%0 Journal Article %@ 1929-0748 %I JMIR Publications %V 14 %N %P e55975 %T Feasibility of Using Resting Heart Rate and Step Counts From Patient-Held Sensors During Clinical Assessment of Medical Emergencies (FUSE): Protocol for Prospective Observational Study in European Hospitals %A Barrington,Jack %A Subbe,Christian %A Kellett,John %A Frischknecht Christensen,Erika %A Brabrand,Mikkel %A Nanayakkara,Prabath %A Alsma,Jelmer %+ North Wales Medical School, Bangor University, Brigantia Building, Bangor, LL57 2AS, United Kingdom, 44 1248 384384, c.subbe@bangor.ac.uk %K mHealth %K emergencies %K heart rate %K step count %K hospital admission %K intensive care %K medical emergency %K smart watch %K feasibility %K clinical assessment %K wearables %K vital signs %K distress %K wearable sensors %K mobility %K device %K mobility data %D 2025 %7 28.4.2025 %9 Protocol %J JMIR Res Protoc %G English %X Background: Abnormalities of vital signs are quantified by comparison with normal ranges, which are those observed in resting healthy populations. It might be more appropriate to compare the vital sign values of an individual in distress with their own usual values recorded when they were stable and well. Sensors from smartwatches or smartphones might make this possible at scale, but the proportion of patients using them is not known. Objective: This study aimed to assess the feasibility of using heart rate and mobility data from patients’ own wearable sensors as part of clinical assessments at the time of presentation to hospitals with medical emergencies, and to quantify the difference between heart rate and the change in daily steps taken by the patient on admission to acute care compared with the previously recorded values at home. Methods: This is an international, multicenter observational study using the flashmob research design. The study will recruit patients aged 18 years and older who present to emergency departments, acute medical departments, or ambulatory emergency care with an acute medical complaint. Main end points of the study include the proportion of patients assessed for an acute complaint who use wearable devices to record vital signs. The study will describe the population that uses devices that collect vital signs in terms of sex, age group, digital literacy, and the severity of illness on presentation (as measured by a standard set of vital signs and frailty). Trends in heart rate and step counts measured in the month before presentation to acute care services will be reported according to discharge or admission status. Data will be collected during a pilot phase and during a single week in centers across Europe. Results: The study has been registered and passed the required approvals in the Netherlands Medical Ethics Committee (MEC-2022-0795) and the United Kingdom Integrated Research Application System (IRAS 321129). Based on the results of a pilot study performed at a single site in the United Kingdom, a flashmob study has been concluded in hospitals throughout Europe in May 2024 and reported in 2025. Conclusions: With the increasing availability of consumer held devices able to record medically relevant information this study will provide information about the availability of these data for clinical use in a number of European settings. International Registered Report Identifier (IRRID): DERR1-10.2196/55975 %M 40293791 %R 10.2196/55975 %U https://www.researchprotocols.org/2025/1/e55975 %U https://doi.org/10.2196/55975 %U http://www.ncbi.nlm.nih.gov/pubmed/40293791 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 14 %N %P e65229 %T Vital Sign and Biochemical Data Collection Using Non-contact Photoplethysmography and the Comestai Mobile Health App: Protocol for an Observational Study %A Zuccotti,Gianvincenzo %A Agnelli,Paolo Osvaldo %A Labati,Lucia %A Cordaro,Erika %A Braghieri,Davide %A Balconi,Simone %A Xodo,Marco %A Losurdo,Fabrizio %A Berra,Cesare Celeste Federico %A Pedretti,Roberto Franco Enrico %A Fiorina,Paolo %A De Pasquale,Sergio Maria %A Calcaterra,Valeria %+ Pediatric Department, Buzzi Children’s Hospital, Via Castelvetro n. 32, Milano, 20154, Italy, 39 0263635321, gianvincenzo.zuccotti@unimi.it %K biochemical data %K mHealth %K mobile app %K non-contact photoplethysmography %K detection %K Comestai %K data accuracy %K monitoring %K vital sign measurement %K screening %D 2025 %7 28.4.2025 %9 Protocol %J JMIR Res Protoc %G English %X Background: Early detection of vital sign changes is key to recognizing patient deterioration promptly, enabling timely interventions and potentially preventing adverse outcomes. Objective: In this study, vital parameters (heart rate, respiratory rate, oxygen saturation, and blood pressure) will be measured using the Comestai app to confirm the accuracy of photoplethysmography methods compared to standard clinical practice devices, analyzing a large and diverse population. In addition, the app will facilitate big data collection to enhance the algorithm’s performance in measuring hemoglobin, glycated hemoglobin, and total cholesterol. Methods: A total of 3000 participants will be consecutively enrolled to achieve the objectives of this study. In all patients, personal data, medical condition, and treatment overview will be recorded. The “by face” method for remote photoplethysmography vital sign data collection involves recording participants’ faces using the front camera of a mobile device (iOS or Android) for approximately 1.5 minutes. Simultaneously, vital signs will be continuously collected for about 1.5 minutes using the reference devices alongside data collected via the Comestai app; biochemical results will also be recorded. The accuracy of the app measurements compared to the reference devices and standard tests will be assessed for all parameters. CIs will be calculated using the bootstrap method. The proposed approach’s effectiveness will be evaluated using various quality criteria, including the mean error, SD, mean absolute error, root mean square error, and mean absolute percentage error. The correlation between measurements obtained using the app and reference devices and standard tests will be evaluated using the Pearson correlation coefficient. Agreement between pairs of measurements (app vs reference devices and standard tests) will be represented using Bland-Altman plots. Sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and likelihood ratios will be calculated to determine the ability of the new app to accurately measure vital signs. Results: Data collection began in June 2024. As of March 25, 2025, we have recruited 1200 participants. The outcomes of the study are expected at the end of 2025. The analysis plan involves verifying and validating the parameters collected from mobile devices via the app, reference devices, and prescheduled blood tests, along with patient demographic data. Conclusions: Our study will enhance and support the accuracy of data on vital sign detection through PPG, also introducing measurements of biochemical risk indicators. The evaluation of a large population will allow for continuous improvement in the performance and accuracy of artificial intelligence algorithms, reducing errors. Expanding research on mobile health solutions like Comestai can support preventive care by validating their effectiveness as screening tools and guiding future health care technology developments. Trial Registration: ClinicalTrials.gov NCT06427564; https://clinicaltrials.gov/study/NCT06427564 %M 40293779 %R 10.2196/65229 %U https://www.researchprotocols.org/2025/1/e65229 %U https://doi.org/10.2196/65229 %U http://www.ncbi.nlm.nih.gov/pubmed/40293779 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 9 %N %P e66215 %T Patient and Clinician Perspectives on Alert-Based Remote Monitoring–First Care for Cardiovascular Implantable Electronic Devices: Semistructured Interview Study Within the Veterans Health Administration %A Kratka,Allison %A Rotering,Thomas L %A Munson,Scott %A Raitt,Merritt H %A Whooley,Mary A %A S Dhruva,Sanket %K cardiovascular implantable electronic device %K CIED %K remote monitoring %K RM %K alert-based monitoring %K remote monitoring–first care %K patient perspectives %K clinician perspectives %K veteran %K pacemaker %K implantable cardioverter-defibrillator %K mobile phone %D 2025 %7 4.4.2025 %9 %J JMIR Cardio %G English %X Background: Patients with cardiovascular implantable electronic devices (CIEDs) typically attend in-person CIED clinic visits at least annually, paired with remote monitoring (RM). As the CIED data available through in-person CIED clinic visits and RM are nearly identical, the 2023 Heart Rhythm Society expert consensus statement introduced “alert-based RM,” an RM-first approach where patients with CIEDs that are consistently and continuously connected to RM, in the absence of recent alerts and other cardiac comorbidities, could attend in-person CIED clinic visits every 24 months or ultimately only as clinically prompted by actionable events identified on RM. However, there is no published information about patient and clinician perspectives on barriers and facilitators to such an RM-first care model. Objective: We aimed to understand patient and clinician perspectives about an RM-first care model for CIED care. Methods: We interviewed 40 rural veteran patients who were experienced with RM with CIEDs and 22 CIED clinicians who were experienced in using RM regarding barriers and facilitators to an RM-first care model. We conducted a reflexive thematic analysis of interviews. Two authors familiarized themselves with the dataset and generated separate codebooks based on the interview guides and inductively coded notes. These 2 authors met and reviewed each other’s codes, sought additional author input, and resolved differences before 1 author coded the remaining interviews and developed candidate themes. These themes were refined, named, and supported with quotations. Results: Patients expressed interest in an RM-first approach, to reduce the burden of long travel times, sometimes in inclement weather, and to enable clinicians to provide care for other patients. However, many preferred routine in-person visits; reasons included a skepticism of the capabilities of RM, a sense that in-person visits provided superior care, and enjoyment of in-person patient-clinician relationships. Clinicians were interested in RM-first care, especially for stable, RM-adherent patients who were not device-dependent. Clinicians most frequently cited the benefit of reducing patient travel burden as well as optimizing clinic space and time to focus on other care such as reviewing routine RM transmissions, but also noted barriers including lack of in-person assessment, patient-perceived diminution of the patient-clinician relationship, possible loss to follow-up, and technological difficulties. Clinicians felt that an RM-first care model should be evaluated for success based on patient satisfaction and assessment of timely addressing of rhythm issues to prevent adverse outcomes. Most clinicians believed that RM-first care represented the future of CIED care. Conclusions: Both patients and CIED clinicians interviewed who were experienced in using RM were open to an RM-first care model that reduces in-person visits but reported some barriers to solely relying on RM and possible diminution of the patient-clinician relationship. Implementation of new RM recommendations will require attention to these perceptions and prioritization of patient-centered approaches. %R 10.2196/66215 %U https://cardio.jmir.org/2025/1/e66215 %U https://doi.org/10.2196/66215 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 9 %N %P e67110 %T Wrist-Worn and Arm-Worn Wearables for Monitoring Heart Rate During Sedentary and Light-to-Vigorous Physical Activities: Device Validation Study %A Schweizer,Theresa %A Gilgen-Ammann,Rahel %K validity %K reliability %K accuracy %K wearable devices %K wearing position %K photoplethysmography %K heart rate %D 2025 %7 21.3.2025 %9 %J JMIR Cardio %G English %X Background: Heart rate (HR) is a vital physiological parameter, serving as an indicator of homeostasis and a key metric for monitoring cardiovascular health and physiological responses. Wearable devices using photoplethysmography (PPG) technology provide noninvasive HR monitoring in real-life settings, but their performance may vary due to factors such as wearing position, blood flow, motion, and device updates. Therefore, ongoing validation of their accuracy and reliability across different activities is essential. Objectives: This study aimed to assess the accuracy and reliability of the HR measurement from the PPG-based Polar Verity Sense and the Polar Vantage V2 devices across a range of physical activities and intensities as well as wearing positions (ie, upper arm, forearm, and both wrists). Methods: Sixteen healthy participants were recruited to participate in this study protocol, which involved 9 activities of varying intensities, ranging from lying down to high-intensity interval training, each repeated twice. The HR measurements from the Verity Sense and Vantage V2 were compared with the criterion measure Polar H10 electrocardiogram (ECG) chest strap. The data were processed to eliminate artifacts and outliers. Accuracy and reliability were assessed using multiple statistical methods, including systematic bias (mean of differences), mean absolute error (MAE) and mean absolute percentage error (MAPE), Pearson product moment correlation coefficient (r), Lin concordance correlation coefficient (CCC), and within-subject coefficient of variation (WSCV). Results: All 16 participants (female=7; male=9; mean 27.4, SD 5.8 years) completed the study. The Verity Sense, worn on the upper arm, demonstrated excellent accuracy across most activities, with a systematic bias of −0.05 bpm, MAE of 1.43 bpm, MAPE of 1.35%, r=1.00, and CCC=1.00. It also demonstrated high reliability across all activities with a WSCV of 2.57% and no significant differences between the 2 sessions. The wrist-worn Vantage V2 demonstrated moderate accuracy with a slight overestimation compared with the ECG and considerable variation in accuracy depending on the activity. For the nondominant wrist, it demonstrated a systematic bias of 2.56 bpm, MAE of 6.41 bpm, MAPE 6.82%, r=0.93, and CCC=0.92. Reliability varied considerably, ranging from a WSCV of 3.64% during postexercise sitting to 23.03% during lying down. Conclusions: The Verity Sense was found to be highly accurate and reliable, outperforming many other wearable HR devices and establishing itself as a strong alternative to ECG-based chest straps, especially when worn on the upper arm. The Vantage V2 was found to have moderate accuracy, with performance highly dependent on activity type and intensity. While it exhibited greater variability and limitations at lower HR, it performed better at higher intensities and outperformed several wrist-worn devices from previous research, particularly during vigorous activities. These findings highlight the importance of device selection and wearing position to ensure the highest possible accuracy in the intended context. %R 10.2196/67110 %U https://cardio.jmir.org/2025/1/e67110 %U https://doi.org/10.2196/67110 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e66347 %T Impact on Patient Outcomes of Continuous Vital Sign Monitoring on Medical Wards: Propensity-Matched Analysis %A Rowland,Bradley %A Saha,Amit %A Motamedi,Vida %A Bundy,Richa %A Winsor,Scott %A McNavish,Daniel %A Lippert,William %A Khanna,Ashish K %+ Department of Internal Medicine, Section of Hospital Medicine, Wake Forest University School of Medicine, 1 Medical Center Boulevard, Winston-Salem, NC, 27157, United States, 1 336 716 9218, barowlan@wakehealth.edu %K clinical %K continuous %K monitoring %K outcomes %K medical ward %K wireless %K wireless monitoring %K vital sign %K ward %K patient outcome %K hospital ward %K clinical outcome %K contemporaneous control %K contemporaneous %K teenager %K young adult %K adult %K monitoring device %K wireless device %K wearable %K patient monitoring %D 2025 %7 11.3.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Continuous and wireless vital sign (VS) monitoring on hospital wards is superior to intermittent VS monitoring at detecting VS abnormalities; however, the impact on clinical outcomes remains to be confirmed. A recent propensity-matched study of primary surgical patients found decreased odds of intensive care unit (ICU) admission and mortality in patients receiving continuous monitoring. Primary surgical patients are inherently different from their medical counterparts who typically have high morbidity, including frailty. Continuous monitoring research has been limited in primary medical patients. Objective: This study aims to evaluate the clinical outcomes of primary medical patients who received either continuous or, as a contemporaneous control, intermittent vital monitoring as the standard of care using propensity matching. Methods: Propensity-matched analysis of a population-based sample of 7971 patients admitted to the medical wards between January 2018 and December 2019 at a single, tertiary United States medical center. The continuous monitoring device measures oxygen saturation, heart rate, respiratory rate, continuous noninvasive blood pressure, and either 3-lead or 5-lead electrocardiogram. Patients received either 12 hours or more of continuous and wireless VS monitoring (n=1450) or intermittent VS monitoring (n=6521). The primary outcome was the odds of a composite of in-hospital mortality or ICU transfer during hospitalization. Secondary outcomes were the odds of individual components of the primary outcome, as well as heart failure (HF), myocardial infarction (MI), acute kidney injury (AKI), and rapid response team (RRT) activations. Results: Those who received intermittent VS monitoring had greater odds of a composite of in-hospital mortality or ICU admission (odds ratio [OR] 2.79, 95% CI 1.89-4.25; P<.001) compared with those who had continuous and wireless VS monitoring. The odds of HF (OR 1.03, 95% CI 0.83-1.28; P=.77), MI (OR 1.58, 95% CI 0.77-3.47; P=.23), AKI (OR 0.74, 95% CI 0.62-1.02; P=.06), and RRT activation (OR 0.94, 95% CI 0.75-1.19; P=.62) were similar in both groups. Conclusions: In this propensity-matched study, medical ward patients who received standard of care intermittent VS monitoring were at nearly 3 times greater odds of transfer to the ICU or death compared with those who received continuous VS monitoring. Our study was primarily limited by the inability to match patients on admission diagnosis due to limitations in electronic health record data. Other limitations included the number of and reasons for false alarms, which can be challenging with continuous monitoring strategies. Given the limitations of this work, these observations need to be confirmed with prospective interventional trials. %M 40068153 %R 10.2196/66347 %U https://www.jmir.org/2025/1/e66347 %U https://doi.org/10.2196/66347 %U http://www.ncbi.nlm.nih.gov/pubmed/40068153 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e53645 %T Effects of Missing Data on Heart Rate Variability Measured From A Smartwatch: Exploratory Observational Study %A Davis-Wilson,Hope %A Hegarty-Craver,Meghan %A Gaur,Pooja %A Boyce,Matthew %A Holt,Jonathan R %A Preble,Edward %A Eckhoff,Randall %A Li,Lei %A Walls,Howard %A Dausch,David %A Temple,Dorota %+ RTI International, 3040 E Cornwallis Rd, Morrisville, NC, 27709, United States, 1 7043404816, hdaviswilson@rti.org %K plethysmography %K electrocardiogram %K missing data %K smartwatch %K wearable %K ECG %K photoplethysmography %K PPG %K mobile phone %K heart rate %K pilot study %K photoplethysmography %K detection %K sensor %K monitoring %K health metric %K measure %K electrocardiogram %K real-world settings %K rest %K physical activity %K remote monitoring %K medical setting %K youth %K adolescent %K teen %K teenager %D 2025 %7 24.2.2025 %9 Original Paper %J JMIR Form Res %G English %X Background: Measuring heart rate variability (HRV) through wearable photoplethysmography sensors from smartwatches is gaining popularity for monitoring many health conditions. However, missing data caused by insufficient wear compliance or signal quality can degrade the performance of health metrics or algorithm calculations. Research is needed on how to best account for missing data and to assess the accuracy of metrics derived from photoplethysmography sensors. Objective: This study aimed to evaluate the influence of missing data on HRV metrics collected from smartwatches both at rest and during activity in real-world settings and to evaluate HRV agreement and consistency between wearable photoplethysmography and gold-standard wearable electrocardiogram (ECG) sensors in real-world settings. Methods: Healthy participants were outfitted with a smartwatch with a photoplethysmography sensor that collected high-resolution interbeat interval (IBI) data to wear continuously (day and night) for up to 6 months. New datasets were created with various amounts of missing data and then compared with the original (reference) datasets. 5-minute windows of each HRV metric (median IBI, SD of IBI values [STDRR], root-mean-square of the difference in successive IBI values [RMSDRR], low-frequency [LF] power, high-frequency [HF] power, and the ratio of LF to HF power [LF/HF]) were compared between the reference and the missing datasets (10%, 20%, 35%, and 60% missing data). HRV metrics calculated from the photoplethysmography sensor were compared with HRV metrics calculated from a chest-worn ECG sensor. Results: At rest, median IBI remained stable until at least 60% of data degradation (P=.24), STDRR remained stable until at least 35% of data degradation (P=.02), and RMSDRR remained stable until at least 35% data degradation (P=.001). During the activity, STDRR remained stable until 20% data degradation (P=.02) while median IBI (P=.01) and RMSDRR P<.001) were unstable at 10% data degradation. LF (rest: P<.001; activity: P<.001), HF (rest: P<.001, activity: P<.001), and LF/HF (rest: P<.001, activity: P<.001) were unstable at 10% data degradation during rest and activity. Median IBI values calculated from photoplethysmography sensors had a moderate agreement (intraclass correlation coefficient [ICC]=0.585) and consistency (ICC=0.589) and LF had moderate consistency (ICC=0.545) with ECG sensors. Other HRV metrics demonstrated poor agreement (ICC=0.071-0.472). Conclusions: This study describes a methodology for the extraction of HRV metrics from photoplethysmography sensor data that resulted in stable and valid metrics while using the least amount of available data. While smartwatches containing photoplethysmography sensors are valuable for remote monitoring of patients, future work is needed to identify best practices for using these sensors to evaluate HRV in medical settings. %M 39993288 %R 10.2196/53645 %U https://formative.jmir.org/2025/1/e53645 %U https://doi.org/10.2196/53645 %U http://www.ncbi.nlm.nih.gov/pubmed/39993288 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 13 %N %P e55298 %T Validity, Accuracy, and Safety Assessment of an Aerobic Interval Training Using an App-Based Prehabilitation Program (PROTEGO MAXIMA Trial) Before Major Surgery: Prospective, Interventional Pilot Study %A Faqar Uz Zaman,Sara Fatima %A Sliwinski,Svenja %A Mohr-Wetzel,Lisa %A Dreilich,Julia %A Filmann,Natalie %A Detemble,Charlotte %A Zmuc,Dora %A Chun,Felix %A Derwich,Wojciech %A Schreiner,Waldemar %A Bechstein,Wolf %A Fleckenstein,Johannes %A Schnitzbauer,Andreas A %+ Department of General, Visceral, Transplant and Thoracic Surgery, University Hospital Frankfurt, Goethe University Frankfurt, Theodor-Stern-Kai 7, Frankfurt am Main, 60590, Germany, 49 69 6301, andreas.schnitzbauer@rub.de %K digital health %K prehab %K major surgery %K surgical oncology %K smartwatches %K safety and quality %K surgery %K surgical %K oncology %K validity %K accuracy %K safety management %K management %K aerobic %K aerobic training %K app %K prehabilitation %K pilot study %K quality of life %K medical device %K wearable %K wearables %K heart rate %D 2025 %7 10.2.2025 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Major surgery is associated with significant morbidity and a reduced quality of life, particularly among older adults and individuals with frailty and impaired functional capacity. Multimodal prehabilitation can enhance functional recovery after surgery and reduce postoperative complications. Digital prehabilitation has the potential to be a resource-sparing and patient-empowering tool that improves patients’ preoperative status; however, little remains known regarding their safety and accuracy as medical devices. Objective: This study aims to test the accuracy and validity of a new software in comparison to the gold-standard electrocardiogram (ECG)-based heart rate measurement. Methods: The PROTEGO MAXIMA trial was a prospective interventional pilot trial assessing the validity, accuracy, and safety of an app-based exercise program. The Prehab App calculates a personalized, risk-stratified aerobic interval training plan based on individual risk factors and utilizes wearables to monitor heart rate. Healthy students and patients undergoing major surgery were enrolled. A structured risk assessment was conducted, followed by a 6-minute walking test and a 37-minute supervised interval session. During the exercise, patients wore app-linked wearables for heart rate and distance measurements, which were compared with standard ECG and treadmill measurements. Safety, accuracy, and usability assessments included testing alarm signals, while the occurrence of adverse events served as the primary and secondary outcome measures. Results: A total of 75 participants were included. The mean heart rate differences between wearables and standard ECG were ≤5 bpm (beats per minute) with a mean absolute percentage error of ≤5%. Regression analysis revealed a significant impact of the BMI (odds ratio 0.90, 95% CI 0.82-0.98, P=.02) and Timed Up and Go Test score (odds ratio 0.12, 95% CI 0.03-0.55, P=.006) on the accuracy of heart rate measurement; 29 (39%) patients experienced adverse events: pain (5/12, 42%), ECG electrode–related skin irritations (2/42, 17%), dizziness (2/42, 17%), shortness of breath (2/42, 17%), and fatigue (1/42, 8%). No cardiovascular or serious adverse events were reported, and no serious device deficiency was detected. There were no indications of clinically meaningful overexertion based on laboratory values measured before and after the 6-minute walking test and exercise. The differences in means and ranges were as follows: lactate (mmol/l), mean 0.04 (range –3 to 6; P=.47); creatinine kinase (U/l), mean 12 (range –7 to 43; P<.001); and sodium (mmol/l), mean –2 (range –11 to 12; P<.001). Conclusions: The interventional trial demonstrated the high safety of the exercise program and the accuracy of heart rate measurements using commercial wearables in patients before major surgery, paving the way for potential remote implementation in the future. Trial Registration: German Clinical Trials Register DRKS00026985; https://drks.de/search/en/trial/DRKS00026985 and European Database on Medical Devices (EUDAMED) CIV-21-07-0307311. International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2022-069394 %M 39928941 %R 10.2196/55298 %U https://mhealth.jmir.org/2025/1/e55298 %U https://doi.org/10.2196/55298 %U http://www.ncbi.nlm.nih.gov/pubmed/39928941 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 9 %N %P e60238 %T Causal Inference for Hypertension Prediction With Wearable Electrocardiogram and Photoplethysmogram Signals: Feasibility Study %A Gong,Ke %A Chen,Yifan %A Song,Xinyue %A Fu,Zhizhong %A Ding,Xiaorong %K hypertension %K causal inference %K wearable physiological signals %K electrocardiogram %K photoplethysmogram %D 2025 %7 23.1.2025 %9 %J JMIR Cardio %G English %X Background: Hypertension is a leading cause of cardiovascular disease and premature death worldwide, and it puts a heavy burden on the health care system. Therefore, it is very important to detect and evaluate hypertension and related cardiovascular events to enable early prevention, detection, and management. Hypertension can be detected in a timely manner with cardiac signals, such as through an electrocardiogram (ECG) and photoplethysmogram (PPG), which can be observed via wearable sensors. Most previous studies predicted hypertension from ECG and PPG signals with extracted features that are correlated with hypertension. However, correlation is sometimes unreliable and may be affected by confounding factors. Objective: The aim of this study was to investigate the feasibility of predicting the risk of hypertension by exploring features that are causally related to hypertension via causal inference methods. Additionally, we paid special attention to and verified the reliability and effectiveness of causality compared to correlation. Methods: We used a large public dataset from the Aurora Project, which was conducted by Microsoft Research. The dataset included diverse individuals who were balanced in terms of gender, age, and the condition of hypertension, with their ECG and PPG signals simultaneously acquired with wrist-worn wearable devices. We first extracted 205 features from the ECG and PPG signals, calculated 6 statistical metrics for these 205 features, and selected some valuable features out of the 205 features under each statistical metric. Then, 6 causal graphs of the selected features for each kind of statistical metric and hypertension were constructed with the equivalent greedy search algorithm. We further fused the 6 causal graphs into 1 causal graph and identified features that were causally related to hypertension from the causal graph. Finally, we used these features to detect hypertension via machine learning algorithms. Results: We validated the proposed method on 405 subjects. We identified 24 causal features that were associated with hypertension. The causal features could detect hypertension with an accuracy of 89%, precision of 92%, and recall of 82%, which outperformed detection with correlation features (accuracy of 85%, precision of 88%, and recall of 77%). Conclusions: The results indicated that the causal inference–based approach can potentially clarify the mechanism of hypertension detection with noninvasive signals and effectively detect hypertension. It also revealed that causality can be more reliable and effective than correlation for hypertension detection and other application scenarios. %R 10.2196/60238 %U https://cardio.jmir.org/2025/1/e60238 %U https://doi.org/10.2196/60238 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e56463 %T Effectiveness of Remote Patient Monitoring Equipped With an Early Warning System in Tertiary Care Hospital Wards: Retrospective Cohort Study %A Lakshman,Pavithra %A Gopal,Priyanka T %A Khurdi,Sheen %+ Hospital Administration, Ramaiah Memorial Hospital, New BEL Rd, M S Ramaiah Nagar, MSRIT Post, Bengaluru, Karnataka, 560054, India, 91 9741592241, dr.pavithra.lakshman@gmail.com %K continuous vitals monitoring %K remote patient monitoring %K early warning system %K hospital wards %K retrospective %K cohort study %K early deterioration monitoring %K patient care %K decision making %K clinical information %D 2025 %7 15.1.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Monitoring vital signs in hospitalized patients is crucial for evaluating their clinical condition. While early warning scores like the modified early warning score (MEWS) are typically calculated 3 to 4 times daily through spot checks, they might not promptly identify early deterioration. Leveraging technologies that provide continuous monitoring of vital signs, combined with an early warning system, has the potential to identify clinical deterioration sooner. This approach empowers health care providers to intervene promptly and effectively. Objective: This study aimed to assess the impact of a Remote Patient Monitoring System (RPMS) with an automated early warning system (R-EWS) on patient safety in noncritical care at a tertiary hospital. R-EWS performance was compared with a simulated Modified Early Warning System (S-MEWS) and a simulated threshold-based alert system (S-Threshold). Methods: Patient outcomes, including intensive care unit (ICU) transfers due to deterioration and discharges for nondeteriorating cases, were analyzed in Ramaiah Memorial Hospital’s general wards with RPMS. Sensitivity, specificity, chi-square test for alert frequency distribution equality, and the average time from the first alert to ICU transfer in the last 24 hours was determined. Alert and patient distribution by tiers and vitals in R-EWS groups were examined. Results: Analyzing 905 patients, including 38 with deteriorations, R-EWS, S-Threshold, and S-MEWS generated more alerts for deteriorating cases. R-EWS showed high sensitivity (97.37%) and low specificity (23.41%), S-Threshold had perfect sensitivity (100%) but low specificity (0.46%), and S-MEWS demonstrated moderate sensitivity (47.37%) and high specificity (81.31%). The average time from initial alert to clinical deterioration was at least 18 hours for RPMS and S-Threshold in deteriorating participants. R-EWS had increased alert frequency and a higher proportion of critical alerts for deteriorating cases. Conclusions: This study underscores R-EWS role in early deterioration detection, emphasizing timely interventions for improved patient outcomes. Continuous monitoring enhances patient safety and optimizes care quality. %M 39813676 %R 10.2196/56463 %U https://www.jmir.org/2025/1/e56463 %U https://doi.org/10.2196/56463 %U http://www.ncbi.nlm.nih.gov/pubmed/39813676 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e65139 %T Comparative Evaluation of Consumer Wearable Devices for Atrial Fibrillation Detection: Validation Study %A Wouters,Femke %A Gruwez,Henri %A Smeets,Christophe %A Pijalovic,Anessa %A Wilms,Wouter %A Vranken,Julie %A Pieters,Zoë %A Van Herendael,Hugo %A Nuyens,Dieter %A Rivero-Ayerza,Maximo %A Vandervoort,Pieter %A Haemers,Peter %A Pison,Laurent %K atrial fibrillation %K AF %K mobile health %K photoplethysmography %K electrocardiography %K smartphone %K consumer wearable device %K wearable devices %K detection %K electrocardiogram %K ECG %K mHealth %D 2025 %7 9.1.2025 %9 %J JMIR Form Res %G English %X Background: Consumer-oriented wearable devices (CWDs) such as smartphones and smartwatches have gained prominence for their ability to detect atrial fibrillation (AF) through proprietary algorithms using electrocardiography or photoplethysmography (PPG)–based digital recordings. Despite numerous individual validation studies, a direct comparison of interdevice performance is lacking. Objective: This study aimed to evaluate and compare the ability of CWDs to distinguish between sinus rhythm and AF. Methods: Patients exhibiting sinus rhythm or AF were enrolled through a cardiology outpatient clinic. The participants were instructed to perform heart rhythm measurements using a handheld 6-lead electrocardiogram (ECG) device (KardiaMobile 6L), a smartwatch-derived single-lead ECG (Apple Watch), and two PPG-based smartphone apps (FibriCheck and Preventicus) in a random sequence, with simultaneous 12-lead reference ECG as the gold standard. Results: A total of 122 participants were included in the study: median age 69 (IQR 61-77) years, 63.9% (n=78) men, 25% (n=30) with AF, 9.8% (n=12) without prior smartphone experience, and 73% (n=89) without experience in using a smartwatch. The sensitivity to detect AF was 100% for all devices. The specificity to detect sinus rhythm was 96.4% (95% CI 89.5%-98.8%) for KardiaMobile 6L, 97.8% (95% CI 91.6%‐99.5%) for Apple Watch, 98.9% (95% CI 92.5%‐99.8%) for FibriCheck, and 97.8% (95% CI 91.5%‐99.4%) for Preventicus (P=.50). Insufficient quality measurements were observed in 10.7% (95% CI 6.3%-17.5%) of cases for both KardiaMobile 6L and Apple Watch, 7.4% (95% CI 3.9%‐13.6%) for FibriCheck, and 14.8% (95% CI 9.5%‐22.2%) for Preventicus (P=.21). Participants preferred Apple Watch over the other devices to monitor their heart rhythm. Conclusions: In this study population, the discrimination between sinus rhythm and AF using CWDs based on ECG or PPG was highly accurate, with no significant variations in performance across the examined devices. Trial Registration: ClinicalTrials.gov NCT06023290; https://clinicaltrials.gov/study/NCT06023290 %R 10.2196/65139 %U https://formative.jmir.org/2025/1/e65139 %U https://doi.org/10.2196/65139 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 13 %N %P e54871 %T Reliability and Accuracy of the Fitbit Charge 4 Photoplethysmography Heart Rate Sensor in Ecological Conditions: Validation Study %A Ceugniez,Maxime %A Devanne,Hervé %A Hermand,Eric %K photoplethysmography %K physical activity %K ecological conditions %K accuracy %K reliability %K Fitbit Charge 4 %K Fitbit %K exercise %K ecological %K wrist-worn device %K device %K sensor %K wearables %K usefulness %K variability %K sensitivity %K heart rate %K heart rate sensor %D 2025 %7 8.1.2025 %9 %J JMIR Mhealth Uhealth %G English %X Background: Wrist-worn photoplethysmography (PPG) sensors allow for continuous heart rate (HR) measurement without the inconveniences of wearing a chest belt. Although green light PPG technology reduces HR measurement motion artifacts, only a limited number of studies have investigated the reliability and accuracy of wearables in non–laboratory-controlled conditions with actual specific and various physical activity movements. Objective: The purpose of this study was to (1) assess the reliability and accuracy of the PPG-based HR sensor of the Fitbit Charge 4 (FC4) in ecological conditions and (2) quantify the potential variability caused by the nature of activities. Methods: We collected HR data from participants who performed badminton, tennis, orienteering running, running, cycling, and soccer while simultaneously wearing the FC4 and the Polar H10 chest belt (criterion sensor). Skin tone was assessed with the Fitzpatrick Skin Scale. Once data from the FC4 and criterion data were synchronized, accuracy and reliability analyses were performed, using intraclass correlation coefficients (ICCs), Lin concordance correlation coefficients (CCCs), mean absolute percentage errors (MAPEs), and Bland-Altman tests. A linear univariate model was also used to evaluate the effect of skin tone on bias. All analyses were stratified by activity and pooled activity types (racket sports and running sports). Results: A total of 77.5 hours of HR recordings from 26 participants (age: mean 21.1, SD 5.8 years) were analyzed. The highest reliability was found for running sports, with ICCs and CCCs of 0.90 and 0.99 for running and 0.80 and 0.93 for orienteering running, respectively, whereas the ICCs and CCCs were 0.37 and 0.78, 0.42 and 0.88, 0.65 and 0.97, and 0.49 and 0.81 for badminton, tennis, cycling, and soccer, respectively. We found the highest accuracy for running (bias: 0.1 beats per minute [bpm]; MAPE 1.2%, SD 4.6%) and the lowest for badminton (bias: −16.5 bpm; MAPE 16.2%, SD 14.4%) and soccer (bias: −16.5 bpm; MAPE 17.5%, SD 20.8%). Limit of agreement (LOA) width and artifact rate followed the same trend. No effect of skin tone was observed on bias. Conclusions: LOA width, bias, and MAPE results found for racket sports and soccer suggest a high sensitivity to motion artifacts for activities that involve “sharp” and random arm movements. In this study, we did not measure arm motion, which limits our results. However, whereas individuals might benefit from using the FC4 for casual training in aerobic sports, we cannot recommend the use of the FC4 for specific purposes requiring high reliability and accuracy, such as research purposes. %R 10.2196/54871 %U https://mhealth.jmir.org/2025/1/e54871 %U https://doi.org/10.2196/54871 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e67256 %T Noninvasive Oral Hyperspectral Imaging–Driven Digital Diagnosis of Heart Failure With Preserved Ejection Fraction: Model Development and Validation Study %A Yang,Xiaomeng %A Li,Zeyan %A Lei,Lei %A Shi,Xiaoyu %A Zhang,Dingming %A Zhou,Fei %A Li,Wenjing %A Xu,Tianyou %A Liu,Xinyu %A Wang,Songyun %A Yuan,Quan %A Yang,Jian %A Wang,Xinyu %A Zhong,Yanfei %A Yu,Lilei %+ Cardiovascular Hospital, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuhan, 430060, China, 86 02788041911, lileiyu@whu.edu.cn %K heart failure with preserved ejection fraction %K HFpEF %K hyperspectral imaging %K HSI %K diagnostic model %K digital health %K Shapley Additive Explanations %K SHAP %K machine learning %K artificial intelligence %K AI %K cardiovascular disease %K predictive modeling %K oral health %D 2025 %7 7.1.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Oral microenvironmental disorders are associated with an increased risk of heart failure with preserved ejection fraction (HFpEF). Hyperspectral imaging (HSI) technology enables the detection of substances that are visually indistinguishable to the human eye, providing a noninvasive approach with extensive applications in medical diagnostics. Objective: The objective of this study is to develop and validate a digital, noninvasive oral diagnostic model for patients with HFpEF using HSI combined with various machine learning algorithms. Methods: Between April 2023 and August 2023, a total of 140 patients were recruited from Renmin Hospital of Wuhan University to serve as the training and internal testing groups for this study. Subsequently, from August 2024 to September 2024, an additional 35 patients were enrolled from Three Gorges University and Yichang Central People’s Hospital to constitute the external testing group. After preprocessing to ensure image quality, spectral and textural features were extracted from the images. We extracted 25 spectral bands from each patient image and obtained 8 corresponding texture features to evaluate the performance of 28 machine learning algorithms for their ability to distinguish control participants from participants with HFpEF. The model demonstrating the optimal performance in both internal and external testing groups was selected to construct the HFpEF diagnostic model. Hyperspectral bands significant for identifying participants with HFpEF were identified for further interpretative analysis. The Shapley Additive Explanations (SHAP) model was used to provide analytical insights into feature importance. Results: Participants were divided into a training group (n=105), internal testing group (n=35), and external testing group (n=35), with consistent baseline characteristics across groups. Among the 28 algorithms tested, the random forest algorithm demonstrated superior performance with an area under the receiver operating characteristic curve (AUC) of 0.884 and an accuracy of 82.9% in the internal testing group, as well as an AUC of 0.812 and an accuracy of 85.7% in the external testing group. For model interpretation, we used the top 25 features identified by the random forest algorithm. The SHAP analysis revealed discernible distinctions between control participants and participants with HFpEF, thereby validating the diagnostic model’s capacity to accurately identify participants with HFpEF. Conclusions: This noninvasive and efficient model facilitates the identification of individuals with HFpEF, thereby promoting early detection, diagnosis, and treatment. Our research presents a clinically advanced diagnostic framework for HFpEF, validated using independent data sets and demonstrating significant potential to enhance patient care. Trial Registration: China Clinical Trial Registry ChiCTR2300078855; https://www.chictr.org.cn/showproj.html?proj=207133 %M 39773415 %R 10.2196/67256 %U https://www.jmir.org/2025/1/e67256 %U https://doi.org/10.2196/67256 %U http://www.ncbi.nlm.nih.gov/pubmed/39773415 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 8 %N %P e60697 %T The Role of Machine Learning in the Detection of Cardiac Fibrosis in Electrocardiograms: Scoping Review %A Handra,Julia %A James,Hannah %A Mbilinyi,Ashery %A Moller-Hansen,Ashley %A O'Riley,Callum %A Andrade,Jason %A Deyell,Marc %A Hague,Cameron %A Hawkins,Nathaniel %A Ho,Kendall %A Hu,Ricky %A Leipsic,Jonathon %A Tam,Roger %+ Faculty of Medicine, University of British Columbia, 2194 Health Sciences Mall, Vancouver, BC, V6T 1Z3, Canada, 1 (604) 822 2421, jhandra@student.ubc.ca %K machine learning %K cardiac fibrosis %K electrocardiogram %K ECG %K detection %K ML %K cardiovascular disease %K review %D 2024 %7 30.12.2024 %9 Review %J JMIR Cardio %G English %X Background: Cardiovascular disease remains the leading cause of mortality worldwide. Cardiac fibrosis impacts the underlying pathophysiology of many cardiovascular diseases by altering structural integrity and impairing electrical conduction. Identifying cardiac fibrosis is essential for the prognosis and management of cardiovascular disease; however, current diagnostic methods face challenges due to invasiveness, cost, and inaccessibility. Electrocardiograms (ECGs) are widely available and cost-effective for monitoring cardiac electrical activity. While ECG-based methods for inferring fibrosis exist, they are not commonly used due to accuracy limitations and the need for cardiac expertise. However, the ECG shows promise as a target for machine learning (ML) applications in fibrosis detection. Objective: This study aims to synthesize and critically evaluate the current state of ECG-based ML approaches for cardiac fibrosis detection. Methods: We conducted a scoping review of research in ECG-based ML applications to identify cardiac fibrosis. Comprehensive searches were performed in PubMed, IEEE Xplore, Scopus, Web of Science, and DBLP databases, including publications up to October 2024. Studies were included if they applied ML techniques to detect cardiac fibrosis using ECG or vectorcardiogram data and provided sufficient methodological details and outcome metrics. Two reviewers independently assessed eligibility and extracted data on the ML models used, their performance metrics, study designs, and limitations. Results: We identified 11 studies evaluating ML approaches for detecting cardiac fibrosis using ECG data. These studies used various ML techniques, including classical (8/11, 73%), ensemble (3/11, 27%), and deep learning models (4/11, 36%). Support vector machines were the most used classical model (6/11, 55%), with the best-performing models of each study achieving accuracies of 77% to 93%. Among deep learning approaches, convolutional neural networks showed promising results, with one study reporting an area under the receiver operating characteristic curve (AUC) of 0.89 when combined with clinical features. Notably, a large-scale convolutional neural network study (n=14,052) achieved an AUC of 0.84 for detecting cardiac fibrosis, outperforming cardiologists (AUC 0.63-0.66). However, many studies had limited sample sizes and lacked external validation, potentially impacting the generalizability of the findings. Variability in reporting methods may affect the reproducibility and applicability of these ML-based approaches. Conclusions: ML-augmented ECG analysis shows promise for accessible and cost-effective detection of cardiac fibrosis. However, there are common limitations with respect to study design and insufficient external validation, raising concerns about the generalizability and clinical applicability of the findings. Inconsistencies in methodologies and incomplete reporting further impede cross-study comparisons. Future work may benefit from using prospective study designs, larger and more clinically and demographically diverse datasets, advanced ML models, and rigorous external validation. Addressing these challenges could pave the way for the clinical implementation of ML-based ECG detection of cardiac fibrosis to improve patient outcomes and health care resource allocation. %M 39753213 %R 10.2196/60697 %U https://cardio.jmir.org/2024/1/e60697 %U https://doi.org/10.2196/60697 %U http://www.ncbi.nlm.nih.gov/pubmed/39753213 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e60944 %T Call for Decision Support for Electrocardiographic Alarm Administration Among Neonatal Intensive Care Unit Staff: Multicenter, Cross-Sectional Survey %A Tang,Xiaoli %A Yang,Xiaochen %A Yuan,Jiajun %A Yang,Jie %A Jin,Qian %A Zhang,Hanting %A Zhao,Liebin %A Guo,Weiwei %+ Shanghai Engineering Research Center of Intelligence Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, 1678 Dongfang Road, Pudong New Area, Shanghai, 200127, China, 86 18930830578, guoweiwei@scmc.com.cn %K ECG alarm %K electrocardiographic %K perception %K practice %K decision-making %K neonatal intensive care unit %K health care providers %K cross-sectional survey %K nationwide %D 2024 %7 20.12.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Previous studies have shown that electrocardiographic (ECG) alarms have high sensitivity and low specificity, have underreported adverse events, and may cause neonatal intensive care unit (NICU) staff fatigue or alarm ignoring. Moreover, prolonged noise stimuli in hospitalized neonates can disrupt neonatal development. Objective: The aim of the study is to conduct a nationwide, multicenter, large-sample cross-sectional survey to identify current practices and investigate the decision-making requirements of health care providers regarding ECG alarms. Methods: We conducted a nationwide, cross-sectional survey of NICU staff working in grade III level A hospitals in 27 Chinese provinces to investigate current clinical practices, perceptions, decision-making processes, and decision-support requirements for clinical ECG alarms. A comparative analysis was conducted on the results using the chi-square, Kruskal-Wallis, or Mann-Whitney U tests. Results: In total, 1019 respondents participated in this study. NICU staff reported experiencing a significant number of nuisance alarms and negative perceptions as well as practices regarding ECG alarms. Compared to nurses, physicians had more negative perceptions. Individuals with higher education levels and job titles had more negative perceptions of alarm systems than those with lower education levels and job titles. The mean difficulty score for decision-making about ECG alarms was 2.96 (SD 0.27) of 5. A total of 62.32% (n=635) respondents reported difficulty in resetting or modifying alarm parameters. Intelligent module–assisted decision support systems were perceived as the most popular form of decision support. Conclusions: This study highlights the negative perceptions and strong decision-making requirements of NICU staff related to ECG alarm handling. Health care policy makers must draw attention to the decision-making requirements and provide adequate decision support in different forms. %M 39705688 %R 10.2196/60944 %U https://www.jmir.org/2024/1/e60944 %U https://doi.org/10.2196/60944 %U http://www.ncbi.nlm.nih.gov/pubmed/39705688 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e60493 %T Resting Heart Rate and Associations With Clinical Measures From the Project Baseline Health Study: Observational Study %A Feng,Kent Y %A Short,Sarah A %A Saeb,Sohrab %A Carroll,Megan K %A Olivier,Christoph B %A Simard,Edgar P %A Swope,Susan %A Williams,Donna %A Eckstrand,Julie %A Pagidipati,Neha %A Shah,Svati H %A Hernandez,Adrian F %A Mahaffey,Kenneth W %+ Verily Life Sciences, 269 E Grand Ave, South San Francisco, CA, 94080, United States, 1 650 495 7100, sarahshort@verily.com %K resting heart rate %K wearable devices %K remote monitoring %K physiology %K PBHS %K Project Baseline Health Study %K Verily Study Watch %K heart rate %K observational study %K cohort study %K wearables %K electrocardiogram %K regression analyses %K socioeconomic status %K medical condition %K vital signs %K laboratory assessments %K physical function %K electronic health %K eHealth %D 2024 %7 20.12.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Though widely used, resting heart rate (RHR), as measured by a wearable device, has not been previously evaluated in a large cohort against a variety of important baseline characteristics. Objective: This study aimed to assess the validity of the RHR measured by a wearable device compared against the gold standard of ECG (electrocardiography), and assess the relationships between device-measured RHR and a broad range of clinical characteristics. Methods: The Project Baseline Health Study (PHBS) captured detailed demographic, occupational, social, lifestyle, and clinical data to generate a deeply phenotyped cohort. We selected an analysis cohort within it, which included participants who had RHR determined by both ECG and the Verily Study Watch (VSW). We examined the correlation between these simultaneous RHR measures and assessed the relationship between VSW RHR and a range of baseline characteristics, including demographic, clinical, laboratory, and functional assessments. Results: From the overall PBHS cohort (N=2502), 875 (35%) participants entered the analysis cohort (mean age 50.9, SD 16.5 years; n=519, 59% female and n=356, 41% male). The mean and SD of VSW RHR was 66.6 (SD 11.2) beats per minute (bpm) for female participants and 64.4 (SD 12.3) bpm for male participants. There was excellent reliability between the two measures of RHR (ECG and VSW) with an intraclass correlation coefficient of 0.946. On univariate analyses, female and male participants had similar baseline characteristics that trended with higher VSW RHR: lack of health care insurance (both P<.05), higher BMI (both P<.001), higher C-reactive protein (both P<.001), presence of type 2 diabetes mellitus (both P<.001) and higher World Health Organization Disability Assessment Schedule (WHODAS) 2.0 score (both P<.001) were associated with higher RHR. On regression analyses, within each domain of baseline characteristics (demographics and socioeconomic status, medical conditions, vitals, physical function, laboratory assessments, and patient-reported outcomes), different characteristics were associated with VSW RHR in female and male participants. Conclusions: RHR determined by the VSW had an excellent correlation with that determined by ECG. Participants with higher VSW RHR had similar trends in socioeconomic status, medical conditions, vitals, laboratory assessments, physical function, and patient-reported outcomes irrespective of sex. However, within each domain of baseline characteristics, different characteristics were most associated with VSW RHR in female and male participants. Trial Registration: ClinicalTrials.gov NCT03154346; https://clinicaltrials.gov/study/NCT03154346 %M 39705694 %R 10.2196/60493 %U https://www.jmir.org/2024/1/e60493 %U https://doi.org/10.2196/60493 %U http://www.ncbi.nlm.nih.gov/pubmed/39705694 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 8 %N %P e56848 %T The Effect of Inhaled Beta-2 Agonists on Heart Rate in Patients With Asthma: Sensor-Based Observational Study %A Khusial,Rishi Jayant %A Sont,Jacob K %A Usmani,Omar S %A Bonini,Matteo %A Chung,Kian Fan %A Fowler,Stephen James %A Honkoop,Persijn J %+ Department of Biomedical Data Sciences, Leiden University Medical Center, Albinusdreef 2, Leiden, 2333 ZA, Netherlands, 31 715261319, r.j.khusial@lumc.nl %K asthma %K mHealth %K side effects %K beta-2 agonists %K inhaler medication %K heart rate %K sensor %K observational study %K asthma management %K cardiac cells %K monitoring %K Fitbit %K inhaler %D 2024 %7 11.12.2024 %9 Original Paper %J JMIR Cardio %G English %X Background: Beta-2 agonists play an important role in the management of asthma. Inhaled long-acting beta-2 agonists (LABAs) and short-acting beta-2 agonists (SABAs) cause bronchodilation by stimulating adrenoceptors. These receptors are also present in cardiac cells and, as a side effect, could also be stimulated by inhaled beta-2 agonists. Objective: This study aims to assess the effect of beta-2 agonists on heart rate (HR). Methods: The data were retrieved from an observational study, the myAirCoach Quantification Campaign. Beta-2 agonist use was registered by self-reported monthly questionnaires and by smart inhalers. HR was monitored continuously with the Fitbit Charge HR tracker (Fitbit Inc). Patients (aged 18 years and older) were recruited if they had uncontrolled asthma and used inhalation medication. Our primary outcome was the difference in HR between LABA and non-LABA users. Secondary outcomes were the difference in HR on days SABAs were used compared to days SABAs were not used and an assessment of the timing of inhaler use during the day. Results: Patients using LABA did not have a clinically relevant higher HR (average 0.8 beats per minute difference) during the day. Around the moment of SABA inhalation itself, the HR does increase steeply, and it takes 138 minutes before it returns to the normal range. Conclusions: This study indicates that LABAs do not have a clinically relevant effect on HR. SABAs are instead associated with a short-term HR increase. Trial Registration: ClinicalTrials.gov NCT02774772; https://clinicaltrials.gov/study/NCT02774772 %M 39661964 %R 10.2196/56848 %U https://cardio.jmir.org/2024/1/e56848 %U https://doi.org/10.2196/56848 %U http://www.ncbi.nlm.nih.gov/pubmed/39661964 %0 Journal Article %@ 2561-3278 %I JMIR Publications %V 9 %N %P e57373 %T Validation of a Wearable Sensor Prototype for Measuring Heart Rate to Prescribe Physical Activity: Cross-Sectional Exploratory Study %A Loro,Fernanda Laís %A Martins,Riane %A Ferreira,Janaína Barcellos %A de Araujo,Cintia Laura Pereira %A Prade,Lucio Rene %A Both,Cristiano Bonato %A Nobre,Jéferson Campos Nobre %A Monteiro,Mariane Borba %A Dal Lago,Pedro %+ Department of Physical Therapy, Universidade Federal de Ciências da Saúde de Porto Alegre - UFCSPA, Rua Sarmento Leite, 245, Porto Alegre, 90050170, Brazil, 55 51999617331, pdallago@ufcspa.edu.br %K heart rate %K wearable device %K HR %K biosensor %K physiological monitor %K wearable system %K medical device %K mobile phone %D 2024 %7 11.12.2024 %9 Original Paper %J JMIR Biomed Eng %G English %X Background: Wearable sensors are rapidly evolving, particularly in health care, due to their ability to facilitate continuous or on-demand physiological monitoring. Objective: This study aimed to design and validate a wearable sensor prototype incorporating photoplethysmography (PPG) and long-range wide area network technology for heart rate (HR) measurement during a functional test. Methods: We conducted a transversal exploratory study involving 20 healthy participants aged between 20 and 30 years without contraindications for physical exercise. Initially, our laboratory developed a pulse wearable sensor prototype for HR monitoring. Following this, the participants were instructed to perform the Incremental Shuttle Walk Test while wearing the Polar H10 HR chest strap sensor (the reference for HR measurement) and the wearable sensor. This test allowed for real-time comparison of HR responses between the 2 devices. Agreement between these measurements was determined using the intraclass correlation coefficient (ICC3.1) and Lin concordance correlation coefficient. The mean absolute percentage error was calculated to evaluate reliability or validity. Cohen d was used to calculate the agreement’s effect size. Results: The mean differences between the Polar H10 and the wearable sensor during the test were –2.6 (95% CI –3.5 to –1.8) for rest HR, –4.1 (95% CI –5.3 to –3) for maximum HR, –2.4 (95% CI –3.5 to –1.4) for mean test HR, and –2.5 (95% CI –3.6 to –1.5) for mean recovery HR. The mean absolute percentage errors were –3% for rest HR, –2.2% for maximum HR, –1.8% for mean test HR, and –1.6% for recovery HR. Excellent agreement was observed between the Polar H10 and the wearable sensor for rest HR (ICC3.1=0.96), mean test HR (ICC3.1=0.92), and mean recovery HR (ICC3.1=0.96). The agreement for maximum HR (ICC3.1=0.78) was considered good. By the Lin concordance correlation coefficient, the agreement was found to be substantial for rest HR (rc=0.96) and recovery HR (rc=0.96), moderate for mean test HR (rc=0.92), and poor for maximum HR (rc=0.78). The power of agreement between the Polar H10 and the wearable sensor prototype was large for baseline HR (Cohen d=0.97), maximum HR (Cohen d=1.18), and mean recovery HR (Cohen d=0.8) and medium for mean test HR (Cohen d= 0.76). Conclusions: The pulse-wearable sensor prototype tested in this study proves to be a valid tool for monitoring HR at rest, during functional tests, and during recovery compared with the Polar H10 reference device used in the laboratory setting. %M 39661434 %R 10.2196/57373 %U https://biomedeng.jmir.org/2024/1/e57373 %U https://doi.org/10.2196/57373 %U http://www.ncbi.nlm.nih.gov/pubmed/39661434 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e65277 %T Evaluating the Sensitivity of Wearable Devices in Posttranscatheter Aortic Valve Implantation Functional Assessment %A An,Jinghui %A Shi,Fengwu %A Wang,Huajun %A Zhang,Hang %A Liu,Su %K aortic valve %K implantation functional %K wearable devices %D 2024 %7 8.11.2024 %9 %J JMIR Mhealth Uhealth %G English %X %R 10.2196/65277 %U https://mhealth.jmir.org/2024/1/e65277 %U https://doi.org/10.2196/65277 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 8 %N %P e54746 %T Comparison of Auscultation Quality Using Contemporary Digital Stethoscopes %A Saraya,Norah %A McBride,Jonathon %A Singh,Karandeep %A Sohail,Omar %A Das,Porag Jeet %+ Department of Learning Health Sciences, University of Michigan, North Ingalls Building, 300 N Ingalls St, Ann Arbor, MI, 48109, United States, 1 734 936 1649, karandeep@health.ucsd.edu %K auscultation %K digital stethoscopes %K valvular heart disease %D 2024 %7 8.11.2024 %9 Research Letter %J JMIR Cardio %G English %X %M 39514245 %R 10.2196/54746 %U https://cardio.jmir.org/2024/1/e54746 %U https://doi.org/10.2196/54746 %U http://www.ncbi.nlm.nih.gov/pubmed/39514245 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e63306 %T Identifying Gravity-Related Artifacts on Ballistocardiography Signals by Comparing Weightlessness and Normal Gravity Recordings (ARTIFACTS): Protocol for an Observational Study %A Albrecht,Urs-Vito %A Mielitz,Annabelle %A Rahman,Kazi Mohammad Abidur %A Kulau,Ulf %+ Department of Digital Medicine, Bielefeld University, Universitätsstraße 25, Bielefeld, 33615, Germany, 49 521 106 ext 86714, urs-vito.albrecht@uni-bielefeld.de %K ballistocardiography %K seismocardiography %K acceleration %K artifact %K weightlessness %K gravity %K observational study %K heartbeat %K blood flow %K intrinsic sensor %K hypotheses %K assessment %K heart-induced %K sensor %K gyroscopes %K cardiovascular %K diagnostic %D 2024 %7 26.9.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Modern ballistocardiography (BCG) and seismocardiography (SCG) use acceleration sensors to measure oscillating recoil movements of the body caused by the heartbeat and blood flow, which are transmitted to the body surface. Acceleration artifacts occur through intrinsic sensor roll, pitch, and yaw movements, assessed by the angular velocities of the respective sensor, during measurements that bias the signal interpretation. Objective: This observational study aims to generate hypotheses on the detection and elimination of acceleration artifacts due to the intrinsic rotation of accelerometers and their differentiation from heart-induced sensor accelerations. Methods: Multimodal data from 4 healthy participants (3 male and 1 female) using BCG-SCG and an electrocardiogram will be collected and serve as a basis for signal characterization, model modulation, and location vector derivation under parabolic flight conditions from µg to 1.8g. The data will be obtained during a parabolic flight campaign (3 times 30 parabolas) between September 24 and July 25 (depending on the flight schedule). To detect the described acceleration artifacts, accelerometers and gyroscopes (6-degree-of-freedom sensors) will be used for measuring acceleration and angular velocities attributed to intrinsic sensor rotation. Changes in acceleration and angular velocities will be explored by conducting descriptive data analysis of resting participants sitting upright in varying gravitational states. Results: A multimodal data set will serve as a basis for research into a noninvasive and gentle method of BCG-SCG with the aid of low-noise and synchronous 3D gyroscopes and 3D acceleration sensors. Hypotheses will be generated related to detecting and eliminating acceleration artifacts due to the intrinsic rotation of accelerometers and gyroscopes (6-degree-of-freedom sensors) and their differentiation from heart-induced sensor accelerations. Data will be collected entirely and exclusively during the parabolic flights, taking place between September 2024 and July 2025. Thus, as of June 2024, no data have been collected yet. The data will be analyzed until December 2025. The results are expected to be published by June 2026. Conclusions: The study will contribute to understanding artificial acceleration bias to signal readings. It will be a first approach for a detection and elimination method. Trial Registration: Deutsches Register Klinische Studien DRKS00034402; https://drks.de/search/en/trial/DRKS00034402 International Registered Report Identifier (IRRID): PRR1-10.2196/63306 %M 39326041 %R 10.2196/63306 %U https://www.researchprotocols.org/2024/1/e63306 %U https://doi.org/10.2196/63306 %U http://www.ncbi.nlm.nih.gov/pubmed/39326041 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 8 %N %P e57241 %T Contactless and Calibration-Free Blood Pressure and Pulse Rate Monitor for Screening and Monitoring of Hypertension: Cross-Sectional Validation Study %A Kapoor,Melissa %A Holman,Blair %A Cohen,Carolyn %+ Mind over Matter Medtech Ltd, Kemp House, 160 City Road, London, EC1V 2NX, United Kingdom, 44 07881 927063, melissa@mind-medtech.com %K remote photoplethysmography %K vital signs %K calibration-free blood pressure monitor %K medical device %K hypertension screening %K home blood pressure monitoring %K vital %K vitals %K device %K devices %K hypertension %K hypertensive %K cardiovascular %K cardiology %K heart %K blood pressure %K monitoring %K monitor %K mHealth %K mobile health %K validation %D 2024 %7 5.8.2024 %9 Original Paper %J JMIR Cardio %G English %X Background: The key to reducing the immense morbidity and mortality burdens of cardiovascular diseases is to help people keep their blood pressure (BP) at safe levels. This requires that more people with hypertension be identified, diagnosed, and given tools to lower their BP. BP monitors are critical to hypertension diagnosis and management. However, there are characteristics of conventional BP monitors (oscillometric cuff sphygmomanometers) that hinder rapid and effective hypertension diagnosis and management. Calibration-free, software-only BP monitors that operate on ubiquitous mobile devices can enable on-demand BP monitoring, overcoming the hardware barriers of conventional BP monitors. Objective: This study aims to investigate the accuracy of a contactless BP monitor software app for classifying the full range of clinically relevant BPs as hypertensive or nonhypertensive and to evaluate its accuracy for measuring the pulse rate (PR) and BP of people with BPs relevant to stage-1 hypertension. Methods: The software app, known commercially as Lifelight, was investigated following the data collection and data analysis methodology outlined in International Organization for Standardization (ISO) 81060-2:2018/AMD 1:2020 “Non-invasive Sphygmomanometers—Part 2: Clinical investigation of automated measurement type.” This validation study was conducted by the independent laboratory Element Materials Technology Boulder (formerly Clinimark). The study generated data from 85 people aged 18-85 years with a wide-ranging distribution of BPs specified in ISO 81060-2:2018/AMD 1:2020. At least 20% were required to have Fitzpatrick scale skin tones of 5 or 6 (ie, dark skin tones). The accuracy of the app’s BP measurements was assessed by comparing its BP measurements with measurements made by dual-observer manual auscultation using the same-arm sequential method specified in ISO 81060-2:2018/AMD 1:2020. The accuracy of the app’s PR measurements was assessed by comparing its measurements with concurrent electroencephalography-derived heart rate values. Results: The app measured PR with an accuracy root-mean-square of 1.3 beats per minute and mean absolute error of 1.1 (SD 0.8) beats per minute. The sensitivity and specificity with which it determined that BPs exceeded the in-clinic systolic threshold for hypertension diagnosis were 70.1% and 71.7%, respectively. These rates are consistent with those reported for conventional BP monitors in a literature review by The National Institute for Health and Care Excellence. The app’s mean error for measuring BP in the range of normotension and stage-1 hypertension (ie, 65/85, 76% of participants) was 6.5 (SD 12.9) mm Hg for systolic BP and 0.4 (SD 10.6) mm Hg for diastolic BP. Mean absolute error was 11.3 (SD 10.0) mm Hg and 8.6 (SD 6.8) mm Hg, respectively. Conclusions: A calibration-free, software-only medical device was independently tested against ISO 81060-2:2018/AMD 1:2020. The safety and performance demonstrated in this study suggest that this technique could be a potential solution for rapid and scalable screening and management of hypertension. %M 39102277 %R 10.2196/57241 %U https://cardio.jmir.org/2024/1/e57241 %U https://doi.org/10.2196/57241 %U http://www.ncbi.nlm.nih.gov/pubmed/39102277 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e54009 %T An App for Navigating Patient Transportation and Acute Stroke Care in Northwestern Ontario Using Machine Learning: Retrospective Study %A Hassan,Ayman %A Benlamri,Rachid %A Diner,Trina %A Cristofaro,Keli %A Dillistone,Lucas %A Khallouki,Hajar %A Ahghari,Mahvareh %A Littlefield,Shalyn %A Siddiqui,Rabail %A MacDonald,Russell %A Savage,David W %+ Thunder Bay Regional Health Research Institute, 980 Oliver Road, Thunder Bay, ON, P7B 6Z6, Canada, 1 8076847580, rabail.siddiqui@tbh.net %K stroke care %K acute stroke %K northwestern %K Ontario %K prediction %K models %K machine learning %K stroke %K cardiovascular %K brain %K neuroscience %K TIA %K transient ischemic attack %K coordinated care %K navigation %K navigating %K mHealth %K mobile health %K app %K apps %K applications %K geomapping %K geography %K geographical %K location %K spatial %K predict %K predictions %K predictive %D 2024 %7 1.8.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: A coordinated care system helps provide timely access to treatment for suspected acute stroke. In Northwestern Ontario (NWO), Canada, communities are widespread with several hospitals offering various diagnostic equipment and services. Thus, resources are limited, and health care providers must often transfer patients with stroke to different hospital locations to ensure the most appropriate care access within recommended time frames. However, health care providers frequently situated temporarily (locum) in NWO or providing care remotely from other areas of Ontario may lack sufficient information and experience in the region to access care for a patient with a time-sensitive condition. Suboptimal decision-making may lead to multiple transfers before definitive stroke care is obtained, resulting in poor outcomes and additional health care system costs. Objective: We aimed to develop a tool to inform and assist NWO health care providers in determining the best transfer options for patients with stroke to provide the most efficient care access. We aimed to develop an app using a comprehensive geomapping navigation and estimation system based on machine learning algorithms. This app uses key stroke-related timelines including the last time the patient was known to be well, patient location, treatment options, and imaging availability at different health care facilities. Methods: Using historical data (2008-2020), an accurate prediction model using machine learning methods was developed and incorporated into a mobile app. These data contained parameters regarding air (Ornge) and land medical transport (3 services), which were preprocessed and cleaned. For cases in which Ornge air services and land ambulance medical transport were both involved in a patient transport process, data were merged and time intervals of the transport journey were determined. The data were distributed for training (35%), testing (35%), and validation (30%) of the prediction model. Results: In total, 70,623 records were collected in the data set from Ornge and land medical transport services to develop a prediction model. Various learning models were analyzed; all learning models perform better than the simple average of all points in predicting output variables. The decision tree model provided more accurate results than the other models. The decision tree model performed remarkably well, with the values from testing, validation, and the model within a close range. This model was used to develop the “NWO Navigate Stroke” system. The system provides accurate results and demonstrates that a mobile app can be a significant tool for health care providers navigating stroke care in NWO, potentially impacting patient care and outcomes. Conclusions: The NWO Navigate Stroke system uses a data-driven, reliable, accurate prediction model while considering all variations and is simultaneously linked to all required acute stroke management pathways and tools. It was tested using historical data, and the next step will to involve usability testing with end users. %R 10.2196/54009 %U https://formative.jmir.org/2024/1/e54009 %U https://doi.org/10.2196/54009 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 11 %N %P e57574 %T Investigating Users’ Attitudes Toward Automated Smartwatch Cardiac Arrest Detection: Cross-Sectional Survey Study %A van den Beuken,Wisse M F %A van Schuppen,Hans %A Demirtas,Derya %A van Halm,Vokko P %A van der Geest,Patrick %A Loer,Stephan A %A Schwarte,Lothar A %A Schober,Patrick %K out-of-hospital cardiac arrest %K wearables %K wearable %K digital health %K smartwatch %K automated cardiac arrest detection %K emergency medicine %K emergency %K cardiology %K heart %K cardiac %K cross sectional %K survey %K surveys %K questionnaire %K questionnaires %K experience %K experiences %K attitude %K attitudes %K opinion %K perception %K perceptions %K perspective %K perspectives %K acceptance %K adoption %K willingness %K intent %K intention %D 2024 %7 25.7.2024 %9 %J JMIR Hum Factors %G English %X Background: Out-of-hospital cardiac arrest (OHCA) is a leading cause of mortality in the developed world. Timely detection of cardiac arrest and prompt activation of emergency medical services (EMS) are essential, yet challenging. Automated cardiac arrest detection using sensor signals from smartwatches has the potential to shorten the interval between cardiac arrest and activation of EMS, thereby increasing the likelihood of survival. Objective: This cross-sectional survey study aims to investigate users’ perspectives on aspects of continuous monitoring such as privacy and data protection, as well as other implications, and to collect insights into their attitudes toward the technology. Methods: We conducted a cross-sectional web-based survey in the Netherlands among 2 groups of potential users of automated cardiac arrest technology: consumers who already own a smartwatch and patients at risk of cardiac arrest. Surveys primarily consisted of closed-ended questions with some additional open-ended questions to provide supplementary insight. The quantitative data were analyzed descriptively, and a content analysis of the open-ended questions was conducted. Results: In the consumer group (n=1005), 90.2% (n=906; 95% CI 88.1%-91.9%) of participants expressed an interest in the technology, and 89% (n=1196; 95% CI 87.3%-90.7%) of the patient group (n=1344) showed interest. More than 75% (consumer group: n= 756; patient group: n=1004) of the participants in both groups indicated they were willing to use the technology. The main concerns raised by participants regarding the technology included privacy, data protection, reliability, and accessibility. Conclusions: The vast majority of potential users expressed a strong interest in and positive attitude toward automated cardiac arrest detection using smartwatch technology. However, a number of concerns were identified, which should be addressed in the development and implementation process to optimize acceptance and effectiveness of the technology. %R 10.2196/57574 %U https://humanfactors.jmir.org/2024/1/e57574 %U https://doi.org/10.2196/57574 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e55361 %T The Accuracy of Pulse Oxygen Saturation, Heart Rate, Blood Pressure, and Respiratory Rate Raised by a Contactless Telehealth Portal: Validation Study %A Gerald Dcruz,Julian %A Yeh,Paichang %+ Docsun Biomedical Holdings, Inc, 6763 32ND Ave N, Saint Petersburg, FL, 33710, United States, 1 (813) 4380045, jan.yeh@docsun.health %K medical devices %K mHealth %K vital signs %K measurements validity %K validation %K validity %K device %K devices %K vital %K vitals %K accuracy %K pulse %K oxygen %K saturation %K heart rate %K blood pressure %K respiration %K respiratory %K telehealth %K telemedicine %K eHealth %K e-health %K self-check %K self-checker %K breathing %K portal %K portals %K self-checking %K self-monitor %K self-monitoring %D 2024 %7 28.6.