TY - JOUR AU - Ding, Y. Eric AU - Han, Dong AU - Whitcomb, Cody AU - Bashar, Khairul Syed AU - Adaramola, Oluwaseun AU - Soni, Apurv AU - Saczynski, Jane AU - Fitzgibbons, P. Timothy AU - Moonis, Majaz AU - Lubitz, A. Steven AU - Lessard, Darleen AU - Hills, True Mellanie AU - Barton, Bruce AU - Chon, Ki AU - McManus, D. David PY - 2019/05/15 TI - Accuracy and Usability of a Novel Algorithm for Detection of Irregular Pulse Using a Smartwatch Among Older Adults: Observational Study JO - JMIR Cardio SP - e13850 VL - 3 IS - 1 KW - mobile health KW - mHealth KW - atrial fibrillation KW - screening KW - photoplethysmography KW - electrocardiography KW - smartwatch N2 - 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. UR - http://cardio.jmir.org/2019/1/e13850/ UR - http://dx.doi.org/10.2196/13850 UR - http://www.ncbi.nlm.nih.gov/pubmed/31758787 ID - info:doi/10.2196/13850 ER - TY - JOUR AU - Baril, Jonathan-F AU - Bromberg, Simon AU - Moayedi, Yasbanoo AU - Taati, Babak AU - Manlhiot, Cedric AU - Ross, Joan Heather AU - Cafazzo, Joseph PY - 2019/05/17 TI - Use of Free-Living Step Count Monitoring for Heart Failure Functional Classification: Validation Study JO - JMIR Cardio SP - e12122 VL - 3 IS - 1 KW - exercise physiology KW - heart rate tracker KW - wrist worn devices KW - Fitbit KW - heart failure KW - steps KW - cardiopulmonary exercise test KW - ambulatory monitoring N2 - 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. UR - http://cardio.jmir.org/2019/1/e12122/ UR - http://dx.doi.org/10.2196/12122 UR - http://www.ncbi.nlm.nih.gov/pubmed/31758777 ID - info:doi/10.2196/12122 ER - TY - JOUR AU - Palacholla, Sita Ramya AU - Fischer, Nils AU - Coleman, Amanda AU - Agboola, Stephen AU - Kirley, Katherine AU - Felsted, Jennifer AU - Katz, Chelsea AU - Lloyd, Stacy AU - Jethwani, Kamal PY - 2019/03/26 TI - Provider- and Patient-Related Barriers to and Facilitators of Digital Health Technology Adoption for Hypertension Management: Scoping Review JO - JMIR Cardio SP - e11951 VL - 3 IS - 1 KW - medical informatics KW - culturally appropriate technology KW - hypertension N2 - Background: The uptake of digital health technology (DHT) has been surprisingly low in clinical practice. Despite showing great promise to improve patient outcomes and disease management, there is limited information on the factors that contribute to the limited adoption of DHT, particularly for hypertension management. Objective: This scoping review provides a comprehensive summary of barriers to and facilitators of DHT adoption for hypertension management reported in the published literature with a focus on provider- and patient-related barriers and facilitators. Methods: This review followed the methodological framework developed by Arskey and O?Malley. Systematic literature searches were conducted on PubMed or Medical Literature Analysis and Retrieval System Online, Cumulative Index to Nursing and Allied Health Literature, and Excerpta Medica database. Articles that reported on barriers to and/or facilitators of digital health adoption for hypertension management published in English between 2008 and 2017 were eligible. Studies not reporting on barriers or facilitators to DHT adoption for management of hypertension were excluded. A total of 2299 articles were identified based on the above criteria after removing duplicates, and they were assessed for eligibility. Of these, 2165 references did not meet the inclusion criteria. After assessing 134 studies in full text, 98 studies were excluded (full texts were either unavailable or studies did not fulfill the inclusion criteria), resulting in a final set of 32 articles. In addition, 4 handpicked articles were also included in the review, making it a total of 36 studies. Results: A total of 36 studies were selected for data extraction after abstract and full-text screening by 2 independent reviewers. All conflicts were resolved by a third reviewer. Thematic analysis was conducted to identify major themes pertaining to barriers and facilitators of DHT from both provider and patient perspectives. The key facilitators of DHT adoption by physicians that were identified include ease of integration with clinical workflow, improvement in patient outcomes, and technology usability and technical support. Technology usability and timely technical support improved self-management and patient experience, and positive impact on patient-provider communication were most frequently reported facilitators for patients. Barriers to use of DHTs reported by physicians include lack of integration with clinical workflow, lack of validation of technology, and