@Article{info:doi/10.2196/18134,
author="Ding, Xiaodong
and Cheng, Feng
and Morris, Robert
and Chen, Cong
and Wang, Yiqin",
title="Machine Learning--Based Signal Quality Evaluation of Single-Period Radial Artery Pulse Waves: Model Development and Validation",
journal="JMIR Med Inform",
year="2020",
month="Jun",
day="22",
volume="8",
number="6",
pages="e18134",
keywords="pulse wave; quality evaluation; single period; segmentation; machine learning",
abstract="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. ",
doi="10.2196/18134",
url="http://medinform.jmir.org/2020/6/e18134/",
url="https://doi.org/10.2196/18134",
url="http://www.ncbi.nlm.nih.gov/pubmed/32568091"
}


@Article{info:doi/10.2196/17299,
author="Ferguson, Caleb
and Inglis, Sally C
and Breen, Paul P
and Gargiulo, Gaetano D
and Byiers, Victoria
and Macdonald, Peter S
and Hickman, Louise D",
title="Clinician Perspectives on the Design and Application of Wearable Cardiac Technologies for Older Adults: Qualitative Study",
journal="JMIR Aging",
year="2020",
month="Jun",
day="18",
volume="3",
number="1",
pages="e17299",
keywords="technology; arrhythmia; monitoring; older people; cardiology; qualitative; wearable",
abstract="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. ",
doi="10.2196/17299",
url="http://aging.jmir.org/2020/1/e17299/",
url="https://doi.org/10.2196/17299",
url="http://www.ncbi.nlm.nih.gov/pubmed/32554377"
}


@Article{info:doi/10.2196/18636,
author="Leenen, Jobbe P L
and Leerentveld, Crista
and van Dijk, Joris D
and van Westreenen, Henderik L
and Schoonhoven, Lisette
and Patijn, Gijsbert A",
title="Current Evidence for Continuous Vital Signs Monitoring by Wearable Wireless Devices in Hospitalized Adults: Systematic Review",
journal="J Med Internet Res",
year="2020",
month="Jun",
day="17",
volume="22",
number="6",
pages="e18636",
keywords="continuous monitoring; patient monitoring; vital signs; clinical deterioration; early deterioration; wearable wireless device; systematic review; monitoring",
abstract="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. ",
doi="10.2196/18636",
url="http://www.jmir.org/2020/6/e18636/",
url="https://doi.org/10.2196/18636",
url="http://www.ncbi.nlm.nih.gov/pubmed/32469323"
}


@Article{info:doi/10.2196/17106,
author="Coffman, Donna L
and Cai, Xizhen
and Li, Runze
and Leonard, Noelle R",
title="Challenges and Opportunities in Collecting and Modeling Ambulatory Electrodermal Activity Data",
journal="JMIR Biomed Eng",
year="2020",
month="Jun",
day="12",
volume="5",
number="1",
pages="e17106",
keywords="electrodermal activity; functional data analysis; ambulatory stress assessment",
abstract="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. ",
doi="10.2196/17106",
url="http://biomedeng.jmir.org/2020/1/e17106/",
url="https://doi.org/10.2196/17106"
}


@Article{info:doi/10.2196/15471,
author="Weenk, Mariska
and Bredie, Sebastian J
and Koeneman, Mats
and Hesselink, Gijs
and van Goor, Harry
and van de Belt, Tom H",
title="Continuous Monitoring of Vital Signs in the General Ward Using Wearable Devices: Randomized Controlled Trial",
journal="J Med Internet Res",
year="2020",
month="Jun",
day="10",
volume="22",
number="6",
pages="e15471",
keywords="remote sensing technology; wireless technology; continuous monitoring; vital signs; wearable electronic devices; remote monitoring; digital health",
abstract="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. ",
doi="10.2196/15471",
url="https://www.jmir.org/2020/6/e15471",
url="https://doi.org/10.2196/15471",
url="http://www.ncbi.nlm.nih.gov/pubmed/32519972"
}


@Article{info:doi/10.2196/16443,
author="Kwon, Soonil
and Hong, Joonki
and Choi, Eue-Keun
and Lee, Byunghwan
and Baik, Changhyun
and Lee, Euijae
and Jeong, Eui-Rim
and Koo, Bon-Kwon
and Oh, Seil
and Yi, Yung",
title="Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study",
journal="J Med Internet Res",
year="2020",
month="May",
day="21",
volume="22",
number="5",
pages="e16443",
keywords="atrial fibrillation; deep learning; diagnosis; photoplethysmography; wearable electronic devices",
abstract="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 ",
doi="10.2196/16443",
url="http://www.jmir.org/2020/5/e16443/",
url="https://doi.org/10.2196/16443",
url="http://www.ncbi.nlm.nih.gov/pubmed/32348254"
}


@Article{info:doi/10.2196/13156,
author="Park, Yong-Seok
and Kim, Sung-Hoon
and Lee, Yoon Se
and Choi, Seung-Ho
and Ku, Seung-Woo
and Hwang, Gyu-Sam",
title="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",
journal="J Med Internet Res",
year="2020",
month="May",
day="15",
volume="22",
number="5",
pages="e13156",
keywords="larynx; blood pressure; photoplethysmography; pulse",
abstract="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. ",
doi="10.2196/13156",
url="https://www.jmir.org/2020/5/e13156",
url="https://doi.org/10.2196/13156",
url="http://www.ncbi.nlm.nih.gov/pubmed/32412413"
}


@Article{info:doi/10.2196/16716,
author="D{\"u}king, Peter
and Giessing, Laura
and Frenkel, Marie Ottilie
and Koehler, Karsten
and Holmberg, Hans-Christer
and Sperlich, Billy",
title="Wrist-Worn Wearables for Monitoring Heart Rate and Energy Expenditure While Sitting or Performing Light-to-Vigorous Physical Activity: Validation Study",
journal="JMIR Mhealth Uhealth",
year="2020",
month="May",
day="6",
volume="8",
number="5",
pages="e16716",
keywords="cardiorespiratory fitness; innovation; smartwatch; technology; wearable; digital health",
abstract="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. ",
doi="10.2196/16716",
url="https://mhealth.jmir.org/2020/5/e16716",
url="https://doi.org/10.2196/16716",
url="http://www.ncbi.nlm.nih.gov/pubmed/32374274"
}


@Article{info:doi/10.2196/14707,
author="Chow, Hsueh-Wen
and Yang, Chao-Ching",
title="Accuracy of Optical Heart Rate Sensing Technology in Wearable Fitness Trackers for Young and Older Adults: Validation and Comparison Study",
journal="JMIR Mhealth Uhealth",
year="2020",
month="Apr",
day="28",
volume="8",
number="4",
pages="e14707",
keywords="pulse; photoplethysmography; wearable device; aerobic exercise",
abstract="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. ",
doi="10.2196/14707",
url="http://mhealth.jmir.org/2020/4/e14707/",
url="https://doi.org/10.2196/14707",
url="http://www.ncbi.nlm.nih.gov/pubmed/32343255"
}


