TY - JOUR AU - Hewage, Sumudu AU - Kularatna, Sanjeewa AU - Parsonage, William AU - Walters, Tomos AU - McPhail, Steven AU - Brain, David AU - Allen, J. Michelle PY - 2025/4/30 TI - Integrating a Mobile App to Enhance Atrial Fibrillation Care: Key Insights From an Implementation Study Guided by the Consolidated Framework for Implementation Research JO - J Med Internet Res SP - e66815 VL - 27 KW - mobile health apps KW - digital health integration KW - health care innovation KW - technology adoption KW - cardiac rehabilitation KW - lifestyle modification KW - implementation science KW - consolidated framework for implementation framework KW - mHealth KW - mobile health KW - app KW - digital health KW - smartphone KW - eHealth KW - telehealth KW - telemedicine KW - digital KW - technology KW - CFIR KW - implementation research KW - cardiac KW - rehabilitation KW - cardiology KW - atrial fibrillation KW - Australia KW - interview N2 - Background: Despite the growing use of mobile health apps in managing chronic heart disease, their integration into routine care remains challenging due to dynamic, context-specific barriers. Objective: This study aimed to identify the key enablers and challenges of implementing a mobile app for cardiac rehabilitation and healthy lifestyles in patients with atrial fibrillation at an Australian cardiology clinic. Methods: We interviewed both clinicians and patients to understand their perspectives about the mobile app and what factors affected the implementation. The two semistructured interview guides used, one for clinicians and one for patients, were developed based on the Consolidated Framework for Implementation Research (CFIR) and nonadoption abandonment, scale-up, spread, and sustainability complexity assessment tool. All interviews were recorded and transcribed, and the transcripts were analyzed inductively to generate codes using a constructionist perspective. These codes were subsequently mapped onto the constructs within the CFIR across its five domains. This framework analysis was followed by examining the interconnections among the constructs to understand their collective impact on the implementation process, thereby identifying key enablers and challenges for the integration efforts. Results: We interviewed 24 participants including 18 patients, whose mean age was 69 (SD 9.2) years, and 6 clinicians, comprising 4 specialist cardiac electrophysiologists and 2 nurses. Patient engagement with the app varied: 3 participants completed the cardiac rehabilitation plan, 1 participant was still actively engaged, 2 participants had partial use, 10 participants downloaded but never used the app, and 2 participants did not download the app. We identified a complex interplay between key determinants across all five CFIR domains, collectively impacting two main elements in the implementation process: (1) acceptability and user engagement with the app and (2) the clinic?s implementation readiness. The app was more likely to be accepted and used by patients who needed to establish healthy lifestyle habits. Those with established healthy lifestyle habits did not indicate that the app provided sufficient added value to justify adoption. Interoperability with other devices and design issues, for example, limited customization options, also negatively impacted the uptake. The clinic?s implementation readiness was limited by various challenges including limited staff availability, insufficient internal communication processes, the absence of an implementation evaluation plan, and lack of clarity around who is funding the app?s use beyond the initial trial. Despite the clinician?s overall inclination toward technology use, diverse opinions on the evidence for short-term cardiac rehabilitation programs in atrial fibrillation critically reduced their commitment to app integration. Conclusions: Mobile health apps have seen rapid expansion and offer clear benefits, yet their integration into complex health systems remains challenging. Whilst our findings are from a single app implementation, they highlight the importance of embedding contextual analysis and proactive strategic planning in the integration process. UR - https://www.jmir.org/2025/1/e66815 UR - http://dx.doi.org/10.2196/66815 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/66815 ER - TY - JOUR AU - Trinkley, E. Katy AU - Simon, T. Steven AU - Rosenberg, A. Michael PY - 2025/4/14 TI - Impact of an Alert-Based Inpatient Clinical Decision Support Tool to Prevent Drug-Induced Long QT Syndrome: Large-Scale, System-Wide Observational Study JO - J Med Internet Res SP - e68256 VL - 27 KW - drug-induced QT prolongation KW - predictive modeling KW - electronic health records KW - clinical decision support KW - alert-based CDS system KW - tools KW - long QT syndrome KW - prevention N2 - Background: Prevention of drug-induced QT prolongation (diLQTS) has been the focus of many system-wide clinical decision support (CDS) tools, which can be directly embedded within the framework of the electronic health record system and triggered to alert in high-risk patients when a known QT-prolonging medication is ordered. Justification for these CDS systems typically lies in the ability to accurately predict which patients are at high risk; however, it is not always evident that identification of risk alone is sufficient for appropriate CDS implementation. Objective: In this investigation, we examined the impact of a system-wide, alert-based, inpatient CDS tool designed to prevent diLQTS across 10 known QT-prolonging medications. Methods: We compared the risk of diLQTS, duration of hospitalization, and in- and out-of-hospital mortality before and after implementation of the CDS system in 178,097 hospitalizations among 102,847 patients. We also compared outcomes between those in whom an alert fired and those in whom it did not, and within the various responses to the alert by providers. Analyses were adjusted for age, sex, race and ethnicity, inpatient location, electrolyte values, and comorbidities, with the latter processed using an unsupervised clustering analysis applied to the top 500 most common medications and diagnosis codes, respectively. Results: We found that the simple, rule-based logic of the CDS (any prior electrocardiograph with heart rate?corrected QT interval (QTc)?500 ms) successfully identified patients at high risk of diLQTS with an odds ratio of 2.28 (95% CI 2.10-2.47, P<.001) among those in whom it fired. However, we did not identify any impact on the risk of diLQTS based on provider responses or on the risk of inpatient, 3-month, 6-month, or 1-year mortality. When compared with rates prior to implementation, the risk of diLQTS was not significantly different after the CDS tools were deployed across the system, although mortality was significantly higher after the tools were implemented. Conclusions: We found that despite successful identification of high-risk patients for diLQTS, deployment of an alert-based CDS did not impact the risk of diLQTS. These findings suggest that quantification of high risk may be insufficient rationale for implementation of a CDS system and that hospital systems should consider evaluation of the system in its entirety prior to adoption to improve clinical outcomes. UR - https://www.jmir.org/2025/1/e68256 UR - http://dx.doi.org/10.2196/68256 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/68256 ER - TY - JOUR AU - Dorsch, P. Michael AU - Flynn, J. Allen AU - Greer, M. Kaitlyn AU - Ganai, Sabah AU - Barnes, D. Geoffrey AU - Zikmund-Fisher, Brian PY - 2025/4/11 TI - A Web-Based Tool to Perform a Values Clarification for Stroke Prevention in Patients With Atrial Fibrillation: Design and Preliminary Testing Study JO - JMIR Cardio SP - e67956 VL - 9 KW - digital health KW - atrial fibrillation KW - stroke prevention KW - shared decision-making KW - values clarification N2 - Background: Atrial fibrillation (AF) is associated with an increased risk of stroke. Oral anticoagulation (OAC) is used for stroke prevention in AF, but it also increases bleeding risk. Clinical guidelines do not definitively recommend for or against OAC for patients with borderline stroke risk. Decision-making may benefit from values clarification exercises to communicate risk trade-offs. Objective: This study aimed to evaluate if a visual with a values clarification alters the understanding of the trade-offs of anticoagulation in AF. Methods: Participants aged 45?64 years were recruited across the United States via an online survey. While answering the survey, they were asked to imagine they were newly diagnosed with AF with a CHA2DS2-VASc (congestive heart failure; hypertension; age ?75 years [doubled]; type 2 diabetes; previous stroke, transient ischemic attack, or thromboembolism [doubled]; vascular disease; age 65 to 75 years; and sex category) score of 1 for men and 2 for women. Eligibility criteria included no diagnosis of AF and no prior OAC use. Participants were randomized to one of three conditions: (1) standard text-based information only (n=255), (2) visual aids showing stroke-risk probabilities (n=218), or (3) visual aids plus a values clarification exercise (visual+VC; n=200). Participants were subrandomized within the 2 visual-based groups to view either a gauge display or an icon array representing stroke risk. All participants read a hypothetical scenario of being newly diagnosed with AF and hypertension. The primary outcome was decision confidence as measured by the SURE (Sure of Myself; Understand Information; Risk-Benefit Ratio; Encouragement) test. Secondary measures included participants? perceived stroke risk reduction, worry about stroke or bleeding, and likelihood to choose OAC. Results: A total of 673 participants completed the survey. The overall SURE test was 61.2% (156/255) for the standard, 66.5% (145/218) for the visual, and 67% (134/200) for the visual+VC group (visual vs standard P=.23; visual+VC vs standard P=.20). Participants were less likely to choose OAC in the visual groups (standard: mean 58.3, SD 30; visual: mean 51.4, SD 32; visual+VC: 51.9, SD 28; P=.03). Participants felt the reduction in stroke risk from an OAC was less in the visual groups (standard: mean 63.8, SD 22; visual: mean 54.2, SD 28; visual+VC: mean 58.6, SD 25; P<.001). Visualization methods (gauge vs icon array) showed no significant differences in overall SURE test results. Participants were less likely to choose OAC and perceived a smaller stroke risk reduction with gauge than icon array (OAC choice: gauge 48.8, icon array 55.4; P=.03; stroke risk reduction: gauge 52.1, icon array 60.4; P=.001). Conclusions: Visual aids can modestly affect decision confidence and perceptions regarding the benefits of OAC but do not significantly alter decision certainty in a scenario where the guidelines do not recommend for or against OAC. Future work should determine the role of a gauge versus icon array visual for decision-making in stroke prevention in AF. UR - https://cardio.jmir.org/2025/1/e67956 UR - http://dx.doi.org/10.2196/67956 ID - info:doi/10.2196/67956 ER - TY - JOUR AU - Du, ShanShan AU - Zhao, Yining PY - 2025/3/14 TI - Enhancing Digital Health Interventions for Medication Adherence: Considerations for Broader Applicability and Long-Term Impact JO - J Med Internet Res SP - e69204 VL - 27 KW - mobile apps KW - digital health KW - atrial fibrillation KW - anticoagulants KW - medication adherence KW - mobile phone UR - https://www.jmir.org/2025/1/e69204 UR - http://dx.doi.org/10.2196/69204 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/69204 ER - TY - JOUR AU - Shi, Xiaoyu AU - Wang, Yijun AU - Wang, Yuhong AU - Wang, Jun AU - Peng, Chen AU - Cheng, Siyi AU - Song, Lingpeng AU - Li, Rui AU - Guo, Fuding AU - Li, Zeyan AU - Duan, Shoupeng AU - Yang, Xiaomeng AU - Zhou, Liping AU - Jiang, Hong AU - Yu, Lilei PY - 2025/3/11 TI - The Effectiveness of Digital Animation?Based Multistage Education for Patients With Atrial Fibrillation Catheter Ablation: Randomized Clinical Trial JO - J Med Internet Res SP - e65685 VL - 27 KW - animation education KW - digital health care KW - atrial fibrillation KW - catheter ablation KW - video KW - mHealth KW - digital care KW - digital health KW - digital animation KW - randomized clinical trial KW - RCT KW - digital education KW - outpatient KW - AFCA KW - atrial fibrillation catheter ablation KW - therapeutic KW - cardiac arrhythmia KW - Asian KW - animations KW - comics N2 - Background: Digital education for outpatient patients with atrial fibrillation (AF) has gradually increased. However, research on digital education for patients undergoing atrial fibrillation catheter ablation (AFCA) is limited. Objective: This study aimed to develop a novel digital animation-based multistage education system and evaluate its quality-of-life benefits for patients with AFCA. Methods: This randomized controlled clinical trial included 208 patients with AF who underwent catheter ablation in the Department of Cardiology at Renmin Hospital of Wuhan University between January 2022 and August 2023. The patients were randomly assigned to the digital animation intervention (n=104) and standard treatment (n=104) groups. The primary outcome was the difference in the quality of life of patients with atrial fibrillation (AF-QoL-18) scores at 3 months. Secondary outcomes included differences in scores on the 5-item Medication Adherence Report Scale (MARS-5), Self-rating Anxiety Scale (SAS), and Self-Rating Depression Scale (SDS) at 3 months. Results: In the digital animation intervention group, the AF-QoL-18 score increased from 38.02 (SD 6.52) to 47.77 (SD 5.74), the MARS-5 score increased from 17.04 (SD 3.03) to 20.13 (SD 2.12), the SAS score decreased from 52.82 (SD 8.08) to 45.39 (SD 6.13), and the SDS score decreased from 54.12 (SD 6.13) to 45.47 (SD 5.94), 3 months post discharge from the hospital. In the conventional treatment group, the AF-QoL-18 score increased from 36.97 (SD 7.00) to 45.31 (SD 5.71), the MARS-5 score increased from 17.14 (SD 3.01) to 18.47 (SD 2.79), the SAS score decreased from 51.83 (SD 7.74) to 47.31 (SD 5.87), and the SDS score decreased from 52.78 (SD 5.21) to 45.37 (SD 6.18). The mean difference in AF-QoL-18 score change between the 2 groups was 1.41 (95% CI 2.42-0.40, P=.006) at 3 months. The mean difference in MARS-5 score change was 1.76 (95% CI 2.42-1.10, P<.001). The mean difference in SAS score was ?2.91 (95% CI ?3.88 to ?1.95, P<.001). Additionally, the mean difference in SDS score was ?1.23 (95% CI ?0.02 to ?2.44, P=.047). Conclusions: Our study introduces a novel digital animation educational approach that provides multidimensional, easily understandable, and multistage education for patients with AF undergoing catheter ablation. This educational model effectively improves postoperative anxiety, depression, medication adherence, and quality of life in patients at 3 months post discharge. Trial Registration: Chinese Clinical Trial Registry ChiCTR2400081673; https://www.chictr.org.cn/showproj.html?proj=201059 UR - https://www.jmir.org/2025/1/e65685 UR - http://dx.doi.org/10.2196/65685 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/65685 ER - TY - JOUR AU - Karamchand, Sumanth AU - Chipamaunga, Tsungai AU - Naidoo, Poobalan AU - Naidoo, Kiolan AU - Rambiritch, Virendra AU - Ho, Kevin AU - Chilton, Robert AU - McMahon, Kyle AU - Leisegang, Rory AU - Weich, Hellmuth AU - Hassan, Karim PY - 2025/3/10 TI - Novel Versus Conventional Sequencing of ?-Blockers, Sodium/Glucose Cotransportor 2 Inhibitors, Angiotensin Receptor-Neprilysin Inhibitors, and Mineralocorticoid Receptor Antagonists in Stable Patients With Heart Failure With Reduced Ejection Fraction (NovCon Sequencing Study): Protocol for a Randomized Controlled Trial JO - JMIR Res Protoc SP - e44027 VL - 14 KW - heart failure KW - SGLT2i KW - sodium/glucose cotransporter 2 inhibitors KW - ARNi KW - angiotensin receptor-neprilysin inhibitors KW - HFrEF KW - heart failure with reduced ejection fraction KW - idiopathic dilated cardiomyopathy KW - heart KW - chronic heart failure KW - patient KW - control KW - clinical KW - adult KW - cardiomyopathy KW - therapy N2 - Background: Chronic heart failure has high morbidity and mortality, with approximately half of the patients dying within 5 years of diagnosis. Recent additions to the armamentarium of anti?heart failure therapies include angiotensin receptor-neprilysin inhibitors (ARNIs) and sodium/glucose cotransporter 2 inhibitors (SGLT2is). Both classes have demonstrated mortality and morbidity benefits. Although these new therapies have morbidity and mortality benefits, it is not known whether rapid initiation is beneficial when compared with the conventional, slower-stepped approach. Many clinicians have been taught that starting with low-dose therapies and gradually increasing the dose is a safe way of intensifying treatment regimens. Pharmacologically, it is rational to use a combination of drugs that target multiple pathological mechanisms, as there is potential synergism and better therapeutic outcomes. Theoretically, the quicker the right combinations are used, the more likely the beneficial effects will be experienced. However, rapid up-titration must be balanced with patient safety and tolerability. Objective: This study aims to determine if early addition of ARNIs, SGLT2is, ?-blockers, and mineralocorticoid receptor antagonists (within 4 weeks), when compared with the same therapies initiated slower (within 6 months), will reduce all-cause mortality and hospitalizations for heart failure in patients with stable heart failure with reduced ejection fraction. Methods: This is a single-center, randomized controlled, double-arm, assessor-blinded, active control, and pragmatic clinical trial. Adults with stable heart failure with reduced ejection fraction and idiopathic dilated cardiomyopathy will be randomized to conventional sequencing (the control arm; over 6 months) of anti?heart failure therapies, and a second arm will receive rapid sequencing (over 4 weeks). Study participants will be followed for 5 years to assess the safety, efficacy, and tolerability of the 2 types of sequencing. Posttrial access and care will be provided to all study participants throughout their lifespan. Results: We are currently in the process of obtaining ethical clearance and funding. Conclusions: We envisage that this study will help support evidence-based medicine and inform clinical practice guidelines on the optimal rate of sequencing of anti?heart failure therapies. A third placebo arm was considered, but costs would be too much and not providing study participants with therapies with known morbidity and mortality benefits may be unethical, in our opinion. Given the post?COVID-19 economic downturn and posttrial access to interventions, a major challenge will be acquiring funding for this study. International Registered Report Identifier (IRRID): PRR1-10.2196/44027 UR - https://www.researchprotocols.org/2025/1/e44027 UR - http://dx.doi.org/10.2196/44027 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/44027 ER - TY - JOUR AU - Guo, XiaoRui AU - Xiao, Liang AU - Liu, Xinyu AU - Chen, Jianxia AU - Tong, Zefang AU - Liu, Ziji PY - 2025/3/4 TI - Enhancing Doctor-Patient Shared Decision-Making: Design of a Novel Collaborative Decision Description Language JO - J Med Internet Res SP - e55341 VL - 27 KW - shared decision-making KW - speech acts KW - agent KW - argumentation KW - interaction protocol N2 - Background: Effective shared decision-making between patients and physicians is crucial for enhancing health care quality and reducing medical errors. The literature shows that the absence of effective methods to facilitate shared decision-making can result in poor patient engagement and unfavorable decision outcomes. Objective: In this paper, we propose a Collaborative Decision Description Language (CoDeL) to model shared decision-making between patients and physicians, offering a theoretical foundation for studying various shared decision scenarios. Methods: CoDeL is based on an extension of the interaction protocol language of Lightweight Social Calculus. The language utilizes speech acts to represent the attitudes of shared decision-makers toward decision propositions, as well as their semantic relationships within dialogues. It supports interactive argumentation among decision makers by embedding clinical evidence into each segment of decision protocols. Furthermore, CoDeL enables personalized decision-making, allowing for the demonstration of characteristics such as persistence, critical thinking, and openness. Results: The feasibility of the approach is demonstrated through a case study of shared decision-making in the disease domain of atrial fibrillation. Our experimental results show that integrating the proposed language with GPT can further enhance its capabilities in interactive decision-making, improving interpretability. Conclusions: The proposed novel CoDeL can enhance doctor-patient shared decision-making in a rational, personalized, and interpretable manner. UR - https://www.jmir.org/2025/1/e55341 UR - http://dx.doi.org/10.2196/55341 UR - http://www.ncbi.nlm.nih.gov/pubmed/40053763 ID - info:doi/10.2196/55341 ER - TY - JOUR AU - Johansson, I. Birgitta AU - Landahl, Jonas AU - Tammelin, Karin AU - Aerts, Erik AU - Lundberg, E. Christina AU - Adiels, Martin AU - Lindgren, Martin AU - Rosengren, Annika AU - Papachrysos, Nikolaos AU - Filipsson Nyström, Helena AU - Sjöland, Helen PY - 2025/2/19 TI - Automated Process for Monitoring of Amiodarone Treatment: Development and Evaluation JO - J Med Internet Res SP - e65473 VL - 27 KW - thyroid function KW - robotics KW - follow-up studies KW - disease management KW - decision support KW - automated process KW - monitoring KW - amiodarone treatment KW - anti-arrhythmic medication KW - anti-arrhythmic KW - development KW - evaluation KW - thyroid KW - liver KW - side effects KW - cardiac dysrhythmias KW - ventricular tachycardia KW - ventricular fibrillation KW - arrhythmia KW - automation KW - robot KW - algorithm KW - clinical decision support system KW - thyroid gland KW - heart KW - atrial fibrillation N2 - Background: Amiodarone treatment requires repeated laboratory evaluations of thyroid and liver function due to potential side effects. Robotic process automation uses software robots to automate repetitive and routine tasks, and their use may be extended to clinical settings. Objective: Thus, this study aimed to develop a robot using a diagnostic classification algorithm to automate repetitive laboratory evaluations for amiodarone follow-up. Methods: We designed a robot and clinical decision support system based on expert clinical advice and current best practices in thyroid and liver disease management. The robot provided recommendations on the time interval to follow-up laboratory testing and management suggestions, while the final decision rested with a physician, acting as a human-in-the-loop. The performance of the robot was compared to the existing real-world manual follow-up routine for amiodarone treatment. Results: Following iterative technical improvements, a robot prototype was validated against physician orders (n=390 paired orders). The robot recommended a mean follow-up time interval of 4.5 (SD 2.4) months compared to the 3.1 (SD 1.4) months ordered by physicians (P<.001). For normal laboratory values, the robot recommended a 6-month follow-up in 281 (72.1%) of cases, whereas physicians did so in only 38 (9.7%) of cases, favoring a 3- to 4-month follow-up (n=227, 58.2%). All patients diagnosed with new side effects (n=12) were correctly detected by the robot, whereas only 8 were by the physician. Conclusions: An automated process, using a software robot and a diagnostic classification algorithm, is a technically and medically reliable alternative for amiodarone follow-up. It may reduce manual labor, decrease the frequency of laboratory testing, and improve the detection of side effects, thereby reducing costs and enhancing patient value. UR - https://www.jmir.org/2025/1/e65473 UR - http://dx.doi.org/10.2196/65473 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/65473 ER - TY - JOUR AU - Dinesen, Birthe AU - Albertsen, Eie Andi AU - Joensen, Ragnvaldsdóttir Elisabet Dortea AU - Spindler, Helle AU - Jensen, Møller Katja AU - Kidholm, Kristian AU - Frost, Lars AU - Dittman, Lars AU - Gunasegaram, Mathushan AU - Johnsen, Paaske Søren AU - Jochumsen, Rovsing Mads AU - Svenstrup, Dorthe PY - 2025/2/18 TI - Future Patient?Telerehabilitation of Patients With Atrial Fibrillation: Protocol for a Multicenter, Mixed Methods, Randomized Controlled Trial JO - JMIR Res Protoc SP - e64259 VL - 14 KW - atrial fibrillation KW - telerehabilitation KW - quality of life KW - research design KW - patient education KW - co-creation KW - randomized controlled trial KW - chronic KW - cardiovascular disease KW - adult KW - aging KW - prevalence KW - comorbidity KW - Future Patient KW - patient engagement KW - primary outcome KW - cost-effectiveness KW - monitoring KW - health care professional KW - digital health KW - remote therapy KW - telehealth N2 - Background: Atrial fibrillation (AF) is a chronic cardiovascular condition with a lifetime risk of 1 in 3 and a prevalence of 3% among adults. AF?s prevalence is predicted to more than double during the next 20 years due to better detection, increasing comorbidities, and an aging population. Due to increased AF prevalence, telerehabilitation has been developed to enhance patient engagement, health care accessibility, and compliance through digital technologies. A telerehabilitation program called ?Future Patient?telerehabilitation of patients with AF (FP-AF)? has been developed to enhance rehabilitation for AF. The FP-AF program comprises two modules: (1) an education and monitoring module using telerehabilitation technologies (4 months) and (2) a follow-up module, where patients can measure steps and access a data and knowledge-sharing portal, HeartPortal, using their digital devices. Those patients in the FP-AF program measure their heart rhythm, pulse, blood pressure, weight, steps, and sleep. Patients also complete web-based questionnaires regarding their well-being and coping with AF. All recorded data are transmitted to the HeartPortal, accessible to patients, relatives, and health care professionals. Objective: This paper aims to describe the research design, outcome measures, and data collection techniques in a clinical trial of the FP-AF program for patients with AF. Methods: This is a multicenter, mixed methods, randomized controlled trial. Patients are recruited from AF clinics serving the North Jutland region of Denmark. The telerehabilitation group will participate in the FP-AF program, while the control group will follow the conventional care regime based on physical visits to the AF clinic. The primary outcome measure is AF-specific health-related quality of life, to be assessed using the Atrial Fibrillation Effect on Quality-of-Life Questionnaire. Secondary outcomes are knowledge of AF; measurement of vital parameters; level of anxiety and depression; degree of motivation; burden of AF; use of the HeartPortal; qualitative exploration of patients?, relatives?, and health care professionals? experiences of participating in the FP-AF program; cost-effectiveness evaluation of the program; and analysis of multiparametric monitoring data. Outcomes are assessed through data from digital technologies, interviews, and questionnaires. Results: Patient enrollment began in January 2023 and will be completed by December 2024, with a total of 208 patients enrolled. Qualitative interviews conducted in spring 2024 will be analyzed and published in peer-reviewed journals in 2025. Data from questionnaires and digital technologies will be analyzed upon study completion and presented at international conferences and published in peer-reviewed journals by the fall of 2025. Conclusions: Results from the FP-AF study will determine whether the FP-AF program can increase quality of life for patients with AF and increase their knowledge of symptoms and living with AF in everyday life compared to conventional AF care. The cost-effectiveness evaluation will determine whether telerehabilitation can be a viable alternative for rehabilitation of patients with AF. Trial Registration: ClinicalTrials.gov NCT06101485; https://clinicaltrials.gov/study/NCT06101485 International Registered Report Identifier (IRRID): DERR1-10.2196/64259 UR - https://www.researchprotocols.org/2025/1/e64259 UR - http://dx.doi.org/10.2196/64259 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/64259 ER - TY - JOUR AU - She, Jou Wan AU - Siriaraya, Panote AU - Iwakoshi, Hibiki AU - Kuwahara, Noriaki AU - Senoo, Keitaro PY - 2025/2/13 TI - An Explainable AI Application (AF?fective) to Support Monitoring of Patients With Atrial Fibrillation After Catheter Ablation: Qualitative Focus Group, Design Session, and Interview Study JO - JMIR Hum Factors SP - e65923 VL - 12 KW - atrial fibrillation KW - explainable artificial intelligence KW - explainable AI KW - user-centered design KW - prevention KW - postablation monitoring N2 - Background: The opaque nature of artificial intelligence (AI) algorithms has led to distrust in medical contexts, particularly in the treatment and monitoring of atrial fibrillation. Although previous studies in explainable AI have demonstrated potential to address this issue, they often focus solely on electrocardiography graphs and lack real-world field insights. Objective: We addressed this gap by incorporating standardized clinical