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: The traditional measurement of heart rate (HR), oxygen saturation (SpO2), blood pressure (BP), and respiratory rate (RR) via physical examination can be challenging, and the recent pandemic has accelerated trends toward telehealth and remote monitoring. Instead of going to the physician to check these vital signs, measuring them at home would be more convenient. Vital sign monitors, also known as physiological parameter monitors, are electronic devices that measure and display biological information about patients under constant monitoring. Objective: The purpose of this study was to validate the accuracy of the pulse SpO2, HR, BP, and RR raised by Docsun Telehealth Portal by comparing it with approved medical devices. Methods: This is a noninvasive, self-check, system-based study conducted to validate the detection of vital signs (SpO2, HR, BP, and RR) raised by Docsun Telehealth Portal. The input for software processing involves facial screening without any accessories on the face, scanning directly through the software application portal. The participant’s facial features are detected and screened for the extraction of necessary readings. Results: For the validation of HR, SpO2, BP, and RR measurements, the main outcomes were the mean of the absolute difference between the respective investigational devices and the reference values as well as the absolute percentage difference between the respective investigational devices and the reference values. If the HR was within ±10% of the reference standard or 5 beats per minute, it was considered acceptable for clinical purposes. The average absolute difference between the Docsun Telehealth Portal and the reference values was 1.41 (SD 1.14) beats per minute. The mean absolute percentage difference was 1.69% (SD 1.37). Therefore, the Docsun Telehealth Portal met the predefined accuracy cutoff for HR measurements. If the RR was within ±10% of the reference standard or 3 breaths per minute, it was considered acceptable for clinical purposes. The average absolute difference between the Docsun Telehealth Portal and the reference values was 0.86 breaths per minute. The mean absolute percentage difference was 4.72%. Therefore, the Docsun Telehealth Portal met the predefined accuracy cutoff for RR measurements. SpO2 levels were considered acceptable if the average absolute difference between the Docsun Telehealth Portal and the reference values was ±3%. The mean absolute percentage difference was 0.59%. Therefore, the Docsun Telehealth Portal met the predefined accuracy cutoff for SpO2 measurements. The Docsun Telehealth Portal predicted systolic BP with an accuracy of 94.81% and diastolic BP with an accuracy of 95.71%. Conclusions: The results of the study show that the accuracy of the HR, BP, SpO2, and RR values raised by the Docsun Telehealth Portal, compared against the clinically approved medical devices, proved to be accurate by meeting predefined accuracy guidelines. %M 38598698 %R 10.2196/55361 %U https://formative.jmir.org/2024/1/e55361 %U https://doi.org/10.2196/55361 %U http://www.ncbi.nlm.nih.gov/pubmed/38598698 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 8 %N %P e57111 %T Accurate Modeling of Ejection Fraction and Stroke Volume With Mobile Phone Auscultation: Prospective Case-Control Study %A Huecker,Martin %A Schutzman,Craig %A French,Joshua %A El-Kersh,Karim %A Ghafghazi,Shahab %A Desai,Ravi %A Frick,Daniel %A Thomas,Jarred Jeremy %+ Department of Emergency Medicine, University of Louisville, 530 South Jackson St., Louisville, KY, 40202, United States, 1 5028525689, martin.huecker@louisville.edu %K ejection fraction %K stroke volume %K auscultation %K digital health %K telehealth %K acoustic recording %K acoustic recordings %K acoustic %K mHealth %K mobile health %K mobile phone %K mobile phones %K heart failure %K heart %K cardiac %K cardiology %K health care costs %K audio %K echocardiographic %K echocardiogram %K ultrasonography %K echocardiography %K accuracy %K monitoring %K telemonitoring %K recording %K recordings %K ejection %K machine learning %K algorithm %K algorithms %D 2024 %7 26.6.2024 %9 Original Paper %J JMIR Cardio %G English %X Background: Heart failure (HF) contributes greatly to morbidity, mortality, and health care costs worldwide. Hospital readmission rates are tracked closely and determine federal reimbursement dollars. No current modality or technology allows for accurate measurement of relevant HF parameters in ambulatory, rural, or underserved settings. This limits the use of telehealth to diagnose or monitor HF in ambulatory patients. Objective: This study describes a novel HF diagnostic technology using audio recordings from a standard mobile phone. Methods: This prospective study of acoustic microphone recordings enrolled convenience samples of patients from 2 different clinical sites in 2 separate areas of the United States. Recordings were obtained at the aortic (second intercostal) site with the patient sitting upright. The team used recordings to create predictive algorithms using physics-based (not neural networks) models. The analysis matched mobile phone acoustic data to ejection fraction (EF) and stroke volume (SV) as evaluated by echocardiograms. Using the physics-based approach to determine features eliminates the need for neural networks and overfitting strategies entirely, potentially offering advantages in data efficiency, model stability, regulatory visibility, and physical insightfulness. Results: Recordings were obtained from 113 participants. No recordings were excluded due to background noise or for any other reason. Participants had diverse racial backgrounds and body surface areas. Reliable echocardiogram data were available for EF from 113 patients and for SV from 65 patients. The mean age of the EF cohort was 66.3 (SD 13.3) years, with female patients comprising 38.3% (43/113) of the group. Using an EF cutoff of ≤40% versus >40%, the model (using 4 features) had an area under the receiver operating curve (AUROC) of 0.955, sensitivity of 0.952, specificity of 0.958, and accuracy of 0.956. The mean age of the SV cohort was 65.5 (SD 12.7) years, with female patients comprising 34% (38/65) of the group. Using a clinically relevant SV cutoff of <50 mL versus >50 mL, the model (using 3 features) had an AUROC of 0.922, sensitivity of 1.000, specificity of 0.844, and accuracy of 0.923. Acoustics frequencies associated with SV were observed to be higher than those associated with EF and, therefore, were less likely to pass through the tissue without distortion. Conclusions: This work describes the use of mobile phone auscultation recordings obtained with unaltered cellular microphones. The analysis reproduced the estimates of EF and SV with impressive accuracy. This technology will be further developed into a mobile app that could bring screening and monitoring of HF to several clinical settings, such as home or telehealth, rural, remote, and underserved areas across the globe. This would bring high-quality diagnostic methods to patients with HF using equipment they already own and in situations where no other diagnostic and monitoring options exist. %M 38924781 %R 10.2196/57111 %U https://cardio.jmir.org/2024/1/e57111 %U https://doi.org/10.2196/57111 %U http://www.ncbi.nlm.nih.gov/pubmed/38924781 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 10 %N %P e51210 %T Heart Rate Monitoring Among Breast Cancer Survivors: Quantitative Study of Device Agreement in a Community-Based Exercise Program %A Page,Lindsey L %A Fanning,Jason %A Phipps,Connor %A Berger,Ann %A Reed,Elizabeth %A Ehlers,Diane %+ Department of Quantitative Health Sciences, Division of Epidemiology, Mayo Clinic Arizona, 200 First Street SW, Rochester, AZ, 55905, United States, 1 408 574 2739, ehlers.diane@mayo.edu %K wearable devices %K exercise prescription %K validity %K photoplethysmography %K monitoring %K wearables %K devices %K exercise %K heart rate %K breast cancer %K cancer %K cancer survivor %K community %K chest monitor %K Fitbit %K recovery %K safety %D 2024 %7 20.6.2024 %9 Original Paper %J JMIR Cancer %G English %X Background: Exercise intensity (eg, target heart rate [HR]) is a fundamental component of exercise prescription to elicit health benefits in cancer survivors. Despite the validity of chest-worn monitors, their feasibility in community and unsupervised exercise settings may be challenging. As wearable technology continues to improve, consumer-based wearable sensors may represent an accessible alternative to traditional monitoring, offering additional advantages. Objective: The purpose of this study was to examine the agreement between the Polar H10 chest monitor and Fitbit Inspire HR for HR measurement in breast cancer survivors enrolled in the intervention arm of a randomized, pilot exercise trial. Methods: Participants included breast cancer survivors (N=14; aged 38-72 years) randomized to a 12-week aerobic exercise program. This program consisted of three 60-minute, moderate-intensity walking sessions per week, either in small groups or one-on-one, facilitated by a certified exercise physiologist and held at local community fitness centers. As originally designed, the exercise prescription included 36 supervised sessions at a fitness center. However, due to the COVID-19 pandemic, the number of supervised sessions varied depending on whether participants enrolled before or after March 2020. During each exercise session, HR (in beats per minute) was concurrently measured via a Polar H10 chest monitor and a wrist-worn Fitbit Inspire HR at 5 stages: pre-exercise rest; midpoint of warm-up; midpoint of exercise session; midpoint of cool-down; and postexercise recovery. The exercise physiologist recorded the participant’s HR from each device at the midpoint of each stage. HR agreement between the Polar H10 and Fitbit Inspire HR was assessed using Lin concordance correlation coefficient (rc) with a 95% CI. Lin rc ranges from 0 to 1.00, with 0 indicating no concordance and 1.00 indicating perfect concordance. Relative error rates were calculated to examine differences across exercise session stages. Results: Data were available for 200 supervised sessions across the sample (session per participant: mean 13.33, SD 13.7). By exercise session stage, agreement between the Polar H10 monitor and the Fitbit was highest during pre-exercise seated rest (rc=0.76, 95% CI 0.70-0.81) and postexercise seated recovery (rc=0.89, 95% CI 0.86-0.92), followed by the midpoint of exercise (rc=0.63, 95% CI 0.55-0.70) and cool-down (rc=0.68, 95% CI 0.60-0.74). The agreement was lowest during warm-up (rc=0.39, 95% CI 0.27-0.49). Relative error rates ranged from –3.91% to 3.09% and were greatest during warm-up (relative error rate: mean –3.91, SD 11.92%). Conclusions: The Fitbit overestimated HR during peak exercise intensity, posing risks for overexercising, which may not be safe for breast cancer survivors’ fitness levels. While the Fitbit Inspire HR may be used to estimate exercise HR, precautions are needed when considering participant safety and data interpretation. Trial Registration: Clinicaltrials.gov NCT03980626; https://clinicaltrials.gov/study/NCT03980626?term=NCT03980626&rank=1 %M 38900505 %R 10.2196/51210 %U https://cancer.jmir.org/2024/1/e51210 %U https://doi.org/10.2196/51210 %U http://www.ncbi.nlm.nih.gov/pubmed/38900505 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e56676 %T Association of Smartwatch-Based Heart Rate and Physical Activity With Cardiorespiratory Fitness Measures in the Community: Cohort Study %A Zhang,Yuankai %A Wang,Xuzhi %A Pathiravasan,Chathurangi H %A Spartano,Nicole L %A Lin,Honghuang %A Borrelli,Belinda %A Benjamin,Emelia J %A McManus,David D %A Larson,Martin G %A Vasan,Ramachandran S %A Shah,Ravi V %A Lewis,Gregory D %A Liu,Chunyu %A Murabito,Joanne M %A Nayor,Matthew %+ Sections of Cardiology and Preventive Medicine and Epidemiology, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, 72 E Concord St, Suite L-516, Boston, MA, 02118, United States, 1 617 638 8771, mnayor@bu.edu %K mobile health %K smartwatch %K heart rate %K physical activity %K cardiorespiratory fitness %K cardiopulmonary exercise testing %D 2024 %7 13.6.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Resting heart rate (HR) and routine physical activity are associated with cardiorespiratory fitness levels. Commercial smartwatches permit remote HR monitoring and step count recording in real-world settings over long periods of time, but the relationship between smartwatch-measured HR and daily steps to cardiorespiratory fitness remains incompletely characterized in the community. Objective: This study aimed to examine the association of nonactive HR and daily steps measured by a smartwatch with a multidimensional fitness assessment via cardiopulmonary exercise testing (CPET) among participants in the electronic Framingham Heart Study. Methods: Electronic Framingham Heart Study participants were enrolled in a research examination (2016-2019) and provided with a study smartwatch that collected longitudinal HR and physical activity data for up to 3 years. At the same examination, the participants underwent CPET on a cycle ergometer. Multivariable linear models were used to test the association of CPET indices with nonactive HR and daily steps from the smartwatch. Results: We included 662 participants (mean age 53, SD 9 years; n=391, 59% women, n=599, 91% White; mean nonactive HR 73, SD 6 beats per minute) with a median of 1836 (IQR 889-3559) HR records and a median of 128 (IQR 65-227) watch-wearing days for each individual. In multivariable-adjusted models, lower nonactive HR and higher daily steps were associated with higher peak oxygen uptake (VO2), % predicted peak VO2, and VO2 at the ventilatory anaerobic threshold, with false discovery rate (FDR)–adjusted P values <.001 for all. Reductions of 2.4 beats per minute in nonactive HR, or increases of nearly 1000 daily steps, corresponded to a 1.3 mL/kg/min higher peak VO2. In addition, ventilatory efficiency (VE/VCO2; FDR-adjusted P=.009), % predicted maximum HR (FDR-adjusted P<.001), and systolic blood pressure-to-workload slope (FDR-adjusted P=.01) were associated with nonactive HR but not associated with daily steps. Conclusions: Our findings suggest that smartwatch-based assessments are associated with a broad array of cardiorespiratory fitness responses in the community, including measures of global fitness (peak VO2), ventilatory efficiency, and blood pressure response to exercise. Metrics captured by wearable devices offer a valuable opportunity to use extensive data on health factors and behaviors to provide a window into individual cardiovascular fitness levels. %M 38870519 %R 10.2196/56676 %U https://www.jmir.org/2024/1/e56676 %U https://doi.org/10.2196/56676 %U http://www.ncbi.nlm.nih.gov/pubmed/38870519 %0 Journal Article %@ 2562-7600 %I JMIR Publications %V 7 %N %P e56616 %T Evaluation of Autonomic Nervous System Function During Sleep by Mindful Breathing Using a Tablet Device: Randomized Controlled Trial %A Togo,Eiichi %A Takami,Miki %A Ishigaki,Kyoko %+ Department of Nursing, Faculty of Nursing, Hyogo University, 2301, Hiraoka-cho Shinzaike, Kakogawa City, 675-0195, Japan, 81 794279516, tougo@hyogo-dai.ac.jp %K mindfulness %K sleep %K cardiac potential %K low frequency %K high frequency %K mobile phone %D 2024 %7 12.6.2024 %9 Original Paper %J JMIR Nursing %G English %X Background: One issue to be considered in universities is the need for interventions to improve sleep quality and educational systems for university students. However, sleep problems remain unresolved. As a clinical practice technique, a mindfulness-based stress reduction method can help students develop mindfulness skills to cope with stress, self-healing skills, and sleep. Objective: We aim to verify the effectiveness of mindful breathing exercises using a tablet device. Methods: In total, 18 nursing students, aged 18-22 years, were randomly assigned and divided equally into mindfulness (Mi) and nonmindfulness (nMi) implementation groups using tablet devices. During the 9-day experimental period, cardiac potentials were measured on days 1, 5, and 9. In each sleep stage (sleep with sympathetic nerve dominance, shallow sleep with parasympathetic nerve dominance, and deep sleep with parasympathetic nerve dominance), low frequency (LF) value, high frequency (HF) value, and LF/HF ratios obtained from the cardiac potentials were evaluated. Results: On day 5, a significant correlation was observed between sleep duration and each sleep stage in both groups. In comparison to each experimental day, the LF and LF/HF ratios of the Mi group were significantly higher on day 1 than on days 5 and 10. LF and HF values in the nMi group were significantly higher on day 1 than on day 5. Conclusions: The correlation between sleep duration and each sleep stage on day 5 suggested that sleep homeostasis in both groups was activated on day 5, resulting in similar changes in sleep stages. During the experimental period, the cardiac potentials in the nMi group showed a wide range of fluctuations, whereas the LF values and LF/HF ratio in the Mi group showed a decreasing trend over time. This finding suggests that implementing mindful breathing exercises using a tablet device may suppress sympathetic activity during sleep. Trial Registration: UMIN-CTR Clinical Trials Registry UMIN000054639; https://tinyurl.com/mu2vdrks %M 38865177 %R 10.2196/56616 %U https://nursing.jmir.org/2024/1/e56616 %U https://doi.org/10.2196/56616 %U http://www.ncbi.nlm.nih.gov/pubmed/38865177 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e54256 %T Performance of a Wearable Ring in Controlled Hypoxia: A Prospective Observational Study %A Mastrototaro,John J %A Leabman,Michael %A Shumate,Joe %A Tompkins,Kim L %+ Movano Inc dba Movano Health, 6800 Koll Center Pkwy, Suite 160, Pleasanton, CA, 94566, United States, 1 408 981 4889, ktompkins@movano.com %K pulse oximetry %K SpO2 %K pulse oximeter %K hypoxia %K hypoxemia %K clinical trial %K accuracy %K digital health %K wearable %K smart ring %K ISO 80601-2-61 %K racial bias %D 2024 %7 5.6.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Over recent years, technological advances in wearables have allowed for continuous home monitoring of heart rate and oxygen saturation. These devices have primarily been used for sports and general wellness and may not be suitable for medical decision-making, especially in saturations below 90% and in patients with dark skin color. Wearable clinical-grade saturation of peripheral oxygen (SpO2) monitoring can be of great value to patients with chronic diseases, enabling them and their clinicians to better manage their condition with reliable real-time and trend data. Objective: This study aimed to determine the SpO2 accuracy of a wearable ring pulse oximeter compared with arterial oxygen saturation (SaO2) in a controlled hypoxia study based on the International Organization for Standardization (ISO) 80601-2-61:2019 standard over the range of 70%-100% SaO2 in volunteers with a broad range of skin color (Fitzpatrick I to VI) during nonmotion conditions. In parallel, accuracy was compared with a calibrated clinical-grade reference pulse oximeter (Masimo Radical-7). Acceptable medical device accuracy was defined as a maximum of 4% root mean square error (RMSE) per the ISO 80601-2-61 standard and a maximum of 3.5% RMSE per the US Food and Drug Administration guidance. Methods: We performed a single-center, blinded hypoxia study of the test device in 11 healthy volunteers at the Hypoxia Research Laboratory, University of California at San Francisco, under the direction of Philip Bickler, MD, PhD, and John Feiner, MD. Each volunteer was connected to a breathing apparatus for the administration of a hypoxic gas mixture. To facilitate frequent blood gas sampling, a radial arterial cannula was placed on either wrist of each participant. One test device was placed on the index finger and another test device was placed on the fingertip. SaO2 analysis was performed using an ABL-90 multi-wavelength oximeter. Results: For the 11 participants included in the analysis, there were 236, 258, and 313 SaO2-SpO2 data pairs for the test device placed on the finger, the test device placed on the fingertip, and the reference device, respectively. The RMSE of the test device for all participants was 2.1% for either finger or fingertip placement, while the Masimo Radical-7 reference pulse oximeter RMSE was 2.8%, exceeding the standard (4% or less) and the Food and Drug Administration guidance (3.5% or less). Accuracy of SaO2-SpO2 paired data from the 4 participants with dark skin in the study was separately analyzed for both test device placements and the reference device. The test and reference devices exceeded the minimum accuracy requirements for a medical device with RMSE at 1.8% (finger) and 1.6% (fingertip) and for the reference device at 2.9%. Conclusions: The wearable ring meets an acceptable standard of accuracy for clinical-grade SpO2 under nonmotion conditions without regard to skin color. Trial Registration: ClinicalTrials.gov NCT05920278; https://clinicaltrials.gov/study/NCT05920278 %M 38838332 %R 10.2196/54256 %U https://formative.jmir.org/2024/1/e54256 %U https://doi.org/10.2196/54256 %U http://www.ncbi.nlm.nih.gov/pubmed/38838332 %0 Journal Article %@ 2291-5222 %I %V 12 %N %P e53964 %T Cardiac Health Assessment Using a Wearable Device Before and After Transcatheter Aortic Valve Implantation: Prospective Study %A Eerdekens,Rob %A Zelis,Jo %A ter Horst,Herman %A Crooijmans,Caia %A van 't Veer,Marcel %A Keulards,Danielle %A Kelm,Marcus %A Archer,Gareth %A Kuehne,Titus %A Brueren,Guus %A Wijnbergen,Inge %A Johnson,Nils %A Tonino,Pim %K aortic valve stenosis %K health watch %K quality of life %K heart %K cardiology %K cardiac %K aortic %K valve %K stenosis %K watch %K smartwatch %K wearables %K 6MWT %K walking %K test %K QoL %K WHOQOL-BREF %K 6-minute walking test %D 2024 %7 3.6.2024 %9 %J JMIR Mhealth Uhealth %G English %X Background: Due to aging of the population, the prevalence of aortic valve stenosis will increase drastically in upcoming years. Consequently, transcatheter aortic valve implantation (TAVI) procedures will also expand worldwide. Optimal selection of patients who benefit with improved symptoms and prognoses is key, since TAVI is not without its risks. Currently, we are not able to adequately predict functional outcomes after TAVI. Quality of life measurement tools and traditional functional assessment tests do not always agree and can depend on factors unrelated to heart disease. Activity tracking using wearable devices might provide a more comprehensive assessment. Objective: This study aimed to identify objective parameters (eg, change in heart rate) associated with improvement after TAVI for severe aortic stenosis from a wearable device. Methods: In total, 100 patients undergoing routine TAVI wore a Philips Health Watch device for 1 week before and after the procedure. Watch data were analyzed offline—before TAVI for 97 patients and after TAVI for 75 patients. Results: Parameters such as the total number of steps and activity time did not change, in contrast to improvements in the 6-minute walking test (6MWT) and physical limitation domain of the transformed WHOQOL-BREF questionnaire. Conclusions: These findings, in an older TAVI population, show that watch-based parameters, such as the number of steps, do not change after TAVI, unlike traditional 6MWT and QoL assessments. Basic wearable device parameters might be less appropriate for measuring treatment effects from TAVI. %R 10.2196/53964 %U https://mhealth.jmir.org/2024/1/e53964 %U https://doi.org/10.2196/53964 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e55953 %T Evaluation of Ambient Sensor Systems for the Early Detection of Heart Failure Decompensation in Older Patients Living at Home Alone: Protocol for a Prospective Cohort Study %A Vögeli,Benjamin %A Arenja,Nisha %A Schütz,Narayan %A Nef,Tobias %A Buluschek,Philipp %A Saner,Hugo %+ ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, Bern, 3008, Switzerland, 41 31 632 7575, hugo.saner@unibe.ch %K heart failure %K home telemonitoring %K digital health %K biomarker %K ambient sensor system %D 2024 %7 31.5.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: The results of telemedicine intervention studies in patients with heart failure (HF) to reduce rehospitalization rate and mortality by early detection of HF decompensation are encouraging. However, the benefits are lower than expected. A possible reason for this could be the fact that vital signs, including blood pressure, heart rate, heart rhythm, and weight changes, may not be ideal indicators of the early stages of HF decompensation but are more sensitive for acute events triggered by ischemic episodes or rhythm disturbances. Preliminary results indicate a potential role of ambient sensor–derived digital biomarkers in this setting. Objective: The aim of this study is to identify changes in ambient sensor system–derived digital biomarkers with a high potential for early detection of HF decompensation. Methods: This is a prospective interventional cohort study. A total of 24 consecutive patients with HF aged 70 years and older, living alone, and hospitalized for HF decompensation will be included. Physical activity in the apartment and toilet visits are quantified using a commercially available, passive, infrared motion sensing system (DomoHealth SA). Heart rate, respiration rate, and toss-and-turns in bed are recorded by using a commercially available Emfit QS device (Emfit Ltd), which is a contact-free piezoelectric sensor placed under the participant’s mattress. Sensor data are visualized on a dedicated dashboard for easy monitoring by health professionals. Digital biomarkers are evaluated for predefined signs of HF decompensation, including particularly decreased physical activity; time spent in bed; increasing numbers of toilet visits at night; and increasing heart rate, respiration rate, and motion in bed at night. When predefined changes in digital biomarkers occur, patients will be called in for clinical evaluation, and N-terminal pro b-type natriuretic peptide measurement (an increase of >30% considered as significant) will be performed. The sensitivity and specificity of the different biomarkers and their combinations for the detection of HF decompensation will be calculated. Results: The study is in the data collection phase. Study recruitment started in February 2024. Data analysis is scheduled to start after all data are collected. As of manuscript submission, 5 patients have been recruited. Results are expected to be published by the end of 2025. Conclusions: The results of this study will add to the current knowledge about opportunities for telemedicine to monitor older patients with HF living at home alone by evaluating the potential of ambient sensor systems for this purpose. Timely recognition of HF decompensation could enable proactive management, potentially reducing health care costs associated with preventable emergency presentations or hospitalizations. Trial Registration: ClinicalTrials.gov NCT06126848; https://clinicaltrials.gov/study/NCT06126848 International Registered Report Identifier (IRRID): PRR1-10.2196/55953 %M 38820577 %R 10.2196/55953 %U https://www.researchprotocols.org/2024/1/e55953 %U https://doi.org/10.2196/55953 %U http://www.ncbi.nlm.nih.gov/pubmed/38820577 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e52647 %T Mobile Electrocardiograms in the Detection of Subclinical Atrial Fibrillation in High-Risk Outpatient Populations: Protocol for an Observational Study %A Mittal,Ajay %A Elkaldi,Yasmine %A Shih,Susana %A Nathu,Riken %A Segal,Mark %+ College of Medicine, University of Florida, 1600 SW Archer Road, Gainesville, FL, 32611, United States, 1 352 615 8883, ajaymittal2400@gmail.com %K mobile ECG %K digital health %K cardiology %K ECG %K electrocardiogram %K atrial fibrillation %K outpatient %K randomized %K controlled trial %K controlled trials %K smartphone %K mobile health %K app %K apps %K feasibility %K effectiveness %K KardiaMobile single-lead ECGs %K mobile phone %D 2024 %7 27.5.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Single-lead, smartphone-based mobile electrocardiograms (ECGs) have the potential to provide a noninvasive, rapid, and cost-effective means of screening for atrial fibrillation (AFib) in outpatient settings. AFib has been associated with various comorbid diseases that prompt further investigation and screening methodologies for at-risk populations. A simple 30-second sinus rhythm strip from the KardiaMobile ECG (AliveCor) can provide an effective screen for cardiac rhythm abnormalities. Objective: The aim of this study is to demonstrate the feasibility of performing Kardia-enabled ECG recordings routinely in outpatient settings in high-risk populations and its potential use in uncovering previous undiagnosed cases of AFib. Specific aim 1 is to determine the feasibility and accuracy of performing routine cardiac rhythm sampling in patients deemed at high risk for AFib. Specific aim 2 is to determine whether routine rhythm sampling in outpatient clinics with high-risk patients can be used cost-effectively in an outpatient clinic without increasing the time it takes for the patient to be seen by a physician. Methods: Participants were recruited across 6 clinic sites across the University of Florida Health Network: University of Florida Health Nephrology, Sleep Center, Ophthalmology, Urology, Neurology, and Pre-Surgical. Participants, aged 18-99 years, who agreed to partake in the study were given a consent form and completed a questionnaire regarding their past medical history and risk factors for cardiovascular disease. Single-lead, 30-second ECGs were taken by the KardiaMobile ECG device. If patients are found to have newly diagnosed AFib, the attending physician is notified, and a 12-lead ECG or standard ECG equivalent will be ordered. Results: As of March 1, 2024, a total of 2339 participants have been enrolled. Of the data collected thus far, the KardiaMobile rhythm strip reported 381 abnormal readings, which are pending analysis from a cardiologist. A total of 78 readings were labeled as possible AFib, 159 readings were labeled unclassified, and 49 were unreadable. Of note, the average age of participants was 61 (SD 10.25) years, and the average self-reported weight was 194 (SD 14.26) pounds. Additionally, 1572 (67.25%) participants report not regularly seeing a cardiologist. Regarding feasibility, the average length of enrolling a patient into the study was 3:30 (SD 0.5) minutes after informed consent was completed, and medical staff across clinic sites (n=25) reported 9 of 10 level of satisfaction with the impact of the screening on clinic flow. Conclusions: Preliminary data show promise regarding the feasibility of using KardiaMobile ECGs for the screening of AFib and prevention of cardiological disease in vulnerable outpatient populations. The use of a single-lead mobile ECG strip can serve as a low-cost, effective AFib screen for implementation across free clinics attempting to provide increased health care accessibility. International Registered Report Identifier (IRRID): DERR1-10.2196/52647 %M 38801762 %R 10.2196/52647 %U https://www.researchprotocols.org/2024/1/e52647 %U https://doi.org/10.2196/52647 %U http://www.ncbi.nlm.nih.gov/pubmed/38801762 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 11 %N %P e55552 %T Remote Short Sessions of Heart Rate Variability Biofeedback Monitored With Wearable Technology: Open-Label Prospective Feasibility Study %A Hirten,Robert P %A Danieletto,Matteo %A Landell,Kyle %A Zweig,Micol %A Golden,Eddye %A Pyzik,Renata %A Kaur,Sparshdeep %A Chang,Helena %A Helmus,Drew %A Sands,Bruce E %A Charney,Dennis %A Nadkarni,Girish %A Bagiella,Emilia %A Keefer,Laurie %A Fayad,Zahi A %+ The Dr Henry D Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY, 10029, United States, 1 212 241 6500, robert.hirten@mountsinai.org %K biofeedback %K digital health %K digital technology %K health care worker %K HCW %K heart rate variability %K mHealth %K mobile health %K mobile phone %K remote monitoring %K smartphone %K wearable devices %D 2024 %7 25.4.2024 %9 Original Paper %J JMIR Ment Health %G English %X Background: Heart rate variability (HRV) biofeedback is often performed with structured education, laboratory-based assessments, and practice sessions. It has been shown to improve psychological and physiological function across populations. However, a means to remotely use and monitor this approach would allow for wider use of this technique. Advancements in wearable and digital technology present an opportunity for the widespread application of this approach. Objective: The primary aim of the study was to determine the feasibility of fully remote, self-administered short sessions of HRV-directed biofeedback in a diverse population of health care workers (HCWs). The secondary aim was to determine whether a fully remote, HRV-directed biofeedback intervention significantly alters longitudinal HRV over the intervention period, as monitored by wearable devices. The tertiary aim was to estimate the impact of this intervention on metrics of psychological well-being. Methods: To determine whether remotely implemented short sessions of HRV biofeedback can improve autonomic metrics and psychological well-being, we enrolled HCWs across 7 hospitals in New York City in the United States. They downloaded our study app, watched brief educational videos about HRV biofeedback, and used a well-studied HRV biofeedback program remotely through their smartphone. HRV biofeedback sessions were used for 5 minutes per day for 5 weeks. HCWs were then followed for 12 weeks after the intervention period. Psychological measures were obtained over the study period, and they wore an Apple Watch for at least 7 weeks to monitor the circadian features of HRV. Results: In total, 127 HCWs were enrolled in the study. Overall, only 21 (16.5%) were at least 50% compliant with the HRV biofeedback intervention, representing a small portion of the total sample. This demonstrates that this study design does not feasibly result in adequate rates of compliance with the intervention. Numerical improvement in psychological metrics was observed over the 17-week study period, although it did not reach statistical significance (all P>.05). Using a mixed effect cosinor model, the mean midline-estimating statistic of rhythm (MESOR) of the circadian pattern of the SD of the interbeat interval of normal sinus beats (SDNN), an HRV metric, was observed to increase over the first 4 weeks of the biofeedback intervention in HCWs who were at least 50% compliant. Conclusions: In conclusion, we found that using brief remote HRV biofeedback sessions and monitoring its physiological effect using wearable devices, in the manner that the study was conducted, was not feasible. This is considering the low compliance rates with the study intervention. We found that remote short sessions of HRV biofeedback demonstrate potential promise in improving autonomic nervous function and warrant further study. Wearable devices can monitor the physiological effects of psychological interventions. %M 38663011 %R 10.2196/55552 %U https://mental.jmir.org/2024/1/e55552 %U https://doi.org/10.2196/55552 %U http://www.ncbi.nlm.nih.gov/pubmed/38663011 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e47525 %T National Tunisian Study of Cardiac Implantable Electronic Devices: Design and Protocol for a Nationwide Multicenter Prospective Observational Study %A Chabrak,Sonia %A Haggui,Abdeddayem %A Allouche,Emna %A Ouali,Sana %A Ben Halima,Afef %A Kacem,Slim %A Krichen,Salma %A Marrakchi,Sonia %A Fehri,Wafa %A Mourali,Mohamed Sami %A Jabbari,Zeineb %A Ben Halima,Manel %A Neffati,Elyes %A Heraiech,Aymen  %A Slim,Mehdi %A Kachboura,Salem %A Gamra,Habib %A Hassine,Majed %A Kraiem,Sondes %A Kammoun,Sofien %A Bezdah,Leila %A Jridi,Gouider %A Bouraoui,Hatem %A Kammoun,Samir %A Hammami,Rania %A Chettaoui,Rafik %A Ben Ameur,Youssef %A Azaiez,Fares %A Tlili,Rami %A Battikh,Kais %A Ben Slima,Hedi %A Chrigui,Rim %A Fazaa,Samia %A Sanaa,Islem %A Ellouz,Yassine %A Mosrati,Mohamed %A Milouchi,Sami %A Jarmouni,Soumaya %A Ayadi,Wacef %A Akrout,Malek %A Razgallah,Rabie %A Neffati,Wissal %A Drissa,Meriem %A Charfeddine,Selma %A Abdessalem,Salem %A Abid,Leila %A Zakhama,Lilia %+ Pasteur Clinic, General and Cardiovascular Clinic of Tunis, Elkhadhra avenue, X 2 Pathway, Tunis, 1003, Tunisia, 216 22 889 349, chabraksonia04@gmail.com %K Tunisia %K study %K pacemaker %K implantable cardioverter defibrillator %K cardiac resynchronization therapy %K design %K complication %D 2024 %7 8.4.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: In Tunisia, the number of cardiac implantable electronic devices (CIEDs) is increasing, owing to the increase in patient life expectancy and expanding indications. Despite their life-saving potential and a significant reduction in population morbidity and mortality, their increased numbers have been associated with the development of multiple early and late complications related to vascular access, pockets, leads, or patient characteristics. Objective: The study aims to identify the rate, type, and predictors of complications occurring within the first year after CIED implantation. It also aims to describe the demographic and epidemiological characteristics of a nationwide sample of patients with CIED in Tunisia. Additionally, the study will evaluate the extent to which Tunisian electrophysiologists follow international guidelines for cardiac pacing and sudden cardiac death prevention. Methods: The Tunisian National Study of Cardiac Implantable Electronic Devices (NATURE-CIED) is a national, multicenter, prospectively monitored study that includes consecutive patients who underwent primary CIED implantation, generator replacement, and upgrade procedure. Patients were enrolled between January 18, 2021, and February 18, 2022, at all Tunisian public and private CIED implantation centers that agreed to participate in the study. All enrolled patients entered a 1-year follow-up period, with 4 consecutive visits at 1, 3, 6, and 12 months after CIED implantation. The collected data are recorded electronically on the clinical suite platform (DACIMA Clinical Suite). Results: The study started on January 18, 2021, and concluded on February 18, 2023. In total, 27 cardiologists actively participated in data collection. Over this period, 1500 patients were enrolled in the study consecutively. The mean age of the patients was 70.1 (SD 15.2) years, with a sex ratio of 1:15. Nine hundred (60%) patients were from the public sector, while 600 (40%) patients were from the private sector. A total of 1298 (86.3%) patients received a conventional pacemaker and 75 (5%) patients received a biventricular pacemaker (CRT-P). Implantable cardioverter defibrillators were implanted in 127 (8.5%) patients. Of these patients, 45 (3%) underwent CRT-D implantation. Conclusions: This study will establish the most extensive contemporary longitudinal cohort of patients undergoing CIED implantation in Tunisia, presenting a significant opportunity for real-world clinical epidemiology. It will address a crucial gap in the management of patients during the perioperative phase and follow-up, enabling the identification of individuals at particularly high risk of complications for optimal care. Trial Registration: ClinicalTrials.gov NCT05361759; https://classic.clinicaltrials.gov/ct2/show/NCT05361759 International Registered Report Identifier (IRRID): RR1-10.2196/47525 %M 38588529 %R 10.2196/47525 %U https://www.researchprotocols.org/2024/1/e47525 %U https://doi.org/10.2196/47525 %U http://www.ncbi.nlm.nih.gov/pubmed/38588529 %0 Journal Article %@ 2561-3278 %I JMIR Publications %V 9 %N %P e46974 %T Validation of a Novel Noninvasive Technology to Estimate Blood Oxygen Saturation Using Green Light: Observational Study %A Gokhale,Sanjay %A Daggubati,Vinoop %A Alexandrakis,Georgios %+ Department of Biomedical Engineering, The University of Texas at Arlington, 500 UTA Blvd, Arlington, TX, 76010, United States, 1 8172645227, rajhanssanjay@gmail.com %K reflectance spectroscopy %K tissue oxygen measurements %K oxygen saturation %K pulse oximeter %K oxyhemoglobin concentration %K oxygen level %K racial disparity %D 2024 %7 27.3.2024 %9 Original Paper %J JMIR Biomed Eng %G English %X Background: Pulse oximeters work within the red-infrared wavelengths. Therefore, these oximeters produce erratic results in dark-skinned subjects and in subjects with cold extremities. Pulse oximetry is routinely performed in patients with fever; however, an elevation in body temperature decreases the affinity of hemoglobin for oxygen, causing a drop in oxygen saturation or oxyhemoglobin concentrations. Objective: We aimed to determine whether our new investigational device, the Shani device or SH1 (US Patent 11191460), detects a drop in oxygen saturation or a decrease in oxyhemoglobin concentrations. Methods: An observational study (phase 1) was performed in two separate groups to validate measurements of hemoglobin and oxygen concentrations, including 39 participants recruited among current university students and staff aged 20-40 years. All volunteers completed baseline readings using the SH1 device and the commercially available Food and Drug Administration–approved pulse oximeter Masimo. SH1 uses two light-emitting diodes in which the emitted wavelengths match with absorption peaks of oxyhemoglobin (hemoglobin combined with oxygen) and deoxyhemoglobin (hemoglobin without oxygen or reduced hemoglobin). Total hemoglobin was calculated as the sum of oxyhemoglobin and deoxyhemoglobin. Subsequently, 16 subjects completed the “heat jacket study” and the others completed the “blood donation study.” Masimo was consistently used on the finger for comparison. The melanin level was accounted for using the von Luschan skin color scale (VLS) and a specifically designed algorithm. We here focus on the results of the heat jacket study, in which the subject wore a double-layered heated jacket and pair of trousers including a network of polythene tubules along with an inlet and outlet. Warm water was circulated to increase the body temperature by 0.5-0.8 °C above the baseline body temperature. We expected a slight drop in oxyhemoglobin concentrations in the heating phase at the tissue level. Results: The mean age of the participants was 24.1 (SD 0.8) years. The skin tone varied from 12 to 36 on the VLS, representing a uniform distribution with one-third of the participants having fair skin, brown skin, and dark skin, respectively. Using a specific algorithm and software, the reflection ratio for oxyhemoglobin was displayed on the screen of the device along with direct hemoglobin values. The SH1 device picked up more minor changes in oxyhemoglobin levels after a change in body temperature compared to the pulse oximeter, with a maximum drop in oxyhemoglobin concentration detected of 6.5% and 2.54%, respectively. Conclusions: Our new investigational device SH1 measures oxygen saturation at the tissue level by reflectance spectroscopy using green wavelengths. This device fared well regardless of skin color. This device can thus eliminate racial disparity in these key biomarker assessments. Moreover, since the light is shone on the wrist, SH1 can be readily miniaturized into a wearable device. %M 38875701 %R 10.2196/46974 %U https://biomedeng.jmir.org/2024/1/e46974 %U https://doi.org/10.2196/46974 %U http://www.ncbi.nlm.nih.gov/pubmed/38875701 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e46098 %T Three-Day Monitoring of Adhesive Single-Lead Electrocardiogram Patch for Premature Ventricular Complex: Prospective Study for Diagnosis Validation and Evaluation of Burden Fluctuation %A Ahn,Hyo-Jeong %A Choi,Eue-Keun %A Lee,So-Ryoung %A Kwon,Soonil %A Song,Hee-Seok %A Lee,Young-Shin %A Oh,Seil %+ Department of Internal Medicine, Seoul National University Hospital, 101 Daehak-ro, Seoul, 03080, Republic of Korea, 82 220720688, choiek417@gmail.com %K premature ventricular complex %K single-lead electrocardiogram %K wearable device %K extended monitor %K electrocardiogram %K EKG %K ECG %K wearable %K heart %K cardiology %K cardiovascular %D 2024 %7 21.3.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Wearable electrocardiogram (ECG) monitoring devices are used worldwide. However, data on the diagnostic yield of an adhesive single-lead ECG patch (SEP) to detect premature ventricular complex (PVC) and the optimal duration of wearing an SEP for PVC burden assessment are limited. Objective: We aimed to validate the diagnostic yield of an SEP (mobiCARE MC-100, Seers Technology) for PVC detection and evaluate the PVC burden variation recorded by the SEP over a 3-day monitoring period. Methods: This is a prospective study of patients with documented PVC on a 12-lead ECG. Patients underwent simultaneous ECG monitoring with the 24-hour Holter monitor and SEP on the first day. On the subsequent second and third days, ECG monitoring was continued using only SEP, and a 3-day extended monitoring was completed. The diagnostic yield of SEP for PVC detection was evaluated by comparison with the results obtained on the first day of Holter monitoring. The PVC burden monitored by SEP for 3 days was used to assess the daily and 6-hour PVC burden variations. The number of patients additionally identified to reach PVC thresholds of 10%, 15%, and 20% during the 3-day extended monitoring by SEP and the clinical factors associated with the higher PVC burden variations were explored. Results: The recruited data of 134 monitored patients (mean age, 54.6 years; males, 45/134, 33.6%) were analyzed. The median daily PVC burden of these patients was 2.4% (IQR 0.2%-10.9%), as measured by the Holter monitor, and 3.3% (IQR 0.3%-11.7%), as measured in the 3-day monitoring by SEP. The daily PVC burden detected on the first day of SEP was in agreement with that of the Holter monitor: the mean difference was –0.07%, with 95% limits of agreement of –1.44% to 1.30%. A higher PVC burden on the first day was correlated with a higher daily (R2=0.34) and 6-hour burden variation (R2=0.48). Three-day monitoring by SEP identified 29% (12/42), 18% (10/56), and 7% (4/60) more patients reaching 10%, 15%, and 20% of daily PVC burden, respectively. Younger age was additionally associated with the identification of clinically significant PVC burden during the extended monitoring period (P=.02). Conclusions: We found that the mobiCARE MC-100 SEP accurately detects PVC with comparable diagnostic yield to the 24-hour Holter monitor. Performing 3-day PVC monitoring with SEP, especially among younger patients, may offer a pragmatic alternative for identifying more individuals exceeding the clinically significant PVC burden threshold. %M 38512332 %R 10.2196/46098 %U https://www.jmir.org/2024/1/e46098 %U https://doi.org/10.2196/46098 %U http://www.ncbi.nlm.nih.gov/pubmed/38512332 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e47803 %T Optimization of Using Multiple Machine Learning Approaches in Atrial Fibrillation Detection Based on a Large-Scale Data Set of 12-Lead Electrocardiograms: Cross-Sectional Study %A Chuang,Beau Bo-Sheng %A Yang,Albert C %+ Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, No 155, Li-Nong St, Sec.2, Beitou District, Taipei, 112304, Taiwan, 886 228267995, accyang@nycu.edu.tw %K machine learning %K atrial fibrillation %K light gradient boosting machine %K power spectral density %K digital health %K electrocardiogram %K machine learning algorithm %K atrial fibrillation detection %K real-time %K detection %K electrocardiography leads %K clinical outcome %D 2024 %7 11.3.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Atrial fibrillation (AF) represents a hazardous cardiac arrhythmia that significantly elevates the risk of stroke and heart failure. Despite its severity, its diagnosis largely relies on the proficiency of health care professionals. At present, the real-time identification of paroxysmal AF is hindered by the lack of automated techniques. Consequently, a highly effective machine learning algorithm specifically designed for AF detection could offer substantial clinical benefits. We hypothesized that machine learning algorithms have the potential to identify and extract features of AF with a high degree of accuracy, given the intricate and distinctive patterns present in electrocardiogram (ECG) recordings of AF. Objective: This study aims to develop a clinically valuable machine learning algorithm that can accurately detect AF and compare different leads’ performances of AF detection. Methods: We used 12-lead ECG recordings sourced from the 2020 PhysioNet Challenge data sets. The Welch method was used to extract power spectral features of the 12-lead ECGs within a frequency range of 0.083 to 24.92 Hz. Subsequently, various machine learning techniques were evaluated and optimized to classify sinus rhythm (SR) and AF based on these power spectral features. Furthermore, we compared the effects of different frequency subbands and different lead selections on machine learning performances. Results: The light gradient boosting machine (LightGBM) was found to be the most effective in classifying AF and SR, achieving an average F1-score of 0.988 across all ECG leads. Among the frequency subbands, the 0.083 to 4.92 Hz range yielded the highest F1-score of 0.985. In interlead comparisons, aVR had the highest performance (F1=0.993), with minimal differences observed between leads. Conclusions: In conclusion, this study successfully used machine learning methodologies, particularly the LightGBM model, to differentiate SR and AF based on power spectral features derived from 12-lead ECGs. The performance marked by an average F1-score of 0.988 and minimal interlead variation underscores the potential of machine learning algorithms to bolster real-time AF detection. This advancement could significantly improve patient care in intensive care units as well as facilitate remote monitoring through wearable devices, ultimately enhancing clinical outcomes. %M 38466973 %R 10.2196/47803 %U https://formative.jmir.org/2024/1/e47803 %U https://doi.org/10.2196/47803 %U http://www.ncbi.nlm.nih.gov/pubmed/38466973 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 8 %N %P e45130 %T Cloud-Based Machine Learning Platform to Predict Clinical Outcomes at Home for Patients With Cardiovascular Conditions Discharged From Hospital: Clinical Trial %A Yang,Phillip C %A Jha,Alokkumar %A Xu,William %A Song,Zitao %A Jamp,Patrick %A Teuteberg,Jeffrey J %+ Stanford University School of Medicine, 300 Pasteur Dr # H2157 Stanford, Palo Alto, CA, 94305-2200, United States, 1 6508048828, phillip@stanford.edu %K smart sensor %K wearable technology %K moving average %K physical activity %K artificial intelligence %K AI %D 2024 %7 1.3.2024 %9 Original Paper %J JMIR Cardio %G English %X Background: Hospitalizations account for almost one-third of the US $4.1 trillion health care cost in the United States. A substantial portion of these hospitalizations are attributed to readmissions, which led to the establishment of the Hospital Readmissions Reduction Program (HRRP) in 2012. The HRRP reduces payments to hospitals with excess readmissions. In 2018, >US $700 million was withheld; this is expected to exceed US $1 billion by 2022. More importantly, there is nothing more physically and emotionally taxing for readmitted patients and demoralizing for hospital physicians, nurses, and administrators. Given this high uncertainty of proper home recovery, intelligent monitoring is needed to predict the outcome of discharged patients to reduce readmissions. Physical activity (PA) is one of the major determinants for overall clinical outcomes in diabetes, hypertension, hyperlipidemia, heart failure, cancer, and mental health issues. These are the exact comorbidities that increase readmission rates, underlining the importance of PA in assessing the recovery of patients by quantitative measurement beyond the questionnaire and survey methods. Objective: This study aims to develop a remote, low-cost, and cloud-based machine learning (ML) platform to enable the precision health monitoring of PA, which may fundamentally alter the delivery of home health care. To validate this technology, we conducted a clinical trial to test the ability of our platform to predict clinical outcomes in discharged patients. Methods: Our platform consists of a wearable device, which includes an accelerometer and a Bluetooth sensor, and an iPhone connected to our cloud-based ML interface to analyze PA remotely and predict clinical outcomes. This system was deployed at a skilled nursing facility where we collected >17,000 person-day data points over 2 years, generating a solid training database. We used these data to train our extreme gradient boosting (XGBoost)–based ML environment to conduct a clinical trial, Activity Assessment of Patients Discharged from Hospital-I, to test the hypothesis that a comprehensive profile of PA would predict clinical outcome. We developed an advanced data-driven analytic platform that predicts the clinical outcome based on accurate measurements of PA. Artificial intelligence or an ML algorithm was used to analyze the data to predict short-term health outcome. Results: We enrolled 52 patients discharged from Stanford Hospital. Our data demonstrated a robust predictive system to forecast health outcome in the enrolled patients based on their PA data. We achieved precise prediction of the patients’ clinical outcomes with a sensitivity of 87%, a specificity of 79%, and an accuracy of 85%. Conclusions: To date, there are no reliable clinical data, using a wearable device, regarding monitoring discharged patients to predict their recovery. We conducted a clinical trial to assess outcome data rigorously to be used reliably for remote home care by patients, health care professionals, and caretakers. %M 38427393 %R 10.2196/45130 %U https://cardio.jmir.org/2024/1/e45130 %U https://doi.org/10.2196/45130 %U http://www.ncbi.nlm.nih.gov/pubmed/38427393 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 8 %N %P e51399 %T Physical Activity, Heart Rate Variability, and Ventricular Arrhythmia During the COVID-19 Lockdown: Retrospective Cohort Study %A Texiwala,Sikander Z %A de Souza,Russell J %A Turner,Suzette %A Singh,Sheldon M %+ Schulich Heart Center, Sunnybrook Health Sciences, Room A222, 2075 Bayview Ave, Toronto, ON, M4N 3M5, Canada, 1 416 480 6100 ext 86359, sheldon.singh@sunnybrook.ca %K implantable cardioverter defibrillator %K heart rate variability %K physical activity %K lockdown %K ICD %K ventricular arrhythmias %K defibrillator %K implementation %D 2024 %7 5.2.2024 %9 Original Paper %J JMIR Cardio %G English %X Background: Ventricular arrhythmias (VAs) increase with stress and national disasters. Prior research has reported that VA did not increase during the onset of the COVID-19 lockdown in March 2020, and the mechanism for this is unknown. Objective: This study aimed to report the presence of VA and changes in 2 factors associated with VA (physical activity and heart rate variability [HRV]) at the onset of COVID-19 lockdown measures in Ontario, Canada. Methods: Patients with implantable cardioverter defibrillator (ICD) followed at a regional cardiac center in Ontario, Canada with data available for both HRV and physical activity between March 1 and 31, 2020, were included. HRV, physical activity, and the presence of VA were determined during the pre- (March 1-10, 2020) and immediate postlockdown (March 11-31) period. When available, these data were determined for the same period in 2019. Results: In total, 68 patients had complete data for 2020, and 40 patients had complete data for 2019. Three (7.5%) patients had VA in March 2019, whereas none had VA in March 2020 (P=.048). Physical activity was reduced during the postlockdown period (mean 2.3, SD 1.6 hours vs mean 2.1, SD 1.6 hours; P=.003). HRV was unchanged during the pre- and postlockdown period (mean 91, SD 30 ms vs mean 92, SD 28 ms; P=.84). Conclusions: VA was infrequent during the COVID-19 pandemic. A reduction in physical activity with lockdown maneuvers may explain this observation. %M 38315512 %R 10.2196/51399 %U https://cardio.jmir.org/2024/1/e51399 %U https://doi.org/10.2196/51399 %U http://www.ncbi.nlm.nih.gov/pubmed/38315512 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 12 %N %P e52035 %T Prolonged mHealth-Based Arrhythmia Monitoring in Patients With Hypertrophic Cardiomyopathy (HCM-PATCH): Protocol for a Single-Center Cohort Study %A Schulze Lammers,Sophia %A Lawrenz,Thorsten %A Lawin,Dennis %A Hoyer,Annika %A Stellbrink,Christoph %A Albrecht,Urs-Vito %+ Department of Cardiology and Intensive Care Medicine, University Hospital Ostwestfalen-Lippe of Bielefeld University, Campus Klinikum Bielefeld, Teutoburger Strasse 50, Bielefeld, 33604, Germany, 49 521 581 3413, s.schulzelammers@uni-bielefeld.de %K hypertrophic cardiomyopathy %K nonsustained ventricular arrhythmia %K sudden cardiac death %K implantable cardioverter-defibrillator %K long-term ECG %K digital medicine %K long-term electrocardiography %D 2023 %7 29.12.2023 %9 Protocol %J JMIR Res Protoc %G English %X Background: Patients with hypertrophic cardiomyopathy (HCM) are at increased risk of sudden cardiac death (SCD) due to ventricular arrhythmias and other arrhythmias. Screening for arrhythmias is mandatory to assess the individual SCD risk, but long-term electrocardiography (ECG) is rarely performed in routine clinical practice. Intensified monitoring may increase the detection rate of ventricular arrhythmias and identify more patients with an increased SCD risk who are potential candidates for the primary prophylactic implantation of an implantable cardioverter-defibrillator. To date, reliable data on the clinical benefit of prolonged arrhythmia monitoring in patients with HCM are rare. Objective: This prospective study aims to measure the prevalence of ventricular arrhythmias in patients with HCM observed by mobile health (mHealth)–based continuous rhythm monitoring over 14 days compared to standard practice (a 24- and 48-h long-term ECG). The frequency of ventricular arrhythmias in this 14-day period is compared with the frequency in the first 24 or 48 hours for the same patient (intraindividual comparison). Methods: Following the sample size calculation, 34 patients with a low or intermediate risk for SCD, assessed by the HCM Risk–SCD calculator, will need to be recruited in this single-center cohort study between June 2023 and February 2024. All patients will receive an ECG patch that records their heart activity over 14 days. In addition, cardiac magnetic resonance imaging and genetic testing data will be integrated into risk stratification. All patients will be asked to complete questionnaires about their symptoms; previous therapy; family history; and, at the end of the study, their experience with the ECG patch-based monitoring. Results: The Hypertrophic Cardiomyopathy: Clinical Impact of a Prolonged mHealth-Based Arrhythmia Monitoring by Single-Channel ECG (HCM-PATCH) study investigates the prevalence of nonsustained ventricular tachycardia (ie, ≥3 consecutive ventricular beats at a rate of 120 beats per minute, lasting for <30 seconds) in low- to intermediate-risk patients with HCM (according to the HCM Risk–SCD calculator) with additional mHealth-based prolonged rhythm monitoring. The study was funded by third-party funding from the Department of Cardiology and Intensive Care Medicine, University Hospital Ostwestfalen-Lippe of Bielefeld University in June 2023 and approved by the institutional review board in May 2023. Data collection began in June 2023, and we plan to end the study in February 2024. Of the 34 patients, 26 have been recruited. Data analysis has not yet taken place. Publication of the results is planned for the fall of 2024. Conclusions: Prolonged mHealth-based rhythm monitoring could lead to differences in the prevalence of arrhythmias compared to 24- and 48-hour long-term ECGs. This may lead to improved identification of patients at high risk and trigger therapeutic interventions that may provide better protection from SCD or atrial fibrillation–related complications such as embolic stroke. Trial Registration: Deutsches Register Klinischer Studien DRKS00032144; https://tinyurl.com/498bkrx8 International Registered Report Identifier (IRRID): DERR1-10.2196/52035 %M 38157231 %R 10.2196/52035 %U https://www.researchprotocols.org/2023/1/e52035 %U https://doi.org/10.2196/52035 %U http://www.ncbi.nlm.nih.gov/pubmed/38157231 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e52519 %T Assessment of Heart Rate Monitoring During Exercise With Smart Wristbands and a Heart Rhythm Patch: Validation and Comparison Study %A Wang,Tse-Lun %A Wu,Hao-Yi %A Wang,Wei-Yun %A Chen,Chao-Wen %A Chien,Wu-Chien %A Chu,Chi-Ming %A Wu,Yi-Syuan %+ Division of Trauma and Surgical Critical Care, Department of Surgery, Kaohsiung Medical University Hospital, Kaohsiung Medical University, PO Box 48, Kaohsiung City, 807, Taiwan, 886 73121101 ext 6020, pu1254@gmail.com %K running %K wearable device %K photoplethysmography %K heart rhythm patch, smart wristband %D 2023 %7 14.12.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: The integration of wearable devices into fitness routines, particularly in military settings, necessitates a rigorous assessment of their accuracy. This study evaluates the precision of heart rate measurements by locally manufactured wristbands, increasingly used in military academies, to inform future device selection for military training activities. Objective: This research aims to assess the reliability of heart rate monitoring in chest straps versus wearable wristbands. Methods: Data on heart rate and acceleration were collected using the Q-Band Q-69 smart wristband (Mobile Action Technology Inc) and compared against the Zephyr Bioharness standard measuring device. The Lin concordance correlation coefficient, Pearson product moment correlation coefficient, and intraclass correlation coefficient were used for reliability analysis. Results: Participants from a Northern Taiwanese medical school were enrolled (January 1-June 31, 2021). The Q-Band Q-69 demonstrated that the mean absolute percentage error (MAPE) of women was observed to be 13.35 (SD 13.47). Comparatively, men exhibited a lower MAPE of 8.54 (SD 10.49). The walking state MAPE was 7.79 for women and 10.65 for men. The wristband’s accuracy generally remained below 10% MAPE in other activities. Pearson product moment correlation coefficient analysis indicated gender-based performance differences, with overall coefficients of 0.625 for women and 0.808 for men, varying across walking, running, and cooldown phases. Conclusions: This study highlights significant gender and activity-dependent variations in the accuracy of the MobileAction Q-Band Q-69 smart wristband. Reduced accuracy was notably observed during running. Occasional extreme errors point to the necessity of caution in relying on such devices for exercise monitoring. The findings emphasize the limitations and potential inaccuracies of wearable technology, especially in high-intensity physical activities. %M 38096010 %R 10.2196/52519 %U https://formative.jmir.org/2023/1/e52519 %U https://doi.org/10.2196/52519 %U http://www.ncbi.nlm.nih.gov/pubmed/38096010 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e49346 %T The Utility of a Novel Electrocardiogram Patch Using Dry Electrodes Technology for Arrhythmia Detection During Exercise and Prolonged Monitoring: Proof-of-Concept Study %A Fruytier,Lonneke A %A Janssen,Daan M %A Campero Jurado,Israel %A van de Sande,Danny AJP %A Lorato,Ilde %A Stuart,Shavini %A Panditha,Pradeep %A de Kok,Margreet %A Kemps,Hareld MC %+ Department of Cardiology, Máxima MC Eindhoven/Veldhoven, De Run 4600, Veldhoven, 5504 DB, Netherlands, 31 408888200, lonneke.fruytier@mmc.nl %K arrhythmia detection %K coronary artery disease %K ECG monitoring %K electrocardiogram %K exercise %K patch %K usability %D 2023 %7 30.11.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Accurate detection of myocardial ischemia and arrhythmias during free-living exercise could play a pivotal role in screening and monitoring for the prevention of exercise-related cardiovascular events in high-risk populations. Although remote electrocardiogram (ECG) solutions are emerging rapidly, existing technology is neither designed nor validated for continuous use during vigorous exercise. Objective: In this proof-of-concept study, we evaluated the usability, signal quality, and accuracy for arrhythmia detection of a single-lead ECG patch platform featuring self-adhesive dry electrode technology in individuals with chronic coronary syndrome. This sensor was evaluated during exercise and for prolonged, continuous monitoring. Methods: We recruited a total of 6 consecutive patients with chronic coronary syndrome scheduled for an exercise stress test (EST) as part of routine cardiac follow-up. Traditional 12-lead ECG recording was combined with monitoring with the ECG patch. Following the EST, the participants continuously wore the sensor for 5 days. Intraclass correlation coefficients (ICC) and Wilcoxon signed rank tests were used to assess the utility of detecting arrhythmias with the patch by comparing the evaluations of 2 blinded assessors. Signal quality during EST and prolonged monitoring was evaluated by using a signal quality indicator. Additionally, connection time was calculated for prolonged ECG monitoring. The comfort and usability of the patch were evaluated by a web-based self-assessment questionnaire. Results: A total of 6 male patients with chronic coronary syndrome (mean age 69.8, SD 6.2 years) completed the study protocol. The patch was worn for a mean of 118.3 (SD 5.6) hours. The level of agreement between the patch and 12-lead ECG was excellent for the detection of premature atrial contractions and premature ventricular contractions during the whole test (ICC=0.998, ICC=1.000). No significant differences in the total number of premature atrial contractions and premature ventricular contractions were detected neither during the entire exercise test (P=.79 and P=.18, respectively) nor during the exercise and recovery stages separately (P=.41, P=.66, P=.18, and P=.66). A total of 1 episode of atrial fibrillation was detected by both methods. Total connection time during recording was between 88% and 100% for all participants. There were no reports of skin irritation, erythema, or pain while wearing the patch. Conclusions: This proof-of-concept study showed that this innovative ECG patch based on self-adhesive dry electrode technology can potentially be used for arrhythmia detection during vigorous exercise. The results suggest that the wearable patch is also usable for prolonged continuous ECG monitoring in free-living conditions and can therefore be of potential use in cardiac rehabilitation and tele-monitoring for the prevention of exercise-related cardiovascular events. Future efforts will focus on optimizing signal quality over time and conducting a larger-scale validation study focusing on both arrhythmia and ischemia detection. %M 38032699 %R 10.2196/49346 %U https://formative.jmir.org/2023/1/e49346 %U https://doi.org/10.2196/49346 %U http://www.ncbi.nlm.nih.gov/pubmed/38032699 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 7 %N %P e45137 %T Accuracy, Usability, and Adherence of Smartwatches for Atrial Fibrillation Detection in Older Adults After Stroke: Randomized Controlled Trial %A Ding,Eric Y %A Tran,Khanh-Van %A Lessard,Darleen %A Wang,Ziyue %A Han,Dong %A Mohagheghian,Fahimeh %A Mensah Otabil,Edith %A Noorishirazi,Kamran %A Mehawej,Jordy %A Filippaios,Andreas %A Naeem,Syed %A Gottbrecht,Matthew F %A Fitzgibbons,Timothy P %A Saczynski,Jane S %A Barton,Bruce %A Chon,Ki %A McManus,David D %+ Department of Medicine, University of Massachusetts Chan Medical School, 55 N Lake Ave, Worcester, MA, 01605, United States, 1 774 455 6571, eric_ding@brown.edu %K accuracy %K atrial fibrillation %K cardiac arrhythmia %K design %K detection %K diagnosis %K electrocardiography %K monitoring %K older adults %K photoplethysmography %K prevention %K remote monitoring %K smartwatch %K stroke %K usability %D 2023 %7 28.11.2023 %9 Original Paper %J JMIR Cardio %G English %X Background: Atrial fibrillation (AF) is a common cause of stroke, and timely diagnosis is critical for secondary prevention. Little is known about smartwatches for AF detection among stroke survivors. We aimed to examine accuracy, usability, and adherence to a smartwatch-based AF monitoring system designed by older stroke survivors and their caregivers. Objective: This study aims to examine the feasibility of smartwatches for AF detection in older stroke survivors. Methods: Pulsewatch is a randomized controlled trial (RCT) in which stroke survivors received either a smartwatch-smartphone dyad for AF detection (Pulsewatch system) plus an electrocardiogram patch or the patch alone for 14 days to assess the accuracy and usability of the system (phase 1). Participants were subsequently rerandomized to potentially 30 additional days of system use to examine adherence to watch wear (phase 2). Participants were aged 50 years or older, had survived an ischemic stroke, and had no major contraindications to oral anticoagulants. The accuracy for AF detection was determined by comparing it to cardiologist-overread electrocardiogram patch, and the usability was assessed with the System Usability Scale (SUS). Adherence was operationalized as daily watch wear time over the 30-day monitoring period. Results: A total of 120 participants were enrolled (mean age 65 years; 50/120, 41% female; 106/120, 88% White). The Pulsewatch system demonstrated 92.9% (95% CI 85.3%-97.4%) accuracy for AF detection. Mean usability score was 65 out of 100, and on average, participants wore the watch for 21.2 (SD 8.3) of the 30 days. Conclusions: Our findings demonstrate that a smartwatch system designed by and for stroke survivors is a viable option for long-term arrhythmia detection among older adults at risk for AF, though it may benefit from strategies to enhance adherence to watch wear. Trial Registration: ClinicalTrials.gov NCT03761394; https://clinicaltrials.gov/study/NCT03761394 International Registered Report Identifier (IRRID): RR2-10.1016/j.cvdhj.2021.07.002 %M 38015598 %R 10.2196/45137 %U https://cardio.jmir.org/2023/1/e45137 %U https://doi.org/10.2196/45137 %U http://www.ncbi.nlm.nih.gov/pubmed/38015598 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 7 %N %P e50701 %T Diagnostic Accuracy of Single-Lead Electrocardiograms Using the Kardia Mobile App and the Apple Watch 4: Validation Study %A Klier,Kristina %A Koch,Lucas %A Graf,Lisa %A Schinköthe,Timo %A Schmidt,Annette %+ Institute of Sport Science, University of the Bundeswehr Munich, Werner-Heisenberg-Weg 39, Neubiberg, 85577, Germany, 49 8960042382, kristina.klier@unibw.de %K accuracy %K electrocardiography %K eHealth %K mHealth %K mobile health %K app %K applications %K mobile monitoring %K electrocardiogram %K ECG %K telemedicine %K diagnostic %K diagnosis %K monitoring %K heart %K cardiology %K mobile phone %D 2023 %7 23.11.2023 %9 Original Paper %J JMIR Cardio %G English %X Background: To date, the 12-lead electrocardiogram (ECG) is the gold standard for cardiological diagnosis in clinical settings. With the advancements in technology, a growing number of smartphone apps and gadgets for recording, visualizing, and evaluating physical performance as well as health data is available. Although this new smart technology is innovative and time- and cost-efficient, less is known about its diagnostic accuracy and reliability. Objective: This study aimed to examine the agreement between the mobile single-lead ECG measurements of the Kardia Mobile App and the Apple Watch 4 compared to the 12-lead gold standard ECG in healthy adults under laboratory conditions. Furthermore, it assessed whether the measurement error of the devices increases with an increasing heart rate. Methods: This study was designed as a prospective quasi-experimental 1-sample measurement, in which no randomization of the sampling was carried out. In total, ECGs at rest from 81 participants (average age 24.89, SD 8.58 years; n=58, 72% male) were recorded and statistically analyzed. Bland-Altman plots were created to graphically illustrate measurement differences. To analyze the agreement between the single-lead ECGs and the 12-lead ECG, Pearson correlation coefficient (r) and Lin concordance correlation coefficient (CCCLin) were calculated. Results: The results showed a higher agreement for the Apple Watch (mean deviation QT: 6.85%; QT interval corrected for heart rate using Fridericia formula [QTcF]: 7.43%) than Kardia Mobile (mean deviation QT: 9.53%; QTcF: 9.78%) even if both tend to underestimate QT and QTcF intervals. For Kardia Mobile, the QT and QTcF intervals correlated significantly with the gold standard (rQT=0.857 and rQTcF=0.727; P<.001). CCCLin corresponded to an almost complete heuristic agreement for the QT interval (0.835), whereas the QTcF interval was in the range of strong agreement (0.682). Further, for the Apple Watch, Pearson correlations were highly significant and in the range of a large effect (rQT=0.793 and rQTcF=0.649; P<.001). CCCLin corresponded to a strong heuristic agreement for both the QT (0.779) and QTcF (0.615) intervals. A small negative correlation between the measurement error and increasing heart rate could be found of each the devices and the reference. Conclusions: Smart technology seems to be a promising and reliable approach for nonclinical health monitoring. Further research is needed to broaden the evidence regarding its validity and usability in different target groups. %M 37995111 %R 10.2196/50701 %U https://cardio.jmir.org/2023/1/e50701 %U https://doi.org/10.2196/50701 %U http://www.ncbi.nlm.nih.gov/pubmed/37995111 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 7 %N %P e50973 %T Barriers and Facilitators Associated With Remote Monitoring Adherence Among Veterans With Pacemakers and Implantable Cardioverter-Defibrillators: Qualitative Cross-Sectional Study %A Dhruva,Sanket S %A Raitt,Merritt H %A Munson,Scott %A Moore,Hans J %A Steele,Pamela %A Rosman,Lindsey %A Whooley,Mary A %+ San Francisco Veterans Affairs Medical Center, 4150 Clement St, Building 203, 111C, San Francisco, CA, 94121, United States, 1 4152214810, sanket.dhruva@ucsf.edu %K cardiac implantable electronic device %K electrophysiology %K pacemaker %K remote monitoring %K veterans %K adherence %D 2023 %7 21.11.2023 %9 Original Paper %J JMIR Cardio %G English %X Background: The Heart Rhythm Society strongly recommends remote monitoring (RM) of cardiovascular implantable electronic devices (CIEDs) because of the clinical outcome benefits to patients. However, many patients do not adhere to RM and, thus, do not achieve these benefits. There has been limited study of patient-level barriers and facilitators to RM adherence; understanding patient perspectives is essential to developing solutions to improve adherence. Objective: We sought to identify barriers and facilitators associated with adherence to RM among veterans with CIEDs followed by the Veterans Health Administration. Methods: We interviewed 40 veterans with CIEDs regarding their experiences with RM. Veterans were stratified into 3 groups based on their adherence to scheduled RM transmissions over the past 2 years: 6 fully adherent (≥95%), 25 partially adherent (≥65% but <95%), and 9 nonadherent (<65%). As the focus was to understand challenges with RM adherence, partially adherent and nonadherent veterans were preferentially weighted for selection. Veterans were mailed a letter stating they would be called to understand their experiences and perspectives of RM and possible barriers, and then contacted beginning 1 week after the letter was mailed. Interviews were structured (some questions allowing for open-ended responses to dive deeper into themes) and focused on 4 predetermined domains: knowledge of RM, satisfaction with RM, reasons for nonadherence, and preferences for health care engagement. Results: Of the 44 veterans contacted, 40 (91%) agreed to participate. The mean veteran age was 75.3 (SD 7.6) years, and 98% (39/40) were men. Veterans had been implanted with their current CIED for an average of 4.4 (SD 2.8) years. A total of 58% (23/40) of veterans recalled a discussion of home monitoring, and 45% (18/40) reported a good understanding of RM; however, when asked to describe RM, their understanding was sometimes incomplete or not correct. Among the 31 fully or partially adherent veterans, nearly all were satisfied with RM. Approximately one-third recalled ever being told the results of a remote transmission. Among partially or nonadherent veterans, only one-fourth reported being contacted by a Department of Veterans Affairs health care professional regarding not having sent a remote transmission; among those who had troubleshooted to ensure they could send remote transmissions, they often relied on the CIED manufacturer for help (this experience was nearly always positive). Most nonadherent veterans felt more comfortable engaging in RM if they received more information or education. Most veterans were interested in being notified of a successful remote transmission and learning the results of their remote transmissions. Conclusions: Veterans with CIEDs often had limited knowledge about RM and did not recall being contacted about nonadherence. When they were contacted and troubleshooted, the experience was positive. These findings provide opportunities to optimize strategies for educating and engaging patients in RM. %M 37988153 %R 10.2196/50973 %U https://cardio.jmir.org/2023/1/e50973 %U https://doi.org/10.2196/50973 %U http://www.ncbi.nlm.nih.gov/pubmed/37988153 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 7 %N %P e47292 %T Characterizing Real-World Implementation of Consumer Wearables for the Detection of Undiagnosed Atrial Fibrillation in Clinical Practice: Targeted Literature Review %A Simonson,Julie K %A Anderson,Misty %A Polacek,Cate %A Klump,Erika %A Haque,Saira N %+ Pfizer Inc, 66 Hudson Blvd E, New York City, NY, 10001, United States, 1 718 208 6842, julie.simonson@pfizer.com %K arrhythmias %K atrial fibrillation %K clinical workflow %K consumer wearable devices %K smartwatches %K wearables %K remote patient monitoring %K virtual care %K mobile phone %D 2023 %7 3.11.2023 %9 Review %J JMIR Cardio %G English %X Background: Atrial fibrillation (AF), the most common cardiac arrhythmia, is often undiagnosed because of lack of awareness and frequent asymptomatic presentation. As AF is associated with increased risk of stroke, early detection is clinically relevant. Several consumer wearable devices (CWDs) have been cleared by the US Food and Drug Administration for irregular heart rhythm detection suggestive of AF. However, recommendations for the use of CWDs for AF detection in clinical practice, especially with regard to pathways for workflows and clinical decisions, remain lacking. Objective: We conducted a targeted literature review to identify articles on CWDs characterizing the current state of wearable technology for AF detection, identifying approaches to implementing CWDs into the clinical workflow, and characterizing provider and patient perspectives on CWDs for patients at risk of AF. Methods: PubMed, ClinicalTrials.gov, UpToDate Clinical Reference, and DynaMed were searched for articles in English published between January 2016 and July 2023. The searches used predefined Medical Subject Headings (MeSH) terms, keywords, and search strings. Articles of interest were specifically on CWDs; articles on ambulatory monitoring tools, tools available by prescription, or handheld devices were excluded. Search results were reviewed for relevancy and discussed among the authors for inclusion. A qualitative analysis was conducted and themes relevant to our study objectives were identified. Results: A total of 31 articles met inclusion criteria: 7 (23%) medical society reports or guidelines, 4 (13%) general reviews, 5 (16%) systematic reviews, 5 (16%) health care provider surveys, 7 (23%) consumer or patient surveys or interviews, and 3 (10%) analytical reports. Despite recognition of CWDs by medical societies, detailed guidelines regarding CWDs for AF detection were limited, as was the availability of clinical tools. A main theme was the lack of pragmatic studies assessing real-world implementation of CWDs for AF detection. Clinicians expressed concerns about data overload; potential for false positives; reimbursement issues; and the need for clinical tools such as care pathways and guidelines, preferably developed or endorsed by professional organizations. Patient-facing challenges included device costs and variability in digital literacy or technology acceptance. Conclusions: This targeted literature review highlights the lack of a comprehensive body of literature guiding real-world implementation of CWDs for AF detection and provides insights for informing additional research and developing appropriate tools and resources for incorporating these devices into clinical practice. The results should also provide an impetus for the active involvement of medical societies and other health care stakeholders in developing appropriate tools and resources for guiding the real-world use of CWDs for AF detection. These resources should target clinicians, patients, and health care systems with the goal of facilitating clinician or patient engagement and using an evidence-based approach for establishing guidelines or frameworks for administrative workflows and patient care pathways. %M 37921865 %R 10.2196/47292 %U https://cardio.jmir.org/2023/1/e47292 %U https://doi.org/10.2196/47292 %U http://www.ncbi.nlm.nih.gov/pubmed/37921865 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 10 %N %P e50891 %T The Feasibility and Reliability of Upper Arm–Worn Apple Watch Heart Rate Monitoring for Surgeons During Surgery: Observational Study %A Yamada,Kazunosuke %A Enokida,Yasuaki %A Kato,Ryuji %A Imaizumi,Jun %A Takada,Takahiro %A Ojima,Hitoshi %+ Department of Gastroenterological Surgery, Gunma Prefectural Cancer Center, 617-1, Nishimachi, Oota City, Gunma, 3730828, Japan, 81 276 38 0771, kazuyama@gunma-cc.jp %K Apple Watch %K heart rate %K surgery %K robot %D 2023 %7 1.11.2023 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Health care professionals, particularly those in surgical settings, face high stress levels, impacting their well-being. Traditional monitoring methods, like using Holter electrocardiogram monitors, are impractical in the operating room, limiting the assessment of physicians’ health. Wrist-worn heart rate monitors, like the Apple Watch, offer promise but are restricted in surgeries due to sterility issues. Objective: This study aims to assess the feasibility and accuracy of using an upper arm–worn Apple Watch for heart rate monitoring during robotic-assisted surgeries, comparing its performance with that of a wrist-worn device to establish a reliable alternative monitoring site. Methods: This study used 2 identical Apple Watch Series 8 devices to monitor the heart rate of surgeons during robotic-assisted surgery. Heart rate data were collected from the wrist-worn and the upper arm–worn devices. Statistical analyses included calculating the mean difference and SD of difference between the 2 devices, constructing Bland-Altman plots, assessing accuracy based on mean absolute error and mean absolute percentage error, and calculating the intraclass correlation coefficient. Results: The mean absolute errors for the whole group and for participants A, B, C, and D were 3.63, 3.58, 2.70, 3.93, and 4.28, respectively, and the mean absolute percentage errors were 3.58%, 3.34%, 2.42%, 4.58%, and 4.00%, respectively. Bland-Altman plots and scatter plots showed no systematic error when comparing the heart rate measurements obtained from the upper arm–worn and the wrist-worn Apple Watches. The intraclass correlation coefficients for participants A, B, C, and D were 0.559, 0.651, 0.508, and 0.563, respectively, with a significance level of P<.001, indicating moderate reliability. Conclusions: The findings of this study suggest that the upper arm is a viable alternative site for monitoring heart rate during surgery using an Apple Watch. The agreement and reliability between the measurements obtained from the upper arm–worn and the wrist-worn devices were good, with no systematic error and a high level of accuracy. These findings have important implications for improving data collection and management of the physical and mental demands of operating room staff during surgery, where wearing a watch on the wrist may not be feasible. %M 37910162 %R 10.2196/50891 %U https://humanfactors.jmir.org/2023/1/e50891 %U https://doi.org/10.2196/50891 %U http://www.ncbi.nlm.nih.gov/pubmed/37910162 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e45760 %T Diagnostic Value of a Wearable Continuous Electrocardiogram Monitoring Device (AT-Patch) for New-Onset Atrial Fibrillation in High-Risk Patients: Prospective Cohort Study %A Kwun,Ju-Seung %A Lee,Jang Hoon %A Park,Bo Eun %A Park,Jong Sung %A Kim,Hyeon Jeong %A Kim,Sun-Hwa %A Jeon,Ki-Hyun %A Cho,Hyoung-won %A Kang,Si-Hyuck %A Lee,Wonjae %A Youn,Tae-Jin %A Chae,In-Ho %A Yoon,Chang-Hwan %+ Cardiovascular Center, Seoul National University Bundang Hospital, 82, Gumi-Ro 173, Bundang-Gu, Seongnam-si, 13620, Republic of Korea, 82 317877052, kunson2@snu.ac.kr %K arrhythmias %K atrial fibrillation %K wearable electronic device %K patch electrocardiogram monitor %K electrocardiogram %K adult %K AT-Patch %K heart failure %K mobile phone %D 2023 %7 18.9.