@Article{info:doi/10.2196/16620,
author="Vesterinen, Ville
and Rinkinen, Niina
and Nummela, Ari",
title="A Contact-Free, Ballistocardiography-Based Monitoring System (Emfit QS) for Measuring Nocturnal Heart Rate and Heart Rate Variability: Validation Study",
journal="JMIR Biomed Eng",
year="2020",
month="Apr",
day="23",
volume="5",
number="1",
pages="e16620",
keywords="wearable technology; cardiac autonomic regulation; monitoring; validity",
abstract="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. ",
doi="10.2196/16620",
url="http://biomedeng.jmir.org/2020/1/e16620/",
url="https://doi.org/10.2196/16620"
}


@Article{info:doi/10.2196/18158,
author="Murali, Srinivasan
and Rincon, Francisco
and Cassina, Tiziano
and Cook, Stephane
and Goy, Jean-Jacques",
title="Heart Rate and Oxygen Saturation Monitoring With a New Wearable Wireless Device in the Intensive Care Unit: Pilot Comparison Trial",
journal="JMIR Biomed Eng",
year="2020",
month="Apr",
day="22",
volume="5",
number="1",
pages="e18158",
keywords="cardiac monitoring; wireless monitor; wearable; cardiology; ICU; respiratory monitoring",
abstract="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 (Dr{\"a}ger-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. ",
doi="10.2196/18158",
url="http://biomedeng.jmir.org/2020/1/e18158/",
url="https://doi.org/10.2196/18158"
}


@Article{info:doi/10.2196/12141,
author="Smeets, Christophe J P
and Lee, Seulki
and Groenendaal, Willemijn
and Squillace, Gabriel
and Vranken, Julie
and De Canni{\`e}re, H{\'e}l{\`e}ne
and Van Hoof, Chris
and Grieten, Lars
and Mullens, Wilfried
and Nijst, Petra
and Vandervoort, Pieter M",
title="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",
journal="JMIR Cardio",
year="2020",
month="Mar",
day="18",
volume="4",
number="1",
pages="e12141",
keywords="congestive heart failure; electric impedance; prognosis",
abstract="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. ",
doi="10.2196/12141",
url="https://cardio.jmir.org/2020/1/e12141",
url="https://doi.org/10.2196/12141",
url="http://www.ncbi.nlm.nih.gov/pubmed/32186520"
}


@Article{info:doi/10.2196/17037,
author="Jeon, Eunjoo
and Oh, Kyusam
and Kwon, Soonhwan
and Son, HyeongGwan
and Yun, Yongkeun
and Jung, Eun-Soo
and Kim, Min Soo",
title="A Lightweight Deep Learning Model for Fast Electrocardiographic Beats Classification With a Wearable Cardiac Monitor: Development and Validation Study",
journal="JMIR Med Inform",
year="2020",
month="Mar",
day="12",
volume="8",
number="3",
pages="e17037",
keywords="path-type ECG sensor system; ECG classification; deep learning; recurrent neural network; fused recurrent neural network",
abstract="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. ",
doi="10.2196/17037",
url="http://medinform.jmir.org/2020/3/e17037/",
url="https://doi.org/10.2196/17037",
url="http://www.ncbi.nlm.nih.gov/pubmed/32163037"
}


@Article{info:doi/10.2196/16811,
author="Hahnen, Christina
and Freeman, Cecilia G
and Haldar, Nilanjan
and Hamati, Jacquelyn N
and Bard, Dylan M
and Murali, Vignesh
and Merli, Geno J
and Joseph, Jeffrey I
and van Helmond, Noud",
title="Accuracy of Vital Signs Measurements by a Smartwatch and a Portable Health Device: Validation Study",
journal="JMIR Mhealth Uhealth",
year="2020",
month="Feb",
day="12",
volume="8",
number="2",
pages="e16811",
keywords="medical devices; mHealth; vital signs; measurements validity",
abstract="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. ",
doi="10.2196/16811",
url="https://mhealth.jmir.org/2020/2/e16811",
url="https://doi.org/10.2196/16811",
url="http://www.ncbi.nlm.nih.gov/pubmed/32049066"
}


@Article{info:doi/10.2196/16409,
author="Rykov, Yuri
and Thach, Thuan-Quoc
and Dunleavy, Gerard
and Roberts, Adam Charles
and Christopoulos, George
and Soh, Chee-Kiong
and Car, Josip",
title="Activity Tracker--Based Metrics as Digital Markers of Cardiometabolic Health in Working Adults: Cross-Sectional Study",
journal="JMIR Mhealth Uhealth",
year="2020",
month="Jan",
day="31",
volume="8",
number="1",
pages="e16409",
keywords="mobile health; metabolic cardiovascular syndrome; fitness trackers; wearable electronic devices; Fitbit; steps; heart rate; physical activity; circadian rhythms; sedentary behavior",
abstract="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. ",
doi="10.2196/16409",
url="http://mhealth.jmir.org/2020/1/e16409/",
url="https://doi.org/10.2196/16409",
url="http://www.ncbi.nlm.nih.gov/pubmed/32012098"
}


@Article{info:doi/10.2196/14857,
author="Inui, Tomohiko
and Kohno, Hiroki
and Kawasaki, Yohei
and Matsuura, Kaoru
and Ueda, Hideki
and Tamura, Yusaku
and Watanabe, Michiko
and Inage, Yuichi
and Yakita, Yasunori
and Wakabayashi, Yutaka
and Matsumiya, Goro",
title="Use of a Smart Watch for Early Detection of Paroxysmal Atrial Fibrillation: Validation Study",
journal="JMIR Cardio",
year="2020",
month="Jan",
day="22",
volume="4",
number="1",
pages="e14857",
keywords="Apple Watch; Fitbit Charge HR; paroxysmal atrial fibrillation; photoplethysmography; mobile health; heart rate; validation; wrist-banded devices",
abstract="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. ",
doi="10.2196/14857",
url="http://cardio.jmir.org/2020/1/e14857/",
url="https://doi.org/10.2196/14857",
url="http://www.ncbi.nlm.nih.gov/pubmed/32012044"
}


@Article{info:doi/10.2196/13756,
author="Salvi, Dario
and Poffley, Emma
and Orchard, Elizabeth
and Tarassenko, Lionel",
title="The Mobile-Based 6-Minute Walk Test: Usability Study and Algorithm Development and Validation",
journal="JMIR Mhealth Uhealth",
year="2020",
month="Jan",
day="3",
volume="8",
number="1",
pages="e13756",
keywords="cardiology; exercise test; pulmonary hypertension; mobile apps; digital signal processing; global positioning system",
abstract="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. ",
doi="10.2196/13756",
url="https://mhealth.jmir.org/2020/1/e13756",
url="https://doi.org/10.2196/13756",
url="http://www.ncbi.nlm.nih.gov/pubmed/31899457"
}