interpretation of electrocardiography graphs into the system and collaborating with cardiologists to co-design and evaluate this approach using real-world patient cases and data. Methods: We conducted a 3-stage iterative design process with 23 cardiologists to co-design, evaluate, and pilot an explainable AI application. In the first stage, we identified 4 physician personas and 7 explainability strategies, which were reviewed in the second stage. A total of 4 strategies were deemed highly effective and feasible for pilot deployment. On the basis of these strategies, we developed a progressive web application and tested it with cardiologists in the third stage. Results: The final progressive web application prototype received above-average user experience evaluations and effectively motivated physicians to adopt it owing to its ease of use, reliable information, and explainable functionality. In addition, we gathered in-depth field insights from cardiologists who used the system in clinical contexts. Conclusions: Our study identified effective explainability strategies, emphasized the importance of curating actionable features and setting accurate expectations, and suggested that many of these insights could apply to other disease care contexts, paving the way for future real-world clinical evaluations. UR - https://humanfactors.jmir.org/2025/1/e65923 UR - http://dx.doi.org/10.2196/65923 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/65923 ER - TY - JOUR AU - Alves, Miguel João AU - Matos, Daniel AU - Martins, Tiago AU - Cavaco, Diogo AU - Carmo, Pedro AU - Galvão, Pedro AU - Costa, Moscoso Francisco AU - Morgado, Francisco AU - Ferreira, Miguel António AU - Freitas, Pedro AU - Dias, Camila Cláudia AU - Rodrigues, Pereira Pedro AU - Adragão, Pedro PY - 2025/2/11 TI - Predicting Atrial Fibrillation Relapse Using Bayesian Networks: Explainable AI Approach JO - JMIR Cardio SP - e59380 VL - 9 KW - artificial intelligence KW - atrial fibrillation KW - Bayesian networks KW - clinical decision-making KW - machine learning KW - prognostic models N2 - Background: Atrial fibrillation (AF) is a prevalent arrhythmia associated with significant morbidity and mortality. Despite advancements in ablation techniques, predicting recurrence of AF remains a challenge, necessitating reliable models to identify patients at risk of relapse. Traditional scoring systems often lack applicability in diverse clinical settings and may not incorporate the latest evidence-based factors influencing AF outcomes. This study aims to develop an explainable artificial intelligence model using Bayesian networks to predict AF relapse postablation, leveraging on easily obtainable clinical variables. Objective: This study aims to investigate the effectiveness of Bayesian networks as a predictive tool for AF relapse following a percutaneous pulmonary vein isolation (PVI) procedure. The objectives include evaluating the model?s performance using various clinical predictors, assessing its adaptability to incorporate new risk factors, and determining its potential to enhance clinical decision-making in the management of AF. Methods: This study analyzed data from 480 patients with symptomatic drug-refractory AF who underwent percutaneous PVI. To predict AF relapse following the procedure, an explainable artificial intelligence model based on Bayesian networks was developed. The model used a variable number of clinical predictors, including age, sex, smoking status, preablation AF type, left atrial volume, epicardial fat, obstructive sleep apnea, and BMI. The predictive performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC-ROC) metrics across different configurations of predictors (5, 6, and 7 variables). Validation was conducted through four distinct sampling techniques to ensure robustness and reliability of the predictions. Results: The Bayesian network model demonstrated promising predictive performance for AF relapse. Using 5 predictors (age, sex, smoking, preablation AF type, and obstructive sleep apnea), the model achieved an AUC-ROC of 0.661 (95% CI 0.603?0.718). Incorporating additional predictors improved performance, with a 6-predictor model (adding BMI) achieving an AUC-ROC of 0.703 (95% CI 0.652?0.753) and a 7-predictor model (adding left atrial volume and epicardial fat) achieving an AUC-ROC of 0.752 (95% CI 0.701?0.800). These results indicate that the model can effectively estimate the risk of AF relapse using readily available clinical variables. Notably, the model maintained acceptable diagnostic accuracy even in scenarios where some predictive features were missing, highlighting its adaptability and potential use in real-world clinical settings. Conclusions: The developed Bayesian network model provides a reliable and interpretable tool for predicting AF relapse in patients undergoing percutaneous PVI. By using easily accessible clinical variables, presenting acceptable diagnostic accuracy, and showing adaptability to incorporate new medical knowledge over time, the model demonstrates a flexibility and robustness that makes it suitable for real-world clinical scenarios. UR - https://cardio.jmir.org/2025/1/e59380 UR - http://dx.doi.org/10.2196/59380 ID - info:doi/10.2196/59380 ER - TY - JOUR AU - Luo, Aijing AU - Chen, Wei AU - Zhu, Hongtao AU - Xie, Wenzhao AU - Chen, Xi AU - Liu, Zhenjiang AU - Xin, Zirui PY - 2025/2/10 TI - Machine Learning in the Management of Patients Undergoing Catheter Ablation for Atrial Fibrillation: Scoping Review JO - J Med Internet Res SP - e60888 VL - 27 KW - atrial fibrillation KW - catheter ablation KW - deep learning KW - patient management KW - prognosis KW - quality assessment tools KW - cardiac arrhythmia KW - public health KW - quality of life KW - severe medical condition KW - electrocardiogram KW - electronic health record KW - morbidity KW - mortality KW - thromboembolism KW - clinical intervention N2 - Background: Although catheter ablation (CA) is currently the most effective clinical treatment for atrial fibrillation, its variable therapeutic effects among different patients present numerous problems. Machine learning (ML) shows promising potential in optimizing the management and clinical outcomes of patients undergoing atrial fibrillation CA (AFCA). Objective: This scoping review aimed to evaluate the current scientific evidence on the application of ML for managing patients undergoing AFCA, compare the performance of various models across specific clinical tasks within AFCA, and summarize the strengths and limitations of ML in this field. Methods: Adhering to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, relevant studies published up to October 7, 2023, were searched from PubMed, Web of Science, Embase, the Cochrane Library, and ScienceDirect. The final included studies were confirmed based on inclusion and exclusion criteria and manual review. The PROBAST (Prediction model Risk Of Bias Assessment Tool) and QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) methodological quality assessment tools were used to review the included studies, and narrative data synthesis was performed on the modeled results provided by these studies. Results: The analysis of 23 included studies showcased the contributions of ML in identifying potential ablation targets, improving ablation strategies, and predicting patient prognosis. The patient data used in these studies comprised demographics, clinical characteristics, various types of imaging (9/23, 39%), and electrophysiological signals (7/23, 30%). In terms of model type, deep learning, represented by convolutional neural networks, was most frequently applied (14/23, 61%). Compared with traditional clinical scoring models or human clinicians, the model performance reported in the included studies was generally satisfactory, but most models (14/23, 61%) showed a high risk of bias due to lack of external validation. Conclusions: Our evidence-based findings suggest that ML is a promising tool for improving the effectiveness and efficiency of managing patients undergoing AFCA. While guiding data preparation and model selection for future studies, this review highlights the need to address prevalent limitations, including lack of external validation, and to further explore model generalization and interpretability. UR - https://www.jmir.org/2025/1/e60888 UR - http://dx.doi.org/10.2196/60888 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/60888 ER - TY - JOUR AU - Smith, Samuel AU - Maisrikrod, Shalisa PY - 2025/2/10 TI - Wearable Electrocardiogram Technology: Help or Hindrance to the Modern Doctor? JO - JMIR Cardio SP - e62719 VL - 9 KW - mobile applications KW - electrocardiogram KW - wearable monitoring KW - app KW - wearable KW - electrocardiograph KW - ECG KW - electrocardiography KW - mobile app KW - tool KW - ischemic KW - arrhythmia KW - wearable ECG KW - doctor KW - smartwatch KW - atrial fibrillation UR - https://cardio.jmir.org/2025/1/e62719 UR - http://dx.doi.org/10.2196/62719 ID - info:doi/10.2196/62719 ER - TY - JOUR AU - Gong, Chan-Juan AU - Zhou, Xiao-Kai AU - Zhang, Zhen-Feng AU - Fang, Yin PY - 2025/1/13 TI - Impact of Preventive Intravenous Amiodarone on Reperfusion Ventricular Fibrillation in Patients With Left Ventricular Hypertrophy Undergoing Open-Heart Surgery: Randomized Controlled Clinical Trial JO - JMIR Form Res SP - e64586 VL - 9 KW - amiodarone KW - left ventricular hypertrophy KW - reperfusion ventricular fibrillation KW - open-heart surgery KW - randomized controlled trial KW - RCT KW - clinical trial KW - ventricular fibrillation KW - vicious arrhythmia KW - aortic cross-clamp KW - surgery KW - effectiveness KW - defibrillation N2 - Background: Ventricular fibrillation (VF) is a vicious arrhythmia usually generated after removal of the aortic cross-clamp (ACC) in patients undergoing open-heart surgery, which could damage cardiomyocytes, especially in patients with left ventricular hypertrophy (LVH). Amiodarone has the prominent properties of converting VF and restoring sinus rhythm. However, few studies concentrated on the effect of amiodarone before ACC release on reducing VF in patients with LVH. Objective: The study was designed to explore the effectiveness of prophylactic intravenous amiodarone in reducing VF after the release of the ACC in patients with LVH. Methods: A total of 54 patients with LVH scheduled for open-heart surgery were enrolled and randomly divided (1:1) into 2 groups?group A (amiodarone group) and group P (placebo-controlled group). Thirty minutes before removal of the ACC, the trial drugs were administered intravenously. In group A, 150 mg of amiodarone was pumped in 15 minutes. In group P, the same volume of normal saline was pumped in 15 minutes. The primary outcome was the incidence of VF 10 minutes after removal of the ACC. Results: The incidence of VF was lower in group A than in group P (30% vs 70%, P=.003). The duration of VF, the number of defibrillations, and the defibrillation energy were also lower in group A than in group P (P<.001, P=.002, and P=.002, respectively). After the end of cardiopulmonary bypass, the heart rate and mean arterial pressure were lower in group A, and the mean pulmonary arterial pressure and the dose of vasoactive drugs were higher than those in group P (P<.001, P<.001, P=.04, and P=.02, respectively). However, there were no significant differences in the use of vasoactive-inotropic agents and hemodynamic status between the 2 groups before the end of surgery. Conclusions: In patients with LVH who undergo open-heart surgery, amiodarone can be safely used to reduce the incidence of VF, the duration of VF, the frequency of defibrillation, and the energy of defibrillation after ACC removal. Trial Registration: Chinese Clinical Trial Registry ChiCTR2000035057; https://www.chictr.org.cn/showprojEN.html?proj=57145 UR - https://formative.jmir.org/2025/1/e64586 UR - http://dx.doi.org/10.2196/64586 ID - info:doi/10.2196/64586 ER - TY - JOUR AU - Wouters, Femke AU - Gruwez, Henri AU - Smeets, Christophe AU - Pijalovic, Anessa AU - Wilms, Wouter AU - Vranken, Julie AU - Pieters, Zoë AU - Van Herendael, Hugo AU - Nuyens, Dieter AU - Rivero-Ayerza, Maximo AU - Vandervoort, Pieter AU - Haemers, Peter AU - Pison, Laurent PY - 2025/1/9 TI - Comparative Evaluation of Consumer Wearable Devices for Atrial Fibrillation Detection: Validation Study JO - JMIR Form Res SP - e65139 VL - 9 KW - atrial fibrillation KW - AF KW - mobile health KW - photoplethysmography KW - electrocardiography KW - smartphone KW - consumer wearable device KW - wearable devices KW - detection KW - electrocardiogram KW - ECG KW - mHealth N2 - Background: Consumer-oriented wearable devices (CWDs) such as smartphones and smartwatches have gained prominence for their ability to detect atrial fibrillation (AF) through proprietary algorithms using electrocardiography or photoplethysmography (PPG)?based digital recordings. Despite numerous individual validation studies, a direct comparison of interdevice performance is lacking. Objective: This study aimed to evaluate and compare the ability of CWDs to distinguish between sinus rhythm and AF. Methods: Patients exhibiting sinus rhythm or AF were enrolled through a cardiology outpatient clinic. The participants were instructed to perform heart rhythm measurements using a handheld 6-lead electrocardiogram (ECG) device (KardiaMobile 6L), a smartwatch-derived single-lead ECG (Apple Watch), and two PPG-based smartphone apps (FibriCheck and Preventicus) in a random sequence, with simultaneous 12-lead reference ECG as the gold standard. Results: A total of 122 participants were included in the study: median age 69 (IQR 61-77) years, 63.9% (n=78) men, 25% (n=30) with AF, 9.8% (n=12) without prior smartphone experience, and 73% (n=89) without experience in using a smartwatch. The sensitivity to detect AF was 100% for all devices. The specificity to detect sinus rhythm was 96.4% (95% CI 89.5%-98.8%) for KardiaMobile 6L, 97.8% (95% CI 91.6%?99.5%) for Apple Watch, 98.9% (95% CI 92.5%?99.8%) for FibriCheck, and 97.8% (95% CI 91.5%?99.4%) for Preventicus (P=.50). Insufficient quality measurements were observed in 10.7% (95% CI 6.3%-17.5%) of cases for both KardiaMobile 6L and Apple Watch, 7.4% (95% CI 3.9%?13.6%) for FibriCheck, and 14.8% (95% CI 9.5%?22.2%) for Preventicus (P=.21). Participants preferred Apple Watch over the other devices to monitor their heart rhythm. Conclusions: In this study population, the discrimination between sinus rhythm and AF using CWDs based on ECG or PPG was highly accurate, with no significant variations in performance across the examined devices. Trial Registration: ClinicalTrials.gov NCT06023290; https://clinicaltrials.gov/study/NCT06023290 UR - https://formative.jmir.org/2025/1/e65139 UR - http://dx.doi.org/10.2196/65139 ID - info:doi/10.2196/65139 ER - TY - JOUR AU - Lolak, Sermkiat AU - Attia, John AU - McKay, J. Gareth AU - Thakkinstian, Ammarin PY - 2025/1/8 TI - Application of Dragonnet and Conformal Inference for Estimating Individualized Treatment Effects for Personalized Stroke Prevention: Retrospective Cohort Study JO - JMIR Cardio SP - e50627 VL - 9 KW - stroke KW - causal effect KW - ITE KW - individual treatment effect KW - Dragonnet KW - conformal inference KW - mortality KW - hospital records KW - hypertension KW - risk factor KW - diabetes KW - dyslipidemia KW - atrial fibrillation KW - machine learning KW - treatment N2 - Background: Stroke is a major cause of death and disability worldwide. Identifying individuals who would benefit most from preventative interventions, such as antiplatelet therapy, is critical for personalized stroke prevention. However, traditional methods for estimating treatment effects often focus on the average effect across a population and do not account for individual variations in risk and treatment response. Objective: This study aimed to estimate the individualized treatment effects (ITEs) for stroke prevention using a novel combination of Dragonnet, a causal neural network, and conformal inference. The study also aimed to determine and validate the causal effects of known stroke risk factors?hypertension (HT), diabetes mellitus (DM), dyslipidemia (DLP), and atrial fibrillation (AF)?using both a conventional causal model and machine learning models. Methods: A retrospective cohort study was conducted using data from 275,247 high-risk patients treated at Ramathibodi Hospital, Thailand, between 2010 and 2020. Patients aged >18 years with HT, DM, DLP, or AF were eligible. The main outcome was ischemic or hemorrhagic stroke, identified using International Classification of Diseases, 10th Revision (ICD-10) codes. Causal effects of the risk factors were estimated using a range of methods, including: (1) propensity score?based methods, such as stratified propensity scores, inverse probability weighting, and doubly robust estimation; (2) structural causal models; (3) double machine learning; and (4) Dragonnet, a causal neural network, which was used together with weighted split-conformal quantile regression to estimate ITEs. Results: AF, HT, and DM were identified as significant stroke risk factors. Average causal risk effect estimates for these risk factors ranged from 0.075 to 0.097 for AF, 0.017 to 0.025 for HT, and 0.006 to 0.010 for DM, depending on the method used. Dragonnet yielded causal risk ratios of 4.56 for AF, 2.44 for HT, and 1.41 for DM, which is comparable to other causal models and the standard epidemiological case-control study. Mean ITE analysis indicated that several patients with DM or DM with HT, who were not receiving antiplatelet treatment at the time of data collection, showed reductions in total risk of ?0.015 and ?0.016, respectively. Conclusions: This study provides a comprehensive evaluation of stroke risk factors and demonstrates the feasibility of using Dragonnet and conformal inference to estimate ITEs of antiplatelet therapy for stroke prevention. The mean ITE analysis suggested that those with DM or DM with HT, who were not receiving antiplatelet treatment at the time of data collection, could potentially benefit from this therapy. The findings highlight the potential of these advanced techniques to inform personalized treatment strategies for stroke, enabling clinicians to identify individuals who are most likely to benefit from specific interventions. UR - https://cardio.jmir.org/2025/1/e50627 UR - http://dx.doi.org/10.2196/50627 ID - info:doi/10.2196/50627 ER - TY - JOUR AU - Handra, Julia AU - James, Hannah AU - Mbilinyi, Ashery AU - Moller-Hansen, Ashley AU - O'Riley, Callum AU - Andrade, Jason AU - Deyell, Marc AU - Hague, Cameron AU - Hawkins, Nathaniel AU - Ho, Kendall AU - Hu, Ricky AU - Leipsic, Jonathon AU - Tam, Roger PY - 2024/12/30 TI - The Role of Machine Learning in the Detection of Cardiac Fibrosis in Electrocardiograms: Scoping Review JO - JMIR Cardio SP - e60697 VL - 8 KW - machine learning KW - cardiac fibrosis KW - electrocardiogram KW - ECG KW - detection KW - ML KW - cardiovascular disease KW - review N2 - Background: Cardiovascular disease remains the leading cause of mortality worldwide. Cardiac fibrosis impacts the underlying pathophysiology of many cardiovascular diseases by altering structural integrity and impairing electrical conduction. Identifying cardiac fibrosis is essential for the prognosis and management of cardiovascular disease; however, current diagnostic methods face challenges due to invasiveness, cost, and inaccessibility. Electrocardiograms (ECGs) are widely available and cost-effective for monitoring cardiac electrical activity. While ECG-based methods for inferring fibrosis exist, they are not commonly used due to accuracy limitations and the need for cardiac expertise. However, the ECG shows promise as a target for machine learning (ML) applications in fibrosis detection. Objective: This study aims to synthesize and critically evaluate the current state of ECG-based ML approaches for cardiac fibrosis detection. Methods: We conducted a scoping review of research in ECG-based ML applications to identify cardiac fibrosis. Comprehensive searches were performed in PubMed, IEEE Xplore, Scopus, Web of Science, and DBLP databases, including publications up to October 2024. Studies were included if they applied ML techniques to detect cardiac fibrosis using ECG or vectorcardiogram data and provided sufficient methodological details and outcome metrics. Two reviewers independently assessed eligibility and extracted data on the ML models used, their performance metrics, study designs, and limitations. Results: We identified 11 studies evaluating ML approaches for detecting cardiac fibrosis using ECG data. These studies used various ML techniques, including classical (8/11, 73%), ensemble (3/11, 27%), and deep learning models (4/11, 36%). Support vector machines were the most used classical model (6/11, 55%), with the best-performing models of each study achieving accuracies of 77% to 93%. Among deep learning approaches, convolutional neural networks showed promising results, with one study reporting an area under the receiver operating characteristic curve (AUC) of 0.89 when combined with clinical features. Notably, a large-scale convolutional neural network study (n=14,052) achieved an AUC of 0.84 for detecting cardiac fibrosis, outperforming cardiologists (AUC 0.63-0.66). However, many studies had limited sample sizes and lacked external validation, potentially impacting the generalizability of the findings. Variability in reporting methods may affect the reproducibility and applicability of these ML-based approaches. Conclusions: ML-augmented ECG analysis shows promise for accessible and cost-effective detection of cardiac fibrosis. However, there are common limitations with respect to study design and insufficient external validation, raising concerns about the generalizability and clinical applicability of the findings. Inconsistencies in methodologies and incomplete reporting further impede cross-study comparisons. Future work may benefit from using prospective study designs, larger and more clinically and demographically diverse datasets, advanced ML models, and rigorous external validation. Addressing these challenges could pave the way for the clinical implementation of ML-based ECG detection of cardiac fibrosis to improve patient outcomes and health care resource allocation. UR - https://cardio.jmir.org/2024/1/e60697 UR - http://dx.doi.org/10.2196/60697 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/60697 ER - TY - JOUR AU - Khusial, Jayant Rishi AU - Sont, K. Jacob AU - Usmani, S. Omar AU - Bonini, Matteo AU - Chung, Fan Kian AU - Fowler, James Stephen AU - Honkoop, J. Persijn PY - 2024/12/11 TI - The Effect of Inhaled Beta-2 Agonists on Heart Rate in Patients With Asthma: Sensor-Based Observational Study JO - JMIR Cardio SP - e56848 VL - 8 KW - asthma KW - mHealth KW - side effects KW - beta-2 agonists KW - inhaler medication KW - heart rate KW - sensor KW - observational study KW - asthma management KW - cardiac cells KW - monitoring KW - Fitbit KW - inhaler N2 - Background: Beta-2 agonists play an important role in the management of asthma. Inhaled long-acting beta-2 agonists (LABAs) and short-acting beta-2 agonists (SABAs) cause bronchodilation by stimulating adrenoceptors. These receptors are also present in cardiac cells and, as a side effect, could also be stimulated by inhaled beta-2 agonists. Objective: This study aims to assess the effect of beta-2 agonists on heart rate (HR). Methods: The data were retrieved from an observational study, the myAirCoach Quantification Campaign. Beta-2 agonist use was registered by self-reported monthly questionnaires and by smart inhalers. HR was monitored continuously with the Fitbit Charge HR tracker (Fitbit Inc). Patients (aged 18 years and older) were recruited if they had uncontrolled asthma and used inhalation medication. Our primary outcome was the difference in HR between LABA and non-LABA users. Secondary outcomes were the difference in HR on days SABAs were used compared to days SABAs were not used and an assessment of the timing of inhaler use during the day. Results: Patients using LABA did not have a clinically relevant higher HR (average 0.8 beats per minute difference) during the day. Around the moment of SABA inhalation itself, the HR does increase steeply, and it takes 138 minutes before it returns to the normal range. Conclusions: This study indicates that LABAs do not have a clinically relevant effect on HR. SABAs are instead associated with a short-term HR increase. Trial Registration: ClinicalTrials.gov NCT02774772; https://clinicaltrials.gov/study/NCT02774772 UR - https://cardio.jmir.org/2024/1/e56848 UR - http://dx.doi.org/10.2196/56848 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/56848 ER - TY - JOUR AU - Shimoo, Satoshi AU - Senoo, Keitaro AU - Okawa, Taku AU - Kawai, Kohei AU - Makino, Masahiro AU - Munakata, Jun AU - Tomura, Nobunari AU - Iwakoshi, Hibiki AU - Nishimura, Tetsuro AU - Shiraishi, Hirokazu AU - Inoue, Keiji AU - Matoba, Satoaki PY - 2024/11/22 TI - Using Machine Learning to Predict the Duration of Atrial Fibrillation: Model Development and Validation JO - JMIR Med Inform SP - e63795 VL - 12 KW - persistent atrial fibrillation KW - atrial fibrillation duration KW - 12-lead electrocardiogram KW - machine learning KW - support system N2 - Background: Atrial fibrillation (AF) is a progressive disease, and its clinical type is classified according to the AF duration: paroxysmal AF, persistent AF (PeAF; AF duration of less than 1 year), and long-standing persistent AF (AF duration of more than 1 year). When considering the indication for catheter ablation, having a long AF duration is considered a risk factor for recurrence, and therefore, the duration of AF is an important factor in determining the treatment strategy for PeAF. Objective: This study aims to improve the accuracy of the cardiologists? diagnosis of the AF duration, and the steps to achieve this goal are to develop a predictive model of the AF duration and validate the support performance of the prediction model. Methods: The study included 272 patients with PeAF (aged 20-90 years), with data obtained between January 1, 2015, and December 31, 2023. Of those, 189 (69.5%) were included in the study, excluding 83 (30.5%) who met the exclusion criteria. Of the 189 patients included, 145 (76.7%) were used as training data to build the machine learning (ML) model and 44 (23.3%) were used as test data for predictive ability of the ML model. Using a questionnaire, 10 cardiologists (group A) evaluated whether the test data (44 patients) included AF of more than a 1-year duration (phase 1). Next, the same questionnaire was performed again after providing the ML model?s answer (phase 2). Subsequently, another 10 cardiologists (group B) were shown the test results of group A, were made aware of the limitations of their own diagnostic abilities, and were then administered the same 2-stage test as group A. Results: The prediction results with the ML model using the test data provided 81.8% accuracy (72% sensitivity and 89% specificity). The mean percentage of correct answers in group A was 63.9% (SD 9.6%) for phase 1 and improved to 71.6% (SD 9.3%) for phase 2 (P=.01). The mean percentage of correct answers in group B was 59.8% (SD 5.3%) for phase 1 and improved to 68.2% (SD 5.9%) for phase 2 (P=.007). The mean percentage of answers that differed from the ML model?s prediction for phase 2 (percentage of answers where cardiologists did not trust the ML model and believed their own determination) was 17.3% (SD 10.3%) in group A and 20.9% (SD 5%) in group B and was not significantly different (P=.85). Conclusions: ML models predicting AF duration improved the diagnostic ability of cardiologists. However, cardiologists did not entirely rely on the ML model?s prediction, even if they were aware of their diagnostic capability limitations. UR - https://medinform.jmir.org/2024/1/e63795 UR - http://dx.doi.org/10.2196/63795 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/63795 ER - TY - JOUR AU - Yoon, Minjae AU - Lee, Hyun Ji AU - Kim, In-Cheol AU - Lee, Ju-Hee AU - Kim, Mi-Na AU - Kim, Hack-Lyoung AU - Lee, Sunki AU - Kim, Jai In AU - Choi, Seonghoon AU - Park, Sung-Ji AU - Hur, Taeho AU - Hussain, Musarrat AU - Lee, Sungyoung AU - Choi, Dong-Ju PY - 2024/11/21 TI - Smartphone App for Improving Self-Awareness of Adherence to Edoxaban Treatment in Patients With Atrial Fibrillation (ADHERE-App Trial): Randomized Controlled Trial JO - J Med Internet Res SP - e65010 VL - 26 KW - mobile apps KW - digital health KW - atrial fibrillation KW - anticoagulants KW - medication adherence KW - mobile phone N2 - Background: Adherence to oral anticoagulant therapy is essential to prevent ischemic stroke in patients with atrial fibrillation (AF). Objective: This study aimed to evaluate whether smartphone app?based interventions improve medication adherence in patients with AF. Methods: This open-label, multicenter randomized controlled trial (ADHERE-App [Self-Awareness of Drug Adherence to Edoxaban Using an Automatic App Feedback System] study) enrolled patients with AF treated with edoxaban for stroke prevention. They were randomly assigned to app-conditioned feedback (intervention; n=248) and conventional treatment (control; n=250) groups. The intervention group received daily alerts via a smartphone app to take edoxaban and measure blood pressure and heart rate at specific times. The control group received only standard, guideline-recommended care. The primary end point was edoxaban adherence, measured by pill count