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: While conventional electrocardiogram monitoring devices are useful for detecting atrial fibrillation, they have considerable drawbacks, including a short monitoring duration and invasive device implantation. The use of patch-type devices circumvents these drawbacks and has shown comparable diagnostic capability for the early detection of atrial fibrillation. Objective: We aimed to determine whether a patch-type device (AT-Patch) applied to patients with a high risk of new-onset atrial fibrillation defined by the congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, stroke, vascular disease, age 65-74 years, sex scale (CHA2DS2-VASc) score had increased detection rates. Methods: In this nonrandomized multicenter prospective cohort study, we enrolled 320 adults aged ≥19 years who had never experienced atrial fibrillation and whose CHA2DS2-VASc score was ≥2. The AT-Patch was attached to each individual for 11 days, and the data were analyzed for arrhythmic events by 2 independent cardiologists. Results: Atrial fibrillation was detected by the AT-Patch in 3.4% (11/320) of patients, as diagnosed by both cardiologists. Interestingly, when participants with or without atrial fibrillation were compared, a previous history of heart failure was significantly more common in the atrial fibrillation group (n=4/11, 36.4% vs n=16/309, 5.2%, respectively; P=.003). When a CHA2DS2-VASc score ≥4 was combined with previous heart failure, the detection rate was significantly increased to 24.4%. Comparison of the recorded electrocardiogram data revealed that supraventricular and ventricular ectopic rhythms were significantly more frequent in the new-onset atrial fibrillation group compared with nonatrial fibrillation group (3.4% vs 0.4%; P=.001 and 5.2% vs 1.2%; P<.001), respectively. Conclusions: This study detected a moderate number of new-onset atrial fibrillations in high-risk patients using the AT-Patch device. Further studies will aim to investigate the value of early detection of atrial fibrillation, particularly in patients with heart failure as a means of reducing adverse clinical outcomes of atrial fibrillation. Trial Registration: ClinicalTrials.gov NCT04857268; https://classic.clinicaltrials.gov/ct2/show/NCT04857268 %M 37721791 %R 10.2196/45760 %U https://www.jmir.org/2023/1/e45760 %U https://doi.org/10.2196/45760 %U http://www.ncbi.nlm.nih.gov/pubmed/37721791 %0 Journal Article %@ 2291-9279 %I JMIR Publications %V 11 %N %P e46351 %T A Serious Game to Self-Regulate Heart Rate Variability as a Technique to Manage Arousal Level Through Cardiorespiratory Biofeedback: Development and Pilot Evaluation Study %A Estrella,Tony %A Alfonso,Carla %A Ramos-Castro,Juan %A Alsina,Aitor %A Capdevila,Lluis %+ Sport Research Institute, Universitat Autònoma de Barcelona, Edifici N, Planta 1, Barcelona, 08193, Spain, 34 93 581 3329, lluis.capdevila@uab.cat %K serious game %K heart rate variability %K biofeedback %K mobile health %K mHealth %K app %K mobile phone %D 2023 %7 24.8.2023 %9 Original Paper %J JMIR Serious Games %G English %X Background: Heart rate variability biofeedback (HRVB) is an established intervention for increasing heart rate variability (HRV) in the clinical context. Using this technique, participants become aware of their HRV through real-time feedback and can self-regulate it. Objective: The aim of this study was 2-fold: first, to develop a serious game that applies the HRVB technique to teach participants to self-regulate HRV and, second, to test the app with participants in a pilot study. Methods: An HRVB app called the FitLab Game was developed for this study. To play the game, users must move the main character up and down the screen, avoiding collisions with obstacles. The wavelength that users must follow to avoid these obstacles is based on the user’s basal heart rate and changes in instantaneous heart rate. To test the FitLab Game, a total of 16 participants (mean age 23, SD 0.69 years) were divided into a control group (n=8) and an experimental group (n=8). A 2 × 2 factorial design was used in each session. Participants in the experimental condition were trained in breathing techniques. Results: Changes in the frequency and time domain parameters of HRV and the game’s performance features were evaluated. Significant changes in the average RR intervals and root mean square of differences between adjacent RR intervals (RMSSD) were found between the groups (P=.02 and P=.04, respectively). Regarding performance, both groups showed a tendency to increase the evaluated outcomes from baseline to the test condition. Conclusions: The results may indicate that playing different levels leads to an improvement in the game’s final score by repeated training. The tendency of changes in HRV may reflect a higher activation of the mental system of attention and control in the experimental group versus the control group. In this context, learning simple, voluntary strategies through a serious game can aid the improvement of self-control and arousal management. The FitLab Game appears to be a promising serious game owing to its ease of use, high engagement, and enjoyability provided by the instantaneous feedback. %M 37616033 %R 10.2196/46351 %U https://games.jmir.org/2023/1/e46351 %U https://doi.org/10.2196/46351 %U http://www.ncbi.nlm.nih.gov/pubmed/37616033 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e48812 %T G Tolerance Prediction Model Using Mobile Device–Measured Cardiac Force Index for Military Aircrew: Observational Study %A Kuo,Ming-Hao %A Lin,You-Jin %A Huang,Wun-Wei %A Chiang,Kwo-Tsao %A Tu,Min-Yu %A Chu,Chi-Ming %A Lai,Chung-Yu %+ Graduate Institute of Aerospace and Undersea Medicine, National Defense Medical Center, Rm 8118, No 161, Sec 6, Minquan E Rd, Neihu Dist, Taipei City, 11490, Taiwan, 886 287923100 ext 19066, multi0912@gmail.com %K G force %K baroreflex %K anti-G straining maneuver %K G tolerance %K cardiac force index %K anti-G suit %K relaxed G tolerance %K straining G tolerance %K cardiac force ratio %D 2023 %7 26.7.2023 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: During flight, G force compels blood to stay in leg muscles and reduces blood flow to the heart. Cardiovascular responses activated by the autonomic nerve system and strengthened by anti-G straining maneuvers can alleviate the challenges faced during G loading. To our knowledge, no definite cardiac information measured using a mobile health device exists for analyzing G tolerance. However, our previous study developed the cardiac force index (CFI) for analyzing the G tolerance of military aircrew. Objective: This study used the CFI to verify participants’ cardiac performance when walking and obtained a formula for predicting an individual’s G tolerance during centrifuge training. Methods: Participants from an air force aircrew undertook high-G training from January 2020 to December 2022. Their heart rate (HR) in beats per minute and activity level per second were recorded using the wearable BioHarness 3.0 device. The CFI was computed using the following formula: weight × activity / HR during resting or walking. Relaxed G tolerance (RGT) and straining G tolerance (SGT) were assessed at a slowly increasing rate of G loading (0.1 G/s) during training. Other demographic factors were included in the multivariate regression to generate a model for predicting G tolerance from the CFI. Results: A total of 213 eligible trainees from a military aircrew were recruited. The average age was 25.61 (SD 3.66) years, and 13.1% (28/213) of the participants were women. The mean resting CFI and walking CFI (WCFI) were 0.016 (SD 0.001) and 0.141 (SD 0.037) kg × G/beats per minute, respectively. The models for predicting RGT and SGT were as follows: RGT = 0.066 × age + 0.043 × (WCFI × 100) – 0.037 × height + 0.015 × systolic blood pressure – 0.010 × HR + 7.724 and SGT = 0.103 × (WCFI × 100) − 0.069 × height + 0.018 × systolic blood pressure + 15.899. Thus, the WCFI is a positive factor for predicting the RGT and SGT before centrifuge training. Conclusions: The WCFI is a vital component of the formula for estimating G tolerance prior to training. The WCFI can be used to monitor physiological conditions against G stress. %M 37494088 %R 10.2196/48812 %U https://mhealth.jmir.org/2023/1/e48812 %U https://doi.org/10.2196/48812 %U http://www.ncbi.nlm.nih.gov/pubmed/37494088 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e45599 %T Accelerometer-Measured Physical Activity Data Sets (Global Physical Activity Data Set Catalogue) That Include Markers of Cardiometabolic Health: Systematic Scoping Review %A Thomas,Jonah J C %A Daley,Amanda J %A Esliger,Dale W %A Kettle,Victoria E %A Coombe,April %A Stamatakis,Emmanuel %A Sanders,James P %+ School of Sport, Exercise and Health Science, Loughborough University, Epinal Way, Loughborough, LE113TU, United Kingdom, 44 01509222222, j.j.c.thomas@lboro.ac.uk %K sedentary behavior %K device measured %K data harmonization %K open science %K big data %D 2023 %7 19.7.2023 %9 Review %J J Med Internet Res %G English %X Background: Cardiovascular disease accounts for 17.9 million deaths globally each year. Many research study data sets have been collected to answer questions regarding the relationship between cardiometabolic health and accelerometer-measured physical activity. This scoping review aimed to map the available data sets that have collected accelerometer-measured physical activity and cardiometabolic health markers. These data were then used to inform the development of a publicly available resource, the Global Physical Activity Data set (GPAD) catalogue. Objective: This review aimed to systematically identify data sets that have measured physical activity using accelerometers and cardiometabolic health markers using either an observational or interventional study design. Methods: Databases, trial registries, and gray literature (inception until February 2021; updated search from February 2021 to September 2022) were systematically searched to identify studies that analyzed data sets of physical activity and cardiometabolic health outcomes. To be eligible for inclusion, data sets must have measured physical activity using an accelerometric device in adults aged ≥18 years; a sample size >400 participants (unless recruited participants in a low- and middle-income country where a sample size threshold was reduced to 100); used an observational, longitudinal, or trial-based study design; and collected at least 1 cardiometabolic health marker (unless only body mass was measured). Two reviewers screened the search results to identify eligible studies, and from these, the unique names of each data set were recorded, and characteristics about each data set were extracted from several sources. Results: A total of 17,391 study reports were identified, and after screening, 319 were eligible, with 122 unique data sets in these study reports meeting the review inclusion criteria. Data sets were found in 49 countries across 5 continents, with the most developed in Europe (n=53) and the least in Africa and Oceania (n=4 and n=3, respectively). The most common accelerometric brand and device wear location was Actigraph and the waist, respectively. Height and body mass were the most frequently measured cardiometabolic health markers in the data sets (119/122, 97.5% data sets), followed by blood pressure (82/122, 67.2% data sets). The number of participants in the included data sets ranged from 103,712 to 120. Once the review processes had been completed, the GPAD catalogue was developed to house all the identified data sets. Conclusions: This review identified and mapped the contents of data sets from around the world that have collected potentially harmonizable accelerometer-measured physical activity and cardiometabolic health markers. The GPAD catalogue is a web-based open-source resource developed from the results of this review, which aims to facilitate the harmonization of data sets to produce evidence that will reduce the burden of disease from physical inactivity. %M 37467026 %R 10.2196/45599 %U https://www.jmir.org/2023/1/e45599 %U https://doi.org/10.2196/45599 %U http://www.ncbi.nlm.nih.gov/pubmed/37467026 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e43340 %T Smartwatch-Based Maximum Oxygen Consumption Measurement for Predicting Acute Mountain Sickness: Diagnostic Accuracy Evaluation Study %A Ye,Xiaowei %A Sun,Mengjia %A Yu,Shiyong %A Yang,Jie %A Liu,Zhen %A Lv,Hailin %A Wu,Boji %A He,Jingyu %A Wang,Xuhong %A Huang,Lan %+ Institute of Cardiovascular Diseases of People's Liberation Army, The Second Affiliated Hospital, Army Medical University (Third Military Medical University), No 183, Xinqiao Street, Shapingba District, Chongqing, 400037, China, 86 23 68755601, huanglan260@126.com %K VO2max %K maximum oxygen consumption %K smartwatch %K cardiopulmonary exercise test %K acute mountain sickness %D 2023 %7 6.7.2023 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Cardiorespiratory fitness plays an important role in coping with hypoxic stress at high altitudes. However, the association of cardiorespiratory fitness with the development of acute mountain sickness (AMS) has not yet been evaluated. Wearable technology devices provide a feasible assessment of cardiorespiratory fitness, which is quantifiable as maximum oxygen consumption (VO2max) and may contribute to AMS prediction. Objective: We aimed to determine the validity of VO2max estimated by the smartwatch test (SWT), which can be self-administered, in order to overcome the limitations of clinical VO2max measurements. We also aimed to evaluate the performance of a VO2max-SWT–based model in predicting susceptibility to AMS. Methods: Both SWT and cardiopulmonary exercise test (CPET) were performed for VO2max measurements in 46 healthy participants at low altitude (300 m) and in 41 of them at high altitude (3900 m). The characteristics of the red blood cells and hemoglobin levels in all the participants were analyzed by routine blood examination before the exercise tests. The Bland-Altman method was used for bias and precision assessment. Multivariate logistic regression was performed to analyze the correlation between AMS and the candidate variables. A receiver operating characteristic curve was used to evaluate the efficacy of VO2max in predicting AMS. Results: VO2max decreased after acute high altitude exposure, as measured by CPET (25.20 [SD 6.46] vs 30.17 [SD 5.01] at low altitude; P<.001) and SWT (26.17 [SD 6.71] vs 31.28 [SD 5.17] at low altitude; P<.001). Both at low and high altitudes, VO2max was slightly overestimated by SWT but had considerable accuracy as the mean absolute percentage error (<7%) and mean absolute error (<2 mL·kg–1·min–1), with a relatively small bias compared with VO2max-CPET. Twenty of the 46 participants developed AMS at 3900 m, and their VO2max was significantly lower than that of those without AMS (CPET: 27.80 [SD 4.55] vs 32.00 [SD 4.64], respectively; P=.004; SWT: 28.00 [IQR 25.25-32.00] vs 32.00 [IQR 30.00-37.00], respectively; P=.001). VO2max-CPET, VO2max-SWT, and red blood cell distribution width-coefficient of variation (RDW-CV) were found to be independent predictors of AMS. To increase the prediction accuracy, we used combination models. The combination of VO2max-SWT and RDW-CV showed the largest area under the curve for all parameters and models, which increased the area under the curve from 0.785 for VO2max-SWT alone to 0.839. Conclusions: Our study demonstrates that the smartwatch device can be a feasible approach for estimating VO2max. In both low and high altitudes, VO2max-SWT showed a systematic bias toward a calibration point, slightly overestimating the proper VO2max when investigated in healthy participants. The SWT-based VO2max at low altitude is an effective indicator of AMS and helps to better identify susceptible individuals following acute high-altitude exposure, particularly by combining the RDW-CV at low altitude. Trial Registration: Chinese Clinical Trial Registry ChiCTR2200059900; https://www.chictr.org.cn/showproj.html?proj=170253 %M 37410528 %R 10.2196/43340 %U https://mhealth.jmir.org/2023/1/e43340 %U https://doi.org/10.2196/43340 %U http://www.ncbi.nlm.nih.gov/pubmed/37410528 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e44642 %T Accuracy of a Standalone Atrial Fibrillation Detection Algorithm Added to a Popular Wristband and Smartwatch: Prospective Diagnostic Accuracy Study %A Selder,Jasper L %A Te Kolste,Henryk Jan %A Twisk,Jos %A Schijven,Marlies %A Gielen,Willem %A Allaart,Cornelis P %+ Department of Cardiology, Amsterdam University Medical Center, De Boelelaan 1117, Amsterdam, 1081 HV, Netherlands, 31 645256921, j.selder@amsterdamumc.nl %K smartwatch %K atrial fibrillation %K algorithm %K fibrillation detection %K wristband %K diagnose %K heart rhythm %K cardioversion %K environment %K software algorithm %K artificial intelligence %K AI %K electrocardiography %K ECG %K EKG %D 2023 %7 26.5.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Silent paroxysmal atrial fibrillation (AF) may be difficult to diagnose, and AF burden is hard to establish. In contrast to conventional diagnostic devices, photoplethysmography (PPG)–driven smartwatches or wristbands allow for long-term continuous heart rhythm assessment. However, most smartwatches lack an integrated PPG-AF algorithm. Adding a standalone PPG-AF algorithm to these wrist devices might open new possibilities for AF screening and burden assessment. Objective: The aim of this study was to assess the accuracy of a well-known standalone PPG-AF detection algorithm added to a popular wristband and smartwatch, with regard to discriminating AF and sinus rhythm, in a group of patients with AF before and after cardioversion (CV). Methods: Consecutive consenting patients with AF admitted for CV in a large academic hospital in Amsterdam, the Netherlands, were asked to wear a Biostrap wristband or Fitbit Ionic smartwatch with Fibricheck algorithm add-on surrounding the procedure. A set of 1-min PPG measurements and 12-lead reference electrocardiograms was obtained before and after CV. Rhythm assessment by the PPG device-software combination was compared with the 12-lead electrocardiogram. Results: A total of 78 patients were included in the Biostrap-Fibricheck cohort (156 measurement sets) and 73 patients in the Fitbit-Fibricheck cohort (143 measurement sets). Of the measurement sets, 19/156 (12%) and 7/143 (5%), respectively, were not classifiable by the PPG algorithm due to bad quality. The diagnostic performance in terms of sensitivity, specificity, positive predictive value, negative predictive value, and accuracy was 98%, 96%, 96%, 99%, 97%, and 97%, 100%, 100%, 97%, and 99%, respectively, at an AF prevalence of ~50%. Conclusions: This study demonstrates that the addition of a well-known standalone PPG-AF detection algorithm to a popular PPG smartwatch and wristband without integrated algorithm yields a high accuracy for the detection of AF, with an acceptable unclassifiable rate, in a semicontrolled environment. %M 37234033 %R 10.2196/44642 %U https://www.jmir.org/2023/1/e44642 %U https://doi.org/10.2196/44642 %U http://www.ncbi.nlm.nih.gov/pubmed/37234033 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 7 %N %P e40524 %T Data Quality Degradation on Prediction Models Generated From Continuous Activity and Heart Rate Monitoring: Exploratory Analysis Using Simulation %A Hearn,Jason %A Van den Eynde,Jef %A Chinni,Bhargava %A Cedars,Ari %A Gottlieb Sen,Danielle %A Kutty,Shelby %A Manlhiot,Cedric %+ Blalock-Taussig-Thomas Heart Center, Johns Hopkins University, 1800 Orleans Street, Baltimore, MD, 21287, United States, 1 410 614 8481, cmanlhi1@jhmi.edu %K wearables %K time series %K data reliability %K prediction models %K hear rate %K monitoring %K data %K reliability %K clinical %K sleep %K data set %K cardiac %K physiological %K accuracy %K consumer %K wearables %K device %D 2023 %7 3.5.2023 %9 Original Paper %J JMIR Cardio %G English %X Background: Limited data accuracy is often cited as a reason for caution in the integration of physiological data obtained from consumer-oriented wearable devices in care management pathways. The effect of decreasing accuracy on predictive models generated from these data has not been previously investigated. Objective: The aim of this study is to simulate the effect of data degradation on the reliability of prediction models generated from those data and thus determine the extent to which lower device accuracy might or might not limit their use in clinical settings. Methods: Using the Multilevel Monitoring of Activity and Sleep in Healthy People data set, which includes continuous free-living step count and heart rate data from 21 healthy volunteers, we trained a random forest model to predict cardiac competence. Model performance in 75 perturbed data sets with increasing missingness, noisiness, bias, and a combination of all 3 perturbations was compared to model performance for the unperturbed data set. Results: The unperturbed data set achieved a mean root mean square error (RMSE) of 0.079 (SD 0.001) in predicting cardiac competence index. For all types of perturbations, RMSE remained stable up to 20%-30% perturbation. Above this level, RMSE started increasing and reached the point at which the model was no longer predictive at 80% for noise, 50% for missingness, and 35% for the combination of all perturbations. Introducing systematic bias in the underlying data had no effect on RMSE. Conclusions: In this proof-of-concept study, the performance of predictive models for cardiac competence generated from continuously acquired physiological data was relatively stable with declining quality of the source data. As such, lower accuracy of consumer-oriented wearable devices might not be an absolute contraindication for their use in clinical prediction models. %M 37133921 %R 10.2196/40524 %U https://cardio.jmir.org/2023/1/e40524 %U https://doi.org/10.2196/40524 %U http://www.ncbi.nlm.nih.gov/pubmed/37133921 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 7 %N %P e44791 %T Feasibility of Artificial Intelligence–Based Electrocardiography Analysis for the Prediction of Obstructive Coronary Artery Disease in Patients With Stable Angina: Validation Study %A Park,Jiesuck %A Yoon,Yeonyee %A Cho,Youngjin %A Kim,Joonghee %+ Department of Cardiology, Seoul National University Bundang Hospital, 82, Gumi-ro 173 beon-gil, Bundang-gu, Seongnam, Gyeonggi-do, 13620, Republic of Korea, 82 31 787 7072, yeonyeeyoon@gmail.com %K artificial intelligence %K AI %K coronary artery disease %K coronary stenosis %K electrocardiography %K stable angina %D 2023 %7 2.5.2023 %9 Original Paper %J JMIR Cardio %G English %X Background: Despite accumulating research on artificial intelligence–based electrocardiography (ECG) algorithms for predicting acute coronary syndrome (ACS), their application in stable angina is not well evaluated. Objective: We evaluated the utility of an existing artificial intelligence–based quantitative electrocardiography (QCG) analyzer in stable angina and developed a new ECG biomarker more suitable for stable angina. Methods: This single-center study comprised consecutive patients with stable angina. The independent and incremental value of QCG scores for coronary artery disease (CAD)–related conditions (ACS, myocardial injury, critical status, ST-elevation myocardial infarction, and left ventricular dysfunction) for predicting obstructive CAD confirmed by invasive angiography was examined. Additionally, ECG signals extracted by the QCG analyzer were used as input to develop a new QCG score. Results: Among 723 patients with stable angina (median age 68 years; male: 470/723, 65%), 497 (69%) had obstructive CAD. QCG scores for ACS and myocardial injury were independently associated with obstructive CAD (odds ratio [OR] 1.09, 95% CI 1.03-1.17 and OR 1.08, 95% CI 1.02-1.16 per 10-point increase, respectively) but did not significantly improve prediction performance compared to clinical features. However, our new QCG score demonstrated better prediction performance for obstructive CAD (area under the receiver operating characteristic curve 0.802) than the original QCG scores, with incremental predictive value in combination with clinical features (area under the receiver operating characteristic curve 0.827 vs 0.730; P<.001). Conclusions: QCG scores developed for acute conditions show limited performance in identifying obstructive CAD in stable angina. However, improvement in the QCG analyzer, through training on comprehensive ECG signals in patients with stable angina, is feasible. %M 37129937 %R 10.2196/44791 %U https://cardio.jmir.org/2023/1/e44791 %U https://doi.org/10.2196/44791 %U http://www.ncbi.nlm.nih.gov/pubmed/37129937 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 12 %N %P e39307 %T Detection of Atrial Fibrillation Using Insertable Cardiac Monitors in Patients With Cryptogenic Stroke in Japan (the LOOK Study): Protocol for a Prospective Multicenter Observational Study %A Suda,Satoshi %A Katano,Takehiro %A Kitagawa,Kazuo %A Iguchi,Yasuyuki %A Fujimoto,Shigeru %A Ono,Kenjiro %A Kano,Osamu %A Takekawa,Hidehiro %A Koga,Masatoshi %A Ihara,Masafumi %A Morimoto,Masafumi %A Yamagami,Hiroshi %A Terasaki,Tadashi %A Yamaguchi,Keiji %A Okubo,Seiji %A Ueno,Yuji %A Ohara,Nobuyuki %A Kamiya,Yuki %A Takeuchi,Masataka %A Yazawa,Yukako %A Terasawa,Yuka %A Doijiri,Ryosuke %A Tsuboi,Yoshifumi %A Sonoda,Kazutaka %A Nomura,Koichi %A Shimoyama,Takashi %A Kutsuna,Akihito %A Kimura,Kazumi %+ Department of Neurology, Nippon Medical School, 1-1-5 Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan, 81 3 3382 2131, suda-sa@nms.ac.jp %K atrial cardiomyopathy %K atrial fibrillation %K biomarker %K cryptogenic stroke %K insertable cardiac monitor %D 2023 %7 13.4.2023 %9 Protocol %J JMIR Res Protoc %G English %X Background: Paroxysmal atrial fibrillation (AF) is a probable cause of cryptogenic stroke (CS), and its detection and treatment are important for the secondary prevention of stroke. Insertable cardiac monitors (ICMs) are clinically effective in screening for AF and are superior to conventional short-term cardiac monitoring. Japanese guidelines for determining clinical indications for ICMs in CS are stricter than those in Western countries. Differences between Japanese and Western guidelines may impact the detection rate and prediction of AF via ICMs in patients with CS. Available data on Japanese patients are limited to small retrospective studies. Furthermore, additional information about AF detection, including the number of episodes, cumulative episode duration, anticoagulation initiation (type and dose of regimen and time of initiation), rate of catheter ablation, role of atrial cardiomyopathy, and stroke recurrence (time of recurrence and cause of the recurrent event), was not provided in the vast majority of previously published studies. Objective: In this study, we aim to identify the proportion and timing of AF detection and risk stratification criteria in patients with CS in real-world settings in Japan. Methods: This is a multicenter, prospective, observational study that aims to use ICMs to evaluate the proportion, timing, and characteristics of AF detection in patients diagnosed with CS. We will investigate the first detection of AF within the initial 6, 12, and 24 months of follow-up after ICM implantation. Patient characteristics, laboratory data, atrial cardiomyopathy markers, serial magnetic resonance imaging findings at baseline, 6, 12, and 24 months after ICM implantation, electrocardiogram readings, transesophageal echocardiography findings, cognitive status, stroke recurrence, and functional outcomes will be compared between patients with AF and patients without AF. Furthermore, we will obtain additional information regarding the number of AF episodes, duration of cumulative AF episodes, and time of anticoagulation initiation. Results: Study recruitment began in February 2020, and thus far, 213 patients have provided written informed consent and are currently in the follow-up phase. The last recruited participant (May 2021) will have completed the 24-month follow-up in May 2023. The main results are expected to be submitted for publication in 2023. Conclusions: The findings of this study will help identify AF markers and generate a risk scoring system with a novel and superior screening algorithm for occult AF detection while identifying candidates for ICM implantation and aiding the development of diagnostic criteria for CS in Japan. Trial Registration: UMIN Clinical Trial Registry UMIN000039809; https://tinyurl.com/3jaewe6a International Registered Report Identifier (IRRID): DERR1-10.2196/39307 %M 37052993 %R 10.2196/39307 %U https://www.researchprotocols.org/2023/1/e39307 %U https://doi.org/10.2196/39307 %U http://www.ncbi.nlm.nih.gov/pubmed/37052993 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e43134 %T Compliance Challenges in a Longitudinal COVID-19 Cohort Using Wearables for Continuous Monitoring: Observational Study %A Mekhael,Mario %A Ho,Chan %A Noujaim,Charbel %A Assaf,Ala %A Younes,Hadi %A El Hajjar,Abdel Hadi %A Chaudhry,Humza A %A Lanier,Brennan %A Chouman,Nour %A Makan,Noor %A Shan,Botao %A Zhang,Yichi %A Dagher,Lilas %A Kreidieh,Omar %A Marrouche,Nassir %A Donnellan,Eoin %+ Tulane University School of Medicine, 1324 Tulane Avenue, 1324 Tulane Ave, Suite A128, New Orleans, LA, 70112, United States, 1 504 988 3072, nmarrouche@tulane.edu %K COVID-19 %K digital health %K wearables %K compliance %K cardiovascular health %K heart disease %K wearable device %K biometric %K remote monitoring %D 2023 %7 5.4.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: The WEAICOR (Wearables to Investigate the Long Term Cardiovascular and Behavioral Impacts of COVID-19) study was a prospective observational study that used continuous monitoring to detect and analyze biometrics. Compliance to wearables was a major challenge when conducting the study and was crucial for the results. Objective: The aim of this study was to evaluate patients’ compliance to wearable wristbands and determinants of compliance in a prospective COVID-19 cohort. Methods: The Biostrap (Biostrap USA LLC) wearable device was used to monitor participants’ biometric data. Compliance was calculated by dividing the total number of days in which transmissions were sent by the total number of days spent in the WEAICOR study. Univariate correlation analyses were performed, with compliance and days spent in the study as dependent variables and age, BMI, sex, symptom severity, and the number of complications or comorbidities as independent variables. Multivariate linear regression was then performed, with days spent in the study as a dependent variable, to assess the power of different parameters in determining the number of days patients spent in the study. Results: A total of 122 patients were included in this study. Patients were on average aged 41.32 years, and 46 (38%) were female. Age was found to correlate with compliance (r=0.23; P=.01). In addition, age (r=0.30; P=.001), BMI (r=0.19; P=.03), and the severity of symptoms (r=0.19; P=.03) were found to correlate with days spent in the WEAICOR study. Per our multivariate analysis, in which days spent in the study was a dependent variable, only increased age was a significant determinant of compliance with wearables (adjusted R2=0.1; β=1.6; P=.01). Conclusions: Compliance is a major obstacle in remote monitoring studies, and the reasons for a lack of compliance are multifactorial. Patient factors such as age, in addition to environmental factors, can affect compliance to wearables. %M 36763647 %R 10.2196/43134 %U https://www.jmir.org/2023/1/e43134 %U https://doi.org/10.2196/43134 %U http://www.ncbi.nlm.nih.gov/pubmed/36763647 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 12 %N %P e44650 %T Clinical Validation of an Artificial Intelligence–Based Tool for Automatic Estimation of Left Ventricular Ejection Fraction and Strain in Echocardiography: Protocol for a Two-Phase Prospective Cohort Study %A Hadjidimitriou,Stelios %A Pagourelias,Efstathios %A Apostolidis,Georgios %A Dimaridis,Ioannis %A Charisis,Vasileios %A Bakogiannis,Constantinos %A Hadjileontiadis,Leontios %A Vassilikos,Vassilios %+ Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, University Campus, Faculty of Engineering, Building D, 6th Fl., Thessaloniki, GR-54124, Greece, 30 2310996319, stelios.hadjidimitriou@gmail.com %K artificial intelligence %K clinical validation %K computer-aided diagnosis %K echocardiography %K ejection fraction %K global longitudinal strain %K left ventricle %K prospective cohort design %K ultrasound %D 2023 %7 13.3.2023 %9 Protocol %J JMIR Res Protoc %G English %X Background: Echocardiography (ECHO) is a type of ultrasonographic procedure for examining the cardiac function and morphology, with functional parameters of the left ventricle (LV), such as the ejection fraction (EF) and global longitudinal strain (GLS), being important indicators. Estimation of LV-EF and LV-GLS is performed either manually or semiautomatically by cardiologists and requires a nonnegligible amount of time, while estimation accuracy depends on scan quality and the clinician’s experience in ECHO, leading to considerable measurement variability. Objective: The aim of this study is to externally validate the clinical performance of a trained artificial intelligence (AI)–based tool that automatically estimates LV-EF and LV-GLS from transthoracic ECHO scans and to produce preliminary evidence regarding its utility. Methods: This is a prospective cohort study conducted in 2 phases. ECHO scans will be collected from 120 participants referred for ECHO examination based on routine clinical practice in the Hippokration General Hospital, Thessaloniki, Greece. During the first phase, 60 scans will be processed by 15 cardiologists of different experience levels and the AI-based tool to determine whether the latter is noninferior in LV-EF and LV-GLS estimation accuracy (primary outcomes) compared to cardiologists. Secondary outcomes include the time required for estimation and Bland-Altman plots and intraclass correlation coefficients to assess measurement reliability for both the AI and cardiologists. In the second phase, the rest of the scans will be examined by the same cardiologists with and without the AI-based tool to primarily evaluate whether the combination of the cardiologist and the tool is superior in terms of correctness of LV function diagnosis (normal or abnormal) to the cardiologist’s routine examination practice, accounting for the cardiologist’s level of ECHO experience. Secondary outcomes include time to diagnosis and the system usability scale score. Reference LV-EF and LV-GLS measurements and LV function diagnoses will be provided by a panel of 3 expert cardiologists. Results: Recruitment started in September 2022, and data collection is ongoing. The results of the first phase are expected to be available by summer 2023, while the study will conclude in May 2024, with the end of the second phase. Conclusions: This study will provide external evidence regarding the clinical performance and utility of the AI-based tool based on prospectively collected ECHO scans in the routine clinical setting, thus reflecting real-world clinical scenarios. The study protocol may be useful to investigators conducting similar research. International Registered Report Identifier (IRRID): DERR1-10.2196/44650 %M 36912875 %R 10.2196/44650 %U https://www.researchprotocols.org/2023/1/e44650 %U https://doi.org/10.2196/44650 %U http://www.ncbi.nlm.nih.gov/pubmed/36912875 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e44818 %T Real-Time Detection of Sleep Apnea Based on Breathing Sounds and Prediction Reinforcement Using Home Noises: Algorithm Development and Validation %A Le,Vu Linh %A Kim,Daewoo %A Cho,Eunsung %A Jang,Hyeryung %A Reyes,Roben Delos %A Kim,Hyunggug %A Lee,Dongheon %A Yoon,In-Young %A Hong,Joonki %A Kim,Jeong-Whun %+ Department of Otorhinolaryngology, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Gyeonggi-do, Seongnam-si, 13620, Republic of Korea, 82 030797405, kimemails7@gmail.com %K sleep apnea %K OSA detection %K home care %K artificial intelligence %K deep learning %K prediction model %K audio %K diagnostic %K home technology %K sound %D 2023 %7 22.2.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Multinight monitoring can be helpful for the diagnosis and management of obstructive sleep apnea (OSA). For this purpose, it is necessary to be able to detect OSA in real time in a noisy home environment. Sound-based OSA assessment holds great potential since it can be integrated with smartphones to provide full noncontact monitoring of OSA at home. Objective: The purpose of this study is to develop a predictive model that can detect OSA in real time, even in a home environment where various noises exist. Methods: This study included 1018 polysomnography (PSG) audio data sets, 297 smartphone audio data sets synced with PSG, and a home noise data set containing 22,500 noises to train the model to predict breathing events, such as apneas and hypopneas, based on breathing sounds that occur during sleep. The whole breathing sound of each night was divided into 30-second epochs and labeled as “apnea,” “hypopnea,” or “no-event,” and the home noises were used to make the model robust to a noisy home environment. The performance of the prediction model was assessed using epoch-by-epoch prediction accuracy and OSA severity classification based on the apnea-hypopnea index (AHI). Results: Epoch-by-epoch OSA event detection showed an accuracy of 86% and a macro F1-score of 0.75 for the 3-class OSA event detection task. The model had an accuracy of 92% for “no-event,” 84% for “apnea,” and 51% for “hypopnea.” Most misclassifications were made for “hypopnea,” with 15% and 34% of “hypopnea” being wrongly predicted as “apnea” and “no-event,” respectively. The sensitivity and specificity of the OSA severity classification (AHI≥15) were 0.85 and 0.84, respectively. Conclusions: Our study presents a real-time epoch-by-epoch OSA detector that works in a variety of noisy home environments. Based on this, additional research is needed to verify the usefulness of various multinight monitoring and real-time diagnostic technologies in the home environment. %M 36811943 %R 10.2196/44818 %U https://www.jmir.org/2023/1/e44818 %U https://doi.org/10.2196/44818 %U http://www.ncbi.nlm.nih.gov/pubmed/36811943 %0 Journal Article %@ 2561-9128 %I JMIR Publications %V 6 %N %P e40474 %T The Accuracy of Wrist-Worn Photoplethysmogram–Measured Heart and Respiratory Rates in Abdominal Surgery Patients: Observational Prospective Clinical Validation Study %A van der Stam,Jonna A %A Mestrom,Eveline H J %A Scheerhoorn,Jai %A Jacobs,Fleur E N B %A Nienhuijs,Simon %A Boer,Arjen-Kars %A van Riel,Natal A W %A de Morree,Helma M %A Bonomi,Alberto G %A Scharnhorst,Volkher %A Bouwman,R Arthur %+ Department of Biomedical Engineering, Eindhoven University of Technology, de Zaale, Eindhoven, 5612 AZ, Netherlands, 31 40 2398675, jonna.vd.stam@catharinaziekenhuis.nl %K Telemetry %K monitoring %K photoplethysmography %K PPG %K photoplethysmogram %K wearable monitoring %K vital parameter %K wearable sensor %K sensor %K heart rate %K respiratory Rate %K respiration %K respiratory %K breathing %K monitoring %K wearable %K postoperative %K post-operative %K vital sign %D 2023 %7 20.2.2023 %9 Original Paper %J JMIR Perioper Med %G English %X Background: Postoperative deterioration is often preceded by abnormal vital parameters. Therefore, vital parameters of postoperative patients are routinely measured by nursing staff. Wrist-worn sensors could potentially provide an alternative tool for the measurement of vital parameters in low-acuity settings. These devices would allow more frequent or even continuous measurements of vital parameters without relying on time-consuming manual measurements, provided their accuracy in this clinical population is established. Objective: This study aimed to assess the accuracy of heart rate (HR) and respiratory rate (RR) measures obtained via a wearable photoplethysmography (PPG) wristband in a cohort of postoperative patients. Methods: The accuracy of the wrist-worn PPG sensor was assessed in 62 post–abdominal surgery patients (mean age 55, SD 15 years; median BMI 34, IQR 25-40 kg/m2). The wearable obtained HR and RR measurements were compared to those of the reference monitor in the postanesthesia or intensive care unit. Bland-Altman and Clarke error grid analyses were performed to determine agreement and clinical accuracy. Results: Data were collected for a median of 1.2 hours per patient. With a coverage of 94% for HR and 34% for RR, the device was able to provide accurate measurements for the large majority of the measurements as 98% and 93% of the measurements were within 5 bpm or 3 rpm of the reference signal. Additionally, 100% of the HR and 98% of the RR measurements were clinically acceptable on Clarke error grid analysis. Conclusions: The wrist-worn PPG device is able to provide measurements of HR and RR that can be seen as sufficiently accurate for clinical applications. Considering the coverage, the device was able to continuously monitor HR and report RR when measurements of sufficient quality were obtained. Trial Registration: ClinicalTrials.gov NCT03923127; https://www.clinicaltrials.gov/ct2/show/NCT03923127 %M 36804173 %R 10.2196/40474 %U https://periop.jmir.org/2023/1/e40474 %U https://doi.org/10.2196/40474 %U http://www.ncbi.nlm.nih.gov/pubmed/36804173 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e40226 %T ChroniSense National Early Warning Score Study: Comparison Study of a Wearable Wrist Device to Measure Vital Signs in Patients Who Are Hospitalized %A Van Velthoven,Michelle Helena %A Oke,Jason %A Kardos,Attila %+ Department of Cardiology, Translational Cardiovascular Research Group, Milton Keynes University Hospital, 8H Standing Way, Eaglestone, Milton Keynes, MK6 5LD, United Kingdom, 44 01908 996 022, attila.kardos@cardiov.ox.ac.uk %K mobile health %K digital health %K wearable %K medical device %K cardiology %K early warning score %K user acceptance %K vital sign %K devices %K monitoring %K clinical decision-making %K decision-making %K respiration rate %K blood pressure %K body temperature %K heart rate %K safety %K use %D 2023 %7 6.2.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Wearable devices could be used to continuously monitor vital signs in patients who are hospitalized, but they require validation. Objective: This study aimed to evaluate the clinical validity of the prototype of a semiautomated wearable wrist device (ChroniSense Polso) to measure vital signs and provide National Early Warning Scores (NEWSs). Methods: Vital signs and NEWSs measured using the wearable device were compared with standard, nurse-lead manual measurements. We enrolled adult patients (aged ≥18 years) who required vital sign measurements at least every 6 hours in a UK teaching district general hospital. Wearable device measurements were not used for clinical decision-making. The primary outcome was the agreement on the individual National Early Warning parameter scores and vital sign measurements: respiratory rate, oxygen saturation, body temperature, systolic blood pressure, and heart rate. Secondary outcomes were the agreement on the total NEWS, incidence of adverse events, and user acceptance. To compare the wearable device measurements with the standard measurements, we analyzed vital sign measurements by limits of agreement (Bland-Altman analysis) and conducted κ agreement analyses for NEWSs. A user experience survey was conducted with questions about comfort of the wrist device, safety, preference, and use. Results: We included 132 participants in the study, with a mean age of 62 (SD 15.81) years; most of them were men (102/132, 77.3%). The highest weighted κ values were found for heart rate (0.69, 95% CI 0.57-0.81 for all 385 measurements) and systolic blood pressure (0.39, 95% CI 0.30-0.47 for all 339 measurements). Weighted κ values were low for respiration rate (0.03, 95% CI −0.001 to 0.05 for all 445 measurements), temperature (0, 95% CI 0-0 for all 231 measurements), and oxygen saturation (−0.11, 95% CI −0.20 to −0.02 for all 187 measurements). Weighted κ using Cicchetti-Allison weights showed κ of 0.20 (95% CI 0.03-0.38) when using all 56 total NEWSs. The user acceptance survey found that approximately half (45/91, 49%) of the participants found it comfortable to wear the device and liked its appearance. Most (85/92, 92%) of them said that they would wear the device during their next hospital visit, and many (74/92, 80%) said that they would recommend it to others. Conclusions: This study shows the promising use of a prototype wearable device to measure vital signs in a hospital setting. Agreement between the standard measurements and wearable device measurements was acceptable for systolic blood pressure and heart rate, but needed to be improved for respiration rate, temperature, and oxygen saturation. Future studies need to improve the clinical validity of this wearable device. Large studies are required to assess clinical outcomes and cost-effectiveness of wearable devices for vital sign measurement. International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2018-028219 %M 36745491 %R 10.2196/40226 %U https://www.jmir.org/2023/1/e40226 %U https://doi.org/10.2196/40226 %U http://www.ncbi.nlm.nih.gov/pubmed/36745491 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 10 %N %P e38096 %T Patients’ Information Needs Related to a Monitoring Implant for Heart Failure: Co-designed Study Based on Affect Stories %A Davat,Ambre %A Martin-Juchat,Fabienne %+ GRESEC - Groupe de Recherche Sur les Enjeux de la Communication, Université Grenoble Alpes, Institut de la communication et des médias - 11 avenue du 8 mai 1945, BP 337, Échirolles, 38434, France, 33 04 56 52 87 12, fabienne.martin-juchat@univ-grenoble-alpes.fr %K co-design %K affect stories %K mixed methods study %K heart failure %K medical implantable device %K mobile health %K mHealth %K remote monitoring %K quantified self %K telehealth %D 2023 %7 23.1.2023 %9 Original Paper %J JMIR Hum Factors %G English %X Background: RealWorld4Clinic is a European consortium that is currently developing an implantable monitoring device for acute heart failure prevention. Objective: This study aimed to identify the main issues and information needs related to this new cardiac implant from the patients’ perspective. Methods: A total of 3 patient collaborators were recruited to help us design the study. During 4 remotely held meetings (each lasting for 2 hours), we defined the main questions and hypotheses together. Next, 26 additional interviews were conducted remotely to test these hypotheses. During both phases, we used affect stories, which are life narratives focusing on affect and the relationship between patients and the care ecosystem, to highlight the main social issues that should be addressed by the research according to the patients. Results: Context of diagnosis, age, and severity of illness strongly influence patient experience. However, these variables do not seem to influence the choice regarding being implanted, which relies mostly on the individual patient’s trust in their physicians. It seems that the major cause of anxiety for the patient is not the implant but the disease itself, although some people may initially be concerned over the idea of becoming a cyborg. Remote monitoring of cardiac implants should draw on existing remote disease management programs focusing on a long-term relationship between the patient and their medical team. Conclusions: Co-design with affect stories is a useful method for quickly identifying the main social issues related to information about a new health technology. %M 36689266 %R 10.2196/38096 %U https://humanfactors.jmir.org/2023/1/e38096 %U https://doi.org/10.2196/38096 %U http://www.ncbi.nlm.nih.gov/pubmed/36689266 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 7 %N %P e41055 %T High-Throughput Assessment of Real-World Medication Effects on QT Interval Prolongation: Observational Study %A Yuan,Neal %A Oesterle,Adam %A Botting,Patrick %A Chugh,Sumeet %A Albert,Christine %A Ebinger,Joseph %A Ouyang,David %+ Division of Cardiology, Department of Medicine, San Francisco Veteran Affairs Medical Center, 4150 Clement Street, San Francisco, CA, 94121, United States, 1 415 221 4810, neal.yuan@ucsf.edu %K electrocardiogram %K QT prolongation %K pharmacovigilance %K drug toxicity %K electronic health records %K ECG %K EHR %K medication monitoring %K medication effects %K clinical data monitoring %K demographic interaction %K comorbidity interaction %K monitoring %K clinical data %K accessibility %K assessment %D 2023 %7 20.1.2023 %9 Original Paper %J JMIR Cardio %G English %X Background: Drug-induced prolongation of the corrected QT interval (QTc) increases the risk for Torsades de Pointes (TdP) and sudden cardiac death. Medication effects on the QTc have been studied in controlled settings but may not be well evaluated in real-world settings where medication effects may be modulated by patient demographics and comorbidities as well as the usage of other concomitant medications. Objective: We demonstrate a new, high-throughput method leveraging electronic health records (EHRs) and the Surescripts pharmacy database to monitor real-world QTc-prolonging medication and potential interacting effects from demographics and comorbidities. Methods: We included all outpatient electrocardiograms (ECGs) from September 2008 to December 2019 at a large academic medical system, which were in sinus rhythm with a heart rate of 40-100 beats per minute, QRS duration of <120 milliseconds, and QTc of 300-700 milliseconds, determined using the Bazett formula. We used prescription information from the Surescripts pharmacy database and EHR medication lists to classify whether a patient was on a medication during an ECG. Negative control ECGs were obtained from patients not currently on the medication but who had been or would be on that medication within 1 year. We calculated the difference in mean QTc between ECGs of patients who are on and those who are off a medication and made comparisons to known medication TdP risks per the CredibleMeds.org database. Using linear regression analysis, we studied the interaction of patient-level demographics or comorbidities on medication-related QTc prolongation. Results: We analyzed the effects of 272 medications on 310,335 ECGs from 159,397 individuals. Medications associated with the greatest QTc prolongation were dofetilide (mean QTc difference 21.52, 95% CI 10.58-32.70 milliseconds), mexiletine (mean QTc difference 18.56, 95% CI 7.70-29.27 milliseconds), amiodarone (mean QTc difference 14.96, 95% CI 13.52-16.33 milliseconds), rifaximin (mean QTc difference 14.50, 95% CI 12.12-17.13 milliseconds), and sotalol (mean QTc difference 10.73, 95% CI 7.09-14.37 milliseconds). Several top QT prolonging medications such as rifaximin, lactulose, cinacalcet, and lenalidomide were not previously known but have plausible mechanistic explanations. Significant interactions were observed between demographics or comorbidities and QTc prolongation with many medications, such as coronary disease and amiodarone. Conclusions: We demonstrate a new, high-throughput technique for monitoring real-world effects of QTc-prolonging medications from readily accessible clinical data. Using this approach, we confirmed known medications for QTc prolongation and identified potential new associations and demographic or comorbidity interactions that could supplement findings in curated databases. Our single-center results would benefit from additional verification in future multisite studies that incorporate larger numbers of patients and ECGs along with more precise medication adherence and comorbidity data. %M 36662566 %R 10.2196/41055 %U https://cardio.jmir.org/2023/1/e41055 %U https://doi.org/10.2196/41055 %U http://www.ncbi.nlm.nih.gov/pubmed/36662566 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 12 %P e42359 %T Accuracy and Systematic Biases of Heart Rate Measurements by Consumer-Grade Fitness Trackers in Postoperative Patients: Prospective Clinical Trial %A Helmer,Philipp %A Hottenrott,Sebastian %A Rodemers,Philipp %A Leppich,Robert %A Helwich,Maja %A Pryss,Rüdiger %A Kranke,Peter %A Meybohm,Patrick %A Winkler,Bernd E %A Sammeth,Michael %+ Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital Würzburg, Oberdürrbacher Str. 6, Würzburg, 97070, Germany, 49 93120130574, helmer_p@ukw.de %K health tracker %K smartwatch %K internet of things %K personalized medicine %K photoplethysmography %K wearable %K Garmin Fenix 6 Pro %K Apple Watch 7 %K Fitbit Sense %K Withings ScanWatch %D 2022 %7 30.12.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Over the recent years, technological advances of wrist-worn fitness trackers heralded a new era in the continuous monitoring of vital signs. So far, these devices have primarily been used for sports. Objective: However, for using these technologies in health care, further validations of the measurement accuracy in hospitalized patients are essential but lacking to date. Methods: We conducted a prospective validation study with 201 patients after moderate to major surgery in a controlled setting to benchmark the accuracy of heart rate measurements in 4 consumer-grade fitness trackers (Apple Watch 7, Garmin Fenix 6 Pro, Withings ScanWatch, and Fitbit Sense) against the clinical gold standard (electrocardiography). Results: All devices exhibited high correlation (r≥0.95; P<.001) and concordance (rc≥0.94) coefficients, with a relative error as low as mean absolute percentage error <5% based on 1630 valid measurements. We identified confounders significantly biasing the measurement accuracy, although not at clinically relevant levels (mean absolute error<5 beats per minute). Conclusions: Consumer-grade fitness trackers appear promising in hospitalized patients for monitoring heart rate. Trial Registration: ClinicalTrials.gov NCT05418881; https://www.clinicaltrials.gov/ct2/show/NCT05418881 %M 36583938 %R 10.2196/42359 %U https://www.jmir.org/2022/12/e42359 %U https://doi.org/10.2196/42359 %U http://www.ncbi.nlm.nih.gov/pubmed/36583938 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 12 %P e37885 %T Comparing a Fitbit Wearable to an Electrocardiogram Gold Standard as a Measure of Heart Rate Under Psychological Stress: A Validation Study %A Gagnon,Joel %A Khau,Michelle %A Lavoie-Hudon,Léandre %A Vachon,François %A Drapeau,Vicky %A Tremblay,Sébastien %+ School of Psychology, Faculty of Social Sciences, Laval University, Pavillon Félix-Antoine-Savard, 1144, 2325, rue des Bibliothèques, Québec, QC, G1V 0A6, Canada, 1 8193830645, joel.gagnon.2@ulaval.ca %K Fitbit device %K wearable %K heart rate %K measurement accuracy %K criterion validity %K interdevice agreement %K psychological stress %K stress %K physiological %K behavioral %K mental health %K well-being %D 2022 %7 21.12.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Wearable devices collect physiological and behavioral data that have the potential to identify individuals at risk of declining mental health and well-being. Past research has mainly focused on assessing the accuracy and the agreement of heart rate (HR) measurement of wearables under different physical exercise conditions. However, the capacity of wearables to sense physiological changes, assessed by increasing HR, caused by a stressful event has not been thoroughly studied. Objective: This study followed 3 objectives: (1) to test the ability of a wearable device (Fitbit Versa 2) to sense an increase in HR upon induction of psychological stress in the laboratory; (2) to assess the accuracy of the wearable device to capture short-term HR variations caused by psychological stress compared to a gold-standard electrocardiogram (ECG) measure (Biopac); and (3) to quantify the degree of agreement between the wearable device and the gold-standard ECG measure across different experimental conditions. Methods: Participants underwent the Trier Social Stress Test protocol, which consists of an oral phase, an arithmetic stress phase, an anticipation phase, and 2 relaxation phases (at the beginning and the end). During the stress protocol, the participants wore a Fitbit Versa 2 and were also connected to a Biopac. A mixed-effect modeling approach was used (1) to assess the effect of experimental conditions on HR, (2) to estimate several metrics of accuracy, and (3) to assess the agreement: the Bland-Altman limits of agreement (LoA), the concordance correlation coefficient, the coverage probability, the total deviation index, and the coefficient of an individual agreement. Mean absolute error and mean absolute percent error were calculated as accuracy indices. Results: A total of 34 university students were recruited for this study (64% of participants were female with a mean age of 26.8 years, SD 8.3). Overall, the results showed significant HR variations across experimental phases. Post hoc tests revealed significant pairwise differences for all phases. Accuracy analyses revealed acceptable accuracy according to the analyzed metrics of accuracy for the Fitbit Versa 2 to capture the short-term variations in psychological stress levels. However, poor indices of agreement between the Fitbit Versa 2 and the Biopac were found. Conclusions: Overall, the results support the use of the Fitbit Versa 2 to capture short-term stress variations. The Fitbit device showed acceptable levels of accuracy but poor agreement with an ECG gold standard. Greater inaccuracy and smaller agreement were found for stressful experimental conditions that induced a higher HR. Fitbit devices can be used in research to measure HR variations caused by stress, although they cannot replace an ECG instrument when precision is of utmost importance. %M 36542432 %R 10.2196/37885 %U https://formative.jmir.org/2022/12/e37885 %U https://doi.org/10.2196/37885 %U http://www.ncbi.nlm.nih.gov/pubmed/36542432 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 12 %P e41163 %T Using Deep Transfer Learning to Detect Hyperkalemia From Ambulatory Electrocardiogram Monitors in Intensive Care Units: Personalized Medicine Approach %A Chiu,I-Min %A Cheng,Jhu-Yin %A Chen,Tien-Yu %A Wang,Yi-Min %A Cheng,Chi-Yung %A Kung,Chia-Te %A Cheng,Fu-Jen %A Yau,Fei-Fei Flora %A Lin,Chun-Hung Richard %+ Department of Computer Science and Engineering, National Sun Yat-sen University, No 70, Lienhai Rd, Kaohsiung City, 804, Taiwan, 886 07 5252000 ext 4301, lin@cse.nsysu.edu.tw %K deep learning %K transfer learning %K hyperkalemia %K electrocardiogram %K ECG monitor %K ICU %K personalized medicine %D 2022 %7 5.12.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Hyperkalemia is a critical condition, especially in intensive care units. So far, there have been no accurate and noninvasive methods for recognizing hyperkalemia events on ambulatory electrocardiogram monitors. Objective: This study aimed to improve the accuracy of hyperkalemia predictions from ambulatory electrocardiogram (ECG) monitors using a personalized transfer learning method; this would be done by training a generic model and refining it with personal data. Methods: This retrospective cohort study used open source data from the Waveform Database Matched Subset of the Medical Information Mart From Intensive Care III (MIMIC-III). We included patients with multiple serum potassium test results and matched ECG data from the MIMIC-III database. A 1D convolutional neural network–based deep learning model was first developed to predict hyperkalemia in a generic population. Once the model achieved a state-of-the-art performance, it was used in an active transfer learning process to perform patient-adaptive heartbeat classification tasks. Results: The results show that by acquiring data from each new patient, the personalized model can improve the accuracy of hyperkalemia detection significantly, from an average of 0.604 (SD 0.211) to 0.980 (SD 0.078), when compared with the generic model. Moreover, the area under the receiver operating characteristic curve level improved from 0.729 (SD 0.240) to 0.945 (SD 0.094). Conclusions: By using the deep transfer learning method, we were able to build a clinical standard model for hyperkalemia detection using ambulatory ECG monitors. These findings could potentially be extended to applications that continuously monitor one’s ECGs for early alerts of hyperkalemia and help avoid unnecessary blood tests. %M 36469396 %R 10.2196/41163 %U https://www.jmir.org/2022/12/e41163 %U https://doi.org/10.2196/41163 %U http://www.ncbi.nlm.nih.gov/pubmed/36469396 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 6 %N 2 %P e36947 %T The First National Program of Remote Cardiac Rehabilitation in Israel–Goal Achievements, Adherence, and Responsiveness in Older Adult Patients: Retrospective Analysis %A Nabutovsky,Irene %A Breitner,Daniel %A Heller,Alexis %A Scheinowitz,Mickey %A Klempfner,Yarin %A Klempfner,Robert %+ Cardiac Prevention and Rehabilitation Institute, Sheba Medical Center, Derech Sheba 2, Ramat Gan, 5265601, Israel, 972 3 5303068, Robert.Klempfner@sheba.health.gov.il %K remote cardiac rehabilitation %K mobile application %K adherence %K elderly patients %K telehealth %K telemedicine %K cardiology %K smartwatch %K wearable %K patient monitoring %D 2022 %7 16.11.2022 %9 Original Paper %J JMIR Cardio %G English %X Background: Remote cardiac rehabilitation (RCR) after myocardial infarction is an innovative Israeli national program in the field of telecardiology. RCR is included in the Israeli health coverage for all citizens. It is generally accepted that telemedicine programs better apply to younger patients because it is thought that they are more technologically literate than are older patients. It has also previously been thought that older patients have difficulty using technology-based programs and attaining program goals. Objective: The objectives of this study were as follows: to study patterns of physical activity, goal achievement, and improvement in functional capacity among patients undergoing RCR over 65 years old compared to those of younger patients; and to identify predictors of better adherence with the RCR program. Methods: A retrospective study of patients post–myocardial infarction were enrolled in a 6-month RCR program. The activity of the patients was monitored using a smartwatch. The data were collected and analyzed by a special telemedicine platform. RCR program goals were as follows: 150 minutes of aerobic activity per week, 120 minutes of the activity in the target heart rate recommended by the exercise physiologist, and 8000 steps per day. Models were created to evaluate variables predicting adherence with the program. Results: Out of 306 patients, 80 were older adults (mean age 70 years, SD 3.4 years). At the end of the program, there was a significant improvement in the functional capacity of all patients (P=.002). Specifically, the older adult group improved from a mean 8.1 (SD 2.8) to 11.2 (SD 12.6). The metabolic equivalents of task (METs) and final MET results were similar among older and younger patients. During the entire program period, the older adult group showed better achievement of program goals compared to younger patients (P=.03). Additionally, we found that younger patient age is an independent predictor of early dropout from the program and completion of program goals (P=.045); younger patients were more likely to experience early program dropout and to complete fewer program goals. Conclusions: Older adult patients demonstrated better compliance and achievement of the goals of the remote rehabilitation program in comparison with younger patients. We found that older age is not a limitation but rather a predictor of better RCR program compliance and program goal achievement. %M 36383410 %R 10.2196/36947 %U https://cardio.jmir.org/2022/2/e36947 %U https://doi.org/10.2196/36947 %U http://www.ncbi.nlm.nih.gov/pubmed/36383410 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 11 %P e36340 %T Measurement of Vital Signs Using Lifelight Remote Photoplethysmography: Results of the VISION-D and VISION-V Observational Studies %A Heiden,Emily %A Jones,Tom %A Brogaard Maczka,Annika %A Kapoor,Melissa %A Chauhan,Milan %A Wiffen,Laura %A Barham,Helen %A Holland,Jeremy %A Saxena,Manish %A Wegerif,Simon %A Brown,Thomas %A Lomax,Mitch %A Massey,Heather %A Rostami,Shahin %A Pearce,Laurence %A Chauhan,Anoop %+ Mind Over Matter Medtech Ltd, Kemp House, London, EC1V 2NX, United Kingdom, 44 7881 927063, melissa@mind-medtech.com %K general practice %K vital signs/methods %K vital signs/standards %K photoplethysmography %K remote photoplethysmography %K remote photoplethysmography %K Lifelight %K contactless %K software %K algorithm development %K algorithm %K blood pressure %K health monitoring %K health technology %K remote monitoring %D 2022 %7 14.11.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: The detection of early changes in vital signs (VSs) enables timely intervention; however, the measurement of VSs requires hands-on technical expertise and is often time-consuming. The contactless measurement of VSs is beneficial to prevent infection, such as during the COVID-19 pandemic. Lifelight is a novel software being developed to measure VSs by remote photoplethysmography based on video captures of the face via the integral camera on mobile phones and tablets. We report two early studies in the development of Lifelight. Objective: The objective of the Vital Sign Comparison Between Lifelight and Standard of Care: Development (VISION-D) study (NCT04763746) was to measure respiratory rate (RR), pulse rate (PR), and blood pressure (BP) simultaneously by using the current standard of care manual methods and the Lifelight software to iteratively refine the software algorithms. The objective of the Vital Sign Comparison Between Lifelight and Standard of Care: Validation (VISION-V) study (NCT03998098) was to validate the use of Lifelight software to accurately measure VSs. Methods: BP, PR, and RR were measured simultaneously using Lifelight, a sphygmomanometer (BP and PR), and the manual counting of RR. Accuracy performance targets for each VS were defined from a systematic literature review of the performance of state-of-the-art VSs technologies. Results: The VISION-D data set (17,233 measurements from 8585 participants) met the accuracy targets for RR (mean error 0.3, SD 3.6 vs target mean error 2.3, SD 5.0; n=7462), PR (mean error 0.3, SD 4.0 vs mean error 2.2, SD 9.2; n=10,214), and diastolic BP (mean error −0.4, SD 8.5 vs mean error 5.5, SD 8.9; n=8951); for systolic BP, the mean error target was met but not the SD (mean error 3.5, SD 16.8 vs mean error 6.7, SD 15.3; n=9233). Fitzpatrick skin type did not affect accuracy. The VISION-V data set (679 measurements from 127 participants) met all the standards: mean error −0.1, SD 3.4 for RR; mean error 1.4, SD 3.8 for PR; mean error 2.8, SD 14.5 for systolic BP; and mean error −0.3, SD 7.0 for diastolic BP. Conclusions: At this early stage in development, Lifelight demonstrates sufficient accuracy in the measurement of VSs to support certification for a Level 1 Conformité Européenne mark. As the use of Lifelight does not require specific training or equipment, the software is potentially useful for the contactless measurement of VSs by nonclinical staff in residential and home care settings. Work is continuing to enhance data collection and processing to achieve the robustness and accuracy required for routine clinical use. International Registered Report Identifier (IRRID): RR2-10.2196/14326 %M 36374541 %R 10.2196/36340 %U https://formative.jmir.org/2022/11/e36340 %U https://doi.org/10.2196/36340 %U http://www.ncbi.nlm.nih.gov/pubmed/36374541 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 6 %N 2 %P e35796 %T Cardiorespiratory Fitness Estimation Based on Heart Rate and Body Acceleration in Adults With Cardiovascular Risk Factors: Validation Study %A Rissanen,Antti-Pekka E %A Rottensteiner,Mirva %A Kujala,Urho M %A Kurkela,Jari L O %A Wikgren,Jan %A Laukkanen,Jari A %+ Department of Sports and Exercise Medicine, Clinicum, University of Helsinki, Urhea-halli, Mäkelänkatu 47, Helsinki, 00550, Finland, 358 9 434 2100, antti-pekka.rissanen@helsinki.fi %K cardiopulmonary exercise test %K cardiorespiratory fitness %K heart rate variability %K hypertension %K type 2 diabetes %K wearable technology %D 2022 %7 25.10.2022 %9 Original Paper %J JMIR Cardio %G English %X Background: Cardiorespiratory fitness (CRF) is an independent risk factor for cardiovascular morbidity and mortality. Adding CRF to conventional risk factors (eg, smoking, hypertension, impaired glucose metabolism, and dyslipidemia) improves the prediction of an individual’s risk for adverse health outcomes such as those related to cardiovascular disease. Consequently, it is recommended to determine CRF as part of individualized risk prediction. However, CRF is not determined routinely in everyday clinical practice. Wearable technologies provide a potential strategy to estimate CRF on a daily basis, and such technologies, which provide CRF estimates based on heart rate and body acceleration, have been developed. However, the validity of such technologies in estimating individual CRF in clinically relevant populations is poorly known. Objective: The objective of this study is to evaluate the validity of a wearable technology, which provides estimated CRF based on heart rate and body acceleration, in working-aged adults with cardiovascular risk factors. Methods: In total, 74 adults (age range 35-64 years; n=56, 76% were women; mean BMI 28.7, SD 4.6 kg/m2) with frequent cardiovascular risk factors (eg, n=64, 86% hypertension; n=18, 24% prediabetes; n=14, 19% type 2 diabetes; and n=51, 69% metabolic syndrome) performed a 30-minute self-paced walk on an indoor track and a cardiopulmonary exercise test on a treadmill. CRF, quantified as peak O2 uptake, was both estimated (self-paced walk: a wearable single-lead electrocardiogram device worn to record continuous beat-to-beat R-R intervals and triaxial body acceleration) and measured (cardiopulmonary exercise test: ventilatory gas analysis). The accuracy of the estimated CRF was evaluated against that of the measured CRF. Results: Measured CRF averaged 30.6 (SD 6.3; range 20.1-49.6) mL/kg/min. In all participants (74/74, 100%), mean difference between estimated and measured CRF was −0.1 mL/kg/min (P=.90), mean absolute error was 3.1 mL/kg/min (95% CI 2.6-3.7), mean absolute percentage error was 10.4% (95% CI 8.5-12.5), and intraclass correlation coefficient was 0.88 (95% CI 0.80-0.92). Similar accuracy was observed in various subgroups (sexes, age, BMI categories, hypertension, prediabetes, and metabolic syndrome). However, mean absolute error was 4.2 mL/kg/min (95% CI 2.6-6.1) and mean absolute percentage error was 16.5% (95% CI 8.6-24.4) in the subgroup of patients with type 2 diabetes (14/74, 19%). Conclusions: The error of the CRF estimate, provided by the wearable technology, was likely below or at least very close to the clinically significant level of 3.5 mL/kg/min in working-aged adults with cardiovascular risk factors, but not in the relatively small subgroup of patients with type 2 diabetes. From a large-scale clinical perspective, the findings suggest that wearable technologies have the potential to estimate individual CRF with acceptable accuracy in clinically relevant populations. %M 36282560 %R 10.2196/35796 %U https://cardio.jmir.org/2022/2/e35796 %U https://doi.org/10.2196/35796 %U http://www.ncbi.nlm.nih.gov/pubmed/36282560 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 9 %P e41241 %T Comparison Between QT and Corrected QT Interval Assessment by an Apple Watch With the AccurBeat Platform and by a 12‑Lead Electrocardiogram With Manual Annotation: Prospective Observational Study %A Chokshi,Sara %A Tologonova,Gulzhan %A Calixte,Rose %A Yadav,Vandana %A Razvi,Naveed %A Lazar,Jason %A Kachnowski,Stan %+ Healthcare Innovation and Technology Lab, 3960 Broadway, New York, NY, 10032, United States, 1 212 543 0100, schokshi@hitlab.org %K artificial intelligence ECG %K AI ECG %K AI wearables %K big data %K cardiovascular medicine %K digital health %K machine learning %D 2022 %7 28.9.