@Article{info:doi/10.2196/15045,
author="Herkert, Cyrille
and Kraal, Jos Johannes
and van Loon, Eline Maria Agnes
and van Hooff, Martijn
and Kemps, Hareld Marijn Clemens",
title="Usefulness of Modern Activity Trackers for Monitoring Exercise Behavior in Chronic Cardiac Patients: Validation Study",
journal="JMIR Mhealth Uhealth",
year="2019",
month="Dec",
day="19",
volume="7",
number="12",
pages="e15045",
keywords="cardiac diseases; activity trackers; energy metabolism; physical activity; validation studies",
abstract="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. ",
doi="10.2196/15045",
url="http://mhealth.jmir.org/2019/12/e15045/",
url="https://doi.org/10.2196/15045",
url="http://www.ncbi.nlm.nih.gov/pubmed/31855191"
}


@Article{info:doi/10.2196/14909,
author="Zhang, Hui
and Zhang, Jie
and Li, Hong-Bao
and Chen, Yi-Xin
and Yang, Bin
and Guo, Yu-Tao
and Chen, Yun-Dai",
title="Validation of Single Centre Pre-Mobile Atrial Fibrillation Apps for Continuous Monitoring of Atrial Fibrillation in a Real-World Setting: Pilot Cohort Study",
journal="J Med Internet Res",
year="2019",
month="Dec",
day="3",
volume="21",
number="12",
pages="e14909",
keywords="atrial fibrillation; photoplethysmography; continuous detection; accuracy; smartphone; smart band; algorithm",
abstract="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. ",
doi="10.2196/14909",
url="https://www.jmir.org/2019/12/e14909",
url="https://doi.org/10.2196/14909",
url="http://www.ncbi.nlm.nih.gov/pubmed/31793887"
}


@Article{info:doi/10.2196/14686,
author="Mazoteras-Pardo, Victoria
and Becerro-De-Bengoa-Vallejo, Ricardo
and Losa-Iglesias, Marta Elena
and L{\'o}pez-L{\'o}pez, Daniel
and Rodr{\'i}guez-Sanz, David
and Casado-Hern{\'a}ndez, Israel
and Calvo-Lobo, Cesar
and Palomo-L{\'o}pez, Patricia",
title="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",
journal="J Med Internet Res",
year="2019",
month="Dec",
day="2",
volume="21",
number="12",
pages="e14686",
keywords="blood pressure; hypertension; kidney disease; mobile apps; software validation",
abstract="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. ",
doi="10.2196/14686",
url="https://www.jmir.org/2019/12/e14686",
url="https://doi.org/10.2196/14686",
url="http://www.ncbi.nlm.nih.gov/pubmed/31789600"
}


@Article{info:doi/10.2196/14926,
author="Wang, Guangyu
and Zhou, Silu
and Rezaei, Shahbaz
and Liu, Xin
and Huang, Anpeng",
title="An Ambulatory Blood Pressure Monitor Mobile Health System for Early Warning for Stroke Risk: Longitudinal Observational Study",
journal="JMIR Mhealth Uhealth",
year="2019",
month="Oct",
day="30",
volume="7",
number="10",
pages="e14926",
keywords="ambulatory blood pressure monitor; mHealth; stroke-risk early warning; abnormal blood pressure data analyzing; longitudinal observational study",
abstract="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. ",
doi="10.2196/14926",
url="http://mhealth.jmir.org/2019/10/e14926/",
url="https://doi.org/10.2196/14926",
url="http://www.ncbi.nlm.nih.gov/pubmed/31670694"
}


@Article{info:doi/10.2196/13757,
author="Graham, Sarah Anne
and Jeste, Dilip V
and Lee, Ellen E
and Wu, Tsung-Chin
and Tu, Xin
and Kim, Ho-Cheol
and Depp, Colin A",
title="Associations Between Heart Rate Variability Measured With a Wrist-Worn Sensor and Older Adults' Physical Function: Observational Study",
journal="JMIR Mhealth Uhealth",
year="2019",
month="Oct",
day="23",
volume="7",
number="10",
pages="e13757",
keywords="wearable technology; aging; electrocardiogram; geriatric assessment",
abstract="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 $\rho$=0.41; P=.003), TUG (n=51; $\rho$=−0.40; P=.004), SF-36 physical composite scores (n=49; $\rho$=0.37; P=.009), and CIRS-G scores (n=52, $\rho$=−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. ",
doi="10.2196/13757",
url="http://mhealth.jmir.org/2019/10/e13757/",
url="https://doi.org/10.2196/13757",
url="http://www.ncbi.nlm.nih.gov/pubmed/31647469"
}


@Article{info:doi/10.2196/14706,
author="Islam, Sheikh Mohammed Shariful
and Cartledge, Susie
and Karmakar, Chandan
and Rawstorn, Jonathan Charles
and Fraser, Steve F
and Chow, Clara
and Maddison, Ralph",
title="Validation and Acceptability of a Cuffless Wrist-Worn Wearable Blood Pressure Monitoring Device Among Users and Health Care Professionals: Mixed Methods Study",
journal="JMIR Mhealth Uhealth",
year="2019",
month="Sep",
day="14",
volume="7",
number="10",
pages="e14706",
keywords="hypertension; cardiovascular disease; wearable device; blood pressure; ambulatory blood pressure monitoring",
abstract="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. ",
doi="10.2196/14706",
url="https://mhealth.jmir.org/2019/10/e14706",
url="https://doi.org/10.2196/14706",
url="http://www.ncbi.nlm.nih.gov/pubmed/31628788"
}


@Article{info:doi/10.2196/14120,
author="M{\"u}ller, Andre Matthias
and Wang, Nan Xin
and Yao, Jiali
and Tan, Chuen Seng
and Low, Ivan Cherh Chiet
and Lim, Nicole
and Tan, Jeremy
and Tan, Agnes
and M{\"u}ller-Riemenschneider, Falk",
title="Heart Rate Measures From Wrist-Worn Activity Trackers in a Laboratory and Free-Living Setting: Validation Study",
journal="JMIR Mhealth Uhealth",
year="2019",
month="Oct",
day="2",
volume="7",
number="10",
pages="e14120",
keywords="eHealth; mHealth; wearable; exercise; measurement; fitness; public health; quantified self",
abstract="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. ",
doi="10.2196/14120",
url="https://mhealth.jmir.org/2019/10/e14120",
url="https://doi.org/10.2196/14120",
url="http://www.ncbi.nlm.nih.gov/pubmed/31579026"
}