at 3 or 6 months. Medication adherence and the proportion of adequate medication adherence, which was defined as ?95% of continuous medication adherence, were evaluated. Results: Medication adherence at 3 or 6 months was not significantly different between the intervention and control groups (median 98%, IQR 95%-100% vs median 98%, IQR 91%-100% at 3 months, P=.06; median 98%, IQR 94.5%-100% vs median 97.5%, IQR 92.8%-100% at 6 months, P=.15). However, the proportion of adequate medication adherence (?95%) was significantly higher in the intervention group at both time points (76.8% vs 64.7% at 3 months, P=.01; 73.9% vs 61% at 6 months, P=.007). Among patients aged >65 years, the intervention group showed a higher medication adherence value and a higher proportion of adequate medication adherence (?95%) at 6 months. Conclusions: There was no difference in edoxaban adherence between the groups. However, the proportion of adequate medication adherence was higher in the intervention group, and the benefit of the smartphone app?based intervention on medication adherence was more pronounced among older patients than among younger patients. Given the low adherence to oral anticoagulants, especially among older adults, using a smartphone app may potentially improve medication adherence. Trial Registration: International Clinical Trials Registry Platform KCT0004754; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=28496&search_page=L International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2021-048777 UR - https://www.jmir.org/2024/1/e65010 UR - http://dx.doi.org/10.2196/65010 UR - http://www.ncbi.nlm.nih.gov/pubmed/39570659 ID - info:doi/10.2196/65010 ER - TY - JOUR AU - Saraya, Norah AU - McBride, Jonathon AU - Singh, Karandeep AU - Sohail, Omar AU - Das, Jeet Porag PY - 2024/11/8 TI - Comparison of Auscultation Quality Using Contemporary Digital Stethoscopes JO - JMIR Cardio SP - e54746 VL - 8 KW - auscultation KW - digital stethoscopes KW - valvular heart disease UR - https://cardio.jmir.org/2024/1/e54746 UR - http://dx.doi.org/10.2196/54746 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/54746 ER - TY - JOUR AU - Suresh Kumar, Sagar AU - Connolly, Patricia AU - Maier, Anja PY - 2024/10/4 TI - Considering User Experience and Behavioral Approaches in the Design of mHealth Interventions for Atrial Fibrillation: Systematic Review JO - J Med Internet Res SP - e54405 VL - 26 KW - atrial fibrillation KW - wearable devices KW - lifestyle modification KW - user experience KW - design for behavior change KW - systems thinking KW - cardiac disease KW - stroke KW - heart disease KW - complication KW - mobile health KW - systematic review KW - usability KW - mHealth KW - intervention N2 - Background: Atrial fibrillation (AF) is a leading chronic cardiac disease associated with an increased risk of stroke, cardiac complications, and general mortality. Mobile health (mHealth) interventions, including wearable devices and apps, can aid in the detection, screening, and management of AF to improve patient outcomes. The inclusion of approaches that consider user experiences and behavior in the design of health care interventions can increase the usability of mHealth interventions, and hence, hopefully, yield an increase in positive outcomes in the lives of users. Objective: This study aims to show how research has considered user experiences and behavioral approaches in designing mHealth interventions for AF detection, screening, and management; the phases of designing complex interventions from the UK Medical Research Council (MRC) were referenced: namely, identification, development, feasibility, evaluation, and implementation. Methods: Studies published until September 7, 2022, that examined user experiences and behavioral approaches associated with mHealth interventions in the context of AF were extracted from multiple databases. The PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines were used. Results: A total of 2219 records were extracted, with only 55 records reporting on usability, user experiences, or behavioral approaches more widely for designing mHealth interventions in the context of AF. When mapping the studies onto the phases of the UK MRC?s guidance for developing and evaluating complex interventions, the following was found: in the identification phase, there were significant differences between the needs of patients and health care workers. In the development phase, user perspectives guided the iterative development of apps, interfaces, and intervention protocols in 4 studies. Most studies (43/55, 78%) assessed the usability of interventions in the feasibility phase as an outcome, although the data collection tools were not designed together with users and stakeholders. Studies that examined the evaluation and implementation phase entailed reporting on challenges in user participation, acceptance, and workflows that could not be captured by studies in the previous phases. To realize the envisaged human behavior intended through treatment, review results highlight the scant inclusion of behavior change approaches for mHealth interventions across multiple levels of sociotechnical health care systems. While interventions at the level of the individual (micro) and the level of communities (meso) were found in the studies reviewed, no studies were found intervening at societal levels (macro). Studies also failed to consider the temporal variation of user goals and feedback in the design of long-term behavioral interventions. Conclusions: In this systematic review, we proposed 2 contributions: first, mapping studies to different phases of the MRC framework for developing and evaluating complex interventions, and second, mapping behavioral approaches to different levels of health care systems. Finally, we discuss the wider implications of our results in guiding future mHealth research. UR - https://www.jmir.org/2024/1/e54405 UR - http://dx.doi.org/10.2196/54405 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/54405 ER - TY - JOUR AU - Magon, Arianna AU - Hendriks, Jeroen AU - Caruso, Rosario PY - 2024/9/13 TI - Developing and Validating a Self-Care Self-Efficacy Scale for Oral Anticoagulation Therapy in Patients With Nonvalvular Atrial Fibrillation: Protocol for a Mixed Methods Study JO - JMIR Res Protoc SP - e51489 VL - 13 KW - oral anticoagulation therapy KW - nonvalvular atrial fibrillation KW - self-care KW - self-efficacy KW - medication adherence KW - patient-centered education KW - scale development KW - validation KW - psychometrics KW - atrial fibrillation KW - education KW - development KW - anticoagulation KW - prevention KW - stroke KW - medication KW - management N2 - Background: Oral anticoagulation therapy (OAC) is the cornerstone treatment for preventing venous thromboembolism and stroke in patients with nonvalvular atrial fibrillation (NVAF). Despite its significance, challenges in adherence and persistence to OAC regimens have been reported, leading to severe health complications. Central to addressing these challenges is the concept of self-efficacy (SE) in medication management. Currently, there is a noticeable gap in available tools specifically designed to measure SE in OAC self-care management, while such tools are crucial for enhancing patient adherence and overall treatment outcomes. Objective: This study aims to develop and validate a novel scale aimed to measure self-care self-efficacy (SCSE) in patients with NVAF under OAC, which is the patients? Self-Care Self-Efficacy Index in Oral Anticoagulation Therapy Management (SCSE-OAC), for English- and Italian-speaking populations. We also seek to assess patients? SE in managing their OAC treatment effectively and to explore the relationship between SE levels and sociodemographic and clinical variables. Methods: Using a multiphase, mixed methods observational study design, we first conceptualize the SCSE-OAC through literature reviews, patient focus groups, and expert consensus. The scale?s content validity will be evaluated through patient and expert reviews, while its construct validity is assessed using exploratory and confirmatory factor analyses, ensuring cross-cultural applicability. Criterion validity will be examined through correlations with clinical outcomes. Reliability will be tested via internal consistency and test-retest reliability measures. The study will involve adult outpatients with NVAF on OAC treatment for a minimum of 3 months, using both e-surveys and paper forms for data collection. Results: It is anticipated that the SCSE-OAC will emerge as a reliable and valid tool for measuring SE in OAC self-care management. It will enable identifying patients at risk of poor adherence due to low SE, facilitating targeted educational interventions. The scale?s validation in both English and Italian-speaking populations will underscore its applicability in diverse clinical settings, contributing significantly to personalized patient-centered care in anticoagulation management. Conclusions: The development and validation of the SCSE-OAC represent a significant advancement in the field of anticoagulation therapy. Validating the index in English- and Italian-speaking populations will enable personalized patient-centered educational interventions, ultimately improving OAC treatment outcomes. The SCSE-OAC?s focus on SCSE introduces a novel approach to identifying and addressing individual patient needs, promoting adherence, and ultimately improving health outcomes. Future endeavors will seek to extend the validation of the SCSE-OAC across diverse cultural and linguistic landscapes, broadening its applicability in global clinical and research settings. This scale-up effort is crucial for establishing a universal standard for measuring SCSE in OAC management, empowering clinicians and researchers worldwide to tailor effective and culturally sensitive interventions. Trial Registration: ClinicalTrials.gov NCT05820854; https://tinyurl.com/2mmypey7 International Registered Report Identifier (IRRID): PRR1-10.2196/51489 UR - https://www.researchprotocols.org/2024/1/e51489 UR - http://dx.doi.org/10.2196/51489 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/51489 ER - TY - JOUR AU - Fan, Jiaqi AU - Dai, Hanyi AU - Guo, Yuchao AU - Xu, Jianguo AU - Wang, Lihan AU - Jiang, Jubo AU - Lin, Xinping AU - Li, Cheng AU - Zhou, Dao AU - Li, Huajun AU - Liu, Xianbao AU - Wang, Jian'an PY - 2024/7/19 TI - Smartwatch-Detected Arrhythmias in Patients After Transcatheter Aortic Valve Replacement (TAVR): Analysis of the SMART TAVR Trial JO - J Med Internet Res SP - e41843 VL - 26 KW - arrhythmias KW - transcatheter aortic valve replacement KW - smartwatch KW - ambulatory electrocardiography KW - smartphone KW - mobile phone N2 - Background: There are limited data available on the development of arrhythmias in patients at risk of high-degree atrioventricular block (HAVB) or complete heart block (CHB) following transcatheter aortic valve replacement (TAVR). Objective: This study aimed to explore the incidence and evolution of arrhythmias by monitoring patients at risk of HAVB or CHB after TAVR using smartwatches. Methods: We analyzed 188 consecutive patients in the prospective SMART TAVR (smartwatch-facilitated early discharge in patients undergoing TAVR) trial. Patients were divided into 2 groups according to the risk of HAVB or CHB. Patients were required to trigger a single-lead electrocardiogram (ECG) recording and send it to the Heart Health App via their smartphone. Physicians in the central ECG core lab would then analyze the ECG. The incidence and timing of arrhythmias and pacemaker implantation within a 30-day follow-up were compared. All arrhythmic events were adjudicated in a central ECG core lab. Results: The mean age of the patients was 73.1 (SD 7.3) years, of whom 105 (55.9%) were men. The mean discharge day after TAVR was 2.0 (SD 1.8) days. There were no statistically significant changes in the evolution of atrial fibrillation or atrial flutter, Mobitz I, Mobitz II, and third-degree atrial ventricular block over time in the first month after TAVR. The incidence of the left bundle branch block (LBBB) increased in the first week and decreased in the subsequent 3 weeks significantly (P<.001). Patients at higher risk of HAVB or CHB received more pacemaker implantation after discharge (n=8, 9.6% vs n=2, 1.9%; P=.04). The incidence of LBBB was higher in the group with higher HAVB or CHB risk (n=47, 56.6% vs n=34, 32.4%; P=.001). The independent predictors for pacemaker implantation were age, baseline atrial fibrillation, baseline right bundle branch block, Mobitz II, and third-degree atrioventricular block detected by the smartwatch. Conclusions: Except for LBBB, no change in arrhythmias was observed over time in the first month after TAVR. A higher incidence of pacemaker implantation after discharge was observed in patients at risk of HAVB or CHB. However, Mobitz II and third-degree atrioventricular block detected by the smartwatch during follow-ups were more valuable indicators to predict pacemaker implantation after discharge from the index TAVR. Trial Registration: ClinicalTrials.gov NCT04454177; https://clinicaltrials.gov/study/NCT04454177 UR - https://www.jmir.org/2024/1/e41843 UR - http://dx.doi.org/10.2196/41843 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/41843 ER - TY - JOUR AU - Li, ming Yi AU - Jia, Yuheng AU - Bai, Lin AU - Yang, Bosen AU - Chen, Mao AU - Peng, Yong PY - 2024/6/7 TI - U-Shaped Relationship Between Fibrinogen Level and 10-year Mortality in Patients With Acute Coronary Syndrome: Prospective Cohort Study JO - JMIR Public Health Surveill SP - e54485 VL - 10 KW - fibrinogen KW - acute coronary syndrome KW - 10-year mortality KW - risk factor KW - coronary artery disease KW - myocardial KW - heart disease KW - inflammatory factor KW - retrospective study KW - Kaplan-Meier analysis KW - mortality KW - all-cause mortality KW - cubic-spline curve KW - regression model UR - https://publichealth.jmir.org/2024/1/e54485 UR - http://dx.doi.org/10.2196/54485 UR - http://www.ncbi.nlm.nih.gov/pubmed/38848124 ID - info:doi/10.2196/54485 ER - TY - JOUR AU - Mittal, Ajay AU - Elkaldi, Yasmine AU - Shih, Susana AU - Nathu, Riken AU - Segal, Mark PY - 2024/5/27 TI - Mobile Electrocardiograms in the Detection of Subclinical Atrial Fibrillation in High-Risk Outpatient Populations: Protocol for an Observational Study JO - JMIR Res Protoc SP - e52647 VL - 13 KW - mobile ECG KW - digital health KW - cardiology KW - ECG KW - electrocardiogram KW - atrial fibrillation KW - outpatient KW - randomized KW - controlled trial KW - controlled trials KW - smartphone KW - mobile health KW - app KW - apps KW - feasibility KW - effectiveness KW - KardiaMobile single-lead ECGs KW - mobile phone N2 - Background: Single-lead, smartphone-based mobile electrocardiograms (ECGs) have the potential to provide a noninvasive, rapid, and cost-effective means of screening for atrial fibrillation (AFib) in outpatient settings. AFib has been associated with various comorbid diseases that prompt further investigation and screening methodologies for at-risk populations. A simple 30-second sinus rhythm strip from the KardiaMobile ECG (AliveCor) can provide an effective screen for cardiac rhythm abnormalities. Objective: The aim of this study is to demonstrate the feasibility of performing Kardia-enabled ECG recordings routinely in outpatient settings in high-risk populations and its potential use in uncovering previous undiagnosed cases of AFib. Specific aim 1 is to determine the feasibility and accuracy of performing routine cardiac rhythm sampling in patients deemed at high risk for AFib. Specific aim 2 is to determine whether routine rhythm sampling in outpatient clinics with high-risk patients can be used cost-effectively in an outpatient clinic without increasing the time it takes for the patient to be seen by a physician. Methods: Participants were recruited across 6 clinic sites across the University of Florida Health Network: University of Florida Health Nephrology, Sleep Center, Ophthalmology, Urology, Neurology, and Pre-Surgical. Participants, aged 18-99 years, who agreed to partake in the study were given a consent form and completed a questionnaire regarding their past medical history and risk factors for cardiovascular disease. Single-lead, 30-second ECGs were taken by the KardiaMobile ECG device. If patients are found to have newly diagnosed AFib, the attending physician is notified, and a 12-lead ECG or standard ECG equivalent will be ordered. Results: As of March 1, 2024, a total of 2339 participants have been enrolled. Of the data collected thus far, the KardiaMobile rhythm strip reported 381 abnormal readings, which are pending analysis from a cardiologist. A total of 78 readings were labeled as possible AFib, 159 readings were labeled unclassified, and 49 were unreadable. Of note, the average age of participants was 61 (SD 10.25) years, and the average self-reported weight was 194 (SD 14.26) pounds. Additionally, 1572 (67.25%) participants report not regularly seeing a cardiologist. Regarding feasibility, the average length of enrolling a patient into the study was 3:30 (SD 0.5) minutes after informed consent was completed, and medical staff across clinic sites (n=25) reported 9 of 10 level of satisfaction with the impact of the screening on clinic flow. Conclusions: Preliminary data show promise regarding the feasibility of using KardiaMobile ECGs for the screening of AFib and prevention of cardiological disease in vulnerable outpatient populations. The use of a single-lead mobile ECG strip can serve as a low-cost, effective AFib screen for implementation across free clinics attempting to provide increased health care accessibility. International Registered Report Identifier (IRRID): DERR1-10.2196/52647 UR - https://www.researchprotocols.org/2024/1/e52647 UR - http://dx.doi.org/10.2196/52647 UR - http://www.ncbi.nlm.nih.gov/pubmed/38801762 ID - info:doi/10.2196/52647 ER - TY - JOUR AU - Liliequist, E. Björn AU - Särnholm, Josefin AU - Skúladóttir, Helga AU - Ólafsdóttir, Eva AU - Ljótsson, Brjánn AU - Braunschweig, Frieder PY - 2024/5/7 TI - Cognitive Behavioral Therapy for Symptom Preoccupation Among Patients With Premature Ventricular Contractions: Nonrandomized Pretest-Posttest Study JO - JMIR Cardio SP - e53815 VL - 8 KW - premature ventricular contractions KW - quality of life KW - symptom preoccupation KW - cognitive behavioral therapy: CBT N2 - Background: Premature ventricular contractions (PVCs) are a common cardiac condition often associated with disabling symptoms and impaired quality of life (QoL). Current treatment strategies have limited effectiveness in reducing symptoms and restoring QoL for patients with PVCs. Symptom preoccupation, involving cardiac-related fear, hypervigilance, and avoidance behavior, is associated with disability in other cardiac conditions and can be effectively targeted by cognitive behavioral therapy (CBT). Objective: The aim of this study was to evaluate the effect of a PVC-specific CBT protocol targeting symptom preoccupation in patients with symptomatic idiopathic PVCs. Methods: Nineteen patients diagnosed with symptomatic idiopathic PVCs and symptom preoccupation underwent PVC-specific CBT over 10 weeks. The treatment was delivered by a licensed psychologist via videoconference in conjunction with online text-based information and homework assignments. The main components of the treatment were exposure to cardiac-related symptoms and reducing cardiac-related avoidance and control behavior. Self-rated measures were collected at baseline, post treatment, and at 3- and 6-month follow-ups. The primary outcome was PVC-specific QoL at posttreatment assessment measured with a PVC-adapted version of the Atrial Fibrillation Effects on Quality of Life questionnaire. Secondary measures included symptom preoccupation measured with the Cardiac Anxiety Questionnaire. PVC burden was evaluated with 5-day continuous electrocardiogram recordings at baseline, post treatment, and 6-month follow-up. Results: We observed large improvements in PVC-specific QoL (Cohen d=1.62, P<.001) and symptom preoccupation (Cohen d=1.73, P<.001) post treatment. These results were sustained at the 3- and 6-month follow-ups. PVC burden, as measured with 5-day continuous electrocardiogram, remained unchanged throughout follow-up. However, self-reported PVC symptoms were significantly lower at posttreatment assessment and at both the 3- and 6-month follow-ups. Reduction in symptom preoccupation had a statistically significant mediating effect of the intervention on PVC-specific QoL in an explorative mediation analysis. Conclusions: This uncontrolled pilot study shows preliminary promising results for PVC-specific CBT as a potentially effective treatment approach for patients with symptomatic idiopathic PVCs and symptom preoccupation. The substantial improvements in PVC-specific QoL and symptom preoccupation, along with the decreased self-reported PVC-related symptoms warrant further investigation in a larger randomized controlled trial. Trial Registration: ClinicalTrials.gov NCT05087238; https://clinicaltrials.gov/study/NCT05087238 UR - https://cardio.jmir.org/2024/1/e53815 UR - http://dx.doi.org/10.2196/53815 UR - http://www.ncbi.nlm.nih.gov/pubmed/38713500 ID - info:doi/10.2196/53815 ER - TY - JOUR AU - Chuang, Bo-Sheng Beau AU - Yang, C. Albert PY - 2024/3/11 TI - Optimization of Using Multiple Machine Learning Approaches in Atrial Fibrillation Detection Based on a Large-Scale Data Set of 12-Lead Electrocardiograms: Cross-Sectional Study JO - JMIR Form Res SP - e47803 VL - 8 KW - machine learning KW - atrial fibrillation KW - light gradient boosting machine KW - power spectral density KW - digital health KW - electrocardiogram KW - machine learning algorithm KW - atrial fibrillation detection KW - real-time KW - detection KW - electrocardiography leads KW - clinical outcome N2 - Background: Atrial fibrillation (AF) represents a hazardous cardiac arrhythmia that significantly elevates the risk of stroke and heart failure. Despite its severity, its diagnosis largely relies on the proficiency of health care professionals. At present, the real-time identification of paroxysmal AF is hindered by the lack of automated techniques. Consequently, a highly effective machine learning algorithm specifically designed for AF detection could offer substantial clinical benefits. We hypothesized that machine learning algorithms have the potential to identify and extract features of AF with a high degree of accuracy, given the intricate and distinctive patterns present in electrocardiogram (ECG) recordings of AF. Objective: This study aims to develop a clinically valuable machine learning algorithm that can accurately detect AF and compare different leads? performances of AF detection. Methods: We used 12-lead ECG recordings sourced from the 2020 PhysioNet Challenge data sets. The Welch method was used to extract power spectral features of the 12-lead ECGs within a frequency range of 0.083 to 24.92 Hz. Subsequently, various machine learning techniques were evaluated and optimized to classify sinus rhythm (SR) and AF based on these power spectral features. Furthermore, we compared the effects of different frequency subbands and different lead selections on machine learning performances. Results: The light gradient boosting machine (LightGBM) was found to be the most effective in classifying AF and SR, achieving an average F1-score of 0.988 across all ECG leads. Among the frequency subbands, the 0.083 to 4.92 Hz range yielded the highest F1-score of 0.985. In interlead comparisons, aVR had the highest performance (F1=0.993), with minimal differences observed between leads. Conclusions: In conclusion, this study successfully used machine learning methodologies, particularly the LightGBM model, to differentiate SR and AF based on power spectral features derived from 12-lead ECGs. The performance marked by an average F1-score of 0.988 and minimal interlead variation underscores the potential of machine learning algorithms to bolster real-time AF detection. This advancement could significantly improve patient care in intensive care units as well as facilitate remote monitoring through wearable devices, ultimately enhancing clinical outcomes. UR - https://formative.jmir.org/2024/1/e47803 UR - http://dx.doi.org/10.2196/47803 UR - http://www.ncbi.nlm.nih.gov/pubmed/38466973 ID - info:doi/10.2196/47803 ER - TY - JOUR AU - Kapoor, Alok AU - Patel, Parth AU - Chennupati, Soumya AU - Mbusa, Daniel AU - Sadiq, Hammad AU - Rampam, Sanjeev AU - Leung, Robert AU - Miller, Megan AU - Vargas, Rivera Kevin AU - Fry, Patrick AU - Lowe, Martin Mary AU - Catalano, Christina AU - Harrison, Charles AU - Catanzaro, Nicholas John AU - Crawford, Sybil AU - Smith, Marie Anne PY - 2024/1/24 TI - Comparing the Efficacy of Targeted and Blast Portal Messaging in Message Opening Rate and Anticoagulation Initiation in Patients With Atrial Fibrillation in the Preventing Preventable Strokes Study II: Prospective Cohort Study JO - JMIR Cardio SP - e49590 VL - 8 KW - anticoagulants KW - atrial fibrillation KW - humans KW - outpatients KW - patient education as topic KW - patient portals N2 - Background: The gap in anticoagulation use among patients with atrial fibrillation (AF) is a major public health threat. Inadequate patient education contributes to this gap. Patient portal?based messaging linked to educational materials may help bridge this gap, but the most effective messaging approach is unknown. Objective: This study aims to compare the responsiveness of patients with AF to an AF or anticoagulation educational message between 2 portal messaging approaches: sending messages targeted at patients with upcoming outpatient appointments 1 week before their scheduled appointment (targeted) versus sending messages to all eligible patients in 1 blast, regardless of appointment scheduling status (blast), at 2 different health systems: the University of Massachusetts Chan Medical School (UMass) and the University of Florida College of Medicine-Jacksonville (UFL). Methods: Using the 2 approaches, we sent patient portal messages to patients with AF and grouped patients by high-risk patients on anticoagulation (group 1), high-risk patients off anticoagulation (group 2), and low-risk patients who may become eligible for anticoagulation in the future (group 3). Risk was classified based on the congestive heart failure, hypertension, age ?75 years, diabetes mellitus, stroke, vascular disease, age between 65 and 74 years, and sex category (CHA2DS2-VASc) score. The messages contained a link to the Upbeat website of the Heart Rhythm Society, which displays print and video materials about AF and anticoagulation. We then tracked message opening, review of the website, anticoagulation use, and administered patient surveys across messaging approaches and sites using Epic Systems (Epic Systems Corporation) electronic health record data and Google website traffic analytics. We then conducted chi-square tests to compare potential differences in the proportion of patients opening messages and other evaluation metrics, adjusting for potential confounders. All statistical analyses were performed in SAS (version 9.4; SAS Institute). Results: We sent 1686 targeted messages and 1450 blast messages. Message opening was significantly higher with the targeted approach for patients on anticoagulation (723/1156, 62.5% vs 382/668, 57.2%; P=.005) and trended the same in patients off anticoagulation; subsequent website reviews did not differ by messaging approach. More patients off anticoagulation at baseline started anticoagulation with the targeted approach than the blast approach (adjusted percentage 9.3% vs 2.1%; P<.001). Conclusions: Patients were more responsive in terms of message opening and subsequent anticoagulation initiation with the targeted approach. UR - https://cardio.jmir.org/2024/1/e49590 UR - http://dx.doi.org/10.2196/49590 UR - http://www.ncbi.nlm.nih.gov/pubmed/38265849 ID - info:doi/10.2196/49590 ER - TY - JOUR AU - Antoniou, Panagiotis AU - Dafli, Eleni AU - Giannakoulas, George AU - Igimbayeva, Gaukhar AU - Visternichan, Olga AU - Kyselov, Serhii AU - Lykhasenko, Ivetta AU - Lashkul, Dmytro AU - Nadareishvili, Ilia AU - Tabagari, Sergo AU - Bamidis, D. Panagiotis PY - 2024/1/23 TI - Education of Patients With Atrial Fibrillation and Evaluation of the Efficacy of a Mobile Virtual Patient Environment: Protocol for a Multicenter Pseudorandomized Controlled Trial JO - JMIR Res Protoc SP - e45946 VL - 13 KW - atrial fibrillation KW - virtual patient KW - scenario based learning KW - technology enhanced learning KW - mHealth KW - mobile health KW - patient engagement KW - patient education KW - cardiac arrhythmia KW - mortality KW - mobile application KW - mobile app KW - health education KW - randomized control trial KW - cardiology KW - cardiac KW - heart KW - Greece KW - Ukraine KW - Kazakhstan KW - clinical decision support systems KW - CDSS KW - virtual patient scenario KW - myocardial infarction KW - arrhythmia KW - stroke N2 - Background: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is a leading cause of mortality and morbidity. Patient knowledge about AF and its management is paramount but often limited. Patients need to be appropriately informed about treatment options, medicinal adherence, and potential consequences of nonadherence, while also understanding treatment goals and expectations from it. Mobile health apps have experienced an explosion both in their availability and acceptance as ?soft