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Abnormal prolongation or shortening of the QT interval is associated with increased risk for ventricular arrhythmias and sudden cardiac death. For continuous monitoring, widespread use, and prevention of cardiac events, advanced wearable technologies are emerging as promising surrogates for conventional 12‑lead electrocardiogram (ECG) QT interval assessment. Previous studies have shown a good agreement between QT and corrected QT (QTc) intervals measured on a smartwatch ECG and a 12-lead ECG, but the clinical accuracy of computerized algorithms for QT and QTc interval measurement from smartwatch ECGs is unclear. Objective: The prospective observational study compared the smartwatch-recorded QT and QTc assessed using AccurKardia’s AccurBeat platform with the conventional 12‑lead ECG annotated manually by a cardiologist. Methods: ECGs were collected from healthy participants (without any known cardiovascular disease) aged >22 years. Two consecutive 30-second ECG readings followed by (within 15 minutes) a 10-second standard 12-lead ECG were recorded for each participant. Characteristics of the participants were compared by sex using a 2-sample t test and Wilcoxon rank sum test. Statistical comparisons of heart rate (HR), QT interval, and QTc interval between the platform and the 12-lead ECG, ECG lead I, and ECG lead II were done using the Wilcoxon sign rank test. Linear regression was used to predict QTc and QT intervals from the ECG based on the platform’s QTc/QT intervals with adjustment for age, sex, and difference in HR measurement. The Bland-Altman method was used to check agreement between various QT and QTc interval measurements. Results: A total of 50 participants (32 female, mean age 46 years, SD 1 year) were included in the study. The result of the regression model using the platform measurements to predict the 12-lead ECG measurements indicated that, in univariate analysis, QT/QTc intervals from the platform significantly predicted QT/QTc intervals from the 12-lead ECG, ECG lead I, and ECG lead II, and this remained significant after adjustment for sex, age, and change in HR. The Bland-Altman plot results found that 96% of the average QTc interval measurements between the platform and QTc intervals from the 12-lead ECG were within the 95% confidence limit of the average difference between the two measurements, with a mean difference of –10.5 (95% limits of agreement –71.43, 50.43). A total of 94% of the average QT interval measurements between the platform and the 12-lead ECG were within the 95% CI of the average difference between the two measurements, with a mean difference of –6.3 (95% limits of agreement –54.54, 41.94). Conclusions: QT and QTc intervals obtained by a smartwatch coupled with the platform’s assessment were comparable to those from a 12-lead ECG. Accordingly, with further refinements, remote monitoring using this technology holds promise for the identification of QT interval prolongation. %M 36169999 %R 10.2196/41241 %U https://formative.jmir.org/2022/9/e41241 %U https://doi.org/10.2196/41241 %U http://www.ncbi.nlm.nih.gov/pubmed/36169999 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 11 %N 2 %P e36335 %T Intervention in the Timeliness of Two Electrocardiography Types for Patients in the Emergency Department With Chest Pain: Randomized Controlled Trial %A Yoo,Suyoung %A Chang,Hansol %A kim,Taerim %A yoon,Hee %A Hwang,Sung Yeon %A Shin,Tae Gun %A Sim,Min Seob %A Jo,Ik joon %A Choi,Jin-Ho %A Cha,Won Chul %+ Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 115, Irwon-ro, Gangnam-gu, Seoul, Seoul, 06355, Republic of Korea, 82 02 1599 3114, docchaster@gmail.com %K imaging %K electrocardiography %K wireless technology %K emergency department %K emergency %K angina %K ECG %K EKG %K cardiology %K chest %K pain %K electrocardiogram %K randomized %K randomization %K heart %K cardiac %K diagnose %K diagnosis %K accuracy %D 2022 %7 13.9.2022 %9 Original Paper %J Interact J Med Res %G English %X Background: In the emergency department (ED), the result obtained using the 12-lead electrocardiography (ECG) is the basis for diagnosing and treating patients with chest pain. It was found that performing ECG at the appropriate time could improve treatment outcomes. Hence, a wearable ECG device with a timer can ensure that the findings are continuously recorded. Objective: We aimed to compare the time accuracy of a single-patch 12-lead ECG (SP-ECG) with that of conventional ECG (C-ECG). We hypothesized that SP-ECG would result in better time accuracy. Methods: Adult patients who visited the emergency room with chest pain but were not in shock were randomly assigned to one of the following 2 groups: the SP-ECG group or the C-ECG group. The final analysis included 33 (92%) of the 36 patients recruited. The primary outcome was the comparison of the time taken by the 2 groups to record the ECG. The average ages of the participants in the SP-ECG and C-ECG groups were 63.7 (SD 18.4) and 58.1 (SD 12.4) years, respectively. Results: With a power of 0.95 and effect sizes of 0.05 and 1.36, the minimum number of samples was calculated. The minimum sample size for each SP-ECG and C-ECG group is 15.36 participants, assuming a 20% dropout rate. As a result, 36 patients with chest pain participated, and 33 of them were analyzed. The timeliness of SP-ECG and C-ECG for the first follow-up ECG was 87.5% and 47.0%, respectively (P=.74). It was 75.0% and 35.2% at the second follow-up, respectively (P=.71). Conclusions: Continuous ECG monitoring with minimal interference from other examinations is feasible and essential in complex ED situations. However, the precision of SP-ECG has not yet been proved. Nevertheless, the application of SP-ECG is expected to improve overcrowding and human resource shortages in EDs, though more research is needed. Trial Registration: ClinicalTrials.gov NCT04114760; https://clinicaltrials.gov/ct2/show/NCT04114760 %M 36099010 %R 10.2196/36335 %U https://www.i-jmr.org/2022/2/e36335 %U https://doi.org/10.2196/36335 %U http://www.ncbi.nlm.nih.gov/pubmed/36099010 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 9 %P e34280 %T Measurement of Heart Rate Using the Withings ScanWatch Device During Free-living Activities: Validation Study %A Giggins,Oonagh M %A Doyle,Julie %A Smith,Suzanne %A Crabtree,Daniel R %A Fraser,Matthew %+ NetwellCASALA, Dundalk Institute of Technology, Dublin Road, Dundalk, A91 K584, Ireland, 353 429370200 ext 2114, oonagh.giggins@dkit.ie %K heart rate %K photoplethysmography %K PPG %K wearable electronic device %K wrist-worn device %K validation study %K heart %K activity %K physical activity %K free-living activity %D 2022 %7 1.9.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Wrist-worn devices that incorporate photoplethysmography (PPG) sensing represent an exciting means of measuring heart rate (HR). A number of studies have evaluated the accuracy of HR measurements produced by these devices in controlled laboratory environments. However, it is also important to establish the accuracy of measurements produced by these devices outside the laboratory, in real-world, consumer use conditions. Objective: This study sought to examine the accuracy of HR measurements produced by the Withings ScanWatch during free-living activities. Methods: A sample of convenience of 7 participants volunteered (3 male and 4 female; mean age 64, SD 10 years; mean height 164, SD 4 cm; mean weight 77, SD 16 kg) to take part in this real-world validation study. Participants were instructed to wear the ScanWatch for a 12-hour period on their nondominant wrist as they went about their day-to-day activities. A Polar H10 heart rate sensor was used as the criterion measure of HR. Participants used a study diary to document activities undertaken during the 12-hour study period. These activities were classified according to the 11 following domains: desk work, eat or drink, exercise, gardening, household activities, self-care, shopping, sitting, sleep, travel, and walking. Validity was assessed using the Bland-Altman analysis, concordance correlation coefficient (CCC), and mean absolute percentage error (MAPE). Results: Across all activity domains, the ScanWatch measured HR with MAPE values <10%, except for the shopping activity domain (MAPE=10.8%). The activity domains that were more sedentary in nature (eg, desk work, eat or drink, and sitting) produced the most accurate HR measurements with a small mean bias and MAPE values <5%. Moderate to strong correlations (CCC=0.526-0.783) were observed between devices for all activity domains, except during the walking activity domain, which demonstrated a weak correlation (CCC=0.164) between devices. Conclusions: The results of this study show that the ScanWatch measures HR with a degree of accuracy that is acceptable for general consumer use; however, it would not be suitable in circumstances where more accurate measurements of HR are required, such as in health care or in clinical trials. Overall, the ScanWatch was less accurate at measuring HR during ambulatory activities (eg, walking, gardening, and household activities) compared to more sedentary activities (eg, desk work, eat or drink, and sitting). Further larger-scale studies examining this device in different populations and during different activities are required. %M 36048505 %R 10.2196/34280 %U https://formative.jmir.org/2022/9/e34280 %U https://doi.org/10.2196/34280 %U http://www.ncbi.nlm.nih.gov/pubmed/36048505 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 8 %P e36741 %T Continuous Remote Patient Monitoring in Patients With Heart Failure (Cascade Study): Protocol for a Mixed Methods Feasibility Study %A Reamer,Courtney %A Chi,Wei Ning %A Gordon,Robert %A Sarswat,Nitasha %A Gupta,Charu %A Gaznabi,Safwan %A White VanGompel,Emily %A Szum,Izabella %A Morton-Jost,Melissa %A Vaughn,Jorma %A Larimer,Karen %A Victorson,David %A Erwin,John %A Halasyamani,Lakshmi %A Solomonides,Anthony %A Padman,Rema %A Shah,Nirav S %+ Department of Medicine, NorthShore University HealthSystem, 2650 Ridge Avenue, Evanston, IL, United States, 1 847 570 2000, CReamer@northshore.org %K continuous remote patient monitoring %K remote patient monitoring %K feasibility %K heart failure %K wearable biosensor %K preliminary efficacy %K mobile phone %D 2022 %7 25.8.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: Heart failure (HF) is a prevalent chronic disease and is associated with increases in mortality and morbidity. HF is a leading cause of hospitalizations and readmissions in the United States. A potentially promising area for preventing HF readmissions is continuous remote patient monitoring (CRPM). Objective: The primary aim of this study is to determine the feasibility and preliminary efficacy of a CRPM solution in patients with HF at NorthShore University HealthSystem. Methods: This study is a feasibility study and uses a wearable biosensor to continuously remotely monitor patients with HF for 30 days after discharge. Eligible patients admitted with an HF exacerbation at NorthShore University HealthSystem are being recruited, and the wearable biosensor is placed before discharge. The biosensor collects physiological ambulatory data, which are analyzed for signs of patient deterioration. Participants are also completing a daily survey through a dedicated study smartphone. If prespecified criteria from the physiological data and survey results are met, a notification is triggered, and a predetermined electronic health record–based pathway of telephonic management is completed. In phase 1, which has already been completed, 5 patients were enrolled and monitored for 30 days after discharge. The results of phase 1 were analyzed, and modifications to the program were made to optimize it. After analysis of the phase 1 results, 15 patients are being enrolled for phase 2, which is a calibration and testing period to enable further adjustments to be made. After phase 2, we will enroll 45 patients for phase 3. The combined results of phases 1, 2, and 3 will be analyzed to determine the feasibility of a CRPM program in patients with HF. Semistructured interviews are being conducted with key stakeholders, including patients, and these results will be analyzed using the affective adaptation of the technology acceptance model. Results: During phase 1, of the 5 patients, 2 (40%) were readmitted during the study period. The study completion rate for phase 1 was 80% (4/5), and the study attrition rate was 20% (1/5). There were 57 protocol deviations out of 150 patient days in phase 1 of the study. The results of phase 1 were analyzed, and the study protocol was adjusted to optimize it for phases 2 and 3. Phase 2 and phase 3 results will be available by the end of 2022. Conclusions: A CRPM program may offer a low-risk solution to improve care of patients with HF after hospital discharge and may help to decrease readmission of patients with HF to the hospital. This protocol may also lay the groundwork for the use of CRPM solutions in other groups of patients considered to be at high risk. International Registered Report Identifier (IRRID): DERR1-10.2196/36741 %M 36006689 %R 10.2196/36741 %U https://www.researchprotocols.org/2022/8/e36741 %U https://doi.org/10.2196/36741 %U http://www.ncbi.nlm.nih.gov/pubmed/36006689 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 6 %N 2 %P e38570 %T Prediction of VO2max From Submaximal Exercise Using the Smartphone Application Myworkout GO: Validation Study of a Digital Health Method %A Helgerud,Jan %A Haglo,Håvard %A Hoff,Jan %+ Medical Rehabilitation Clinic, Myworkout, Ingvald Ystgaards veg 23, Trondheim, 7047, Norway, 47 92621619, havard@treningsklinikken.no %K high-intensity interval training %K cardiovascular health %K physical inactivity %K endurance training %K measurement accuracy %D 2022 %7 4.8.2022 %9 Original Paper %J JMIR Cardio %G English %X Background: Physical inactivity remains the largest risk factor for the development of cardiovascular disease worldwide. Wearable devices have become a popular method of measuring activity-based outcomes and facilitating behavior change to increase cardiorespiratory fitness (CRF) or maximal oxygen consumption (VO2max) and reduce weight. However, it is critical to determine their accuracy in measuring these variables. Objective: This study aimed to determine the accuracy of using a smartphone and the application Myworkout GO for submaximal prediction of VO2max. Methods: Participants included 162 healthy volunteers: 58 women and 104 men (17-73 years old). The study consisted of 3 experimental tests randomized to 3 separate days. One-day VO2max was assessed with Metamax II, with the participant walking or running on the treadmill. On the 2 other days, the application Myworkout GO used standardized high aerobic intensity interval training (HIIT) on the treadmill to predict VO2max. Results: There were no significant differences between directly measured VO2max (mean 49, SD 14 mL/kg/min) compared with the VO2max predicted by Myworkout GO (mean 50, SD 14 mL/kg/min). The direct and predicted VO2max values were highly correlated, with an R2 of 0.97 (P<.001) and standard error of the estimate (SEE) of 2.2 mL/kg/min, with no sex differences. Conclusions: Myworkout GO accurately calculated VO2max, with an SEE of 4.5% in the total group. The submaximal HIIT session (4 x 4 minutes) incorporated in the application was tolerated well by the participants. We present health care providers and their patients with a more accurate and practical version of health risk estimation. This might increase physical activity and improve exercise habits in the general population. %M 35925653 %R 10.2196/38570 %U https://cardio.jmir.org/2022/2/e38570 %U https://doi.org/10.2196/38570 %U http://www.ncbi.nlm.nih.gov/pubmed/35925653 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 7 %P e34669 %T High-Resolution Digital Phenotypes From Consumer Wearables and Their Applications in Machine Learning of Cardiometabolic Risk Markers: Cohort Study %A Zhou,Weizhuang %A Chan,Yu En %A Foo,Chuan Sheng %A Zhang,Jingxian %A Teo,Jing Xian %A Davila,Sonia %A Huang,Weiting %A Yap,Jonathan %A Cook,Stuart %A Tan,Patrick %A Chin,Calvin Woon-Loong %A Yeo,Khung Keong %A Lim,Weng Khong %A Krishnaswamy,Pavitra %+ Institute for Infocomm Research, Agency for Science Technology and Research (A*STAR), 1 Fusionopolis Way, #21-01, Connexis (South Tower), Singapore, 138632, Singapore, 65 64082450, pavitrak@i2r.a-star.edu.sg %K wearable device %K heart rate %K cardiometabolic disease %K risk prediction %K digital phenotypes %K polygenic risk scores %K time series analysis %K machine learning %K free-living %D 2022 %7 29.7.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Consumer-grade wearable devices enable detailed recordings of heart rate and step counts in free-living conditions. Recent studies have shown that summary statistics from these wearable recordings have potential uses for longitudinal monitoring of health and disease states. However, the relationship between higher resolution physiological dynamics from wearables and known markers of health and disease remains largely uncharacterized. Objective: We aimed to derive high-resolution digital phenotypes from observational wearable recordings and to examine their associations with modifiable and inherent markers of cardiometabolic disease risk. Methods: We introduced a principled framework to extract interpretable high-resolution phenotypes from wearable data recorded in free-living conditions. The proposed framework standardizes the handling of data irregularities; encodes contextual information regarding the underlying physiological state at any given time; and generates a set of 66 minimally redundant features across active, sedentary, and sleep states. We applied our approach to a multimodal data set, from the SingHEART study (NCT02791152), which comprises heart rate and step count time series from wearables, clinical screening profiles, and whole genome sequences from 692 healthy volunteers. We used machine learning to model nonlinear relationships between the high-resolution phenotypes on the one hand and clinical or genomic risk markers for blood pressure, lipid, weight and sugar abnormalities on the other. For each risk type, we performed model comparisons based on Brier scores to assess the predictive value of high-resolution features over and beyond typical baselines. We also qualitatively characterized the wearable phenotypes for participants who had actualized clinical events. Results: We found that the high-resolution features have higher predictive value than typical baselines for clinical markers of cardiometabolic disease risk: the best models based on high-resolution features had 17.9% and 7.36% improvement in Brier score over baselines based on age and gender and resting heart rate, respectively (P<.001 in each case). Furthermore, heart rate dynamics from different activity states contain distinct information (maximum absolute correlation coefficient of 0.15). Heart rate dynamics in sedentary states are most predictive of lipid abnormalities and obesity, whereas patterns in active states are most predictive of blood pressure abnormalities (P<.001). Moreover, in comparison with standard measures, higher resolution patterns in wearable heart rate recordings are better able to represent subtle physiological dynamics related to genomic risk for cardiometabolic disease (improvement of 11.9%-22.0% in Brier scores; P<.001). Finally, illustrative case studies reveal connections between these high-resolution phenotypes and actualized clinical events, even for borderline profiles lacking apparent cardiometabolic risk markers. Conclusions: High-resolution digital phenotypes recorded by consumer wearables in free-living states have the potential to enhance the prediction of cardiometabolic disease risk and could enable more proactive and personalized health management. %M 35904853 %R 10.2196/34669 %U https://www.jmir.org/2022/7/e34669 %U https://doi.org/10.2196/34669 %U http://www.ncbi.nlm.nih.gov/pubmed/35904853 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 6 %N 1 %P e31230 %T Continuous mHealth Patch Monitoring for the Algorithm-Based Detection of Atrial Fibrillation: Feasibility and Diagnostic Accuracy Study %A Santala,Onni E %A Lipponen,Jukka A %A Jäntti,Helena %A Rissanen,Tuomas T %A Tarvainen,Mika P %A Laitinen,Tomi P %A Laitinen,Tiina M %A Castrén,Maaret %A Väliaho,Eemu-Samuli %A Rantula,Olli A %A Naukkarinen,Noora S %A Hartikainen,Juha E K %A Halonen,Jari %A Martikainen,Tero J %+ School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Yliopistonranta 1 PO BOX 1627, Kuopio, FI-70211, Finland, 358 503010879, elmeris@uef.fi %K atrial fibrillation %K heart rate variability %K HRV %K algorithm %K stroke %K mobile health %K mHealth %K Awario analysis Service, screening %K risk %K stroke risk %K heart rate %K feasibility %K reliability %K artificial intelligence %K mobile patch %K wearable %K arrhythmia %K screening %D 2022 %7 21.6.2022 %9 Original Paper %J JMIR Cardio %G English %X Background: The detection of atrial fibrillation (AF) is a major clinical challenge as AF is often paroxysmal and asymptomatic. Novel mobile health (mHealth) technologies could provide a cost-effective and reliable solution for AF screening. However, many of these techniques have not been clinically validated. Objective: The purpose of this study is to evaluate the feasibility and reliability of artificial intelligence (AI) arrhythmia analysis for AF detection with an mHealth patch device designed for personal well-being. Methods: Patients (N=178) with an AF (n=79, 44%) or sinus rhythm (n=99, 56%) were recruited from the emergency care department. A single-lead, 24-hour, electrocardiogram-based heart rate variability (HRV) measurement was recorded with the mHealth patch device and analyzed with a novel AI arrhythmia analysis software. Simultaneously registered 3-lead electrocardiograms (Holter) served as the gold standard for the final rhythm diagnostics. Results: Of the HRV data produced by the single-lead mHealth patch, 81.5% (3099/3802 hours) were interpretable, and the subject-based median for interpretable HRV data was 99% (25th percentile=77% and 75th percentile=100%). The AI arrhythmia detection algorithm detected AF correctly in all patients in the AF group and suggested the presence of AF in 5 patients in the control group, resulting in a subject-based AF detection accuracy of 97.2%, a sensitivity of 100%, and a specificity of 94.9%. The time-based AF detection accuracy, sensitivity, and specificity of the AI arrhythmia detection algorithm were 98.7%, 99.6%, and 98.0%, respectively. Conclusions: The 24-hour HRV monitoring by the mHealth patch device enabled accurate automatic AF detection. Thus, the wearable mHealth patch device with AI arrhythmia analysis is a novel method for AF screening. Trial Registration: ClinicalTrials.gov NCT03507335; https://clinicaltrials.gov/ct2/show/NCT03507335 %M 35727618 %R 10.2196/31230 %U https://cardio.jmir.org/2022/1/e31230 %U https://doi.org/10.2196/31230 %U http://www.ncbi.nlm.nih.gov/pubmed/35727618 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 6 %N 1 %P e35615 %T Surveillance of Arrhythmia in Patients After Myocardial Infarction Using Wearable Electrocardiogram Patch Devices: Prospective Cohort Study %A Kwun,Ju-Seung %A Yoon,Chang-Hwan %A Kim,Sun-Hwa %A Jeon,Ki-Hyun %A Kang,Si-Hyuck %A Lee,Wonjae %A Youn,Tae-Jin %A Chae,In-Ho %+ Seoul National University Bundang Hospital, 82 Gumi-Ro 173 Beon-Gil Bundang-Gu, Gyeonggi-Do, Seongnam-Si, 13620, Republic of Korea, 82 31 787 7052, changhwanyoon@gmail.com %K myocardial infarction %K arrhythmia %K wearable electronic device %K wearable %K ECG %K electrocardiogram %K patch %K patch devices %K atrial fibrillation %K heart %K rhythm %K cardiology %K cardiologist %K cohort study %K tachycardia %K beta-blocker %D 2022 %7 9.6.2022 %9 Original Paper %J JMIR Cardio %G English %X Background: Acute myocardial infarction may be associated with new-onset arrhythmias. Patients with myocardial infarction may manifest serious arrhythmias such as ventricular tachyarrhythmias or atrial fibrillation. Frequent, prolonged electrocardiogram (ECG) monitoring can prevent devastating outcomes caused by these arrhythmias. Objective: We aimed to investigate the incidence of arrhythmias in patients following myocardial infarction using a patch-type device—AT-Patch (ATP-C120; ATsens). Methods: This study is a nonrandomized, single-center, prospective cohort study. We evaluated 71 patients who had had a myocardial infarction and had been admitted to our hospital. The ATP-C120 device was attached to the patient for 11 days and analyzed by 2 cardiologists for new-onset arrhythmic events. Results: One participant was concordantly diagnosed with atrial fibrillation. The cardiologists diagnosed atrial premature beats in 65 (92%) and 60 (85%) of 71 participants, and ventricular premature beats in 38 (54%) and 44 (62%) participants, respectively. Interestingly, 40 (56%) patients showed less than 2 minutes of sustained paroxysmal atrial tachycardia confirmed by both cardiologists. Among participants with atrial tachycardia, the use of β-blockers was significantly lower compared with patients without tachycardia (70% vs 90%, P=.04). However, different dosages of β-blockers did not make a significant difference. Conclusions: Wearable ECG monitoring patch devices are easy to apply and can correlate symptoms and ECG rhythm disturbances in patients following myocardial infarction. Further study is necessary regarding clinical implications and appropriate therapies for arrhythmias detected early after myocardial infarction to prevent adverse outcomes. %M 35679117 %R 10.2196/35615 %U https://cardio.jmir.org/2022/1/e35615 %U https://doi.org/10.2196/35615 %U http://www.ncbi.nlm.nih.gov/pubmed/35679117 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 5 %P e37970 %T Comparison Between the 24-hour Holter Test and 72-hour Single-Lead Electrocardiogram Monitoring With an Adhesive Patch-Type Device for Atrial Fibrillation Detection: Prospective Cohort Study %A Kwon,Soonil %A Lee,So-Ryoung %A Choi,Eue-Keun %A Ahn,Hyo-Jeong %A Song,Hee-Seok %A Lee,Young-Shin %A Oh,Seil %A Lip,Gregory Y H %+ Department of Internal Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, Republic of Korea, 82 2 2072 0688, choiek417@gmail.com %K atrial fibrillation %K diagnosis %K electrocardiogram %K wearable device %K health monitoring %K Holter %K cardiac %K arrhythmia %K electrocardiogram %K ECG %K EKG %K digital tool %K cardiology %K patient monitoring %K outpatient clinic %K cardiac health %K diagnostic %K patient %K clinician %K digital health %D 2022 %7 9.5.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: There is insufficient evidence for the use of single-lead electrocardiogram (ECG) monitoring with an adhesive patch-type device (APD) over an extended period compared to that of the 24-hour Holter test for atrial fibrillation (AF) detection. Objective: In this paper, we aimed to compare AF detection by the 24-hour Holter test and 72-hour single-lead ECG monitoring using an APD among patients with AF. Methods: This was a prospective, single-center cohort study. A total of 210 patients with AF with clinical indications for the Holter test at cardiology outpatient clinics were enrolled in the study. The study participants were equipped with both the Holter device and APD for the first 24 hours. Subsequently, only the APD continued ECG monitoring for an additional 48 hours. AF detection during the first 24 hours was compared between the two devices. The diagnostic benefits of extended monitoring using the APD were evaluated. Results: A total of 200 patients (mean age 60 years; n=141, 70.5% male; and n=59, 29.5% female) completed 72-hour ECG monitoring with the APD. During the first 24 hours, both monitoring methods detected AF in the same 40/200 (20%) patients (including 20 patients each with paroxysmal and persistent AF). Compared to the 24-hour Holter test, the APD increased the AF detection rate by 1.5-fold (58/200; 29%) and 1.6-fold (64/200; 32%) with 48- and 72-hour monitoring, respectively. With the APD, the number of newly discovered patients with paroxysmal AF was 20/44 (45.5%), 18/44 (40.9%), and 6/44 (13.6%) at 24-, 48-, and 72-hour monitoring, respectively. Compared with 24-hour Holter monitoring, 72-hour monitoring with the APD increased the detection rate of paroxysmal AF by 2.2-fold (44/20). Conclusions: Compared to the 24-hour Holter test, AF detection could be improved with 72-hour single-lead ECG monitoring with the APD. %M 35532989 %R 10.2196/37970 %U https://www.jmir.org/2022/5/e37970 %U https://doi.org/10.2196/37970 %U http://www.ncbi.nlm.nih.gov/pubmed/35532989 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 4 %P e35945 %T Development and Effectiveness of a Mobile Health Intervention in Improving Health Literacy and Self-management of Patients With Multimorbidity and Heart Failure: Protocol for a Randomized Controlled Trial %A Bas-Sarmiento,Pilar %A Fernández-Gutiérrez,Martina %A Poza-Méndez,Miriam %A Marín-Paz,Antonio Jesús %A Paloma-Castro,Olga %A Romero-Sánchez,José Manuel %A , %+ Department of Nursing and Physiotherapy, University of Cádiz, Ana de Viya Avenue, 52, Cádiz, 11009, Spain, 34 956028100, martina.fernandez@uca.es %K complex health needs %K health literacy %K heart failure %K mHealth %K multimorbidity %D 2022 %7 29.4.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: Patients with multimorbidity and complex health needs are defined as a priority by the World Health Organization (WHO) and the European Union. There is a need to develop appropriate strategies with effective measures to meet the challenge of chronicity, reorienting national health systems. The increasing expansion of mobile health (mHealth) interventions in patient communication, the reduction of health inequalities, improved access to health care resources, adherence to treatment, and self-care of chronic diseases all point to an optimistic outlook. However, only few mobile apps demonstrate their effectiveness in these patients, which is diminished when they are not based on evidence, or when they are not designed by and for users with different levels of health literacy (HL). Objective: This study aims to evaluate the efficacy of an mHealth intervention relative to routine clinical practice in improving HL and self-management in patients with multimorbidity with heart failure (HF) and complex health needs. Methods: This is a randomized, multicenter, blinded clinical trial evaluating 2 groups, namely, a control group (standard clinical practice) and an intervention group (standard clinical practice and an ad hoc designed mHealth intervention previously developed), for 12 months. Results: The contents of the mHealth intervention will address user-perceived needs based on the development of user stories regarding diet, physical exercise, cardiac rehabilitation, therapeutic adherence, warning signs and symptoms, and emotional management. These contents have been validated by expert consensus. The creation and development of the contents of the mHealth intervention (app) took 18 months and was completed during 2021. The mobile app is expected to be developed by the end of 2022, after which it will be applied to the experimental group as an adjunct to standard clinical care during 12 months. Conclusions: The trial will demonstrate whether the mobile app improves HL and self-management in patients with HF and complex health needs, improves therapeutic adherence, and reduces hospital admissions. This study can serve as a starting point for developing other mHealth tools in other pathologies and for their generalization to other contexts. Trial Registration: ClinicalTrials.gov NCT04725526; https://tinyurl.com/bd8va27w International Registered Report Identifier (IRRID): DERR1-10.2196/35945 %M 35486437 %R 10.2196/35945 %U https://www.researchprotocols.org/2022/4/e35945 %U https://doi.org/10.2196/35945 %U http://www.ncbi.nlm.nih.gov/pubmed/35486437 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 6 %N 1 %P e31501 %T The Effect of Wearable Tracking Devices on Cardiorespiratory Fitness Among Inactive Adults: Crossover Study %A Larsen,Lisbeth Hoejkjaer %A Lauritzen,Maja Hedegaard %A Sinkjaer,Mikkel %A Kjaer,Troels W %+ Department of Neurology, Zealand University Hospital, Sygehusvej 10, Roskilde, 4000, Denmark, 45 41558592, lisbla@regionsjaelland.dk %K activity tracking %K cardiorespiratory fitness %K mHealth %K mobile health %K motivation %K physical activity %K self-monitoring %K wearable %K cardio %K fitness %K cardiorespiratory %K behavior change %D 2022 %7 15.3.2022 %9 Original Paper %J JMIR Cardio %G English %X Background: Modern lifestyle is associated with a high prevalence of physical inactivity. Objective: This study aims to investigate the effect of a wearable tracking device on cardiorespiratory fitness among inactive adults and to explore if personal characteristics and health outcomes can predict adoption of the device. Methods: In total, 62 inactive adults were recruited for this study. A control period (4 weeks) was followed by an intervention period (8 weeks) where participants were instructed to register and follow their physical activity (PA) behavior on a wrist-worn tracking device. Data collected included estimated cardiorespiratory fitness, body composition, blood pressure, perceived stress levels, and self-reported adoption of using the tracking device. Results: In total, 50 participants completed the study (mean age 48, SD 13 years, 84% women). Relative to the control period, participants increased cardiorespiratory fitness by 1.52 mL/kg/minute (95% CI 0.82-2.22; P<.001), self-reported PA by 140 minutes per week (95% CI 93.3-187.1; P<.001), daily step count by 982 (95% CI 492-1471; P<.001), and participants’ fat percentage decreased by 0.48% (95% CI –0.84 to –0.13; P=.009). No difference was observed in blood pressure (systolic: 95% CI –2.16 to 3.57, P=.63; diastolic: 95% CI –0.70 to 2.55; P=.27) or perceived stress (95% CI –0.86 to 1.78; P=.49). No associations were found between adoption of the wearable tracking device and age, gender, personality, or education. However, participants with a low perceived stress at baseline were more likely to rate the use of a wearable tracking device highly motivating. Conclusions: Tracking health behavior using a wearable tracking device increases PA resulting in an improved cardiorespiratory fitness among inactive adults. %M 35289763 %R 10.2196/31501 %U https://cardio.jmir.org/2022/1/e31501 %U https://doi.org/10.2196/31501 %U http://www.ncbi.nlm.nih.gov/pubmed/35289763 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 3 %P e33635 %T Heart Rate Measurement Accuracy of Fitbit Charge 4 and Samsung Galaxy Watch Active2: Device Evaluation Study %A Nissen,Michael %A Slim,Syrine %A Jäger,Katharina %A Flaucher,Madeleine %A Huebner,Hanna %A Danzberger,Nina %A Fasching,Peter A %A Beckmann,Matthias W %A Gradl,Stefan %A Eskofier,Bjoern M %+ Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Schlossplatz 4, Erlangen, 91054, Germany, 49 913185 28990, michael.nissen@fau.de %K wearable validation %K heart rate validation %K Fitbit Charge 4 %K Samsung Galaxy Watch Active2 %K heart rate accuracy %K fitness tracker accuracy %K wearable accuracy %K wearable %K Fitbit %K heart rate %K fitness tracker %K fitness %K cardiovascular %D 2022 %7 1.3.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Fitness trackers and smart watches are frequently used to collect data in longitudinal medical studies. They allow continuous recording in real-life settings, potentially revealing previously uncaptured variabilities of biophysiological parameters and diseases. Adequate device accuracy is a prerequisite for meaningful research. Objective: This study aims to assess the heart rate recording accuracy in two previously unvalidated devices: Fitbit Charge 4 and Samsung Galaxy Watch Active2. Methods: Participants performed a study protocol comprising 5 resting and sedentary, 2 low-intensity, and 3 high-intensity exercise phases, lasting an average of 19 minutes 27 seconds. Participants wore two wearables simultaneously during all activities: Fitbit Charge 4 and Samsung Galaxy Watch Active2. Reference heart rate data were recorded using a medically certified Holter electrocardiogram. The data of the reference and evaluated devices were synchronized and compared at 1-second intervals. The mean, mean absolute error, mean absolute percentage error, Lin concordance correlation coefficient, Pearson correlation coefficient, and Bland-Altman plots were analyzed. Results: A total of 23 healthy adults (mean age 24.2, SD 4.6 years) participated in our study. Overall, and across all activities, the Fitbit Charge 4 slightly underestimated the heart rate, whereas the Samsung Galaxy Watch Active2 overestimated it (−1.66 beats per minute [bpm]/3.84 bpm). The Fitbit Charge 4 achieved a lower mean absolute error during resting and sedentary activities (seated rest: 7.8 vs 9.4; typing: 8.1 vs 11.6; laying down [left]: 7.2 vs 9.4; laying down [back]: 6.0 vs 8.6; and walking slowly: 6.8 vs 7.7 bpm), whereas the Samsung Galaxy Watch Active2 performed better during and after low- and high-intensity activities (standing up: 12.3 vs 9.0; walking fast: 6.1 vs 5.8; stairs: 8.8 vs 6.9; squats: 15.7 vs 6.1; resting: 9.6 vs 5.6 bpm). Conclusions: Device accuracy varied with activity. Overall, both devices achieved a mean absolute percentage error of just <10%. Thus, they were considered to produce valid results based on the limits established by previous work in the field. Neither device reached sufficient accuracy during seated rest or keyboard typing. Thus, both devices may be eligible for use in respective studies; however, researchers should consider their individual study requirements. %M 35230250 %R 10.2196/33635 %U https://formative.jmir.org/2022/3/e33635 %U https://doi.org/10.2196/33635 %U http://www.ncbi.nlm.nih.gov/pubmed/35230250 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 2 %P e24916 %T Continuous Noninvasive Remote Automated Blood Pressure Monitoring With Novel Wearable Technology: A Preliminary Validation Study %A McGillion,Michael H %A Dvirnik,Nazari %A Yang,Stephen %A Belley-Côté,Emilie %A Lamy,Andre %A Whitlock,Richard %A Marcucci,Maura %A Borges,Flavia K %A Duceppe,Emmanuelle %A Ouellette,Carley %A Bird,Marissa %A Carroll,Sandra L %A Conen,David %A Tarride,Jean-Eric %A Harsha,Prathiba %A Scott,Ted %A Good,Amber %A Gregus,Krysten %A Sanchez,Karla %A Benoit,Pamela %A Owen,Julian %A Harvey,Valerie %A Peter,Elizabeth %A Petch,Jeremy %A Vincent,Jessica %A Graham,Michelle %A Devereaux,P J %+ School of Nursing, McMaster University, 1280 Main St W, Hamilton, ON, L8S 4L8, Canada, 1 905 525 9140 ext 26140, mmcgill@mcmaster.ca %K validation study %K continuous vital signs monitor %K continuous non-invasive blood pressure monitoring %K wearable %K blood pressure %K monitoring %K validation %K mHealth %K vital sign %K biosensor %K accuracy %K usability %D 2022 %7 28.2.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wearable continuous monitoring biosensor technologies have the potential to transform postoperative care with early detection of impending clinical deterioration. Objective: Our aim was to validate the accuracy of Cloud DX Vitaliti continuous vital signs monitor (CVSM) continuous noninvasive blood pressure (cNIBP) measurements in postsurgical patients. A secondary aim was to examine user acceptance of the Vitaliti CVSM with respect to comfort, ease of application, sustainability of positioning, and aesthetics. Methods: Included participants were ≥18 years old and recovering from surgery in a cardiac intensive care unit (ICU). We targeted a maximum recruitment of 80 participants for verification and acceptance testing. We also oversampled to minimize the effect of unforeseen interruptions and other challenges to the study. Validation procedures were according to the International Standards Organization (ISO) 81060-2:2018 standards for wearable, cuffless blood pressure (BP) measuring devices. Baseline BP was determined from the gold-standard ICU arterial catheter. The Vitaliti CVSM was calibrated against the reference arterial catheter. In static (seated in bed) and supine positions, 3 cNIBP measurements, each 30 seconds, were taken for each patient with the Vitaliti CVSM and an invasive arterial catheter. At the conclusion of each test session, captured cNIBP measurements were extracted using MediCollector BEDSIDE data extraction software, and Vitaliti CVSM measurements were extracted to a secure laptop through a cable connection. The errors of these determinations were calculated. Participants were interviewed about device acceptability. Results: The validation analysis included data for 20 patients. The average times from calibration to first measurement in the static position and to first measurement in the supine position were 133.85 seconds (2 minutes 14 seconds) and 535.15 seconds (8 minutes 55 seconds), respectively. The overall mean errors of determination for the static position were –0.621 (SD 4.640) mm Hg for systolic blood pressure (SBP) and 0.457 (SD 1.675) mm Hg for diastolic blood pressure (DBP). Errors of determination were slightly higher for the supine position, at 2.722 (SD 5.207) mm Hg for SBP and 2.650 (SD 3.221) mm Hg for DBP. The majority rated the Vitaliti CVSM as comfortable. This study was limited to evaluation of the device during a very short validation period after calibration (ie, that commenced within 2 minutes after calibration and lasted for a short duration of time). Conclusions: We found that the Cloud DX’s Vitaliti CVSM demonstrated cNIBP measurement in compliance with ISO 81060-2:2018 standards in the context of evaluation that commenced within 2 minutes of device calibration; this device was also well-received by patients in a postsurgical ICU setting. Future studies will examine the accuracy of the Vitaliti CVSM in ambulatory contexts, with attention to assessment over a longer duration and the impact of excessive patient motion on data artifacts and signal quality. Trial Registration: ClinicalTrials.gov NCT03493867; https://clinicaltrials.gov/ct2/show/NCT03493867 %M 34876396 %R 10.2196/24916 %U https://mhealth.jmir.org/2022/2/e24916 %U https://doi.org/10.2196/24916 %U http://www.ncbi.nlm.nih.gov/pubmed/34876396 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 9 %N 1 %P e34058 %T Understanding Cardiology Practitioners’ Interpretations of Electrocardiograms: An Eye-Tracking Study %A Tahri Sqalli,Mohammed %A Al-Thani,Dena %A Elshazly,Mohamed B %A Al-Hijji,Mohammed %A Alahmadi,Alaa %A Sqalli Houssaini,Yahya %+ Information and Computing Technology Division, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, 34110, Qatar, 974 50588170, mtahrisqalli@hbku.edu.qa %K eye tracking %K electrocardiogram %K ECG interpretation %K cardiology practitioners %K human-computer interaction %K cardiology %K ECG %D 2022 %7 9.2.2022 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Visual expertise refers to advanced visual skills demonstrated when performing domain-specific visual tasks. Prior research has emphasized the fact that medical experts rely on such perceptual pattern-recognition skills when interpreting medical images, particularly in the field of electrocardiogram (ECG) interpretation. Analyzing and modeling cardiology practitioners’ visual behavior across different levels of expertise in the health care sector is crucial. Namely, understanding such acquirable visual skills may help train less experienced clinicians to interpret ECGs accurately. Objective: This study aims to quantify and analyze through the use of eye-tracking technology differences in the visual behavior and methodological practices for different expertise levels of cardiology practitioners such as medical students, cardiology nurses, technicians, fellows, and consultants when interpreting several types of ECGs. Methods: A total of 63 participants with different levels of clinical expertise took part in an eye-tracking study that consisted of interpreting 10 ECGs with different cardiac abnormalities. A counterbalanced within-subjects design was used with one independent variable consisting of the expertise level of the cardiology practitioners and two dependent variables of eye-tracking metrics (fixations count and fixation revisitations). The eye movements data revealed by specific visual behaviors were analyzed according to the accuracy of interpretation and the frequency with which interpreters visited different parts/leads on a standard 12-lead ECG. In addition, the median and SD in the IQR for the fixations count and the mean and SD for the ECG lead revisitations were calculated. Results: Accuracy of interpretation ranged between 98% among consultants, 87% among fellows, 70% among technicians, 63% among nurses, and finally 52% among medical students. The results of the eye fixations count, and eye fixation revisitations indicate that the less experienced cardiology practitioners need to interpret several ECG leads more carefully before making any decision. However, more experienced cardiology practitioners rely on their skills to recognize the visual signal patterns of different cardiac abnormalities, providing an accurate ECG interpretation. Conclusions: The results show that visual expertise for ECG interpretation is linked to the practitioner’s role within the health care system and the number of years of practical experience interpreting ECGs. Cardiology practitioners focus on different ECG leads and different waveform abnormalities according to their role in the health care sector and their expertise levels. %M 35138258 %R 10.2196/34058 %U https://humanfactors.jmir.org/2022/1/e34058 %U https://doi.org/10.2196/34058 %U http://www.ncbi.nlm.nih.gov/pubmed/35138258 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 2 %P e34778 %T Patient-Led Mass Screening for Atrial Fibrillation in the Older Population Using Handheld Electrocardiographic Devices Integrated With a Clinician-Coordinated Remote Central Monitoring System: Protocol for a Randomized Controlled Trial and Process Evaluation %A Wong,Kam Cheong %A Nguyen,Tu N %A Marschner,Simone %A Turnbull,Samual %A Burns,Mason Jenner %A Ne,Jia Yi Anna %A Gopal,Vishal %A Indrawansa,Anupama Balasuriya %A Trankle,Steven A %A Usherwood,Tim %A Kumar,Saurabh %A Lindley,Richard I %A Chow,Clara K %+ Westmead Applied Research Centre, Faculty of Medicine and Health, The University of Sydney, Level 6, Block K, Entrance 10, Westmead Hospital, Hawkesbury Road, Westmead, 2145, Australia, 1 2 8890 3125, clara.chow@sydney.edu.au %K atrial fibrillation %K screening %K handheld %K electrocardiogram %K ECG %K acceptability %K user perception %K user experience %K barrier %K enabler %K older adults %K elderly %K feasibility %K effectiveness %K implementation %K monitoring %K aging %K cardiovascular %K cardiology %K heart disease %K mobile phone %D 2022 %7 1.2.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: Atrial fibrillation (AF) is common in older people and increases the risk of stroke. The feasibility and effectiveness of the implementation of a patient-led AF screening program for older people are unknown. Objective: This study aims to examine the feasibility and effectiveness of an AF screening program comprising patient-led monitoring of single-lead electrocardiograms (ECGs) with clinician-coordinated central monitoring to diagnose AF among community-dwelling people aged ≥75 years in Australia. Methods: This is a nationwide randomized controlled implementation trial conducted via the internet and remotely among 200 community-dwelling adults aged ≥75 years with no known AF. Randomization will be performed in a 1:1 allocation ratio for the intervention versus control. Intervention group participants will be enrolled in the monitoring program at randomization. They will receive a handheld single-lead ECG device and training on the self-recording of ECGs on weekdays and submit their ECGs via their smartphones. The control group participants will receive usual care from their general practitioners for the initial 6 months and then commence the 6-month monitoring program. The ECGs will be reviewed centrally by trained personnel. Participants and their general practitioners will be notified of AF and other clinically significant ECG abnormalities. Results: This study will establish the feasibility and effectiveness of implementing the intervention in this patient population. The primary clinical outcome is the AF detection rate, and the primary feasibility outcome is the patient satisfaction score. Other outcomes include appropriate use of anticoagulant therapy, participant recruitment rate, program engagement (eg, frequency of ECG transmission), agreement in ECG interpretation between the device automatic algorithm and clinicians, the proportion of participants who complete the trial and number of dropouts, and the impact of frailty on feasibility and outcomes. We will conduct a qualitative evaluation to examine the barriers to and acceptability and enablers of implementation. Ethics approval was obtained from the human research ethics committee at the University of Sydney (project number 2020/680). The results will be disseminated via conventional scientific forums, including peer-reviewed publications and presentations at national and international conferences. Conclusions: By incorporating an integrated health care approach involving patient empowerment, centralized clinician-coordinated ECG monitoring, and facilitation of primary care and specialist services, it is possible to diagnose and treat AF early to reduce stroke risk. This study will provide new information on how to implement AF screening using digital health technology practicably and feasibly for older and frail populations residing in the community. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12621000184875; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380877 International Registered Report Identifier (IRRID): DERR1-10.2196/34778 %M 35103614 %R 10.2196/34778 %U https://www.researchprotocols.org/2022/2/e34778 %U https://doi.org/10.2196/34778 %U http://www.ncbi.nlm.nih.gov/pubmed/35103614 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 1 %P e27487 %T Accuracy Assessment of Oura Ring Nocturnal Heart Rate and Heart Rate Variability in Comparison With Electrocardiography in Time and Frequency Domains: Comprehensive Analysis %A Cao,Rui %A Azimi,Iman %A Sarhaddi,Fatemeh %A Niela-Vilen,Hannakaisa %A Axelin,Anna %A Liljeberg,Pasi %A Rahmani,Amir M %+ Department of Electrical Engineering and Computer Science, University of California, 1407 Palo Verde Rd, Irvine, CA, 92617, United States, 1 6266883017, caor6@uci.edu %K electrocardiography %K ECG %K wearable device %K heart rate variability %K Oura smart ring %D 2022 %7 18.1.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Photoplethysmography is a noninvasive and low-cost method to remotely and continuously track vital signs. The Oura Ring is a compact photoplethysmography-based smart ring, which has recently drawn attention to remote health monitoring and wellness applications. The ring is used to acquire nocturnal heart rate (HR) and HR variability (HRV) parameters ubiquitously. However, these parameters are highly susceptible to motion artifacts and environmental noise. Therefore, a validity assessment of the parameters is required in everyday settings. Objective: This study aims to evaluate the accuracy of HR and time domain and frequency domain HRV parameters collected by the Oura Ring against a medical grade chest electrocardiogram monitor. Methods: We conducted overnight home-based monitoring using an Oura Ring and a Shimmer3 electrocardiogram device. The nocturnal HR and HRV parameters of 35 healthy individuals were collected and assessed. We evaluated the parameters within 2 tests, that is, values collected from 5-minute recordings (ie, short-term HRV analysis) and the average values per night sleep. A linear regression method, the Pearson correlation coefficient, and the Bland–Altman plot were used to compare the measurements of the 2 devices. Results: Our findings showed low mean biases of the HR and HRV parameters collected by the Oura Ring in both the 5-minute and average-per-night tests. In the 5-minute test, the error variances of the parameters were different. The parameters provided by the Oura Ring dashboard (ie, HR and root mean square of successive differences [RMSSD]) showed relatively low error variance compared with the HRV parameters extracted from the normal interbeat interval signals. The Pearson correlation coefficient tests (P<.001) indicated that HR, RMSSD, average of normal heart beat intervals (AVNN), and percentage of successive normal beat-to-beat intervals that differ by more than 50 ms (pNN50) had high positive correlations with the baseline values; SD of normal beat-to-beat intervals (SDNN) and high frequency (HF) had moderate positive correlations, and low frequency (LF) and LF:HF ratio had low positive correlations. The HR, RMSSD, AVNN, and pNN50 had narrow 95% CIs; however, SDNN, LF, HF, and LF:HF ratio had relatively wider 95% CIs. In contrast, the average-per-night test showed that the HR, RMSSD, SDNN, AVNN, pNN50, LF, and HF had high positive relationships (P<.001), and the LF:HF ratio had a moderate positive relationship (P<.001). The average-per-night test also indicated considerably lower error variances than the 5-minute test for the parameters. Conclusions: The Oura Ring could accurately measure nocturnal HR and RMSSD in both the 5-minute and average-per-night tests. It provided acceptable nocturnal AVNN, pNN50, HF, and SDNN accuracy in the average-per-night test but not in the 5-minute test. In contrast, the LF and LF:HF ratio of the ring had high error rates in both tests. %M 35040799 %R 10.2196/27487 %U https://www.jmir.org/2022/1/e27487 %U https://doi.org/10.2196/27487 %U http://www.ncbi.nlm.nih.gov/pubmed/35040799 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 5 %N 2 %P e31316 %T Effects of Urban Green Space on Cardiovascular and Respiratory Biomarkers in Chinese Adults: Panel Study Using Digital Tracking Devices %A Yang,Lin %A Chan,Ka Long %A Yuen,John W M %A Wong,Frances K Y %A Han,Lefei %A Ho,Hung Chak %A Chang,Katherine K P %A Ho,Yuen Shan %A Siu,Judy Yuen-Man %A Tian,Linwei %A Wong,Man Sing %+ School of Nursing, The Hong Kong Polytechnic University, Hung Hom Campus, GH519, Hong Kong, Hong Kong, 852 2766 6419, frances.wong@polyu.edu.hk %K green space %K biomarker %K cardiovascular disease %K respiratory disease %D 2021 %7 30.12.2021 %9 Original Paper %J JMIR Cardio %G English %X Background: The health benefits of urban green space have been widely reported in the literature; however, the biological mechanisms remain unexplored, and a causal relationship cannot be established between green space exposure and cardiorespiratory health. Objective: Our aim was to conduct a panel study using personal tracking devices to continuously collect individual exposure data from healthy Chinese adults aged 50 to 64 years living in Hong Kong. Methods: A panel of cardiorespiratory biomarkers was tested each week for a period of 5 consecutive weeks. Data on weekly exposure to green space, air pollution, and the physical activities of individual participants were collected by personal tracking devices. The effects of green space exposure measured by the normalized difference vegetation index (NDVI) at buffer zones of 100, 250, and 500 meters on a panel of cardiorespiratory biomarkers were estimated by a generalized linear mixed-effects model, with adjustment for confounding variables of sociodemographic characteristics, exposure to air pollutants and noise, exercise, and nutrient intake. Results: A total of 39 participants (mean age 56.4 years, range 50-63 years) were recruited and followed up for 5 consecutive weeks. After adjustment for sex, income, occupation, physical activities, dietary intake, noise, and air pollution, significant negative associations with the NDVI for the 250-meter buffer zone were found in total cholesterol (–21.6% per IQR increase in NDVI, 95% CI –32.7% to –10.6%), low-density lipoprotein (–14.9%, 95% CI –23.4% to –6.4%), glucose (–11.2%, 95% CI –21.9% to –0.5%), and high-sensitivity C-reactive protein (–41.3%, 95% CI –81.7% to –0.9%). Similar effect estimates were found for the 100-meter and 250-meter buffer zones. After adjustment for multiple testing, the effect estimates of glucose and high-sensitivity C-reactive protein were no longer significant. Conclusions: The health benefits of green space can be found in some metabolic and inflammatory biomarkers. Further studies are warranted to establish the causal relationship between green space and cardiorespiratory health. %M 34967754 %R 10.2196/31316 %U https://cardio.jmir.org/2021/2/e31316 %U https://doi.org/10.2196/31316 %U http://www.ncbi.nlm.nih.gov/pubmed/34967754 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 5 %N 2 %P e27765 %T Validation of Heart Rate Extracted From Wrist-Based Photoplethysmography in the Perioperative Setting: Prospective Observational Study %A Mestrom,Eveline %A Deneer,Ruben %A Bonomi,Alberto G %A Margarito,Jenny %A Gelissen,Jos %A Haakma,Reinder %A Korsten,Hendrikus H M %A Scharnhorst,Volkher %A Bouwman,R Arthur %+ Department of Anesthesiology, Catharina Hospital Eindhoven, Michelangelolaan 2, Eindhoven, 5623 EJ, Netherlands, 31 646067638, eveline.mestrom@catharinaziekenhuis.nl %K validation %K heart rate %K photoplethysmography %K perioperative patients %K unobtrusive sensing %D 2021 %7 4.11.2021 %9 Original Paper %J JMIR Cardio %G English %X Background: Measurement of heart rate (HR) through an unobtrusive, wrist-worn optical HR monitor (OHRM) could enable earlier recognition of patient deterioration in low acuity settings and enable timely intervention. Objective: The goal of this study was to assess the agreement between the HR extracted from the OHRM and the gold standard 5-lead electrocardiogram (ECG) connected to a patient monitor during surgery and in the recovery period. Methods: In patients undergoing surgery requiring anesthesia, the HR reported by the patient monitor’s ECG module was recorded and stored simultaneously with the photopletysmography (PPG) from the OHRM attached to the patient’s wrist. The agreement between the HR reported by the patient’s monitor and the HR extracted from the OHRM’s PPG signal was assessed using Bland-Altman analysis during the surgical and recovery phase. Results: A total of 271.8 hours of data in 99 patients was recorded simultaneously by the OHRM and patient monitor. The median coverage was 86% (IQR 65%-95%) and did not differ significantly between surgery and recovery (Wilcoxon paired difference test P=.17). Agreement analysis showed the limits of agreement (LoA) of the difference between the OHRM and the ECG HR were within the range of 5 beats per minute (bpm). The mean bias was –0.14 bpm (LoA between –3.08 bpm and 2.79 bpm) and –0.19% (LoA between –5 bpm to 5 bpm) for the PPG- measured HR compared to the ECG-measured HR during surgery; during recovery, it was –0.11 bpm (LoA between –2.79 bpm and 2.59 bpm) and –0.15% (LoA between –3.92% and 3.64%). Conclusions: This study shows that an OHRM equipped with a PPG sensor can measure HR within the ECG reference standard of –5 bpm to 5 bpm or –10% to 10% in the perioperative setting when the PPG signal is of sufficient quality. This implies that an OHRM can be considered clinically acceptable for HR monitoring in low acuity hospitalized patients. %M 34734834 %R 10.2196/27765 %U https://cardio.jmir.org/2021/2/e27765 %U https://doi.org/10.2196/27765 %U http://www.ncbi.nlm.nih.gov/pubmed/34734834 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 10 %P e29933 %T Automatic Mobile Health Arrhythmia Monitoring for the Detection of Atrial Fibrillation: Prospective Feasibility, Accuracy, and User Experience Study %A Santala,Onni E %A Halonen,Jari %A Martikainen,Susanna %A Jäntti,Helena %A Rissanen,Tuomas T %A Tarvainen,Mika P %A Laitinen,Tomi P %A Laitinen,Tiina M %A Väliaho,Eemu-Samuli %A Hartikainen,Juha E K %A Martikainen,Tero J %A Lipponen,Jukka A %+ School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Yliopistonranta 1, P O BOX 1627, Kuopio, FI-70211, Finland, 358 503010879, elmeris@uef.fi %K atrial fibrillation %K ECG %K algorithm %K stroke %K mHealth %K user experience %K Awario analysis Service %K Suunto Movesense %K cardiology %K digital health %K mobile health %K wearable device %K heart belt %K arrhythmia monitor %K heart monitor %D 2021 %7 22.10.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Atrial fibrillation (AF) is the most common tachyarrhythmia and associated with a risk of stroke. The detection and diagnosis of AF represent a major clinical challenge due to AF’s asymptomatic and intermittent nature. Novel consumer-grade mobile health (mHealth) products with automatic arrhythmia detection could be an option for long-term electrocardiogram (ECG)-based rhythm monitoring and AF detection. Objective: We evaluated the feasibility and accuracy of a wearable automated mHealth arrhythmia monitoring system, including a consumer-grade, single-lead heart rate belt ECG device (heart belt), a mobile phone application, and a cloud service with an artificial intelligence (AI) arrhythmia detection algorithm for AF detection. The specific aim of this proof-of-concept study was to test the feasibility of the entire sequence of operations from ECG recording to AI arrhythmia analysis and ultimately to final AF detection. Methods: Patients (n=159) with an AF (n=73) or sinus rhythm (n=86) were recruited from the emergency department. A single-lead heart belt ECG was recorded for 24 hours. Simultaneously registered 3-lead ECGs (Holter) served as the gold standard for the final rhythm diagnostics and as a reference device in a user experience survey with patients over 65 years of age (high-risk group). Results: The heart belt provided a high-quality ECG recording for visual interpretation resulting in 100% accuracy, sensitivity, and specificity of AF detection. The accuracy of AF detection with the automatic AI arrhythmia detection from the heart belt ECG recording was also high (97.5%), and the sensitivity and specificity were 100% and 95.4%, respectively. The correlation between the automatic estimated AF burden and the true AF burden from Holter recording was >0.99 with a mean burden error of 0.05 (SD 0.26) hours. The heart belt demonstrated good user experience and did not significantly interfere with the patient’s daily activities. The patients preferred the heart belt over Holter ECG for rhythm monitoring (85/110, 77% heart belt vs 77/109, 71% Holter, P=.049). Conclusions: A consumer-grade, single-lead ECG heart belt provided good-quality ECG for rhythm diagnosis. The mHealth arrhythmia monitoring system, consisting of heart-belt ECG, a mobile phone application, and an automated AF detection achieved AF detection with high accuracy, sensitivity, and specificity. In addition, the mHealth arrhythmia monitoring system showed good user experience. Trial Registration: ClinicalTrials.gov NCT03507335; https://clinicaltrials.gov/ct2/show/NCT03507335 %M 34677135 %R 10.2196/29933 %U https://mhealth.jmir.org/2021/10/e29933 %U https://doi.org/10.2196/29933 %U http://www.ncbi.nlm.nih.gov/pubmed/34677135 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 10 %P e28039 %T Ensemble Learning-Based Pulse Signal Recognition: Classification Model Development Study %A Yan,Jianjun %A Cai,Xianglei %A Chen,Songye %A Guo,Rui %A Yan,Haixia %A Wang,Yiqin %+ Institute of Intelligent Perception and Diagnosis, School of Mechanical and Power Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China, 86 21 64252074, jjyan@ecust.edu.cn %K wrist pulse %K ensemble learning %K support vector machine %K deep convolutional neural network %K pulse signal %K machine learning %K traditional Chinese medicine %K pulse classification %K pulse analysis %K fully connected neural network %K synthetic minority oversampling technique %K feature extraction %D 2021 %7 21.10.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: In pulse signal analysis and identification, time domain and time frequency domain analysis methods can obtain interpretable structured data and build classification models using traditional machine learning methods. Unstructured data, such as pulse signals, contain rich information about the state of the cardiovascular system, and local features of unstructured data can be extracted and classified using deep learning. Objective: The objective of this paper was to comprehensively use machine learning and deep learning classification methods to fully exploit the information about pulse signals. Methods: Structured data were obtained by using time domain and time frequency domain analysis methods. A classification model was built using a support vector machine (SVM), a deep convolutional neural network (DCNN) kernel was used to extract local features of the unstructured data, and the stacking method was used to fuse the above classification results for decision making. Results: The highest average accuracy of 0.7914 was obtained using only a single classifier, while the average accuracy obtained using the ensemble learning approach was 0.8330. Conclusions: Ensemble learning can effectively use information from structured and unstructured data to improve classification accuracy through decision-level fusion. This study provides a new idea and method for pulse signal classification, which is of practical value for pulse diagnosis objectification. %M 34673537 %R 10.2196/28039 %U https://medinform.jmir.org/2021/10/e28039 %U https://doi.org/10.2196/28039 %U http://www.ncbi.nlm.nih.gov/pubmed/34673537 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 10 %P e30916 %T The Accuracy of Tidal Volume Measured With a Smart Shirt During Tasks of Daily Living in Healthy Subjects: Cross-sectional Study %A Mannée,Denise %A de Jongh,Frans %A van Helvoort,Hanneke %+ Department of Pulmonary Disease, Radboud University Medical Centre, Geert Grooteplein Zuid 10, Nijmegen, 6525 GA, Netherlands, 31 24 361 1111, denise.mannee@radboudumc.nl %K telemonitoring %K Hexoskin smart shirt %K smart textiles %K textile sensors %K accuracy %K healthy subjects %K calibration %K tidal volume %D 2021 %7 18.10.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: The Hexoskin is a smart shirt that can take continuous and objective measurements and could be part of a potential telemonitoring system. Objective: The aim of this study was to determine the accuracy of the calibrated Hexoskin in measuring tidal volumes (TVs) in comparison to spirometry during various tasks. Methods: In a cross-sectional study, the TV of 15 healthy subjects was measured while performing seven tasks using spirometry and the Hexoskin. These tasks were performed during two sessions; between sessions, all equipment was removed. A one-time spirometer-based calibration per task was determined in session 1 and applied to the corresponding task in both sessions. Bland-Altman analysis was used to determine the agreement between TV that was measured with the Hexoskin and that measured with spirometry. A priori, we determined that the bias had to be less than ±5%, with limits of agreement (LOA) of less than ±15%. Lung volumes were measured and had to have LOA of less than ±0.150 L. Results: In the first session, all tasks had a median bias within the criteria (±0.6%). In the second session, biases were ±8.9%; only two tasks met the criteria. In both sessions, LOA were within the criteria in six out of seven tasks (±14.7%). LOA of lung volumes were greater than 0.150 L. Conclusions: The Hexoskin was able to correctly measure TV in healthy subjects during various tasks. However, after reapplication of the equipment, calibration factors were not able to be reused to obtain results within the determined boundaries. Trial Registration: Netherlands Trial Register NL6934; https://www.trialregister.nl/trial/6934 %M 34661546 %R 10.2196/30916 %U https://formative.jmir.org/2021/10/e30916 %U https://doi.org/10.2196/30916 %U http://www.ncbi.nlm.nih.gov/pubmed/34661546 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 5 %N 2 %P e27720 %T Clinic Time Required for Remote and In-Person Management of Patients With Cardiac Devices: Time and Motion Workflow Evaluation %A Seiler,Amber %A Biundo,Eliana %A Di Bacco,Marco %A Rosemas,Sarah %A Nicolle,Emmanuelle %A Lanctin,David %A Hennion,Juliette %A de Melis,Mirko %A Van Heel,Laura %+ Medtronic, 8200 Coral Sea Ct NE, Mounds View, MN, 55112, United States, 1 800 633 8766, david.lanctin@medtronic.com %K cardiac implantable electronic devices %K remote monitoring %K patient management %K clinic efficiency %K digital health %K mobile phone %D 2021 %7 15.10.2021 %9 Original Paper %J JMIR Cardio %G English %X Background: The number of patients with cardiac implantable electronic device (CIED) is increasing, creating a substantial workload for device clinics. Objective: This study aims to characterize the workflow and quantify clinic staff time requirements for managing patients with CIEDs. Methods: A time and motion workflow evaluation was performed in 11 US and European CIEDs clinics. Workflow tasks were repeatedly timed during 1 business week of observation at each clinic; these observations included all device models and manufacturers. The mean cumulative staff time required to review a remote device transmission and an in-person clinic visit were calculated, including all necessary clinical and administrative tasks. The annual staff time to manage a patient with a CIED was modeled using CIED transmission volumes, clinical guidelines, and the published literature. Results: A total of 276 in-person clinic visits and 2173 remote monitoring activities were observed. Mean staff time required per remote transmission ranged from 9.4 to 13.5 minutes for therapeutic devices (pacemaker, implantable cardioverter-defibrillator, and cardiac resynchronization therapy) and from 11.3 to 12.9 minutes for diagnostic devices such as insertable cardiac monitors (ICMs). Mean staff time per in-person visit ranged from 37.8 to 51.0 and from 39.9 to 45.8 minutes for therapeutic devices and ICMs, respectively. Including all remote and in-person follow-ups, the estimated annual time to manage a patient with a CIED ranged from 1.6 to 2.4 hours for therapeutic devices and from 7.7 to 9.3 hours for ICMs. Conclusions: The CIED patient management workflow is complex and requires significant staff time. Understanding process steps and time requirements informs the implementation of efficiency improvements, including remote solutions. Future research should examine heterogeneity in patient management processes to identify the most efficient workflow. %M 34156344 %R 10.2196/27720 %U https://cardio.jmir.org/2021/2/e27720 %U https://doi.org/10.2196/27720 %U http://www.ncbi.nlm.nih.gov/pubmed/34156344 %0 Journal Article %@ 2369-3762 %I JMIR Publications %V 7 %N 4 %P e26675 %T Interpretation of a 12-Lead Electrocardiogram by Medical Students: Quantitative Eye-Tracking Approach %A Tahri Sqalli,Mohammed %A Al-Thani,Dena %A Elshazly,Mohamed B %A Al-Hijji,‪Mohammed %+ Information and Computing Technology Division, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, 34110, Qatar, 974 50588170, mtahrisqalli@hbku.edu.qa %K eye tracking %K electrocardiogram %K ECG interpretation %K medical education %K human-computer interaction %K medical student %K eye %K tracking %K interpretation %K ECG %D 2021 %7 14.10.2021 %9 Original Paper %J JMIR Med Educ %G English %X Background: Accurate interpretation of a 12-lead electrocardiogram (ECG) demands high levels of skill and expertise. Early training in medical school plays an important role in building the ECG interpretation skill. Thus, understanding how medical students perform the task of interpretation is important for improving this skill. Objective: We aimed to use eye tracking as a tool to research how eye fixation can be used to gain a deeper understanding of how medical students interpret ECGs. Methods: In total, 16 medical students were recruited to interpret 10 different ECGs each. Their eye movements were recorded using an eye tracker. Fixation heatmaps of where the students looked were generated from the collected data set. Statistical analysis was conducted on the fixation count and duration using the Mann-Whitney U test and the Kruskal-Wallis test. Results: The average percentage of correct interpretations was 55.63%, with an SD of 4.63%. After analyzing the average fixation duration, we found that medical students study the three lower leads (rhythm strips) the most using a top-down approach: lead II (mean=2727 ms, SD=456), followed by leads V1 (mean=1476 ms, SD=320) and V5 (mean=1301 ms, SD=236). We also found that medical students develop a personal system of interpretation that adapts to the nature and complexity of the diagnosis. In addition, we found that medical students consider some leads as their guiding point toward finding a hint leading to the correct interpretation. Conclusions: The use of eye tracking successfully provides a quantitative explanation of how medical students learn to interpret a 12-lead ECG. %M 34647899 %R 10.2196/26675 %U https://mededu.jmir.org/2021/4/e26675 %U https://doi.org/10.2196/26675 %U http://www.ncbi.nlm.nih.gov/pubmed/34647899 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 10 %P e25163 %T Digital Tracking of Physical Activity, Heart Rate, and Inhalation Behavior in Patients With Pulmonary Arterial Hypertension Treated With Inhaled Iloprost: Observational Study (VENTASTEP) %A Stollfuss,Barbara %A Richter,Manuel %A Drömann,Daniel %A Klose,Hans %A Schwaiblmair,Martin %A Gruenig,Ekkehard %A Ewert,Ralf %A Kirchner,Martin C %A Kleinjung,Frank %A Irrgang,Valeska %A Mueller,Christian %+ Bayer Vital GmbH, Building K 56, 1D321, Leverkusen, 51368, Germany, 49 2143046587, christian.mueller4@bayer.com %K 6-minute walk distance %K 6MWD %K Breelib %K daily physical activity %K digital monitoring %K health-related quality of life %K iloprost %K Ventavis %K inhalation behavior %K mobile phone %K pulmonary arterial hypertension %K PAH %K sleeping behavior %K behavior %K sleep %K monitoring %K physical activity %K heart %K cardiology %D 2021 %7 8.10.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Pulmonary arterial hypertension restricts the ability of patients to perform routine physical activities. As part of pulmonary arterial hypertension treatment, inhaled iloprost can be administered via a nebulizer that tracks inhalation behavior. Pulmonary arterial hypertension treatment is guided by intermittent clinical measurements, such as 6-minute walk distance, assessed during regular physician visits. Continuous digital monitoring of physical activity may facilitate more complete assessment of the impact of pulmonary arterial hypertension on daily life. Physical activity tracking with a wearable has not yet been assessed with simultaneous tracking of pulmonary arterial hypertension medication intake. Objective: We aimed to digitally track the physical parameters of patients with pulmonary arterial hypertension who were starting treatment with iloprost using a Breelib nebulizer. The primary objective was to investigate correlations between changes in digital physical activity measures and changes in traditional clinical measures and health-related quality of life over 3 months. Secondary objectives were to evaluate inhalation behavior, adverse events, and changes in heart rate and sleep quality. Methods: We conducted a prospective, multicenter observational study of adults with pulmonary arterial hypertension in World Health Organization functional class III who were adding inhaled iloprost to existing pulmonary arterial hypertension therapy. Daily distance walked, step count, number of standing-up events, heart rate, and 6-minute walk distance were digitally captured using smartwatch (Apple Watch Series 2) and smartphone (iPhone 6S) apps during a 3-month observation period (which began when iloprost treatment began). Before and at the end of the observation period (within 2 weeks), we also evaluated 6-minute walk distance, Borg dyspnea, functional class, B-type natriuretic peptide (or N-terminal pro–B-type natriuretic peptide) levels, health-related quality of life (EQ-5D questionnaire), and sleep quality (Pittsburgh Sleep Quality Index). Results: Of 31 patients, 18 were included in the full analysis (observation period: median 91.5 days, IQR 88.0 to 92.0). Changes from baseline in traditional and digital 6-minute walk distance were moderately correlated (r=0.57). Physical activity (daily distance walked: median 0.4 km, IQR –0.2 to 1.9; daily step count: median 591, IQR −509 to 2413) and clinical measures (traditional 6-minute walk distance: median 26 m, IQR 0 to 40) changed concordantly from baseline to the end of the observation period. Health-related quality of life showed little change. Total sleep score and resting heart rate slightly decreased. Distance walked and step count showed short-term increases after each iloprost inhalation. No new safety signals were identified (safety analysis set: n=30). Conclusions: Our results suggest that despite challenges, parallel monitoring of physical activity, heart rate, and iloprost inhalation is feasible in patients with pulmonary arterial hypertension and may complement traditional measures in guiding treatment; however, the sample size of this study limits generalizability. Trial Registration: ClinicalTrials.gov NCT03293407; https://clinicaltrials.gov/ct2/show/NCT03293407 International Registered Report Identifier (IRRID): RR2-10.2196/12144 %M 34623313 %R 10.2196/25163 %U https://www.jmir.org/2021/10/e25163 %U https://doi.org/10.2196/25163 %U http://www.ncbi.nlm.nih.gov/pubmed/34623313 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 10 %P e30051 %T Remote Blood Pressure Monitoring With a Wearable Photoplethysmographic Device (Senbiosys): Protocol for a Single-Center Prospective Clinical Trial %A Schukraft,Sara %A Boukhayma,Assim %A Cook,Stéphane %A Caizzone,Antonino %+ Department of Cardiology, University and Hospital Fribourg, Chemin des Pensionnats 2, Fribourg, 1708, Switzerland, 41 0786579025, sara.schukraft@yahoo.com %K continuous blood pressure monitoring %K photoplethysmography %K arterial line %K Senbiosys %K wearable devices %K blood pressure %K remote monitoring %K continuous monitoring %K mHealth %K mobile health %D 2021 %7 7.10.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Wearable devices can provide user-friendly, accurate, and continuous blood pressure (BP) monitoring to assess patients’ vital signs and achieve remote patient management. Remote BP monitoring can substantially improve BP control. The newest cuffless BP monitoring devices have emerged in patient care using photoplethysmography. Objective: The Senbiosys trial aims to compare BP measurements of a new device capturing a photoplethysmography signal on the finger versus invasive measurements performed in patients with an arterial catheter in the intensive care unit (ICU) or referred for a coronarography at the Hospital of Fribourg. Methods: The Senbiosys study is a single-center, single-arm, prospective trial. The study population consists of adult patients undergoing coronarography or patients in the ICU with an arterial catheter in place. This study will enroll 35 adult patients, including 25 patients addressed for a coronarography and 10 patients in the ICU. The primary outcome is the assessment of mean bias (95% CI) for systolic BP, diastolic BP, and mean BP between noninvasive and invasive BP measurements. Secondary outcomes include a reliability index (Qualification Index) for BP epochs and count of qualified epochs. Results: Patient recruitment started in June 2021. Results are expected to be published by December 2021. Conclusions: The findings of the Senbiosys trial are expected to improve remote BP monitoring. The diagnosis and treatment of hypertension should benefit from these advancements. Trial Registration: ClinicalTrials.gov NCT04379986; https://clinicaltrials.gov/ct2/show/NCT04379986 International Registered Report Identifier (IRRID): PRR1-10.2196/30051 %M 34617912 %R 10.2196/30051 %U https://www.researchprotocols.org/2021/10/e30051 %U https://doi.org/10.2196/30051 %U http://www.ncbi.nlm.nih.gov/pubmed/34617912 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 10 %P e26476 %T Validation of Fitbit Charge 2 Sleep and Heart Rate Estimates Against Polysomnographic Measures in Shift Workers: Naturalistic Study %A Stucky,Benjamin %A Clark,Ian %A Azza,Yasmine %A Karlen,Walter %A Achermann,Peter %A Kleim,Birgit %A Landolt,Hans-Peter %+ Institute of Pharmacology and Toxicology, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland, 41 44 635 59 53, landolt@pharma.uzh.ch %K wearables %K actigraphy %K polysomnography %K validation %K multisensory %K mobile phone %D 2021 %7 5.10.