@Article{info:doi/10.2196/13909,
author="Peters, Gregory Andrew
and Wong, Matthew L
and Joseph, Joshua W
and Sanchez, Leon D",
title="Pulse Rate Variability in Emergency Physicians During Shifts: Pilot Cross-Sectional Study",
journal="JMIR Mhealth Uhealth",
year="2019",
month="Oct",
day="2",
volume="7",
number="10",
pages="e13909",
keywords="emergency medicine; burnout; photoplethysmography; emergency physicians; physician wellness; stress; heart rate variability; pulse rate variability",
abstract="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{\texttimes}103 vs 3.4{\texttimes}103; P=.79), high-frequency power (8.5{\texttimes}103 vs 8.3{\texttimes}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. ",
doi="10.2196/13909",
url="https://mhealth.jmir.org/2019/10/e13909",
url="https://doi.org/10.2196/13909",
url="http://www.ncbi.nlm.nih.gov/pubmed/31579017"
}


@Article{info:doi/10.2196/13400,
author="Gibson, Kim
and Al-Naji, Ali
and Fleet, Julie-Anne
and Steen, Mary
and Chahl, Javaan
and Huynh, Jasmine
and Morris, Scott",
title="Noncontact Heart and Respiratory Rate Monitoring of Preterm Infants Based on a Computer Vision System: Protocol for a Method Comparison Study",
journal="JMIR Res Protoc",
year="2019",
month="Aug",
day="29",
volume="8",
number="8",
pages="e13400",
keywords="heart rate; respiratory rate; infant; electrocardiography; computers",
abstract="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 ",
doi="10.2196/13400",
url="https://www.researchprotocols.org/2019/8/e13400",
url="https://doi.org/10.2196/13400"
}


@Article{info:doi/10.2196/11164,
author="Ogink, Paula AM
and de Jong, Jelske M
and Koeneman, Mats
and Weenk, Mariska
and Engelen, Lucien JLPG
and van Goor, Harry
and van de Belt, Tom H
and Bredie, Sebastian JH",
title="Feasibility of a New Cuffless Device for Ambulatory Blood Pressure Measurement in Patients With Hypertension: Mixed Methods Study",
journal="J Med Internet Res",
year="2019",
month="Jun",
day="19",
volume="21",
number="6",
pages="e11164",
keywords="ambulatory blood pressure monitoring; home blood pressure monitoring; cuffless blood pressure device; hypertension",
abstract="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. ",
doi="10.2196/11164",
url="http://www.jmir.org/2019/6/e11164/",
url="https://doi.org/10.2196/11164",
url="http://www.ncbi.nlm.nih.gov/pubmed/31219050"
}


@Article{info:doi/10.2196/13641,
author="Giebel, Godwin Denk
and Gissel, Christian",
title="Accuracy of mHealth Devices for Atrial Fibrillation Screening: Systematic Review",
journal="JMIR Mhealth Uhealth",
year="2019",
month="Jun",
day="16",
volume="7",
number="6",
pages="e13641",
keywords="mHealth; atrial fibrillation; wearable; app",
abstract="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. ",
doi="10.2196/13641",
url="http://mhealth.jmir.org/2019/6/e13641/",
url="https://doi.org/10.2196/13641",
url="http://www.ncbi.nlm.nih.gov/pubmed/31199337"
}


@Article{info:doi/10.2196/13327,
author="Kwon, Soon Bin
and Ahn, Joong Woo
and Lee, Seung Min
and Lee, Joonnyong
and Lee, Dongheon
and Hong, Jeeyoung
and Kim, Hee Chan
and Yoon, Hyung-Jin",
title="Estimating Maximal Oxygen Uptake From Daily Activity Data Measured by a Watch-Type Fitness Tracker: Cross-Sectional Study",
journal="JMIR Mhealth Uhealth",
year="2019",
month="Jun",
day="13",
volume="7",
number="6",
pages="e13327",
keywords="cardiorespiratory fitness; oxygen consumption; fitness tracker",
abstract="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. ",
doi="10.2196/13327",
url="https://mhealth.jmir.org/2019/6/e13327/",
url="https://doi.org/10.2196/13327",
url="http://www.ncbi.nlm.nih.gov/pubmed/31199336"
}


@Article{info:doi/10.2196/12770,
author="Kwon, Soonil
and Hong, Joonki
and Choi, Eue-Keun
and Lee, Euijae
and Hostallero, David Earl
and Kang, Wan Ju
and Lee, Byunghwan
and Jeong, Eui-Rim
and Koo, Bon-Kwon
and Oh, Seil
and Yi, Yung",
title="Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study",
journal="JMIR Mhealth Uhealth",
year="2019",
month="Jun",
day="6",
volume="7",
number="6",
pages="e12770",
keywords="atrial fibrillation; deep learning; photoplethysmography; pulse oximetry; diagnosis",
abstract="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. ",
doi="10.2196/12770",
url="http://mhealth.jmir.org/2019/6/e12770/",
url="https://doi.org/10.2196/12770",
url="http://www.ncbi.nlm.nih.gov/pubmed/31199302"
}


@Article{info:doi/10.2196/12866,
author="Buekers, Joren
and Theunis, Jan
and De Boever, Patrick
and Vaes, Anouk W
and Koopman, Maud
and Janssen, Eefje VM
and Wouters, Emiel FM
and Spruit, Martijn A
and Aerts, Jean-Marie",
title="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",
journal="JMIR Mhealth Uhealth",
year="2019",
month="Jun",
day="6",
volume="7",
number="6",
pages="e12866",
keywords="COPD; oxygen saturation; finger pulse oximeter; wearable sensor; nocturnal desaturation; telemonitoring",
abstract="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. ",
doi="10.2196/12866",
url="https://mhealth.jmir.org/2019/6/e12866/",
url="https://doi.org/10.2196/12866",
url="http://www.ncbi.nlm.nih.gov/pubmed/31199331"
}


@Article{info:doi/10.2196/12122,
author="Baril, Jonathan-F
and Bromberg, Simon
and Moayedi, Yasbanoo
and Taati, Babak
and Manlhiot, Cedric
and Ross, Heather Joan
and Cafazzo, Joseph",
title="Use of Free-Living Step Count Monitoring for Heart Failure Functional Classification: Validation Study",
journal="JMIR Cardio",
year="2019",
month="May",
day="17",
volume="3",
number="1",
pages="e12122",
keywords="exercise physiology; heart rate tracker; wrist worn devices; Fitbit; heart failure; steps; cardiopulmonary exercise test; ambulatory monitoring",
abstract="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. ",
doi="10.2196/12122",
url="http://cardio.jmir.org/2019/1/e12122/",
url="https://doi.org/10.2196/12122",
url="http://www.ncbi.nlm.nih.gov/pubmed/31758777"
}