interventions? for patient engagement and education; however, the prolific nature of such solutions revealed a gap in the evidence base regarding their efficacy and impact. Virtual patients (VPs), interactive computer simulations, have been used as learning activities in modern health care education. VPs demonstrably improved cognitive and behavioral skills; hence, they have been effectively implemented across undergraduate and postgraduate curricula. However, their application in patient education has been rather limited so far. Objective: This work aims to implement and evaluate the efficacy of a mobile-deployed VP regimen for the education and engagement of patients with AF on crucial topics regarding their condition. A mobile VP app is being developed with the goal of each VP being a simple scenario with a set goal and very specific messages and will be subsequently attempted and evaluated. Methods: A mobile VP player app is being developed so as to be used for the design of 3 educational scenarios for AF management. A pseudorandomized controlled trial for the efficacy of VPs is planned to be executed at 3 sites in Greece, Ukraine, and Kazakhstan for patients with AF. The Welch t test will be used to demonstrate the performance of patients? evaluation of the VP experience. Results: Our study is at the development stage. A preliminary study regarding the system?s development and feasibility was initiated in December 2022. The results of our study are expected to be available in 2024 or when the needed sample size is achieved. Conclusions: This study aims to evaluate and demonstrate the first significant evidence for the value of VP resources in outreach and training endeavors for empowering and patients with AF and fostering healthy habits among them. International Registered Report Identifier (IRRID): PRR1-10.2196/45946 UR - https://www.researchprotocols.org/2024/1/e45946 UR - http://dx.doi.org/10.2196/45946 UR - http://www.ncbi.nlm.nih.gov/pubmed/38261376 ID - info:doi/10.2196/45946 ER - TY - JOUR AU - Chang, Hao-Yun AU - Wu, Hui-Wen AU - Hung, Chi-Sheng AU - Chen, Ying-Hsien AU - Huang, Ching-Chang AU - Yang, Li-Tan AU - Hwang, Shin-Tsyr AU - Yu, Jiun-Yu AU - Lee, Jen-Kuang AU - Ho, Yi-Lwun PY - 2024/1/8 TI - Costs and Cardiovascular Benefits of a Fourth-Generation Synchronous Telehealth Program on Mortality and Cardiovascular Outcomes for Patients With Atrial Fibrillation: Retrospective Cohort Study JO - J Med Internet Res SP - e48748 VL - 26 KW - atrial fibrillation KW - cardiovascular death KW - fourth-generation synchronous program KW - ischemic stroke KW - telehealth N2 - Background: The prevalence of atrial fibrillation (AF) continues to increase in modern aging society. Patients with AF are at high risk for multiple adverse cardiovascular events, including heart failure, stroke, and mortality. Improved medical care is needed for patients with AF to enhance their quality of life and limit their medical resource utilization. With advances in the internet and technology, telehealth programs are now widely used in medical care. A fourth-generation telehealth program offers synchronous and continuous medical attention in response to physiological parameters measured at home. Although we have previously shown the benefits of this telehealth program for some patients with a high risk of cardiovascular disease, its benefits for patients with AF remains uncertain. Objective: This study aims to investigate the benefits of participating in a fourth-generation telehealth program for patients with AF in relation to cardiovascular outcomes. Methods: This was a retrospective cohort study. We retrospectively searched the medical records database of a tertiary medical center in Northern Taiwan between January 2007 and December 2017. We screened 5062 patients with cardiovascular disease and enrolled 537 patients with AF, of which 279 participated in the telehealth program and 258 did not. Bias was reduced using the inverse probability of treatment weighting adjustment based on the propensity score. Outcomes were collected and analyzed, including all-cause readmission, admission for heart failure, acute coronary syndrome, ischemic stroke, systemic embolism, bleeding events, all-cause mortality, and cardiovascular death within the follow-up period. Total medical expenses and medical costs in different departments were also compared. Subgroup analyses were conducted on ischemic stroke stratified by several subgroup variables. Results: The mean follow-up period was 3.0 (SD 1.7) years for the telehealth group and 3.4 (SD 1.9) years for the control group. After inverse probability of treatment weighting adjustment, the patients in the telehealth program had significantly fewer ischemic strokes (2.0 vs 4.5 events per 100 person-years; subdistribution hazard ratio [SHR] 0.45, 95% CI 0.22-0.92) and cardiovascular deaths (2.5 vs 5.9 events per 100 person-years; SHR 0.43, 95% CI 0.18-0.99) at the follow-up. The telehealth program particularly benefited patients comorbid with vascular disease (SHR 0.11, 95% CI 0.02-0.53 vs SHR 1.16, 95% CI 0.44-3.09; P=.01 for interaction). The total medical expenses during follow-up were similar in the telehealth and control groups. Conclusions: This study demonstrated the benefits of participating in the fourth-generation telehealth program for patients with AF by significantly reducing their ischemic stroke risk while spending the same amount on medical expenses. UR - https://www.jmir.org/2024/1/e48748 UR - http://dx.doi.org/10.2196/48748 UR - http://www.ncbi.nlm.nih.gov/pubmed/38190237 ID - info:doi/10.2196/48748 ER - TY - JOUR AU - Schulze Lammers, Sophia AU - Lawrenz, Thorsten AU - Lawin, Dennis AU - Hoyer, Annika AU - Stellbrink, Christoph AU - Albrecht, Urs-Vito PY - 2023/12/29 TI - Prolonged mHealth-Based Arrhythmia Monitoring in Patients With Hypertrophic Cardiomyopathy (HCM-PATCH): Protocol for a Single-Center Cohort Study JO - JMIR Res Protoc SP - e52035 VL - 12 KW - hypertrophic cardiomyopathy KW - nonsustained ventricular arrhythmia KW - sudden cardiac death KW - implantable cardioverter-defibrillator KW - long-term ECG KW - digital medicine KW - long-term electrocardiography N2 - Background: Patients with hypertrophic cardiomyopathy (HCM) are at increased risk of sudden cardiac death (SCD) due to ventricular arrhythmias and other arrhythmias. Screening for arrhythmias is mandatory to assess the individual SCD risk, but long-term electrocardiography (ECG) is rarely performed in routine clinical practice. Intensified monitoring may increase the detection rate of ventricular arrhythmias and identify more patients with an increased SCD risk who are potential candidates for the primary prophylactic implantation of an implantable cardioverter-defibrillator. To date, reliable data on the clinical benefit of prolonged arrhythmia monitoring in patients with HCM are rare. Objective: This prospective study aims to measure the prevalence of ventricular arrhythmias in patients with HCM observed by mobile health (mHealth)?based continuous rhythm monitoring over 14 days compared to standard practice (a 24- and 48-h long-term ECG). The frequency of ventricular arrhythmias in this 14-day period is compared with the frequency in the first 24 or 48 hours for the same patient (intraindividual comparison). Methods: Following the sample size calculation, 34 patients with a low or intermediate risk for SCD, assessed by the HCM Risk?SCD calculator, will need to be recruited in this single-center cohort study between June 2023 and February 2024. All patients will receive an ECG patch that records their heart activity over 14 days. In addition, cardiac magnetic resonance imaging and genetic testing data will be integrated into risk stratification. All patients will be asked to complete questionnaires about their symptoms; previous therapy; family history; and, at the end of the study, their experience with the ECG patch-based monitoring. Results: The Hypertrophic Cardiomyopathy: Clinical Impact of a Prolonged mHealth-Based Arrhythmia Monitoring by Single-Channel ECG (HCM-PATCH) study investigates the prevalence of nonsustained ventricular tachycardia (ie, ?3 consecutive ventricular beats at a rate of 120 beats per minute, lasting for <30 seconds) in low- to intermediate-risk patients with HCM (according to the HCM Risk?SCD calculator) with additional mHealth-based prolonged rhythm monitoring. The study was funded by third-party funding from the Department of Cardiology and Intensive Care Medicine, University Hospital Ostwestfalen-Lippe of Bielefeld University in June 2023 and approved by the institutional review board in May 2023. Data collection began in June 2023, and we plan to end the study in February 2024. Of the 34 patients, 26 have been recruited. Data analysis has not yet taken place. Publication of the results is planned for the fall of 2024. Conclusions: Prolonged mHealth-based rhythm monitoring could lead to differences in the prevalence of arrhythmias compared to 24- and 48-hour long-term ECGs. This may lead to improved identification of patients at high risk and trigger therapeutic interventions that may provide better protection from SCD or atrial fibrillation?related complications such as embolic stroke. Trial Registration: Deutsches Register Klinischer Studien DRKS00032144; https://tinyurl.com/498bkrx8 International Registered Report Identifier (IRRID): DERR1-10.2196/52035 UR - https://www.researchprotocols.org/2023/1/e52035 UR - http://dx.doi.org/10.2196/52035 UR - http://www.ncbi.nlm.nih.gov/pubmed/38157231 ID - info:doi/10.2196/52035 ER - TY - JOUR AU - Ding, Y. Eric AU - Tran, Khanh-Van AU - Lessard, Darleen AU - Wang, Ziyue AU - Han, Dong AU - Mohagheghian, Fahimeh AU - Mensah Otabil, Edith AU - Noorishirazi, Kamran AU - Mehawej, Jordy AU - Filippaios, Andreas AU - Naeem, Syed AU - Gottbrecht, F. Matthew AU - Fitzgibbons, P. Timothy AU - Saczynski, S. Jane AU - Barton, Bruce AU - Chon, Ki AU - McManus, D. David PY - 2023/11/28 TI - Accuracy, Usability, and Adherence of Smartwatches for Atrial Fibrillation Detection in Older Adults After Stroke: Randomized Controlled Trial JO - JMIR Cardio SP - e45137 VL - 7 KW - accuracy KW - atrial fibrillation KW - cardiac arrhythmia KW - design KW - detection KW - diagnosis KW - electrocardiography KW - monitoring KW - older adults KW - photoplethysmography KW - prevention KW - remote monitoring KW - smartwatch KW - stroke KW - usability N2 - Background: Atrial fibrillation (AF) is a common cause of stroke, and timely diagnosis is critical for secondary prevention. Little is known about smartwatches for AF detection among stroke survivors. We aimed to examine accuracy, usability, and adherence to a smartwatch-based AF monitoring system designed by older stroke survivors and their caregivers. Objective: This study aims to examine the feasibility of smartwatches for AF detection in older stroke survivors. Methods: Pulsewatch is a randomized controlled trial (RCT) in which stroke survivors received either a smartwatch-smartphone dyad for AF detection (Pulsewatch system) plus an electrocardiogram patch or the patch alone for 14 days to assess the accuracy and usability of the system (phase 1). Participants were subsequently rerandomized to potentially 30 additional days of system use to examine adherence to watch wear (phase 2). Participants were aged 50 years or older, had survived an ischemic stroke, and had no major contraindications to oral anticoagulants. The accuracy for AF detection was determined by comparing it to cardiologist-overread electrocardiogram patch, and the usability was assessed with the System Usability Scale (SUS). Adherence was operationalized as daily watch wear time over the 30-day monitoring period. Results: A total of 120 participants were enrolled (mean age 65 years; 50/120, 41% female; 106/120, 88% White). The Pulsewatch system demonstrated 92.9% (95% CI 85.3%-97.4%) accuracy for AF detection. Mean usability score was 65 out of 100, and on average, participants wore the watch for 21.2 (SD 8.3) of the 30 days. Conclusions: Our findings demonstrate that a smartwatch system designed by and for stroke survivors is a viable option for long-term arrhythmia detection among older adults at risk for AF, though it may benefit from strategies to enhance adherence to watch wear. Trial Registration: ClinicalTrials.gov NCT03761394; https://clinicaltrials.gov/study/NCT03761394 International Registered Report Identifier (IRRID): RR2-10.1016/j.cvdhj.2021.07.002 UR - https://cardio.jmir.org/2023/1/e45137 UR - http://dx.doi.org/10.2196/45137 UR - http://www.ncbi.nlm.nih.gov/pubmed/38015598 ID - info:doi/10.2196/45137 ER - TY - JOUR AU - Simonson, K. Julie AU - Anderson, Misty AU - Polacek, Cate AU - Klump, Erika AU - Haque, N. Saira PY - 2023/11/3 TI - Characterizing Real-World Implementation of Consumer Wearables for the Detection of Undiagnosed Atrial Fibrillation in Clinical Practice: Targeted Literature Review JO - JMIR Cardio SP - e47292 VL - 7 KW - arrhythmias KW - atrial fibrillation KW - clinical workflow KW - consumer wearable devices KW - smartwatches KW - wearables KW - remote patient monitoring KW - virtual care KW - mobile phone N2 - Background: Atrial fibrillation (AF), the most common cardiac arrhythmia, is often undiagnosed because of lack of awareness and frequent asymptomatic presentation. As AF is associated with increased risk of stroke, early detection is clinically relevant. Several consumer wearable devices (CWDs) have been cleared by the US Food and Drug Administration for irregular heart rhythm detection suggestive of AF. However, recommendations for the use of CWDs for AF detection in clinical practice, especially with regard to pathways for workflows and clinical decisions, remain lacking. Objective: We conducted a targeted literature review to identify articles on CWDs characterizing the current state of wearable technology for AF detection, identifying approaches to implementing CWDs into the clinical workflow, and characterizing provider and patient perspectives on CWDs for patients at risk of AF. Methods: PubMed, ClinicalTrials.gov, UpToDate Clinical Reference, and DynaMed were searched for articles in English published between January 2016 and July 2023. The searches used predefined Medical Subject Headings (MeSH) terms, keywords, and search strings. Articles of interest were specifically on CWDs; articles on ambulatory monitoring tools, tools available by prescription, or handheld devices were excluded. Search results were reviewed for relevancy and discussed among the authors for inclusion. A qualitative analysis was conducted and themes relevant to our study objectives were identified. Results: A total of 31 articles met inclusion criteria: 7 (23%) medical society reports or guidelines, 4 (13%) general reviews, 5 (16%) systematic reviews, 5 (16%) health care provider surveys, 7 (23%) consumer or patient surveys or interviews, and 3 (10%) analytical reports. Despite recognition of CWDs by medical societies, detailed guidelines regarding CWDs for AF detection were limited, as was the availability of clinical tools. A main theme was the lack of pragmatic studies assessing real-world implementation of CWDs for AF detection. Clinicians expressed concerns about data overload; potential for false positives; reimbursement issues; and the need for clinical tools such as care pathways and guidelines, preferably developed or endorsed by professional organizations. Patient-facing challenges included device costs and variability in digital literacy or technology acceptance. Conclusions: This targeted literature review highlights the lack of a comprehensive body of literature guiding real-world implementation of CWDs for AF detection and provides insights for informing additional research and developing appropriate tools and resources for incorporating these devices into clinical practice. The results should also provide an impetus for the active involvement of medical societies and other health care stakeholders in developing appropriate tools and resources for guiding the real-world use of CWDs for AF detection. These resources should target clinicians, patients, and health care systems with the goal of facilitating clinician or patient engagement and using an evidence-based approach for establishing guidelines or frameworks for administrative workflows and patient care pathways. UR - https://cardio.jmir.org/2023/1/e47292 UR - http://dx.doi.org/10.2196/47292 UR - http://www.ncbi.nlm.nih.gov/pubmed/37921865 ID - info:doi/10.2196/47292 ER - TY - JOUR AU - Chen, Mu AU - Li, Cheng AU - Zhang, Jiwei AU - Cui, Xin AU - Tian, Wenqi AU - Liao, Peng AU - Wang, Qunshan AU - Sun, Jian AU - Luo, Li AU - Wu, Hong AU - Li, Yi-Gang PY - 2023/10/17 TI - Cancer and Atrial Fibrillation Comorbidities Among 25 Million Citizens in Shanghai, China: Medical Insurance Database Study JO - JMIR Public Health Surveill SP - e40149 VL - 9 KW - cardiovascular KW - malignancy KW - arrhythmia KW - cardio-oncology KW - prevalence KW - epidemiology KW - atrial fibrillation N2 - Background: With population aging, the prevalence of both cancer and atrial fibrillation (AF) have increased. However, there is scarce epidemiological data concerning the comorbid state of cancer and AF in low- and middle-income countries, including China. Objective: We aimed to evaluate the site-, sex-, and age-specific profiles of cancer and AF comorbidities in Chinese populations. Methods: Data from the Shanghai Municipal Health Commission database between 2015 and 2020 were screened, covering all medical records of Shanghai residents with medical insurance. Site-specific cancer profiles were evaluated for the population with AF relative to the age- and sex-adjusted population of residents without AF. The sex distribution and peak age of cancer diagnosis were also assessed. Results: A total of 25,964,447 adult patients were screened. Among them, 22,185 patients presented cancers comorbid with AF (median 77, IQR 67-82 years of age; men: n=13,631, 61.44%), while 839,864 presented cancers without AF (median 67, IQR 57-72 years of age; men: n=419,020, 49.89%), thus yielding a higher cancer prevalence among residents with AF (8.27%) than among those without AF (6.05%; P<.001). In the population with AF, the most prevalent cancer type was lung cancer, followed by colorectal, male genital organ, stomach, breast, liver, bladder, thyroid, leukemia, and esophageal cancers. AF was associated with an average of nearly 1.4-fold (prevalence ratio [PR] 1.37, 95% CI 1.35-1.38) increased prevalence of cancer after adjusting for age and sex. For site-specific analyses, an increased prevalence of cancer in the population with AF was observed in 20 of 21 cancer sites. This increased prevalence was most prominent for nonsolid tumors, including multiple myeloma (PR 2.56, 95% CI 2.28-2.87), leukemia (PR 1.73, 95% CI 1.57-1.90), and non-Hodgkin lymphoma (PR 1.59, 95% CI 1.43-1.77); intrathoracic malignancies, including mediastinum (PR 2.34, 95% CI 1.89-2.90), lung (PR 1.64, 95% CI 1.59-1.69), and esophageal cancers (PR 1.41, 95% CI 1.28-1.56); bone and soft tissue neoplasms (PR 1.56, 95% CI 1.37-1.77); and kidney cancer (PR 1.53, 95% CI 1.36-1.72). Cancer prevalence in the population with AF relative to that in the population without AF was higher in men than in women in 14 of 18 cancer sites, and female predominance was only observed for thyroid cancer. The peak age of index cancer diagnosis was lower in the population with AF (age group: 70-74 years) than in that without AF (age group: 75-79 years), especially for specific cancer types, including thyroid, central nervous system, mediastinum, esophageal, bladder, and biliary cancers. Conclusions: Patients with AF are associated with increased prevalence, heightened male predominance, and younger peak age of cancer. Further studies are needed to determine whether early screening of specific cancers is cost-effective and beneficial for patients with AF. UR - https://publichealth.jmir.org/2023/1/e40149 UR - http://dx.doi.org/10.2196/40149 UR - http://www.ncbi.nlm.nih.gov/pubmed/37847541 ID - info:doi/10.2196/40149 ER - TY - JOUR AU - Kwun, Ju-Seung AU - Lee, Hoon Jang AU - Park, Eun Bo AU - Park, Sung Jong AU - Kim, Jeong Hyeon AU - Kim, Sun-Hwa AU - Jeon, Ki-Hyun AU - Cho, Hyoung-won AU - Kang, Si-Hyuck AU - Lee, Wonjae AU - Youn, Tae-Jin AU - Chae, In-Ho AU - Yoon, Chang-Hwan PY - 2023/9/18 TI - Diagnostic Value of a Wearable Continuous Electrocardiogram Monitoring Device (AT-Patch) for New-Onset Atrial Fibrillation in High-Risk Patients: Prospective Cohort Study JO - J Med Internet Res SP - e45760 VL - 25 KW - arrhythmias KW - atrial fibrillation KW - wearable electronic device KW - patch electrocardiogram monitor KW - electrocardiogram KW - adult KW - AT-Patch KW - heart failure KW - mobile phone N2 - Background: While conventional electrocardiogram monitoring devices are useful for detecting atrial fibrillation, they have considerable drawbacks, including a short monitoring duration and invasive device implantation. The use of patch-type devices circumvents these drawbacks and has shown comparable diagnostic capability for the early detection of atrial fibrillation. Objective: We aimed to determine whether a patch-type device (AT-Patch) applied to patients with a high risk of new-onset atrial fibrillation defined by the congestive heart failure, hypertension, age ?75 years, diabetes mellitus, stroke, vascular disease, age 65-74 years, sex scale (CHA2DS2-VASc) score had increased detection rates. Methods: In this nonrandomized multicenter prospective cohort study, we enrolled 320 adults aged ?19 years who had never experienced atrial fibrillation and whose CHA2DS2-VASc score was ?2. The AT-Patch was attached to each individual for 11 days, and the data were analyzed for arrhythmic events by 2 independent cardiologists. Results: Atrial fibrillation was detected by the AT-Patch in 3.4% (11/320) of patients, as diagnosed by both cardiologists. Interestingly, when participants with or without atrial fibrillation were compared, a previous history of heart failure was significantly more common in the atrial fibrillation group (n=4/11, 36.4% vs n=16/309, 5.2%, respectively; P=.003). When a CHA2DS2-VASc score ?4 was combined with previous heart failure, the detection rate was significantly increased to 24.4%. Comparison of the recorded electrocardiogram data revealed that supraventricular and ventricular ectopic rhythms were significantly more frequent in the new-onset atrial fibrillation group compared with nonatrial fibrillation group (3.4% vs 0.4%; P=.001 and 5.2% vs 1.2%; P<.001), respectively. Conclusions: This study detected a moderate number of new-onset atrial fibrillations in high-risk patients using the AT-Patch device. Further studies will aim to investigate the value of early detection of atrial fibrillation, particularly in patients with heart failure as a means of reducing adverse clinical outcomes of atrial fibrillation. Trial Registration: ClinicalTrials.gov NCT04857268; https://classic.clinicaltrials.gov/ct2/show/NCT04857268 UR - https://www.jmir.org/2023/1/e45760 UR - http://dx.doi.org/10.2196/45760 UR - http://www.ncbi.nlm.nih.gov/pubmed/37721791 ID - info:doi/10.2196/45760 ER - TY - JOUR AU - Selder, L. Jasper AU - Te Kolste, Jan Henryk AU - Twisk, Jos AU - Schijven, Marlies AU - Gielen, Willem AU - Allaart, P. Cornelis PY - 2023/5/26 TI - Accuracy of a Standalone Atrial Fibrillation Detection Algorithm Added to a Popular Wristband and Smartwatch: Prospective Diagnostic Accuracy Study JO - J Med Internet Res SP - e44642 VL - 25 KW - smartwatch KW - atrial fibrillation KW - algorithm KW - fibrillation detection KW - wristband KW - diagnose KW - heart rhythm KW - cardioversion KW - environment KW - software algorithm KW - artificial intelligence KW - AI KW - electrocardiography KW - ECG KW - EKG N2 - Background: Silent paroxysmal atrial fibrillation (AF) may be difficult to diagnose, and AF burden is hard to establish. In contrast to conventional diagnostic devices, photoplethysmography (PPG)?driven smartwatches or wristbands allow for long-term continuous heart rhythm assessment. However, most smartwatches lack an integrated PPG-AF algorithm. Adding a standalone PPG-AF algorithm to these wrist devices might open new possibilities for AF screening and burden assessment. Objective: The aim of this study was to assess the accuracy of a well-known standalone PPG-AF detection algorithm added to a popular wristband and smartwatch, with regard to discriminating AF and sinus rhythm, in a group of patients with AF before and after cardioversion (CV). Methods: Consecutive consenting patients with AF admitted for CV in a large academic hospital in Amsterdam, the Netherlands, were asked to wear a Biostrap wristband or Fitbit Ionic smartwatch with Fibricheck algorithm add-on surrounding the procedure. A set of 1-min PPG measurements and 12-lead reference electrocardiograms was obtained before and after CV. Rhythm assessment by the PPG device-software combination was compared with the 12-lead electrocardiogram. Results: A total of 78 patients were included in the Biostrap-Fibricheck cohort (156 measurement sets) and 73 patients in the Fitbit-Fibricheck cohort (143 measurement sets). Of the measurement sets, 19/156 (12%) and 7/143 (5%), respectively, were not classifiable by the PPG algorithm due to bad quality. The diagnostic performance in terms of sensitivity, specificity, positive predictive value, negative predictive value, and accuracy was 98%, 96%, 96%, 99%, 97%, and 97%, 100%, 100%, 97%, and 99%, respectively, at an AF prevalence of ~50%. Conclusions: This study demonstrates that the addition of a well-known standalone PPG-AF detection algorithm to a popular PPG smartwatch and wristband without integrated algorithm yields a high accuracy for the detection of AF, with an acceptable unclassifiable rate, in a semicontrolled environment. UR - https://www.jmir.org/2023/1/e44642 UR - http://dx.doi.org/10.2196/44642 UR - http://www.ncbi.nlm.nih.gov/pubmed/37234033 ID - info:doi/10.2196/44642 ER - TY - JOUR AU - He, Ying AU - Tang, Zhijie AU - Sun, Guozhen AU - Cai, Cheng AU - Wang, Yao AU - Yang, Gang AU - Bao, ZhiPeng PY - 2023/5/3 TI - Effectiveness of a Mindfulness Meditation App Based on an Electroencephalography-Based Brain-Computer Interface in Radiofrequency Catheter Ablation for Patients With Atrial Fibrillation: Pilot Randomized Controlled Trial JO - JMIR Mhealth Uhealth SP - e44855 VL - 11 KW - atrial fibrillation KW - radiofrequency catheter ablation KW - mindfulness meditation KW - brain computer interface KW - mHealth KW - smartphone app KW - randomized controlled trial N2 - Background: Radiofrequency catheter ablation (RFCA) for patients with atrial fibrillation (AF) can generate considerable physical and psychological discomfort under conscious sedation. App-based mindfulness meditation combined with an electroencephalography (EEG)-based brain-computer interface (BCI) shows promise as effective and accessible adjuncts in medical practice. Objective: This study aimed to investigate the effectiveness of a BCI-based mindfulness meditation app in improving the experience of patients with AF during RFCA. Methods: This single-center pilot randomized controlled trial involved 84 eligible patients with AF scheduled for RFCA, who were randomized 1:1 to the intervention and control groups. Both groups received a standardized RFCA procedure and a conscious sedative regimen. Patients in the control group were administered conventional care, while those in the intervention group received BCI-based app?delivered mindfulness meditation from a research nurse. The primary outcomes were the changes in the numeric rating scale, State Anxiety Inventory, and Brief Fatigue Inventory scores. Secondary outcomes were the differences in hemodynamic parameters (heart rate, blood pressure, and peripheral oxygen saturation), adverse events, patient-reported pain, and the doses of sedative drugs used in ablation. Results: BCI-based app?delivered mindfulness meditation, compared to conventional care, resulted in a significantly lower mean numeric rating scale (mean 4.6, SD 1.7 [app-based mindfulness meditation] vs mean 5.7, SD 2.1 [conventional care]; P=.008), State Anxiety Inventory (mean 36.7, SD 5.5 vs mean 42.3, SD 7.2; P<.001), and Brief Fatigue Inventory (mean 3.4, SD 2.3 vs mean 4.7, SD 2.2; P=.01) scores. No significant differences were observed in hemodynamic parameters or the amounts of parecoxib and dexmedetomidine used in RFCA between the 2 groups. The intervention group exhibited a significant decrease in fentanyl use compared to the control group, with a mean dose of 3.96 (SD 1.37) mcg/kg versus 4.85 (SD 1.25) mcg/kg in the control group (P=.003).The incidence of adverse events was lower in the intervention group (5/40) than in the control group (10/40), though this difference was not significant (P=.15). Conclusions: BCI-based app?delivered mindfulness meditation effectively relieved physical and psychological discomfort and may reduce the doses of sedative medication used in RFCA for patients with AF. Trial Registration: ClinicalTrials.gov NCT05306015; https://clinicaltrials.gov/ct2/show/NCT05306015 UR - https://mhealth.jmir.org/2023/1/e44855 UR - http://dx.doi.org/10.2196/44855 UR - http://www.ncbi.nlm.nih.gov/pubmed/37133926 ID - info:doi/10.2196/44855 ER - TY - JOUR AU - Suda, Satoshi AU - Katano, Takehiro AU - Kitagawa, Kazuo AU - Iguchi, Yasuyuki AU - Fujimoto, Shigeru AU - Ono, Kenjiro AU - Kano, Osamu AU - Takekawa, Hidehiro AU - Koga, Masatoshi AU - Ihara, Masafumi AU - Morimoto, Masafumi