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Multisensor fitness trackers offer the ability to longitudinally estimate sleep quality in a home environment with the potential to outperform traditional actigraphy. To benefit from these new tools for objectively assessing sleep for clinical and research purposes, multisensor wearable devices require careful validation against the gold standard of sleep polysomnography (PSG). Naturalistic studies favor validation. Objective: This study aims to validate the Fitbit Charge 2 against portable home PSG in a shift-work population composed of 59 first responder police officers and paramedics undergoing shift work. Methods: A reliable comparison between the two measurements was ensured through the data-driven alignment of a PSG and Fitbit time series that was recorded at night. Epoch-by-epoch analyses and Bland-Altman plots were used to assess sensitivity, specificity, accuracy, the Matthews correlation coefficient, bias, and limits of agreement. Results: Sleep onset and offset, total sleep time, and the durations of rapid eye movement (REM) sleep and non–rapid-eye movement sleep stages N1+N2 and N3 displayed unbiased estimates with nonnegligible limits of agreement. In contrast, the proprietary Fitbit algorithm overestimated REM sleep latency by 29.4 minutes and wakefulness after sleep onset (WASO) by 37.1 minutes. Epoch-by-epoch analyses indicated better specificity than sensitivity, with higher accuracies for WASO (0.82) and REM sleep (0.86) than those for N1+N2 (0.55) and N3 (0.78) sleep. Fitbit heart rate (HR) displayed a small underestimation of 0.9 beats per minute (bpm) and a limited capability to capture sudden HR changes because of the lower time resolution compared to that of PSG. The underestimation was smaller in N2, N3, and REM sleep (0.6-0.7 bpm) than in N1 sleep (1.2 bpm) and wakefulness (1.9 bpm), indicating a state-specific bias. Finally, Fitbit suggested a distribution of all sleep episode durations that was different from that derived from PSG and showed nonbiological discontinuities, indicating the potential limitations of the staging algorithm. Conclusions: We conclude that by following careful data processing processes, the Fitbit Charge 2 can provide reasonably accurate mean values of sleep and HR estimates in shift workers under naturalistic conditions. Nevertheless, the generally wide limits of agreement hamper the precision of quantifying individual sleep episodes. The value of this consumer-grade multisensor wearable in terms of tackling clinical and research questions could be enhanced with open-source algorithms, raw data access, and the ability to blind participants to their own sleep data. %M 34609317 %R 10.2196/26476 %U https://www.jmir.org/2021/10/e26476 %U https://doi.org/10.2196/26476 %U http://www.ncbi.nlm.nih.gov/pubmed/34609317 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 5 %N 2 %P e28731 %T Moderation of the Stressor-Strain Process in Interns by Heart Rate Variability Measured With a Wearable and Smartphone App: Within-Subject Design Using Continuous Monitoring %A de Vries,Herman %A Kamphuis,Wim %A Oldenhuis,Hilbrand %A van der Schans,Cees %A Sanderman,Robbert %+ Professorship Personalized Digital Health, Hanze University of Applied Sciences, Zernikeplein 11, Groningen, 9747 AS, Netherlands, 31 0031 50 5953572, h.j.de.vries@pl.hanze.nl %K stress %K strain %K burnout %K resilience %K heart rate variability %K sleep %K wearables %K digital health %K sensors %K ecological momentary assessment %K mobile phone %D 2021 %7 4.10.2021 %9 Original Paper %J JMIR Cardio %G English %X Background: The emergence of smartphones and wearable sensor technologies enables easy and unobtrusive monitoring of physiological and psychological data related to an individual’s resilience. Heart rate variability (HRV) is a promising biomarker for resilience based on between-subject population studies, but observational studies that apply a within-subject design and use wearable sensors in order to observe HRV in a naturalistic real-life context are needed. Objective: This study aims to explore whether resting HRV and total sleep time (TST) are indicative and predictive of the within-day accumulation of the negative consequences of stress and mental exhaustion. The tested hypotheses are that demands are positively associated with stress and resting HRV buffers against this association, stress is positively associated with mental exhaustion and resting HRV buffers against this association, stress negatively impacts subsequent-night TST, and previous-evening mental exhaustion negatively impacts resting HRV, while previous-night TST buffers against this association. Methods: In total, 26 interns used consumer-available wearables (Fitbit Charge 2 and Polar H7), a consumer-available smartphone app (Elite HRV), and an ecological momentary assessment smartphone app to collect resilience-related data on resting HRV, TST, and perceived demands, stress, and mental exhaustion on a daily basis for 15 weeks. Results: Multiple linear regression analysis of within-subject standardized data collected on 2379 unique person-days showed that having a high resting HRV buffered against the positive association between demands and stress (hypothesis 1) and between stress and mental exhaustion (hypothesis 2). Stress did not affect TST (hypothesis 3). Finally, mental exhaustion negatively predicted resting HRV in the subsequent morning but TST did not buffer against this (hypothesis 4). Conclusions: To our knowledge, this study provides first evidence that having a low within-subject resting HRV may be both indicative and predictive of the short-term accumulation of the negative effects of stress and mental exhaustion, potentially forming a negative feedback loop. If these findings can be replicated and expanded upon in future studies, they may contribute to the development of automated resilience interventions that monitor daily resting HRV and aim to provide users with an early warning signal when a negative feedback loop forms, to prevent the negative impact of stress on long-term health outcomes. %M 34319877 %R 10.2196/28731 %U https://cardio.jmir.org/2021/2/e28731 %U https://doi.org/10.2196/28731 %U http://www.ncbi.nlm.nih.gov/pubmed/34319877 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 9 %P e30819 %T Early Detection of Symptom Exacerbation in Patients With SARS-CoV-2 Infection Using the Fitbit Charge 3 (DEXTERITY): Pilot Evaluation %A Yamagami,Kan %A Nomura,Akihiro %A Kometani,Mitsuhiro %A Shimojima,Masaya %A Sakata,Kenji %A Usui,Soichiro %A Furukawa,Kenji %A Takamura,Masayuki %A Okajima,Masaki %A Watanabe,Kazuyoshi %A Yoneda,Takashi %+ Department of Cardiovascular Medicine, Graduate School of Medical Sciences, Kanazawa University, 13-1 Takaramachi, Kanazawa, Japan, 81 076 265 2000, anomura@med.kanazawa-u.ac.jp %K COVID-19 %K silent hypoxia %K wearable device %K Fitbit %K estimated oxygen variation %K detection %K infectious disease %K pilot study %K symptom %K outpatient %K oxygen %K sleep %K wearable %D 2021 %7 16.9.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Some patients with COVID-19 experienced sudden death due to rapid symptom deterioration. Thus, it is important to predict COVID-19 symptom exacerbation at an early stage prior to increasing severity in patients. Patients with COVID-19 could experience a unique “silent hypoxia” at an early stage of the infection when they are apparently asymptomatic, but with rather low SpO2 (oxygen saturation) levels. In order to continuously monitor SpO2 in daily life, a high-performance wearable device, such as the Apple Watch or Fitbit, has become commercially available to monitor several biometric data including steps, resting heart rate (RHR), physical activity, sleep quality, and estimated oxygen variation (EOV). Objective: This study aimed to test whether EOV measured by the wearable device Fitbit can predict COVID-19 symptom exacerbation. Methods: We recruited patients with COVID-19 from August to November 2020. Patients were asked to wear the Fitbit for 30 days, and biometric data including EOV and RHR were extracted. EOV is a relative physiological measure that reflects users’ SpO2 levels during sleep. We defined a high EOV signal as a patient’s oxygen level exhibiting a significant dip and recovery within the index period, and a high RHR signal as daily RHR exceeding 5 beats per day compared with the minimum RHR of each patient in the study period. We defined successful prediction as the appearance of those signals within 2 days before the onset of the primary outcome. The primary outcome was the composite of deaths of all causes, use of extracorporeal membrane oxygenation, use of mechanical ventilation, oxygenation, and exacerbation of COVID-19 symptoms, irrespective of readmission. We also assessed each outcome individually as secondary outcomes. We made weekly phone calls to discharged patients to check on their symptoms. Results: We enrolled 23 patients with COVID-19 diagnosed by a positive SARS-CoV-2 polymerase chain reaction test. The patients had a mean age of 50.9 (SD 20) years, and 70% (n=16) were female. Each patient wore the Fitbit for 30 days. COVID-19 symptom exacerbation occurred in 6 (26%) patients. We were successful in predicting exacerbation using EOV signals in 4 out of 5 cases (sensitivity=80%, specificity=90%), whereas the sensitivity and specificity of high RHR signals were 50% and 80%, respectively, both lower than those of high EOV signals. Coincidental obstructive sleep apnea syndrome confirmed by polysomnography was detected in 1 patient via consistently high EOV signals. Conclusions: This pilot study successfully detected early COVID-19 symptom exacerbation by measuring EOV, which may help to identify the early signs of COVID-19 exacerbation. Trial Registration: University Hospital Medical Information Network Clinical Trials Registry UMIN000041421; https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000047290 %M 34516390 %R 10.2196/30819 %U https://formative.jmir.org/2021/9/e30819 %U https://doi.org/10.2196/30819 %U http://www.ncbi.nlm.nih.gov/pubmed/34516390 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 9 %P e27547 %T A Chest Patch for Continuous Vital Sign Monitoring: Clinical Validation Study During Movement and Controlled Hypoxia %A Morgado Areia,Carlos %A Santos,Mauro %A Vollam,Sarah %A Pimentel,Marco %A Young,Louise %A Roman,Cristian %A Ede,Jody %A Piper,Philippa %A King,Elizabeth %A Gustafson,Owen %A Harford,Mirae %A Shah,Akshay %A Tarassenko,Lionel %A Watkinson,Peter %+ Critical Care Research Group, Nuffield Department of Clinical Neurosciences, University of Oxford, Kadoorie Research Centre, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, United Kingdom, 44 1865 231440, carlos.morgadoareia@ndcn.ox.ac.uk %K clinical validation %K chest patch %K vital signs %K remote monitoring %K wearable %K heart rate %K respiratory rate %D 2021 %7 15.9.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: The standard of care in general wards includes periodic manual measurements, with the data entered into track-and-trigger charts, either on paper or electronically. Wearable devices may support health care staff, improve patient safety, and promote early deterioration detection in the interval between periodic measurements. However, regulatory standards for ambulatory cardiac monitors estimating heart rate (HR) and respiratory rate (RR) do not specify performance criteria during patient movement or clinical conditions in which the patient’s oxygen saturation varies. Therefore, further validation is required before clinical implementation and deployment of any wearable system that provides continuous vital sign measurements. Objective: The objective of this study is to determine the agreement between a chest-worn patch (VitalPatch) and a gold standard reference device for HR and RR measurements during movement and gradual desaturation (modeling a hypoxic episode) in a controlled environment. Methods: After the VitalPatch and gold standard devices (Philips MX450) were applied, participants performed different movements in seven consecutive stages: at rest, sit-to-stand, tapping, rubbing, drinking, turning pages, and using a tablet. Hypoxia was then induced, and the participants’ oxygen saturation gradually reduced to 80% in a controlled environment. The primary outcome measure was accuracy, defined as the mean absolute error (MAE) of the VitalPatch estimates when compared with HR and RR gold standards (3-lead electrocardiography and capnography, respectively). We defined these as clinically acceptable if the rates were within 5 beats per minute for HR and 3 respirations per minute (rpm) for RR. Results: Complete data sets were acquired for 29 participants. In the movement phase, the HR estimates were within prespecified limits for all movements. For RR, estimates were also within the acceptable range, with the exception of the sit-to-stand and turning page movements, showing an MAE of 3.05 (95% CI 2.48-3.58) rpm and 3.45 (95% CI 2.71-4.11) rpm, respectively. For the hypoxia phase, both HR and RR estimates were within limits, with an overall MAE of 0.72 (95% CI 0.66-0.78) beats per minute and 1.89 (95% CI 1.75-2.03) rpm, respectively. There were no significant differences in the accuracy of HR and RR estimations between normoxia (≥90%), mild (89.9%-85%), and severe hypoxia (<85%). Conclusions: The VitalPatch was highly accurate throughout both the movement and hypoxia phases of the study, except for RR estimation during the two types of movements. This study demonstrated that VitalPatch can be safely tested in clinical environments to support earlier detection of cardiorespiratory deterioration. Trial Registration: ISRCTN Registry ISRCTN61535692; https://www.isrctn.com/ISRCTN61535692 %M 34524087 %R 10.2196/27547 %U https://www.jmir.org/2021/9/e27547 %U https://doi.org/10.2196/27547 %U http://www.ncbi.nlm.nih.gov/pubmed/34524087 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 9 %P e31129 %T Automated Detection of Acute Myocardial Infarction Using Asynchronous Electrocardiogram Signals—Preview of Implementing Artificial Intelligence With Multichannel Electrocardiographs Obtained From Smartwatches: Retrospective Study %A Han,Changho %A Song,Youngjae %A Lim,Hong-Seok %A Tae,Yunwon %A Jang,Jong-Hwan %A Lee,Byeong Tak %A Lee,Yeha %A Bae,Woong %A Yoon,Dukyong %+ Department of Biomedical Systems Informatics, Yonsei University College of Medicine, 363, Dongbaekjukjeon-daero, Giheung-gu, Yongin, 16995, Republic of Korea, 82 3151898450, dukyong.yoon@yonsei.ac.kr %K wearables %K smartwatches %K asynchronous electrocardiogram %K artificial intelligence %K deep learning %K automatic diagnosis %K myocardial infarction %K timely diagnosis %K machine learning %K digital health %K cardiac health %K cardiology %D 2021 %7 10.9.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: When using a smartwatch to obtain electrocardiogram (ECG) signals from multiple leads, the device has to be placed on different parts of the body sequentially. The ECG signals measured from different leads are asynchronous. Artificial intelligence (AI) models for asynchronous ECG signals have barely been explored. Objective: We aimed to develop an AI model for detecting acute myocardial infarction using asynchronous ECGs and compare its performance with that of the automatic ECG interpretations provided by a commercial ECG analysis software. We sought to evaluate the feasibility of implementing multiple lead–based AI-enabled ECG algorithms on smartwatches. Moreover, we aimed to determine the optimal number of leads for sufficient diagnostic power. Methods: We extracted ECGs recorded within 24 hours from each visit to the emergency room of Ajou University Medical Center between June 1994 and January 2018 from patients aged 20 years or older. The ECGs were labeled on the basis of whether a diagnostic code corresponding to acute myocardial infarction was entered. We derived asynchronous ECG lead sets from standard 12-lead ECG reports and simulated a situation similar to the sequential recording of ECG leads via smartwatches. We constructed an AI model based on residual networks and self-attention mechanisms by randomly masking each lead channel during the training phase and then testing the model using various targeting lead sets with the remaining lead channels masked. Results: The performance of lead sets with 3 or more leads compared favorably with that of the automatic ECG interpretations provided by a commercial ECG analysis software, with 8.1%-13.9% gain in sensitivity when the specificity was matched. Our results indicate that multiple lead-based AI-enabled ECG algorithms can be implemented on smartwatches. Model performance generally increased as the number of leads increased (12-lead sets: area under the receiver operating characteristic curve [AUROC] 0.880; 4-lead sets: AUROC 0.858, SD 0.008; 3-lead sets: AUROC 0.845, SD 0.011; 2-lead sets: AUROC 0.813, SD 0.018; single-lead sets: AUROC 0.768, SD 0.001). Considering the short amount of time needed to measure additional leads, measuring at least 3 leads—ideally more than 4 leads—is necessary for minimizing the risk of failing to detect acute myocardial infarction occurring in a certain spatial location or direction. Conclusions: By developing an AI model for detecting acute myocardial infarction with asynchronous ECG lead sets, we demonstrated the feasibility of multiple lead-based AI-enabled ECG algorithms on smartwatches for automated diagnosis of cardiac disorders. We also demonstrated the necessity of measuring at least 3 leads for accurate detection. Our results can be used as reference for the development of other AI models using sequentially measured asynchronous ECG leads via smartwatches for detecting various cardiac disorders. %M 34505839 %R 10.2196/31129 %U https://www.jmir.org/2021/9/e31129 %U https://doi.org/10.2196/31129 %U http://www.ncbi.nlm.nih.gov/pubmed/34505839 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 9 %P e29018 %T Adherence to Telemonitoring Therapy for Medicaid Patients With Hypertension: Case Study %A Park,Sulki %A Kum,Hye-Chung %A Morrisey,Michael A %A Zheng,Qi %A Lawley,Mark A %+ Department of Health Policy and Management, Texas A&M University, 212 Adriance Lab Rd, College Station, TX, 77843, United States, 1 979 436 9439, kum@tamu.edu %K telemedicine %K hypertension %K Medicaid %K blood pressure %K pulse %K telemonitoring %K mobile phone %D 2021 %7 6.9.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Almost 50% of the adults in the United States have hypertension. Although clinical trials indicate that home blood pressure monitoring can be effective in managing hypertension, the reported results might not materialize in practice because of patient adherence problems. Objective: The aims of this study are to characterize the adherence of Medicaid patients with hypertension to daily telemonitoring, identify the impacts of adherence reminder calls, and investigate associations with blood pressure control. Methods: This study targeted Medicaid patients with hypertension from the state of Texas. A total of 180 days of blood pressure and pulse data in 2016-2018 from a telemonitoring company were analyzed for mean transmission rate and mean blood pressure change. The first 30 days of data were excluded because of startup effects. The protocols required the patients to transmit readings by a specified time daily. Patients not transmitting their readings received an adherence reminder call to troubleshoot problems and encourage transmission. The patients were classified into adherent and nonadherent cohorts; adherent patients were those who transmitted data on at least 80% of the days. Results: The mean patient age was 73.2 (SD 11.7) years. Of the 823 patients, 536 (65.1%) were women, and 660 (80.2%) were urban residents. The adherent cohort (475/823, 57.7%) had mean transmission rates of 74.9% before the adherence reminder call and 91.3% after the call, whereas the nonadherent cohort (348/823, 42.3%) had mean transmission rates of 39% and 58% before and after the call, respectively. From month 1 to month 5, the transmission rates dropped by 1.9% and 10.2% for the adherent and nonadherent cohorts, respectively. The systolic and diastolic blood pressure values improved by an average of 2.2 and 0.7 mm Hg (P<.001 and P=.004), respectively, for the adherent cohort during the study period, whereas only the systolic blood pressure value improved by an average of 1.6 mm Hg (P=.02) for the nonadherent cohort. Conclusions: Although we found that patients can achieve high levels of adherence, many experience adherence problems. Although adherence reminder calls help, they may not be sufficient. Telemonitoring lowered blood pressure, as has been observed in clinical trials. Furthermore, blood pressure control was positively associated with adherence. %M 34486977 %R 10.2196/29018 %U https://www.jmir.org/2021/9/e29018 %U https://doi.org/10.2196/29018 %U http://www.ncbi.nlm.nih.gov/pubmed/34486977 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 8 %P e28974 %T Diagnostic Accuracy of Smartwatches for the Detection of Cardiac Arrhythmia: Systematic Review and Meta-analysis %A Nazarian,Scarlet %A Lam,Kyle %A Darzi,Ara %A Ashrafian,Hutan %+ Imperial College London, 10th Floor QEQM Building, St Mary’s Hospital, Praed Street, London, W2 1NY, United Kingdom, 44 7799 871 597, hutan@researchtrials.net %K wearables %K smartwatch %K cardiac arrhythmia %K atrial fibrillation %K cardiology %K mHealth %K wearable devices %K screening %K diagnostics %K accuracy %D 2021 %7 27.8.2021 %9 Review %J J Med Internet Res %G English %X Background: Significant morbidity, mortality, and financial burden are associated with cardiac rhythm abnormalities. Conventional investigative tools are often unsuccessful in detecting cardiac arrhythmias because of their episodic nature. Smartwatches have gained popularity in recent years as a health tool for the detection of cardiac rhythms. Objective: This study aims to systematically review and meta-analyze the diagnostic accuracy of smartwatches in the detection of cardiac arrhythmias. Methods: A systematic literature search of the Embase, MEDLINE, and Cochrane Library databases was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to identify studies reporting the use of a smartwatch for the detection of cardiac arrhythmia. Summary estimates of sensitivity, specificity, and area under the curve were attempted using a bivariate model for the diagnostic meta-analysis. Studies were examined for quality using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Results: A total of 18 studies examining atrial fibrillation detection, bradyarrhythmias and tachyarrhythmias, and premature contractions were analyzed, measuring diagnostic accuracy in 424,371 subjects in total. The signals analyzed by smartwatches were based on photoplethysmography. The overall sensitivity, specificity, and accuracy of smartwatches for detecting cardiac arrhythmias were 100% (95% CI 0.99-1.00), 95% (95% CI 0.93-0.97), and 97% (95% CI 0.96-0.99), respectively. The pooled positive predictive value and negative predictive value for detecting cardiac arrhythmias were 85% (95% CI 0.79-0.90) and 100% (95% CI 1.0-1.0), respectively. Conclusions: This review demonstrates the evolving field of digital disease detection. The current diagnostic accuracy of smartwatch technology for the detection of cardiac arrhythmias is high. Although the innovative drive of digital devices in health care will continue to gain momentum toward screening, the process of accurate evidence accrual and regulatory standards ready to accept their introduction is strongly needed. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42020213237; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=213237. %M 34448706 %R 10.2196/28974 %U https://www.jmir.org/2021/8/e28974 %U https://doi.org/10.2196/28974 %U http://www.ncbi.nlm.nih.gov/pubmed/34448706 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 8 %P e23425 %T Enhancing Healthcare Access–Smartphone Apps in Arrhythmia Screening: Viewpoint %A Książczyk,Marcin %A Dębska-Kozłowska,Agnieszka %A Warchoł,Izabela %A Lubiński,Andrzej %+ Department of Interventional Cardiology and Cardiac Arrhythmias, Medical University of Lodz, Ul. Żeromskiego 113, Łódź, 90-549, Poland, 48 42 639 35 63, marcin_ksiazczyk@interia.pl %K arrhythmia screening %K atrial fibrillation %K mobile electrocardiography %K mobile health %K phonocardiography %K photoplethysmography %K seismocardiography %K stroke prevention %D 2021 %7 27.8.2021 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Atrial fibrillation is the most commonly reported arrhythmia and, if undiagnosed or untreated, may lead to thromboembolic events. It is therefore desirable to provide screening to patients in order to detect atrial arrhythmias. Specific mobile apps and accessory devices, such as smartphones and smartwatches, may play a significant role in monitoring heart rhythm in populations at high risk of arrhythmia. These apps are becoming increasingly common among patients and professionals as a part of mobile health. The rapid development of mobile health solutions may revolutionize approaches to arrhythmia screening. In this viewpoint paper, we assess the availability of smartphone and smartwatch apps and evaluate their efficacy for monitoring heart rhythm and arrhythmia detection. The findings obtained so far suggest they are on the right track to improving the efficacy of early detection of atrial fibrillation, thus lowering the risk of stroke and reducing the economic burden placed on public health. %M 34448723 %R 10.2196/23425 %U https://mhealth.jmir.org/2021/8/e23425 %U https://doi.org/10.2196/23425 %U http://www.ncbi.nlm.nih.gov/pubmed/34448723 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 5 %N 2 %P e21186 %T User Perceptions and Experiences of a Handheld 12-Lead Electrocardiographic Device in a Clinical Setting: Usability Evaluation %A Wong,Kam Cheong %A Thiagalingam,Aravinda %A Kumar,Saurabh %A Marschner,Simone %A Kunwar,Ritu %A Bailey,Jannine %A Kok,Cindy %A Usherwood,Tim %A Chow,Clara K %+ Westmead Applied Research Centre, Faculty of Medicine and Health, The University of Sydney, Level 6, Block K, Entrance 10, Westmead Hospital, Hawkesbury Road, Westmead, 2145, Australia, 61 2 8890 3125, kam.wong@sydney.edu.au %K handheld %K electrocardiogram %K ECG %K acceptability %K usability %K user perception %K user experience %K atrial fibrillation %K long QT %K screening %D 2021 %7 26.8.2021 %9 Original Paper %J JMIR Cardio %G English %X Background: Cardiac arrhythmias are a leading cause of death. The mainstay method for diagnosing arrhythmias (eg, atrial fibrillation) and cardiac conduction disorders (eg, prolonged corrected QT interval [QTc]) is by using 12-lead electrocardiography (ECG). Handheld 12-lead ECG devices are emerging in the market. In tandem with emerging technology options, evaluations of device usability should go beyond validation of the device in a controlled laboratory setting and assess user perceptions and experiences, which are crucial for successful implementation in clinical practice. Objective: This study aimed to evaluate clinician and patient perceptions and experiences, regarding the usability of a handheld 12-lead ECG device compared to a conventional 12-lead ECG machine, and generalizability of this user-centered approach. Methods: International Organization for Standardization Guidelines on Usability and the Technology Acceptance Model were integrated to form the framework for this study, which was conducted in outpatient clinics and cardiology wards at Westmead Hospital, New South Wales, Australia. Each patient underwent 2 ECGs (1 by each device) in 2 postures (supine and standing) acquired in random sequence. The times taken by clinicians to acquire the first ECG (efficiency) using the devices were analyzed using linear regression. Electrocardiographic parameters (QT interval, QTc interval, heart rate, PR interval, QRS interval) and participant satisfaction surveys were collected. Device reliability was assessed by evaluating the mean difference of QTc measurements within ±15 ms, intraclass correlation coefficient, and level of agreement of the devices in detecting atrial fibrillation and prolonged QTc. Clinicians’ perceptions and feedback were assessed with semistructured interviews based on the Technology Acceptance Model. Results: A total of 100 patients (age: mean 57.9 years, SD 15.2; sex: male: n=64, female n=36) and 11 clinicians (experience acquiring ECGs daily or weekly 10/11, 91%) participated, and 783 ECGs were acquired. Mean differences in QTc measurements of both handheld and conventional devices were within ±15 ms with high intraclass correlation coefficients (range 0.90-0.96), and the devices had a good level of agreement in diagnosing atrial fibrillation and prolonged QTc (κ=0.68-0.93). Regardless of device, QTc measurements when patients were standing were longer duration than QTc measurements when patients were supine. Clinicians’ ECG acquisition times improved with usage (P<.001). Clinicians reported that device characteristics (small size, light weight, portability, and wireless ECG transmission) were highly desired features. Most clinicians agreed that the handheld device could be used for clinician-led mass screening with enhancement in efficiency by increasing user training. Regardless of device, patients reported that they felt comfortable when they were connected to the ECG devices. Conclusions: Reliability and usability of the handheld 12-lead ECG device were comparable to those of a conventional ECG machine. The user-centered evaluation approach helped us identify remediable action to improve the efficiency in using the device and identified highly desirable device features that could potentially help mass screening and remote assessment of patients. The approach could be applied to evaluate and better understand the acceptability and usability of new medical devices. %M 34435958 %R 10.2196/21186 %U https://cardio.jmir.org/2021/2/e21186 %U https://doi.org/10.2196/21186 %U http://www.ncbi.nlm.nih.gov/pubmed/34435958 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 8 %P e17411 %T Evaluating the Validity and Utility of Wearable Technology for Continuously Monitoring Patients in a Hospital Setting: Systematic Review %A Patel,Vikas %A Orchanian-Cheff,Ani %A Wu,Robert %+ Faculty of Medicine, University of Toronto, 1 King's College Cir, Toronto, ON, M5S 1A8, Canada, 1 4169756585, vik.patel@mail.utoronto.ca %K wearable %K inpatient %K continuous monitoring %D 2021 %7 18.8.2021 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: The term posthospital syndrome has been used to describe the condition in which older patients are transiently frail after hospitalization and have a high chance of readmission. Since low activity and poor sleep during hospital stay may contribute to posthospital syndrome, the continuous monitoring of such parameters by using affordable wearables may help to reduce the prevalence of this syndrome. Although there have been systematic reviews of wearables for physical activity monitoring in hospital settings, there are limited data on the use of wearables for measuring other health variables in hospitalized patients. Objective: This systematic review aimed to evaluate the validity and utility of wearable devices for monitoring hospitalized patients. Methods: This review involved a comprehensive search of 7 databases and included articles that met the following criteria: inpatients must be aged >18 years, the wearable devices studied in the articles must be used to continuously monitor patients, and wearables should monitor biomarkers other than solely physical activity (ie, heart rate, respiratory rate, blood pressure, etc). Only English-language studies were included. From each study, we extracted basic demographic information along with the characteristics of the intervention. We assessed the risk of bias for studies that validated their wearable readings by using a modification of the Consensus-Based Standards for the Selection of Health Status Measurement Instruments. Results: Of the 2012 articles that were screened, 14 studies met the selection criteria. All included articles were observational in design. In total, 9 different commercial wearables for various body locations were examined in this review. The devices collectively measured 7 different health parameters across all studies (heart rate, sleep duration, respiratory rate, oxygen saturation, skin temperature, blood pressure, and fall risk). Only 6 studies validated their results against a reference device or standard. There was a considerable risk of bias in these studies due to the low number of patients in most of the studies (4/6, 67%). Many studies that validated their results found that certain variables were inaccurate and had wide limits of agreement. Heart rate and sleep were the parameters with the most evidence for being valid for in-hospital monitoring. Overall, the mean patient completion rate across all 14 studies was >90%. Conclusions: The included studies suggested that wearable devices show promise for monitoring the heart rate and sleep of patients in hospitals. Many devices were not validated in inpatient settings, and the readings from most of the devices that were validated in such settings had wide limits of agreement when compared to gold standards. Even some medical-grade devices were found to perform poorly in inpatient settings. Further research is needed to determine the accuracy of hospitalized patients’ digital biomarker readings and eventually determine whether these wearable devices improve health outcomes. %M 34406121 %R 10.2196/17411 %U https://mhealth.jmir.org/2021/8/e17411 %U https://doi.org/10.2196/17411 %U http://www.ncbi.nlm.nih.gov/pubmed/34406121 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 8 %P e24762 %T Development and Validation of an Arterial Pressure-Based Cardiac Output Algorithm Using a Convolutional Neural Network: Retrospective Study Based on Prospective Registry Data %A Yang,Hyun-Lim %A Jung,Chul-Woo %A Yang,Seong Mi %A Kim,Min-Soo %A Shim,Sungho %A Lee,Kook Hyun %A Lee,Hyung-Chul %+ Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea, 82 2 2072 0640, vital@snu.ac.kr %K cardiac output %K deep learning %K arterial pressure %D 2021 %7 16.8.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: Arterial pressure-based cardiac output (APCO) is a less invasive method for estimating cardiac output without concerns about complications from the pulmonary artery catheter (PAC). However, inaccuracies of currently available APCO devices have been reported. Improvements to the algorithm by researchers are impossible, as only a subset of the algorithm has been released. Objective: In this study, an open-source algorithm was developed and validated using a convolutional neural network and a transfer learning technique. Methods: A retrospective study was performed using data from a prospective cohort registry of intraoperative bio-signal data from a university hospital. The convolutional neural network model was trained using the arterial pressure waveform as input and the stroke volume (SV) value as the output. The model parameters were pretrained using the SV values from a commercial APCO device (Vigileo or EV1000 with the FloTrac algorithm) and adjusted with a transfer learning technique using SV values from the PAC. The performance of the model was evaluated using absolute error for the PAC on the testing dataset from separate periods. Finally, we compared the performance of the deep learning model and the FloTrac with the SV values from the PAC. Results: A total of 2057 surgical cases (1958 training and 99 testing cases) were used in the registry. In the deep learning model, the absolute errors of SV were 14.5 (SD 13.4) mL (10.2 [SD 8.4] mL in cardiac surgery and 17.4 [SD 15.3] mL in liver transplantation). Compared with FloTrac, the absolute errors of the deep learning model were significantly smaller (16.5 [SD 15.4] and 18.3 [SD 15.1], P<.001). Conclusions: The deep learning–based APCO algorithm showed better performance than the commercial APCO device. Further improvement of the algorithm developed in this study may be helpful for estimating cardiac output accurately in clinical practice and optimizing high-risk patient care. %M 34398790 %R 10.2196/24762 %U https://medinform.jmir.org/2021/8/e24762 %U https://doi.org/10.2196/24762 %U http://www.ncbi.nlm.nih.gov/pubmed/34398790 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 8 %P e25415 %T Assessing Electrocardiogram and Respiratory Signal Quality of a Wearable Device (SensEcho): Semisupervised Machine Learning-Based Validation Study %A Xu,Haoran %A Yan,Wei %A Lan,Ke %A Ma,Chenbin %A Wu,Di %A Wu,Anshuo %A Yang,Zhicheng %A Wang,Jiachen %A Zang,Yaning %A Yan,Muyang %A Zhang,Zhengbo %+ Centre for Artificial Intelligence in Medicine, Medical Innovation Research Department, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, Beijing, 100853, China, 86 13693321644, zhengbozhang301@gmail.com %K signal quality %K electrocardiogram %K respiratory signal %K isolation forest %K machine learning %K mobile health %D 2021 %7 12.8.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: With the development and promotion of wearable devices and their mobile health (mHealth) apps, physiological signals have become a research hotspot. However, noise is complex in signals obtained from daily lives, making it difficult to analyze the signals automatically and resulting in a high false alarm rate. At present, screening out the high-quality segments of the signals from huge-volume data with few labels remains a problem. Signal quality assessment (SQA) is essential and is able to advance the valuable information mining of signals. Objective: The aims of this study were to design an SQA algorithm based on the unsupervised isolation forest model to classify the signal quality into 3 grades: good, acceptable, and unacceptable; validate the algorithm on labeled data sets; and apply the algorithm on real-world data to evaluate its efficacy. Methods: Data used in this study were collected by a wearable device (SensEcho) from healthy individuals and patients. The observation windows for electrocardiogram (ECG) and respiratory signals were 10 and 30 seconds, respectively. In the experimental procedure, the unlabeled training set was used to train the models. The validation and test sets were labeled according to preset criteria and used to evaluate the classification performance quantitatively. The validation set consisted of 3460 and 2086 windows of ECG and respiratory signals, respectively, whereas the test set was made up of 4686 and 3341 windows of signals, respectively. The algorithm was also compared with self-organizing maps (SOMs) and 4 classic supervised models (logistic regression, random forest, support vector machine, and extreme gradient boosting). One case validation was illustrated to show the application effect. The algorithm was then applied to 1144 cases of ECG signals collected from patients and the detected arrhythmia false alarms were calculated. Results: The quantitative results showed that the ECG SQA model achieved 94.97% and 95.58% accuracy on the validation and test sets, respectively, whereas the respiratory SQA model achieved 81.06% and 86.20% accuracy on the validation and test sets, respectively. The algorithm was superior to SOM and achieved moderate performance when compared with the supervised models. The example case showed that the algorithm was able to correctly classify the signal quality even when there were complex pathological changes in the signals. The algorithm application results indicated that some specific types of arrhythmia false alarms such as tachycardia, atrial premature beat, and ventricular premature beat could be significantly reduced with the help of the algorithm. Conclusions: This study verified the feasibility of applying the anomaly detection unsupervised model to SQA. The application scenarios include reducing the false alarm rate of the device and selecting signal segments that can be used for further research. %M 34387554 %R 10.2196/25415 %U https://mhealth.jmir.org/2021/8/e25415 %U https://doi.org/10.2196/25415 %U http://www.ncbi.nlm.nih.gov/pubmed/34387554 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 8 %P e27466 %T Enabling Wearable Pulse Transit Time-Based Blood Pressure Estimation for Medically Underserved Areas and Health Equity: Comprehensive Evaluation Study %A Ganti,Venu %A Carek,Andrew M %A Jung,Hewon %A Srivatsa,Adith V %A Cherry,Deborah %A Johnson,Levather Neicey %A Inan,Omer T %+ School of Electrical and Computer Engineering, Georgia Institute of Technology, 85 5th St NW, Atlanta, GA, 30308, United States, 1 2406434250, vganti6@gatech.edu %K wearable sensing %K pulse transit time %K cuffless blood pressure %K noninvasive blood pressure estimation %K health equity %K mobile phone %D 2021 %7 2.8.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Noninvasive and cuffless approaches to monitor blood pressure (BP), in light of their convenience and accuracy, have paved the way toward remote screening and management of hypertension. However, existing noninvasive methodologies, which operate on mechanical, electrical, and optical sensing modalities, have not been thoroughly evaluated in demographically and racially diverse populations. Thus, the potential accuracy of these technologies in populations where they could have the greatest impact has not been sufficiently addressed. This presents challenges in clinical translation due to concerns about perpetuating existing health disparities. Objective: In this paper, we aim to present findings on the feasibility of a cuffless, wrist-worn, pulse transit time (PTT)–based device for monitoring BP in a diverse population. Methods: We recruited a diverse population through a collaborative effort with a nonprofit organization working with medically underserved areas in Georgia. We used our custom, multimodal, wrist-worn device to measure the PTT through seismocardiography, as the proximal timing reference, and photoplethysmography, as the distal timing reference. In addition, we created a novel data-driven beat-selection algorithm to reduce noise and improve the robustness of the method. We compared the wearable PTT measurements with those from a finger-cuff continuous BP device over the course of several perturbations used to modulate BP. Results: Our PTT-based wrist-worn device accurately monitored diastolic blood pressure (DBP) and mean arterial pressure (MAP) in a diverse population (N=44 participants) with a mean absolute difference of 2.90 mm Hg and 3.39 mm Hg for DBP and MAP, respectively, after calibration. Meanwhile, the mean absolute difference of our systolic BP estimation was 5.36 mm Hg, a grade B classification based on the Institute for Electronics and Electrical Engineers standard. We have further demonstrated the ability of our device to capture the commonly observed demographic differences in underlying arterial stiffness. Conclusions: Accurate DBP and MAP estimation, along with grade B systolic BP estimation, using a convenient wearable device can empower users and facilitate remote BP monitoring in medically underserved areas, thus providing widespread hypertension screening and management for health equity. %M 34338646 %R 10.2196/27466 %U https://mhealth.jmir.org/2021/8/e27466 %U https://doi.org/10.2196/27466 %U http://www.ncbi.nlm.nih.gov/pubmed/34338646 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 7 %P e24171 %T The Challenges and Pitfalls of Detecting Sleep Hypopnea Using a Wearable Optical Sensor: Comparative Study %A Zhang,Zhongxing %A Qi,Ming %A Hügli,Gordana %A Khatami,Ramin %+ Center for Sleep Medicine, Sleep Research and Epileptology, Clinic Barmelweid AG, Barmelweid, CH-5017, Switzerland, 41 62 857 22 38, zhongxing.zhang@barmelweid.ch %K obstructive sleep apnea %K wearable devices %K smartwatch %K oxygen saturation %K near-infrared spectroscopy %K continuous positive airway pressure therapy %K photoplethysmography %D 2021 %7 29.7.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Obstructive sleep apnea (OSA) is the most prevalent respiratory sleep disorder occurring in 9% to 38% of the general population. About 90% of patients with suspected OSA remain undiagnosed due to the lack of sleep laboratories or specialists and the high cost of gold-standard in-lab polysomnography diagnosis, leading to a decreased quality of life and increased health care burden in cardio- and cerebrovascular diseases. Wearable sleep trackers like smartwatches and armbands are booming, creating a hope for cost-efficient at-home OSA diagnosis and assessment of treatment (eg, continuous positive airway pressure [CPAP] therapy) effectiveness. However, such wearables are currently still not available and cannot be used to detect sleep hypopnea. Sleep hypopnea is defined by ≥30% drop in breathing and an at least 3% drop in peripheral capillary oxygen saturation (Spo2) measured at the fingertip. Whether the conventional measures of oxygen desaturation (OD) at the fingertip and at the arm or wrist are identical is essentially unknown. Objective: We aimed to compare event-by-event arm OD (arm_OD) with fingertip OD (finger_OD) in sleep hypopneas during both naïve sleep and CPAP therapy. Methods: Thirty patients with OSA underwent an incremental, stepwise CPAP titration protocol during all-night in-lab video-polysomnography monitoring (ie, 1-h baseline sleep without CPAP followed by stepwise increments of 1 cmH2O pressure per hour starting from 5 to 8 cmH2O depending on the individual). Arm_OD of the left biceps muscle and finger_OD of the left index fingertip in sleep hypopneas were simultaneously measured by frequency-domain near-infrared spectroscopy and video-polysomnography photoplethysmography, respectively. Bland-Altman plots were used to illustrate the agreements between arm_OD and finger_OD during baseline sleep and under CPAP. We used t tests to determine whether these measurements significantly differed. Results: In total, 534 obstructive apneas and 2185 hypopneas were recorded. Of the 2185 hypopneas, 668 (30.57%) were collected during baseline sleep and 1517 (69.43%), during CPAP sleep. The mean difference between finger_OD and arm_OD was 2.86% (95% CI 2.67%-3.06%, t667=28.28; P<.001; 95% limits of agreement [LoA] –2.27%, 8.00%) during baseline sleep and 1.83% (95% CI 1.72%-1.94%, t1516=31.99; P<.001; 95% LoA –2.54%, 6.19%) during CPAP. Using the standard criterion of 3% saturation drop, arm_OD only recognized 16.32% (109/668) and 14.90% (226/1517) of hypopneas at baseline and during CPAP, respectively. Conclusions: arm_OD is 2% to 3% lower than standard finger_OD in sleep hypopnea, probably because the measured arm_OD originates physiologically from arterioles, venules, and capillaries; thus, the venous blood adversely affects its value. Our findings demonstrate that the standard criterion of ≥3% OD drop at the arm or wrist is not suitable to define hypopnea because it could provide large false-negative results in diagnosing OSA and assessing CPAP treatment effectiveness. %M 34326039 %R 10.2196/24171 %U https://www.jmir.org/2021/7/e24171 %U https://doi.org/10.2196/24171 %U http://www.ncbi.nlm.nih.gov/pubmed/34326039 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 10 %N 2 %P e27823 %T Early Identification of COVID-19 Infection Using Remote Cardiorespiratory Monitoring: Three Case Reports %A Polsky,Michael %A Moraveji,Neema %+ Pulmonary Associates of Richmond, 1000 Boulders Parkway, Suite 101, North Chesterfield, VA, 23225, United States, 1 804 320 4243, mpolsky@paraccess.com %K COVID-19 %K remote patient monitoring %K wearable sensors %K monitoring %K case study %K preidentification %K lung %K data collection %K respiration %K prediction %D 2021 %7 16.6.2021 %9 Original Paper %J Interact J Med Res %G English %X Background: The adoption of remote patient monitoring (RPM) in routine medical care requires increased understanding of the physiologic changes accompanying disease development and the proactive interventions that will improve outcomes. Objective: The aim of this study is to present three case reports that highlight the capability of RPM to enable early identification of viral infection with COVID-19 in patients with chronic respiratory disease. Methods: Patients at a large pulmonary practice who were enrolled in a respiratory RPM program and who had contracted COVID-19 were identified. The RPM system (Spire Health) contains three components: (1) Health Tags (Spire Health), undergarment waistband-adhered physiologic monitors that include a respiratory rate sensor; (2) an app on a smartphone; and (3) a web dashboard for use by respiratory therapists. The physiologic data of 9 patients with COVID out of 1000 patients who were enrolled for monitoring were retrospectively reviewed, and 3 instances were identified where the RPM system had notified clinicians of physiologic deviation due to the viral infection. Results: Physiologic deviations from respective patient baselines occurred during infection onset and, although the infection manifested differently in each case, were identified by the RPM system. In the first case, the patient was symptomatic; in the second case, the patient was presymptomatic; and in the third case, the patient varied from asymptomatic to mildly symptomatic. Conclusions: RPM systems intended for long-term use and that use patient-specific baselines can highlight physiologic changes early in the course of acute disease, such as COVID-19 infection. These cases demonstrate opportunities for earlier diagnosis, treatment, and isolation. This study supports the need for further research into how RPM can be effectively integrated into clinical practice. %M 34086588 %R 10.2196/27823 %U https://www.i-jmr.org/2021/2/e27823 %U https://doi.org/10.2196/27823 %U http://www.ncbi.nlm.nih.gov/pubmed/34086588 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 6 %P e27496 %T Wireless Home Blood Pressure Monitoring System With Automatic Outcome-Based Feedback and Financial Incentives to Improve Blood Pressure in People With Hypertension: Protocol for a Randomized Controlled Trial %A Bilger,Marcel %A Koong,Agnes Ying Leng %A Phoon,Ian Kwong Yun %A Tan,Ngiap Chuan %A Bahadin,Juliana %A Bairavi,Joann %A Batcagan-Abueg,Ada Portia M %A Finkelstein,Eric A %+ Health Economics and Policy, Vienna University of Economics and Business, Welthandelsplatz 1, Building D4, Room: Third Floor / D4.3.280, Vienna, Austria, 1020, Austria, 43 1 31336 5861, marcel.bilger@wu.ac.at %K telemedicine %K home blood pressure monitoring %K behavior change %K hypertension %K financial incentive %K medication adherence %K remote titration %D 2021 %7 9.6.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Hypertension is prevalent in Singapore and is a major risk factor for cardiovascular morbidity and mortality and increased health care costs. Strategies to lower blood pressure include lifestyle modifications and home blood pressure monitoring. Nonetheless, adherence to home blood pressure monitoring remains low. This protocol details an algorithm for remote management of primary care patients with hypertension. Objective: The objective of this study was to determine whether wireless home blood pressure monitoring with or without financial incentives is more effective at reducing systolic blood pressure than nonwireless home blood pressure monitoring (usual care). Methods: This study was designed as a randomized controlled open-label superiority study. A sample size of 224 was required to detect differences of 10 mmHg in average systolic blood pressure. Participants were to be randomized, in the ratio of 2:3:3, into 1 of 3 parallel study arms :(1) usual care, (2) wireless home blood pressure monitoring, and (3) wireless home blood pressure monitoring with financial incentives. The primary outcome was the mean change in systolic blood pressure at month 6. The secondary outcomes were the mean reduction in diastolic blood pressure, cost of financial incentives, time taken for the intervention, adherence to home blood pressure monitoring, effectiveness of the framing of financial incentives in decreasing nonadherence to blood pressure self-monitoring and the adherence to antihypertensive medication at month 6. Results: This study was approved by SingHealth Centralised Institutional Review Board and registered. Between January 24, 2018 and July 10, 2018, 42 participants (18.75% of the required sample size) were enrolled, and 33 participants completed the month 6 assessment by January 31, 2019. Conclusions: Due to unforeseen events, the study was stopped prematurely; therefore, no results are available. Depending on the blood pressure information received from the patients, the algorithm can trigger immediate blood pressure advice (eg, Accident and Emergency department visit advice for extremely high blood pressure), weekly feedback on blood pressure monitoring, medication titration, or skipping of routine follow-ups. The inclusion of financial incentives framed as health capital provides a novel idea on how to promote adherence to remote monitoring, and ultimately, improve chronic disease management. Trial Registration: ClinicalTrials.gov NCT 03368417; https://clinicaltrials.gov/ct2/show/NCT03368417 International Registered Report Identifier (IRRID): DERR1-10.2196/27496 %M 34106085 %R 10.2196/27496 %U https://www.researchprotocols.org/2021/6/e27496 %U https://doi.org/10.2196/27496 %U http://www.ncbi.nlm.nih.gov/pubmed/34106085 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 6 %P e22748 %T App-Based Versus Standard Six-Minute Walk Test in Pulmonary Hypertension: Mixed Methods Study %A Salvi,Dario %A Poffley,Emma %A Tarassenko,Lionel %A Orchard,Elizabeth %+ Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Old Road Campus Research Building, Oxford, United Kingdom, 44 1865 617675, dario.salvi.work@gmail.com %K cardiology %K exercise test %K pulmonary hypertension %K mobile apps %K GPS %D 2021 %7 7.6.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Pulmonary arterial hypertension (PAH) is a chronic disease of the pulmonary vasculature that can lead to heart failure and premature death. Assessment of patients with PAH includes performing a 6-minute walk test (6MWT) in clinics. We developed a smartphone app to compute the walked distance (6MWD) indoors, by counting U-turns, and outdoors, by using satellite positioning. Objective: The goal of the research was to assess (1) accuracy of the indoor 6MWTs in clinical settings, (2) validity and test-retest reliability of outdoor 6MWTs in the community, (3) compliance, usability, and acceptance of the app, and (4) feasibility of pulse oximetry during 6MWTs. Methods: We tested the app on 30 PAH patients over 6 months. Patients were asked to perform 3 conventional 6MWTs in clinic while using the app in the indoor mode and one or more app-based 6MWTs in outdoor mode in the community per month. Results: Bland-Altman analysis of 70 pairs of conventional versus app-based indoor 6MWDs suggests that the app is sometimes inaccurate (14.6 m mean difference, lower and upper limit of agreement: –133.35 m to 162.55 m). The comparison of 69 pairs of conventional 6MWDs and community-based outdoor 6MWDs within 7 days shows that community tests are strongly related to those performed in clinic (correlation 0.89), but the interpretation of the distance should consider that differences above the clinically significant threshold are not uncommon. Analysis of 89 pairs of outdoor tests performed by the same patient within 7 days shows that community-based tests are repeatable (intraclass correlation 0.91, standard error of measurement 36.97 m, mean coefficient of variation 12.45%). Questionnaires and semistructured interviews indicate that the app is usable and well accepted, but motivation to use it could be affected if the data are not used for clinical decision, which may explain low compliance in 52% of our cohort. Analysis of pulse oximetry data indicates that conventional pulse oximeters are unreliable if used during a walk. Conclusions: App-based outdoor 6MWTs in community settings are valid, repeatable, and well accepted by patients. More studies would be needed to assess the benefits of using the app in clinical practice. Trial Registration: ClinicalTrials.gov NCT04633538; https://clinicaltrials.gov/ct2/show/NCT04633538 %M 34096876 %R 10.2196/22748 %U https://mhealth.jmir.org/2021/6/e22748 %U https://doi.org/10.2196/22748 %U http://www.ncbi.nlm.nih.gov/pubmed/34096876 %0 Journal Article %@ 2369-2529 %I JMIR Publications %V 8 %N 2 %P e25996 %T Zero-Effort Ambient Heart Rate Monitoring Using Ballistocardiography Detected Through a Seat Cushion: Prototype Development and Preliminary Study %A Malik,Ahmed Raza %A Boger,Jennifer %+ Department of Systems Design Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada, 1 5198884567 ext 38328, jboger@uwaterloo.ca %K ballistocardiography %K heart rate %K ambient health monitoring %K zero-effort technology %K continuous wavelet transform %D 2021 %7 31.5.2021 %9 Original Paper %J JMIR Rehabil Assist Technol %G English %X Background: Cardiovascular diseases are a leading cause of death worldwide and result in significant economic costs to health care systems. The prevalence of cardiovascular conditions that require monitoring is expected to increase as the average age of the global population continues to rise. Although an accurate cardiac assessment can be performed at medical centers, frequent visits for assessment are not feasible for most people, especially those with limited mobility. Monitoring of vital signs at home is becoming an increasingly desirable, accessible, and practical alternative. As wearable devices are not the ideal solution for everyone, it is necessary to develop parallel and complementary approaches. Objective: This research aims to develop a zero-effort, unobtrusive, cost-effective, and portable option for home-based ambient heart rate monitoring. Methods: The prototype seat cushion uses load cells to acquire a user’s ballistocardiogram (BCG). The analog signal from the load cells is amplified and filtered by a signal-conditioning circuit before being digitally recorded. A pilot study with 20 participants was conducted to analyze the prototype’s ability to capture the BCG during five real-world tasks: sitting still, watching a video on a computer screen, reading, using a computer, and having a conversation. A novel algorithm based on the continuous wavelet transform was developed to extract the heart rate by detecting the largest amplitude values (J-peaks) in the BCG signal. Results: The pilot study data showed that the BCG signals from all five tasks had sufficiently large portions to extract heart rate. The continuous wavelet transform–based algorithm for J-peak detection demonstrated an overall accuracy of 91.4% compared with electrocardiography. Excluding three outliers that had significantly noisy BCG data, the algorithm achieved 94.6% accuracy, which was aligned with that of wearable devices. Conclusions: This study suggests that BCG acquired through a seat cushion is a viable alternative to wearable technologies. The prototype seat cushion presented in this study is an example of a relatively accessible, affordable, portable, and unobtrusive zero-effort approach to achieve frequent home-based ambient heart rate monitoring. %M 34057420 %R 10.2196/25996 %U https://rehab.jmir.org/2021/2/e25996 %U https://doi.org/10.2196/25996 %U http://www.ncbi.nlm.nih.gov/pubmed/34057420 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 5 %P e25079 %T Pain Recognition With Electrocardiographic Features in Postoperative Patients: Method Validation Study %A Kasaeyan Naeini,Emad %A Subramanian,Ajan %A Calderon,Michael-David %A Zheng,Kai %A Dutt,Nikil %A Liljeberg,Pasi %A Salantera,Sanna %A Nelson,Ariana M %A Rahmani,Amir M %+ Department of Computer Science, University of California, Irvine, DBH Bldg, 3rd Fl., Irvine, CA, 92617, United States, 1 9496106661, ekasaeya@uci.edu %K pain assessment %K recognition %K health monitoring %K wearable electronics %K machine learning %D 2021 %7 28.5.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: There is a strong demand for an accurate and objective means of assessing acute pain among hospitalized patients to help clinicians provide pain medications at a proper dosage and in a timely manner. Heart rate variability (HRV) comprises changes in the time intervals between consecutive heartbeats, which can be measured through acquisition and interpretation of electrocardiography (ECG) captured from bedside monitors or wearable devices. As increased sympathetic activity affects the HRV, an index of autonomic regulation of heart rate, ultra–short-term HRV analysis can provide a reliable source of information for acute pain monitoring. In this study, widely used HRV time and frequency domain measurements are used in acute pain assessments among postoperative patients. The existing approaches have only focused on stimulated pain in healthy subjects, whereas, to the best of our knowledge, there is no work in the literature building models using real pain data and on postoperative patients. Objective: The objective of our study was to develop and evaluate an automatic and adaptable pain assessment algorithm based on ECG features for assessing acute pain in postoperative patients likely experiencing mild to moderate pain. Methods: The study used a prospective observational design. The sample consisted of 25 patient participants aged 18 to 65 years. In part 1 of the study, a transcutaneous electrical nerve stimulation unit was employed to obtain baseline discomfort thresholds for the patients. In part 2, a multichannel biosignal acquisition device was used as patients were engaging in non-noxious activities. At all times, pain intensity was measured using patient self-reports based on the Numerical Rating Scale. A weak supervision framework was inherited for rapid training data creation. The collected labels were then transformed from 11 intensity levels to 5 intensity levels. Prediction models were developed using 5 different machine learning methods. Mean prediction accuracy was calculated using leave-one-out cross-validation. We compared the performance of these models with the results from a previously published research study. Results: Five different machine learning algorithms were applied to perform a binary classification of baseline (BL) versus 4 distinct pain levels (PL1 through PL4). The highest validation accuracy using 3 time domain HRV features from a BioVid research paper for baseline versus any other pain level was achieved by support vector machine (SVM) with 62.72% (BL vs PL4) to 84.14% (BL vs PL2). Similar results were achieved for the top 8 features based on the Gini index using the SVM method, with an accuracy ranging from 63.86% (BL vs PL4) to 84.79% (BL vs PL2). Conclusions: We propose a novel pain assessment method for postoperative patients using ECG signal. Weak supervision applied for labeling and feature extraction improves the robustness of the approach. Our results show the viability of using a machine learning algorithm to accurately and objectively assess acute pain among hospitalized patients. International Registered Report Identifier (IRRID): RR2-10.2196/17783 %M 34047710 %R 10.2196/25079 %U https://www.jmir.org/2021/5/e25079 %U https://doi.org/10.2196/25079 %U http://www.ncbi.nlm.nih.gov/pubmed/34047710 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 5 %P e24044 %T Bispectral Index Alterations and Associations With Autonomic Changes During Hypnosis in Trauma Center Researchers: Formative Evaluation Study %A Dunham,C Michael %A Burger,Amanda J %A Hileman,Barbara M %A Chance,Elisha A %A Hutchinson,Amy E %+ St Elizabeth Youngstown Hospital, 1044 Belmont Avenue, Youngstown, OH, 44501, United States, 1 330 480 3907, dunham.michael@sbcglobal.net %K bispectral index %K hypnosis %K heart rate variability %K electromyography %K skin conductance %K skin temperature %K respiratory rate %K expired carbon dioxide %K neurofeedback %D 2021 %7 26.5.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Previous work performed by our group demonstrated that intermittent reductions in bispectral index (BIS) values were found during neurofeedback following mindfulness instructions. Hypnosis was induced to enhance reductions in BIS values. Objective: This study aims to assess physiologic relaxation and explore its associations with BIS values using autonomic monitoring. Methods: Each session consisted of reading a 4-minute baseline neutral script and playing an 18-minute hypnosis tape to 3 researchers involved in the BIS neurofeedback study. In addition to BIS monitoring, autonomic monitoring was performed, and this included measures of electromyography (EMG), skin temperature, skin conductance, respiratory rate, expired carbon dioxide, and heart rate variability. The resulting data were analyzed using two-tailed t tests, correlation analyses, and multivariate linear regression analyses. Results: We found that hypnosis was associated with reductions in BIS (P<.001), EMG (P<.001), respiratory rate (P<.001), skin conductance (P=.006), and very low frequency power (P=.04); it was also associated with increases in expired carbon dioxide (P<.001), skin temperature (P=.04), high frequency power (P<.001), and successive heart interbeat interval difference (P=.04) values. Decreased BIS values were associated with reduced EMG measures (R=0.76; P<.001), respiratory rate (R=0.35; P=.004), skin conductance (R=0.57; P<.001), and low frequency power (R=0.32; P=.01) and with increased high frequency power (R=−0.53; P<.001), successive heart interbeat interval difference (R=−0.32; P=.009), and heart interbeat interval SD (R=−0.26; P=.04) values. Conclusions: Hypnosis appeared to induce mental and physical relaxation, enhance parasympathetic neural activation, and attenuate sympathetic nervous system activity, changes that were associated with BIS values. Findings from this preliminary formative evaluation suggest that the current hypnosis model may be useful for assessing autonomic physiological associations with changes in BIS values, thus motivating us to proceed with a larger investigation in trauma center nurses and physicians. %M 34037529 %R 10.2196/24044 %U https://formative.jmir.org/2021/5/e24044 %U https://doi.org/10.2196/24044 %U http://www.ncbi.nlm.nih.gov/pubmed/34037529 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 5 %P e24470 %T Performance of a Mobile Single-Lead Electrocardiogram Technology for Atrial Fibrillation Screening in a Semirural African Population: Insights From “The Heart of Ethiopia: Focus on Atrial Fibrillation” (TEFF-AF) Study %A Pitman,Bradley M %A Chew,Sok-Hui %A Wong,Christopher X %A Jaghoori,Amenah %A Iwai,Shinsuke %A Thomas,Gijo %A Chew,Andrew %A Sanders,Prashanthan %A Lau,Dennis H %+ Centre for Heart Rhythm Disorders, The University of Adelaide, 1 Port Rd, Adelaide, 5000, Australia, 61 8313 9000, dennis.h.lau@adelaide.edu.au %K atrial fibrillation %K screening %K sub-Saharan Africa %K single-lead ECG %D 2021 %7 19.5.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Atrial fibrillation (AF) screening using mobile single-lead electrocardiogram (ECG) devices has demonstrated variable sensitivity and specificity. However, limited data exists on the use of such devices in low-resource countries. Objective: The goal of the research was to evaluate the utility of the KardiaMobile device’s (AliveCor Inc) automated algorithm for AF screening in a semirural Ethiopian population. Methods: Analysis was performed on 30-second single-lead ECG tracings obtained using the KardiaMobile device from 1500 TEFF-AF (The Heart of Ethiopia: Focus on Atrial Fibrillation) study participants. We evaluated the performance of the KardiaMobile automated algorithm against cardiologists’ interpretations of 30-second single-lead ECG for AF screening. Results: A total of 1709 single-lead ECG tracings (including repeat tracing on 209 occasions) were analyzed from 1500 Ethiopians (63.53% [953/1500] male, mean age 35 [SD 13] years) who presented for AF screening. Initial successful rhythm decision (normal or possible AF) with one single-lead ECG tracing was lower with the KardiaMobile automated algorithm versus manual verification by cardiologists (1176/1500, 78.40%, vs 1455/1500, 97.00%; P<.001). Repeat single-lead ECG tracings in 209 individuals improved overall rhythm decision, but the KardiaMobile automated algorithm remained inferior (1301/1500, 86.73%, vs 1479/1500, 98.60%; P<.001). The key reasons underlying unsuccessful KardiaMobile automated rhythm determination include poor quality/noisy tracings (214/408, 52.45%), frequent ectopy (22/408, 5.39%), and tachycardia (>100 bpm; 167/408, 40.93%). The sensitivity and specificity of rhythm decision using KardiaMobile automated algorithm were 80.27% (1168/1455) and 82.22% (37/45), respectively. Conclusions: The performance of the KardiaMobile automated algorithm was suboptimal when used for AF screening. However, the KardiaMobile single-lead ECG device remains an excellent AF screening tool with appropriate clinician input and repeat tracing. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12619001107112; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=378057&isReview=true %M 34009129 %R 10.2196/24470 %U https://mhealth.jmir.org/2021/5/e24470 %U https://doi.org/10.2196/24470 %U http://www.ncbi.nlm.nih.gov/pubmed/34009129 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 5 %P e26469 %T Feasibility of a Waistband-Type Wireless Wearable Electrocardiogram Monitoring System Based on a Textile Electrode: Development and Usability Study %A Gwon,Danbi %A Cho,Hakyung %A Shin,Hangsik %+ Department of Biomedical Engineering, Chonnam National University, 50 Daehak-ro, Yeosu, 59626, Republic of Korea, 82 61 659 7362, hangsik.shin@jnu.ac.kr %K electrocardiogram %K telehealth %K telemetry %K telemonitoring %K textile electrode %K wearable system %K smartphone %K mobile phone %D 2021 %7 11.5.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Electrocardiogram (ECG) monitoring in daily life is essential for effective management of cardiovascular disease, a leading cause of death. Wearable ECG measurement systems in the form of clothing have been proposed to replace Holter monitors used for clinical ECG monitoring; however, they have limitations in daily use because they compress the upper body and, in doing so, cause discomfort during wear. Objective: The purpose of this study was to develop a wireless wearable ECG monitoring system that includes a textile ECG electrode that can be applied to the lining of pants and can be used in the same way that existing lower clothing is worn, without compression to the upper body. Methods: A textile electrode with stretchable characteristics was fabricated by knitting a conductive yarn together with polyester-polyurethane fiber, which was then coated with silver compound; an ECG electrode was developed by placing it on an elastic band in a modified limb lead configuration. In addition, a system with analog-to-digital conversion, wireless communication, and a smartphone app was developed, allowing users to be able to check and store their own ECGs in real time. A signal processing algorithm was also developed to remove noise from the obtained signal and to calculate the heart rate. To evaluate the ECG and heart rate measurement performance of the developed module, a comparative evaluation with a commercial device was performed. ECGs were measured for 5 minutes each in standing, sitting, and lying positions; the mean absolute percentage errors of heart rates measured with both systems were then compared. Results: The system was developed in the form of a belt buckle with a size of 53 × 45 × 12 mm (width × height × depth) and a weight of 23 g. In a qualitative evaluation, it was confirmed that the P-QRS-T waveform was clearly observed in ECGs obtained with the wearable system. From the results of the heart rate estimation, the developed system could track changes in heart rate as calculated by a commercial ECG measuring device; in addition, the mean absolute percentage errors of heart rates were 1.80%, 2.84%, and 2.48% in the standing, sitting, and lying positions, respectively. Conclusions: The developed system was able to effectively measure ECG and calculate heart rate simply through being worn as existing clothing without upper body pressure. It is anticipated that general usability can be secured through further evaluation under more diverse conditions. %M 33973860 %R 10.2196/26469 %U https://mhealth.jmir.org/2021/5/e26469 %U https://doi.org/10.2196/26469 %U http://www.ncbi.nlm.nih.gov/pubmed/33973860 %0 Journal Article %@ 2561-3278 %I JMIR Publications %V 6 %N 2 %P e22911 %T Wearable Bioimpedance Monitoring: Viewpoint for Application in Chronic Conditions %A Groenendaal,Willemijn %A Lee,Seulki %A van Hoof,Chris %+ Imec the Netherlands / Holst Centre, High Tech Campus 31, Eindhoven, 5656AE, Netherlands, 31 404020400, willemijn.groenendaal@imec.nl %K wearable monitoring %K bioimpedance %K impedance pneumography %K impedance cardiography %K body composition %K imaging %D 2021 %7 11.5.2021 %9 Viewpoint %J JMIR Biomed Eng %G English %X Currently, nearly 6 in 10 US adults are suffering from at least one chronic condition. Wearable technology could help in controlling the health care costs by remote monitoring and early detection of disease worsening. However, in recent years, there have been disappointments in wearable technology with respect to reliability, lack of feedback, or lack of user comfort. One of the promising sensor techniques for wearable monitoring of chronic disease is bioimpedance, which is a noninvasive, versatile sensing method that can be applied in different ways to extract a wide range of health care parameters. Due to the changes in impedance caused by either breathing or blood flow, time-varying signals such as respiration and cardiac output can be obtained with bioimpedance. A second application area is related to body composition and fluid status (eg, pulmonary congestion monitoring in patients with heart failure). Finally, bioimpedance can be used for continuous and real-time imaging (eg, during mechanical ventilation). In this viewpoint, we evaluate the use of wearable bioimpedance monitoring for application in chronic conditions, focusing on the current status, recent improvements, and challenges that still need to be tackled. %M 38907374 %R 10.2196/22911 %U https://biomedeng.jmir.org/2021/2/e22911 %U https://doi.org/10.2196/22911 %U http://www.ncbi.nlm.nih.gov/pubmed/38907374 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 5 %N 1 %P e26259 %T A Wearable Ballistocardiography Device for Estimating Heart Rate During Positive Airway Pressure Therapy: Investigational Study Among the General Population %A Gardner,Mark %A Randhawa,Sharmil %A Malouf,Gordon %A Reynolds,Karen %+ Medical Device Research Institute, Flinders University, 1284 South Road, Clovelly Park, 5042, Australia, 61 0448121126, mark.gardner@sydney.edu.au %K heart rate %K ballistocardiography %K sleep apnea %K positive airway pressure %K gyroscope %K Kalman filter %D 2021 %7 5.5.2021 %9 Original Paper %J JMIR Cardio %G English %X Background: Obstructive sleep apnea (OSA) is a condition in which a person’s airway is obstructed during sleep, thus disturbing their sleep. People with OSA are at a higher risk of developing heart problems. OSA is commonly treated with a positive airway pressure (PAP) therapy device, which is used during sleep. The PAP therapy setup provides a good opportunity to monitor the heart health of people with OSA, but no simple, low-cost method is available for the PAP therapy device to monitor heart rate (HR). Objective: This study aims to develop a simple, low-cost device to monitor the HR of people with OSA during PAP therapy. This device was then tested on a small group of participants to investigate the feasibility of the device. Methods: A low-cost and simple device to monitor HR was created by attaching a gyroscope to a PAP mask, thus integrating HR monitoring into PAP therapy. The gyroscope signals were then analyzed to detect heartbeats, and a Kalman filter was used to produce a more accurate and consistent HR signal. In this study, 19 participants wore the modified PAP mask while the mask was connected to a PAP device. Participants lay in 3 common sleeping positions and then underwent 2 different PAP therapy modes to determine if these affected the accuracy of the HR estimation. Results: Before the PAP device was turned on, the median HR error was <5 beats per minute, although the HR estimation error increased when participants lay on their side compared with when participants lay on their back. Using the different PAP therapy modes did not significantly increase the HR error. Conclusions: These results show that monitoring HR from gyroscope signals in a PAP mask is possible during PAP therapy for different sleeping positions and PAP therapy modes, suggesting that long-term HR monitoring of OSA during PAP therapy may be possible. %M 33949952 %R 10.2196/26259 %U https://cardio.jmir.org/2021/1/e26259 %U https://doi.org/10.2196/26259 %U http://www.ncbi.nlm.nih.gov/pubmed/33949952 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 4 %P e27503 %T Validation of the Withings ScanWatch as a Wrist-Worn Reflective Pulse Oximeter: Prospective Interventional Clinical Study %A Kirszenblat,Romain %A Edouard,Paul %+ Withings, 2 rue Maurice Hartmann, Issy-Les-Moulineaux, 92130, France, 33 141460460, romain.kirszenblat@withings.com %K connected watch %K COPD %K COVID-19 %K neural network %K pulse oxygen saturation %K reflective pulse oximeter %K sleep apnea syndrome %K SpO2 %K Withings ScanWatch %K wearable %K respiratory %K oxygen %K respiratory disease %K oximeter %K validation %K accuracy %K safety %D 2021 %7 26.4.