@Article{info:doi/10.2196/13850,
author="Ding, Eric Y
and Han, Dong
and Whitcomb, Cody
and Bashar, Syed Khairul
and Adaramola, Oluwaseun
and Soni, Apurv
and Saczynski, Jane
and Fitzgibbons, Timothy P
and Moonis, Majaz
and Lubitz, Steven A
and Lessard, Darleen
and Hills, Mellanie True
and Barton, Bruce
and Chon, Ki
and McManus, David D",
title="Accuracy and Usability of a Novel Algorithm for Detection of Irregular Pulse Using a Smartwatch Among Older Adults: Observational Study",
journal="JMIR Cardio",
year="2019",
month="May",
day="15",
volume="3",
number="1",
pages="e13850",
keywords="mobile health; mHealth; atrial fibrillation; screening; photoplethysmography; electrocardiography; smartwatch",
abstract="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. ",
doi="10.2196/13850",
url="http://cardio.jmir.org/2019/1/e13850/",
url="https://doi.org/10.2196/13850",
url="http://www.ncbi.nlm.nih.gov/pubmed/31758787"
}


@Article{info:doi/10.2196/11959,
author="Hao, Yiming
and Cheng, Feng
and Pham, Minh
and Rein, Hayley
and Patel, Devashru
and Fang, Yuchen
and Feng, Yiyi
and Yan, Jin
and Song, Xueyang
and Yan, Haixia
and Wang, Yiqin",
title="A Noninvasive, Economical, and Instant-Result Method to Diagnose and Monitor Type 2 Diabetes Using Pulse Wave: Case-Control Study",
journal="JMIR Mhealth Uhealth",
year="2019",
month="Apr",
day="23",
volume="7",
number="4",
pages="e11959",
keywords="type 2 diabetes; hypertension; hyperlipidemia; pulse wave analysis; diagnosis",
abstract="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. ",
doi="10.2196/11959",
url="http://mhealth.jmir.org/2019/4/e11959/",
url="https://doi.org/10.2196/11959",
url="http://www.ncbi.nlm.nih.gov/pubmed/31012863"
}


@Article{info:doi/10.2196/12772,
author="Wetterholm, Madeleine
and Bonn, Stephanie Erika
and Alexandrou, Christina
and L{\"o}f, Marie
and Trolle Lagerros, Ylva",
title="Validation of Two Automatic Blood Pressure Monitors With the Ability to Transfer Data via Bluetooth",
journal="J Med Internet Res",
year="2019",
month="Apr",
day="17",
volume="21",
number="4",
pages="e12772",
keywords="blood pressure monitors; diabetes mellitus, type 2; hypertension; methods; mHealth; self-care; self-management",
abstract="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. ",
doi="10.2196/12772",
url="https://www.jmir.org/2019/4/e12772/",
url="https://doi.org/10.2196/12772",
url="http://www.ncbi.nlm.nih.gov/pubmed/30994459"
}


@Article{info:doi/10.2196/10140,
author="Cho, Youngjun
and Julier, Simon J
and Bianchi-Berthouze, Nadia",
title="Instant Stress: Detection of Perceived Mental Stress Through Smartphone Photoplethysmography and Thermal Imaging",
journal="JMIR Ment Health",
year="2019",
month="Apr",
day="09",
volume="6",
number="4",
pages="e10140",
keywords="stress detection; mobile applications; photoplethysmography; thermography; psychophysiology; heart rate variability; physiological computing; affective computing; machine learning",
abstract="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. ",
doi="10.2196/10140",
url="https://mental.jmir.org/2019/4/e10140/",
url="https://doi.org/10.2196/10140",
url="http://www.ncbi.nlm.nih.gov/pubmed/30964440"
}


@Article{info:doi/10.2196/12284,
author="Proesmans, Tine
and Mortelmans, Christophe
and Van Haelst, Ruth
and Verbrugge, Frederik
and Vandervoort, Pieter
and Vaes, Bert",
title="Mobile Phone--Based Use of the Photoplethysmography Technique to Detect Atrial Fibrillation in Primary Care: Diagnostic Accuracy Study of the FibriCheck App",
journal="JMIR Mhealth Uhealth",
year="2019",
month="Mar",
day="27",
volume="7",
number="3",
pages="e12284",
keywords="atrial fibrillation; electrocardiography; photoplethysmography; mobile phone; algorithm",
abstract="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. ",
doi="10.2196/12284",
url="http://mhealth.jmir.org/2019/3/e12284/",
url="https://doi.org/10.2196/12284",
url="http://www.ncbi.nlm.nih.gov/pubmed/30916656"
}


@Article{info:doi/10.2196/11889,
author="Falter, Maarten
and Budts, Werner
and Goetschalckx, Kaatje
and Cornelissen, V{\'e}ronique
and Buys, Roselien",
title="Accuracy of Apple Watch Measurements for Heart Rate and Energy Expenditure in Patients With Cardiovascular Disease: Cross-Sectional Study",
journal="JMIR Mhealth Uhealth",
year="2019",
month="Mar",
day="19",
volume="7",
number="3",
pages="e11889",
keywords="mobile health; heart rate; energy expenditure; validation; Apple Watch; wrist-worn devices; cardiovascular rehabilitation",
abstract="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. ",
doi="10.2196/11889",
url="http://mhealth.jmir.org/2019/3/e11889/",
url="https://doi.org/10.2196/11889",
url="http://www.ncbi.nlm.nih.gov/pubmed/30888332"
}


@Article{info:doi/10.2196/10828,
author="Nelson, Benjamin W
and Allen, Nicholas B",
title="Accuracy of Consumer Wearable Heart Rate Measurement During an Ecologically Valid 24-Hour Period: Intraindividual Validation Study",
journal="JMIR Mhealth Uhealth",
year="2019",
month="Mar",
day="11",
volume="7",
number="3",
pages="e10828",
keywords="electrocardiography; Apple Watch 3; digital health; Fitbit Charge 2; heart rate; mobile health; passive sensing; photoplethysmography; wearables",
abstract="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 (<{\textpm}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. ",
doi="10.2196/10828",
url="https://mhealth.jmir.org/2019/3/e10828/",
url="https://doi.org/10.2196/10828",
url="http://www.ncbi.nlm.nih.gov/pubmed/30855232"
}


@Article{info:doi/10.2196/11437,
author="Fan, Yong-Yan
and Li, Yan-Guang
and Li, Jian
and Cheng, Wen-Kun
and Shan, Zhao-Liang
and Wang, Yu-Tang
and Guo, Yu-Tao",
title="Diagnostic Performance of a Smart Device With Photoplethysmography Technology for Atrial Fibrillation Detection: Pilot Study (Pre-mAFA II Registry)",
journal="JMIR Mhealth Uhealth",
year="2019",
month="Mar",
day="05",
volume="7",
number="3",
pages="e11437",
keywords="atrial fibrillation; photoplethysmography; detection; accuracy; mobile phone; smart band; algorithm",
abstract="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) ",
doi="10.2196/11437",
url="http://mhealth.jmir.org/2019/3/e11437/",
url="https://doi.org/10.2196/11437",
url="http://www.ncbi.nlm.nih.gov/pubmed/30835243"
}