AU - Yamagami, Hiroshi AU - Terasaki, Tadashi AU - Yamaguchi, Keiji AU - Okubo, Seiji AU - Ueno, Yuji AU - Ohara, Nobuyuki AU - Kamiya, Yuki AU - Takeuchi, Masataka AU - Yazawa, Yukako AU - Terasawa, Yuka AU - Doijiri, Ryosuke AU - Tsuboi, Yoshifumi AU - Sonoda, Kazutaka AU - Nomura, Koichi AU - Shimoyama, Takashi AU - Kutsuna, Akihito AU - Kimura, Kazumi PY - 2023/4/13 TI - Detection of Atrial Fibrillation Using Insertable Cardiac Monitors in Patients With Cryptogenic Stroke in Japan (the LOOK Study): Protocol for a Prospective Multicenter Observational Study JO - JMIR Res Protoc SP - e39307 VL - 12 KW - atrial cardiomyopathy KW - atrial fibrillation KW - biomarker KW - cryptogenic stroke KW - insertable cardiac monitor N2 - Background: Paroxysmal atrial fibrillation (AF) is a probable cause of cryptogenic stroke (CS), and its detection and treatment are important for the secondary prevention of stroke. Insertable cardiac monitors (ICMs) are clinically effective in screening for AF and are superior to conventional short-term cardiac monitoring. Japanese guidelines for determining clinical indications for ICMs in CS are stricter than those in Western countries. Differences between Japanese and Western guidelines may impact the detection rate and prediction of AF via ICMs in patients with CS. Available data on Japanese patients are limited to small retrospective studies. Furthermore, additional information about AF detection, including the number of episodes, cumulative episode duration, anticoagulation initiation (type and dose of regimen and time of initiation), rate of catheter ablation, role of atrial cardiomyopathy, and stroke recurrence (time of recurrence and cause of the recurrent event), was not provided in the vast majority of previously published studies. Objective: In this study, we aim to identify the proportion and timing of AF detection and risk stratification criteria in patients with CS in real-world settings in Japan. Methods: This is a multicenter, prospective, observational study that aims to use ICMs to evaluate the proportion, timing, and characteristics of AF detection in patients diagnosed with CS. We will investigate the first detection of AF within the initial 6, 12, and 24 months of follow-up after ICM implantation. Patient characteristics, laboratory data, atrial cardiomyopathy markers, serial magnetic resonance imaging findings at baseline, 6, 12, and 24 months after ICM implantation, electrocardiogram readings, transesophageal echocardiography findings, cognitive status, stroke recurrence, and functional outcomes will be compared between patients with AF and patients without AF. Furthermore, we will obtain additional information regarding the number of AF episodes, duration of cumulative AF episodes, and time of anticoagulation initiation. Results: Study recruitment began in February 2020, and thus far, 213 patients have provided written informed consent and are currently in the follow-up phase. The last recruited participant (May 2021) will have completed the 24-month follow-up in May 2023. The main results are expected to be submitted for publication in 2023. Conclusions: The findings of this study will help identify AF markers and generate a risk scoring system with a novel and superior screening algorithm for occult AF detection while identifying candidates for ICM implantation and aiding the development of diagnostic criteria for CS in Japan. Trial Registration: UMIN Clinical Trial Registry UMIN000039809; https://tinyurl.com/3jaewe6a International Registered Report Identifier (IRRID): DERR1-10.2196/39307 UR - https://www.researchprotocols.org/2023/1/e39307 UR - http://dx.doi.org/10.2196/39307 UR - http://www.ncbi.nlm.nih.gov/pubmed/37052993 ID - info:doi/10.2196/39307 ER - TY - JOUR AU - Shapira-Daniels, Ayelet AU - Kornej, Jelena AU - Spartano, L. Nicole AU - Wang, Xuzhi AU - Zhang, Yuankai AU - Pathiravasan, H. Chathurangi AU - Liu, Chunyu AU - Trinquart, Ludovic AU - Borrelli, Belinda AU - McManus, D. David AU - Murabito, M. Joanne AU - Benjamin, J. Emelia AU - Lin, Honghuang PY - 2023/3/6 TI - Step Count, Self-reported Physical Activity, and Predicted 5-Year Risk of Atrial Fibrillation: Cross-sectional Analysis JO - J Med Internet Res SP - e43123 VL - 25 KW - atrial fibrillation KW - physical activity KW - fitness tracker KW - cardiovascular epidemiology KW - fitness KW - exercise KW - tracker KW - cardiology KW - heart KW - walk KW - step count KW - smartwatch KW - wearable KW - risk KW - cross-sectional analysis N2 - Background: Physical inactivity is a known risk factor for atrial fibrillation (AF). Wearable devices, such as smartwatches, present an opportunity to investigate the relation between daily step count and AF risk. Objective: The objective of this study was to investigate the association between daily step count and the predicted 5-year risk of AF. Methods: Participants from the electronic Framingham Heart Study used an Apple smartwatch. Individuals with diagnosed AF were excluded. Daily step count, watch wear time (hours and days), and self-reported physical activity data were collected. Individuals? 5-year risk of AF was estimated, using the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE)?AF score. The relation between daily step count and predicted 5-year AF risk was examined via linear regression, adjusting for age, sex, and wear time. Secondary analyses examined effect modification by sex and obesity (BMI?30 kg/m2), as well as the relation between self-reported physical activity and predicted 5-year AF risk. Results: We examined 923 electronic Framingham Heart Study participants (age: mean 53, SD 9 years; female: n=563, 61%) who had a median daily step count of 7227 (IQR 5699-8970). Most participants (n=823, 89.2%) had a <2.5% CHARGE-AF risk. Every 1000 steps were associated with a 0.08% lower CHARGE-AF risk (P<.001). A stronger association was observed in men and individuals with obesity. In contrast, self-reported physical activity was not associated with CHARGE-AF risk. Conclusions: Higher daily step counts were associated with a lower predicted 5-year risk of AF, and this relation was stronger in men and participants with obesity. The utility of a wearable daily step counter for AF risk reduction merits further investigation. UR - https://www.jmir.org/2023/1/e43123 UR - http://dx.doi.org/10.2196/43123 UR - http://www.ncbi.nlm.nih.gov/pubmed/36877540 ID - info:doi/10.2196/43123 ER - TY - JOUR AU - Han, Dong AU - Ding, Y. Eric AU - Cho, Chaeho AU - Jung, Haewook AU - Dickson, L. Emily AU - Mohagheghian, Fahimeh AU - Peitzsch, G. Andrew AU - DiMezza, Danielle AU - Tran, Khanh-Van AU - McManus, D. David AU - Chon, H. Ki PY - 2023/2/13 TI - A Smartwatch System for Continuous Monitoring of Atrial Fibrillation in Older Adults After Stroke or Transient Ischemic Attack: Application Design Study JO - JMIR Cardio SP - e41691 VL - 7 KW - atrial fibrillation KW - stroke KW - smartwatch app KW - smartphone apps KW - wearable devices KW - user experience KW - older adults KW - mobile phone N2 - Background: The prevalence of atrial fibrillation (AF) increases with age and can lead to stroke. Therefore, older adults may benefit the most from AF screening. However, older adult populations tend to lag more than younger groups in the adoption of, and comfort with, the use of mobile health (mHealth) apps. Furthermore, although mobile apps that can detect AF are available to the public, most are designed for intermittent AF detection and for younger users. No app designed for long-term AF monitoring has released detailed system design specifications that can handle large data collections, especially in this age group. Objective: This study aimed to design an innovative smartwatch-based AF monitoring mHealth solution in collaboration with older adult participants and clinicians. Methods: The Pulsewatch system is designed to link smartwatches and smartphone apps, a website for data verification, and user data organization on a cloud server. The smartwatch in the Pulsewatch system is designed to continuously monitor the pulse rate with embedded AF detection algorithms, and the smartphone in the Pulsewatch system is designed to serve as the data-transferring hub to the cloud storage server. Results: We implemented the Pulsewatch system based on the functionality that patients and caregivers recommended. The user interfaces of the smartwatch and smartphone apps were specifically designed for older adults at risk for AF. We improved our Pulsewatch system based on feedback from focus groups consisting of patients with stroke and clinicians. The Pulsewatch system was used by the intervention group for up to 6 weeks in the 2 phases of our randomized clinical trial. At the conclusion of phase 1, 90 trial participants who had used the Pulsewatch app and smartwatch for 14 days completed a System Usability Scale to assess the usability of the Pulsewatch system; of 88 participants, 56 (64%) endorsed that the smartwatch app is ?easy to use.? For phases 1 and 2 of the study, we collected 9224.4 hours of smartwatch recordings from the participants. The longest recording streak in phase 2 was 21 days of consecutive recordings out of the 30 days of data collection. Conclusions: This is one of the first studies to provide a detailed design for a smartphone-smartwatch dyad for ambulatory AF monitoring. In this paper, we report on the system?s usability and opportunities to increase the acceptability of mHealth solutions among older patients with cognitive impairment. Trial Registration: ClinicalTrials.gov NCT03761394; https://www.clinicaltrials.gov/ct2/show/NCT03761394 International Registered Report Identifier (IRRID): RR2-10.1016/j.cvdhj.2021.07.002 UR - https://cardio.jmir.org/2023/1/e41691 UR - http://dx.doi.org/10.2196/41691 UR - http://www.ncbi.nlm.nih.gov/pubmed/36780211 ID - info:doi/10.2196/41691 ER - TY - JOUR AU - Campo, David AU - Elie, Valery AU - de Gallard, Tristan AU - Bartet, Pierre AU - Morichau-Beauchant, Tristan AU - Genain, Nicolas AU - Fayol, Antoine AU - Fouassier, David AU - Pasteur-Rousseau, Adrien AU - Puymirat, Etienne AU - Nahum, Julien PY - 2022/11/4 TI - Atrial Fibrillation Detection With an Analog Smartwatch: Prospective Clinical Study and Algorithm Validation JO - JMIR Form Res SP - e37280 VL - 6 IS - 11 KW - atrial fibrillation KW - mobile health KW - mHealth KW - diagnosis KW - electrocardiogram KW - ECG KW - smartwatch KW - smart technology KW - wearable KW - cardiology KW - cardiac KW - heart failure KW - heart disease KW - cardiovascular KW - morbidity KW - automatic detection KW - algorithm KW - physician KW - sensor KW - digital health N2 - Background: Atrial fibrillation affects approximately 4% of the world?s population and is one of the major causes of stroke, heart failure, sudden death, and cardiovascular morbidity. It can be difficult to diagnose when asymptomatic or in the paroxysmal stage, and its natural history is not well understood. New wearables and connected devices offer an opportunity to improve on this situation. Objective: We aimed to validate an algorithm for the automatic detection of atrial fibrillation from a single-lead electrocardiogram taken with a smartwatch. Methods: Eligible patients were recruited from 4 sites in Paris, France. Electrocardiograms (12-lead reference and single lead) were captured simultaneously. The electrocardiograms were reviewed by independent, blinded board-certified cardiologists. The sensitivity and specificity of the algorithm to detect atrial fibrillation and normal sinus rhythm were calculated. The quality of single-lead electrocardiograms (visibility and polarity of waves, interval durations, heart rate) was assessed in comparison with the gold standard (12-lead electrocardiogram). Results: A total of 262 patients (atrial fibrillation: n=100, age: mean 74.3 years, SD 12.3; normal sinus rhythm: n=113, age: 61.8 years, SD 14.3; other arrhythmia: n=45, 66.9 years, SD 15.2; unreadable electrocardiograms: n=4) were included in the final analysis; 6.9% (18/262) were classified as Noise by the algorithm. Excluding other arrhythmias and Noise, the sensitivity for atrial fibrillation detection was 0.963 (95% CI lower bound 0.894), and the specificity was 1.000 (95% CI lower bound 0.967). Visibility and polarity accuracies were similar (1-lead electrocardiogram: P waves: 96.9%, QRS complexes: 99.2%, T waves: 91.2%; 12-lead electrocardiogram: P waves: 100%, QRS complexes: 98.8%, T waves: 99.5%). P-wave visibility accuracy was 99% (99/100) for patients with atrial fibrillation and 95.7% (155/162) for patients with normal sinus rhythm, other arrhythmias, and unreadable electrocardiograms. The absolute values of the mean differences in PR duration and QRS width were <3 ms, and more than 97% were <40 ms. The mean difference between the heart rates from the 1-lead electrocardiogram calculated by the algorithm and those calculated by cardiologists was 0.55 bpm. Conclusions: The algorithm demonstrated great diagnostic performance for atrial fibrillation detection. The smartwatch?s single-lead electrocardiogram also demonstrated good quality for physician use in daily routine care. Trial Registration: ClinicalTrials.gov NCT04351386; http://clinicaltrials.gov/ct2/show/NCT04351386 UR - https://formative.jmir.org/2022/11/e37280 UR - http://dx.doi.org/10.2196/37280 UR - http://www.ncbi.nlm.nih.gov/pubmed/35481559 ID - info:doi/10.2196/37280 ER - TY - JOUR AU - Isakadze, Nino AU - Molello, Nancy AU - MacFarlane, Zane AU - Gao, Yumin AU - Spaulding, M. Erin AU - Commodore Mensah, Yvonne AU - Marvel, A. Francoise AU - Khoury, Shireen AU - Marine, E. Joseph AU - Michos, D. Erin AU - Spragg, David AU - Berger, D. Ronald AU - Calkins, Hugh AU - Cooper, A. Lisa AU - Martin, S. Seth PY - 2022/10/31 TI - The Virtual Inclusive Digital Health Intervention Design to Promote Health Equity (iDesign) Framework for Atrial Fibrillation: Co-design and Development Study JO - JMIR Hum Factors SP - e38048 VL - 9 IS - 4 KW - atrial fibrillation KW - digital health intervention KW - human-centered design KW - health equity KW - smartphone KW - mobile application KW - cardiac KW - cardiology KW - virtual meeting KW - virtual health KW - medication adherence N2 - Background: Smartphone ownership and mobile app use are steadily increasing in individuals of diverse racial and ethnic backgrounds living in the United States. Growing adoption of technology creates a perfect opportunity for digital health interventions to increase access to health care. To successfully implement digital health interventions and engage users, intervention development should be guided by user input, which is best achieved by the process of co-design. Digital health interventions co-designed with the active engagement of users have the potential to increase the uptake of guideline recommendations, which can reduce morbidity and mortality and advance health equity. Objective: We aimed to co-design a digital health intervention for patients with atrial fibrillation, the most common cardiac arrhythmia, with patient, caregiver, and clinician feedback and to describe our approach to human-centered design for building digital health interventions. Methods: We conducted virtual meetings with patients with atrial fibrillation (n=8), their caregivers, and clinicians (n=8). We used the following 7 steps in our co-design process: step 1, a virtual meeting focused on defining challenges and empathizing with problems that are faced in daily life by individuals with atrial fibrillation and clinicians; step 2, a virtual meeting focused on ideation and brainstorming the top challenges identified during the first meeting; step 3, individualized onboarding of patients with an existing minimally viable version of the atrial fibrillation app; step 4, virtual prototyping of the top 3 ideas generated during ideation; step 5, further ranking by the study investigators and engineers of the ideas that were generated during ideation but were not chosen as top-3 solutions to be prototyped in step 4; step 6, ongoing engineering work to incorporate top-priority features in the app; and step 7, obtaining further feedback from patients and testing the atrial fibrillation digital health intervention in a pilot clinical study. Results: The top challenges identified by patients and caregivers included addressing risk factor modification, medication adherence, and guidance during atrial fibrillation episodes. Challenges identified by clinicians were complementary and included patient education, addressing modifiable atrial fibrillation risk factors, and remote atrial fibrillation episode management. Patients brainstormed more than 30 ideas to address the top challenges, and the clinicians generated more than 20 ideas. Ranking of the ideas informed several novel or modified features aligned with the Theory of Health Behavior Change, features that were geared toward risk factor modification; patient education; rhythm, symptom, and trigger correlation for remote atrial fibrillation management; and social support. Conclusions: We co-designed an atrial fibrillation digital health intervention in partnership with patients, caregivers, and clinicians by virtually engaging in collaborative creation through the design process. We summarize our experience and describe a flexible approach to human-centered design for digital health intervention development that can guide innovative clinical investigators. UR - https://humanfactors.jmir.org/2022/4/e38048 UR - http://dx.doi.org/10.2196/38048 UR - http://www.ncbi.nlm.nih.gov/pubmed/36315217 ID - info:doi/10.2196/38048 ER - TY - JOUR AU - Stacy, John AU - Kim, Rachel AU - Barrett, Christopher AU - Sekar, Balaviknesh AU - Simon, Steven AU - Banaei-Kashani, Farnoush AU - Rosenberg, A. Michael PY - 2022/8/11 TI - Qualitative Evaluation of an Artificial Intelligence?Based Clinical Decision Support System to Guide Rhythm Management of Atrial Fibrillation: Survey Study JO - JMIR Form Res SP - e36443 VL - 6 IS - 8 KW - Clinical decision support system KW - machine learning KW - supervised learning KW - reinforcement learning KW - atrial fibrillation KW - rhythm strategy N2 - Background: Despite the numerous studies evaluating various rhythm control strategies for atrial fibrillation (AF), determination of the optimal strategy in a single patient is often based on trial and error, with no one-size-fits-all approach based on international guidelines/recommendations. The decision, therefore, remains personal and lends itself well to help from a clinical decision support system, specifically one guided by artificial intelligence (AI). QRhythm utilizes a 2-stage machine learning (ML) model to identify the optimal rhythm management strategy in a given patient based on a set of clinical factors, in which the model first uses supervised learning to predict the actions of an expert clinician and identifies the best strategy through reinforcement learning to obtain the best clinical outcome?a composite of symptomatic recurrence, hospitalization, and stroke. Objective: We qualitatively evaluated a novel, AI-based, clinical decision support system (CDSS) for AF rhythm management, called QRhythm, which uses both supervised and reinforcement learning to recommend either a rate control or one of 3 types of rhythm control strategies?external cardioversion, antiarrhythmic medication, or ablation?based on individual patient characteristics. Methods: Thirty-three clinicians, including cardiology attendings and fellows and internal medicine attendings and residents, performed an assessment of QRhythm, followed by a survey to assess relative comfort with automated CDSS in rhythm management and to examine areas for future development. Results: The 33 providers were surveyed with training levels ranging from resident to fellow to attending. Of the characteristics of the app surveyed, safety was most important to providers, with an average importance rating of 4.7 out of 5 (SD 0.72). This priority was followed by clinical integrity (a desire for the advice provided to make clinical sense; importance rating 4.5, SD 0.9), backward interpretability (transparency in the population used to create the algorithm; importance rating 4.3, SD 0.65), transparency of the algorithm (reasoning underlying the decisions made; importance rating 4.3, SD 0.88), and provider autonomy (the ability to challenge the decisions made by the model; importance rating 3.85, SD 0.83). Providers who used the app ranked the integrity of recommendations as their highest concern with ongoing clinical use of the model, followed by efficacy of the application and patient data security. Trust in the app varied; 1 (17%) provider responded that they somewhat disagreed with the statement, ?I trust the recommendations provided by the QRhythm app,? 2 (33%) providers responded with neutrality to the statement, and 3 (50%) somewhat agreed with the statement. Conclusions: Safety of ML applications was the highest priority of the providers surveyed, and trust of such models remains varied. Widespread clinical acceptance of ML in health care is dependent on how much providers trust the algorithms. Building this trust involves ensuring transparency and interpretability of the model. UR - https://formative.jmir.org/2022/8/e36443 UR - http://dx.doi.org/10.2196/36443 UR - http://www.ncbi.nlm.nih.gov/pubmed/35969422 ID - info:doi/10.2196/36443 ER - TY - JOUR AU - Santala, E. Onni AU - Lipponen, A. Jukka AU - Jäntti, Helena AU - Rissanen, T. Tuomas AU - Tarvainen, P. Mika AU - Laitinen, P. Tomi AU - Laitinen, M. Tiina AU - Castrén, Maaret AU - Väliaho, Eemu-Samuli AU - Rantula, A. Olli AU - Naukkarinen, S. Noora AU - Hartikainen, K. Juha E. AU - Halonen, Jari AU - Martikainen, J. Tero PY - 2022/6/21 TI - Continuous mHealth Patch Monitoring for the Algorithm-Based Detection of Atrial Fibrillation: Feasibility and Diagnostic Accuracy Study JO - JMIR Cardio SP - e31230 VL - 6 IS - 1 KW - atrial fibrillation KW - heart rate variability KW - HRV KW - algorithm KW - stroke KW - mobile health KW - mHealth KW - Awario analysis Service, screening KW - risk KW - stroke risk KW - heart rate KW - feasibility KW - reliability KW - artificial intelligence KW - mobile patch KW - wearable KW - arrhythmia KW - screening N2 - Background: The detection of atrial fibrillation (AF) is a major clinical challenge as AF is often paroxysmal and asymptomatic. Novel mobile health (mHealth) technologies could provide a cost-effective and reliable solution for AF screening. However, many of these techniques have not been clinically validated. Objective: The purpose of this study is to evaluate the feasibility and reliability of artificial intelligence (AI) arrhythmia analysis for AF detection with an mHealth patch device designed for personal well-being. Methods: Patients (N=178) with an AF (n=79, 44%) or sinus rhythm (n=99, 56%) were recruited from the emergency care department. A single-lead, 24-hour, electrocardiogram-based heart rate variability (HRV) measurement was recorded with the mHealth patch device and analyzed with a novel AI arrhythmia analysis software. Simultaneously registered 3-lead electrocardiograms (Holter) served as the gold standard for the final rhythm diagnostics. Results: Of the HRV data produced by the single-lead mHealth patch, 81.5% (3099/3802 hours) were interpretable, and the subject-based median for interpretable HRV data was 99% (25th percentile=77% and 75th percentile=100%). The AI arrhythmia detection algorithm detected AF correctly in all patients in the AF group and suggested the presence of AF in 5 patients in the control group, resulting in a subject-based AF detection accuracy of 97.2%, a sensitivity of 100%, and a specificity of 94.9%. The time-based AF detection accuracy, sensitivity, and specificity of the AI arrhythmia detection algorithm were 98.7%, 99.6%, and 98.0%, respectively. Conclusions: The 24-hour HRV monitoring by the mHealth patch device enabled accurate automatic AF detection. Thus, the wearable mHealth patch device with AI arrhythmia analysis is a novel method for AF screening. Trial Registration: ClinicalTrials.gov NCT03507335; https://clinicaltrials.gov/ct2/show/NCT03507335 UR - https://cardio.jmir.org/2022/1/e31230 UR - http://dx.doi.org/10.2196/31230 UR - http://www.ncbi.nlm.nih.gov/pubmed/35727618 ID - info:doi/10.2196/31230 ER - TY - JOUR AU - Kwun, Ju-Seung AU - Yoon, Chang-Hwan AU - Kim, Sun-Hwa AU - Jeon, Ki-Hyun AU - Kang, Si-Hyuck AU - Lee, Wonjae AU - Youn, Tae-Jin AU - Chae, In-Ho PY - 2022/6/9 TI - Surveillance of Arrhythmia in Patients After Myocardial Infarction Using Wearable Electrocardiogram Patch Devices: Prospective Cohort Study JO - JMIR Cardio SP - e35615 VL - 6 IS - 1 KW - myocardial infarction KW - arrhythmia KW - wearable electronic device KW - wearable KW - ECG KW - electrocardiogram KW - patch KW - patch devices KW - atrial fibrillation KW - heart KW - rhythm KW - cardiology KW - cardiologist KW - cohort study KW - tachycardia KW - beta-blocker N2 - Background: Acute myocardial infarction may be associated with new-onset arrhythmias. Patients with myocardial infarction may manifest serious arrhythmias such as ventricular tachyarrhythmias or atrial fibrillation. Frequent, prolonged electrocardiogram (ECG) monitoring can prevent devastating outcomes caused by these arrhythmias. Objective: We aimed to investigate the incidence of arrhythmias in patients following myocardial infarction using a patch-type device?AT-Patch (ATP-C120; ATsens). Methods: This study is a nonrandomized, single-center, prospective cohort study. We evaluated 71 patients who had had a myocardial infarction and had been admitted to our hospital. The ATP-C120 device was attached to the patient for 11 days and analyzed by 2 cardiologists for new-onset arrhythmic events. Results: One participant was concordantly diagnosed with atrial fibrillation. The cardiologists diagnosed atrial premature beats in 65 (92%) and 60 (85%) of 71 participants, and ventricular premature beats in 38 (54%) and 44 (62%) participants, respectively. Interestingly, 40 (56%) patients showed less than 2 minutes of sustained paroxysmal atrial tachycardia confirmed by both cardiologists. Among participants with atrial tachycardia, the use of ?-blockers was significantly lower compared with patients without tachycardia (70% vs 90%, P=.04). However, different dosages of ?