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: A decrease in the level of pulse oxygen saturation as measured by pulse oximetry (SpO2) is an indicator of hypoxemia that may occur in various respiratory diseases, such as chronic obstructive pulmonary disease (COPD), sleep apnea syndrome, and COVID-19. Currently, no mass-market wrist-worn SpO2 monitor meets the medical standards for pulse oximeters. Objective: The main objective of this monocentric and prospective clinical study with single-blind analysis was to test and validate the accuracy of the reflective pulse oximeter function of the Withings ScanWatch to measure SpO2 levels at different stages of hypoxia. The secondary objective was to confirm the safety of this device when used as intended. Methods: To achieve these objectives, we included 14 healthy participants aged 23-39 years in the study, and we induced several stable plateaus of arterial oxygen saturation (SaO2) ranging from 100%-70% to mimic nonhypoxic conditions and then mild, moderate, and severe hypoxic conditions. We measured the SpO2 level with a Withings ScanWatch on each participant’s wrist and the SaO2 from blood samples with a co-oximeter, the ABL90 hemoximeter (Radiometer Medical ApS). Results: After removal of the inconclusive measurements, we obtained 275 and 244 conclusive measurements with the two ScanWatches on the participants’ right and left wrists, respectively, evenly distributed among the 3 predetermined SpO2 groups: SpO2≤80%, 80%90 min during typical day-to-day activities and while sleeping. After filtering, ECG data were processed using a combination of discrete wavelet transforms and peak-finding methods to identify R-R intervals. P-P intervals were edited for deletion using methods based on outlier detection and by removing sections affected by motion artifacts. Common HRV metrics were compared, including mean N-N, SD of N-N intervals, percentage of subsequent differences >50 ms (pNN50), root mean square of successive differences, low-frequency power (LF), and high-frequency power. Validity was assessed using root mean square error (RMSE) and Pearson correlation coefficient (R2). Results: Data sets for 10 days and 9 corresponding nights were acquired. The mean RMSE was 182 ms (SD 48) during the day and 158 ms (SD 67) at night. R2 ranged from 0.00 to 0.66, with 2 of 19 (2 nights) trials considered moderate, 7 of 19 (2 days, 5 nights) fair, and 10 of 19 (8 days, 2 nights) poor. Deleting sections thought to be affected by motion artifacts had a minimal impact on the accuracy of PRV measures. Significant HRV and PRV differences were found for LF during the day and R-R, SDNN, pNN50, and LF at night. For 8 of the 9 matched day and night data sets, R2 values were higher at night (P=.08). P-P intervals were less sensitive to rapid R-R interval changes. Conclusions: Owing to overall poor concurrent validity and inconsistency among participant data, PRV was found to be a poor surrogate for HRV under free-living conditions. These findings suggest that free-living HRV measurements would benefit from examining alternate sensing methods, such as multiwavelength PPG and wearable ECG. %R 10.2196/17355 %U http://biomedeng.jmir.org/2020/1/e17355/ %U https://doi.org/10.2196/17355 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 9 %P e20488 %T Digital Cardiovascular Biomarker Responses to Transcutaneous Cervical Vagus Nerve Stimulation: State-Space Modeling, Prediction, and Simulation %A Gazi,Asim H %A Gurel,Nil Z %A Richardson,Kristine L S %A Wittbrodt,Matthew T %A Shah,Amit J %A Vaccarino,Viola %A Bremner,J Douglas %A Inan,Omer T %+ School of Electrical and Computer Engineering, Georgia Institute of Technology, North Ave NW, Atlanta, GA, 30332, United States, 1 4693608083, asim.gazi@gatech.edu %K vagus nerve stimulation %K noninvasive %K wearable sensing %K digital biomarkers %K dynamic models %K state space %K biomarker %K cardiovascular %K neuromodulation %K bioelectronic medicine %D 2020 %7 22.9.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Transcutaneous cervical vagus nerve stimulation (tcVNS) is a promising alternative to implantable stimulation of the vagus nerve. With demonstrated potential in myriad applications, ranging from systemic inflammation reduction to traumatic stress attenuation, closed-loop tcVNS during periods of risk could improve treatment efficacy and reduce ineffective delivery. However, achieving this requires a deeper understanding of biomarker changes over time. Objective: The aim of the present study was to reveal the dynamics of relevant cardiovascular biomarkers, extracted from wearable sensing modalities, in response to tcVNS. Methods: Twenty-four human subjects were recruited for a randomized double-blind clinical trial, for whom electrocardiography and photoplethysmography were used to measure heart rate and photoplethysmogram amplitude responses to tcVNS, respectively. Modeling these responses in state-space, we (1) compared the biomarkers in terms of their predictability and active vs sham differentiation, (2) studied the latency between stimulation onset and measurable effects, and (3) visualized the true and model-simulated biomarker responses to tcVNS. Results: The models accurately predicted future heart rate and photoplethysmogram amplitude values with root mean square errors of approximately one-fifth the standard deviations of the data. Moreover, (1) the photoplethysmogram amplitude showed superior predictability (P=.03) and active vs sham separation compared to heart rate; (2) a consistent delay of greater than 5 seconds was found between tcVNS onset and cardiovascular effects; and (3) dynamic characteristics differentiated responses to tcVNS from the sham stimulation. Conclusions: This work furthers the state of the art by modeling pertinent biomarker responses to tcVNS. Through subsequent analysis, we discovered three key findings with implications related to (1) wearable sensing devices for bioelectronic medicine, (2) the dominant mechanism of action for tcVNS-induced effects on cardiovascular physiology, and (3) the existence of dynamic biomarker signatures that can be leveraged when titrating therapy in closed loop. Trial Registration: ClinicalTrials.gov NCT02992899; https://clinicaltrials.gov/ct2/show/NCT02992899 International Registered Report Identifier (IRRID): RR2-10.1016/j.brs.2019.08.002 %M 32960179 %R 10.2196/20488 %U http://mhealth.jmir.org/2020/9/e20488/ %U https://doi.org/10.2196/20488 %U http://www.ncbi.nlm.nih.gov/pubmed/32960179 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 9 %P e17983 %T Effect of Prior Health Knowledge on the Usability of Two Home Medical Devices: Usability Study %A Chaniaud,Noémie %A Métayer,Natacha %A Megalakaki,Olga %A Loup-Escande,Emilie %+ Centre de Rercherche en Psychologie: Cognition Psychisme et Organisations, Université Picardie Jules Verne, Pôle Campus Sud - Bâtiment E - 3ème étage 1, Chemin du Thil - CS 52 501, Amiens, 80 025 CEDEX 1, France, 33 3 22 82 70 59, noemie.chaniaud@u-picardie.fr %K usability %K prior health knowledge %K mHealth %K home medical devices %K blood pressure monitor %K pulse oximeter %D 2020 %7 21.9.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Studies on the usability of health care devices are becoming more common, although usability standards are not necessarily specified and followed. Yet, there is little knowledge about the impact of the context of use on the usability outcome. It is specified in the usability standard (ISO 9241-11, 2018) of a device that it may be affected by its context of use and especially by the characteristics of its users. Among these, prior health knowledge (ie, knowledge about human body functioning) is crucial. However, no study has shown that prior health knowledge influences the usability of medical devices.  Objective: Our study aimed to fill this gap by analyzing the relationship between the usability of two home medical devices (soon to be used in the context of ambulatory surgery) and prior health knowledge through an experimental approach. Methods: For assessing the usability of two home medical devices (blood pressure monitor and pulse oximeter), user tests were conducted among 149 students. A mixed-methods approach (subjective vs objective) using a variety of standard instruments was adopted (direct observation, video analysis, and questionnaires). Participants completed a questionnaire to show the extent of their previous health knowledge and then operated both devices randomly. Efficiency (ie, handling time) and effectiveness (ie, number of handling errors) measures were collected by video analysis. Satisfaction measures were collected by a questionnaire (system usability scale [SUS]). The qualitative observational data were coded using inductive analysis by two independent researchers specialized in cognitive psychology and cognitive ergonomics. Correlational analyses and clusters were performed to test how usability relates to sociodemographic characteristics and prior health knowledge. Results: The results indicated a lack of usability for both devices. Regarding the blood pressure monitor (137 participants), users made approximately 0.77 errors (SD 1.49), and the mean SUS score was 72.4 (SD 21.07), which is considered “satisfactory.” The pulse oximeter (147 participants) appeared easier to use, but participants made more errors (mean 0.99, SD 0.92), and the mean SUS score was 71.52 (SD 17.29), which is considered “satisfactory.” The results showed a low negative and significant correlation only between the effectiveness of the two devices and previous knowledge (blood pressure monitor: r=−0.191, P=.03; pulse oximeter: r=−0.263, P=.001). More subtly, we experimentally identified the existence of a threshold level (χ²2,146=10.9, P=.004) for health knowledge to correctly use the pulse oximeter, but this was missing for the blood pressure monitor. Conclusions: This study has the following two contributions: (1) a theoretical interest highlighting the importance of user characteristics including prior health knowledge on usability outcomes and (2) an applied interest to provide recommendations to designers and medical staff. %M 32955454 %R 10.2196/17983 %U http://mhealth.jmir.org/2020/9/e17983/ %U https://doi.org/10.2196/17983 %U http://www.ncbi.nlm.nih.gov/pubmed/32955454 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 9 %P e18253 %T Relationship Between Chronic Stress and Heart Rate Over Time Modulated by Gender in a Cohort of Office Workers: Cross-Sectional Study Using Wearable Technologies %A van Kraaij,Alex Wilhelmus Jacobus %A Schiavone,Giuseppina %A Lutin,Erika %A Claes,Stephan %A Van Hoof,Chris %+ OnePlanet Research Center, imec-the Netherlands, Bronland 10, Wageningen, 6708 WH, Netherlands, 31 404020400, alex.vankraaij@imec.nl %K chronic stress %K heart rate %K circadian rhythm %K gender %K age %K wearable device %D 2020 %7 9.9.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Chronic stress is increasing in prevalence and is associated with several physical and mental disorders. Although it is proven that acute stress changes physiology, much less is known about the relationship between physiology and long-term stress. Continuous measurement of vital signs in daily life and chronic stress detection algorithms could serve this purpose. For this, it is paramount to model the effects of chronic stress on human physiology and include other cofounders, such as demographics, enabling the enrichment of a population-wide approach with individual variations. Objective: The main objectives of this study were to investigate the effect of chronic stress on heart rate (HR) over time while correcting for weekdays versus weekends and to test a possible modulation effect by gender and age in a healthy cohort. Methods: Throughout 2016 and 2017, healthy employees of technology companies were asked to participate in a 5-day observation stress study. They were required to wear two wearables, of which one included an electrocardiogram sensor. The derived HR was averaged per hour and served as an output for a mixed design model including a trigonometric fit over time with four harmonics (periods of 24, 12, 8, and 6 hours), gender, age, whether it was a workday or weekend day, and a chronic stress score derived from the Perceived Stress Scale (PSS) as predictors. Results: The study included 328 subjects, of which 142 were female and 186 were male participants, with a mean age of 38.9 (SD 10.2) years and a mean PSS score of 13.7 (SD 6.0). As main effects, gender (χ21=24.02, P<.001); the hour of the day (χ21=73.22, P<.001); the circadian harmonic (χ22=284.4, P<.001); and the harmonic over 12 hours (χ22=242.1, P<.001), over 8 hours (χ22=23.78, P<.001), and over 6 hours (χ22=82.96, P<.001) had a significant effect on HR. Two three-way interaction effects were found. The interaction of age, whether it was a workday or weekend day, and the circadian harmonic over time were significantly correlated with HR (χ22=7.13, P=.03), as well as the interaction of gender, PSS score, and the circadian harmonic over time (χ22=7.59, P=.02). Conclusions: The results show a relationship between HR and the three-way interaction of chronic stress, gender, and the circadian harmonic. The modulation by gender might be related to evolution-based energy utilization strategies, as suggested in related literature studies. More research, including daily cortisol assessment, longer recordings, and a wider population, should be performed to confirm this interpretation. This would enable the development of more complete and personalized models of chronic stress. %M 32902392 %R 10.2196/18253 %U http://www.jmir.org/2020/9/e18253/ %U https://doi.org/10.2196/18253 %U http://www.ncbi.nlm.nih.gov/pubmed/32902392 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 8 %P e17699 %T Using Smart Bracelets to Assess Heart Rate Among Students During Physical Education Lessons: Feasibility, Reliability, and Validity Study %A Sun,Jiangang %A Liu,Yang %+ School of Physical Education and Sport Training, Shanghai University of Sport, 650 Qingyuanhuan Rd, Shanghai, 200438, China, 86 21 6550 7989, docliuyang@hotmail.com %K physical education %K heart rate %K validation %K feasibility %K reliability %K Fizzo %K Polar %K wrist-worn devices %K physical education lesson %K monitoring %D 2020 %7 5.8.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: An increasing number of wrist-worn wearables are being examined in the context of health care. However, studies of their use during physical education (PE) lessons remain scarce. Objective: We aim to examine the reliability and validity of the Fizzo Smart Bracelet (Fizzo) in measuring heart rate (HR) in the laboratory and during PE lessons. Methods: In Study 1, 11 healthy subjects (median age 22.0 years, IQR 3.75 years) twice completed a test that involved running on a treadmill at 6 km/h for 12 minutes and 12 km/h for 5 minutes. During the test, participants wore two Fizzo devices, one each on their left and right wrists, to measure their HR. At the same time, the Polar Team2 Pro (Polar), which is worn on the chest, was used as the standard. In Study 2, we went to 10 schools and measured the HR of 24 students (median age 14.0 years, IQR 2.0 years) during PE lessons. During the PE lessons, each student wore a Polar device on their chest and a Fizzo on their right wrist to measure HR data. At the end of the PE lessons, the students and their teachers completed a questionnaire where they assessed the feasibility of Fizzo. The measurements taken by the left wrist Fizzo and the right wrist Fizzo were compared to estimate reliability, while the Fizzo measurements were compared to the Polar measurements to estimate validity. To measure reliability, intraclass correlation coefficients (ICC), mean difference (MD), standard error of measurement (SEM), and mean absolute percentage errors (MAPE) were used. To measure validity, ICC, limits of agreement (LOA), and MAPE were calculated and Bland-Altman plots were constructed. Percentage values were used to estimate the feasibility of Fizzo. Results: The Fizzo showed excellent reliability and validity in the laboratory and moderate validity in a PE lesson setting. In Study 1, reliability was excellent (ICC>0.97; MD<0.7; SEM<0.56; MAPE<1.45%). The validity as determined by comparing the left wrist Fizzo and right wrist Fizzo was excellent (ICC>0.98; MAPE<1.85%). Bland-Altman plots showed a strong correlation between left wrist Fizzo measurements (bias=0.48, LOA=–3.94 to 4.89 beats per minute) and right wrist Fizzo measurements (bias=0.56, LOA=–4.60 to 5.72 beats per minute). In Study 2, the validity of the Fizzo was lower compared to that found in Study 1 but still moderate (ICC>0.70; MAPE<9.0%). The Fizzo showed broader LOA in the Bland-Altman plots during the PE lessons (bias=–2.60, LOA=–38.89 to 33.69 beats per minute). Most participants considered the Fizzo very comfortable and easy to put on. All teachers thought the Fizzo was helpful. Conclusions: When participants ran on a treadmill in the laboratory, both left and right wrist Fizzo measurements were accurate. The validity of the Fizzo was lower in PE lessons but still reached a moderate level. The Fizzo is feasible for use during PE lessons. %M 32663136 %R 10.2196/17699 %U http://mhealth.jmir.org/2020/8/e17699/ %U https://doi.org/10.2196/17699 %U http://www.ncbi.nlm.nih.gov/pubmed/32663136 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 7 %P e18761 %T A Novel Smartphone App for the Measurement of Ultra–Short-Term and Short-Term Heart Rate Variability: Validity and Reliability Study %A Chen,Yung-Sheng %A Lu,Wan-An %A Pagaduan,Jeffrey C %A Kuo,Cheng-Deng %+ Department of Medical Research, Taipei Veterans General Hospital, Number 201, Section 2, Shipai Rd, Beitou District, Taipei, 112, Taiwan, 886 932981776, cdkuo23@gmail.com %K heart rate variability %K smartphone %K reproducibility %K limits of agreement %K autonomic nervous function %D 2020 %7 31.7.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Smartphone apps for heart rate variability (HRV) measurement have been extensively developed in the last decade. However, ultra–short-term HRV recordings taken by wearable devices have not been examined. Objective: The aims of this study were the following: (1) to compare the validity and reliability of ultra–short-term and short-term HRV time-domain and frequency-domain variables in a novel smartphone app, Pulse Express Pro (PEP), and (2) to determine the agreement of HRV assessments between an electrocardiogram (ECG) and PEP. Methods: In total, 60 healthy adults were recruited to participate in this study (mean age 22.3 years [SD 3.0 years], mean height 168.4 cm [SD 8.0 cm], mean body weight 64.2 kg [SD 11.5 kg]). A 5-minute resting HRV measurement was recorded via ECG and PEP in a sitting position. Standard deviation of normal R-R interval (SDNN), root mean square of successive R-R interval (RMSSD), proportion of NN50 divided by the total number of RR intervals (pNN50), normalized very-low–frequency power (nVLF), normalized low-frequency power (nLF), and normalized high-frequency power (nHF) were analyzed within 9 time segments of HRV recordings: 0-1 minute, 1-2 minutes, 2-3 minutes, 3-4 minutes, 4-5 minutes, 0-2 minutes, 0-3 minutes, 0-4 minutes, and 0-5 minutes (standard). Standardized differences (ES), intraclass correlation coefficients (ICC), and the Spearman product-moment correlation were used to compare the validity and reliability of each time segment to the standard measurement (0-5 minutes). Limits of agreement were assessed by using Bland-Altman plot analysis. Results: Compared to standard measures in both ECG and PEP, pNN50, SDNN, and RMSSD variables showed trivial ES (<0.2) and very large to nearly perfect ICC and Spearman correlation coefficient values in all time segments (>0.8). The nVLF, nLF, and nHF demonstrated a variation of ES (from trivial to small effects, 0.01-0.40), ICC (from moderate to nearly perfect, 0.39-0.96), and Spearman correlation coefficient values (from moderate to nearly perfect, 0.40-0.96). Furthermore, the Bland-Altman plots showed relatively narrow values of mean difference between the ECG and PEP after consecutive 1-minute recordings for SDNN, RMSSD, and pNN50. Acceptable limits of agreement were found after consecutive 3-minute recordings for nLF and nHF. Conclusions: Using the PEP app to facilitate a 1-minute ultra–short-term recording is suggested for time-domain HRV indices (SDNN, RMSSD, and pNN50) to interpret autonomic functions during stabilization. When using frequency-domain HRV indices (nLF and nHF) via the PEP app, a recording of at least 3 minutes is needed for accurate measurement. %M 32735219 %R 10.2196/18761 %U https://mhealth.jmir.org/2020/7/e18761 %U https://doi.org/10.2196/18761 %U http://www.ncbi.nlm.nih.gov/pubmed/32735219 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 7 %P e13737 %T Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology %A Prinable,Joseph %A Jones,Peter %A Boland,David %A Thamrin,Cindy %A McEwan,Alistair %+ School of Electrical and Information Engineering, The University of Sydney, Room 402, Building J03, Maze Crescent, Darlington, 2006, Australia, 61 404035701, joseph.prinable@sydney.edu.au %K photoplethysmogram %K respiration %K asthma monitoring %K LSTM %D 2020 %7 31.7.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: There has been a recent increased interest in monitoring health using wearable sensor technologies; however, few have focused on breathing. The ability to monitor breathing metrics may have indications both for general health as well as respiratory conditions such as asthma, where long-term monitoring of lung function has shown promising utility. Objective: In this paper, we explore a long short-term memory (LSTM) architecture and predict measures of interbreath intervals, respiratory rate, and the inspiration-expiration ratio from a photoplethysmogram signal. This serves as a proof-of-concept study of the applicability of a machine learning architecture to the derivation of respiratory metrics. Methods: A pulse oximeter was mounted to the left index finger of 9 healthy subjects who breathed at controlled respiratory rates. A respiratory band was used to collect a reference signal as a comparison. Results: Over a 40-second window, the LSTM model predicted a respiratory waveform through which breathing metrics could be derived with a bias value and 95% CI. Metrics included inspiration time (–0.16 seconds, –1.64 to 1.31 seconds), expiration time (0.09 seconds, –1.35 to 1.53 seconds), respiratory rate (0.12 breaths per minute, –2.13 to 2.37 breaths per minute), interbreath intervals (–0.07 seconds, –1.75 to 1.61 seconds), and the inspiration-expiration ratio (0.09, –0.66 to 0.84). Conclusions: A trained LSTM model shows acceptable accuracy for deriving breathing metrics and could be useful for long-term breathing monitoring in health. Its utility in respiratory disease (eg, asthma) warrants further investigation. %M 32735229 %R 10.2196/13737 %U http://mhealth.jmir.org/2020/7/e13737/ %U https://doi.org/10.2196/13737 %U http://www.ncbi.nlm.nih.gov/pubmed/32735229 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 7 %P e19781 %T QardioArm Blood Pressure Monitoring in a Population With Type 2 Diabetes: Validation Study %A Mazoteras-Pardo,Victoria %A Becerro-De-Bengoa-Vallejo,Ricardo %A Losa-Iglesias,Marta Elena %A Martínez-Jiménez,Eva María %A Calvo-Lobo,César %A Romero-Morales,Carlos %A López-López,Daniel %A Palomo-López,Patricia %+ Faculty of Health Sciences, Universidad Rey Juan Carlos, Avenida de Atenas S/N, Alcorcón, Spain, 34 91 488 8508, marta.losa@urjc.es %K blood pressure %K hypertension %K type 2 diabetes %K mobile applications %K software validation %D 2020 %7 24.7.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Home blood pressure monitoring has many benefits, even more so, in populations prone to high blood pressure, such as persons with diabetes. Objective: The purpose of this research was to validate the QardioArm mobile device in a sample of individuals with noninsulin-dependent type 2 diabetes in accordance with the guidelines of the second International Protocol of the European Society of Hypertension. Methods: The sample consisted of 33 patients with type 2 diabetes. To evaluate the validity of QardioArm by comparing its data with that obtained with a digital sphygmomanometer (Omron M3 Intellisense), two nurses collected diastolic blood pressure, systolic blood pressure, and heart rate with both devices. Results: The analysis indicated that the test device QardioArm met all the validation requirements using a sample population with type 2 diabetes. Conclusions: This paper reports the first validation of QardioArm in a population of individuals with noninsulin-dependent type 2 diabetes. QardioArm for home monitoring of blood pressure and heart rate met the requirements of the second International Protocol of the European Society of Hypertension. %M 32706672 %R 10.2196/19781 %U http://www.jmir.org/2020/7/e19781/ %U https://doi.org/10.2196/19781 %U http://www.ncbi.nlm.nih.gov/pubmed/32706672 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 7 %P e15331 %T Usability of Wearable Devices With a Novel Cardiac Force Index for Estimating the Dynamic Cardiac Function: Observational Study %A Hsiao,Po-Jen %A Chiu,Chih-Chien %A Lin,Ke-Hsin %A Hu,Fu-Kang %A Tsai,Pei-Jan %A Wu,Chun-Ting %A Pang,Yuan-Kai %A Lin,Yu %A Kuo,Ming-Hao %A Chen,Kang-Hua %A Wu,Yi-Syuan %A Wu,Hao-Yi %A Chang,Ya-Ting %A Chang,Yu-Tien %A Cheng,Chia-Shiang %A Chuu,Chih-Pin %A Lin,Fu-Huang %A Chang,Chi-Wen %A Li,Yuan-Kuei %A Chan,Jenq-Shyong %A Chu,Chi-Ming %+ Division of Biostatistics and Medical Informatics, Department of Epidemiology, School of Public Health, National Defense Medical Center, Neihu 114, Taipei, Taiwan, 1 886 2 87923100, chuchiming@web.de %K cardiac force %K running %K acceleration %K physical activity %K heart rate %D 2020 %7 21.7.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Long-distance running can be a form of stress to the heart. Technological improvements combined with the public’s gradual turn toward mobile health (mHealth), self-health, and exercise effectiveness have resulted in the widespread use of wearable exercise products. The monitoring of dynamic cardiac function changes during running and running performance should be further studied. Objective: We investigated the relationship between dynamic cardiac function changes and finish time for 3000-meter runs. Using a wearable device based on a novel cardiac force index (CFI), we explored potential correlations among 3000-meter runners with stronger and weaker cardiac functions during running. Methods: This study used the American product BioHarness 3.0 (Zephyr Technology Corporation), which can measure basic physiological parameters including heart rate, respiratory rate, temperature, maximum oxygen consumption, and activity. We investigated the correlations among new physiological parameters, including CFI = weight * activity / heart rate, cardiac force ratio (CFR) = CFI of running / CFI of walking, and finish times for 3000-meter runs. Results: The results showed that waist circumference, smoking, and CFI were the significant factors for qualifying in the 3000-meter run. The prediction model was as follows: ln (3000 meters running performance pass probability / fail results probability) = –2.702 – 0.096 × [waist circumference] – 1.827 × [smoke] + 0.020 × [ACi7]. If smoking and the ACi7 were controlled, contestants with a larger waist circumference tended to fail the qualification based on the formula above. If waist circumference and ACi7 were controlled, smokers tended to fail more often than nonsmokers. Finally, we investigated a new calculation method for monitoring cardiac status during exercise that uses the CFI of walking for the runner as a reference to obtain the ratio between the cardiac force of exercise and that of walking (CFR) to provide a standard for determining if the heart is capable of exercise. A relationship is documented between the CFR and the performance of 3000-meter runs in a healthy 22-year-old person. During the running period, data are obtained while participant slowly runs 3000 meters, and the relationship between the CFR and time is plotted. The runner’s CFR varies with changes in activity. Since the runner’s acceleration increases, the CFR quickly increases to an explosive peak, indicating the runner’s explosive power. At this period, the CFI revealed a 3-fold increase (CFR=3) in a strong heart. After a time lapse, the CFR is approximately 2.5 during an endurance period until finishing the 3000-meter run. Similar correlation is found in a runner with a weak heart, with the CFR at the beginning period being 4 and approximately 2.5 thereafter. Conclusions: In conclusion, the study results suggested that measuring the real-time CFR changes could be used in a prediction model for 3000-meter running performance. %M 32706725 %R 10.2196/15331 %U https://mhealth.jmir.org/2020/7/e15331 %U https://doi.org/10.2196/15331 %U http://www.ncbi.nlm.nih.gov/pubmed/32706725 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 7 %P e15873 %T Experiences With Wearable Activity Data During Self-Care by Chronic Heart Patients: Qualitative Study %A Andersen,Tariq Osman %A Langstrup,Henriette %A Lomborg,Stine %+ Department of Computer Science, University of Copenhagen, Universitetsparken 5, Copenhagen, 2100, Denmark, 45 26149169, tariq@di.ku.dk %K consumer health information %K wearable electronic devices %K self-care %K chronic illness %K patient experiences %D 2020 %7 20.7.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Most commercial activity trackers are developed as consumer devices and not as clinical devices. The aim is to monitor and motivate sport activities, healthy living, and similar wellness purposes, and the devices are not designed to support care management in a clinical context. There are great expectations for using wearable sensor devices in health care settings, and the separate realms of wellness tracking and disease self-monitoring are increasingly becoming blurred. However, patients’ experiences with activity tracking technologies designed for use outside the clinical context have received little academic attention. Objective: This study aimed to contribute to understanding how patients with a chronic disease experience activity data from consumer self-tracking devices related to self-care and their chronic illness. Our research question was: “How do patients with heart disease experience activity data in relation to self-care and chronic illness?” Methods: We conducted a qualitative interview study with patients with chronic heart disease (n=27) who had an implanted cardioverter-defibrillator. Patients were invited to wear a FitBit Alta HR wearable activity tracker for 3-12 months and provide their perspectives on their experiences with step, sleep, and heart rate data. The average age was 57.2 years (25 men and 2 women), and patients used the tracker for 4-49 weeks (mean 26.1 weeks). Semistructured interviews (n=66) were conducted with patients 2–3 times and were analyzed iteratively in workshops using thematic analysis and abductive reasoning logic. Results: Of the 27 patients, 18 related the heart rate, sleep, and step count data directly to their heart disease. Wearable activity trackers actualized patients’ experiences across 3 dimensions with a spectrum of contrasting experiences: (1) knowing, which spanned gaining insight and evoking doubts; (2) feeling, which spanned being reassured and becoming anxious; and (3) evaluating, which spanned promoting improvements and exposing failure. Conclusions: Patients’ experiences could reside more on one end of the spectrum, could reside across all 3 dimensions, or could combine contrasting positions and even move across the spectrum over time. Activity data from wearable devices may be a resource for self-care; however, the data may simultaneously constrain and create uncertainty, fear, and anxiety. By showing how patients experience self-tracking data across dimensions of knowing, feeling, and evaluating, we point toward the richness and complexity of these data experiences in the context of chronic illness and self-care. %M 32706663 %R 10.2196/15873 %U https://www.jmir.org/2020/7/e15873 %U https://doi.org/10.2196/15873 %U http://www.ncbi.nlm.nih.gov/pubmed/32706663 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 7 %P e18012 %T Mobile Personal Health Care System for Noninvasive, Pervasive, and Continuous Blood Pressure Monitoring: Development and Usability Study %A Mena,Luis J %A Félix,Vanessa G %A Ostos,Rodolfo %A González,Armando J %A Martínez-Peláez,Rafael %A Melgarejo,Jesus D %A Maestre,Gladys E %+ Academic Unit of Computing, Master Program in Applied Sciences, Universidad Politecnica de Sinaloa, Carretera Municipal Libre Mazatlan-Higueras Km. 3, Mazatlan, 82199, Mexico, 52 6691800695 ext 140, lmena@upsin.edu.mx %K mHealth %K photoplethysmography %K blood pressure monitoring %K hypertension %D 2020 %7 20.7.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Smartphone-based blood pressure (BP) monitoring using photoplethysmography (PPG) technology has emerged as a promising approach to empower users with self-monitoring for effective diagnosis and control of hypertension. Objective: This study aimed to develop a mobile personal health care system for noninvasive, pervasive, and continuous estimation of BP level and variability, which is user friendly for elderly people. Methods: The proposed approach was integrated by a self-designed cuffless, calibration-free, wireless, and wearable PPG-only sensor and a native purposely designed smartphone app using multilayer perceptron machine learning techniques from raw signals. We performed a development and usability study with three older adults (mean age 61.3 years, SD 1.5 years; 66% women) to test the usability and accuracy of the smartphone-based BP monitor. Results: The employed artificial neural network model had good average accuracy (>90%) and very strong correlation (>0.90) (P<.001) for predicting the reference BP values of our validation sample (n=150). Bland-Altman plots showed that most of the errors for BP prediction were less than 10 mmHg. However, according to the Association for the Advancement of Medical Instrumentation and British Hypertension Society standards, only diastolic blood pressure prediction met the clinically accepted accuracy thresholds. Conclusions: With further development and validation, the proposed system could provide a cost-effective strategy to improve the quality and coverage of health care, particularly in rural zones, areas lacking physicians, and areas with solitary elderly populations. %M 32459642 %R 10.2196/18012 %U https://mhealth.jmir.org/2020/7/e18012 %U https://doi.org/10.2196/18012 %U http://www.ncbi.nlm.nih.gov/pubmed/32459642 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 6 %P e18134 %T Machine Learning–Based Signal Quality Evaluation of Single-Period Radial Artery Pulse Waves: Model Development and Validation %A Ding,Xiaodong %A Cheng,Feng %A Morris,Robert %A Chen,Cong %A Wang,Yiqin %+ Shanghai Key Laboratory of Health Identification and Assessment, Laboratory of Traditional Chinese Medicine Four Diagnostic Information, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai, 201203, China, 86 21 51322271, wangyiqin2380@vip.sina.com %K pulse wave %K quality evaluation %K single period %K segmentation %K machine learning %D 2020 %7 22.6.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: The radial artery pulse wave is a widely used physiological signal for disease diagnosis and personal health monitoring because it provides insight into the overall health of the heart and blood vessels. Periodic radial artery pulse signals are subsequently decomposed into single pulse wave periods (segments) for physiological parameter evaluations. However, abnormal periods frequently arise due to external interference, the inherent imperfections of current segmentation methods, and the quality of the pulse wave signals. Objective: The objective of this paper was to develop a machine learning model to detect abnormal pulse periods in real clinical data. Methods: Various machine learning models, such as k-nearest neighbor, logistic regression, and support vector machines, were applied to classify the normal and abnormal periods in 8561 segments extracted from the radial pulse waves of 390 outpatients. The recursive feature elimination method was used to simplify the classifier. Results: It was found that a logistic regression model with only four input features can achieve a satisfactory result. The area under the receiver operating characteristic curve from the test set was 0.9920. In addition, these classifiers can be easily interpreted. Conclusions: We expect that this model can be applied in smart sport watches and watchbands to accurately evaluate human health status. %M 32568091 %R 10.2196/18134 %U http://medinform.jmir.org/2020/6/e18134/ %U https://doi.org/10.2196/18134 %U http://www.ncbi.nlm.nih.gov/pubmed/32568091 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 3 %N 1 %P e17299 %T Clinician Perspectives on the Design and Application of Wearable Cardiac Technologies for Older Adults: Qualitative Study %A Ferguson,Caleb %A Inglis,Sally C %A Breen,Paul P %A Gargiulo,Gaetano D %A Byiers,Victoria %A Macdonald,Peter S %A Hickman,Louise D %+ Western Sydney Nursing & Midwifery Research Centre, Western Sydney Local Health District and Western Sydney University, Blacktown Hospital, Marcel Crescent, Blacktown, 2148, Australia, 61 410207543, c.ferguson@westernsydney.edu.au %K technology %K arrhythmia %K monitoring %K older people %K cardiology %K qualitative %K wearable %D 2020 %7 18.6.2020 %9 Original Paper %J JMIR Aging %G English %X Background: New wearable devices (for example, AliveCor or Zio patch) offer promise in detecting arrhythmia and monitoring cardiac health status, among other clinically useful parameters in older adults. However, the clinical utility and usability from the perspectives of clinicians is largely unexplored. Objective: This study aimed to explore clinician perspectives on the use of wearable cardiac monitoring technology for older adults. Methods: A descriptive qualitative study was conducted using semistructured focus group interviews. Clinicians were recruited through purposive sampling of physicians, nurses, and allied health staff working in 3 tertiary-level hospitals. Verbatim transcripts were analyzed using thematic content analysis to identify themes. Results: Clinicians representing physicians, nurses, and allied health staff working in 3 tertiary-level hospitals completed 4 focus group interviews between May 2019 and July 2019. There were 50 participants (28 men and 22 women), including cardiologists, geriatricians, nurses, and allied health staff. The focus groups generated the following 3 overarching, interrelated themes: (1) the current state of play, understanding the perceived challenges of patient cardiac monitoring in hospitals, (2) priorities in cardiac monitoring, what parameters new technologies should measure, and (3) cardiac monitoring of the future, “the ideal device.” Conclusions: There remain pitfalls related to the design of wearable cardiac technology for older adults that present clinical challenges. These pitfalls and challenges likely negatively impact the uptake of wearable cardiac monitoring in routine clinical care. Partnering with clinicians and patients in the co-design of new wearable cardiac monitoring technologies is critical to optimize the use of these devices and their uptake in clinical care. %M 32554377 %R 10.2196/17299 %U http://aging.jmir.org/2020/1/e17299/ %U https://doi.org/10.2196/17299 %U http://www.ncbi.nlm.nih.gov/pubmed/32554377 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 6 %P e18636 %T Current Evidence for Continuous Vital Signs Monitoring by Wearable Wireless Devices in Hospitalized Adults: Systematic Review %A Leenen,Jobbe P L %A Leerentveld,Crista %A van Dijk,Joris D %A van Westreenen,Henderik L %A Schoonhoven,Lisette %A Patijn,Gijsbert A %+ Department of Surgery, Isala, Dr. van Heesweg 2, Zwolle, 8025 AB, Netherlands, 31 384245654, j.p.l.leenen@isala.nl %K continuous monitoring %K patient monitoring %K vital signs %K clinical deterioration %K early deterioration %K wearable wireless device %K systematic review %K monitoring %D 2020 %7 17.6.2020 %9 Review %J J Med Internet Res %G English %X Background: Continuous monitoring of vital signs by using wearable wireless devices may allow for timely detection of clinical deterioration in patients in general wards in comparison to detection by standard intermittent vital signs measurements. A large number of studies on many different wearable devices have been reported in recent years, but a systematic review is not yet available to date. Objective: The aim of this study was to provide a systematic review for health care professionals regarding the current evidence about the validation, feasibility, clinical outcomes, and costs of wearable wireless devices for continuous monitoring of vital signs. Methods: A systematic and comprehensive search was performed using PubMed/MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials from January 2009 to September 2019 for studies that evaluated wearable wireless devices for continuous monitoring of vital signs in adults. Outcomes were structured by validation, feasibility, clinical outcomes, and costs. Risk of bias was determined by using the Mixed Methods Appraisal Tool, quality assessment of diagnostic accuracy studies 2nd edition, or quality of health economic studies tool. Results: In this review, 27 studies evaluating 13 different wearable wireless devices were included. These studies predominantly evaluated the validation or the feasibility outcomes of these devices. Only a few studies reported the clinical outcomes with these devices and they did not report a significantly better clinical outcome than the standard tools used for measuring vital signs. Cost outcomes were not reported in any study. The quality of the included studies was predominantly rated as low or moderate. Conclusions: Wearable wireless continuous monitoring devices are mostly still in the clinical validation and feasibility testing phases. To date, there are no high quality large well-controlled studies of wearable wireless devices available that show a significant clinical benefit or cost-effectiveness. Such studies are needed to help health care professionals and administrators in their decision making regarding implementation of these devices on a large scale in clinical practice or in-home monitoring. %M 32469323 %R 10.2196/18636 %U http://www.jmir.org/2020/6/e18636/ %U https://doi.org/10.2196/18636 %U http://www.ncbi.nlm.nih.gov/pubmed/32469323 %0 Journal Article %@ 2561-3278 %I JMIR Publications %V 5 %N 1 %P e17106 %T Challenges and Opportunities in Collecting and Modeling Ambulatory Electrodermal Activity Data %A Coffman,Donna L %A Cai,Xizhen %A Li,Runze %A Leonard,Noelle R %+ Department of Epidemiology and Biostatistics, Temple University, 1301 Cecil B. Moore Ave, Ritter Annex, 9th floor, Philadelphia, PA, 19122, United States, 1 2152044420, dcoffman@temple.edu %K electrodermal activity %K functional data analysis %K ambulatory stress assessment %D 2020 %7 12.6.2020 %9 Original Paper %J JMIR Biomed Eng %G English %X Background: Ambulatory assessment of electrodermal activity (EDA) is an emerging technique for capturing individuals’ autonomic responses to real-life events. There is currently little guidance available for processing and analyzing such data in an ambulatory setting. Objective: This study aimed to describe and implement several methods for preprocessing and constructing features for use in modeling ambulatory EDA data, particularly for measuring stress. Methods: We used data from a study examining the effects of stressful tasks on EDA of adolescent mothers (AMs). A biosensor band recorded EDA 4 times per second and was worn during an approximately 2-hour assessment that included a 10-min mother-child videotaped interaction. The initial processing included filtering noise and motion artifacts. Results: We constructed the features of the EDA data, including the number of peaks and their amplitude as well as EDA reactivity, quantified as the rate at which AMs returned to baseline EDA following an EDA peak. Although the pattern of EDA varied substantially across individuals, various features of EDA may be computed for all individuals enabling within- and between-individual analyses and comparisons. Conclusions: The algorithms we developed can be used to construct features for dry-electrode ambulatory EDA, which can be used by other researchers to study stress and anxiety. %M 34888487 %R 10.2196/17106 %U http://biomedeng.jmir.org/2020/1/e17106/ %U https://doi.org/10.2196/17106 %U http://www.ncbi.nlm.nih.gov/pubmed/34888487 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 6 %P e15471 %T Continuous Monitoring of Vital Signs in the General Ward Using Wearable Devices: Randomized Controlled Trial %A Weenk,Mariska %A Bredie,Sebastian J %A Koeneman,Mats %A Hesselink,Gijs %A van Goor,Harry %A van de Belt,Tom H %+ Radboud University Medical Center, PO Box 9101, Nijmegen, 6500 HB, Netherlands, 31 621894189, mariska.weenk@radboudumc.nl %K remote sensing technology %K wireless technology %K continuous monitoring %K vital signs %K wearable electronic devices %K remote monitoring %K digital health %D 2020 %7 10.6.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Wearable devices can be used for continuous patient monitoring in the general ward, increasing patient safety. Little is known about the experiences and expectations of patients and health care professionals regarding continuous monitoring with these devices. Objective: This study aimed to identify positive and negative effects as well as barriers and facilitators for the use of two wearable devices: ViSi Mobile (VM) and HealthPatch (HP). Methods: In this randomized controlled trial, 90 patients admitted to the internal medicine and surgical wards of a university hospital in the Netherlands were randomly assigned to continuous vital sign monitoring using VM or HP and a control group. Users’ experiences and expectations were addressed using semistructured interviews. Nurses, physician assistants, and medical doctors were also interviewed. Interviews were analyzed using thematic content analysis. Psychological distress was assessed using the State Trait Anxiety Inventory and the Pain Catastrophizing Scale. The System Usability Scale was used to assess the usability of both devices. Results: A total of 60 patients, 20 nurses, 3 physician assistants, and 6 medical doctors were interviewed. We identified 47 positive and 30 negative effects and 19 facilitators and 36 barriers for the use of VM and HP. Frequently mentioned topics included earlier identification of clinical deterioration, increased feelings of safety, and VM lines and electrodes. No differences related to psychological distress and usability were found between randomization groups or devices. Conclusions: Both devices were well received by most patients and health care professionals, and the majority of them encouraged the idea of monitoring vital signs continuously in the general ward. This comprehensive overview of barriers and facilitators of using wireless devices may serve as a guide for future researchers, developers, and health care institutions that consider implementing continuous monitoring in the ward. Trial Registration: Clinicaltrials.gov NCT02933307; http://clinicaltrials.gov/ct2/show/NCT02933307. %M 32519972 %R 10.2196/15471 %U https://www.jmir.org/2020/6/e15471 %U https://doi.org/10.2196/15471 %U http://www.ncbi.nlm.nih.gov/pubmed/32519972 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 5 %P e18092 %T Smart Shirts for Monitoring Physiological Parameters: Scoping Review %A Khundaqji,Hamzeh %A Hing,Wayne %A Furness,James %A Climstein,Mike %+ Faculty of Health Sciences & Medicine, Bond University, 2 Promethean Way, Gold Coast, 4226, Australia, 61 0431443642, hamzeh.khundaqji@student.bond.edu.au %K wearable electronic devices %K biomedical technology %K telemedicine %K fitness trackers %K sports %K exercise %K physiology %K clinical decision making %K vital signs %D 2020 %7 27.5.2020 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: The recent trends of technological innovation and widescale digitization as potential solutions to challenges in health care, sports, and emergency service operations have led to the conception of smart textile technology. In health care, these smart textile systems present the potential to aid preventative medicine and early diagnosis through continuous, noninvasive tracking of physical and mental health while promoting proactive involvement of patients in their medical management. In areas such as sports and emergency response, the potential to provide comprehensive and simultaneous physiological insights across multiple body systems is promising. However, it is currently unclear what type of evidence exists surrounding the use of smart textiles for the monitoring of physiological outcome measures across different settings. Objective: This scoping review aimed to systematically survey the existing body of scientific literature surrounding smart textiles in their most prevalent form, the smart shirt, for monitoring physiological outcome measures. Methods: A total of 5 electronic bibliographic databases were systematically searched (Ovid Medical Literature Analysis and Retrieval System Online, Excerpta Medica database, Scopus, Cumulative Index to Nursing and Allied Health Literature, and SPORTDiscus). Publications from the inception of the database to June 24, 2019 were reviewed. Nonindexed literature relevant to this review was also systematically searched. The results were then collated, summarized, and reported. Results: Following the removal of duplicates, 7871 citations were identified. On the basis of title and abstract screening, 7632 citations were excluded, whereas 239 were retrieved and assessed for eligibility. Of these, 101 citations were included in the final analysis. Included studies were categorized into four themes: (1) prototype design, (2) validation, (3) observational, and (4) reviews. Among the 101 analyzed studies, prototype design was the most prevalent theme (50/101, 49.5%), followed by validation (29/101, 28.7%), observational studies (21/101, 20.8%), and reviews (1/101, 0.1%). Presented prototype designs ranged from those capable of monitoring one physiological metric to those capable of monitoring several simultaneously. In 29 validation studies, 16 distinct smart shirts were validated against reference technology under various conditions and work rates, including rest, submaximal exercise, and maximal exercise. The identified observational studies used smart shirts in clinical, healthy, and occupational populations for aims such as early diagnosis and stress detection. One scoping review was identified, investigating the use of smart shirts for electrocardiograph signal monitoring in cardiac patients. Conclusions: Although smart shirts have been found to be valid and reliable in the monitoring of specific physiological metrics, results were variable for others, demonstrating the need for further systematic validation. Analysis of the results has also demonstrated gaps in knowledge, such as a considerable lag of validation and observational studies in comparison with prototype design and limited investigation using smart shirts in pediatric, elite sports, and emergency service populations. %M 32348279 %R 10.2196/18092 %U http://mhealth.jmir.org/2020/5/e18092/ %U https://doi.org/10.2196/18092 %U http://www.ncbi.nlm.nih.gov/pubmed/32348279 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 5 %P e16443 %T Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study %A Kwon,Soonil %A Hong,Joonki %A Choi,Eue-Keun %A Lee,Byunghwan %A Baik,Changhyun %A Lee,Euijae %A Jeong,Eui-Rim %A Koo,Bon-Kwon %A Oh,Seil %A Yi,Yung %+ Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, Republic of Korea, 82 2 2072 0688, choiek417@gmail.com %K atrial fibrillation %K deep learning %K diagnosis %K photoplethysmography %K wearable electronic devices %D 2020 %7 21.5.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Continuous photoplethysmography (PPG) monitoring with a wearable device may aid the early detection of atrial fibrillation (AF). Objective: We aimed to evaluate the diagnostic performance of a ring-type wearable device (CardioTracker, CART), which can detect AF using deep learning analysis of PPG signals. Methods: Patients with persistent AF who underwent cardioversion were recruited prospectively. We recorded PPG signals at the finger with CART and a conventional pulse oximeter before and after cardioversion over a period of 15 min (each instrument). Cardiologists validated the PPG rhythms with simultaneous single-lead electrocardiography. The PPG data were transmitted to a smartphone wirelessly and analyzed with a deep learning algorithm. We also validated the deep learning algorithm in 20 healthy subjects with sinus rhythm (SR). Results: In 100 study participants, CART generated a total of 13,038 30-s PPG samples (5850 for SR and 7188 for AF). Using the deep learning algorithm, the diagnostic accuracy, sensitivity, specificity, positive-predictive value, and negative-predictive value were 96.9%, 99.0%, 94.3%, 95.6%, and 98.7%, respectively. Although the diagnostic accuracy decreased with shorter sample lengths, the accuracy was maintained at 94.7% with 10-s measurements. For SR, the specificity decreased with higher variability of peak-to-peak intervals. However, for AF, CART maintained consistent sensitivity regardless of variability. Pulse rates had a lower impact on sensitivity than on specificity. The performance of CART was comparable to that of the conventional device when using a proper threshold. External validation showed that 94.99% (16,529/17,400) of the PPG samples from the control group were correctly identified with SR. Conclusions: A ring-type wearable device with deep learning analysis of PPG signals could accurately diagnose AF without relying on electrocardiography. With this device, continuous monitoring for AF may be promising in high-risk populations. Trial Registration: ClinicalTrials.gov NCT04023188; https://clinicaltrials.gov/ct2/show/NCT04023188 %M 32348254 %R 10.2196/16443 %U http://www.jmir.org/2020/5/e16443/ %U https://doi.org/10.2196/16443 %U http://www.ncbi.nlm.nih.gov/pubmed/32348254 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 5 %P e13156 %T Real-Time Monitoring of Blood Pressure Using Digitalized Pulse Arrival Time Calculation Technology for Prompt Detection of Sudden Hypertensive Episodes During Laryngeal Microsurgery: Retrospective Observational Study %A Park,Yong-Seok %A Kim,Sung-Hoon %A Lee,Yoon Se %A Choi,Seung-Ho %A Ku,Seung-Woo %A Hwang,Gyu-Sam %+ Department of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea, 82 2 3010 0617, shkimans@amc.seoul.kr %K larynx %K blood pressure %K photoplethysmography %K pulse %D 2020 %7 15.5.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Laryngeal microsurgery (LMS) is often accompanied by a sudden increase in blood pressure (BP) during surgery because of stimulation around the larynx. This sudden change in the hemodynamic status is not immediately reflected in a casual cuff-type measurement that takes intermittent readings every 3 to 5 min. Objective: This study aimed to investigate the potential of pulse arrival time (PAT) as a marker for a BP surge, which usually occurs in patients undergoing LMS. Methods: Intermittent measurements of BP and electrocardiogram (ECG) and photoplethysmogram (PPG) signals were recorded during LMS. PAT was defined as the interval between the R-peak on the ECG and the maximum slope on the PPG. Mean PAT values before and after BP increase were compared. PPG-related parameters and the correlations between changes in these variables were calculated. Results: BP surged because of laryngoscopic manipulation (mean systolic BP [SBP] from 115.3, SD 21.4 mmHg, to 159.9, SD 25.2 mmHg; P<.001), whereas PAT decreased significantly (from mean 460.6, SD 51.9 ms, to 405.8, SD 50.1 ms; P<.001) in most of the cases. The change in SBP showed a significant correlation with the inverse of the PAT (r=0.582; P<.001). Receiver-operating characteristic curve analysis indicated that an increase of 11.5% in the inverse of the PAT could detect a 40% increase in SBP, and the area under the curve was 0.814. Conclusions: During LMS, where invasive arterial catheterization is not always possible, PAT shows good correlation with SBP and may, therefore, have the potential to identify abrupt BP surges during laryngoscopic manipulations in a noninvasive manner. %M 32412413 %R 10.2196/13156 %U https://www.jmir.org/2020/5/e13156 %U https://doi.org/10.2196/13156 %U http://www.ncbi.nlm.nih.gov/pubmed/32412413 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 5 %P e16716 %T Wrist-Worn Wearables for Monitoring Heart Rate and Energy Expenditure While Sitting or Performing Light-to-Vigorous Physical Activity: Validation Study %A Düking,Peter %A Giessing,Laura %A Frenkel,Marie Ottilie %A Koehler,Karsten %A Holmberg,Hans-Christer %A Sperlich,Billy %+ Integrative and Experimental Exercise Science, Department of Sport Science, University of Würzburg, Judenbühlweg 11, Würzburg, 97082, Germany, 49 931 31 ext 8479, peterdueking@gmx.de %K cardiorespiratory fitness %K innovation %K smartwatch %K technology %K wearable %K digital health %D 2020 %7 6.5.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Physical activity reduces the incidences of noncommunicable diseases, obesity, and mortality, but an inactive lifestyle is becoming increasingly common. Innovative approaches to monitor and promote physical activity are warranted. While individual monitoring of physical activity aids in the design of effective interventions to enhance physical activity, a basic prerequisite is that the monitoring devices exhibit high validity. Objective: Our goal was to assess the validity of monitoring heart rate (HR) and energy expenditure (EE) while sitting or performing light-to-vigorous physical activity with 4 popular wrist-worn wearables (Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa). Methods: While wearing the 4 different wearables, 25 individuals performed 5 minutes each of sitting, walking, and running at different velocities (ie, 1.1 m/s, 1.9 m/s, 2.7 m/s, 3.6 m/s, and 4.1 m/s), as well as intermittent sprints. HR and EE were compared to common criterion measures: Polar-H7 chest belt for HR and indirect calorimetry for EE. Results: While monitoring HR at different exercise intensities, the standardized typical errors of the estimates were 0.09-0.62, 0.13-0.88, 0.62-1.24, and 0.47-1.94 for the Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, respectively. Depending on exercise intensity, the corresponding coefficients of variation were 0.9%-4.3%, 2.2%-6.7%, 2.9%-9.2%, and 4.1%-19.1%, respectively, for the 4 wearables. While monitoring EE at different exercise intensities, the standardized typical errors of the estimates were 0.34-1.84, 0.32-1.33, 0.46-4.86, and 0.41-1.65 for the Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, respectively. Depending on exercise intensity, the corresponding coefficients of variation were 13.5%-27.1%, 16.3%-28.0%, 15.9%-34.5%, and 8.0%-32.3%, respectively. Conclusions: The Apple Watch Series 4 provides the highest validity (ie, smallest error rates) when measuring HR while sitting or performing light-to-vigorous physical activity, followed by the Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, in that order. The Apple Watch Series 4 and Polar Vantage V are suitable for valid HR measurements at the intensities tested, but HR data provided by the Garmin Fenix 5 and Fitbit Versa should be interpreted with caution due to higher error rates at certain intensities. None of the 4 wrist-worn wearables should be employed to monitor EE at the intensities and durations tested. %M 32374274 %R 10.2196/16716 %U https://mhealth.jmir.org/2020/5/e16716 %U https://doi.org/10.2196/16716 %U http://www.ncbi.nlm.nih.gov/pubmed/32374274 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 4 %P e14707 %T Accuracy of Optical Heart Rate Sensing Technology in Wearable Fitness Trackers for Young and Older Adults: Validation and Comparison Study %A Chow,Hsueh-Wen %A Yang,Chao-Ching %+ Graduate Institute of Physical Education, Health & Leisure Studies, National Cheng Kung University, No. 1 University Rd, East District, Tainan City, Taiwan, 886 62757575 ext 81806, hwchow@mail.ncku.edu.tw %K pulse %K photoplethysmography %K wearable device %K aerobic exercise %D 2020 %7 28.4.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wearable fitness trackers are devices that can record and enhance physical activity among users. Recently, photoplethysmography (PPG) devices that use optical heart rate sensors to detect heart rate in real time have become popular and help in monitoring and controlling exercise intensity. Although the benefits of using optical heart rate monitors have been highlighted through studies, the accuracy of the readouts these commercial devices generate has not been widely assessed for different age groups, especially for the East Asian population with Fitzpatrick skin type III or IV. Objective: This study aimed to examine the accuracy of 2 wearable fitness trackers with PPG to monitor heart rate in real time during moderate exercise in young and older adults. Methods: A total of 20 young adults and 20 older adults were recruited for this study. All participants were asked to undergo a series of sedentary and moderate physical activities using indoor aerobic exercise equipment. In this study, the Polar H7 chest-strapped heart rate monitor was used as the criterion measure in 2 fitness trackers, namely Xiaomi Mi Band 2 and Garmin Vivosmart HR+. The real-time, second-by-second heart rate data obtained from both devices were recorded using the broadcast heart rate mode. To critically analyze the results, multiple statistical parameters including the mean absolute percentage error (MAPE), Lin concordance correlation coefficient (CCC), intraclass correlation coefficient, the Pearson product moment correlation coefficient, and the Bland-Altman coefficient were determined to examine the performances of the devices. Results: Both test devices exhibited acceptable overall accuracy as heart rate sensors based on several statistical tests. Notably, the MAPE values were below 10% (the designated threshold) in both devices (GarminYoung=3.77%; GarminSenior=4.73%; XiaomiYoung=7.69%; and XiaomiSenior=6.04%). The scores for reliability test of CCC for Garmin were 0.92 (Young) and 0.80 (Senior), whereas those for Xiaomi were 0.76 (Young) and 0.73 (Senior). However, the results obtained using the Bland-Altman analysis indicated that both test optical devices underestimated the average heart rate. More importantly, the study documented some unexpected outlier readings reported by these devices when used on certain participants. Conclusions: The study reveals that commonly used optical heart rate sensors, such as the ones used herein, generally produce accurate heart rate readings irrespective of the age of the user. However, users should avoid relying entirely on these readings to indicate exercise intensities, as these devices have a tendency to produce erroneous, extreme readings, which might misinterpret the real-time exercise intensity. Future studies should therefore emphasize the occurrence rate of such errors, as this will likely benefit the development of improved models of heart rate sensors. %M 32343255 %R 10.2196/14707 %U http://mhealth.jmir.org/2020/4/e14707/ %U https://doi.org/10.2196/14707 %U http://www.ncbi.nlm.nih.gov/pubmed/32343255 %0 Journal Article %@ 2561-3278 %I JMIR Publications %V 5 %N 1 %P e16620 %T A Contact-Free, Ballistocardiography-Based Monitoring System (Emfit QS) for Measuring Nocturnal Heart Rate and Heart Rate Variability: Validation Study %A Vesterinen,Ville %A Rinkinen,Niina %A Nummela,Ari %+ KIHU - Research Institute for Olympic Sports, Rautpohjankatu 6, Jyväskylä, 40700, Finland, 358 505451049, ville.vesterinen@kihu.fi %K wearable technology %K cardiac autonomic regulation %K monitoring %K validity %D 2020 %7 23.4.2020 %9 Original Paper %J JMIR Biomed Eng %G English %X Background: Heart rate (HR) and heart rate variability (HRV) measurements are widely used to monitor stress and recovery status in sedentary people and athletes. However, effective HRV monitoring should occur on a daily basis because sparse measurements do not allow for a complete view of the stress-recovery balance. Morning electrocardiography (ECG) measurements with HR straps are time-consuming and arduous to perform every day, and thus compliance with regular measurements is poor. Contact-free, ballistocardiography (BCG)-based Emfit QS is effortless for daily monitoring. However, to the best of our knowledge, there is no study on the accuracy of nocturnal HR and HRV measured via BCG under real-life conditions. Objective: The aim of this study was to evaluate the accuracy of Emfit QS in measuring nocturnal HR and HRV. Methods: Healthy participants (n=20) completed nocturnal HR and HRV recordings at home using Emfit QS and an ECG-based reference device (Firstbeat BG2) during sleep. Emfit QS measures BCG by a ferroelectret sensor installed under a bed mattress. HR and the root mean square of successive differences between RR intervals (RMSSD) were determined for 3-minute epochs and the sleep period mean. Results: A trivial mean bias was observed in the mean HR (mean –0.8 bpm [beats per minute], SD 2.3 bpm, P=.15) and Ln (natural logarithm) RMSSD (mean –0.05 ms, SD 0.25 ms, P=.33) between Emfit QS and ECG. In addition, very large correlations were found in the mean values of HR (r=0.90, P<.001) and Ln RMSSD (r=0.89, P<.001) between the devices. A greater amount of erroneous or missing data (P<.001) was observed in the Emfit QS measurements (28.3%, SD 14.4%) compared with the reference device (1.1%, SD 2.3%). The results showed that 5.0% of the mean HR and Ln RMSSD values were outside the limits of agreement. Conclusions: Based on the present results, Emfit QS provides nocturnal HR and HRV data with an acceptable, small mean bias when calculating the mean of the sleep period. Thus, Emfit QS has the potential to be used for the long-term monitoring of nocturnal HR and HRV. However, further research is needed to assess reliability in HR and HRV detection. %R 10.2196/16620 %U http://biomedeng.jmir.org/2020/1/e16620/ %U https://doi.org/10.2196/16620 %0 Journal Article %@ 2561-3278 %I JMIR Publications %V 5 %N 1 %P e18158 %T Heart Rate and Oxygen Saturation Monitoring With a New Wearable Wireless Device in the Intensive Care Unit: Pilot Comparison Trial %A Murali,Srinivasan %A Rincon,Francisco %A Cassina,Tiziano %A Cook,Stephane %A Goy,Jean-Jacques %+ University Hospital Fribourg, Rue des Pensionnats 5-7, Fribourg, , Switzerland, 41 792136465, jjgoy@goyman.com %K cardiac monitoring %K wireless monitor %K wearable %K cardiology %K ICU %K respiratory monitoring %D 2020 %7 22.4.2020 %9 Original Paper %J JMIR Biomed Eng %G English %X Background: Continuous cardiac monitoring with wireless sensors is an attractive option for early detection of arrhythmia and conduction disturbances and the prevention of adverse events leading to patient deterioration. We present a new sensor design (SmartCardia), a wearable wireless biosensor patch, for continuous cardiac and oxygen saturation (SpO2) monitoring. Objective: This study aimed to test the clinical value of a new wireless sensor device (SmartCardia) and its usefulness in monitoring the heart rate (HR) and SpO2 of patients. Methods: We performed an observational study and monitored the HR and SpO2 of patients admitted to the intensive care unit (ICU). We compared the device under test (SmartCardia) with the ICU-grade monitoring system (Dräger-Healthcare). We defined optimal correlation between the gold standard and the wireless system as <10% difference for HR and <4% difference for SpO2. Data loss and discrepancy between the two systems were critically analyzed. Results: A total of 58 ICU patients (42 men and 16 women), with a mean age of 71 years (SD 11), were included in this study. A total of 13.49 (SD 5.53) hours per patient were recorded. This represents a total recorded period of 782.3 hours. The mean difference between the HR detected by the SmartCardia patch and the ICU monitor was 5.87 (SD 16.01) beats per minute (bias=–5.66, SD 16.09). For SpO2, the average difference was 3.54% (SD 3.86; bias=2.9, SD 4.36) for interpretable values. SmartCardia’s patch measures SpO2 only under low-to-no activity conditions and otherwise does not report a value. Data loss and noninterpretable values of SpO2 represented 26% (SD 24) of total measurements. Conclusions: The SmartCardia device demonstrated clinically acceptable accuracy for HR and SpO2 monitoring in ICU patients. %R 10.2196/18158 %U http://biomedeng.jmir.org/2020/1/e18158/ %U https://doi.org/10.2196/18158 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 4 %N 1 %P e12141 %T The Added Value of In-Hospital Tracking of the Efficacy of Decongestion Therapy and Prognostic Value of a Wearable Thoracic Impedance Sensor in Acutely Decompensated Heart Failure With Volume Overload: Prospective Cohort Study %A Smeets,Christophe J P %A Lee,Seulki %A Groenendaal,Willemijn %A Squillace,Gabriel %A Vranken,Julie %A De Cannière,Hélène %A Van Hoof,Chris %A Grieten,Lars %A Mullens,Wilfried %A Nijst,Petra %A Vandervoort,Pieter M %+ Mobile Health Unit, Faculty of Medicine and Life Sciences, Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium, 32 89212011, Christophe.smeets@uhasselt.be %K congestive heart failure %K electric impedance %K prognosis %D 2020 %7 18.3.2020 %9 Original Paper %J JMIR Cardio %G English %X Background: Incomplete relief of congestion in acute decompensated heart failure (HF) is related to poor outcomes. However, congestion can be difficult to evaluate, stressing the urgent need for new objective approaches. Due to its inverse correlation with tissue hydration, continuous bioimpedance monitoring might be an effective method for serial fluid status assessments. Objective: This study aimed to determine whether in-hospital bioimpedance monitoring can be used to track fluid changes (ie, the efficacy of decongestion therapy) and the relationships between bioimpedance changes and HF hospitalization and all-cause mortality. Methods: A wearable bioimpedance monitoring device was used for thoracic impedance measurements. Thirty-six patients with signs of acute decompensated HF and volume overload were included. Changes in the resistance at 80 kHz (R80kHz) were analyzed, with fluid balance (fluid in/out) used as a reference. Patients were divided into two groups depending on the change in R80kHz during hospitalization: increase in R80kHz or decrease in R80kHz. Clinical outcomes in terms of HF rehospitalization and all-cause mortality were studied at 30 days and 1 year of follow-up. Results: During hospitalization, R80kHz increased for 24 patients, and decreased for 12 patients. For the total study sample, a moderate negative correlation was found between changes in fluid balance (in/out) and relative changes in R80kHz during hospitalization (rs=-0.51, P<.001). Clinical outcomes at both 30 days and 1 year of follow-up were significantly better for patients with an increase in R80kHz. At 1 year of follow-up, 88% (21/24) of patients with an increase in R80kHz were free from all-cause mortality, compared with 50% (6/12) of patients with a decrease in R80kHz (P=.01); 75% (18/24) and 25% (3/12) were free from all-cause mortality and HF hospitalization, respectively (P=.01). A decrease in R80kHz resulted in a significant hazard ratio of 4.96 (95% CI 1.82-14.37, P=.003) on the composite endpoint. Conclusions: The wearable bioimpedance device was able to track changes in fluid status during hospitalization and is a convenient method to assess the efficacy of decongestion therapy during hospitalization. Patients who do not show an improvement in thoracic impedance tend to have worse clinical outcomes, indicating the potential use of thoracic impedance as a prognostic parameter. %M 32186520 %R 10.2196/12141 %U https://cardio.jmir.org/2020/1/e12141 %U https://doi.org/10.2196/12141 %U http://www.ncbi.nlm.nih.gov/pubmed/32186520 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 3 %P e17037 %T A Lightweight Deep Learning Model for Fast Electrocardiographic Beats Classification With a Wearable Cardiac Monitor: Development and Validation Study %A Jeon,Eunjoo %A Oh,Kyusam %A Kwon,Soonhwan %A Son,HyeongGwan %A Yun,Yongkeun %A Jung,Eun-Soo %A Kim,Min Soo %+ Technology Research, Samsung SDS, 56 Seongchon-ro, Seocho-gu, Seoul, 06765, Republic of Korea, 82 2 6155 3808, minsoo07.kim@samsung.com %K path-type ECG sensor system %K ECG classification %K deep learning %K recurrent neural network %K fused recurrent neural network %D 2020 %7 12.3.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: Electrocardiographic (ECG) monitors have been widely used for diagnosing cardiac arrhythmias for decades. However, accurate analysis of ECG signals is difficult and time-consuming work because large amounts of beats need to be inspected. In order to enhance ECG beat classification, machine learning and deep learning methods have been studied. However, existing studies have limitations in model rigidity, model complexity, and inference speed. Objective: To classify ECG beats effectively and efficiently, we propose a baseline model with recurrent neural networks (RNNs). Furthermore, we also propose a lightweight model with fused RNN for speeding up the prediction time on central processing units (CPUs). Methods: We used 48 ECGs from the MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) Arrhythmia Database, and 76 ECGs were collected with S-Patch devices developed by Samsung SDS. We developed both baseline and lightweight models on the MXNet framework. We trained both models on graphics processing units and measured both models’ inference times on CPUs. Results: Our models achieved overall beat classification accuracies of 99.72% for the baseline model with RNN and 99.80% for the lightweight model with fused RNN. Moreover, our lightweight model reduced the inference time on CPUs without any loss of accuracy. The inference time for the lightweight model for 24-hour ECGs was 3 minutes, which is 5 times faster than the baseline model. Conclusions: Both our baseline and lightweight models achieved cardiologist-level accuracies. Furthermore, our lightweight model is competitive on CPU-based wearable hardware. %M 32163037 %R 10.2196/17037 %U http://medinform.jmir.org/2020/3/e17037/ %U https://doi.org/10.2196/17037 %U http://www.ncbi.nlm.nih.gov/pubmed/32163037 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 2 %P e16811 %T Accuracy of Vital Signs Measurements by a Smartwatch and a Portable Health Device: Validation Study %A Hahnen,Christina %A Freeman,Cecilia G %A Haldar,Nilanjan %A Hamati,Jacquelyn N %A Bard,Dylan M %A Murali,Vignesh %A Merli,Geno J %A Joseph,Jeffrey I %A van Helmond,Noud %+ Department of Anesthesiology, Cooper Medical School of Rowan University, Cooper University Health Care, One Cooper Plaza, 2nd Floor Dorrance, Suite# D206, Camden, NJ, 08103, United States, 1 8563422000, vanhelmond-noud@cooperhealth.edu %K medical devices %K mHealth %K vital signs %K measurements validity %D 2020 %7 12.2.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: New consumer health devices are being developed to easily monitor multiple physiological parameters on a regular basis. Many of these vital sign measurement devices have yet to be formally studied in a clinical setting but have already spread widely throughout the consumer market. Objective: The aim of this study was to investigate the accuracy and precision of heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), and oxygen saturation (SpO2) measurements of 2 novel all-in-one monitoring devices, the BodiMetrics Performance Monitor and the Everlast smartwatch. Methods: We enrolled 127 patients (>18 years) from the Thomas Jefferson University Hospital Preadmission Testing Center. SBP and HR were measured by both investigational devices. In addition, the Everlast watch was utilized to measure DBP, and the BodiMetrics Performance Monitor was utilized to measure SpO2. After 5 min of quiet sitting, four hospital-grade standard and three investigational vital sign measurements were taken, with 60 seconds in between each measurement. The reference vital sign measurements were calculated by determining the average of the two standard measurements that bounded each investigational measurement. Using this method, we determined three comparison pairs for each investigational device in each subject. After excluding data from 42 individuals because of excessive variation in sequential standard measurements per prespecified dropping rules, data from 85 subjects were used for final analysis. Results: Of 85 participants, 36 (42%) were women, and the mean age was 53 (SD 21) years. The accuracy guidelines were only met for the HR measurements in both devices. SBP measurements deviated 16.9 (SD 13.5) mm Hg and 5.3 (SD 4.7) mm Hg from the reference values for the Everlast and BodiMetrics devices, respectively. The mean absolute difference in DBP measurements for the Everlast smartwatch was 8.3 (SD 6.1) mm Hg. The mean absolute difference between BodiMetrics and reference SpO2 measurements was 3.02%. Conclusions: Both devices we investigated met accuracy guidelines for HR measurements, but they failed to meet the predefined accuracy guidelines for other vital sign measurements. Continued sale of consumer physiological monitors without prior validation and approval procedures is a public health concern. %M 32049066 %R 10.2196/16811 %U https://mhealth.jmir.org/2020/2/e16811 %U https://doi.org/10.2196/16811 %U http://www.ncbi.nlm.nih.gov/pubmed/32049066 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 1 %P e16409 %T Activity Tracker–Based Metrics as Digital Markers of Cardiometabolic Health in Working Adults: Cross-Sectional Study %A Rykov,Yuri %A Thach,Thuan-Quoc %A Dunleavy,Gerard %A Roberts,Adam Charles %A Christopoulos,George %A Soh,Chee-Kiong %A Car,Josip %+ Centre for Population Health Sciences, Lee Kong Chian School of Medicine, Nanyang Technological University, CSB, 18th Floor, 11 Mandalay Rd, Singapore, 308232, Singapore, 65 87660342, yuri.rykov@ntu.edu.sg %K mobile health %K metabolic cardiovascular syndrome %K fitness trackers %K wearable electronic devices %K Fitbit %K steps %K heart rate %K physical activity %K circadian rhythms %K sedentary behavior %D 2020 %7 31.1.