@Article{info:doi/10.2196/12369,
author="Huang, Ching-Chang
and Chen, Ying-Hsien
and Hung, Chi-Sheng
and Lee, Jen-Kuang
and Hsu, Tse-Pin
and Wu, Hui-Wen
and Chuang, Pao-Yu
and Chen, Ming-Fong
and Ho, Yi-Lwun",
title="Assessment of the Relationship Between Ambient Temperature and Home Blood Pressure in Patients From a Web-Based Synchronous Telehealth Care Program: Retrospective Study",
journal="J Med Internet Res",
year="2019",
month="Mar",
day="04",
volume="21",
number="3",
pages="e12369",
keywords="ambient temperature; home blood pressure; antihypertensive agents; retrospective studies",
abstract="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{\textdegree}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. ",
doi="10.2196/12369",
url="http://www.jmir.org/2019/3/e12369/",
url="https://doi.org/10.2196/12369",
url="http://www.ncbi.nlm.nih.gov/pubmed/30829574"
}


@Article{info:doi/10.2196/11606,
author="Li, Ka Hou Christien
and White, Francesca Anne
and Tipoe, Timothy
and Liu, Tong
and Wong, Martin CS
and Jesuthasan, Aaron
and Baranchuk, Adrian
and Tse, Gary
and Yan, Bryan P",
title="The Current State of Mobile Phone Apps for Monitoring Heart Rate, Heart Rate Variability, and Atrial Fibrillation: Narrative Review",
journal="JMIR Mhealth Uhealth",
year="2019",
month="Feb",
day="15",
volume="7",
number="2",
pages="e11606",
keywords="mobile phone apps; atrial fibrillation; heart rate; arrhythmia; photoplethysmography; electrocardiography; mobile health",
abstract="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. ",
doi="10.2196/11606",
url="http://mhealth.jmir.org/2019/2/e11606/",
url="https://doi.org/10.2196/11606",
url="http://www.ncbi.nlm.nih.gov/pubmed/30767904"
}


@Article{info:doi/10.2196/12419,
author="Conn, Nicholas J
and Schwarz, Karl Q
and Borkholder, David A",
title="In-Home Cardiovascular Monitoring System for Heart Failure: Comparative Study",
journal="JMIR Mhealth Uhealth",
year="2019",
month="Jan",
day="18",
volume="7",
number="1",
pages="e12419",
keywords="ballistocardiogram; BCG; blood pressure; ECG; electrocardiogram; heart failure; Internet of Things; IoT; photoplethysmogram; PPG; remote monitoring; SpO2; stroke volume",
abstract="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. ",
doi="10.2196/12419",
url="http://mhealth.jmir.org/2019/1/e12419/",
url="https://doi.org/10.2196/12419",
url="http://www.ncbi.nlm.nih.gov/pubmed/30664492"
}


@Article{info:doi/10.2196/11896,
author="Zhang, Jia
and T{\"u}shaus, Laura
and Nu{\~{n}}o Mart{\'i}nez, N{\'e}stor
and Moreo, Monica
and Verastegui, Hector
and Hartinger, Stella M
and M{\"a}usezahl, Daniel
and Karlen, Walter",
title="Data Integrity--Based Methodology and Checklist for Identifying Implementation Risks of Physiological Sensing in Mobile Health Projects: Quantitative and Qualitative Analysis",
journal="JMIR Mhealth Uhealth",
year="2018",
month="Dec",
day="14",
volume="6",
number="12",
pages="e11896",
keywords="physiological monitoring; data completeness; data quality; signal quality; medical sensors; implementation research; content analysis; mHealth; digital health",
abstract="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. ",
doi="10.2196/11896",
url="http://mhealth.jmir.org/2018/12/e11896/",
url="https://doi.org/10.2196/11896",
url="http://www.ncbi.nlm.nih.gov/pubmed/30552079"
}


@Article{info:doi/10.2196/10802,
author="Downey, Candice
and Randell, Rebecca
and Brown, Julia
and Jayne, David G",
title="Continuous Versus Intermittent Vital Signs Monitoring Using a Wearable, Wireless Patch in Patients Admitted to Surgical Wards: Pilot Cluster Randomized Controlled Trial",
journal="J Med Internet Res",
year="2018",
month="Dec",
day="11",
volume="20",
number="12",
pages="e10802",
keywords="general surgery; monitoring; physiological; randomized controlled trial; vital signs",
abstract="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) ",
doi="10.2196/10802",
url="https://www.jmir.org/2018/12/e10802/",
url="https://doi.org/10.2196/10802",
url="http://www.ncbi.nlm.nih.gov/pubmed/30538086"
}


@Article{info:doi/10.2196/12048,
author="Lee, Jang Ho
and Park, Yu Rang
and Kweon, Solbi
and Kim, Seulgi
and Ji, Wonjun
and Choi, Chang-Min",
title="A Cardiopulmonary Monitoring System for Patient Transport Within Hospitals Using Mobile Internet of Things Technology: Observational Validation Study",
journal="JMIR Mhealth Uhealth",
year="2018",
month="Nov",
day="14",
volume="6",
number="11",
pages="e12048",
keywords="wearable device; patient safety; intrahospital transport; oxygen saturation; heart rate; mobile application; real-time monitoring",
abstract="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. ",
doi="10.2196/12048",
url="http://mhealth.jmir.org/2018/11/e12048/",
url="https://doi.org/10.2196/12048",
url="http://www.ncbi.nlm.nih.gov/pubmed/30429115"
}


@Article{info:doi/10.2196/10126,
author="Steijlen, Annemarijn SM
and Jansen, Kaspar MB
and Albayrak, Armagan
and Verschure, Derk O
and Van Wijk, Diederik F",
title="A Novel 12-Lead Electrocardiographic System for Home Use: Development and Usability Testing",
journal="JMIR Mhealth Uhealth",
year="2018",
month="Jul",
day="30",
volume="6",
number="7",
pages="e10126",
keywords="12-lead ECG system; electrocardiography; home use; handheld; user-centered design",
abstract="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. ",
doi="10.2196/10126",
url="http://mhealth.jmir.org/2018/7/e10126/",
url="https://doi.org/10.2196/10126",
url="http://www.ncbi.nlm.nih.gov/pubmed/30061094"
}