lack of technology usability and technical support. Finally, lack of technology usability and technical support, interference with patient-provider relationship, and lack of validation of technology were the most commonly reported barriers by patients. Conclusions: Findings suggest the settings and context in which DHTs are implemented and individuals involved in implementation influence adoption. Finally, to fully realize the potential of digitally enabled hypertension management, there is a greater need to validate these technologies to provide patients and providers with reliable and accurate information on both clinical outcomes and cost effectiveness. UR - http://cardio.jmir.org/2019/1/e11951/ UR - http://dx.doi.org/10.2196/11951 UR - http://www.ncbi.nlm.nih.gov/pubmed/31758771 ID - info:doi/10.2196/11951 ER - TY - JOUR AU - Guthrie, L. Nicole AU - Berman, A. Mark AU - Edwards, L. Katherine AU - Appelbaum, J. Kevin AU - Dey, Sourav AU - Carpenter, Jason AU - Eisenberg, M. David AU - Katz, L. David PY - 2019/03/12 TI - Achieving Rapid Blood Pressure Control With Digital Therapeutics: Retrospective Cohort and Machine Learning Study JO - JMIR Cardio SP - e13030 VL - 3 IS - 1 KW - hypertension KW - mobile health KW - mHealth KW - lifestyle medicine KW - digital therapeutics KW - digital medicine KW - machine learning, behavioral therapy N2 - Background: Behavioral therapies, such as electronic counseling and self-monitoring dispensed through mobile apps, have been shown to improve blood pressure, but the results vary and long-term engagement is a challenge. Machine learning is a rapidly advancing discipline that can be used to generate predictive and responsive models for the management and treatment of chronic conditions and shows potential for meaningfully improving outcomes. Objective: The objectives of this retrospective analysis were to examine the effect of a novel digital therapeutic on blood pressure in adults with hypertension and to explore the ability of machine learning to predict participant completion of the intervention. Methods: Participants with hypertension, who engaged with the digital intervention for at least 2 weeks and had paired blood pressure values, were identified from the intervention database. Participants were required to be ?18 years old, reside in the United States, and own a smartphone. The digital intervention offers personalized behavior therapy, including goal setting, skill building, and self-monitoring. Participants reported blood pressure values at will, and changes were calculated using averages of baseline and final values for each participant. Machine learning was used to generate a model of participants who would complete the intervention. Random forest models were trained at days 1, 3, and 7 of the intervention, and the generalizability of the models was assessed using leave-one-out cross-validation. Results: The primary cohort comprised 172 participants with hypertension, having paired blood pressure values, who were engaged with the intervention. Of the total, 86.1% participants were women, the mean age was 55.0 years (95% CI 53.7-56.2), baseline systolic blood pressure was 138.9 mmHg (95% CI 136.6-141.3), and diastolic was 86.2 mmHg (95% CI 84.8-87.7). Mean change was ?11.5 mmHg for systolic blood pressure and ?5.9 mmHg for diastolic blood pressure over a mean of 62.6 days (P<.001). Among participants with stage 2 hypertension, mean change was ?17.6 mmHg for systolic blood pressure and ?8.8 mmHg for diastolic blood pressure. Changes in blood pressure remained significant in a mixed-effects model accounting for the baseline systolic blood pressure, age, gender, and body mass index (P<.001). A total of 43% of the participants tracking their blood pressure at 12 weeks achieved the 2017 American College of Cardiology/American Heart Association definition of blood pressure control. The 7-day predictive model for intervention completion was trained on 427 participants, and the area under the receiver operating characteristic curve was .78. Conclusions: Reductions in blood pressure were observed in adults with hypertension who used the digital therapeutic. The degree of blood pressure reduction was clinically meaningful and achieved rapidly by a majority of the studied participants. Greater improvement was observed in participants with more severe hypertension at baseline. A successful proof of concept for using machine learning to predict intervention completion was presented. UR - http://cardio.jmir.org/2019/1/e13030/ UR - http://dx.doi.org/10.2196/13030 UR - http://www.ncbi.nlm.nih.gov/pubmed/31758792 ID - info:doi/10.2196/13030 ER - TY - JOUR AU - Geerse, Carlijn AU - van Slobbe, Cher AU - van Triet, Edda AU - Simonse, Lianne PY - 2019/05/03 TI - Design of a Care Pathway for Preventive Blood Pressure Monitoring: Qualitative Study JO - JMIR Cardio SP - e13048 VL - 3 IS - 1 KW - eHealth KW - blood pressure monitoring KW - at-risk patients KW - secondary preventive care KW - care pathway KW - design N2 - Background: Electronic health (eHealth) services could provide a solution for monitoring the blood pressure of at-risk patients while also decreasing expensive doctor visits. However, a major barrier to their implementation is the lack of integration into organizations. Objective: Our aim was to design a Care Pathway for monitoring the blood pressure of at-risk patients, in order to increase eHealth implementation in secondary preventive care. Methods: A qualitative design study was used in this research. Data were collected by conducting visual mapping sessions including semistructured interviews with hypertension patients and doctors. The data were transcribed and coded and thereafter mapped into a Care Pathway. Results: Four themes emerged from the results: (1) the current approach to blood pressure measuring has disadvantages, (2) risk and lifestyle factors of blood pressure measuring need to be considered, (3) there are certain influences of the at-home context on measuring blood pressure, and (4) new touchpoints between patients and health professionals need to be designed. These in-depth insights combined with the visualization of the current blood pressure process resulted in our Care Pathway design for monitoring the blood pressure of at-risk patients as secondary preventive care. Conclusions: The Care Pathway guides the implementation of eHealth devices for blood pressure self-measurement. It showcases the pathway of at-risk patients and increases their involvement in managing their blood pressure. It serves as a basis for a new service using eHealth. UR - http://cardio.jmir.org/2019/1/e13048/ UR - http://dx.doi.org/10.2196/13048 UR - http://www.ncbi.nlm.nih.gov/pubmed/31758790 ID - info:doi/10.2196/13048 ER - TY - JOUR AU - Treskes, Willem Roderick AU - Maan, C. Arie AU - Verwey, Florence Harriette AU - Schot, Robert AU - Beeres, Anna Saskia Lambertha Maria AU - Tops, F. Laurens AU - Van Der Velde, Tjeerd Enno AU - Schalij, Jan Martin AU - Slats, Margaretha Annelies PY - 2019/03/19 TI - Mobile Health for Central Sleep Apnea Screening Among Patients With Stable Heart Failure: Single-Cohort, Open, Prospective Trial JO - JMIR Cardio SP - e9894 VL - 3 IS - 1 KW - mobile health KW - central sleep apnea KW - heart failure KW - prevention KW - screening KW - mobile phone N2 - Background: Polysomnography is the gold standard for detection of central sleep apnea in patients with stable heart failure. However, this procedure is costly, time consuming, and a burden to the patient and therefore unsuitable as a screening method. An electronic health (eHealth) app to measure overnight oximetry may be an acceptable screening alternative, as it can be automatically analyzed and is less burdensome to patients. Objective: This study aimed to assess whether overnight pulse oximetry using a smartphone-compatible oximeter can be used to detect central sleep apnea in a population with stable heart failure. Methods: A total of 26 patients with stable heart failure underwent one night of both a polygraph examination and overnight saturation using a smartphone-compatible oximeter. The primary endpoint was agreement between the oxygen desaturation index (ODI) above or below 15 on the smartphone-compatible oximeter and the diagnosis of the polygraph. Results: The median age of patients was 66.4 (interquartile range, 62-71) years and 92% were men. The median body mass index was 27.1 (interquartile range, 24.4-30.8) kg/m2. Two patients were excluded due to incomplete data, and two other patients were excluded because they could not use a smartphone. Seven patients had central sleep apnea, and 6 patients had obstructive sleep apnea. Of the 7 (of 22, 32%) patients with central sleep apnea that were included in the analysis, 3 (13%) had an ODI?15. Of all patients without central sleep apnea, 8 (36%) had an ODI<15. The McNemar test yielded a P value of .55. Conclusions: Oxygen desaturation measured by this smartphone-compatible oximeter is a weak predictor of central sleep apnea in patients with stable heart failure. UR - http://cardio.jmir.org/2019/1/e9894/ UR - http://dx.doi.org/10.2196/cardio.9894 UR - http://www.ncbi.nlm.nih.gov/pubmed/31758786 ID - info:doi/10.2196/cardio.9894 ER -