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Greater adoption of wearable devices with multiple sensors may enhance personalized health monitoring, facilitate early detection of some diseases, and further scale up population health screening. However, few studies have explored the utility of data from wearable fitness trackers in cardiovascular and metabolic disease risk prediction. Objective: This study aimed to investigate the associations between a range of activity metrics derived from a wearable consumer-grade fitness tracker and major modifiable biomarkers of cardiometabolic disease in a working-age population. Methods: This was a cross-sectional study of 83 working adults. Participants wore Fitbit Charge 2 for 21 consecutive days and went through a health assessment, including fasting blood tests. The following clinical biomarkers were collected: BMI, waist circumference, waist-to-hip ratio, blood pressure, triglycerides (TGs), high-density lipoprotein (HDL) and low-density lipoprotein cholesterol, and blood glucose. We used a range of wearable-derived metrics based on steps, heart rate (HR), and energy expenditure, including measures of stability of circadian activity rhythms, sedentary time, and time spent at various intensities of physical activity. Spearman rank correlation was used for preliminary analysis. Multiple linear regression adjusted for potential confounders was used to determine the extent to which each metric of activity was associated with continuous clinical biomarkers. In addition, pairwise multiple regression was used to investigate the significance and mutual dependence of activity metrics when two or more of them had significant association with the same outcome from the previous step of the analysis. Results: The participants were predominantly middle aged (mean age 44.3 years, SD 12), Chinese (62/83, 75%), and male (64/83, 77%). Blood biomarkers of cardiometabolic disease (HDL cholesterol and TGs) were significantly associated with steps-based activity metrics independent of age, gender, ethnicity, education, and shift work, whereas body composition biomarkers (BMI, waist circumference, and waist-to-hip ratio) were significantly associated with energy expenditure–based and HR-based metrics when adjusted for the same confounders. Steps-based interdaily stability of circadian activity rhythm was strongly associated with HDL (beta=5.4 per 10% change; 95% CI 1.8 to 9.0; P=.005) and TG (beta=−27.7 per 10% change; 95% CI −48.4 to −7.0; P=.01). Average daily steps were negatively associated with TG (beta=−6.8 per 1000 steps; 95% CI −13.0 to −0.6; P=.04). The difference between average HR and resting HR was significantly associated with BMI (beta=−.5; 95% CI −1.0 to −0.1; P=.01) and waist circumference (beta=−1.3; 95% CI −2.4 to −0.2; P=.03). Conclusions: Wearable consumer-grade fitness trackers can provide acceptably accurate and meaningful information, which might be used in the risk prediction of cardiometabolic disease. Our results showed the beneficial effects of stable daily patterns of locomotor activity for cardiometabolic health. Study findings should be further replicated with larger population studies. %M 32012098 %R 10.2196/16409 %U http://mhealth.jmir.org/2020/1/e16409/ %U https://doi.org/10.2196/16409 %U http://www.ncbi.nlm.nih.gov/pubmed/32012098 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 4 %N 1 %P e14857 %T Use of a Smart Watch for Early Detection of Paroxysmal Atrial Fibrillation: Validation Study %A Inui,Tomohiko %A Kohno,Hiroki %A Kawasaki,Yohei %A Matsuura,Kaoru %A Ueda,Hideki %A Tamura,Yusaku %A Watanabe,Michiko %A Inage,Yuichi %A Yakita,Yasunori %A Wakabayashi,Yutaka %A Matsumiya,Goro %+ Department of Cardiovascular Surgery, University of Chiba, 1-8-1, Inohana, Chuo-ku, Chiba, Japan, 81 432262567, nuinui5762@yahoo.co.jp %K Apple Watch %K Fitbit Charge HR %K paroxysmal atrial fibrillation %K photoplethysmography %K mobile health %K heart rate %K validation %K wrist-banded devices %D 2020 %7 22.1.2020 %9 Original Paper %J JMIR Cardio %G English %X Background: Wearable devices with photoplethysmography (PPG) technology can be useful for detecting paroxysmal atrial fibrillation (AF), which often goes uncaptured despite being a leading cause of stroke. Objective: This study is the first part of a 2-phase study that aimed at developing a method for immediate detection of paroxysmal AF using PPG-integrated wearable devices. In this study, the diagnostic performance of 2 major smart watches, Apple Watch Series 3 and Fitbit (FBT) Charge HR Wireless Activity Wristband, each equipped with a PPG sensor, was compared, and the pulse rate data outputted from those devices were analyzed for precision and accuracy in reference to the heart rate data from electrocardiography (ECG) during AF. Methods: A total of 40 subjects from patients who underwent cardiac surgery at a single center between September 2017 and March 2018 were monitored for postoperative AF using telemetric ECG and PPG devices. AF was diagnosed using a 12-lead ECG by qualified physicians. Each subject was given a pair of smart watches, Apple Watch and FBT, for simultaneous pulse rate monitoring. The heart rate of all subjects was also recorded on the telemetry system. Time series pulse rate trends and heart rate trends were created and analyzed for trend pattern similarities. Those trend data were then used to determine the accuracy of PPG-based pulse rate measurements in reference to ECG-based heart rate measurements during AF. Results: Of the 20 AF events in group FBT, 6 (30%) showed a moderate or higher correlation (cross-correlation function>0.40) between pulse rate trend patterns and heart rate trend patterns. Of the 16 AF events in group Apple Watch (workout [W] mode), 12 (75%) showed a moderate or higher correlation between the 2 trend patterns. Linear regression analyses also showed a significant correlation between the pulse rates and the heart rates during AF in the subjects with Apple Watch. This correlation was not observed with FBT. The regression formula for Apple Watch W mode and FBT was X=14.203 + 0.841Y and X=58.225 + 0.228Y, respectively (where X denotes the mean of all average pulse rates during AF and Y denotes the mean of all corresponding average heart rates during AF), and the coefficient of determination (R2) was 0.685 and 0.057, respectively (P<.001 and .29, respectively). Conclusions: In this validation study, the detection precision of AF and measurement accuracy during AF were both better with Apple Watch W mode than with FBT. %M 32012044 %R 10.2196/14857 %U http://cardio.jmir.org/2020/1/e14857/ %U https://doi.org/10.2196/14857 %U http://www.ncbi.nlm.nih.gov/pubmed/32012044 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 1 %P e13756 %T The Mobile-Based 6-Minute Walk Test: Usability Study and Algorithm Development and Validation %A Salvi,Dario %A Poffley,Emma %A Orchard,Elizabeth %A Tarassenko,Lionel %+ Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Old Road Campus Research Building, Oxford, OX3 7DQ, United Kingdom, 44 1865617679, dario.salvi@eng.ox.ac.uk %K cardiology %K exercise test %K pulmonary hypertension %K mobile apps %K digital signal processing %K global positioning system %D 2020 %7 3.1.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The 6-min walk test (6MWT) is a convenient method for assessing functional capacity in patients with cardiopulmonary conditions. It is usually performed in the context of a hospital clinic and thus requires the involvement of hospital staff and facilities, with their associated costs. Objective: This study aimed to develop a mobile phone–based system that allows patients to perform the 6MWT in the community. Methods: We developed 2 algorithms to compute the distance walked during a 6MWT using sensors embedded in a mobile phone. One algorithm makes use of the global positioning system to track the location of the phone when outdoors and hence computes the distance travelled. The other algorithm is meant to be used indoors and exploits the inertial sensors built into the phone to detect U-turns when patients walk back and forth along a corridor of fixed length. We included these algorithms in a mobile phone app, integrated with wireless pulse oximeters and a back-end server. We performed Bland-Altman analysis of the difference between the distances estimated by the phone and by a reference trundle wheel on 49 indoor tests and 30 outdoor tests, with 11 different mobile phones (both Apple iOS and Google Android operating systems). We also assessed usability aspects related to the app in a discussion group with patients and clinicians using a technology acceptance model to guide discussion. Results: The mean difference between the mobile phone-estimated distances and the reference values was −2.013 m (SD 7.84 m) for the indoor algorithm and −0.80 m (SD 18.56 m) for the outdoor algorithm. The absolute maximum difference was, in both cases, below the clinically significant threshold. A total of 2 pulmonary hypertension patients, 1 cardiologist, 2 physiologists, and 1 nurse took part in the discussion group, where issues arising from the use of the 6MWT in hospital were identified. The app was demonstrated to be usable, and the 2 patients were keen to use it in the long term. Conclusions: The system described in this paper allows patients to perform the 6MWT at a place of their convenience. In addition, the use of pulse oximetry allows more information to be generated about the patient’s health status and, possibly, be more relevant to the real-life impact of their condition. Preliminary assessment has shown that the developed 6MWT app is highly accurate and well accepted by its users. Further tests are needed to assess its clinical value. %M 31899457 %R 10.2196/13756 %U https://mhealth.jmir.org/2020/1/e13756 %U https://doi.org/10.2196/13756 %U http://www.ncbi.nlm.nih.gov/pubmed/31899457 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 12 %P e15045 %T Usefulness of Modern Activity Trackers for Monitoring Exercise Behavior in Chronic Cardiac Patients: Validation Study %A Herkert,Cyrille %A Kraal,Jos Johannes %A van Loon,Eline Maria Agnes %A van Hooff,Martijn %A Kemps,Hareld Marijn Clemens %+ Máxima Medical Center, Flow, Center for Prevention, Telemedicine and Rehabilitation in Chronic Disease, Dominee Theodor Fliednerstraat 1, Eindhoven, 5631 BM, Netherlands, 31 408888200, cyrille.herkert@mmc.nl %K cardiac diseases %K activity trackers %K energy metabolism %K physical activity %K validation studies %D 2019 %7 19.12.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Improving physical activity (PA) is a core component of secondary prevention and cardiac (tele)rehabilitation. Commercially available activity trackers are frequently used to monitor and promote PA in cardiac patients. However, studies on the validity of these devices in cardiac patients are scarce. As cardiac patients are being advised and treated based on PA parameters measured by these devices, it is highly important to evaluate the accuracy of these parameters in this specific population. Objective: The aim of this study was to determine the accuracy and responsiveness of 2 wrist-worn activity trackers, Fitbit Charge 2 (FC2) and Mio Slice (MS), for the assessment of energy expenditure (EE) in cardiac patients. Methods: EE assessed by the activity trackers was compared with indirect calorimetry (Oxycon Mobile [OM]) during a laboratory activity protocol. Two groups were assessed: patients with stable coronary artery disease (CAD) with preserved left ventricular ejection fraction (LVEF) and patients with heart failure with reduced ejection fraction (HFrEF). Results: A total of 38 patients were included: 19 with CAD and 19 with HFrEF (LVEF 31.8%, SD 7.6%). The CAD group showed no significant difference in total EE between FC2 and OM (47.5 kcal, SD 112 kcal; P=.09), in contrast to a significant difference between MS and OM (88 kcal, SD 108 kcal; P=.003). The HFrEF group showed significant differences in EE between FC2 and OM (38 kcal, SD 57 kcal; P=.01), as well as between MS and OM (106 kcal, SD 167 kcal; P=.02). Agreement of the activity trackers was low in both groups (CAD: intraclass correlation coefficient [ICC] FC2=0.10, ICC MS=0.12; HFrEF: ICC FC2=0.42, ICC MS=0.11). The responsiveness of FC2 was poor, whereas MS was able to detect changes in cycling loads only. Conclusions: Both activity trackers demonstrated low accuracy in estimating EE in cardiac patients and poor performance to detect within-patient changes in the low-to-moderate exercise intensity domain. Although the use of activity trackers in cardiac patients is promising and could enhance daily exercise behavior, these findings highlight the need for population-specific devices and algorithms. %M 31855191 %R 10.2196/15045 %U http://mhealth.jmir.org/2019/12/e15045/ %U https://doi.org/10.2196/15045 %U http://www.ncbi.nlm.nih.gov/pubmed/31855191 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 12 %P e14909 %T Validation of Single Centre Pre-Mobile Atrial Fibrillation Apps for Continuous Monitoring of Atrial Fibrillation in a Real-World Setting: Pilot Cohort Study %A Zhang,Hui %A Zhang,Jie %A Li,Hong-Bao %A Chen,Yi-Xin %A Yang,Bin %A Guo,Yu-Tao %A Chen,Yun-Dai %+ Department of Cardiology, Chinese PLA General Hospital, 28 Fuxing Rd, Beijing, 100853, China, 86 13810021492, guoyutao2010@126.com %K atrial fibrillation %K photoplethysmography %K continuous detection %K accuracy %K smartphone %K smart band %K algorithm %D 2019 %7 3.12.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: Atrial fibrillation is the most common recurrent arrhythmia in clinical practice, with most clinical events occurring outside the hospital. Low detection and nonadherence to guidelines are the primary obstacles to atrial fibrillation management. Photoplethysmography is a novel technology developed for atrial fibrillation screening. However, there has been limited validation of photoplethysmography-based smart devices for the detection of atrial fibrillation and its underlying clinical factors impacting detection. Objective: This study aimed to explore the feasibility of photoplethysmography-based smart devices for the detection of atrial fibrillation in real-world settings. Methods: Subjects aged ≥18 years (n=361) were recruited from September 14 to October 16, 2018, for screening of atrial fibrillation with active measurement, initiated by the users, using photoplethysmography-based smart wearable devices (ie, a smart band or smart watches). Of these, 200 subjects were also automatically and periodically monitored for 14 days with a smart band. The baseline diagnosis of “suspected” atrial fibrillation was confirmed by electrocardiogram and physical examination. The sensitivity and accuracy of photoplethysmography-based smart devices for monitoring atrial fibrillation were evaluated. Results: A total of 2353 active measurement signals and 23,864 periodic measurement signals were recorded. Eleven subjects were confirmed to have persistent atrial fibrillation, and 20 were confirmed to have paroxysmal atrial fibrillation. Smart devices demonstrated >91% predictive ability for atrial fibrillation. The sensitivity and specificity of devices in detecting atrial fibrillation among active recording of the 361 subjects were 100% and about 99%, respectively. For subjects with persistent atrial fibrillation, 127 (97.0%) active measurements and 2240 (99.2%) periodic measurements were identified as atrial fibrillation by the algorithm. For subjects with paroxysmal atrial fibrillation, 36 (17%) active measurements and 717 (19.8%) periodic measurements were identified as atrial fibrillation by the algorithm. All persistent atrial fibrillation cases could be detected as “atrial fibrillation episodes” by the photoplethysmography algorithm on the first monitoring day, while 14 (70%) patients with paroxysmal atrial fibrillation demonstrated “atrial fibrillation episodes” within the first 6 days. The average time to detect paroxysmal atrial fibrillation was 2 days (interquartile range: 1.25-5.75) by active measurement and 1 day (interquartile range: 1.00-2.00) by periodic measurement (P=.10). The first detection time of atrial fibrillation burden of <50% per 24 hours was 4 days by active measurement and 2 days by periodic measurementThe first detection time of atrial fibrillation burden of >50% per 24 hours was 1 day for both active and periodic measurements (active measurement: P=.02, periodic measurement: P=.03). Conclusions: Photoplethysmography-based smart devices demonstrated good atrial fibrillation predictive ability in both active and periodic measurements. However, atrial fibrillation type could impact detection, resulting in increased monitoring time. Trial Registration: Chinese Clinical Trial Registry of the International Clinical Trials Registry Platform of the World Health Organization ChiCTR-OOC-17014138; http://www.chictr.org.cn/showprojen.aspx?proj=24191. %M 31793887 %R 10.2196/14909 %U https://www.jmir.org/2019/12/e14909 %U https://doi.org/10.2196/14909 %U http://www.ncbi.nlm.nih.gov/pubmed/31793887 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 12 %P e14686 %T QardioArm Upper Arm Blood Pressure Monitor Against Omron M3 Upper Arm Blood Pressure Monitor in Patients With Chronic Kidney Disease: A Validation Study According to the European Society of Hypertension International Protocol Revision 2010 %A Mazoteras-Pardo,Victoria %A Becerro-De-Bengoa-Vallejo,Ricardo %A Losa-Iglesias,Marta Elena %A López-López,Daniel %A Rodríguez-Sanz,David %A Casado-Hernández,Israel %A Calvo-Lobo,Cesar %A Palomo-López,Patricia %+ School of Nursing, Physiotherapy, and Podiatry, Universidad Complutense de Madrid, Plaza Ramón y Cajal, 3, Madrid, 28040, Spain, 34 913941544, cescalvo@ucm.es %K blood pressure %K hypertension %K kidney disease %K mobile apps %K software validation %D 2019 %7 2.12.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: Hypertension is considered as a main risk factor for chronic kidney disease development and progression. Thus, the control and evaluation of this disease with new software and devices are especially important in patients who suffer from chronic kidney disease. Objective: This study aimed to validate the QardioArm mobile device, which is used for blood pressure (BP) self-measurement in patients who suffer from chronic kidney disease, by following the European Society of Hypertension International Protocol 2 (ESH-IP2) guidelines. Methods: A validation study was carried out by following the ESH-IP2 guidelines. A sample of 33 patients with chronic kidney disease self-measured their BP by using the QardioArm and Omron M3 Intellisense devices. Heart rate (HR), diastolic BP, and systolic BP were measured. Results: The QardioArm fulfilled the ESH-IP2 validation criteria in patients who suffered from chronic kidney disease. Conclusions: Thus, this study is considered as the first validation using a wireless upper arm oscillometric device connected to an app to measure BP and HR meeting the ESH-IP2 requirements in patients who suffer from chronic kidney disease. New validation studies following the ESH-IP2 guidelines should be carried out using different BP devices in patients with specific diseases. %M 31789600 %R 10.2196/14686 %U https://www.jmir.org/2019/12/e14686 %U https://doi.org/10.2196/14686 %U http://www.ncbi.nlm.nih.gov/pubmed/31789600 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 10 %P e14926 %T An Ambulatory Blood Pressure Monitor Mobile Health System for Early Warning for Stroke Risk: Longitudinal Observational Study %A Wang,Guangyu %A Zhou,Silu %A Rezaei,Shahbaz %A Liu,Xin %A Huang,Anpeng %+ National Institute of Health Data Science, Peking University, 38 Xueyuan Road, Haidian District, Beijing, 100191, China, 86 1062755832, hapku@pku.edu.cn %K ambulatory blood pressure monitor %K mHealth %K stroke-risk early warning %K abnormal blood pressure data analyzing %K longitudinal observational study %D 2019 %7 30.10.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Stroke, as a leading cause of death around the globe, has become a heavy burden on our society. Studies show that stroke can be predicted and prevented if a person’s blood pressure (BP) status is appropriately monitored via an ambulatory blood pressure monitor (ABPM) system. However, currently there exists no efficient and user-friendly ABPM system to provide early warning for stroke risk in real-time. Moreover, most existing ABPM devices measure BP during the deflation of the cuff, which fails to reflect blood pressure accurately. Objective: In this study, we sought to develop a new ABPM mobile health (mHealth) system that was capable of monitoring blood pressure during inflation and could detect early stroke-risk signals in real-time. Methods: We designed an ABPM mHealth system that is based on mobile network infrastructure and mobile apps. The proposed system contains two major parts: a new ABPM device in which an inflation-type BP measurement algorithm is embedded, and an abnormal blood pressure data analysis algorithm for stroke-risk prediction services at our health data service center. For evaluation, the ABPM device was first tested using simulated signals and compared with the gold standard of a mercury sphygmomanometer. Then, the performance of our proposed mHealth system was evaluated in an observational study. Results: The results are presented in two main parts: the device test and the longitudinal observational studies of the presented system. The average measurement error of the new ABPM device with the inflation-type algorithm was less than 0.55 mmHg compared to a reference device using simulated signals. Moreover, the results of correlation coefficients and agreement analyses show that there is a strong linear correlation between our device and the standard mercury sphygmomanometer. In the case of the system observational study, we collected a data set with 88 features, including real-time data, user information, and user records. Our abnormal blood pressure data analysis algorithm achieved the best performance, with an area under the curve of 0.904 for the low risk level, 0.756 for the caution risk level, and 0.912 for the high-risk level. Our system enables a patient to be aware of their risk in real-time, which improves medication adherence with risk self-management. Conclusions: To our knowledge, this device is the first ABPM device that measures blood pressure during the inflation process and has obtained a government medical license. Device tests and longitudinal observational studies were conducted in Peking University hospitals, and they showed the device’s high accuracy for BP measurements, its efficiency in detecting early signs of stroke, and its efficiency at providing an early warning for stroke risk. %M 31670694 %R 10.2196/14926 %U http://mhealth.jmir.org/2019/10/e14926/ %U https://doi.org/10.2196/14926 %U http://www.ncbi.nlm.nih.gov/pubmed/31670694 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 10 %P e13757 %T Associations Between Heart Rate Variability Measured With a Wrist-Worn Sensor and Older Adults’ Physical Function: Observational Study %A Graham,Sarah Anne %A Jeste,Dilip V %A Lee,Ellen E %A Wu,Tsung-Chin %A Tu,Xin %A Kim,Ho-Cheol %A Depp,Colin A %+ Sam and Rose Stein Institute for Research on Aging, University of California San Diego, 9500 Gilman Drive, #0664, La Jolla, CA, 92093-0664, United States, 1 858 534 5433, sagraham@ucsd.edu %K wearable technology %K aging %K electrocardiogram %K geriatric assessment %D 2019 %7 23.10.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Heart rate variability (HRV), or variation in beat-to-beat intervals of the heart, is a quantitative measure of autonomic regulation of the cardiovascular system. Low HRV derived from electrocardiogram (ECG) recordings is reported to be related to physical frailty in older adults. Recent advances in wearable technology offer opportunities to more easily integrate monitoring of HRV into regular clinical geriatric health assessments. However, signals obtained from ECG versus wearable photoplethysmography (PPG) devices are different, and a critical first step preceding their widespread use is to determine whether HRV metrics derived from PPG devices also relate to older adults’ physical function. Objective: This study aimed to investigate associations between HRV measured with a wrist-worn PPG device, the Empatica E4 sensor, and validated clinical measures of both objective and self-reported physical function in a cohort of older adults living independently within a continuing care senior housing community. Our primary hypothesis was that lower HRV would be associated with lower physical function. In addition, we expected that HRV would explain a significant proportion of variance in measures of physical health status. Methods: We evaluated 77 participants from an ongoing study of older adults aged between 65 and 95 years. The assessments encompassed a thorough examination of domains typically included in a geriatric health evaluation. We collected HRV data with the Empatica E4 device and examined bivariate correlations between HRV quantified with the triangular index (HRV TI) and 3 widely used and validated measures of physical functioning—the Short Physical Performance Battery (SPPB), Timed Up and Go (TUG), and Medical Outcomes Study Short Form 36 (SF-36) physical composite scores. We further investigated the additional predictive power of HRV TI on physical health status, as characterized by SF-36 physical composite scores and Cumulative Illness Rating Scale for Geriatrics (CIRS-G) scores, using generalized estimating equation regression analyses with backward elimination. Results: We observed significant associations of HRV TI with SPPB (n=52; Spearman ρ=0.41; P=.003), TUG (n=51; ρ=−0.40; P=.004), SF-36 physical composite scores (n=49; ρ=0.37; P=.009), and CIRS-G scores (n=52, ρ=−0.43; P=.001). In addition, the HRV TI explained a significant proportion of variance in SF-36 physical composite scores (R2=0.28 vs 0.11 without HRV) and CIRS-G scores (R2=0.33 vs 0.17 without HRV). Conclusions: The HRV TI measured with a relatively novel wrist-worn PPG device was related to both objective (SPPB and TUG) and self-reported (SF-36 physical composite) measures of physical function. In addition, the HRV TI explained additional variance in self-reported physical function and cumulative illness severity beyond traditionally measured aspects of physical health. Future steps include longitudinal tracking of changes in both HRV and physical function, which will add important insights regarding the predictive value of HRV as a biomarker of physical health in older adults. %M 31647469 %R 10.2196/13757 %U http://mhealth.jmir.org/2019/10/e13757/ %U https://doi.org/10.2196/13757 %U http://www.ncbi.nlm.nih.gov/pubmed/31647469 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 10 %P e14706 %T Validation and Acceptability of a Cuffless Wrist-Worn Wearable Blood Pressure Monitoring Device Among Users and Health Care Professionals: Mixed Methods Study %A Islam,Sheikh Mohammed Shariful %A Cartledge,Susie %A Karmakar,Chandan %A Rawstorn,Jonathan Charles %A Fraser,Steve F %A Chow,Clara %A Maddison,Ralph %+ Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Faculty of Health, Deakin University, 221 Burwood Highway, Burwood, VIC 3125, Australia, 61 451733373, shariful@deakin.edu.au %K hypertension %K cardiovascular disease %K wearable device %K blood pressure %K ambulatory blood pressure monitoring %D 2019 %7 14.9.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Blood pressure (BP) is an important modifiable cardiovascular risk factor, yet its long-term monitoring remains problematic. Wearable cuffless devices enable the capture of multiple BP measures during everyday activities and could improve BP monitoring, but little is known about their validity or acceptability. Objective: This study aimed to validate a wrist-worn cuffless wearable BP device (Model T2; TMART Technologies Limited) and assess its acceptability among users and health care professionals. Methods: A mixed methods study was conducted to examine the validity and comparability of a wearable cuffless BP device against ambulatory and home devices. BP was measured simultaneously over 24 hours using wearable and ambulatory devices and over 7 days using wearable and home devices. Pearson correlation coefficients compared the degree of association between the measures, and limits of agreement (LOA; Bland-Altman plots) were generated to assess measurement bias. Semistructured interviews were conducted with users and 10 health care professionals to assess acceptability, facilitators, and barriers to using the wearable device. Interviews were audio recorded, transcribed, and analyzed. Results: A total of 9090 BP measurements were collected from 20 healthy volunteers (mean 20.3 years, SD 5.4; N=10 females). Mean (SD) systolic BP (SBP)/diastolic BP (DBP) measured using the ambulatory (24 hours), home (7 days), and wearable (7 days) devices were 126 (SD 10)/75 (SD 6) mm Hg, 112 (SD 10)/71 (SD 9) mm Hg and 125 (SD 4)/77 (SD 3) mm Hg, respectively. Mean (LOA) biases and precision between the wearable and ambulatory devices over 24 hours were 0.5 (−10.1 to 11.1) mm Hg for SBP and 2.24 (−17.6 to 13.1) mm Hg for DBP. The mean biases (LOA) and precision between the wearable and home device over 7 days were −12.7 (−28.7 to 3.4) mm Hg for SBP and −5.6 (−20.5 to 9.2) mm Hg for DBP. The wearable BP device was well accepted by participants who found the device easy to wear and use. Both participants and health care providers agreed that the wearable cuffless devices were easy to use and that they could be used to improve BP monitoring. Conclusions: Wearable BP measures compared well against a gold-standard ambulatory device, indicating potential for this user-friendly method to augment BP management, particularly by enabling long-term monitoring that could improve treatment titration and increase understanding of users’ BP response during daily activity and stressors. %M 31628788 %R 10.2196/14706 %U https://mhealth.jmir.org/2019/10/e14706 %U https://doi.org/10.2196/14706 %U http://www.ncbi.nlm.nih.gov/pubmed/31628788 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 10 %P e14120 %T Heart Rate Measures From Wrist-Worn Activity Trackers in a Laboratory and Free-Living Setting: Validation Study %A Müller,Andre Matthias %A Wang,Nan Xin %A Yao,Jiali %A Tan,Chuen Seng %A Low,Ivan Cherh Chiet %A Lim,Nicole %A Tan,Jeremy %A Tan,Agnes %A Müller-Riemenschneider,Falk %+ Health Systems & Behavioral Sciences, Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Tahir Foundation Building, 12 Science Drive 2, #09-01C, Singapore, 117549, Singapore, 65 82997548, ephamm@nus.edu.sg %K eHealth %K mHealth %K wearable %K exercise %K measurement %K fitness %K public health %K quantified self %D 2019 %7 2.10.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wrist-worn activity trackers are popular, and an increasing number of these devices are equipped with heart rate (HR) measurement capabilities. However, the validity of HR data obtained from such trackers has not been thoroughly assessed outside the laboratory setting. Objective: This study aimed to investigate the validity of HR measures of a high-cost consumer-based tracker (Polar A370) and a low-cost tracker (Tempo HR) in the laboratory and free-living settings. Methods: Participants underwent a laboratory-based cycling protocol while wearing the two trackers and the chest-strapped Polar H10, which acted as criterion. Participants also wore the devices throughout the waking hours of the following day during which they were required to conduct at least one 10-min bout of moderate-to-vigorous physical activity (MVPA) to ensure variability in the HR signal. We extracted 10-second values from all devices and time-matched HR data from the trackers with those from the Polar H10. We calculated intraclass correlation coefficients (ICCs), mean absolute errors, and mean absolute percentage errors (MAPEs) between the criterion and the trackers. We constructed decile plots that compared HR data from Tempo HR and Polar A370 with criterion measures across intensity deciles. We investigated how many HR data points within the MVPA zone (≥64% of maximum HR) were detected by the trackers. Results: Of the 57 people screened, 55 joined the study (mean age 30.5 [SD 9.8] years). Tempo HR showed moderate agreement and large errors (laboratory: ICC 0.51 and MAPE 13.00%; free-living: ICC 0.71 and MAPE 10.20%). Polar A370 showed moderate-to-strong agreement and small errors (laboratory: ICC 0.73 and MAPE 6.40%; free-living: ICC 0.83 and MAPE 7.10%). Decile plots indicated increasing differences between Tempo HR and the criterion as HRs increased. Such trend was less pronounced when considering the Polar A370 HR data. Tempo HR identified 62.13% (1872/3013) and 54.27% (5717/10,535) of all MVPA time points in the laboratory phase and free-living phase, respectively. Polar A370 detected 81.09% (2273/2803) and 83.55% (9323/11,158) of all MVPA time points in the laboratory phase and free-living phase, respectively. Conclusions: HR data from the examined wrist-worn trackers were reasonably accurate in both the settings, with the Polar A370 showing stronger agreement with the Polar H10 and smaller errors. Inaccuracies increased with increasing HRs; this was pronounced for Tempo HR. %M 31579026 %R 10.2196/14120 %U https://mhealth.jmir.org/2019/10/e14120 %U https://doi.org/10.2196/14120 %U http://www.ncbi.nlm.nih.gov/pubmed/31579026 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 10 %P e13909 %T Pulse Rate Variability in Emergency Physicians During Shifts: Pilot Cross-Sectional Study %A Peters,Gregory Andrew %A Wong,Matthew L %A Joseph,Joshua W %A Sanchez,Leon D %+ Department of Emergency Medicine, Beth Israel Deaconess Medical Center, 1 Deaconess Rd, Boston, MA, 02215, United States, 1 6177542339, greg.peters826@gmail.com %K emergency medicine %K burnout %K photoplethysmography %K emergency physicians %K physician wellness %K stress %K heart rate variability %K pulse rate variability %D 2019 %7 2.10.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The high prevalence of physician burnout, particularly in emergency medicine, has garnered national attention in recent years. Objective means of measuring stress while at work can facilitate research into stress reduction interventions, and wearable photoplethysmography (PPG) technology has been proposed as a potential solution. However, the use of low-burden wearable biosensors to study training and clinical practice among emergency physicians (EP) remains untested. Objective: This pilot study aimed to (1) determine the feasibility of recording on-shift photoplethysmographic data from EP, (2) assess the quality of these data, and (3) calculate standard pulse rate variability (PRV) metrics from the acquired dataset and examine patterns in these variables over the course of an academic year. Methods: A total of 21 EP wore PPG biosensors on their wrists during clinical work in the emergency department during a 9-hour shift. Recordings were collected during the first quarter of the academic year, then again during the fourth quarter of the same year for comparison. The overall rate of usable data collection per time was computed. Standard pulse rate (PR) and PRV metrics from these two time points were calculated and entered into Student t tests. Results: More than 400 hours of data were entered into these analyses. Interpretable data were captured during 8.54% of the total recording time overall. In the fourth quarter of the academic year compared with the first quarter, there was no significant difference in median PR (75.8 vs 76.8; P=.57), mean R-R interval (0.81 vs 0.80; P=.32), SD of R-R interval (0.11 vs 0.11; P=.93), root mean square of successive difference of R-R interval (0.81 vs 0.80; P=.96), low-frequency power (3.5×103 vs 3.4×103; P=.79), high-frequency power (8.5×103 vs 8.3×103; P=.91), or low-frequency to high-frequency ratio (0.42 vs 0.41; P=.43), respectively. Power estimates for each of these tests exceeded .90. A secondary analysis of the resident-only subgroup similarly showed no significant differences over time, despite power estimates greater than .80. Conclusions: Although the use of PPG biosensors to record real-time physiological data from EP while providing clinical care seems operationally feasible, this study fails to support the notion that such an approach can efficiently provide reliable estimates of metrics of interest. No significant differences in PR or PRV metrics were found at the end of the year compared with the beginning. Although these methods may offer useful applications to other domains, it may currently have limited utility in the contexts of physician training and wellness. %M 31579017 %R 10.2196/13909 %U https://mhealth.jmir.org/2019/10/e13909 %U https://doi.org/10.2196/13909 %U http://www.ncbi.nlm.nih.gov/pubmed/31579017 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 8 %N 8 %P e13400 %T Noncontact Heart and Respiratory Rate Monitoring of Preterm Infants Based on a Computer Vision System: Protocol for a Method Comparison Study %A Gibson,Kim %A Al-Naji,Ali %A Fleet,Julie-Anne %A Steen,Mary %A Chahl,Javaan %A Huynh,Jasmine %A Morris,Scott %+ School of Nursing and Midwifery, University of South Australia, North Terrace, City East, Adelaide, 5000, Australia, 61 83022706, kim.gibson@unisa.edu.au %K heart rate %K respiratory rate %K infant %K electrocardiography %K computers %D 2019 %7 29.08.2019 %9 Protocol %J JMIR Res Protoc %G English %X Background: Biomedical research in the application of noncontact methods to measure heart rate (HR) and respiratory rate (RR) in the neonatal population has produced mixed results. This paper describes and discusses a protocol for conducting a method comparison study, which aims to determine the accuracy of a proposed noncontact computer vision system to detect HR and RR relative to the HR and RR obtained by 3-lead electrocardiogram (ECG) in preterm infants in the neonatal unit. Objective: The aim of this preliminary study is to determine the accuracy of a proposed noncontact computer vision system to detect HR and RR relative to the HR and RR obtained by 3-lead ECG in preterm infants in the neonatal unit. Methods: A single-center cross-sectional study was planned to be conducted in the neonatal unit at Flinders Medical Centre, South Australia, in May 2018. A total of 10 neonates and their ECG monitors will be filmed concurrently for 10 min using digital cameras. Advanced image processing techniques are to be applied later to determine their physiological data at 3 intervals. These data will then be compared with the ECG readings at the same points in time. Results: Study enrolment began in May 2018. Results of this study were published in July 2019. Conclusions: The study will analyze the data obtained by the noncontact system in comparison to data obtained by ECG, identify factors that may influence data extraction and accuracy when filming infants, and provide recommendations for how this noncontact system may be implemented into clinical applications. International Registered Report Identifier (IRRID): RR1-10.2196/13400 %M 31469077 %R 10.2196/13400 %U https://www.researchprotocols.org/2019/8/e13400 %U https://doi.org/10.2196/13400 %U http://www.ncbi.nlm.nih.gov/pubmed/31469077 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 6 %P e11164 %T Feasibility of a New Cuffless Device for Ambulatory Blood Pressure Measurement in Patients With Hypertension: Mixed Methods Study %A Ogink,Paula AM %A de Jong,Jelske M %A Koeneman,Mats %A Weenk,Mariska %A Engelen,Lucien JLPG %A van Goor,Harry %A van de Belt,Tom H %A Bredie,Sebastian JH %+ Department of Internal Medicine, Radboud University Medical Center, Geert Grooteplein 8, Nijmegen,, Netherlands, 31 243618819, bas.bredie@radboudumc.nl %K ambulatory blood pressure monitoring %K home blood pressure monitoring %K cuffless blood pressure device %K hypertension %D 2019 %7 19.06.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: Frequent home blood pressure (BP) measurements result in a better estimation of the true BP. However, traditional cuff-based BP measurements are troublesome for patients. Objective: This study aimed to evaluate the feasibility of a cuffless device for ambulatory systolic blood pressure (SBP) measurement. Methods: This was a mixed method feasibility study in patients with hypertension. Performance of ambulatory SBPs with the device was analyzed quantitatively by intrauser reproducibility and comparability to a classic home BP monitor. Correct use by the patients was checked with video, and user-friendliness was assessed using a validated questionnaire, the System Usability Scale (SUS). Patient experiences were assessed using qualitative interviews. Results: A total of 1020 SBP measurements were performed using the Checkme monitor in 11 patients with hypertension. Duplicate SBPs showed a high intrauser correlation (R=0.86, P<.001). SBPs measured by the Checkme monitor did not correlate well with those of the different home monitors (R=0.47, P=.007). However, the mean SBPs measured by the Checkme and home monitors over the 3-week follow-up were strongly correlated (R=0.75, P=.008). In addition, 36.4% (n=4) of the participants performed the Checkme measurements without any mistakes. The mean SUS score was 86.4 (SD 8.3). The most important facilitator was the ease of using the Checkme monitor. Most important barriers included the absence of diastolic BP and the incidental difficulties in obtaining an SBP result. Conclusions: Given the good intrauser reproducibility, user-friendliness, and patient experience, all of which facilitate patients to perform frequent measurements, cuffless BP monitoring may change the way patients measure their BP at home in the context of ambulant hypertension management. %M 31219050 %R 10.2196/11164 %U http://www.jmir.org/2019/6/e11164/ %U https://doi.org/10.2196/11164 %U http://www.ncbi.nlm.nih.gov/pubmed/31219050 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 6 %P e13641 %T Accuracy of mHealth Devices for Atrial Fibrillation Screening: Systematic Review %A Giebel,Godwin Denk %A Gissel,Christian %+ Health Economics, Department of Economics and Business, Justus Liebig University, Licher Strasse 62, Giessen, 35394, Germany, 49 641 99 22070, christian.gissel@wirtschaft.uni-giessen.de %K mHealth %K atrial fibrillation %K wearable %K app %D 2019 %7 16.6.2019 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Mobile health (mHealth) devices can be used for the diagnosis of atrial fibrillation. Early diagnosis allows better treatment and prevention of secondary diseases like stroke. Although there are many different mHealth devices to screen for atrial fibrillation, their accuracy varies due to different technological approaches. Objective: We aimed to systematically review available studies that assessed the accuracy of mHealth devices in screening for atrial fibrillation. The goal of this review was to provide a comprehensive overview of available technologies, specific characteristics, and accuracy of all relevant studies. Methods: PubMed and Web of Science databases were searched from January 2014 until January 2019. Our systematic review was performed according to the Preferred Reporting Items for Systematic Review and Meta-Analyses. We restricted the search by year of publication, language, noninvasive methods, and focus on diagnosis of atrial fibrillation. Articles not including information about the accuracy of devices were excluded. Results: We found 467 relevant studies. After removing duplicates and excluding ineligible records, 22 studies were included. The accuracy of mHealth devices varied among different technologies, their application settings, and study populations. We described and summarized the eligible studies. Conclusions: Our systematic review identifies different technologies for screening for atrial fibrillation with mHealth devices. A specific technology’s suitability depends on the underlying form of atrial fibrillation to be diagnosed. With the suitable use of mHealth, early diagnosis and treatment of atrial fibrillation are possible. Successful application of mHealth technologies could contribute to significantly reducing the cost of illness of atrial fibrillation. %M 31199337 %R 10.2196/13641 %U http://mhealth.jmir.org/2019/6/e13641/ %U https://doi.org/10.2196/13641 %U http://www.ncbi.nlm.nih.gov/pubmed/31199337 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 6 %P e13327 %T Estimating Maximal Oxygen Uptake From Daily Activity Data Measured by a Watch-Type Fitness Tracker: Cross-Sectional Study %A Kwon,Soon Bin %A Ahn,Joong Woo %A Lee,Seung Min %A Lee,Joonnyong %A Lee,Dongheon %A Hong,Jeeyoung %A Kim,Hee Chan %A Yoon,Hyung-Jin %+ Interdisciplinary Program in Bioengineering, Seoul National University, 103 Daehak-ro, Jongro-gu, Seoul, 03080, Republic of Korea, 82 2 740 8596, hjyoon@snu.ac.kr %K cardiorespiratory fitness %K oxygen consumption %K fitness tracker %D 2019 %7 13.6.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Cardiorespiratory fitness (CRF), an important index of physical fitness, is the ability to inhale and provide oxygen to the exercising muscle. However, despite its importance, the current gold standard for measuring CRF is impractical, requiring maximal exercise from the participants. Objective: This study aimed to develop a convenient and practical estimation model for CRF using data collected from daily life with a wristwatch-type device. Methods: A total of 191 subjects, aged 20 to 65 years, participated in this study. Maximal oxygen uptake (VO2 max), a standard measure of CRF, was measured with a maximal exercise test. Heart rate (HR) and physical activity data were collected using a commercial wristwatch-type fitness tracker (Fitbit; Fitbit Charge; Fitbit) for 3 consecutive days. Maximal activity energy expenditure (aEEmax) and slope between HR and physical activity were calculated using a linear regression. A VO2 max estimation model was built using multiple linear regression with data on age, sex, height, percent body fat, aEEmax, and the slope. The result was validated with 2 different cross-validation methods. Results: aEEmax showed a moderate correlation with VO2 max (r=0.50). The correlation coefficient for the multiple linear regression model was 0.81, and the SE of estimate (SEE) was 3.518 mL/kg/min. The regression model was cross-validated through the predicted residual error sum of square (PRESS). The PRESS correlation coefficient was 0.79, and the PRESS SEE was 3.667 mL/kg/min. The model was further validated by dividing it into different subgroups and calculating the constant error (CE) where a low CE showed that the model does not significantly overestimate or underestimate VO2 max. Conclusions: This study proposes a CRF estimation method using data collected by a wristwatch-type fitness tracker without any specific protocol for a wide range of the population. %M 31199336 %R 10.2196/13327 %U https://mhealth.jmir.org/2019/6/e13327/ %U https://doi.org/10.2196/13327 %U http://www.ncbi.nlm.nih.gov/pubmed/31199336 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 6 %P e12770 %T Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study %A Kwon,Soonil %A Hong,Joonki %A Choi,Eue-Keun %A Lee,Euijae %A Hostallero,David Earl %A Kang,Wan Ju %A Lee,Byunghwan %A Jeong,Eui-Rim %A Koo,Bon-Kwon %A Oh,Seil %A Yi,Yung %+ Department of Internal Medicine, Seoul National University Hospital, 101 Daehang-ro, Jongno-gu, Seoul, 03080, Republic of Korea, 82 2 2072 0688, choiek417@gmail.com %K atrial fibrillation %K deep learning %K photoplethysmography %K pulse oximetry %K diagnosis %D 2019 %7 6.6.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wearable devices have evolved as screening tools for atrial fibrillation (AF). A photoplethysmographic (PPG) AF detection algorithm was developed and applied to a convenient smartphone-based device with good accuracy. However, patients with paroxysmal AF frequently exhibit premature atrial complexes (PACs), which result in poor unmanned AF detection, mainly because of rule-based or handcrafted machine learning techniques that are limited in terms of diagnostic accuracy and reliability. Objective: This study aimed to develop deep learning (DL) classifiers using PPG data to detect AF from the sinus rhythm (SR) in the presence of PACs after successful cardioversion. Methods: We examined 75 patients with AF who underwent successful elective direct-current cardioversion (DCC). Electrocardiogram and pulse oximetry data over a 15-min period were obtained before and after DCC and labeled as AF or SR. A 1-dimensional convolutional neural network (1D-CNN) and recurrent neural network (RNN) were chosen as the 2 DL architectures. The PAC indicator estimated the burden of PACs on the PPG dataset. We defined a metric called the confidence level (CL) of AF or SR diagnosis and compared the CLs of true and false diagnoses. We also compared the diagnostic performance of 1D-CNN and RNN with previously developed AF detectors (support vector machine with root-mean-square of successive difference of RR intervals and Shannon entropy, autocorrelation, and ensemble by combining 2 previous methods) using 10 5-fold cross-validation processes. Results: Among the 14,298 training samples containing PPG data, 7157 samples were obtained during the post-DCC period. The PAC indicator estimated 29.79% (2132/7157) of post-DCC samples had PACs. The diagnostic accuracy of AF versus SR was 99.32% (70,925/71,410) versus 95.85% (68,602/71,570) in 1D-CNN and 98.27% (70,176/71,410) versus 96.04% (68,736/71,570) in RNN methods. The area under receiver operating characteristic curves of the 2 DL classifiers was 0.998 (95% CI 0.995-1.000) for 1D-CNN and 0.996 (95% CI 0.993-0.998) for RNN, which were significantly higher than other AF detectors (P<.001). If we assumed that the dataset could emulate a sufficient number of patients in training, both DL classifiers improved their diagnostic performances even further especially for the samples with a high burden of PACs. The average CLs for true versus false classification were 98.56% versus 78.75% for 1D-CNN and 98.37% versus 82.57% for RNN (P<.001 for all cases). Conclusions: New DL classifiers could detect AF using PPG monitoring signals with high diagnostic accuracy even with frequent PACs and could outperform previously developed AF detectors. Although diagnostic performance decreased as the burden of PACs increased, performance improved when samples from more patients were trained. Moreover, the reliability of the diagnosis could be indicated by the CL. Wearable devices sensing PPG signals with DL classifiers should be validated as tools to screen for AF. %M 31199302 %R 10.2196/12770 %U http://mhealth.jmir.org/2019/6/e12770/ %U https://doi.org/10.2196/12770 %U http://www.ncbi.nlm.nih.gov/pubmed/31199302 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 6 %P e12866 %T Wearable Finger Pulse Oximetry for Continuous Oxygen Saturation Measurements During Daily Home Routines of Patients With Chronic Obstructive Pulmonary Disease (COPD) Over One Week: Observational Study %A Buekers,Joren %A Theunis,Jan %A De Boever,Patrick %A Vaes,Anouk W %A Koopman,Maud %A Janssen,Eefje VM %A Wouters,Emiel FM %A Spruit,Martijn A %A Aerts,Jean-Marie %+ Measure, Model & Manage Bioresponses, Department of Biosystems, KU Leuven, Kasteelpark Arenberg 30, Leuven, 3000, Belgium, 32 16321434, jean-marie.aerts@kuleuven.be %K COPD %K oxygen saturation %K finger pulse oximeter %K wearable sensor %K nocturnal desaturation %K telemonitoring %D 2019 %7 6.6.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Chronic obstructive pulmonary disease (COPD) patients can suffer from low blood oxygen concentrations. Peripheral blood oxygen saturation (SpO2), as assessed by pulse oximetry, is commonly measured during the day using a spot check, or continuously during one or two nights to estimate nocturnal desaturation. Sampling at this frequency may overlook natural fluctuations in SpO2. Objective: This study used wearable finger pulse oximeters to continuously measure SpO2 during daily home routines of COPD patients and assess natural SpO2 fluctuations. Methods: A total of 20 COPD patients wore a WristOx2 pulse oximeter for 1 week to collect continuous SpO2 measurements. A SenseWear Armband simultaneously collected actigraphy measurements to provide contextual information. SpO2 time series were preprocessed and data quality was assessed afterward. Mean SpO2, SpO2 SD, and cumulative time spent with SpO2 below 90% (CT90) were calculated for every (1) day, (2) day in rest, and (3) night to assess SpO2 fluctuations. Results: A high percentage of valid SpO2 data (daytime: 93.27%; nocturnal: 99.31%) could be obtained during a 7-day monitoring period, except during moderate-to-vigorous physical activity (MVPA) (67.86%). Mean nocturnal SpO2 (89.9%, SD 3.4) was lower than mean daytime SpO2 in rest (92.1%, SD 2.9; P<.001). On average, SpO2 in rest ranged over 10.8% (SD 4.4) within one day. Highly varying CT90 values between different nights led to 50% (10/20) of the included patients changing categories between desaturator and nondesaturator over the course of 1 week. Conclusions: Continuous SpO2 measurements with wearable finger pulse oximeters identified significant SpO2 fluctuations between and within multiple days and nights of patients with COPD. Continuous SpO2 measurements during daily home routines of patients with COPD generally had high amounts of valid data, except for motion artifacts during MVPA. The identified fluctuations can have implications for telemonitoring applications that are based on daily SpO2 spot checks. CT90 values can vary greatly from night to night in patients with a nocturnal mean SpO2 around 90%, indicating that these patients cannot be consistently categorized as desaturators or nondesaturators. We recommend using wearable sensors for continuous SpO2 measurements over longer time periods to determine the clinical relevance of the identified SpO2 fluctuations. %M 31199331 %R 10.2196/12866 %U https://mhealth.jmir.org/2019/6/e12866/ %U https://doi.org/10.2196/12866 %U http://www.ncbi.nlm.nih.gov/pubmed/31199331 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 3 %N 1 %P e12122 %T Use of Free-Living Step Count Monitoring for Heart Failure Functional Classification: Validation Study %A Baril,Jonathan-F %A Bromberg,Simon %A Moayedi,Yasbanoo %A Taati,Babak %A Manlhiot,Cedric %A Ross,Heather Joan %A Cafazzo,Joseph %K exercise physiology %K heart rate tracker %K wrist worn devices %K Fitbit %K heart failure %K steps %K cardiopulmonary exercise test %K ambulatory monitoring %D 2019 %7 17.05.2019 %9 Original Paper %J JMIR Cardio %G English %X Background: The New York Heart Association (NYHA) functional classification system has poor inter-rater reproducibility. A previously published pilot study showed a statistically significant difference between the daily step counts of heart failure (with reduced ejection fraction) patients classified as NYHA functional class II and III as measured by wrist-worn activity monitors. However, the study’s small sample size severely limits scientific confidence in the generalizability of this finding to a larger heart failure (HF) population. Objective: This study aimed to validate the pilot study on a larger sample of patients with HF with reduced ejection fraction (HFrEF) and attempt to characterize the step count distribution to gain insight into a more objective method of assessing NYHA functional class. Methods: We repeated the analysis performed during the pilot study on an independently recorded dataset comprising a total of 50 patients with HFrEF (35 NYHA II and 15 NYHA III) patients. Participants were monitored for step count with a Fitbit Flex for a period of 2 weeks in a free-living environment. Results: Comparing group medians, patients exhibiting NYHA class III symptoms had significantly lower recorded 2-week mean daily total step count (3541 vs 5729 [steps], P=.04), lower 2-week maximum daily total step count (10,792 vs 5904 [steps], P=.03), lower 2-week recorded mean daily mean step count (4.0 vs 2.5 [steps/minute], P=.04,), and lower 2-week mean and 2-week maximum daily per minute step count maximums (88.1 vs 96.1 and 111.0 vs 123.0 [steps/minute]; P=.02 and .004, respectively). Conclusions: Patients with NYHA II and III symptoms differed significantly by various aggregate measures of free-living step count including the (1) mean and (2) maximum daily total step count as well as by the (3) mean of daily mean step count and by the (4) mean and (5) maximum of the daily per minute step count maximum. These findings affirm that the degree of exercise intolerance of NYHA II and III patients as a group is quantifiable in a replicable manner. This is a novel and promising finding that suggests the existence of a possible, completely objective measure of assessing HF functional class, something which would be a great boon in the continuing quest to improve patient outcomes for this burdensome and costly disease. %M 31758777 %R 10.2196/12122 %U http://cardio.jmir.org/2019/1/e12122/ %U https://doi.org/10.2196/12122 %U http://www.ncbi.nlm.nih.gov/pubmed/31758777 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 3 %N 1 %P e13850 %T Accuracy and Usability of a Novel Algorithm for Detection of Irregular Pulse Using a Smartwatch Among Older Adults: Observational Study %A Ding,Eric Y %A Han,Dong %A Whitcomb,Cody %A Bashar,Syed Khairul %A Adaramola,Oluwaseun %A Soni,Apurv %A Saczynski,Jane %A Fitzgibbons,Timothy P %A Moonis,Majaz %A Lubitz,Steven A %A Lessard,Darleen %A Hills,Mellanie True %A Barton,Bruce %A Chon,Ki %A McManus,David D %+ Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, 368 Plantation Street, Albert Sherman Center, AS7-1046, Worcester, MA, 01605, United States, 1 4086607644, eric.ding@umassmed.edu %K mobile health %K mHealth %K atrial fibrillation %K screening %K photoplethysmography %K electrocardiography %K smartwatch %D 2019 %7 15.05.2019 %9 Original Paper %J JMIR Cardio %G English %X Background: Atrial fibrillation (AF) is often paroxysmal and minimally symptomatic, hindering its diagnosis. Smartwatches may enhance AF care by facilitating long-term, noninvasive monitoring. Objective: This study aimed to examine the accuracy and usability of arrhythmia discrimination using a smartwatch. Methods: A total of 40 adults presenting to a cardiology clinic wore a smartwatch and Holter monitor and performed scripted movements to simulate activities of daily living (ADLs). Participants’ clinical and sociodemographic characteristics were abstracted from medical records. Participants completed a questionnaire assessing different domains of the device’s usability. Pulse recordings were analyzed blindly using a real-time realizable algorithm and compared with gold-standard Holter monitoring. Results: The average age of participants was 71 (SD 8) years; most participants had AF risk factors and 23% (9/39) were in AF. About half of the participants owned smartphones, but none owned smartwatches. Participants wore the smartwatch for 42 (SD 14) min while generating motion noise to simulate ADLs. The algorithm determined 53 of the 314 30-second noise-free pulse segments as consistent with AF. Compared with the gold standard, the algorithm demonstrated excellent sensitivity (98.2%), specificity (98.1%), and accuracy (98.1%) for identifying irregular pulse. Two-thirds of participants considered the smartwatch highly usable. Younger age and prior cardioversion were associated with greater overall comfort and comfort with data privacy with using a smartwatch for rhythm monitoring, respectively. Conclusions: A real-time realizable algorithm analyzing smartwatch pulse recordings demonstrated high accuracy for identifying pulse irregularities among older participants. Despite advanced age, lack of smartwatch familiarity, and high burden of comorbidities, participants found the smartwatch to be highly acceptable. %M 31758787 %R 10.2196/13850 %U http://cardio.jmir.org/2019/1/e13850/ %U https://doi.org/10.2196/13850 %U http://www.ncbi.nlm.nih.gov/pubmed/31758787 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 4 %P e11959 %T A Noninvasive, Economical, and Instant-Result Method to Diagnose and Monitor Type 2 Diabetes Using Pulse Wave: Case-Control Study %A Hao,Yiming %A Cheng,Feng %A Pham,Minh %A Rein,Hayley %A Patel,Devashru %A Fang,Yuchen %A Feng,Yiyi %A Yan,Jin %A Song,Xueyang %A Yan,Haixia %A Wang,Yiqin %+ Shanghai Key Laboratory of Health Identification and Assessment/Laboratory of Traditional Chinese Medicine Four Diagnostic Information, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai,, China, 86 21 51322447, wangyiqin2380@sina.com %K type 2 diabetes %K hypertension %K hyperlipidemia %K pulse wave analysis %K diagnosis %D 2019 %7 23.04.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: We should pay more attention to the long-term monitoring and early warning of type 2 diabetes and its complications. The traditional blood glucose tests are traumatic and cannot effectively monitor the development of diabetic complications. The development of mobile health is changing rapidly. Therefore, we are interested in developing a new noninvasive, economical, and instant-result method to accurately diagnose and monitor type 2 diabetes and its complications. Objective: We aimed to determine whether type 2 diabetes and its complications, including hypertension and hyperlipidemia, could be diagnosed and monitored by using pulse wave. Methods: We collected the pulse wave parameters from 50 healthy people, 139 diabetic patients without hypertension and hyperlipidemia, 133 diabetic patients with hypertension, 70 diabetic patients with hyperlipidemia, and 75 diabetic patients with hypertension and hyperlipidemia. The pulse wave parameters showing significant differences among these groups were identified. Various machine learning models such as linear discriminant analysis, support vector machines (SVMs), and random forests were applied to classify the control group, diabetic patients, and diabetic patients with complications. Results: There were significant differences in several pulse wave parameters among the 5 groups. The parameters height of tidal wave (h3), time distance between the start point of pulse wave and dominant wave (t1), and width of percussion wave in its one-third height position (W) increase and the height of dicrotic wave (h5) decreases when people develop diabetes. The parameters height of dominant wave (h1), h3, and height of dicrotic notch (h4) are found to be higher in diabetic patients with hypertension, whereas h5 is lower in diabetic patients with hyperlipidemia. For detecting diabetes, the method with the highest out-of-sample prediction accuracy is SVM with polynomial kernel. The algorithm can detect diabetes with 96.35% accuracy. However, all the algorithms have a low accuracy when predicting diabetic patients with hypertension and hyperlipidemia (below 70%). Conclusions: The results demonstrated that the noninvasive and convenient pulse-taking diagnosis described in this paper has the potential to become a low-cost and accurate method to monitor the development of diabetes. We are collecting more data to improve the accuracy for detecting hypertension and hyperlipidemia among diabetic patients. Mobile devices such as sport bands, smart watches, and other diagnostic tools are being developed based on the pulse wave method to improve the diagnosis and monitoring of diabetes, hypertension, and hyperlipidemia. %M 31012863 %R 10.2196/11959 %U http://mhealth.jmir.org/2019/4/e11959/ %U https://doi.org/10.2196/11959 %U http://www.ncbi.nlm.nih.gov/pubmed/31012863 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 4 %P e12772 %T Validation of Two Automatic Blood Pressure Monitors With the Ability to Transfer Data via Bluetooth %A Wetterholm,Madeleine %A Bonn,Stephanie Erika %A Alexandrou,Christina %A Löf,Marie %A Trolle Lagerros,Ylva %+ Clinical Epidemiology Unit, Department of Medicine Solna, Karolinska Institutet, Eugeniahemmet T2, Stockholm, SE 171 76, Sweden, 46 851779183, madeleine.wetterholm@ki.se %K blood pressure monitors %K diabetes mellitus, type 2 %K hypertension %K methods %K mHealth %K self-care %K self-management %D 2019 %7 17.04.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: Patients with chronic diseases are in need of regular health controls. Diabetes mellitus type 2 is currently the most prevalent chronic metabolic disease. A majority of diabetic patients have at least one comorbid chronic disease, where hypertension is the most common. The standard for blood pressure (BP) measurement is manual BP monitoring at health care clinics. Nevertheless, several advantages of self-measured BP have been documented. With BP data transfer from an automatic BP monitor via Bluetooth to software, for example, a smartphone app, home measurement could effectively be integrated into regular care. Objective: The aim of this study was to validate two commercially available automatic BP monitors with the ability to transfer BP data via Bluetooth (Beurer BM 85 and Andersson Lifesense BDR 2.0), against manual BP monitoring in patients with type 2 diabetes. Methods: A total of 181 participants with type 2 diabetes were recruited from 6 primary care centers in Stockholm, Sweden. BP was first measured using a manual BP monitor and then measured using the two automatic BP monitors. The mean differences between the automatic and manual measurements were calculated by subtracting the manual BP monitor measurement from the automatic monitor measurement. Validity of the two automatic BP monitors was further assessed using Spearman rank correlation coefficients and the Bland-Altman method. Results: In total, 180 participants, 119 men and 61 women, were included. The mean age was 60.1 (SD 11.4) years and the mean body mass index was 30.4 (SD 5.4) kg/m2. The mean difference between the Beurer BM 85 and the manual BP monitor was 11.1 (SD 11.2) mmHg for systolic blood pressure (SBP) and 8.0 (SD 8.1) mmHg for diastolic blood pressure (DBP). The mean difference between the Andersson Lifesense BDR 2.0 and the manual BP monitor was 3.2 (SD 10.8) mmHg for SBP and 4.2 (SD 7.2) mmHg for DBP. The automatic BP measurements were significantly correlated (P<.001) with the manual BP measurement values (Andersson Lifesense BDR 2.0: r=0.78 for SBP and r=0.71 for DBP; Beurer BM 85: r=0.78 for SBP and r=0.69 for DBP). Conclusions: The two automatic BP monitors validated measure sufficiently accurate on a group level, with the Andersson Lifesense BDR 2.0 more often falling within the ranges for what is acceptable in clinical practice compared with the Beurer BM 85. %M 30994459 %R 10.2196/12772 %U https://www.jmir.org/2019/4/e12772/ %U https://doi.org/10.2196/12772 %U http://www.ncbi.nlm.nih.gov/pubmed/30994459 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 6 %N 4 %P e10140 %T Instant Stress: Detection of Perceived Mental Stress Through Smartphone Photoplethysmography and Thermal Imaging %A Cho,Youngjun %A Julier,Simon J %A Bianchi-Berthouze,Nadia %+ Department of Computer Science, University College London, 1.06, 66-72 Gower Street, London,, United Kingdom, 44 20 3108 7177, youngjun.cho@ucl.ac.uk %K stress detection %K mobile applications %K photoplethysmography %K thermography %K psychophysiology %K heart rate variability %K physiological computing %K affective computing %K machine learning %D 2019 %7 09.04.2019 %9 Original Paper %J JMIR Ment Health %G English %X Background: A smartphone is a promising tool for daily cardiovascular measurement and mental stress monitoring. A smartphone camera–based photoplethysmography (PPG) and a low-cost thermal camera can be used to create cheap, convenient, and mobile monitoring systems. However, to ensure reliable monitoring results, a person must remain still for several minutes while a measurement is being taken. This is cumbersome and makes its use in real-life situations impractical. Objective: We proposed a system that combines PPG and thermography with the aim of improving cardiovascular signal quality and detecting stress responses quickly. Methods: Using a smartphone camera with a low-cost thermal camera added on, we built a novel system that continuously and reliably measures 2 different types of cardiovascular events: (1) blood volume pulse and (2) vasoconstriction/dilation-induced temperature changes of the nose tip. 17 participants, involved in stress-inducing mental workload tasks, measured their physiological responses to stressors over a short time period (20 seconds) immediately after each task. Participants reported their perceived stress levels on a 10-cm visual analog scale. For the instant stress inference task, we built novel low-level feature sets representing cardiovascular variability. We then used the automatic feature learning capability of artificial neural networks to improve the mapping between the extracted features and the self-reported ratings. We compared our proposed method with existing hand-engineered features-based machine learning methods. Results: First, we found that the measured PPG signals presented high quality cardiac cyclic information (mean pSQI: 0.755; SD 0.068). We also found that the measured thermal changes of the nose tip presented high-quality breathing cyclic information and filtering helped extract vasoconstriction/dilation-induced patterns with fewer respiratory effects (mean pSQI: from 0.714 to 0.157). Second, we found low correlations between the self-reported stress scores and the existing metrics of the cardiovascular signals (ie, heart rate variability and thermal directionality) from short measurements, suggesting they were not very dependent upon one another. Third, we tested the performance of the instant perceived stress inference method. The proposed method achieved significantly higher accuracies than existing precrafted features-based methods. In addition, the 17-fold leave-one-subject-out cross-validation results showed that combining both modalities produced higher accuracy than using PPG or thermal imaging only (PPG+Thermal: 78.33%; PPG: 68.53%; Thermal: 58.82%). The multimodal results are comparable to the state-of-the-art stress recognition methods that require long-term measurements. Finally, we explored effects of different data labeling strategies on the sensitivity of our inference methods. Our results showed the need for separation of and normalization between individual data. Conclusions: The results demonstrate the feasibility of using smartphone-based imaging for instant stress detection. Given that this approach does not need long-term measurements requiring attention and reduced mobility, we believe it is more suitable for mobile mental health care solutions in the wild. %M 30964440 %R 10.2196/10140 %U https://mental.jmir.org/2019/4/e10140/ %U https://doi.org/10.2196/10140 %U http://www.ncbi.nlm.nih.gov/pubmed/30964440 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 3 %P e12284 %T Mobile Phone–Based Use of the Photoplethysmography Technique to Detect Atrial Fibrillation in Primary Care: Diagnostic Accuracy Study of the FibriCheck App %A Proesmans,Tine %A Mortelmans,Christophe %A Van Haelst,Ruth %A Verbrugge,Frederik %A Vandervoort,Pieter %A Vaes,Bert %+ Department of Public Health and Primary Care, University of Leuven, Blok J, Kapucijnenvoer 33, Leuven, 3000, Belgium, 32 16337468, bert.vaes@kuleuven.be %K atrial fibrillation %K electrocardiography %K photoplethysmography %K mobile phone %K algorithm %D 2019 %7 27.03.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Mobile phone apps using photoplethysmography (PPG) technology through their built-in camera are becoming an attractive alternative for atrial fibrillation (AF) screening because of their low cost, convenience, and broad accessibility. However, some important questions concerning their diagnostic accuracy remain to be answered. Objective: This study tested the diagnostic accuracy of the FibriCheck AF algorithm for the detection of AF on the basis of mobile phone PPG and single-lead electrocardiography (ECG) signals. Methods: A convenience sample of patients aged 65 years and above, with or without a known history of AF, was recruited from 17 primary care facilities. Patients with an active pacemaker rhythm were excluded. A PPG signal was obtained with the rear camera of an iPhone 5S. Simultaneously, a single‑lead ECG was registered using a dermal patch with a wireless connection to the same mobile phone. PPG and single-lead ECG signals were analyzed using the FibriCheck AF algorithm. At the same time, a 12‑lead ECG was obtained and interpreted offline by independent cardiologists to determine the presence of AF. Results: A total of 45.7% (102/223) subjects were having AF. PPG signal quality was sufficient for analysis in 93% and single‑lead ECG quality was sufficient in 94% of the participants. After removing insufficient quality measurements, the sensitivity and specificity were 96% (95% CI 89%-99%) and 97% (95% CI 91%-99%) for the PPG signal versus 95% (95% CI 88%-98%) and 97% (95% CI 91%-99%) for the single‑lead ECG, respectively. False-positive results were mainly because of premature ectopic beats. PPG and single‑lead ECG techniques yielded adequate signal quality in 196 subjects and a similar diagnosis in 98.0% (192/196) subjects. Conclusions: The FibriCheck AF algorithm can accurately detect AF on the basis of mobile phone PPG and single-lead ECG signals in a primary care convenience sample. %M 30916656 %R 10.2196/12284 %U http://mhealth.jmir.org/2019/3/e12284/ %U https://doi.org/10.2196/12284 %U http://www.ncbi.nlm.nih.gov/pubmed/30916656 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 3 %P e11889 %T Accuracy of Apple Watch Measurements for Heart Rate and Energy Expenditure in Patients With Cardiovascular Disease: Cross-Sectional Study %A Falter,Maarten %A Budts,Werner %A Goetschalckx,Kaatje %A Cornelissen,Véronique %A Buys,Roselien %+ Department of Rehabilitation Sciences, KU Leuven, Herestraat 49 - Bus 1501, Leuven,, Belgium, 32 48 638 81 76, roselien.buys@kuleuven.be %K mobile health %K heart rate %K energy expenditure %K validation %K Apple Watch %K wrist-worn devices %K cardiovascular rehabilitation %D 2019 %7 19.03.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wrist-worn tracking devices such as the Apple Watch are becoming more integrated in health care. However, validation studies of these consumer devices remain scarce. Objectives: This study aimed to assess if mobile health technology can be used for monitoring home-based exercise in future cardiac rehabilitation programs. The purpose was to determine the accuracy of the Apple Watch in measuring heart rate (HR) and estimating energy expenditure (EE) during a cardiopulmonary exercise test (CPET) in patients with cardiovascular disease. Methods: Forty patients (mean age 61.9 [SD 15.2] yrs, 80% male) with cardiovascular disease (70% ischemic, 22.5% valvular, 7.5% other) completed a graded maximal CPET on a cycle ergometer while wearing an Apple Watch. A 12-lead electrocardiogram (ECG) was used to measure HR; indirect calorimetry was used for EE. HR was analyzed at three levels of intensity (seated rest, HR1; moderate intensity, HR2; maximal performance, HR3) for 30 seconds. The EE of the entire test was used. Bias or mean difference (MD), standard deviation of difference (SDD), limits of agreement (LoA), mean absolute error (MAE), mean absolute percentage error (MAPE), and intraclass correlation coefficients (ICCs) were calculated. Bland-Altman plots and scatterplots were constructed. Results: SDD for HR1, HR2, and HR3 was 12.4, 16.2, and 12.0 bpm, respectively. Bias and LoA (lower, upper LoA) were 3.61 (–20.74, 27.96) for HR1, 0.91 (–30.82, 32.63) for HR2, and –1.82 (–25.27, 21.63) for HR3. MAE was 6.34 for HR1, 7.55 for HR2, and 6.90 for HR3. MAPE was 10.69% for HR1, 9.20% for HR2, and 6.33% for HR3. ICC was 0.729 (P<.001) for HR1, 0.828 (P<.001) for HR2, and 0.958 (P<.001) for HR3. Bland-Altman plots and scatterplots showed good correlation without systematic error when comparing Apple Watch with ECG measurements. SDD for EE was 17.5 kcal. Bias and LoA were 30.47 (–3.80, 64.74). MAE was 30.77; MAPE was 114.72%. ICC for EE was 0.797 (P<.001). The Bland-Altman plot and a scatterplot directly comparing Apple Watch and indirect calorimetry showed systematic bias with an overestimation of EE by the Apple Watch. Conclusions: In patients with cardiovascular disease, the Apple Watch measures HR with clinically acceptable accuracy during exercise. If confirmed, it might be considered safe to incorporate the Apple Watch in HR-guided training programs in the setting of cardiac rehabilitation. At this moment, however, it is too early to recommend the Apple Watch for cardiac rehabilitation. Also, the Apple Watch systematically overestimates EE in this group of patients. Caution might therefore be warranted when using the Apple Watch for measuring EE. %M 30888332 %R 10.2196/11889 %U http://mhealth.jmir.org/2019/3/e11889/ %U https://doi.org/10.2196/11889 %U http://www.ncbi.nlm.nih.gov/pubmed/30888332 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 3 %P e10828 %T Accuracy of Consumer Wearable Heart Rate Measurement During an Ecologically Valid 24-Hour Period: Intraindividual Validation Study %A Nelson,Benjamin W %A Allen,Nicholas B %+ Department of Psychology, University of Oregon, 1227 University Street, Eugene, OR, 97403, United States, 1 3108014595, bwn@uoregon.edu %K electrocardiography %K Apple Watch 3 %K digital health %K Fitbit Charge 2 %K heart rate %K mobile health %K passive sensing %K photoplethysmography %K wearables %D 2019 %7 11.03.