@Article{info:doi/10.2196/mhealth.9884,
author="Lee, Jie-Eun
and Lee, Dong Hwa
and Oh, Tae Jung
and Kim, Kyoung Min
and Choi, Sung Hee
and Lim, Soo
and Park, Young Joo
and Park, Do Joon
and Jang, Hak Chul
and Moon, Jae Hoon",
title="Clinical Feasibility of Monitoring Resting Heart Rate Using a Wearable Activity Tracker in Patients With Thyrotoxicosis: Prospective Longitudinal Observational Study",
journal="JMIR Mhealth Uhealth",
year="2018",
month="Jul",
day="13",
volume="6",
number="7",
pages="e159",
keywords="activity tracker; wearable device; heart rate; thyrotoxicosis; hyperthyroidism; Graves' disease",
abstract="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) ",
doi="10.2196/mhealth.9884",
url="http://mhealth.jmir.org/2018/7/e159/",
url="https://doi.org/10.2196/mhealth.9884",
url="http://www.ncbi.nlm.nih.gov/pubmed/30006328"
}


@Article{info:doi/10.2196/10108,
author="Sartor, Francesco
and Papini, Gabriele
and Cox, Lieke Gertruda Elisabeth
and Cleland, John",
title="Methodological Shortcomings of Wrist-Worn Heart Rate Monitors Validations",
journal="J Med Internet Res",
year="2018",
month="Jul",
day="02",
volume="20",
number="7",
pages="e10108",
keywords="sensor technology; accuracy; wearable; telemonitoring",
doi="10.2196/10108",
url="http://www.jmir.org/2018/7/e10108/",
url="https://doi.org/10.2196/10108",
url="http://www.ncbi.nlm.nih.gov/pubmed/29967000"
}


@Article{info:doi/10.2196/mhealth.9604,
author="Conn, Nicholas J
and Schwarz, Karl Q
and Borkholder, David A",
title="Nontraditional Electrocardiogram and Algorithms for Inconspicuous In-Home Monitoring: Comparative Study",
journal="JMIR Mhealth Uhealth",
year="2018",
month="May",
day="28",
volume="6",
number="5",
pages="e120",
keywords="algorithms; delineation; ECG; EDB; electrocardiogram; Internet of Things; IoT; MITDB; signal quality; wearable",
abstract="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. ",
doi="10.2196/mhealth.9604",
url="http://mhealth.jmir.org/2018/5/e120/",
url="https://doi.org/10.2196/mhealth.9604"
}


@Article{info:doi/10.2196/mhealth.8429,
author="Gliner, Vadim
and Behar, Joachim
and Yaniv, Yael",
title="Novel Method to Efficiently Create an mHealth App: Implementation of a Real-Time Electrocardiogram R Peak Detector",
journal="JMIR Mhealth Uhealth",
year="2018",
month="May",
day="22",
volume="6",
number="5",
pages="e118",
keywords="atrial fibrillation; arrhythmia; heart rate variability; MATLAB Mobile; mobile device",
abstract="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. ",
doi="10.2196/mhealth.8429",
url="http://mhealth.jmir.org/2018/5/e118/",
url="https://doi.org/10.2196/mhealth.8429",
url="http://www.ncbi.nlm.nih.gov/pubmed/29789276"
}


@Article{info:doi/10.2196/cardio.9534,
author="Kehl, Devin
and Zimmer, Raymond
and Sudan, Madhuri
and Kedan, Ilan",
title="Handheld Ultrasound as a Novel Predictive Tool in Atrial Fibrillation: Prediction of Outcomes Following Electrical Cardioversion",
journal="JMIR Cardio",
year="2018",
month="Mar",
day="08",
volume="2",
number="1",
pages="e7",
keywords="atrial fibrillation; cardioversion; recurrence; inferior vena cava; hand held ultrasound; point of care",
abstract="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. ",
doi="10.2196/cardio.9534",
url="http://cardio.jmir.org/2018/1/e7/",
url="https://doi.org/10.2196/cardio.9534",
url="http://www.ncbi.nlm.nih.gov/pubmed/31758780"
}


@Article{info:doi/10.2196/mhealth.8946,
author="Kang, Si-Hyuck
and Joe, Byunggill
and Yoon, Yeonyee
and Cho, Goo-Yeong
and Shin, Insik
and Suh, Jung-Won",
title="Cardiac Auscultation Using Smartphones: Pilot Study",
journal="JMIR Mhealth Uhealth",
year="2018",
month="Feb",
day="28",
volume="6",
number="2",
pages="e49",
keywords="cardiac auscultation; physical examination; smartphone; mobile health care; telemedicine",
abstract="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) ",
doi="10.2196/mhealth.8946",
url="http://mhealth.jmir.org/2018/2/e49/",
url="https://doi.org/10.2196/mhealth.8946",
url="http://www.ncbi.nlm.nih.gov/pubmed/29490899"
}


@Article{info:doi/10.2196/cardio.8574,
author="Abt, Grant
and Bray, James
and Benson, Amanda Clare",
title="Measuring Moderate-Intensity Exercise with the Apple Watch: Validation Study",
journal="JMIR Cardio",
year="2018",
month="Feb",
day="28",
volume="2",
number="1",
pages="e6",
keywords="smartwatch; wearables; technology; physical activity; cardiovascular health, Apple Watch",
abstract="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. ",
doi="10.2196/cardio.8574",
url="http://cardio.jmir.org/2018/1/e6/",
url="https://doi.org/10.2196/cardio.8574",
url="http://www.ncbi.nlm.nih.gov/pubmed/31758766"
}


@Article{info:doi/10.2196/cardio.8199,
author="Rozanski, Gabriela M
and Aqui, Anthony
and Sivakumaran, Shajicaa
and Mansfield, Avril",
title="Consumer Wearable Devices for Activity Monitoring Among Individuals After a Stroke: A Prospective Comparison",
journal="JMIR Cardio",
year="2018",
month="Jan",
day="04",
volume="2",
number="1",
pages="e1",
keywords="physical activity; heart rate; accelerometry; stroke rehabilitation; walking",
abstract="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 ($\rho$=.78 and $\rho$=.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 ($\rho$=.42, P=.12) or GAR ($\rho$=.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 ($\rho$=.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. ",
doi="10.2196/cardio.8199",
url="http://cardio.jmir.org/2018/1/e1/",
url="https://doi.org/10.2196/cardio.8199",
url="http://www.ncbi.nlm.nih.gov/pubmed/31758760"
}


@Article{info:doi/10.2196/cardio.8301,
author="Moayedi, Yasbanoo
and Abdulmajeed, Raghad
and Duero Posada, Juan
and Foroutan, Farid
and Alba, Ana Carolina
and Cafazzo, Joseph
and Ross, Heather Joan",
title="Assessing the Use of Wrist-Worn Devices in Patients With Heart Failure: Feasibility Study",
journal="JMIR Cardio",
year="2017",
month="Dec",
day="19",
volume="1",
number="2",
pages="e8",
keywords="MeSH: exercise physiology; heart rate tracker; wrist worn devices; Fitbit; Apple watch; heart failure; steps",
abstract="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. ",
doi="10.2196/cardio.8301",
url="http://cardio.jmir.org/2017/2/e8/",
url="https://doi.org/10.2196/cardio.8301",
url="http://www.ncbi.nlm.nih.gov/pubmed/31758789"
}