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wrist-worn smart watches and fitness monitors (ie, wearables) have become widely adopted by consumers and are gaining increased attention from researchers for their potential contribution to naturalistic digital measurement of health in a scalable, mobile, and unobtrusive way. Various studies have examined the accuracy of these devices in controlled laboratory settings (eg, treadmill and stationary bike); however, no studies have investigated the heart rate accuracy of wearables during a continuous and ecologically valid 24-hour period of actual consumer device use conditions. Objective: The aim of this study was to determine the heart rate accuracy of 2 popular wearable devices, the Apple Watch 3 and Fitbit Charge 2, as compared with the gold standard reference method, an ambulatory electrocardiogram (ECG), during consumer device use conditions in an individual. Data were collected across 5 daily conditions, including sitting, walking, running, activities of daily living (ADL; eg, chores, brushing teeth), and sleeping. Methods: One participant, (first author; 29-year-old Caucasian male) completed a 24-hour ecologically valid protocol by wearing 2 popular wrist wearable devices (Apple Watch 3 and Fitbit Charge 2). In addition, an ambulatory ECG (Vrije Universiteit Ambulatory Monitoring System) was used as the gold standard reference method, which resulted in the collection of 102,740 individual heartbeats. A single-subject design was used to keep all variables constant except for wearable devices while providing a rapid response design to provide initial assessment of wearable accuracy for allowing the research cycle to keep pace with technological advancements. Accuracy of these devices compared with the gold standard ECG was assessed using mean error, mean absolute error, and mean absolute percent error. These data were supplemented with Bland-Altman analyses and concordance class correlation to assess agreement between devices. Results: The Apple Watch 3 and Fitbit Charge 2 were generally highly accurate across the 24-hour condition. Specifically, the Apple Watch 3 had a mean difference of −1.80 beats per minute (bpm), a mean absolute error percent of 5.86%, and a mean agreement of 95% when compared with the ECG across 24 hours. The Fitbit Charge 2 had a mean difference of −3.47 bpm, a mean absolute error of 5.96%, and a mean agreement of 91% when compared with the ECG across 24 hours. These findings varied by condition. Conclusions: The Apple Watch 3 and the Fitbit Charge 2 provided acceptable heart rate accuracy (<±10%) across the 24 hour and during each activity, except for the Apple Watch 3 during the daily activities condition. Overall, these findings provide preliminary support that these devices appear to be useful for implementing ambulatory measurement of cardiac activity in research studies, especially those where the specific advantages of these methods (eg, scalability, low participant burden) are particularly suited to the population or research question. %M 30855232 %R 10.2196/10828 %U https://mhealth.jmir.org/2019/3/e10828/ %U https://doi.org/10.2196/10828 %U http://www.ncbi.nlm.nih.gov/pubmed/30855232 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 3 %P e11437 %T Diagnostic Performance of a Smart Device With Photoplethysmography Technology for Atrial Fibrillation Detection: Pilot Study (Pre-mAFA II Registry) %A Fan,Yong-Yan %A Li,Yan-Guang %A Li,Jian %A Cheng,Wen-Kun %A Shan,Zhao-Liang %A Wang,Yu-Tang %A Guo,Yu-Tao %+ Department of Cardiology, Chinese People's Liberation Army General Hospital, 28 Fuxing Rd, Beijing, 100853, China, 86 13810021492, guoyutao2010@126.com %K atrial fibrillation %K photoplethysmography %K detection %K accuracy %K mobile phone %K smart band %K algorithm %D 2019 %7 05.03.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia. The asymptomatic nature and paroxysmal frequency of AF lead to suboptimal early detection. A novel technology, photoplethysmography (PPG), has been developed for AF screening. However, there has been limited validation of mobile phone and smart band apps with PPG compared to 12-lead electrocardiograms (ECG). Objective: We investigated the feasibility and accuracy of a mobile phone and smart band for AF detection using pulse data measured by PPG. Methods: A total of 112 consecutive inpatients were recruited from the Chinese PLA General Hospital from March 15 to April 1, 2018. Participants were simultaneously tested with mobile phones (HUAWEI Mate 9, HUAWEI Honor 7X), smart bands (HUAWEI Band 2), and 12-lead ECG for 3 minutes. Results: In all, 108 patients (56 with normal sinus rhythm, 52 with persistent AF) were enrolled in the final analysis after excluding four patients with unclear cardiac rhythms. The corresponding sensitivity and specificity of the smart band PPG were 95.36% (95% CI 92.00%-97.40%) and 99.70% (95% CI 98.08%-99.98%), respectively. The positive predictive value of the smart band PPG was 99.63% (95% CI 97.61%-99.98%), the negative predictive value was 96.24% (95% CI 93.50%-97.90%), and the accuracy was 97.72% (95% CI 96.11%-98.70%). Moreover, the diagnostic sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of mobile phones with PPG for AF detection were over 94%. There was no significant difference after further statistical analysis of the results from the different smart devices compared with the gold-standard ECG (P>.99). Conclusions: The algorithm based on mobile phones and smart bands with PPG demonstrated good performance in detecting AF and may represent a convenient tool for AF detection in at-risk individuals, allowing widespread screening of AF in the population. Trial Registration: Chinese Clinical Trial Registry ChiCTR-OOC-17014138; http://www.chictr.org.cn/showproj.aspx?proj=24191 (Archived by WebCite at http://www.webcitation/76WXknvE6) %M 30835243 %R 10.2196/11437 %U http://mhealth.jmir.org/2019/3/e11437/ %U https://doi.org/10.2196/11437 %U http://www.ncbi.nlm.nih.gov/pubmed/30835243 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 3 %P e12369 %T Assessment of the Relationship Between Ambient Temperature and Home Blood Pressure in Patients From a Web-Based Synchronous Telehealth Care Program: Retrospective Study %A Huang,Ching-Chang %A Chen,Ying-Hsien %A Hung,Chi-Sheng %A Lee,Jen-Kuang %A Hsu,Tse-Pin %A Wu,Hui-Wen %A Chuang,Pao-Yu %A Chen,Ming-Fong %A Ho,Yi-Lwun %+ Graduate Institute of Clinical Medicine, Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, 7 Chung-Shan South Road, Taipei, 10002, Taiwan, 886 223123456 ext 66373, ylho@ntu.edu.tw %K ambient temperature %K home blood pressure %K antihypertensive agents %K retrospective studies %D 2019 %7 04.03.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: Decreased ambient temperature significantly increases office blood pressure, but few studies have evaluated the effect of ambient temperature on home blood pressure. Objective: We aimed to investigate the relationship between short-term ambient temperature exposure and home blood pressure. Methods: We recruited patients with chronic cardiovascular diseases from a telehealth care program at a university-affiliated hospital. Blood pressure was measured at home by patients or their caregivers. We obtained hourly meteorological data for Taipei (temperature, relative humidity, and wind speed) for the same time period from the Central Weather Bureau, Taiwan. Results: From 2009 to 2013, we enrolled a total of 253 patients. Mean patient age was 70.28 (SD 13.79) years, and 66.0% (167/253) of patients were male. We collected a total of 110,715 home blood pressure measurements. Ambient temperature had a negative linear effect on all 3 home blood pressure parameters after adjusting for demographic and clinical factors and antihypertensive agents. A 1°C decrease was associated with a 0.5492-mm Hg increase in mean blood pressure, a 0.6841-mm Hg increase in systolic blood pressure, and a 0.2709-mm Hg increase in diastolic blood pressure. This temperature effect on home blood pressure was less prominent in patients with diabetes or hypertension. Antihypertensive agents modified this negative effect of temperature on home blood pressure to some extent, and angiotensin receptor blockers had the most favorable results. Conclusions: Short-term exposure to low ambient temperature significantly increased home blood pressure in patients with chronic cardiovascular diseases. Antihypertensive agents may modify this effect. %M 30829574 %R 10.2196/12369 %U http://www.jmir.org/2019/3/e12369/ %U https://doi.org/10.2196/12369 %U http://www.ncbi.nlm.nih.gov/pubmed/30829574 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 2 %P e11606 %T The Current State of Mobile Phone Apps for Monitoring Heart Rate, Heart Rate Variability, and Atrial Fibrillation: Narrative Review %A Li,Ka Hou Christien %A White,Francesca Anne %A Tipoe,Timothy %A Liu,Tong %A Wong,Martin CS %A Jesuthasan,Aaron %A Baranchuk,Adrian %A Tse,Gary %A Yan,Bryan P %+ Division of Cardiology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, 9/F, Lui Che Woo Clinical Sciences Building, Prince of Wales Hospital, Shatin, Hong Kong,, China (Hong Kong), 852 35051750, bryan.yan@cuhk.edu.hk %K mobile phone apps %K atrial fibrillation %K heart rate %K arrhythmia %K photoplethysmography %K electrocardiography %K mobile health %D 2019 %7 15.02.2019 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Mobile phone apps capable of monitoring arrhythmias and heart rate (HR) are increasingly used for screening, diagnosis, and monitoring of HR and rhythm disorders such as atrial fibrillation (AF). These apps involve either the use of (1) photoplethysmographic recording or (2) a handheld external electrocardiographic recording device attached to the mobile phone or wristband. Objective: This review seeks to explore the current state of mobile phone apps in cardiac rhythmology while highlighting shortcomings for further research. Methods: We conducted a narrative review of the use of mobile phone devices by searching PubMed and EMBASE from their inception to October 2018. Potentially relevant papers were then compared against a checklist for relevance and reviewed independently for inclusion, with focus on 4 allocated topics of (1) mobile phone monitoring, (2) AF, (3) HR, and (4) HR variability (HRV). Results: The findings of this narrative review suggest that there is a role for mobile phone apps in the diagnosis, monitoring, and screening for arrhythmias and HR. Photoplethysmography and handheld electrocardiograph recorders are the 2 main techniques adopted in monitoring HR, HRV, and AF. Conclusions: A number of studies have demonstrated high accuracy of a number of different mobile devices for the detection of AF. However, further studies are warranted to validate their use for large scale AF screening. %M 30767904 %R 10.2196/11606 %U http://mhealth.jmir.org/2019/2/e11606/ %U https://doi.org/10.2196/11606 %U http://www.ncbi.nlm.nih.gov/pubmed/30767904 %0 Journal Article %@ 2561-3278 %I JMIR Publications %V 4 %N 1 %P e10740 %T Automatic Near Real-Time Outlier Detection and Correction in Cardiac Interbeat Interval Series for Heart Rate Variability Analysis: Singular Spectrum Analysis-Based Approach %A Lang,Michael %+ Graduate School of Excellence Computational Engineering, Technische Universität Darmstadt, Dolivostraße 15, Darmstadt, 64293, Germany, 49 61511624401, michael.lang@ieee.org %K change-point detection %K cumulative sum %K forecasting %K heart rate variability %K R-R series %K singular spectrum analysis %K ventricular premature complexes %D 2019 %7 30.01.2019 %9 Original Paper %J JMIR Biomed Eng %G English %X Background: Heart rate variability (HRV) is derived from the series of R-R intervals extracted from an electrocardiographic (ECG) measurement. Ideally all components of the R-R series are the result of sinoatrial node depolarization. However, the actual R-R series are contaminated by outliers due to heart rhythm disturbances such as ectopic beats, which ought to be detected and corrected appropriately before HRV analysis. Objective: We have introduced a novel, lightweight, and near real-time method to detect and correct anomalies in the R-R series based on the singular spectrum analysis (SSA). This study aimed to assess the performance of the proposed method in terms of (1) detection performance (sensitivity, specificity, and accuracy); (2) root mean square error (RMSE) between the actual N-N series and the approximated outlier-cleaned R-R series; and (3) how it benchmarks against a competitor in terms of the relative RMSE. Methods: A lightweight SSA-based change-point detection procedure, improved through the use of a cumulative sum control chart with adaptive thresholds to reduce detection delays, monitored the series of R-R intervals in real time. Upon detection of an anomaly, the corrupted segment was substituted with the respective outlier-cleaned approximation obtained using recurrent SSA forecasting. Next, N-N intervals from a 5-minute ECG segment were extracted from each of the 18 records in the MIT-BIH Normal Sinus Rhythm Database. Then, for each such series, a number (randomly drawn integer between 1 and 6) of simulated ectopic beats were inserted at random positions within the series and results were averaged over 1000 Monte Carlo runs. Accordingly, 18,000 R-R records corresponding to 5-minute ECG segments were used to assess the detection performance whereas another 180,000 (10,000 for each record) were used to assess the error introduced in the correction step. Overall 198,000 R-R series were used in this study. Results: The proposed SSA-based algorithm reliably detected outliers in the R-R series and achieved an overall sensitivity of 96.6%, specificity of 98.4% and accuracy of 98.4%. Furthermore, it compared favorably in terms of discrepancies of the cleaned R-R series compared with the actual N-N series, outperforming an established correction method on average by almost 30%. Conclusions: The proposed algorithm, which leverages the power and versatility of the SSA to both automatically detect and correct artifacts in the R-R series, provides an effective and efficient complementary method and a potential alternative to the current manual-editing gold standard. Other important characteristics of the proposed method include the ability to operate in near real-time, the almost entirely model-free nature of the framework which does not require historical training data, and its overall low computational complexity. %R 10.2196/10740 %U https://biomedeng.jmir.org/2019/1/e10740/ %U https://doi.org/10.2196/10740 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 1 %P e12419 %T In-Home Cardiovascular Monitoring System for Heart Failure: Comparative Study %A Conn,Nicholas J %A Schwarz,Karl Q %A Borkholder,David A %+ Microsystems Engineering, Rochester Institute of Technology, 168 Lomb Memorial Drive, Rochester, NY,, United States, 1 585 475 6067, david.borkholder@rit.edu %K ballistocardiogram %K BCG %K blood pressure %K ECG %K electrocardiogram %K heart failure %K Internet of Things %K IoT %K photoplethysmogram %K PPG %K remote monitoring %K SpO2 %K stroke volume %D 2019 %7 18.01.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: There is a pressing need to reduce the hospitalization rate of heart failure patients to limit rising health care costs and improve outcomes. Tracking physiologic changes to detect early deterioration in the home has the potential to reduce hospitalization rates through early intervention. However, classical approaches to in-home monitoring have had limited success, with patient adherence cited as a major barrier. This work presents a toilet seat–based cardiovascular monitoring system that has the potential to address low patient adherence as it does not require any change in habit or behavior. Objective: The objective of this work was to demonstrate that a toilet seat–based cardiovascular monitoring system with an integrated electrocardiogram, ballistocardiogram, and photoplethysmogram is capable of clinical-grade measurements of systolic and diastolic blood pressure, stroke volume, and peripheral blood oxygenation. Methods: The toilet seat–based estimates of blood pressure and peripheral blood oxygenation were compared to a hospital-grade vital signs monitor for 18 subjects over an 8-week period. The estimated stroke volume was validated on 38 normative subjects and 111 subjects undergoing a standard echocardiogram at a hospital clinic for any underlying condition, including heart failure. Results: Clinical grade accuracy was achieved for all of the seat measurements when compared to their respective gold standards. The accuracy of diastolic blood pressure and systolic blood pressure is 1.2 (SD 6.0) mm Hg (N=112) and –2.7 (SD 6.6) mm Hg (N=89), respectively. Stroke volume has an accuracy of –2.5 (SD 15.5) mL (N=149) compared to an echocardiogram gold standard. Peripheral blood oxygenation had an RMS error of 2.3% (N=91). Conclusions: A toilet seat–based cardiovascular monitoring system has been successfully demonstrated with blood pressure, stroke volume, and blood oxygenation accuracy consistent with gold standard measures. This system will be uniquely positioned to capture trend data in the home that has been previously unattainable. Demonstration of the clinical benefit of the technology requires additional algorithm development and future clinical trials, including those targeting a reduction in heart failure hospitalizations. %M 30664492 %R 10.2196/12419 %U http://mhealth.jmir.org/2019/1/e12419/ %U https://doi.org/10.2196/12419 %U http://www.ncbi.nlm.nih.gov/pubmed/30664492 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 12 %P e11896 %T Data Integrity–Based Methodology and Checklist for Identifying Implementation Risks of Physiological Sensing in Mobile Health Projects: Quantitative and Qualitative Analysis %A Zhang,Jia %A Tüshaus,Laura %A Nuño Martínez,Néstor %A Moreo,Monica %A Verastegui,Hector %A Hartinger,Stella M %A Mäusezahl,Daniel %A Karlen,Walter %+ Mobile Health Systems Lab, Institute of Robotics and Intelligent Systems, Department of Health Sciences and Technology, ETH Zurich, Building BAA, Lengghalde 5, Zurich, 8092, Switzerland, 41 44 63 3 77 54, walter.karlen@ieee.org %K physiological monitoring %K data completeness %K data quality %K signal quality %K medical sensors %K implementation research %K content analysis %K mHealth %K digital health %D 2018 %7 14.12.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Mobile health (mHealth) technologies have the potential to bring health care closer to people with otherwise limited access to adequate health care. However, physiological monitoring using mobile medical sensors is not yet widely used as adding biomedical sensors to mHealth projects inherently introduces new challenges. Thus far, no methodology exists to systematically evaluate these implementation challenges and identify the related risks. Objective: This study aimed to facilitate the implementation of mHealth initiatives with mobile physiological sensing in constrained health systems by developing a methodology to systematically evaluate potential challenges and implementation risks. Methods: We performed a quantitative analysis of physiological data obtained from a randomized household intervention trial that implemented sensor-based mHealth tools (pulse oximetry combined with a respiratory rate assessment app) to monitor health outcomes of 317 children (aged 6-36 months) that were visited weekly by 1 of 9 field workers in a rural Peruvian setting. The analysis focused on data integrity such as data completeness and signal quality. In addition, we performed a qualitative analysis of pretrial usability and semistructured posttrial interviews with a subset of app users (7 field workers and 7 health care center staff members) focusing on data integrity and reasons for loss thereof. Common themes were identified using a content analysis approach. Risk factors of each theme were detailed and then generalized and expanded into a checklist by reviewing 8 mHealth projects from the literature. An expert panel evaluated the checklist during 2 iterations until agreement between the 5 experts was achieved. Results: Pulse oximetry signals were recorded in 78.36% (12,098/15,439) of subject visits where tablets were used. Signal quality decreased for 1 and increased for 7 field workers over time (1 excluded). Usability issues were addressed and the workflow was improved. Users considered the app easy and logical to use. In the qualitative analysis, we constructed a thematic map with the causes of low data integrity. We sorted them into 5 main challenge categories: environment, technology, user skills, user motivation, and subject engagement. The obtained categories were translated into detailed risk factors and presented in the form of an actionable checklist to evaluate possible implementation risks. By visually inspecting the checklist, open issues and sources for potential risks can be easily identified. Conclusions: We developed a data integrity–based methodology to assess the potential challenges and risks of sensor-based mHealth projects. Aiming at improving data integrity, implementers can focus on the evaluation of environment, technology, user skills, user motivation, and subject engagement challenges. We provide a checklist to assist mHealth implementers with a structured evaluation protocol when planning and preparing projects. %M 30552079 %R 10.2196/11896 %U http://mhealth.jmir.org/2018/12/e11896/ %U https://doi.org/10.2196/11896 %U http://www.ncbi.nlm.nih.gov/pubmed/30552079 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 20 %N 12 %P e10802 %T Continuous Versus Intermittent Vital Signs Monitoring Using a Wearable, Wireless Patch in Patients Admitted to Surgical Wards: Pilot Cluster Randomized Controlled Trial %A Downey,Candice %A Randell,Rebecca %A Brown,Julia %A Jayne,David G %+ Leeds Institute of Biomedical & Clinical Sciences, University of Leeds, Clinical Sciences Building, St James’s University Hospital, Leeds, LS9 7TF, United Kingdom, 44 7544783204, c.l.downey@leeds.ac.uk %K general surgery %K monitoring %K physiological %K randomized controlled trial %K vital signs %D 2018 %7 11.12.2018 %9 Original Paper %J J Med Internet Res %G English %X Background: Vital signs monitoring is a universal tool for the detection of postoperative complications; however, unwell patients can be missed between traditional observation rounds. New remote monitoring technologies promise to convey the benefits of continuous monitoring to patients in general wards. Objective: The aim of this pilot study was to evaluate whether continuous remote vital signs monitoring is a practical and acceptable way of monitoring surgical patients and to optimize the delivery of a definitive trial. Methods: We performed a prospective, cluster-randomized, parallel-group, unblinded, controlled pilot study. Patients admitted to 2 surgical wards at a large tertiary hospital received either continuous and intermittent vital signs monitoring or intermittent monitoring alone using an early warning score system. Continuous monitoring was provided by a wireless patch, worn on the patient’s chest, with data transmitted wirelessly every 2 minutes to a central monitoring station or a mobile device carried by the patient’s nurse. The primary outcome measure was time to administration of antibiotics in sepsis. The secondary outcome measures included the length of hospital stay, 30-day readmission rate, mortality, and patient acceptability. Results: Overall, 226 patients were randomized between January and June 2017. Of 226 patients, 140 were randomized to continuous remote monitoring and 86 to intermittent monitoring alone. On average, patients receiving continuous monitoring were administered antibiotics faster after evidence of sepsis (626 minutes, n=22, 95% CI 431.7-820.3 minutes vs 1012.8 minutes, n=12, 95% CI 425.0-1600.6 minutes), had a shorter average length of hospital stay (13.3 days, 95% CI 11.3-15.3 days vs 14.6 days, 95% CI 11.5-17.7 days), and were less likely to require readmission within 30 days of discharge (11.4%, 95% CI 6.16-16.7 vs 20.9%, 95% CI 12.3-29.5). Wide CIs suggest these differences are not statistically significant. Patients found the monitoring device to be acceptable in terms of comfort and perceived an enhanced sense of safety, despite 24% discontinuing the intervention early. Conclusions: Remote continuous vital signs monitoring on surgical wards is practical and acceptable to patients. Large, well-controlled studies in high-risk populations are required to determine whether the observed trends translate into a significant benefit for continuous over intermittent monitoring. Trial Registration: International Standard Randomised Controlled Trial Number ISRCTN60999823; http://www.isrctn.com /ISRCTN60999823 (Archived by WebCite at http://www.webcitation.org/73ikP6OQz) %M 30538086 %R 10.2196/10802 %U https://www.jmir.org/2018/12/e10802/ %U https://doi.org/10.2196/10802 %U http://www.ncbi.nlm.nih.gov/pubmed/30538086 %0 Journal Article %@ 2561-3278 %I JMIR Publications %V 3 %N 1 %P e11057 %T Auralife Instant Blood Pressure App in Measuring Resting Heart Rate: Validation Study %A Plante,Timothy B %A O'Kelly,Anna C %A Urrea,Bruno %A Macfarlane,Zane T %A Appel,Lawrence J %A Miller III,Edgar R %A Blumenthal,Roger S %A Martin,Seth S %+ Department of Medicine, Larner College of Medicine, University of Vermont, 360 S Park Drive, Room 206B, Colchester, VT, 05446, United States, 1 8026563688, timothy.plante@uvm.edu %K mHealth %K digital health %K heart rate %K validation study %K photoplethysmography %K medical informatics %K mobile phones %D 2018 %7 21.11.2018 %9 Original Paper %J JMIR Biomed Eng %G English %X Background: mHealth apps that measure heart rate using pulse photoplethysmography (PPG) are classified as class II (moderate-risk) Food and Drug Administration devices; therefore, these devices need clinical validation prior to public release. The Auralife Instant Blood Pressure app (AuraLife IBP app) is an mHealth app that measures blood pressure inaccurately based on a previous validation study. Its ability to measure heart rate has not been previously reported. Objective: The objective of our study was to assess the accuracy and precision of the AuraLife IBP app in measuring heart rate. Methods: We enrolled 85 adults from ambulatory clinics. Two measurements were obtained using the AuraLife IBP app, and 2 other measurements were achieved with a oscillometric device. The order of devices was randomized. Accuracy was assessed by calculating the relative and absolute mean differences between heart rate measurements obtained using each AuraLife IBP app and an average of both standard heart rate measurements. Precision was assessed by calculating the relative and absolute mean differences between individual measurements in the pair for each device. Results: The relative and absolute mean (SD) differences between the devices were 1.1 (3.5) and 2.8 (2.4) beats per minute (BPM), respectively. Meanwhile, the within-device relative and absolute mean differences, respectively, were <0.1 (2.2) and 1.7 (1.4) BPM for the standard device and −0.1 (3.2) and 2.2 (2.3) BPM for the AuraLife IBP app. Conclusions: The AuraLife IBP app had a high degree of accuracy and precision in the measurement of heart rate. This supports the use of PPG technology in smartphones for monitoring resting heart rate. %R 10.2196/11057 %U http://biomedeng.jmir.org/2018/1/e11057/ %U https://doi.org/10.2196/11057 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 11 %P e12048 %T A Cardiopulmonary Monitoring System for Patient Transport Within Hospitals Using Mobile Internet of Things Technology: Observational Validation Study %A Lee,Jang Ho %A Park,Yu Rang %A Kweon,Solbi %A Kim,Seulgi %A Ji,Wonjun %A Choi,Chang-Min %+ Department of Pulmonology and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro, 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea, 82 2 3010 5902, ccm@amc.seoul.kr %K wearable device %K patient safety %K intrahospital transport %K oxygen saturation %K heart rate %K mobile application %K real-time monitoring %D 2018 %7 14.11.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: During intrahospital transport, adverse events are inevitable. Real-time monitoring can be helpful for preventing these events during intrahospital transport. Objective: We attempted to determine the viability of risk signal detection using wearable devices and mobile apps during intrahospital transport. An alarm was sent to clinicians in the event of oxygen saturation below 90%, heart rate above 140 or below 60 beats per minute (bpm), and network errors. We validated the reliability of the risk signal transmitted over the network. Methods: We used two wearable devices to monitor oxygen saturation and heart rate for 23 patients during intrahospital transport for diagnostic workup or rehabilitation. To determine the agreement between the devices, records collected every 4 seconds were matched and imputation was performed if no records were collected at the same time by both devices. We used intraclass correlation coefficients (ICC) to evaluate the relationships between the two devices. Results: Data for 21 patients were delivered to the cloud over LTE, and data for two patients were delivered over Wi-Fi. Monitoring devices were used for 20 patients during intrahospital transport for diagnostic work up and for three patients during rehabilitation. Three patients using supplemental oxygen before the study were included. In our study, the ICC for the heart rate between the two devices was 0.940 (95% CI 0.939-0.942) and that of oxygen saturation was 0.719 (95% CI 0.711-0.727). Systemic error analyzed with Bland-Altman analysis was 0.428 for heart rate and –1.404 for oxygen saturation. During the study, 14 patients had 20 risk signals: nine signals for eight patients with less than 90% oxygen saturation, four for four patients with a heart rate of 60 bpm or less, and seven for five patients due to network error. Conclusions: We developed a system that notifies the health care provider of the risk level of a patient during transportation using a wearable device and a mobile app. Although there were some problems such as missing values and network errors, this paper is meaningful in that the previously mentioned risk detection system was validated with actual patients. %M 30429115 %R 10.2196/12048 %U http://mhealth.jmir.org/2018/11/e12048/ %U https://doi.org/10.2196/12048 %U http://www.ncbi.nlm.nih.gov/pubmed/30429115 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 7 %P e10126 %T A Novel 12-Lead Electrocardiographic System for Home Use: Development and Usability Testing %A Steijlen,Annemarijn SM %A Jansen,Kaspar MB %A Albayrak,Armagan %A Verschure,Derk O %A Van Wijk,Diederik F %+ Design Engineering, Faculty of Industrial Design Engineering, Delft University of Technology, 32-B-3-040, Landbergstraat 15, Delft, 2628 CE, Netherlands, 31 152781819, a.s.m.steijlen@tudelft.nl %K 12-lead ECG system %K electrocardiography %K home use %K handheld %K user-centered design %D 2018 %7 30.07.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Cardiovascular diseases (CVD) are the leading cause of morbidity and mortality worldwide. Early diagnosis is of pivotal importance for patients with cardiac arrhythmias and ischemia to minimize the consequences like strokes and myocardial infarctions. The chance of capturing signals of arrhythmias or ischemia is substantially high when a 12-lead electrocardiogram (ECG) can be recorded at the moment when a patient experiences the symptoms. However, until now, available diagnostic systems (Holter monitors and other wearable ECG sensors) have not enabled patients to record a reliable 12-lead ECG at home. Objective: The objective of this project was to develop a user-friendly system that enables persons with cardiac complaints to record a reliable 12-lead ECG at home to improve the diagnostic process and, consequently, reduce the time between the onset of symptoms and adequate treatment. Methods: Using an iterative design approach, ECGraph was developed. The system consists of an ECG measurement system and a mobile app, which were developed with the help of several concept tests. To evaluate the design, a prototype of the final design was built and a final technical performance test and usability test were executed. Results: The ECG measurement system consists of a belt and 4 limb straps. Ten wet Ag/AgCl electrodes are placed in the belt to optimize skin-electrode contact. The product is controlled via an app on the mobile phone of the user. Once a person experiences symptoms, he or she can put on the belt and record ECGs within a few minutes. Short instructions, supported by visualizations, offer guidance during use. ECGs are sent wirelessly to the caregiver, and the designated expert can quickly interpret the results. Usability tests with the final prototype (n=6) showed that the participants were able to put on the product within 8 minutes during first-time use. However, we expect that the placement of the product can be executed faster when the user becomes more familiar with the product. Areas of improvement focus mainly on confidence during product use. In the technical performance test, a 12-lead ECG was made and reproduced 6 times. Conclusions: We developed a new 12-lead ECG system for home use. The product is expected to be more user-friendly than current hospital ECG systems and is designed to record more reliable data than current ECG systems for home use, which makes it suitable for expert interpretation. The system has great potential to be incorporated into an outpatient practice, so that arrhythmias and ischemia can be diagnosed and treated as early as possible. %M 30061094 %R 10.2196/10126 %U http://mhealth.jmir.org/2018/7/e10126/ %U https://doi.org/10.2196/10126 %U http://www.ncbi.nlm.nih.gov/pubmed/30061094 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 7 %P e159 %T Clinical Feasibility of Monitoring Resting Heart Rate Using a Wearable Activity Tracker in Patients With Thyrotoxicosis: Prospective Longitudinal Observational Study %A Lee,Jie-Eun %A Lee,Dong Hwa %A Oh,Tae Jung %A Kim,Kyoung Min %A Choi,Sung Hee %A Lim,Soo %A Park,Young Joo %A Park,Do Joon %A Jang,Hak Chul %A Moon,Jae Hoon %+ Department of Internal Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro, 173 Beon-gil, Sungnam-si, 13620, Republic Of Korea, 82 31 787 7068, jaemoon76@gmail.com %K activity tracker %K wearable device %K heart rate %K thyrotoxicosis %K hyperthyroidism %K Graves’ disease %D 2018 %7 13.07.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Symptoms and signs of thyrotoxicosis are nonspecific and assessing its clinical status is difficult with conventional physical examinations and history taking. Increased heart rate (HR) is one of the easiest signs to quantify this, and current wearable devices can monitor HR. Objective: We assessed the association between thyroid function and resting HR measured by a wearable activity tracker (WD-rHR) and evaluated the clinical feasibility of using this method in patients with thyrotoxicosis. Methods: Thirty patients with thyrotoxicosis and 10 controls were included in the study. Participants were instructed to use the wearable activity tracker during the study period so that activity and HR data could be collected. The primary study outcomes were verification of changes in WD-rHR during thyrotoxicosis treatment and associations between WD-rHR and thyroid function. Linear and logistic model generalized estimating equation analyses were performed and the results were compared to conventionally obtained resting HR during clinic visits (on-site resting HR) and the Hyperthyroidism Symptom Scale. Results: WD-rHR was higher in thyrotoxic patients than in the control groups and decreased in association with improvement of thyrotoxicosis. A one standard deviation–increase of WD-rHR of about 11 beats per minute (bpm) was associated with the increase of serum free T4 levels (beta=.492, 95% CI 0.367-0.616, P<.001) and thyrotoxicosis risk (odds ratio [OR] 3.840, 95% CI 2.113-6.978, P<.001). Although the Hyperthyroidism Symptom Scale showed similar results with WD-rHR, a 1 SD-increase of on-site rHR (about 16 beats per minute) showed a relatively lower beta and OR (beta=.396, 95% CI 0.204-0.588, P<.001; OR 2.114, 95% CI 1.365-3.273, P<.001) compared with WD-rHR. Conclusions: Heart rate data measured by a wearable device showed reasonable predictability of thyroid function. This simple, easy-to-measure parameter is clinically feasible and has the potential to manage thyroid dysfunction. Trial Registration: ClinicalTrials.gov NCT03009357; https://clinicaltrials.gov/ct2/show/NCT03009357 (Archived by WebCite at http://www.webcitation.org/70h55Llyg) %M 30006328 %R 10.2196/mhealth.9884 %U http://mhealth.jmir.org/2018/7/e159/ %U https://doi.org/10.2196/mhealth.9884 %U http://www.ncbi.nlm.nih.gov/pubmed/30006328 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 20 %N 7 %P e10108 %T Methodological Shortcomings of Wrist-Worn Heart Rate Monitors Validations %A Sartor,Francesco %A Papini,Gabriele %A Cox,Lieke Gertruda Elisabeth %A Cleland,John %+ Personal Health, Philips Research, High Tech Campus, Eindhoven,, Netherlands, 31 61 550 9627, francesco.sartor@philips.com %K sensor technology %K accuracy %K wearable %K telemonitoring %D 2018 %7 02.07.2018 %9 Viewpoint %J J Med Internet Res %G English %X Wearable sensor technology could have an important role for clinical research and in delivering health care. Accordingly, such technology should undergo rigorous evaluation prior to market launch, and its performance should be supported by evidence-based marketing claims. Many studies have been published attempting to validate wrist-worn photoplethysmography (PPG)-based heart rate monitoring devices, but their contrasting results question the utility of this technology. The reason why many validations did not provide conclusive evidence of the validity of wrist-worn PPG-based heart rate monitoring devices is mostly methodological. The validation strategy should consider the nature of data provided by both the investigational and reference devices. There should be uniformity in the statistical approach to the analyses employed in these validation studies. The investigators should test the technology in the population of interest and in a setting appropriate for intended use. Device industries and the scientific community require robust standards for the validation of new wearable sensor technology. %M 29967000 %R 10.2196/10108 %U http://www.jmir.org/2018/7/e10108/ %U https://doi.org/10.2196/10108 %U http://www.ncbi.nlm.nih.gov/pubmed/29967000 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 5 %P e120 %T Nontraditional Electrocardiogram and Algorithms for Inconspicuous In-Home Monitoring: Comparative Study %A Conn,Nicholas J %A Schwarz,Karl Q %A Borkholder,David A %+ Microsystems Engineering, Rochester Institute of Technology, 168 Lomb Memorial Drive, Rochester, NY, 14623, United States, 1 585 475 6067, david.borkholder@rit.edu %K algorithms %K delineation %K ECG %K EDB %K electrocardiogram %K Internet of Things %K IoT %K MITDB %K signal quality %K wearable %D 2018 %7 28.05.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wearable and connected in-home medical devices are typically utilized in uncontrolled environments and often measure physiologic signals at suboptimal locations. Motion artifacts and reduced signal-to-noise ratio, compared with clinical grade equipment, results in a highly variable signal quality that can change significantly from moment to moment. The use of signal quality classification algorithms and robust feature delineation algorithms designed to achieve high accuracy on poor quality physiologic signals can prove beneficial in addressing concerns associated with measurement accuracy, confidence, and clinical validity. Objective: The objective of this study was to demonstrate the successful extraction of clinical grade measures using a custom signal quality classification algorithm for the rejection of poor-quality regions and a robust QRS delineation algorithm from a nonstandard electrocardiogram (ECG) integrated into a toilet seat; a device plagued by many of the same challenges as wearable technologies and other Internet of Things–based medical devices. Methods: The present algorithms were validated using a study of 25 normative subjects and 29 heart failure (HF) subjects. Measurements captured from a toilet seat-based buttocks electrocardiogram were compared with a simultaneously captured 12-lead clinical grade ECG. The ECG lead with the highest morphological correlation to buttocks electrocardiogram was used to determine the accuracy of the heart rate (HR), heart rate variability (HRV), which used the standard deviation of the normal-to-normal (SDNN) intervals between sinus beats, QRS duration, and the corrected QT interval (QTc). These algorithms were benchmarked using the MIT-BIH Arrhythmia Database (MITDB) and European ST-T Database (EDB), which are standardized databases commonly used to test QRS detection algorithms. Results: Clinical grade accuracy was achieved for all buttocks electrocardiogram measures compared with standard Lead II. For the normative cohort, the mean was −0.0 (SD 0.3) bpm (N=141 recordings) for HR accuracy and −1.0 (SD 3.4) ms for HRV (N=135). The QRS duration and the QTc interval had an accuracy of −0.5 (SD 6.6) ms (N=85) and 14.5 (SD 11.1) ms (N=85), respectively. In the HF cohort, the accuracy for HR, HRV, QRS duration, and QTc interval was 0.0 (SD 0.3) bpm (N=109), −6.6 (SD 13.2) ms (N=99), 2.9 (SD 11.5) ms (N=59), and 11.2 (SD 19.1) ms (N=58), respectively. When tested on MITDB and EDB, the algorithms presented herein had an overall sensitivity and positive predictive value of over 99.82% (N=900,059 total beats), which is comparable to best in-class algorithms tuned specifically for use with these databases. Conclusions: The present algorithmic approach to data analysis of noisy physiologic data was successfully demonstrated using a toilet seat-based ECG remote monitoring system. This approach to the analysis of physiologic data captured from wearable and connected devices has future potential to enable new types of monitoring devices, providing new insights through daily, inconspicuous in-home monitoring. %R 10.2196/mhealth.9604 %U http://mhealth.jmir.org/2018/5/e120/ %U https://doi.org/10.2196/mhealth.9604 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 5 %P e118 %T Novel Method to Efficiently Create an mHealth App: Implementation of a Real-Time Electrocardiogram R Peak Detector %A Gliner,Vadim %A Behar,Joachim %A Yaniv,Yael %+ Technion-IIT, Silver Building, Biomedical Engineering, Haifa, 32000, Israel, 972 48294124, yaely@bm.technion.ac.il %K atrial fibrillation %K arrhythmia %K heart rate variability %K MATLAB Mobile %K mobile device %D 2018 %7 22.05.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: In parallel to the introduction of mobile communication devices with high computational power and internet connectivity, high-quality and low-cost health sensors have also become available. However, although the technology does exist, no clinical mobile system has been developed to monitor the R peaks from electrocardiogram recordings in real time with low false positive and low false negative detection. Implementation of a robust electrocardiogram R peak detector for various arrhythmogenic events has been hampered by the lack of an efficient design that will conserve battery power without reducing algorithm complexity or ease of implementation. Objective: Our goals in this paper are (1) to evaluate the suitability of the MATLAB Mobile platform for mHealth apps and whether it can run on any phone system, and (2) to embed in the MATLAB Mobile platform a real-time electrocardiogram R peak detector with low false positive and low false negative detection in the presence of the most frequent arrhythmia, atrial fibrillation. Methods: We implemented an innovative R peak detection algorithm that deals with motion artifacts, electrical drift, breathing oscillations, electrical spikes, and environmental noise by low-pass filtering. It also fixes the signal polarity and deals with premature beats by heuristic filtering. The algorithm was trained on the annotated non–atrial fibrillation MIT-BIH Arrhythmia Database and tested on the atrial fibrillation MIT-BIH Arrhythmia Database. Finally, the algorithm was implemented on mobile phones connected to a mobile electrocardiogram device using the MATLAB Mobile platform. Results: Our algorithm precisely detected the R peaks with a sensitivity of 99.7% and positive prediction of 99.4%. These results are superior to some state-of-the-art algorithms. The algorithm performs similarly on atrial fibrillation and non–atrial fibrillation patient data. Using MATLAB Mobile, we ran our algorithm in less than an hour on both the iOS and Android system. Our app can accurately analyze 1 minute of real-time electrocardiogram signals in less than 1 second on a mobile phone. Conclusions: Accurate real-time identification of heart rate on a beat-to-beat basis in the presence of noise and atrial fibrillation events using a mobile phone is feasible. %M 29789276 %R 10.2196/mhealth.8429 %U http://mhealth.jmir.org/2018/5/e118/ %U https://doi.org/10.2196/mhealth.8429 %U http://www.ncbi.nlm.nih.gov/pubmed/29789276 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 2 %N 1 %P e7 %T Handheld Ultrasound as a Novel Predictive Tool in Atrial Fibrillation: Prediction of Outcomes Following Electrical Cardioversion %A Kehl,Devin %A Zimmer,Raymond %A Sudan,Madhuri %A Kedan,Ilan %+ Cedars Sinai Heart Institute, Suite 403, 250 N Robertson Blvd, Beverly Hills, CA, 90211, United States, 1 3103853496, kedani@cshs.org %K atrial fibrillation %K cardioversion %K recurrence %K inferior vena cava %K hand held ultrasound %K point of care %D 2018 %7 08.03.2018 %9 Original Paper %J JMIR Cardio %G English %X Background: Atrial fibrillation (AF) recurrence after successful direct current cardioversion (CV) is common, and clinical predictors may be useful. We evaluated the risk of early AF recurrence according to inferior vena cava (IVC) measurements by handheld ultrasound (HHU) at the time of CV. Objective: Assess HHU and objectively obtained measurements acquired at the point of care as potential clinical predictors of future clinical outcomes in patients with AF undergoing CV. Methods: Maximum IVC diameter (IVCd) and collapsibility with inspiration were measured by the Vscan HHU (General Electric Healthcare Division) in 128 patients immediately before and after successful CV for AF. Patients were followed by chart review for recurrence of AF. Results: Mean IVCd was 2.16 cm in AF pre-CV and 2.01 cm in sinus rhythm post-CV (P<.001). AF recurred within 30 days of CV in 34 of 128 patients (26.6%). Among patients with IVCd <2.1 cm pre-CV and decrease in IVCd post-CV, AF recurrence was 12.1%, compared to 31.6% in patients not meeting these parameters (odds ratio [OR] 0.299, P=.04). This association persisted after adjustment for age, ejection fraction <50%, left atrial enlargement, and amiodarone use (adjusted OR 0.185, P=.01). Among patients with IVCd post-CV <1.7 cm, AF recurrence was 13.5%, compared to 31.9% in patients not meeting this parameter (OR 0.185, P=.01). IVC parameters did not predict AF recurrence at 180 or 365 days. Conclusions: The presence of a normal IVCd pre-CV that becomes smaller post-CV and the presence of a small IVCd post-CV were each independently associated with reduced likelihood of early, but not late, AF recurrence. HHU assessment of IVCd at the time of CV may be useful to identify patients at low risk of early recurrence of AF after CV. %M 31758780 %R 10.2196/cardio.9534 %U http://cardio.jmir.org/2018/1/e7/ %U https://doi.org/10.2196/cardio.9534 %U http://www.ncbi.nlm.nih.gov/pubmed/31758780 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 2 %P e49 %T Cardiac Auscultation Using Smartphones: Pilot Study %A Kang,Si-Hyuck %A Joe,Byunggill %A Yoon,Yeonyee %A Cho,Goo-Yeong %A Shin,Insik %A Suh,Jung-Won %+ Division of Cardiology, Cardiovascular Center, Seoul National University Bundang Hospital, 166 Gumi-ro, Bundang-gu, Seongnam-si, 13620, Republic Of Korea, 82 31 787 7016, suhjw1@gmail.com %K cardiac auscultation %K physical examination %K smartphone %K mobile health care %K telemedicine %D 2018 %7 28.02.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Cardiac auscultation is a cost-effective, noninvasive screening tool that can provide information about cardiovascular hemodynamics and disease. However, with advances in imaging and laboratory tests, the importance of cardiac auscultation is less appreciated in clinical practice. The widespread use of smartphones provides opportunities for nonmedical expert users to perform self-examination before hospital visits. Objective: The objective of our study was to assess the feasibility of cardiac auscultation using smartphones with no add-on devices for use at the prehospital stage. Methods: We performed a pilot study of patients with normal and pathologic heart sounds. Heart sounds were recorded on the skin of the chest wall using 3 smartphones: the Samsung Galaxy S5 and Galaxy S6, and the LG G3. Recorded heart sounds were processed and classified by a diagnostic algorithm using convolutional neural networks. We assessed diagnostic accuracy, as well as sensitivity, specificity, and predictive values. Results: A total of 46 participants underwent heart sound recording. After audio file processing, 30 of 46 (65%) heart sounds were proven interpretable. Atrial fibrillation and diastolic murmur were significantly associated with failure to acquire interpretable heart sounds. The diagnostic algorithm classified the heart sounds into the correct category with high accuracy: Galaxy S5, 90% (95% CI 73%-98%); Galaxy S6, 87% (95% CI 69%-96%); and LG G3, 90% (95% CI 73%-98%). Sensitivity, specificity, positive predictive value, and negative predictive value were also acceptable for the 3 devices. Conclusions: Cardiac auscultation using smartphones was feasible. Discrimination using convolutional neural networks yielded high diagnostic accuracy. However, using the built-in microphones alone, the acquisition of reproducible and interpretable heart sounds was still a major challenge. Trial Registration: ClinicalTrials.gov NCT03273803; https://clinicaltrials.gov/ct2/show/NCT03273803 (Archived by WebCite at http://www.webcitation.org/6x6g1fHIu) %M 29490899 %R 10.2196/mhealth.8946 %U http://mhealth.jmir.org/2018/2/e49/ %U https://doi.org/10.2196/mhealth.8946 %U http://www.ncbi.nlm.nih.gov/pubmed/29490899 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 2 %N 1 %P e6 %T Measuring Moderate-Intensity Exercise with the Apple Watch: Validation Study %A Abt,Grant %A Bray,James %A Benson,Amanda Clare %+ School of Life Sciences, The University of Hull, Cottingham Road, Kingston upon Hull, HU6 7RX, United Kingdom, 44 01482463397, g.abt@hull.ac.uk %K smartwatch %K wearables %K technology %K physical activity %K cardiovascular health, Apple Watch %D 2018 %7 28.02.2018 %9 Original Paper %J JMIR Cardio %G English %X Background: Moderate fitness levels and habitual exercise have a protective effect for cardiovascular disease, stroke, type 2 diabetes, and all-cause mortality. The Apple Watch displays exercise completed at an intensity of a brisk walk or above using a green “exercise” ring. However, it is unknown if the exercise ring accurately represents an exercise intensity comparable to that defined as moderate-intensity. In order for health professionals to prescribe exercise intensity with confidence, consumer wearable devices need to be accurate and precise if they are to be used as part of a personalized medicine approach to disease management. Objective: The aim of this study was to examine the validity and reliability of the Apple Watch for measuring moderate-intensity exercise, as defined as 40-59% oxygen consumption reserve (VO2R). Methods: Twenty recreationally active participants completed resting oxygen consumption (VO2rest) and maximal oxygen consumption (VO2 max) tests prior to a series of 5-minute bouts of treadmill walking at increasing speed while wearing an Apple Watch on both wrists, and with oxygen consumption measured continuously. Five-minute exercise bouts were added until the Apple Watch advanced the green “exercise” ring by 5 minutes (defined as the treadmill inflection speed). Validity was examined using a one-sample t-test, with interdevice and intradevice reliability reported as the standardized typical error and intraclass correlation. Results: The mean %VO2R at the treadmill inflection speed was 30% (SD 7) for both Apple Watches. There was a large underestimation of moderate-intensity exercise (left hand: mean difference = -10% [95% CI -14 to -7], d=-1.4; right hand: mean difference = -10% [95% CI -13 to -7], d=-1.5) when compared to the criterion of 40% VO2R. Standardized typical errors for %VO2R at the treadmill inflection speed were small to moderate, with intraclass correlations higher within trials compared to between trials. Conclusions: The Apple Watch threshold for moderate-intensity exercise was lower than the criterion, which would lead to an overestimation of moderate-intensity exercise minutes completed throughout the day. %M 31758766 %R 10.2196/cardio.8574 %U http://cardio.jmir.org/2018/1/e6/ %U https://doi.org/10.2196/cardio.8574 %U http://www.ncbi.nlm.nih.gov/pubmed/31758766 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 2 %N 1 %P e1 %T Consumer Wearable Devices for Activity Monitoring Among Individuals After a Stroke: A Prospective Comparison %A Rozanski,Gabriela M %A Aqui,Anthony %A Sivakumaran,Shajicaa %A Mansfield,Avril %+ Mobility Team, Toronto Rehabilitation Institute-University Health Network, University Centre, 550 University Avenue, Toronto, ON, M5G 2A2, Canada, 1 416 597 3422 ext 3872, gabriela.rozanski@uhn.ca %K physical activity %K heart rate %K accelerometry %K stroke rehabilitation %K walking %D 2018 %7 04.01.2018 %9 Original Paper %J JMIR Cardio %G English %X Background: Activity monitoring is necessary to investigate sedentary behavior after a stroke. Consumer wearable devices are an attractive alternative to research-grade technology, but measurement properties have not been established. Objective: The purpose of this study was to determine the accuracy of 2 wrist-worn fitness trackers: Fitbit Charge HR (FBT) and Garmin Vivosmart (GAR). Methods: Adults attending in- or outpatient therapy for stroke (n=37) wore FBT and GAR each on 2 separate days, in addition to an X6 accelerometer and Actigraph chest strap monitor. Step counts and heart rate data were extracted, and the agreement between devices was determined using Pearson or Spearman correlation and paired t or Wilcoxon signed rank tests (one- and two-sided). Subgroup analyses were conducted. Results: Step counts from FBT and GAR positively correlated with the X6 accelerometer (ρ=.78 and ρ=.65, P<.001, respectively) but were significantly lower (P<.01). For individuals using a rollator, there was no significant correlation between step counts from the X6 accelerometer and either FBT (ρ=.42, P=.12) or GAR (ρ=.30, P=.27). Heart rate from Actigraph, FBT, and GAR demonstrated responsiveness to changes in activity. Both FBT and GAR positively correlated with Actigraph for average heart rate (r=.53 and .75, P<.01, respectively) and time in target zone (ρ=.49 and .74, P<.01, respectively); these measures were not significantly different, but nonequivalence was found. Conclusions: FBT and GAR had moderate to strong correlation with best available reference measures of walking activity in individuals with subacute stroke. Accuracy appears to be lower among rollator users and varies according to heart rhythm. Consumer wearables may be a viable option for large-scale studies of physical activity. %M 31758760 %R 10.2196/cardio.8199 %U http://cardio.jmir.org/2018/1/e1/ %U https://doi.org/10.2196/cardio.8199 %U http://www.ncbi.nlm.nih.gov/pubmed/31758760 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 1 %N 2 %P e8 %T Assessing the Use of Wrist-Worn Devices in Patients With Heart Failure: Feasibility Study %A Moayedi,Yasbanoo %A Abdulmajeed,Raghad %A Duero Posada,Juan %A Foroutan,Farid %A Alba,Ana Carolina %A Cafazzo,Joseph %A Ross,Heather Joan %+ Ted Rogers Centre of Excellence in Heart Function, University Health Network, Toronto General Hospital, 190 Elizabeth St, Toronto, ON, M5G 2C4, Canada, 1 416 340 3482, heather.ross@uhn.ca %K MeSH: exercise physiology %K heart rate tracker %K wrist worn devices %K Fitbit %K Apple watch %K heart failure %K steps %D 2017 %7 19.12.2017 %9 Original Paper %J JMIR Cardio %G English %X Background: Exercise capacity and raised heart rate (HR) are important prognostic markers in patients with heart failure (HF). There has been significant interest in wrist-worn devices that track activity and HR. Objective: We aimed to assess the feasibility and accuracy of HR and activity tracking of the Fitbit and Apple Watch. Methods: We conducted a two-phase study assessing the accuracy of HR by Apple Watch and Fitbit in healthy participants. In Phase 1, 10 healthy individuals wore a Fitbit, an Apple Watch, and a GE SEER Light 5-electrode Holter monitor while exercising on a cycle ergometer with a 10-watt step ramp protocol from 0-100 watts. In Phase 2, 10 patients with HF and New York Heart Association (NYHA) Class II-III symptoms wore wrist devices for 14 days to capture overall step count/exercise levels. Results: Recorded HR by both wrist-worn devices had the best agreement with Holter readings at a workload of 60-100 watts when the rate of change of HR is less dynamic. Fitbit recorded a mean 8866 steps/day for NYHA II patients versus 4845 steps/day for NYHA III patients (P=.04). In contrast, Apple Watch recorded a mean 7027 steps/day for NYHA II patients and 4187 steps/day for NYHA III patients (P=.08). Conclusions: Both wrist-based devices are best suited for static HR rate measurements. In an outpatient setting, these devices may be adequate for average HR in patients with HF. When assessing exercise capacity, the Fitbit better differentiated patients with NYHA II versus NYHA III by the total number of steps recorded. This exploratory study indicates that these wrist-worn devices show promise in prognostication of HF in the continuous monitoring of outpatients. %M 31758789 %R 10.2196/cardio.8301 %U http://cardio.jmir.org/2017/2/e8/ %U https://doi.org/10.2196/cardio.8301 %U http://www.ncbi.nlm.nih.gov/pubmed/31758789 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 11 %P e393 %T Bioimpedance Alerts from Cardiovascular Implantable Electronic Devices: Observational Study of Diagnostic Relevance and Clinical Outcomes %A Smeets,Christophe JP %A Vranken,Julie %A Van der Auwera,Jo %A Verbrugge,Frederik H %A Mullens,Wilfried %A Dupont,Matthias %A Grieten,Lars %A De Cannière,Hélène %A Lanssens,Dorien %A Vandenberk,Thijs %A Storms,Valerie %A Thijs,Inge M %A Vandervoort,Pieter M %+ Department of Cardiology, Ziekenhuis Oost-Limburg, Schiepse Bos 6, Genk,, Belgium, 32 089 32 15 26, christophe.smeets@uhasselt.be %K defibrillators, implantable %K cardiac resynchronization therapy %K telemedicine %K electric impedance %K algorithms %K call centers %D 2017 %7 23.11.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: The use of implantable cardioverter-defibrillators (ICDs) and cardiac resynchronization therapy (CRT) devices is expanding in the treatment of heart failure. Most of the current devices are equipped with remote monitoring functions, including bioimpedance for fluid status monitoring. The question remains whether bioimpedance measurements positively impact clinical outcome. Objective: The aim of this study was to provide a comprehensive overview of the clinical interventions taken based on remote bioimpedance monitoring alerts and their impact on clinical outcome. Methods: This is a single-center observational study of consecutive ICD and CRT patients (n=282) participating in protocol-driven remote follow-up. Bioimpedance alerts were analyzed with subsequently triggered interventions. Results: A total of 55.0% (155/282) of patients had an ICD or CRT device equipped with a remote bioimpedance algorithm. During 34 (SD 12) months of follow-up, 1751 remote monitoring alarm notifications were received (2.2 per patient-year of follow-up), comprising 2096 unique alerts (2.6 per patient-year of follow-up). Since 591 (28.2%) of all incoming alerts were bioimpedance-related, patients with an ICD or CRT including a bioimpedance algorithm had significantly more alerts (3.4 versus 1.8 alerts per patient-year of follow-up, P<.001). Bioimpedance-only alerts resulted in a phone contact in 91.0% (498/547) of cases, which triggered an actual intervention in 15.9% (87/547) of cases, since in 75.1% (411/547) of cases reenforcing heart failure education sufficed. Overall survival was lower in patients with a cardiovascular implantable electronic device with a bioimpedance algorithm; however, this difference was driven by differences in baseline characteristics (adjusted hazard ratio of 2.118, 95% CI 0.845-5.791). No significant differences between both groups were observed in terms of the number of follow-up visits in the outpatient heart failure clinic, the number of hospital admissions with a primary diagnosis of heart failure, or mean length of hospital stay. Conclusions: Bioimpedance-only alerts constituted a substantial amount of incoming alerts when turned on during remote follow-up and triggered an additional intervention in only 16% of cases since in 75% of cases, providing general heart failure education sufficed. The high frequency of heart failure education that was provided could have contributed to fewer heart failure–related hospitalizations despite significant differences in baseline characteristics. %M 29170147 %R 10.2196/jmir.8066 %U http://www.jmir.org/2017/11/e393/ %U https://doi.org/10.2196/jmir.8066 %U http://www.ncbi.nlm.nih.gov/pubmed/29170147 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 10 %P e157 %T Fitbit Charge HR Wireless Heart Rate Monitor: Validation Study Conducted Under Free-Living Conditions %A Gorny,Alexander Wilhelm %A Liew,Seaw Jia %A Tan,Chuen Seng %A Müller-Riemenschneider,Falk %+ Saw Swee Hock School of Public Health, National University of Singapore, Tahir Foundation Building #10-01, 12 Science Drive 2, Singapore, 117549, Singapore, 65 65164988, alexander_gorny@u.nus.edu %K heart rate %K photoplethysmography %K telemedicine %K validation studies %D 2017 %7 20.10.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Many modern smart watches and activity trackers feature an optical sensor that estimates the wearer’s heart rate. Recent studies have evaluated the performance of these consumer devices in the laboratory. Objective: The objective of our study was to examine the accuracy and sensitivity of a common wrist-worn tracker device in measuring heart rates and detecting 1-min bouts of moderate to vigorous physical activity (MVPA) under free-living conditions. Methods: Ten healthy volunteers were recruited from a large university in Singapore to participate in a limited field test, followed by a month of continuous data collection. During the field test, each participant would wear one Fitbit Charge HR activity tracker and one Polar H6 heart rate monitor. Fitbit measures were accessed at 1-min intervals, while Polar readings were available for 10-s intervals. We derived intraclass correlation coefficients (ICCs) for individual participants comparing heart rate estimates. We applied Centers for Disease Control and Prevention heart rate zone cut-offs to ascertain the sensitivity and specificity of Fitbit in identifying 1-min epochs falling into MVPA heart rate zone. Results: We collected paired heart rate data for 2509 1-min epochs in 10 individuals under free-living conditions of 3 to 6 hours. The overall ICC comparing 1-min Fitbit measures with average 10-s Polar H6 measures for the same epoch was .83 (95% CI .63-.91). On average, the Fitbit tracker underestimated heart rate measures by −5.96 bpm (standard error, SE=0.18). At the low intensity heart rate zone, the underestimate was smaller at −4.22 bpm (SE=0.15). This underestimate grew to −16.2 bpm (SE=0.74) in the MVPA heart rate zone. Fitbit devices detected 52.9% (192/363) of MVPA heart rate zone epochs correctly. Positive and negative predictive values were 86.1% (192/223) and 92.52% (2115/2286), respectively. During subsequent 1 month of continuous data collection (270 person-days), only 3.9% of 1-min epochs could be categorized as MVPA according to heart rate zones. This measure was affected by decreasing wear time and adherence over the period of follow-up. Conclusions: Under free-living conditions, Fitbit trackers are affected by significant systematic errors. Improvements in tracker accuracy and sensitivity when measuring MVPA are required before they can be considered for use in the context of exercise prescription to promote better health. %M 29055881 %R 10.2196/mhealth.8233 %U http://mhealth.jmir.org/2017/10/e157/ %U https://doi.org/10.2196/mhealth.8233 %U http://www.ncbi.nlm.nih.gov/pubmed/29055881 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 8 %P e129 %T Clinical Validation of Heart Rate Apps: Mixed-Methods Evaluation Study %A Vandenberk,Thijs %A Stans,Jelle %A Mortelmans,Christophe %A Van Haelst,Ruth %A Van Schelvergem,Gertjan %A Pelckmans,Caroline %A Smeets,Christophe JP %A Lanssens,Dorien %A De Cannière,Hélène %A Storms,Valerie %A Thijs,Inge M %A Vaes,Bert %A Vandervoort,Pieter M %+ Mobile Health Unit, Faculty of Medicine and Life Sciences, Hasselt University, Martelarenlaan 42, Hasselt, 3600, Belgium, 32 11268111, thijs.vandenberk@uhasselt.be %K heart rate %K software validation %K remote sensing technology %D 2017 %7 25.8.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Photoplethysmography (PPG) is a proven way to measure heart rate (HR). This technology is already available in smartphones, which allows measuring HR only by using the smartphone. Given the widespread availability of smartphones, this creates a scalable way to enable mobile HR monitoring. An essential precondition is that these technologies are as reliable and accurate as the current clinical (gold) standards. At this moment, there is no consensus on a gold standard method for the validation of HR apps. This results in different validation processes that do not always reflect the veracious outcome of comparison. Objective: The aim of this paper was to investigate and describe the necessary elements in validating and comparing HR apps versus standard technology. Methods: The FibriCheck (Qompium) app was used in two separate prospective nonrandomized studies. In the first study, the HR of the FibriCheck app was consecutively compared with 2 different Food and Drug Administration (FDA)-cleared HR devices: the Nonin oximeter and the AliveCor Mobile ECG. In the second study, a next step in validation was performed by comparing the beat-to-beat intervals of the FibriCheck app to a synchronized ECG recording. Results: In the first study, the HR (BPM, beats per minute) of 88 random subjects consecutively measured with the 3 devices showed a correlation coefficient of .834 between FibriCheck and Nonin, .88 between FibriCheck and AliveCor, and .897 between Nonin and AliveCor. A single way analysis of variance (ANOVA; P=.61 was executed to test the hypothesis that there were no significant differences between the HRs as measured by the 3 devices. In the second study, 20,298 (ms) R-R intervals (RRI)–peak-to-peak intervals (PPI) from 229 subjects were analyzed. This resulted in a positive correlation (rs=.993, root mean square deviation [RMSE]=23.04 ms, and normalized root mean square error [NRMSE]=0.012) between the PPI from FibriCheck and the RRI from the wearable ECG. There was no significant difference (P=.92) between these intervals. Conclusions: Our findings suggest that the most suitable method for the validation of an HR app is a simultaneous measurement of the HR by the smartphone app and an ECG system, compared on the basis of beat-to-beat analysis. This approach could lead to more correct assessments of the accuracy of HR apps. %R 10.2196/mhealth.7254 %U http://mhealth.jmir.org/2017/8/e129/ %U https://doi.org/10.2196/mhealth.7254 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 1 %N 2 %P e2 %T Assessing the Utility of a Novel SMS- and Phone-Based System for Blood Pressure Control in Hypertensive Patients: Feasibility Study %A Peters,Robert Mattson %A Shivakumar,Nishkala %A Xu,Ran %A Javaherian,Kavon %A Sink,Eric %A Patel,Kunjan %A Brown,Angela %A Huynh,Justin %A Blanchard,Melvin %A Ross,Will %A Byrd,Jonathan %+ Washington University in St. Louis School of Medicine, Grant Medical Clinic, 114 North Taylor Avenue., St. Louis, MO, 63108, United States, 1 314 534 8600, byrdj@wustl.edu %K telemedicine %K hypertension %K quality improvement %K text messaging %K primary care %K eHealth %K mHealth %K disease management %D 2017 %7 27.07.2017 %9 Original Paper %J JMIR Cardio %G English %X Background: Although hypertension (HTN) is a major modifiable risk factor for arterial damage, blood pressure (BP) remains poorly controlled in the hypertensive population. Telemedicine is a promising adjunct intervention that may complement traditional therapies and improve adherence rates; however, current approaches have multiple barriers to entry, including the use of relatively expensive Bluetooth devices or the dependence on smart phone utilization, which tend to exclude low-income and more elderly populations. Objective: The aim of this study was to design and implement a new phone call- and short message service text messaging-based intervention, Epharmix’s EpxHypertension, in a quality improvement project that demonstrates the feasibility of this system for BP control in a family medicine setting. Methods: We recruited 174 patients from a community clinic in St Louis from a database of patients diagnosed with HTN. An automated call or text messaging system was used to monitor patient-reported BPs. If determined to be elevated, physicians were notified by an email, text, or electronic medical record alert. Mean systolic BPs (SBPs) and diastolic BPs (DBPs) were compared at the beginning and end of 12 weeks. Results: After 12 weeks on the system, patients with a baseline SBP of 140 mm Hg or higher reduced SBP by 10.8 mm Hg (95% CI −14.5 to −7.2, P<.001) and DBP by 6.6 mm Hg (95% CI −9.9 to −3.4, P=.002), but no significant changes were observed in overall BPs and BPs in the group with baseline SBP less than 140 mm Hg. Conclusions: EpxHypertension provides a viable means to control HTN in patients with high baseline BPs despite previous therapy. This community implementation study demonstrates the feasibility of implementing EpxHypertension across a primary care setting without the need for smartphones or Bluetooth-linked BP cuffs. Future studies should evaluate its effectiveness in a randomized control trial compared with standard of care. %M 31758763 %R 10.2196/cardio.7915 %U http://cardio.jmir.org/2017/2/e2/ %U https://doi.org/10.2196/cardio.7915 %U http://www.ncbi.nlm.nih.gov/pubmed/31758763 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 6 %P e76 %T Direct Adherence Measurement Using an Ingestible Sensor Compared With Self-Reporting in High-Risk Cardiovascular Disease Patients Who Knew They Were Being Measured: A Prospective Intervention %A Thompson,David %A Mackay,Teresa %A Matthews,Maria %A Edwards,Judith %A Peters,Nicholas S %A Connolly,Susan B %+ National Heart and Lung Institute, Imperial College London, 4th Floor Imperial Centre for Translational and Experimental Medicine, Du Cane Road, London, W12 0NN, United Kingdom, 44 2075941880, n.peters@imperial.ac.uk %K cardiac prevention and rehabilitation %K adherence %K mHealth %K remote monitoring %K cardiovascular diseases %K primary prevention %K medication adherence %K telemedicine %D 2017 %7 12.06.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Use of appropriate cardioprotective medication is a cornerstone of cardiovascular disease prevention, but less-than-optimal patient adherence is common. Thus, strategies for improving adherence are recommended to adopt a multifaceted approach. Objective: The objective of our study was to test a system comprising a biodegradable, ingestible sensor for direct measurement of medication ingestion in a group of patients at elevated cardiovascular risk attending a cardiac prevention and rehabilitation program. Methods: In this prospective intervention trial in a single group of 21 patients running from April 2014 to June 2015, we measured adherence by self-report and adherence determined objectively by the system. The sensor emits a signal when it encounters the acidic environment of the stomach, detectable by an externally worn patch and linked software app. Longitudinal adherence data in the form of daily progress charts for sensed dosing events as compared with scheduled dosing are visible to patients on their tablet computer’s medication dosing app, thus providing patients with continuous medication adherence feedback. We sought feedback on patient acceptability by questionnaire assessment. Participants used the system for the 12-week period of their cardiac prevention and rehabilitation program. Results: Only 1 patient at initial assessment and 1 patient at end-of-program assessment reported often missing medication. The remaining patients reported never missing medication or had missing data. Only 12 (57%) of patients overall achieved system-determined adherence of 80% or more, and 3 patients had scores below 40%. Participants reported high levels of acceptability. Conclusions: This integrated system was well tolerated in a group of 21 patients over an appreciable time frame. Its ability to measure adherence reveals the sizeable disconnect between patient self-reported adherence and actual medication taking and has promising potential for clinical use as a tool to encourage better medication-taking behavior due to its ability to provide continuous patient-level feedback. %M 28606895 %R 10.2196/mhealth.6998 %U http://mhealth.jmir.org/2017/6/e76/ %U https://doi.org/10.2196/mhealth.6998 %U http://www.ncbi.nlm.nih.gov/pubmed/28606895 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 1 %N 1 %P e1 %T Bioimpedance-Based Heart Failure Deterioration Prediction Using a Prototype Fluid Accumulation Vest-Mobile Phone Dyad: An Observational Study %A Darling,Chad Eric %A Dovancescu,Silviu %A Saczynski,Jane S %A Riistama,Jarno %A Sert Kuniyoshi,Fatima %A Rock,Joseph %A Meyer,Theo E %A McManus,David D %+ UMass Memorial Health Care, Department of Emergency Medicine, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA, 01655, United States, 1 508 421 1464, chad.darling@umassmed.edu %K telemedicine %K outpatient monitoring %K heart failure %K electric impedance %D 2017 %7 13.03.2017 %9 Original Paper %J JMIR Cardio %G English %X Background: Recurrent heart failure (HF) events are common in patients discharged after acute decompensated heart failure (ADHF). New patient-centered technologies are needed to aid in detecting HF decompensation. Transthoracic bioimpedance noninvasively measures pulmonary fluid retention. Objective: The objectives of our study were to (1) determine whether transthoracic bioimpedance can be measured daily with a novel, noninvasive, wearable fluid accumulation vest (FAV) and transmitted using a mobile phone and (2) establish whether an automated algorithm analyzing daily thoracic bioimpedance values would predict recurrent HF events. Methods: We prospectively enrolled patients admitted for ADHF. Participants were trained to use a FAV–mobile phone dyad and asked to transmit bioimpedance measurements for 45 consecutive days. We examined the performance of an algorithm analyzing changes in transthoracic bioimpedance as a predictor of HF events (HF readmission, diuretic uptitration) over a 75-day follow-up. Results: We observed 64 HF events (18 HF readmissions and 46 diuretic uptitrations) in the 106 participants (67 years; 63.2%, 67/106, male; 48.1%, 51/106, with prior HF) who completed follow-up. History of HF was the only clinical or laboratory factor related to recurrent HF events (P=.04). Among study participants with sufficient FAV data (n=57), an algorithm analyzing thoracic bioimpedance showed 87% sensitivity (95% CI 82-92), 70% specificity (95% CI 68-72), and 72% accuracy (95% CI 70-74) for identifying recurrent HF events. Conclusions: Patients discharged after ADHF can measure and transmit daily transthoracic bioimpedance using a FAV–mobile phone dyad. Algorithms analyzing thoracic bioimpedance may help identify patients at risk for recurrent HF events after hospital discharge. %M 31758769 %R 10.2196/cardio.6057 %U http://cardio.jmir.org/2017/1/e1/ %U https://doi.org/10.2196/cardio.6057 %U http://www.ncbi.nlm.nih.gov/pubmed/31758769 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 3 %P e33 %T Resting and Postexercise Heart Rate Detection From Fingertip and Facial Photoplethysmography Using a Smartphone Camera: A Validation Study %A Yan,Bryan P %A Chan,Christy KY %A Li,Christien KH %A To,Olivia TL %A Lai,William HS %A Tse,Gary %A Poh,Yukkee C %A Poh,Ming-Zher %+ Division of Cardiology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong and Prince of Wales Hospital, 9/F, Division of Cardiology, Department of Medicine and Therapeutics, Clinical Sciences Building, Prince of Wales Hospital, Shatin, N.T., Hong Kong,, China (Hong Kong), 852 2632 3142, bryan.yan@cuhk.edu.hk %K heart rate %K mobile apps %K photoplethysmography %K smartphone %K mobile phone %D 2017 %7 13.03.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Modern smartphones allow measurement of heart rate (HR) by detecting pulsatile photoplethysmographic (PPG) signals with built-in cameras from the fingertips or the face, without physical contact, by extracting subtle beat-to-beat variations of skin color. Objective: The objective of our study was to evaluate the accuracy of HR measurements at rest and after exercise using a smartphone-based PPG detection app. Methods: A total of 40 healthy participants (20 men; mean age 24.7, SD 5.2 years; von Luschan skin color range 14-27) underwent treadmill exercise using the Bruce protocol. We recorded simultaneous PPG signals for each participant by having them (1) facing the front camera and (2) placing their index fingertip over an iPhone’s back camera. We analyzed the PPG signals from the Cardiio-Heart Rate Monitor + 7 Minute Workout (Cardiio) smartphone app for HR measurements compared with a continuous 12-lead electrocardiogram (ECG) as the reference. Recordings of 20 seconds’ duration each were acquired at rest, and immediately after moderate- (50%-70% maximum HR) and vigorous- (70%-85% maximum HR) intensity exercise, and repeated successively until return to resting HR. We used Bland-Altman plots to examine agreement between ECG and PPG-estimated HR. The accuracy criterion was root mean square error (RMSE) ≤5 beats/min or ≤10%, whichever was greater, according to the American National Standards Institute/Association for the Advancement of Medical Instrumentation EC-13 standard. Results: We analyzed a total of 631 fingertip and 626 facial PPG measurements. Fingertip PPG-estimated HRs were strongly correlated with resting ECG HR (r=.997, RMSE=1.03 beats/min or 1.40%), postmoderate-intensity exercise (r=.994, RMSE=2.15 beats/min or 2.53%), and postvigorous-intensity exercise HR (r=.995, RMSE=2.01 beats/min or 1.93%). The correlation of facial PPG-estimated HR was stronger with resting ECG HR (r=.997, RMSE=1.02 beats/min or 1.44%) than with postmoderate-intensity exercise (r=.982, RMSE=3.68 beats/min or 4.11%) or with postvigorous-intensity exercise (r=.980, RMSE=3.84 beats/min or 3.73%). Bland-Altman plots showed better agreement between ECG and fingertip PPG-estimated HR than between ECG and facial PPG-estimated HR. Conclusions: We found that HR detection by the Cardiio smartphone app was accurate at rest and after moderate- and vigorous-intensity exercise in a healthy young adult sample. Contact-free facial PPG detection is more convenient but is less accurate than finger PPG due to body motion after exercise. %M 28288955 %R 10.2196/mhealth.7275 %U http://mhealth.jmir.org/2017/3/e33/ %U https://doi.org/10.2196/mhealth.7275 %U http://www.ncbi.nlm.nih.gov/pubmed/28288955