@Article{info:doi/10.2196/jmir.8066,
author="Smeets, Christophe JP
and Vranken, Julie
and Van der Auwera, Jo
and Verbrugge, Frederik H
and Mullens, Wilfried
and Dupont, Matthias
and Grieten, Lars
and De Canni{\`e}re, H{\'e}l{\`e}ne
and Lanssens, Dorien
and Vandenberk, Thijs
and Storms, Valerie
and Thijs, Inge M
and Vandervoort, Pieter M",
title="Bioimpedance Alerts from Cardiovascular Implantable Electronic Devices: Observational Study of Diagnostic Relevance and Clinical Outcomes",
journal="J Med Internet Res",
year="2017",
month="Nov",
day="23",
volume="19",
number="11",
pages="e393",
keywords="defibrillators, implantable; cardiac resynchronization therapy; telemedicine; electric impedance; algorithms; call centers",
abstract="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. ",
doi="10.2196/jmir.8066",
url="http://www.jmir.org/2017/11/e393/",
url="https://doi.org/10.2196/jmir.8066",
url="http://www.ncbi.nlm.nih.gov/pubmed/29170147"
}


@Article{info:doi/10.2196/mhealth.8233,
author="Gorny, Alexander Wilhelm
and Liew, Seaw Jia
and Tan, Chuen Seng
and M{\"u}ller-Riemenschneider, Falk",
title="Fitbit Charge HR Wireless Heart Rate Monitor: Validation Study Conducted Under Free-Living Conditions",
journal="JMIR Mhealth Uhealth",
year="2017",
month="Oct",
day="20",
volume="5",
number="10",
pages="e157",
keywords="heart rate; photoplethysmography; telemedicine; validation studies",
abstract="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. ",
doi="10.2196/mhealth.8233",
url="http://mhealth.jmir.org/2017/10/e157/",
url="https://doi.org/10.2196/mhealth.8233",
url="http://www.ncbi.nlm.nih.gov/pubmed/29055881"
}


@Article{info:doi/10.2196/mhealth.7254,
author="Vandenberk, Thijs
and Stans, Jelle
and Mortelmans, Christophe
and Van Haelst, Ruth
and Van Schelvergem, Gertjan
and Pelckmans, Caroline
and Smeets, Christophe JP
and Lanssens, Dorien
and De Canni{\`e}re, H{\'e}l{\`e}ne
and Storms, Valerie
and Thijs, Inge M
and Vaes, Bert
and Vandervoort, Pieter M",
title="Clinical Validation of Heart Rate Apps: Mixed-Methods Evaluation Study",
journal="JMIR Mhealth Uhealth",
year="2017",
month="Aug",
day="25",
volume="5",
number="8",
pages="e129",
keywords="heart rate; software validation; remote sensing technology",
abstract="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. ",
doi="10.2196/mhealth.7254",
url="http://mhealth.jmir.org/2017/8/e129/",
url="https://doi.org/10.2196/mhealth.7254"
}


@Article{info:doi/10.2196/cardio.7915,
author="Peters, Robert Mattson
and Shivakumar, Nishkala
and Xu, Ran
and Javaherian, Kavon
and Sink, Eric
and Patel, Kunjan
and Brown, Angela
and Huynh, Justin
and Blanchard, Melvin
and Ross, Will
and Byrd, Jonathan",
title="Assessing the Utility of a Novel SMS- and Phone-Based System for Blood Pressure Control in Hypertensive Patients: Feasibility Study",
journal="JMIR Cardio",
year="2017",
month="Jul",
day="27",
volume="1",
number="2",
pages="e2",
keywords="telemedicine; hypertension; quality improvement; text messaging; primary care; eHealth; mHealth; disease management",
abstract="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. ",
doi="10.2196/cardio.7915",
url="http://cardio.jmir.org/2017/2/e2/",
url="https://doi.org/10.2196/cardio.7915",
url="http://www.ncbi.nlm.nih.gov/pubmed/31758763"
}


@Article{info:doi/10.2196/mhealth.6998,
author="Thompson, David
and Mackay, Teresa
and Matthews, Maria
and Edwards, Judith
and Peters, Nicholas S
and Connolly, Susan B",
title="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",
journal="JMIR Mhealth Uhealth",
year="2017",
month="Jun",
day="12",
volume="5",
number="6",
pages="e76",
keywords="cardiac prevention and rehabilitation; adherence; mHealth; remote monitoring; cardiovascular diseases; primary prevention; medication adherence; telemedicine",
abstract="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. ",
doi="10.2196/mhealth.6998",
url="http://mhealth.jmir.org/2017/6/e76/",
url="https://doi.org/10.2196/mhealth.6998",
url="http://www.ncbi.nlm.nih.gov/pubmed/28606895"
}


@Article{info:doi/10.2196/cardio.6057,
author="Darling, Chad Eric
and Dovancescu, Silviu
and Saczynski, Jane S
and Riistama, Jarno
and Sert Kuniyoshi, Fatima
and Rock, Joseph
and Meyer, Theo E
and McManus, David D",
title="Bioimpedance-Based Heart Failure Deterioration Prediction Using a Prototype Fluid Accumulation Vest-Mobile Phone Dyad: An Observational Study",
journal="JMIR Cardio",
year="2017",
month="Mar",
day="13",
volume="1",
number="1",
pages="e1",
keywords="telemedicine; outpatient monitoring; heart failure; electric impedance",
abstract="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. ",
doi="10.2196/cardio.6057",
url="http://cardio.jmir.org/2017/1/e1/",
url="https://doi.org/10.2196/cardio.6057",
url="http://www.ncbi.nlm.nih.gov/pubmed/31758769"
}


@Article{info:doi/10.2196/mhealth.7275,
author="Yan, Bryan P
and Chan, Christy KY
and Li, Christien KH
and To, Olivia TL
and Lai, William HS
and Tse, Gary
and Poh, Yukkee C
and Poh, Ming-Zher",
title="Resting and Postexercise Heart Rate Detection From Fingertip and Facial Photoplethysmography Using a Smartphone Camera: A Validation Study",
journal="JMIR Mhealth Uhealth",
year="2017",
month="Mar",
day="13",
volume="5",
number="3",
pages="e33",
keywords="heart rate; mobile apps; photoplethysmography; smartphone; mobile phone",
abstract="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. ",
doi="10.2196/mhealth.7275",
url="http://mhealth.jmir.org/2017/3/e33/",
url="https://doi.org/10.2196/mhealth.7275",
url="http://www.ncbi.nlm.nih.gov/pubmed/28288955"
}