-blockers did not make a significant difference. Conclusions: Wearable ECG monitoring patch devices are easy to apply and can correlate symptoms and ECG rhythm disturbances in patients following myocardial infarction. Further study is necessary regarding clinical implications and appropriate therapies for arrhythmias detected early after myocardial infarction to prevent adverse outcomes. UR - https://cardio.jmir.org/2022/1/e35615 UR - http://dx.doi.org/10.2196/35615 UR - http://www.ncbi.nlm.nih.gov/pubmed/35679117 ID - info:doi/10.2196/35615 ER - TY - JOUR AU - Kwon, Soonil AU - Lee, So-Ryoung AU - Choi, Eue-Keun AU - Ahn, Hyo-Jeong AU - Song, Hee-Seok AU - Lee, Young-Shin AU - Oh, Seil AU - Lip, H. Gregory Y. PY - 2022/5/9 TI - Comparison Between the 24-hour Holter Test and 72-hour Single-Lead Electrocardiogram Monitoring With an Adhesive Patch-Type Device for Atrial Fibrillation Detection: Prospective Cohort Study JO - J Med Internet Res SP - e37970 VL - 24 IS - 5 KW - atrial fibrillation KW - diagnosis KW - electrocardiogram KW - wearable device KW - health monitoring KW - Holter KW - cardiac KW - arrhythmia KW - ECG KW - EKG KW - digital tool KW - cardiology KW - patient monitoring KW - outpatient clinic KW - cardiac health KW - diagnostic KW - patient KW - clinician KW - digital health N2 - Background: There is insufficient evidence for the use of single-lead electrocardiogram (ECG) monitoring with an adhesive patch-type device (APD) over an extended period compared to that of the 24-hour Holter test for atrial fibrillation (AF) detection. Objective: In this paper, we aimed to compare AF detection by the 24-hour Holter test and 72-hour single-lead ECG monitoring using an APD among patients with AF. Methods: This was a prospective, single-center cohort study. A total of 210 patients with AF with clinical indications for the Holter test at cardiology outpatient clinics were enrolled in the study. The study participants were equipped with both the Holter device and APD for the first 24 hours. Subsequently, only the APD continued ECG monitoring for an additional 48 hours. AF detection during the first 24 hours was compared between the two devices. The diagnostic benefits of extended monitoring using the APD were evaluated. Results: A total of 200 patients (mean age 60 years; n=141, 70.5% male; and n=59, 29.5% female) completed 72-hour ECG monitoring with the APD. During the first 24 hours, both monitoring methods detected AF in the same 40/200 (20%) patients (including 20 patients each with paroxysmal and persistent AF). Compared to the 24-hour Holter test, the APD increased the AF detection rate by 1.5-fold (58/200; 29%) and 1.6-fold (64/200; 32%) with 48- and 72-hour monitoring, respectively. With the APD, the number of newly discovered patients with paroxysmal AF was 20/44 (45.5%), 18/44 (40.9%), and 6/44 (13.6%) at 24-, 48-, and 72-hour monitoring, respectively. Compared with 24-hour Holter monitoring, 72-hour monitoring with the APD increased the detection rate of paroxysmal AF by 2.2-fold (44/20). Conclusions: Compared to the 24-hour Holter test, AF detection could be improved with 72-hour single-lead ECG monitoring with the APD. UR - https://www.jmir.org/2022/5/e37970 UR - http://dx.doi.org/10.2196/37970 UR - http://www.ncbi.nlm.nih.gov/pubmed/35532989 ID - info:doi/10.2196/37970 ER - TY - JOUR AU - Tenbult, Nicole AU - Kraal, Jos AU - Brouwers, Rutger AU - Spee, Ruud AU - Eijsbouts, Sabine AU - Kemps, Hareld PY - 2022/4/29 TI - Adherence to a Multidisciplinary Lifestyle Program for Patients With Atrial Fibrillation and Obesity: Feasibility Study JO - JMIR Form Res SP - e32625 VL - 6 IS - 4 KW - cardiac rehabilitation KW - atrial fibrillation KW - obesity KW - participation KW - completion KW - adherence KW - lifestyle N2 - Background: Atrial fibrillation is commonly associated with obesity. Observational studies have shown that weight loss is associated with improved prognosis and a decrease in atrial fibrillation frequency and severity. However, despite these benefits, nonadherence to lifestyle programs is common. Objective: In this study, we evaluated adherence to and feasibility of a multidisciplinary lifestyle program focusing on behavior change in patients with atrial fibrillation and obesity. Methods: Patients with atrial fibrillation and obesity participated in a 1-year goal-oriented cardiac rehabilitation program. After baseline assessment, the first 3 months included a cardiac rehabilitation intervention with 4 fixed modules: lifestyle counseling (with an advanced nurse practitioner), exercise training, dietary consultation, and psychosocial therapy; relaxation sessions were an additional optional treatment module. An advanced nurse practitioner monitored the personal lifestyle of each individual patient, with assessments and consultations at 3 months (ie, immediately after the intervention) and at the end of the year (ie, 9 months after the intervention). At each timepoint, level of physical activity, personal goals and progress, atrial fibrillation symptoms and frequency (Atrial Fibrillation Severity Scale), psychosocial stress (Generalized Anxiety Disorder?7), and depression (Patient Health Questionnaire?9) were assessed. The primary endpoints were adherence (defined as the number of visits attended as percentage of the number of planned visits) and completion rates of the cardiac rehabilitation intervention (defined as performing at least of 80% of the prescribed sessions). In addition, we performed an exploratory analysis of effects of the cardiac rehabilitation program on weight and atrial fibrillation symptom frequency and severity. Results: Patients with atrial fibrillation and obesity (male: n=8; female: n=2; age: mean 57.2 years, SD 9.0; baseline weight: mean 107.2 kg, SD 11.8; baseline BMI: mean 32.4 kg/m2, SD 3.5) were recruited. Of the 10 participants, 8 participants completed the 3-month cardiac rehabilitation intervention, and 2 participants did not complete the cardiac rehabilitation intervention (both because of personal issues). Adherence to the fixed treatment modules was 95% (mean 3.8 sessions attended out of mean 4 planned) for lifestyle counseling, 86% (mean 15.2 sessions attended out of mean 17.6 planned) for physiotherapy sessions, 88% (mean 3.7 sessions attended out of mean 4.1 planned) for dietician consultations, and 60% (mean 0.6 sessions attended out of mean 1.0 planned) for psychosocial therapy; 70% of participants (7/10) were referred to the optional relaxation sessions, for which adherence was 86% (mean 2 sessions attended out of mean 2.4 planned). The frequency of atrial fibrillation symptoms was reduced immediately after the intervention (before: mean 35.6, SD 3.8; after: mean 31.2, SD 3.3), and this was sustained at 12 months (mean 24.8, SD 3.2). The severity of atrial fibrillation complaints immediately after the intervention (mean 20.0, SD 3.7) and at 12 months (mean 9.3, SD 3.6) were comparable to that at baseline (mean 16.6, SD 3.3). Conclusions: A 1-year multidisciplinary lifestyle program for obese patients with atrial fibrillation was found to be feasible, with high adherence and completion rates. Exploratory analysis revealed a sustained reduction in atrial fibrillation symptoms; however, these results remain to be confirmed in large-scale studies. UR - https://formative.jmir.org/2022/4/e32625 UR - http://dx.doi.org/10.2196/32625 UR - http://www.ncbi.nlm.nih.gov/pubmed/35486435 ID - info:doi/10.2196/32625 ER - TY - JOUR AU - Luo, Xueyan AU - Xu, Wei AU - Ming, Wai-Kit AU - Jiang, Xinchan AU - Yuan, Quan AU - Lai, Han AU - Huang, Chunji AU - Zhong, Xiaoni PY - 2022/4/19 TI - Cost-Effectiveness of Mobile Health?Based Integrated Care for Atrial Fibrillation: Model Development and Data Analysis JO - J Med Internet Res SP - e29408 VL - 24 IS - 4 KW - mobile health KW - integrated care KW - ABC pathway KW - atrial fibrillation KW - model-based KW - cost-effectiveness KW - health economic evaluation N2 - Background: Mobile health (mHealth) technology is increasingly used in disease management. Using mHealth tools to integrate and streamline care has improved clinical outcomes of patients with atrial fibrillation (AF). Objective: The aim of this study was to investigate the potential clinical and health economic outcomes of mHealth-based integrated care for AF from the perspective of a public health care provider in China. Methods: A Markov model was designed to compare outcomes of mHealth-based care and usual care in a hypothetical cohort of patients with AF in China. The time horizon was 30 years with monthly cycles. Model outcomes measured were direct medical cost, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratio (ICER). Sensitivity analyses were performed to examine the robustness of the base-case results. Results: In the base-case analysis, mHealth-based care gained higher QALYs of 0.0730 with an incurred cost of US $1090. Using US $33,438 per QALY (three times the gross domestic product) as the willingness-to-pay threshold, mHealth-based care was cost-effective, with an ICER of US $14,936 per QALY. In one-way sensitivity analysis, no influential factor with a threshold value was identified. In probabilistic sensitivity analysis, mHealth-based care was accepted as cost-effective in 92.33% of 10,000 iterations. Conclusions: This study assessed the expected cost-effectiveness of applying mHealth-based integrated care for AF according to a model-based health economic evaluation. The exploration suggested the potential cost-effective use of mHealth apps in streamlining and integrating care via the Atrial fibrillation Better Care (ABC) pathway for AF in China. Future economic evaluation alongside randomized clinical trials is highly warranted to verify the suggestion and investigate affecting factors such as geographical variations in patient characteristics, identification of subgroups, and constraints on local implementation. UR - https://www.jmir.org/2022/4/e29408 UR - http://dx.doi.org/10.2196/29408 UR - http://www.ncbi.nlm.nih.gov/pubmed/35438646 ID - info:doi/10.2196/29408 ER - TY - JOUR AU - Laranjo, Liliana AU - Shaw, Tim AU - Trivedi, Ritu AU - Thomas, Stuart AU - Charlston, Emma AU - Klimis, Harry AU - Thiagalingam, Aravinda AU - Kumar, Saurabh AU - Tan, C. Timothy AU - Nguyen, N. Tu AU - Marschner, Simone AU - Chow, Clara PY - 2022/4/13 TI - Coordinating Health Care With Artificial Intelligence?Supported Technology for Patients With Atrial Fibrillation: Protocol for a Randomized Controlled Trial JO - JMIR Res Protoc SP - e34470 VL - 11 IS - 4 KW - atrial fibrillation KW - interactive voice response KW - artificial intelligence KW - conversational agent KW - mobile phone N2 - Background: Atrial fibrillation (AF) is an increasingly common chronic health condition for which integrated care that is multidisciplinary and patient-centric is recommended yet challenging to implement. Objective: The aim of Coordinating Health Care With Artificial Intelligence?Supported Technology in AF is to evaluate the feasibility and potential efficacy of a digital intervention (AF-Support) comprising preprogrammed automated telephone calls (artificial intelligence conversational technology), SMS text messages, and emails, as well as an educational website, to support patients with AF in self-managing their condition and coordinate primary and secondary care follow-up. Methods: Coordinating Health Care With Artificial Intelligence?Supported Technology in AF is a 6-month randomized controlled trial of adult patients with AF (n=385), who will be allocated in a ratio of 4:1 to AF-Support or usual care, with postintervention semistructured interviews. The primary outcome is AF-related quality of life, and the secondary outcomes include cardiovascular risk factors, outcomes, and health care use. The 4:1 allocation design enables a detailed examination of the feasibility, uptake, and process of the implementation of AF-Support. Participants with new or ongoing AF will be recruited from hospitals and specialist-led clinics in Sydney, New South Wales, Australia. AF-Support has been co-designed with clinicians, researchers, information technologists, and patients. Automated telephone calls will occur 7 times, with the first call triggered to commence 24 to 48 hours after enrollment. Calls follow a standard flow but are customized to vary depending on patients? responses. Calls assess AF symptoms, and participants? responses will trigger different system responses based on prespecified protocols, including the identification of red flags requiring escalation. Randomization will be performed electronically, and allocation concealment will be ensured. Because of the nature of this trial, only outcome assessors and data analysts will be blinded. For the primary outcome, groups will be compared using an analysis of covariance adjusted for corresponding baseline values. Randomized trial data analysis will be performed according to the intention-to-treat principle, and qualitative data will be thematically analyzed. Results: Ethics approval was granted by the Western Sydney Local Health District Human Ethics Research Committee, and recruitment started in December 2020. As of December 2021, a total of 103 patients had been recruited. Conclusions: This study will address the gap in knowledge with respect to the role of postdischarge digital care models for supporting patients with AF. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12621000174886; https://www.australianclinicaltrials.gov.au/anzctr/trial/ACTRN12621000174886 International Registered Report Identifier (IRRID): DERR1-10.2196/34470 UR - https://www.researchprotocols.org/2022/4/e34470 UR - http://dx.doi.org/10.2196/34470 UR - http://www.ncbi.nlm.nih.gov/pubmed/35416784 ID - info:doi/10.2196/34470 ER - TY - JOUR AU - Merschel, Steve AU - Reinhardt, Lars PY - 2022/3/28 TI - Analyzability of Photoplethysmographic Smartwatch Data by the Preventicus Heartbeats Algorithm During Everyday Life: Feasibility Study JO - JMIR Form Res SP - e29479 VL - 6 IS - 3 KW - photoplethysmography KW - wearable KW - smartwatch KW - heart rate monitoring KW - cardiac arrhythmia screening KW - atrial fibrillation KW - signal quality KW - activity profile N2 - Background: Continuous heart rate monitoring via mobile health technologies based on photoplethysmography (PPG) has great potential for the early detection of sustained cardiac arrhythmias such as atrial fibrillation. However, PPG measurements are impaired by motion artifacts. Objective: The aim of this investigation was to evaluate the analyzability of smartwatch-derived PPG data during everyday life and to determine the relationship between the analyzability of the data and the activity level of the participant. Methods: A total of 41 (19 female and 22 male) adults in good cardiovascular health (aged 19-79 years) continuously wore a smartwatch equipped with a PPG sensor and a 3D accelerometer (Cardio Watch 287, Corsano Health BV) for a period of 24 hours that represented their individual daily routine. For each participant, smartwatch data were analyzed on a 1-minute basis by an algorithm designed for heart rhythm analysis (Preventicus Heartbeats, Preventicus GmbH). As outcomes, the percentage of analyzable data (PAD) and the mean acceleration (ACC) were calculated. To map changes of the ACC and PAD over the course of one day, the 24-hour period was divided into 8 subintervals comprising 3 hours each. Results: Univariate analysis of variance showed a large effect (?p2> 0.6; P<.001) of time interval (phase) on the ACC and PAD. The PAD ranged between 34% and 100%, with an average of 71.5% for the whole day, which is equivalent to a period of 17.2 hours. Between midnight and 6 AM, the mean values were the highest for the PAD (>94%) and the lowest for the ACC (<6×10-3 m/s2). Regardless of the time of the day, the correlation between the PAD and ACC was strong (r=?0.64). A linear regression analysis for the averaged data resulted in an almost perfect coefficient of determination (r2=0.99). Conclusions: This study showed a large relationship between the activity level and the analyzability of smartwatch-derived PPG data. Given the high yield of analyzable data during the nighttime, continuous arrhythmia screening seems particularly effective during sleep phases. UR - https://formative.jmir.org/2022/3/e29479 UR - http://dx.doi.org/10.2196/29479 UR - http://www.ncbi.nlm.nih.gov/pubmed/35343902 ID - info:doi/10.2196/29479 ER - TY - JOUR AU - Tahri Sqalli, Mohammed AU - Al-Thani, Dena AU - Elshazly, B. Mohamed AU - Al-Hijji, Mohammed AU - Alahmadi, Alaa AU - Sqalli Houssaini, Yahya PY - 2022/2/9 TI - Understanding Cardiology Practitioners? Interpretations of Electrocardiograms: An Eye-Tracking Study JO - JMIR Hum Factors SP - e34058 VL - 9 IS - 1 KW - eye tracking KW - electrocardiogram KW - ECG interpretation KW - cardiology practitioners KW - human-computer interaction KW - cardiology KW - ECG N2 - Background: Visual expertise refers to advanced visual skills demonstrated when performing domain-specific visual tasks. Prior research has emphasized the fact that medical experts rely on such perceptual pattern-recognition skills when interpreting medical images, particularly in the field of electrocardiogram (ECG) interpretation. Analyzing and modeling cardiology practitioners? visual behavior across different levels of expertise in the health care sector is crucial. Namely, understanding such acquirable visual skills may help train less experienced clinicians to interpret ECGs accurately. Objective: This study aims to quantify and analyze through the use of eye-tracking technology differences in the visual behavior and methodological practices for different expertise levels of cardiology practitioners such as medical students, cardiology nurses, technicians, fellows, and consultants when interpreting several types of ECGs. Methods: A total of 63 participants with different levels of clinical expertise took part in an eye-tracking study that consisted of interpreting 10 ECGs with different cardiac abnormalities. A counterbalanced within-subjects design was used with one independent variable consisting of the expertise level of the cardiology practitioners and two dependent variables of eye-tracking metrics (fixations count and fixation revisitations). The eye movements data revealed by specific visual behaviors were analyzed according to the accuracy of interpretation and the frequency with which interpreters visited different parts/leads on a standard 12-lead ECG. In addition, the median and SD in the IQR for the fixations count and the mean and SD for the ECG lead revisitations were calculated. Results: Accuracy of interpretation ranged between 98% among consultants, 87% among fellows, 70% among technicians, 63% among nurses, and finally 52% among medical students. The results of the eye fixations count, and eye fixation revisitations indicate that the less experienced cardiology practitioners need to interpret several ECG leads more carefully before making any decision. However, more experienced cardiology practitioners rely on their skills to recognize the visual signal patterns of different cardiac abnormalities, providing an accurate ECG interpretation. Conclusions: The results show that visual expertise for ECG interpretation is linked to the practitioner?s role within the health care system and the number of years of practical experience interpreting ECGs. Cardiology practitioners focus on different ECG leads and different waveform abnormalities according to their role in the health care sector and their expertise levels. UR - https://humanfactors.jmir.org/2022/1/e34058 UR - http://dx.doi.org/10.2196/34058 UR - http://www.ncbi.nlm.nih.gov/pubmed/35138258 ID - info:doi/10.2196/34058 ER - TY - JOUR AU - Wong, Cheong Kam AU - Nguyen, N. Tu AU - Marschner, Simone AU - Turnbull, Samual AU - Burns, Jenner Mason AU - Ne, Anna Jia Yi AU - Gopal, Vishal AU - Indrawansa, Balasuriya Anupama AU - Trankle, A. Steven AU - Usherwood, Tim AU - Kumar, Saurabh AU - Lindley, I. Richard AU - Chow, K. Clara PY - 2022/2/1 TI - Patient-Led Mass Screening for Atrial Fibrillation in the Older Population Using Handheld Electrocardiographic Devices Integrated With a Clinician-Coordinated Remote Central Monitoring System: Protocol for a Randomized Controlled Trial and Process Evaluation JO - JMIR Res Protoc SP - e34778 VL - 11 IS - 2 KW - atrial fibrillation KW - screening KW - handheld KW - electrocardiogram KW - ECG KW - acceptability KW - user perception KW - user experience KW - barrier KW - enabler KW - older adults KW - elderly KW - feasibility KW - effectiveness KW - implementation KW - monitoring KW - aging KW - cardiovascular KW - cardiology KW - heart disease KW - mobile phone N2 - Background: Atrial fibrillation (AF) is common in older people and increases the risk of stroke. The feasibility and effectiveness of the implementation of a patient-led AF screening program for older people are unknown. Objective: This study aims to examine the feasibility and effectiveness of an AF screening program comprising patient-led monitoring of single-lead electrocardiograms (ECGs) with clinician-coordinated central monitoring to diagnose AF among community-dwelling people aged ?75 years in Australia. Methods: This is a nationwide randomized controlled implementation trial conducted via the internet and remotely among 200 community-dwelling adults aged ?75 years with no known AF. Randomization will be performed in a 1:1 allocation ratio for the intervention versus control. Intervention group participants will be enrolled in the monitoring program at randomization. They will receive a handheld single-lead ECG device and training on the self-recording of ECGs on weekdays and submit their ECGs via their smartphones. The control group participants will receive usual care from their general practitioners for the initial 6 months and then commence the 6-month monitoring program. The ECGs will be reviewed centrally by trained personnel. Participants and their general practitioners will be notified of AF and other clinically significant ECG abnormalities. Results: This study will establish the feasibility and effectiveness of implementing the intervention in this patient population. The primary clinical outcome is the AF detection rate, and the primary feasibility outcome is the patient satisfaction score. Other outcomes include appropriate use of anticoagulant therapy, participant recruitment rate, program engagement (eg, frequency of ECG transmission), agreement in ECG interpretation between the device automatic algorithm and clinicians, the proportion of participants who complete the trial and number of dropouts, and the impact of frailty on feasibility and outcomes. We will conduct a qualitative evaluation to examine the barriers to and acceptability and enablers of implementation. Ethics approval was obtained from the human research ethics committee at the University of Sydney (project number 2020/680). The results will be disseminated via conventional scienti?c forums, including peer-reviewed publications and presentations at national and international conferences. Conclusions: By incorporating an integrated health care approach involving patient empowerment, centralized clinician-coordinated ECG monitoring, and facilitation of primary care and specialist services, it is possible to diagnose and treat AF early to reduce stroke risk. This study will provide new information on how to implement AF screening using digital health technology practicably and feasibly for older and frail populations residing in the community. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12621000184875; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380877 International Registered Report Identifier (IRRID): DERR1-10.2196/34778 UR - https://www.researchprotocols.org/2022/2/e34778 UR - http://dx.doi.org/10.2196/34778 UR - http://www.ncbi.nlm.nih.gov/pubmed/35103614 ID - info:doi/10.2196/34778 ER - TY - JOUR AU - Senoo, Keitaro AU - Miki, Tomonori AU - Ohkura, Takashi AU - Iwakoshi, Hibiki AU - Nishimura, Tetsuro AU - Shiraishi, Hirokazu AU - Teramukai, Satoshi AU - Matoba, Satoaki PY - 2022/1/7 TI - A Smartphone App to Improve Oral Anticoagulation Adherence in Patients With Atrial Fibrillation: Prospective Observational Study JO - JMIR Mhealth Uhealth SP - e30807 VL - 10 IS - 1 KW - atrial fibrillation KW - smartphone app KW - anticoagulants KW - drug adherence KW - education KW - patient involvement N2 - Background: Poor adherence to oral anticoagulation in elderly patients with atrial fibrillation (AF) has been shown to negatively impact health care costs, morbidity, and mortality. Although various methods such as automated reminders, counseling, telephone support, and patient education have been effective in improving medication adherence, the burden on health care providers has been considerable. Recently, an attempt has been made to improve medication adherence without burdening health care providers by using smartphone apps; however, the use of the app for elderly patients with AF is still limited. Objective: The purpose of this study was to determine whether the newly developed smartphone app for patients with AF (the Smart AF), which integrates education, automatic reminder, and patient engagement strategies with a simple user interface, can improve medication adherence in elderly patients with AF. Methods: Patient enrollment was carried out by obtaining informed consent from patients with AF attending Kyoto Prefectural University of Medicine hospital between May 2019 and September 2020. Follow-up was planned at 1, 3, and 6 months after enrollment, and questionnaire reminders were automatically sent to patient apps at designated follow-up time points. A questionnaire-based survey of medication adherence was performed electronically using the self-reported 8-item Morisky Medication Adherence Scale (MMAS-8) as the survey tool. Results: A total of 136 patients with AF were enrolled in this study. During the follow-up period, 112 (82%) patients underwent follow-up at 1 month, 107 (79%) at 3 months, and 96 (71%) at 6 months. The mean age of the enrolled patients was 64.3 years (SD 9.6), and male participants accounted for 79.4% (108/136) of the study population. The mean CHADS2 (congestive heart failure, hypertension, age, diabetes, previous stroke, or transient ischemic attack) score was 1.2, with hypertension being the most common comorbidity. At the time of enrollment, 126 (93%) and 10 (7%) patients were taking direct oral anticoagulants and warfarin, respectively. For medication adherence as measured according to the MMAS-8, MMAS scores at 1 month, 3 months, and 6 months were significantly improved compared with baseline MMAS scores (all P values less than .01). The overall improvement in medication adherence achieved by the 6-month intervention was as follows: 77.8% (14/18) of the patients in the high adherence group (score=8) at baseline remained in the same state, 45.3% (24/53) of the patients in the medium adherence group (score=6 to <8) at baseline moved to the high adherence group, and 72% (18/25) of the patients in the low adherence group (score <6) moved to either the medium or high adherence group. Conclusions: The Smart AF app improved medication adherence among elderly patients with AF. In the realm of medication management, an approach using a mobile health technology that emphasizes education, automatic reminder, and patient engagement may be helpful. UR - https://mhealth.jmir.org/2022/1/e30807 UR - http://dx.doi.org/10.2196/30807 UR - http://www.ncbi.nlm.nih.gov/pubmed/34894626 ID - info:doi/10.2196/30807 ER - TY - JOUR AU - Alamgir, Asma AU - Mousa, Osama AU - Shah, Zubair PY - 2021/12/17 TI - Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review JO - JMIR Med Inform SP - e30798 VL - 9 IS - 12 KW - artificial intelligence KW - machine learning KW - deep learning KW - cardiac arrest KW - predict N2 - Background: Cardiac arrest is a life-threatening cessation of activity in the heart. Early prediction of cardiac arrest is important, as it allows for the necessary measures to be taken to prevent or intervene during the onset. Artificial intelligence (AI) technologies and big data have been increasingly used to enhance the ability to predict and prepare for the patients at risk. Objective: This study aims to explore the use of AI technology in predicting cardiac arrest as reported in the literature. Methods: A scoping review was conducted in line with the guidelines of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping reviews. Scopus, ScienceDirect, Embase, the Institute of Electrical and Electronics Engineers, and Google Scholar were searched to identify relevant studies. Backward reference list checks of the included studies were also conducted. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively. Results: Out of 697 citations retrieved, 41 studies were included in the review, and 6 were added after backward citation checking. The included studies reported the use of AI in the prediction of cardiac arrest. Of the 47 studies, we were able to classify the approaches taken by the studies into 3 different categories: 26 (55%) studies predicted cardiac arrest by analyzing specific parameters or variables of the patients, whereas 16 (34%) studies developed an AI-based warning system. The remaining 11% (5/47) of studies focused on distinguishing patients at high risk of cardiac arrest from patients who were not at risk. Two studies focused on the pediatric population, and the rest focused on adults (45/47, 96%). Most of the studies used data sets with a size of <10,000 samples (32/47, 68%). Machine learning models were the most prominent branch of AI used in the prediction of cardiac arrest in the studies (38/47, 81%), and the most used algorithm was the neural network (23/47, 49%). K-fold cross-validation was the most used algorithm evaluation tool reported in the studies (24/47, 51%). Conclusions: AI is extensively used to predict cardiac arrest in different patient settings. Technology is expected to play an integral role in improving cardiac medicine. There is a need for more reviews to learn the obstacles to the implementation of AI technologies in clinical settings. Moreover, research focusing on how to best provide clinicians with support to understand, adapt, and implement this technology in their practice is also necessary. UR - https://medinform.jmir.org/2021/12/e30798 UR - http://dx.doi.org/10.2196/30798 UR - http://www.ncbi.nlm.nih.gov/pubmed/34927595 ID - info:doi/10.2196/30798 ER - TY - JOUR AU - Kim, S. Rachel AU - Simon, Steven AU - Powers, Brett AU - Sandhu, Amneet AU - Sanchez, Jose AU - Borne, T. Ryan AU - Tumolo, Alexis AU - Zipse, Matthew AU - West, Jason J. AU - Aleong, Ryan AU - Tzou, Wendy AU - Rosenberg, A. Michael PY - 2021/12/6 TI - Machine Learning Methodologies for Prediction of Rhythm-Control Strategy in Patients Diagnosed With Atrial Fibrillation: Observational, Retrospective, Case-Control Study JO - JMIR Med Inform SP - e29225 VL - 9 IS - 12 KW - atrial fibrillation KW - rhythm-control KW - machine learning KW - ablation KW - antiarrhythmia agents KW - data science KW - biostatistics KW - artificial intelligence N2 - Background: The identification of an appropriate rhythm management strategy for patients diagnosed with atrial fibrillation (AF) remains a major challenge for providers. Although clinical trials have identified subgroups of patients in whom a rate- or rhythm-control strategy might be indicated to improve outcomes, the wide range of presentations and risk factors among patients presenting with AF makes such approaches challenging. The strength of electronic health records is the ability to build in logic to guide management decisions, such that the system can automatically identify patients in whom a rhythm-control strategy is more likely and can promote efficient referrals to specialists. However, like any clinical decision support tool, there is a balance between interpretability and accurate prediction. Objective: This study aims to create an electronic health record?based prediction tool to guide patient referral to specialists for rhythm-control management by comparing different machine learning algorithms. Methods: We compared machine learning models of increasing complexity and used up to 50,845 variables to predict the rhythm-control strategy in 42,022 patients within the University of Colorado Health system at the time of AF diagnosis. Models were evaluated on the basis of their classification accuracy, defined by the F1 score and other metrics, and interpretability, captured by inspection of the relative importance of each predictor. Results: We found that age was by far the strongest single predictor of a rhythm-control strategy but that greater accuracy could be achieved with more complex models incorporating neural networks and more predictors for each participant. We determined that the impact of better prediction models was notable primarily in the rate of inappropriate referrals for rhythm-control, in which more complex models provided an average of 20% fewer inappropriate referrals than simpler, more interpretable models. Conclusions: We conclude that any health care system seeking to incorporate algorithms to guide rhythm management for patients with AF will need to address this trade-off between prediction accuracy and model interpretability. UR - https://medinform.jmir.org/2021/12/e29225 UR - http://dx.doi.org/10.2196/29225 UR - http://www.ncbi.nlm.nih.gov/pubmed/34874889 ID - info:doi/10.2196/29225 ER - TY - JOUR AU - Matthiesen, Stina AU - Diederichsen, Zöga Søren AU - Hansen, Hartmann Mikkel Klitzing AU - Villumsen, Christina AU - Lassen, Højbjerg Mats Christian AU - Jacobsen, Karl Peter AU - Risum, Niels AU - Winkel, Gregers Bo AU - Philbert, T. Berit AU - Svendsen, Hastrup Jesper AU - Andersen, Osman Tariq PY - 2021/11/26 TI - Clinician Preimplementation Perspectives of a Decision-Support Tool for the Prediction of Cardiac Arrhythmia Based on Machine Learning: Near-Live Feasibility and Qualitative Study JO - JMIR Hum Factors SP - e26964 VL - 8 IS - 4 KW - cardiac arrhythmia KW - short-term prediction KW - clinical decision support systems KW - machine learning KW - artificial intelligence KW - preimplementation KW - qualitative study KW - implantable cardioverter defibrillator KW - remote follow-up KW - sociotechnical N2 - Background: Artificial intelligence (AI), such as machine learning (ML), shows great promise for improving clinical decision-making in cardiac diseases by outperforming statistical-based models. However, few AI-based tools have been implemented in cardiology clinics because of the sociotechnical challenges during transitioning from algorithm development to real-world implementation. Objective: This study explored how an ML-based tool for predicting ventricular tachycardia and ventricular fibrillation (VT/VF) could support clinical decision-making in the remote monitoring of patients with an implantable cardioverter defibrillator (ICD). Methods: Seven experienced electrophysiologists participated in a near-live feasibility and qualitative study, which included walkthroughs of 5 blinded retrospective patient cases, use of the prediction tool, and questionnaires and interview questions. All sessions were video recorded, and sessions evaluating the prediction tool were transcribed verbatim. Data were analyzed through an inductive qualitative approach based on grounded theory. Results: The prediction tool was found to have potential for supporting decision-making in ICD remote monitoring by providing reassurance, increasing confidence, acting as a second opinion, reducing information search time, and enabling delegation of decisions to nurses and technicians. However, the prediction tool did not lead to changes in clinical action and was found less useful in cases where the quality of data was poor or when VT/VF predictions were found to be irrelevant for evaluating the patient. Conclusions: When transitioning from AI development to testing its feasibility for clinical implementation, we need to consider the following: expectations must be aligned with the intended use of AI; trust in the prediction tool is likely to emerge from real-world use; and AI accuracy is relational and dependent on available information and local workflows. Addressing the sociotechnical gap between the development and implementation of clinical decision-support tools based on ML in cardiac care is essential for succeeding with adoption. It is suggested to include clinical end-users, clinical contexts, and workflows throughout the overall iterative approach to design, development, and implementation. UR - https://humanfactors.jmir.org/2021/4/e26964 UR - http://dx.doi.org/10.2196/26964 UR - http://www.ncbi.nlm.nih.gov/pubmed/34842528 ID - info:doi/10.2196/26964 ER - TY - JOUR AU - Kapoor, Alok AU - Hayes, Anna AU - Patel, Jay AU - Patel, Harshal AU - Andrade, Andreza AU - Mazor, Kathleen AU - Possidente, Carl AU - Nolen, Kimberly AU - Hegeman-Dingle, Rozelle AU - McManus, David PY - 2021/11/19 TI - Usability and Perceived Usefulness of the AFib 2gether Mobile App in a Clinical Setting: Single-Arm Intervention Study JO - JMIR Cardio SP - e27016 VL - 5 IS - 2 KW - shared decision-making KW - mobile health KW - stroke risk KW - anticoagulation risk KW - anticoagulation KW - atrial fibrillation KW - anticoagulation therapy KW - atrial flutter KW - mobile phone N2 - Background: Although the American Heart Association and other professional societies have recommended shared decision-making as a way for patients with atrial fibrillation (AF) or atrial flutter to make informed decisions about using anticoagulation (AC), the best method for facilitating shared decision-making remains uncertain. Objective: The aim of this study is to assess the AFib 2gether mobile app for usability, perceived usefulness, and the extent and nature of shared decision-making that occurred for clinical encounters between patients with AF and their cardiology providers in which the app was used. Methods: We identified patients visiting a cardiology provider between October 2019 and May 2020. We measured usability from patients and providers using the Mobile App Rating Scale. From the 8 items of the Mobile App Rating Scale, we reported the average score (out of 5) for domains of functionality, esthetics, and overall quality. We administered a 3-item questionnaire to patients relating to their perceived usefulness of the app and a separate 3-item questionnaire to providers to measure their perceived usefulness of the app. We performed a chart review to track the occurrence of AC within 6 months of the index visit. We also audio recorded a subset of the encounters to identify evidence of shared decision-making. Results: We facilitated shared decision-making visits for 37 patients visiting 13 providers. In terms of usability, patients? average ratings of functionality, esthetics, and overall quality were 4.51 (SD 0.61), 4.26 (SD 0.51), and 4.24 (SD 0.89), respectively. In terms of usefulness, 41% (15/37) of patients agreed that the app improved their knowledge regarding AC, and 62% (23/37) agreed that the app helped clarify to their provider their preferences regarding AC. Among providers, 79% (27/34) agreed that the app helped clarify their patients? preferences, 82% (28/34) agreed that the app saved them time, and 59% (20/34) agreed that the app helped their patients make decisions about AC. In addition, 32% (12/37) of patients started AC after their shared decision-making visits. We audio recorded 25 encounters. Of these, 84% (21/25) included the mention of AC for AF, 44% (11/25) included the discussion of multiple options for AC, 72% (18/25) included a provider recommendation for AC, and 48% (12/25) included the evidence of patient involvement in the discussion. Conclusions: Patients and providers rated the app with high usability and perceived usefulness. Moreover, one-third of the patients began AC, and approximately 50% (12/25) of the encounters showed evidence of patient involvement in decision-making. In the future, we plan to study the effect of the app on a larger sample and with a controlled study design. Trial Registration: ClinicalTrials.gov NCT04118270; https://clinicaltrials.gov/ct2/show/NCT04118270 International Registered Report Identifier (IRRID): RR2-21986 UR - https://cardio.jmir.org/2021/2/e27016 UR - http://dx.doi.org/10.2196/27016 UR - http://www.ncbi.nlm.nih.gov/pubmed/34806997 ID - info:doi/10.2196/27016 ER - TY - JOUR AU - Elkin, L. Peter AU - Mullin, Sarah AU - Mardekian, Jack AU - Crowner, Christopher AU - Sakilay, Sylvester AU - Sinha, Shyamashree AU - Brady, Gary AU - Wright, Marcia AU - Nolen, Kimberly AU - Trainer, JoAnn AU - Koppel, Ross AU - Schlegel, Daniel AU - Kaushik, Sashank AU - Zhao, Jane AU - Song, Buer AU - Anand, Edwin PY - 2021/11/9 TI - Using Artificial Intelligence With Natural Language Processing to Combine Electronic Health Record?s Structured and Free Text Data to Identify Nonvalvular Atrial Fibrillation to Decrease Strokes and Death: Evaluation and Case-Control Study JO - J Med Internet Res SP - e28946 VL - 23 IS - 11 KW - afib KW - atrial fibrillation KW - artificial intelligence KW - NVAF KW - natural language processing KW - stroke risk KW - bleed risk KW - CHA2DS2-VASc KW - HAS-BLED KW - bio-surveillance N2 - Background: Nonvalvular atrial fibrillation (NVAF) affects almost 6 million Americans and is a major contributor to stroke but is significantly undiagnosed and undertreated despite explicit guidelines for oral anticoagulation. Objective: The aim of this study is to investigate whether the use of semisupervised natural language processing (NLP) of electronic health record?s (EHR) free-text information combined with structured EHR data improves NVAF discovery and treatment and perhaps offers a method to prevent thousands of deaths and save billions of dollars. Methods: We abstracted 96,681 participants from the University of Buffalo faculty practice?s EHR. NLP was used to index the notes and compare the ability to identify NVAF, congestive heart failure, hypertension, age ?75 years, diabetes mellitus, stroke or transient ischemic attack, vascular disease, age 65 to 74 years, sex category (CHA2DS2-VASc), and Hypertension, Abnormal liver/renal function, Stroke history, Bleeding history or predisposition, Labile INR, Elderly, Drug/alcohol usage (HAS-BLED) scores using unstructured data (International Classification of Diseases codes) versus structured and unstructured data from clinical notes. In addition, we analyzed data from 63,296,120 participants in the Optum and Truven databases to determine the NVAF frequency, rates of CHA2DS2?VASc ?2, and no contraindications to oral anticoagulants, rates of stroke and death in the untreated population, and first year?s costs after stroke. Results: The structured-plus-unstructured method would have identified 3,976,056 additional true NVAF cases (P<.001) and improved sensitivity for CHA2DS2-VASc and HAS-BLED scores compared with the structured data alone (P=.002 and P<.001, respectively), causing a 32.1% improvement. For the United States, this method would prevent an estimated 176,537 strokes, save 10,575 lives, and save >US $13.5 billion. Conclusions: Artificial intelligence?informed bio-surveillance combining NLP of free-text information with structured EHR data improves data completeness, prevents thousands of strokes, and saves lives and funds. This method is applicable to many disorders with profound public health consequences. UR - https://www.jmir.org/2021/11/e28946 UR - http://dx.doi.org/10.2196/28946 UR - http://www.ncbi.nlm.nih.gov/pubmed/34751659 ID - info:doi/10.2196/28946 ER - TY - JOUR AU - Santala, E. Onni AU - Halonen, Jari AU - Martikainen, Susanna AU - Jäntti, Helena AU - Rissanen, T. Tuomas AU - Tarvainen, P. Mika AU - Laitinen, P. Tomi AU - Laitinen, M. Tiina AU - Väliaho, Eemu-Samuli AU - Hartikainen, K. Juha E. AU - Martikainen, J. Tero AU - Lipponen, A. Jukka PY - 2021/10/22 TI - Automatic Mobile Health Arrhythmia Monitoring for the Detection of Atrial Fibrillation: Prospective Feasibility, Accuracy, and User Experience Study JO - JMIR Mhealth Uhealth SP - e29933 VL - 9 IS - 10 KW - atrial fibrillation KW - ECG KW - algorithm KW - stroke KW - mHealth KW - user experience KW - Awario analysis Service KW - Suunto Movesense KW - cardiology KW - digital health KW - mobile health KW - wearable device KW - heart belt KW - arrhythmia monitor KW - heart monitor N2 - Background: Atrial fibrillation (AF) is the most common tachyarrhythmia and associated with a risk of stroke. The detection and diagnosis of AF represent a major clinical challenge due to AF?s asymptomatic and intermittent nature. Novel consumer-grade mobile health (mHealth) products with automatic arrhythmia detection could be an option for long-term electrocardiogram (ECG)-based rhythm monitoring and AF detection. Objective: We evaluated the feasibility and accuracy of a wearable automated mHealth arrhythmia monitoring system, including a consumer-grade, single-lead heart rate belt ECG device (heart belt), a mobile phone application, and a cloud service with an artificial intelligence (AI) arrhythmia detection algorithm for AF detection. The specific aim of this proof-of-concept study was to test the feasibility of the entire sequence of operations from ECG recording to AI arrhythmia analysis and ultimately to final AF detection. Methods: Patients (n=159) with an AF (n=73) or sinus rhythm (n=86) were recruited from the emergency department. A single-lead heart belt ECG was recorded for 24 hours. Simultaneously registered 3-lead ECGs (Holter) served as the gold standard for the final rhythm diagnostics and as a reference device in a user experience survey with patients over 65 years of age (high-risk group). Results: The heart belt provided a high-quality ECG recording for visual interpretation resulting in 100% accuracy, sensitivity, and specificity of AF detection. The accuracy of AF detection with the automatic AI arrhythmia detection from the heart belt ECG recording was also high (97.5%), and the sensitivity and specificity were 100% and 95.4%, respectively. The correlation between the automatic estimated AF burden and the true AF burden from Holter recording was >0.99 with a mean burden error of 0.05 (SD 0.26) hours. The heart belt demonstrated good user experience and did not significantly interfere with the patient?s daily activities. The patients preferred the heart belt over Holter ECG for rhythm monitoring (85/110, 77% heart belt vs 77/109, 71% Holter, P=.049). Conclusions: A consumer-grade, single-lead ECG heart belt provided good-quality ECG for rhythm diagnosis. The mHealth arrhythmia monitoring system, consisting of heart-belt ECG, a mobile phone application, and an automated AF detection achieved AF detection with high accuracy, sensitivity, and specificity. In addition, the mHealth arrhythmia monitoring system showed good user experience. Trial Registration: ClinicalTrials.gov NCT03507335; https://clinicaltrials.gov/ct2/show/NCT03507335 UR - https://mhealth.jmir.org/2021/10/e29933 UR - http://dx.doi.org/10.2196/29933 UR - http://www.ncbi.nlm.nih.gov/pubmed/34677135 ID - info:doi/10.2196/29933 ER - TY - JOUR AU - Seiler, Amber AU - Biundo, Eliana AU - Di Bacco, Marco AU - Rosemas, Sarah AU - Nicolle, Emmanuelle AU - Lanctin, David AU - Hennion, Juliette AU - de Melis, Mirko AU - Van Heel, Laura PY - 2021/10/15 TI - Clinic Time Required for Remote and In-Person Management of Patients With Cardiac Devices: Time and Motion Workflow Evaluation JO - JMIR Cardio SP - e27720 VL - 5 IS - 2 KW - cardiac implantable electronic devices KW - remote monitoring KW - patient management KW - clinic efficiency KW - digital health KW - mobile phone N2 - Background: The number of patients with cardiac implantable electronic device (CIED) is increasing, creating a substantial workload for device clinics. Objective: This study aims to characterize the workflow and quantify clinic staff time requirements for managing patients with CIEDs. Methods: A time and motion workflow evaluation was performed in 11 US and European CIEDs clinics. Workflow tasks were repeatedly timed during 1 business week of observation at each clinic; these observations included all device models and manufacturers. The mean cumulative staff time required to review a remote device transmission and an in-person clinic visit were calculated, including all necessary clinical and administrative tasks. The annual staff time to manage a patient with a CIED was modeled using CIED transmission volumes, clinical guidelines, and the published literature. Results: A total of 276 in-person clinic visits and 2173 remote monitoring activities were observed. Mean staff time required per remote transmission ranged from 9.4 to 13.5 minutes for therapeutic devices (pacemaker, implantable cardioverter-defibrillator, and cardiac resynchronization therapy) and from 11.3 to 12.9 minutes for diagnostic devices such as insertable cardiac monitors (ICMs). Mean staff time per in-person visit ranged from 37.8 to 51.0 and from 39.9 to 45.8 minutes for therapeutic devices and ICMs, respectively. Including all remote and in-person follow-ups, the estimated annual time to manage a patient with a CIED ranged from 1.6 to 2.4 hours for therapeutic devices and from 7.7 to 9.3 hours for ICMs. Conclusions: The CIED patient management workflow is complex and requires significant staff time. Understanding process steps and time requirements informs the implementation of efficiency improvements, including remote solutions. Future research should examine heterogeneity in patient management processes to identify the most efficient workflow. UR - https://cardio.jmir.org/2021/2/e27720 UR - http://dx.doi.org/10.2196/27720 UR - http://www.ncbi.nlm.nih.gov/pubmed/34156344 ID - info:doi/10.2196/27720 ER - TY - JOUR AU - Kovoor, G. Joshua AU - McIntyre, Daniel AU - Chik, B. William W. AU - Chow, K. Clara AU - Thiagalingam, Aravinda PY - 2021/10/11 TI - Clinician-Created Educational Video Resources for Shared Decision-making in the Outpatient Management of Chronic Disease: Development and Evaluation Study JO - J Med Internet Res SP - e26732 VL - 23 IS - 10 KW - Shared decision-making KW - chronic disease KW - outpatients KW - audiovisual aids KW - atrial fibrillation KW - educational technology KW - teaching materials KW - referral and consultation KW - physician-patient relations KW - physicians N2 - Background: The provision of reliable patient education is essential for shared decision-making. However, many clinicians are reluctant to use commonly available resources, as they are generic and may contain information of insufficient quality. Clinician-created educational materials, accessed during the waiting time prior to consultation, can potentially benefit clinical practice if developed in a time- and resource-efficient manner. Objective: The aim of this study is to evaluate the utility of educational videos in improving patient decision-making, as well as consultation satisfaction and anxiety, within the outpatient management of chronic disease (represented by atrial fibrillation). The approach involves clinicians creating audiovisual patient education in a time- and resource-efficient manner for opportunistic delivery, using mobile smart devices with internet access, during waiting time before consultation. Methods: We implemented this educational approach in outpatient clinics and collected patient responses through an electronic survey. The educational module was a web-based combination of 4 short videos viewed sequentially, followed by a patient experience survey using 5-point Likert scales and 0-100 visual analogue scales. The clinician developed the audiovisual module over a 2-day span while performing usual clinical tasks, using existing hardware and software resources (laptop and tablet). Patients presenting for the outpatient management of atrial fibrillation accessed the module during waiting time before their consultation using either a URL or Quick Response (QR) code on a provided tablet or their own mobile smart devices. The primary outcome of the study was the module?s utility in improving patient decision-making ability, as measured on a 0-100 visual analogue scale. Secondary outcomes were the level of patient satisfaction with the videos, measured with 5-point Likert scales, in addition to the patient?s value for clinician narration and the module?s utility in improving anxiety and long-term treatment adherence, as represented on 0-100 visual analogue scales. Results: This study enrolled 116 patients presenting for the outpatient management of atrial fibrillation. The proportion of responses that were ?very satisfied? with the educational video content across the 4 videos ranged from 93% (86/92) to 96.3% (104/108) and this was between 98% (90/92) and 99.1% (107/108) for ?satisfied? or ?very satisfied.? There were no reports of dissatisfaction for the first 3 videos, and only 1% (1/92) of responders reported dissatisfaction for the fourth video. The median reported scores (on 0-100 visual analogue scales) were 90 (IQR 82.5-97) for improving patient decision-making, 89 (IQR 81-95) for reducing consultation anxiety, 90 (IQR 81-97) for improving treatment adherence, and 82 (IQR 70-90) for the clinician?s narration adding benefit to the patient experience. Conclusions: Clinician-created educational videos for chronic disease management resulted in improvements in patient-reported informed decision-making ability and expected long-term treatment adherence, as well as anxiety reduction. This form of patient education was also time efficient as it used the sunk time cost of waiting time to provide education without requiring additional clinician input. UR - https://www.jmir.org/2021/10/e26732 UR - http://dx.doi.org/10.2196/26732 UR - http://www.ncbi.nlm.nih.gov/pubmed/34633292 ID - info:doi/10.2196/26732 ER - TY - JOUR AU - Hsieh, Hui-Ling AU - Kao, Chi-Wen AU - Cheng, Shu-Meng AU - Chang, Yue-Cune PY - 2021/9/22 TI - A Web-Based Integrated Management Program for Improving Medication Adherence and Quality of Life, and Reducing Readmission in Patients With Atrial Fibrillation: Randomized Controlled Trial JO - J Med Internet Res SP - e30107 VL - 23 IS - 9 KW - web-based program KW - atrial fibrillation KW - coping strategy KW - medication adherence KW - readmission KW - health-related quality of life N2 - Background: Atrial fibrillation (AF) is related to a variety of chronic diseases and life-threatening complications. It is estimated that by 2050, there will be 72 million patients with AF in Asia, of which 2.9 million will have AF-associated stroke. AF has become a major issue for health care systems. Objective: We aimed to evaluate the effects of a web-based integrated management program on improving coping strategies, medication adherence, and health-related quality of life (HRQoL) in patients with AF, and to detect the effect on decreasing readmission events. Methods: The parallel-group, single-blind, prospective randomized controlled trial recruited patients with AF from a medical center in northern Taiwan and divided them randomly into intervention and control groups. Patients in the intervention group received the web-based integrated management program, whereas those in the control group received usual care. The measurement tools included the Brief Coping Orientation to Problems Experienced (COPE) scale, Medication Adherence Rating Scale (MARS), the three-level version of the EuroQoL five-dimension self-report questionnaire (EQ-5D-3L), and readmission events 2 years after initiating the intervention. Data were collected at 4 instances (baseline, 1 month, 3 months, and 6 months after initiating the intervention), and analyzed with generalized estimating equations (GEEs). Results: A total of 231 patients were recruited and allocated into an intervention (n=115) or control (n=116) group. The mean age of participants was 73.08 (SD 11.71) years. Most participants were diagnosed with paroxysmal AF (171/231, 74%), and the most frequent comorbidity was hypertension (162/231, 70.1%). Compared with the control group, the intervention group showed significantly greater improvement in approach coping strategies, medication adherence, and HRQoL at 1, 3, and 6 months (all P<.05). In addition, the intervention group showed significantly fewer readmission events within 2 years (OR 0.406, P=.03), compared with the control group. Conclusions: The web-based integrated management program can significantly improve patients' coping strategy and medication adherence. Therefore, it can empower patients to maintain disease stability, which is a major factor in improving their HRQoL and reducing readmission events within 2 years. Trial Registration: ClinicalTrials.gov NCT04813094; https://clinicaltrials.gov/ct2/show/NCT04813094. UR - https://www.jmir.org/2021/9/e30107 UR - http://dx.doi.org/10.2196/30107 UR - http://www.ncbi.nlm.nih.gov/pubmed/34550084 ID - info:doi/10.2196/30107 ER - TY - JOUR AU - Nazarian, Scarlet AU - Lam, Kyle AU - Darzi, Ara AU - Ashrafian, Hutan PY - 2021/8/27 TI - Diagnostic Accuracy of Smartwatches for the Detection of Cardiac Arrhythmia: Systematic Review and Meta-analysis JO - J Med Internet Res SP - e28974 VL - 23 IS - 8 KW - wearables KW - smartwatch KW - cardiac arrhythmia KW - atrial fibrillation KW - cardiology KW - mHealth KW - wearable devices KW - screening KW - diagnostics KW - accuracy N2 - Background: Significant morbidity, mortality, and financial burden are associated with cardiac rhythm abnormalities. Conventional investigative tools are often unsuccessful in detecting cardiac arrhythmias because of their episodic nature. Smartwatches have gained popularity in recent years as a health tool for the detection of cardiac rhythms. Objective: This study aims to systematically review and meta-analyze the diagnostic accuracy of smartwatches in the detection of cardiac arrhythmias. Methods: A systematic literature search of the Embase, MEDLINE, and Cochrane Library databases was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to identify studies reporting the use of a smartwatch for the detection of cardiac arrhythmia. Summary estimates of sensitivity, specificity, and area under the curve were attempted using a bivariate model for the diagnostic meta-analysis. Studies were examined for quality using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Results: A total of 18 studies examining atrial fibrillation detection, bradyarrhythmias and tachyarrhythmias, and premature contractions were analyzed, measuring diagnostic accuracy in 424,371 subjects in total. The signals analyzed by smartwatches were based on photoplethysmography. The overall sensitivity, specificity, and accuracy of smartwatches for detecting cardiac arrhythmias were 100% (95% CI 0.99-1.00), 95% (95% CI 0.93-0.97), and 97% (95% CI 0.96-0.99), respectively. The pooled positive predictive value and negative predictive value for detecting cardiac arrhythmias were 85% (95% CI 0.79-0.90) and 100% (95% CI 1.0-1.0), respectively. Conclusions: This review demonstrates the evolving field of digital disease detection. The current diagnostic accuracy of smartwatch technology for the detection of cardiac arrhythmias is high. Although the innovative drive of digital devices in health care will continue to gain momentum toward screening, the process of accurate evidence accrual and regulatory standards ready to accept their introduction is strongly needed. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42020213237; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=213237. UR - https://www.jmir.org/2021/8/e28974 UR - http://dx.doi.org/10.2196/28974 UR - http://www.ncbi.nlm.nih.gov/pubmed/34448706 ID - info:doi/10.2196/28974 ER - TY - JOUR AU - Ksi??czyk, Marcin AU - D?bska-Koz?owska, Agnieszka AU - Warcho?, Izabela AU - Lubi?ski, Andrzej PY - 2021/8/27 TI - Enhancing Healthcare Access?Smartphone Apps in Arrhythmia Screening: Viewpoint JO - JMIR Mhealth Uhealth SP - e23425 VL - 9 IS - 8 KW - arrhythmia screening KW - atrial fibrillation KW - mobile electrocardiography KW - mobile health KW - phonocardiography KW - photoplethysmography KW - seismocardiography KW - stroke prevention UR - https://mhealth.jmir.org/2021/8/e23425 UR - http://dx.doi.org/10.2196/23425 UR - http://www.ncbi.nlm.nih.gov/pubmed/34448723 ID - info:doi/10.2196/23425 ER - TY - JOUR AU - de Castro, Paul Kim AU - Chiu, Henrison Harold AU - De Leon-Yao, Cheska Ronna AU - Almelor-Sembrana, Lorraine AU - Dans, Miguel Antonio PY - 2021/8/12 TI - A Patient Decision Aid for Anticoagulation Therapy in Patients With Nonvalvular Atrial Fibrillation: Development and Pilot Study JO - JMIR Cardio SP - e23464 VL - 5 IS - 2 KW - shared decision-making KW - patient decision aid KW - atrial fibrillation KW - anticoagulation KW - stroke prevention KW - mHealth KW - mobile health N2 - Background: Atrial fibrillation (AF) is one of the most common predisposing factors for ischemic stroke worldwide. Because of this, patients with AF are prescribed anticoagulant medications to decrease the risk. The availability of different options for oral anticoagulation makes it difficult for some patients to decide a preferred choice of medication. Clinical guidelines often recommend enhancing the decision-making process of patients by increasing their involvement in health decisions. In particular, the use of patient decision aids (PDAs) in patients with AF was associated with increased knowledge and increased likelihood of making a choice. However, the majority of available PDAs are from Western countries. Objective: We aimed to develop and pilot test a PDA to help patients with nonvalvular AF choose an oral anticoagulant for stroke prevention in the local setting. Outcomes were (1) reduction in patient decisional conflict, (2) improvement in patient knowledge, and (3) patient and physician acceptability. Methods: We followed the International Patient Decision Aid Standards (IPDAS) to develop a mobile app?based PDA for anticoagulation therapy in patients with nonvalvular AF. Focus group discussions identified decisional needs, which were subsequently incorporated into the PDA to compare choices for anticoagulation. Based on recommendations, the prototype PDA was rendered by at least 30 patients and 30 physicians. Decisional conflict and patient knowledge were tested before and after the PDA was implemented. Patient acceptability and physician acceptability were measured after each encounter. Results: Anticoagulant options were compared by the PDA using three factors that were identified (impact on stroke and bleeding risk, and price). The comparisons were presented as tables and graphs. The prototype PDA was rendered by 30 doctors and 37 patients for pilot testing. The mean duration of the encounters was 15 minutes. The decisional conflict score reduced by 35 points (100-point scale; P<.001). The AF knowledge score improved from 10 to 15 (P<.001). The PDA was acceptable for both patients and doctors. Conclusions: Our study showed that an app-based PDA for anticoagulation therapy in patients with nonvalvular AF (1) reduced patient decisional conflict, (2) improved patient knowledge, and (3) was acceptable to patients and physicians. A PDA is potentially acceptable and useful in our setting. A randomized controlled trial is warranted to test its effectiveness compared to usual care. PDAs for other conditions should also be developed. UR - https://cardio.jmir.org/2021/2/e23464 UR - http://dx.doi.org/10.2196/23464 UR - http://www.ncbi.nlm.nih.gov/pubmed/34385138 ID - info:doi/10.2196/23464 ER - TY - JOUR AU - Dinesen, Birthe AU - Dam Gade, Josefine AU - Skov Schacksen, Cathrine AU - Spindler, Helle AU - Eie Albertsen, Andi AU - Dittmann, Lars AU - Jochumsen, Mads AU - Svenstrup Møller, Dorthe PY - 2021/7/19 TI - The Danish Future Patient Telerehabilitation Program for Patients With Atrial Fibrillation: Design and Pilot Study in Collaboration With Patients and Their Spouses JO - JMIR Cardio SP - e27321 VL - 5 IS - 2 KW - atrial fibrillation KW - cardiac rehabilitation KW - telerehabilitation KW - patient education N2 - Background: Atrial fibrillation (AF) is the most common cardiac arrhythmia and is predicted to more than double in prevalence over the next 20 years. Tailored patient education is recommended as an important aspect of AF care. Current guidelines emphasize that patients become more active participants in the management of their own disease, yet there are no rehabilitation programs for patients with AF in the Danish health care system. Through participatory design, we developed the Future Patient Telerehabilitation (TR) Programs, A and B, for patients with AF. The 2 programs are based on HeartPortal and remote monitoring, together with educational modules. Objective: The aim of this pilot study is to evaluate and compare the feasibility of the 2 programs of TR for patients with AF. Methods: This pilot study was conducted between December 2019 and March 2020. The pilot study consisted of testing the 2 TR programs, A and B, in two phases: (1) treatment at the AF clinic and (2) TR at home. The primary outcome of the study was the usability of technologies for self-monitoring and the context of the TR programs as seen from patients? perspectives. Secondary outcomes were the development of patients? knowledge of AF, development of clinical data, and understanding the expectations and experiences of patients and spouses. Data were collected through interviews, questionnaires, and clinical measurements from home monitoring devices. Statistical analyses were performed using the IBM SPSS Statistics version 26. Qualitative data were analyzed using NVivo 12.0. Results: Through interviews, patients articulated the following themes about participating in a TR program: usefulness of the HeartPortal, feeling more secure living with AF, community of practice living with AF, and measuring heart rhythm makes good sense. Through interviews, the spouses of patients with AF expressed that they had gained increased knowledge about AF and how to support their spouses living with AF in everyday life. Results from the responses to the Jessa AF Knowledge Questionnaire support the qualitative data, as they showed that patients in program B acquired increased knowledge about AF at follow-up compared with baseline. No significant differences were found in the number of electrocardiography recordings between the 2 groups. Conclusions: Patients with AF and their spouses were positive about the TR program and they found the TR program useful, especially because it created an increased sense of security, knowledge about mastering their symptoms, and a community of practice linking patients with AF and their spouses and health care personnel. To assess all the benefits of the Future Patient?TR Program for patients with AF, it needs to be tested in a comprehensive randomized controlled trial. Trial Registration: ClinicalTrials.gov NCT04493437; https://clinicaltrials.gov/ct2/show/NCT04493437. UR - https://cardio.jmir.org/2021/2/e27321 UR - http://dx.doi.org/10.2196/27321 UR - http://www.ncbi.nlm.nih.gov/pubmed/34279239 ID - info:doi/10.2196/27321 ER - TY - JOUR AU - Pitman, M. Bradley AU - Chew, Sok-Hui AU - Wong, X. Christopher AU - Jaghoori, Amenah AU - Iwai, Shinsuke AU - Thomas, Gijo AU - Chew, Andrew AU - Sanders, Prashanthan AU - Lau, H. Dennis PY - 2021/5/19 TI - Performance of a Mobile Single-Lead Electrocardiogram Technology for Atrial Fibrillation Screening in a Semirural African Population: Insights From ?The Heart of Ethiopia: Focus on Atrial Fibrillation? (TEFF-AF) Study JO - JMIR Mhealth Uhealth SP - e24470 VL - 9 IS - 5 KW - atrial fibrillation KW - screening KW - sub-Saharan Africa KW - single-lead ECG N2 - Background: Atrial fibrillation (AF) screening using mobile single-lead electrocardiogram (ECG) devices has demonstrated variable sensitivity and specificity. However, limited data exists on the use of such devices in low-resource countries. Objective: The goal of the research was to evaluate the utility of the KardiaMobile device?s (AliveCor Inc) automated algorithm for AF screening in a semirural Ethiopian population. Methods: Analysis was performed on 30-second single-lead ECG tracings obtained using the KardiaMobile device from 1500 TEFF-AF (The Heart of Ethiopia: Focus on Atrial Fibrillation) study participants. We evaluated the performance of the KardiaMobile automated algorithm against cardiologists? interpretations of 30-second single-lead ECG for AF screening. Results: A total of 1709 single-lead ECG tracings (including repeat tracing on 209 occasions) were analyzed from 1500 Ethiopians (63.53% [953/1500] male, mean age 35 [SD 13] years) who presented for AF screening. Initial successful rhythm decision (normal or possible AF) with one single-lead ECG tracing was lower with the KardiaMobile automated algorithm versus manual verification by cardiologists (1176/1500, 78.40%, vs 1455/1500, 97.00%; P<.001). Repeat single-lead ECG tracings in 209 individuals improved overall rhythm decision, but the KardiaMobile automated algorithm remained inferior (1301/1500, 86.73%, vs 1479/1500, 98.60%; P<.001). The key reasons underlying unsuccessful KardiaMobile automated rhythm determination include poor quality/noisy tracings (214/408, 52.45%), frequent ectopy (22/408, 5.39%), and tachycardia (>100 bpm; 167/408, 40.93%). The sensitivity and specificity of rhythm decision using KardiaMobile automated algorithm were 80.27% (1168/1455) and 82.22% (37/45), respectively. Conclusions: The performance of the KardiaMobile automated algorithm was suboptimal when used for AF screening. However, the KardiaMobile single-lead ECG device remains an excellent AF screening tool with appropriate clinician input and repeat tracing. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12619001107112; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=378057&isReview=true UR - https://mhealth.jmir.org/2021/5/e24470 UR - http://dx.doi.org/10.2196/24470 UR - http://www.ncbi.nlm.nih.gov/pubmed/34009129 ID - info:doi/10.2196/24470 ER - TY - JOUR AU - Biersteker, E. Tom AU - Schalij, J. Martin AU - Treskes, W. Roderick PY - 2021/4/28 TI - Impact of Mobile Health Devices for the Detection of Atrial Fibrillation: Systematic Review JO - JMIR Mhealth Uhealth SP - e26161 VL - 9 IS - 4 KW - eHealth KW - mHealth KW - telemedicine KW - cardiology KW - atrial fibrillation KW - systematic review N2 - Background: Atrial fibrillation (AF) is the most common arrhythmia, and its prevalence is increasing. Early diagnosis is important to reduce the risk of stroke. Mobile health (mHealth) devices, such as single-lead electrocardiogram (ECG) devices, have been introduced to the worldwide consumer market over the past decade. Recent studies have assessed the usability of these devices for detection of AF, but it remains unclear if the use of mHealth devices leads to a higher AF detection rate. Objective: The goal of the research was to conduct a systematic review of the diagnostic detection rate of AF by mHealth devices compared with traditional outpatient follow-up. Study participants were aged 16 years or older and had an increased risk for an arrhythmia and an indication for ECG follow-up?for instance, after catheter ablation or presentation to the emergency department with palpitations or (near) syncope. The intervention was the use of an mHealth device, defined as a novel device for the diagnosis of rhythm disturbances, either a handheld electronic device or a patch-like device worn on the patient?s chest. Control was standard (traditional) outpatient care, defined as follow-up via general practitioner or regular outpatient clinic visits with a standard 12-lead ECG or Holter monitoring. The main outcome measures were the odds ratio (OR) of AF detection rates. Methods: Two reviewers screened the search results, extracted data, and performed a risk of bias assessment. A heterogeneity analysis was performed, forest plot made to summarize the results of the individual studies, and albatross plot made to allow the P values to be interpreted in the context of the study sample size. Results: A total of 3384 articles were identified after a database search, and 14 studies with a 4617 study participants were selected. All studies but one showed a higher AF detection rate in the mHealth group compared with the control group (OR 1.00-35.71), with all RCTs showing statistically significant increases of AF detection (OR 1.54-19.16). Statistical heterogeneity between studies was considerable, with a Q of 34.1 and an I2 of 61.9, and therefore it was decided to not pool the results into a meta-analysis. Conclusions: Although the results of 13 of 14 studies support the effectiveness of mHealth interventions compared with standard care, study results could not be pooled due to considerable clinical and statistical heterogeneity. However, smartphone-connectable ECG devices provide patients with the ability to document a rhythm disturbance more easily than with standard care, which may increase empowerment and engagement with regard to their illness. Clinicians must beware of overdiagnosis of AF, as it is not yet clear when an mHealth-detected episode of AF must be deemed significant. UR - https://mhealth.jmir.org/2021/4/e26161 UR - http://dx.doi.org/10.2196/26161 UR - http://www.ncbi.nlm.nih.gov/pubmed/33908885 ID - info:doi/10.2196/26161 ER - TY - JOUR AU - Lamberigts, Marie AU - Van Hoof, Lucas AU - Proesmans, Tine AU - Vandervoort, Pieter AU - Grieten, Lars AU - Haemers, Peter AU - Rega, Filip PY - 2021/4/15 TI - Remote Heart Rhythm Monitoring by Photoplethysmography-Based Smartphone Technology After Cardiac Surgery: Prospective Observational Study JO - JMIR Mhealth Uhealth SP - e26519 VL - 9 IS - 4 KW - cardiac surgery KW - postoperative follow-up KW - cardiac rehabilitation KW - postoperative arrhythmias KW - atrial fibrillation KW - photoplethysmography KW - home-monitoring N2 - Background: Atrial fibrillation (AF) is the most common arrhythmia after cardiac surgery, yet the precise incidence and significance of arrhythmias after discharge home need to be better defined. Photoplethysmography (PPG)-based smartphone apps are promising tools to enable early detection and follow-up of arrhythmias. Objective: By using a PPG-based smartphone app, we aimed to gain more insight into the prevalence of AF and other rhythm-related complications upon discharge home after cardiac surgery and evaluate the implementation of this app into routine clinical care. Methods: In this prospective, single-center trial, patients recovering from cardiac surgery were asked to register their heart rhythm 3 times daily using a Food and Drug Administration?approved PPG-based app, for either 30 or 60 days after discharge home. Patients with permanent AF or a permanent pacemaker were excluded. Results: We included 24 patients (mean age 60.2 years, SD 12 years; 15/23, 65% male) who underwent coronary artery bypass grafting and/or valve surgery. During hospitalization, 39% (9/23) experienced postoperative AF. After discharge, the PPG app reported AF or atrial flutter in 5 patients. While the app notified flutter in 1 patient, this was a false positive, as electrocardiogram revealed a 2nd-degree, 2:1 atrioventricular block necessitating a permanent pacemaker. AF was confirmed in 4 patients (4/23, 17%) and interestingly, was associated with an underlying postoperative complication in 2 participants (pneumonia n=1, pericardial tamponade n=1). A significant increase in the proportion of measurements indicating sinus rhythm was observed when comparing the first to the second month of follow-up (P<.001). In the second month of follow-up, compliance was significantly lower with 2.2 (SD 0.7) measurements per day versus 3.0 (SD 0.8) measurements per day in the first month (P=.002). The majority of participants (17/23, 74%), as well as the surveyed primary care physicians, experienced positive value by using the app as they felt more involved in the postoperative rehabilitation. Conclusions: Implementation of smartphone-based PPG technology enables detection of AF and other rhythm-related complications after cardiac surgery. An association between AF detection and an underlying complication was found in 2 patients. Therefore, smartphone-based PPG technology may supplement rehabilitation after cardiac surgery by acting as a sentinel for underlying complications, rhythm-related or otherwise. UR - https://mhealth.jmir.org/2021/4/e26519 UR - http://dx.doi.org/10.2196/26519 UR - http://www.ncbi.nlm.nih.gov/pubmed/33856357 ID - info:doi/10.2196/26519 ER - TY - JOUR AU - Yang, Yun Tien AU - Huang, Li AU - Malwade, Shwetambara AU - Hsu, Chien-Yi AU - Chen, Ching Yang PY - 2021/4/9 TI - Diagnostic Accuracy of Ambulatory Devices in Detecting Atrial Fibrillation: Systematic Review and Meta-analysis JO - JMIR Mhealth Uhealth SP - e26167 VL - 9 IS - 4 KW - atrial fibrillation KW - ambulatory devices KW - electrocardiogram KW - photoplethysmography KW - diagnostic accuracy KW - ubiquitous health KW - mobile health KW - technology KW - ambulatory device N2 - Background: Atrial fibrillation (AF) is the most common cardiac arrhythmia worldwide. Early diagnosis of AF is crucial for preventing AF-related morbidity, mortality, and economic burden, yet the detection of the disease remains challenging. The 12-lead electrocardiogram (ECG) is the gold standard for the diagnosis of AF. Because of technological advances, ambulatory devices may serve as convenient screening tools for AF. Objective: The objective of this review was to investigate the diagnostic accuracy of 2 relatively new technologies used in ambulatory devices, non-12-lead ECG and photoplethysmography (PPG), in detecting AF. We performed a meta-analysis to evaluate the diagnostic accuracy of non-12-lead ECG and PPG compared to the reference standard, 12-lead ECG. We also conducted a subgroup analysis to assess the impact of study design and participant recruitment on diagnostic accuracy. Methods: This systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. MEDLINE and EMBASE were systematically searched for articles published from January 1, 2015 to January 23, 2021. A bivariate model was used to pool estimates of sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and area under the summary receiver operating curve (SROC) as the main diagnostic measures. Study quality was evaluated using the quality assessment of diagnostic accuracy studies (QUADAS-2) tool. Results: Our search resulted in 16 studies using either non-12-lead ECG or PPG for detecting AF, comprising 3217 participants and 7623 assessments. The pooled estimates of sensitivity, specificity, PLR, NLR, and diagnostic odds ratio for the detection of AF were 89.7% (95% CI 83.2%-93.9%), 95.7% (95% CI 92.0%-97.7%), 20.64 (95% CI 10.10-42.15), 0.11 (95% CI 0.06-0.19), and 224.75 (95% CI 70.10-720.56), respectively, for the automatic interpretation of non-12-lead ECG measurements and 94.7% (95% CI 93.3%-95.8%), 97.6% (95% CI 94.5%-99.0%), 35.51 (95% CI 18.19-69.31), 0.05 (95% CI 0.04-0.07), and 730.79 (95% CI 309.33-1726.49), respectively, for the automatic interpretation of PPG measurements. Conclusions: Both non-12-lead ECG and PPG offered high diagnostic accuracies for AF. Detection employing automatic analysis techniques may serve as a useful preliminary screening tool before administering a gold standard test, which generally requires competent physician analyses. Subgroup analysis indicated variations of sensitivity and specificity between studies that recruited low-risk and high-risk populations, warranting future validity tests in the general population. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42020179937; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=179937 UR - https://mhealth.jmir.org/2021/4/e26167 UR - http://dx.doi.org/10.2196/26167 UR - http://www.ncbi.nlm.nih.gov/pubmed/33835039 ID - info:doi/10.2196/26167 ER - TY - JOUR AU - Beerten, Gabriël Simon AU - Proesmans, Tine AU - Vaes, Bert PY - 2021/4/7 TI - A Heart Rate Monitoring App (FibriCheck) for Atrial Fibrillation in General Practice: Pilot Usability Study JO - JMIR Form Res SP - e24461 VL - 5 IS - 4 KW - atrial fibrillation KW - smartphone app KW - FibriCheck KW - primary care N2 - Background: Atrial fibrillation (AF) is a major risk factor for stroke. The current opportunistic screening procedure consists of pulse palpation and an electrocardiogram when an irregular rhythm is found. Smartphone apps that measure heart rhythm could be useful in increasing the detection of AF in a primary care setting. Objective: We conducted a pilot study with the smartphone app FibriCheck to assess whether the introduction of such an app is feasible. Methods: Four general practices across Flanders provided patient data for the study. Inclusion criteria for participants were aged 65 or older and a CHARGE-AF score of at least 10%. We excluded patients with known AF or a pacemaker. Participants were asked to measure at least twice a day with FibriCheck (for at least 14 days). They were provided the 36-Item Short Form Survey (SF-36) questionnaire both before and after the study, as well as different surveys concerning their user experience and general perception of technology. Results: There were 92 participants (36 women and 56 men). The study population was relatively homogenous concerning risk factors and medication use at baseline. During the study period, 5/86 (6%) participants were found to have AF (6 dropouts). The average study period was 23 days and the average number of measurements per day was 2.1. Patient compliance was variable, but high. On the whole, there were no appreciable changes in quality of life. The overall user experience and satisfaction were very high. Conclusions: FibriCheck is a relatively easy-to-use smartphone app to complement AF screening in primary care. Its implementation in this setting is certainly achievable, and one can expect high rates of patient compliance. Based on these results, a planned cluster randomized trial will be going ahead. Trial Registration: ClinicalTrials.gov NCT03509493; https://clinicaltrials.gov/ct2/show/NCT03509493 UR - https://formative.jmir.org/2021/4/e24461 UR - http://dx.doi.org/10.2196/24461 UR - http://www.ncbi.nlm.nih.gov/pubmed/33825692 ID - info:doi/10.2196/24461 ER - TY - JOUR AU - Tran, Kim-Anh AU - Pollock, William Neal AU - Rhéaume, Caroline AU - Razdan, Sonya Payal AU - Fortier, Félix-Antoine AU - Dutil-Fafard, Lara AU - Morin, Camille AU - Monnot, Pierre-Marie David AU - Huot-Lavoie, Maxime AU - Simard-Sauriol, Philippe AU - Chandavong, Sam AU - Le Pabic, Geneviève AU - LeBlanc, Jean-Philippe AU - Lafond, Daniel AU - Marion, Andréanne AU - Archambault, Michel Patrick PY - 2021/3/29 TI - Evidence Supporting the Management of Medical Conditions During Long-Duration Spaceflight: Protocol for a Scoping Review JO - JMIR Res Protoc SP - e24323 VL - 10 IS - 3 KW - spaceflight KW - astronauts KW - microgravity KW - weightlessness KW - acute coronary syndrome KW - arrhythmia KW - atrial fibrillation KW - eye penetration KW - intraocular foreign body KW - herniated disk KW - nephrolithiasis KW - pulmonary embolism KW - retinal detachment KW - sepsis KW - stroke KW - spaceflight associated neuro-ocular syndrome N2 - Background: Future long-duration space exploration missions, such as traveling to Mars, will create an increase in communication time delays and disruptions and remove the viability of emergency returns to Earth for timely medical treatment. Thus, higher levels of medical autonomy are necessary. Crew selection is proposed as the first line of defense to minimize medical risk for future missions; however, the second proposed line of defense is medical preparedness and crew member autonomy. In an effort to develop a decision support system, the Canadian Space Agency mandated a team of scientists from Thales Research and Technology Canada (Québec, QC) and Université Laval (Québec, QC) to create an evidence-based medical condition database linking mission-critical human conditions with key causal factors, diagnostic and treatment information, and probable outcomes. Objective: To complement this database, we are currently conducting a scoping review to better understand the depth and breadth of evidence about managing medical conditions in space. Methods: This scoping review will adhere to quality standards for scoping reviews, employing Levac, Colquhoun, and O?Brien's 6-stage methodology; the reported results will follow the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping reviews. In stage 1, we identified the research question in collaboration with the Canadian Space Agency (CSA), the main knowledge user. We prioritized 10 medical conditions: (1) acute coronary syndrome, (2) atrial fibrillation, (3) eye penetration, (4) herniated disk, (5) nephrolithiasis, (6) pulmonary embolism, (7) retinal detachment, (8) sepsis, (9) stroke, and (10) spaceflight associated neuro-ocular syndrome. In stage 2, with the help of an information specialist from Cochrane Canada Francophone, papers were identified through searches of the following databases: ARC, Embase, IeeeXplore, Medline Ovid, PsychINFO, and Web of Science. In stage 3, studies will be selected and assessed using a 3-step process and emerging, refined exclusion criteria. In stage 4, the data will be charted in a table based on parameters required by the CSA and developed using Google spreadsheets for shared access. In stage 5, evidence-based descriptive summaries will be produced for each condition, as well as descriptive analyses of collected data. Finally, in stage 6, the findings will be shared with the CSA to guide the completion of this project. Results: This study was planned in December 2018. Stage 1 has been completed. The initial database search strategy with all target conditions combined identified a total of 10,403 citations to review through title and abstract screening and after duplicate removal. We plan to complete stages 2-6 by the beginning of 2021. Conclusions: This scoping review will map the literature on the management of 10 priority medical conditions in space. It will also enable us to identify knowledge gaps that must be addressed in future research, ensuring successful and medically safe future missions as humankind embarks upon new frontiers of space exploration. International Registered Report Identifier (IRRID): DERR1-10.2196/24323 UR - https://www.researchprotocols.org/2021/3/e24323 UR - http://dx.doi.org/10.2196/24323 UR - http://www.ncbi.nlm.nih.gov/pubmed/33779571 ID - info:doi/10.2196/24323 ER - TY - JOUR AU - Särnholm, Josefin AU - Skúladóttir, Helga AU - Rück, Christian AU - Klavebäck, Sofia AU - Ólafsdóttir, Eva AU - Pedersen, S. Susanne AU - Braunschweig, Frieder AU - Ljótsson, Brjánn PY - 2021/3/2 TI - Internet-Delivered Exposure-Based Therapy for Symptom Preoccupation in Atrial Fibrillation: Uncontrolled Pilot Trial JO - JMIR Cardio SP - e24524 VL - 5 IS - 1 KW - atrial fibrillation KW - arrhythmia KW - cognitive behavior therapy KW - quality of life KW - anxiety N2 - Background: Atrial fibrillation (AF) is the most common cardiac arrhythmia in the adult population. AF is associated with a poor quality of life (QoL) and, in many patients, current medical treatments are inadequate in alleviating AF symptoms (eg, palpitations). Patients often present with symptom preoccupation in terms of symptom fear, avoidance, and control behaviors. Internet-delivered cognitive behavior therapy is effective for treating other somatic disorders but has never been evaluated in patients with AF. Objective: The aim of this study is to evaluate the efficacy and feasibility of AF-specific internet-delivered cognitive behavior therapy. Methods: We conducted an uncontrolled pilot study in which 19 patients with symptomatic paroxysmal AF underwent internet-delivered cognitive behavior therapy. Participants completed self-assessments at pretreatment, posttreatment, and at a 6-month follow-up along with handheld electrocardiogram measurements with symptom registration. The treatment lasted 10 weeks and included exposure to physical sensations, reduction in avoidance behavior, and behavioral activation. Results: We observed large within-group improvements in the primary outcome, AF-specific QoL (Cohen d=0.80; P<.001), and in symptom preoccupation (Cohen d=1.24; P<.001) at posttreatment; the results were maintained at the 6-month follow-up. Treatment satisfaction and adherence rates were also high. We observed an increased AF burden, measured by electrocardiogram, at the 6-month follow-up, but a significant decrease was observed in the overestimation of AF symptoms at posttreatment and 6-month follow-up. Exploratory mediation analysis showed that a reduction in symptom preoccupation mediated the effects of internet-delivered cognitive behavior therapy on AF-specific QoL. Conclusions: This study presents preliminary evidence for the potential efficacy and feasibility of a novel approach in treating patients with symptomatic AF with internet-delivered cognitive behavior therapy. Trial Registration: ClinicalTrials.gov NCT02694276; https://clinicaltrials.gov/ct2/show/NCT02694276 UR - https://cardio.jmir.org/2021/1/e24524 UR - http://dx.doi.org/10.2196/24524 UR - http://www.ncbi.nlm.nih.gov/pubmed/33650972 ID - info:doi/10.2196/24524 ER - TY - JOUR AU - Kapoor, Alok AU - Andrade, Andreza AU - Hayes, Anna AU - Mazor, Kathleen AU - Possidente, Carl AU - Nolen, Kim AU - Hegeman-Dingle, Rozelle AU - McManus, David PY - 2021/2/24 TI - Usability, Perceived Usefulness, and Shared Decision-Making Features of the AFib 2gether Mobile App: Protocol for a Single-Arm Intervention Study JO - JMIR Res Protoc SP - e21986 VL - 10 IS - 2 KW - shared decision making KW - mobile health KW - stroke risk KW - anticoagulation risk KW - anticoagulation education KW - atrial fibrillation KW - anticoagulation therapy KW - anticoagulation KW - atrial flutter KW - mobile phone N2 - Background: The Centers for Disease Control and Prevention has estimated that atrial fibrillation (AF) affects between 2.7 million and 6.1 million people in the United States. Those who have AF tend to have a much higher stroke risk than others. Although most individuals with AF benefit from anticoagulation (AC) therapy, a significant majority are hesitant to start it. To add, providers often struggle in helping patients negotiate the decision to start AC therapy. To assist in the communication between patients and providers regarding preferences and knowledge about AC therapy, different strategies are being used to try and solve this problem. In this research study, we will have patients and providers utilize the AFib 2gether app with hopes that it will create a platform for shared decision making regarding the prevention of stroke in patients with AF receiving AC therapy. Objective: The aim of our study is to measure several outcomes related to encounters between patients and their cardiology providers where AFib 2gether is used. These outcomes include usability and perceived usefulness of the app from the perspective of patients and providers. In addition, we will assess the extent and nature of shared decision making. Methods: Eligible patients and providers will evaluate the AFib 2gether mobile app for usability and perceived usefulness in facilitating shared decision making regarding understanding the patient?s risk of stroke and whether or not to start AC therapy. Both patients and providers will review the app and complete multiple questionnaires about the usability and perceived usefulness of the mobile app in a clinical setting. We will also audio-record a subset of encounters to assess for evidence of shared decision making. Results: Enrollment in the AFib 2gether shared decision-making study is still ongoing for both patients and providers. The first participant enrolled on November 22, 2019. Analysis and publishing of results are expected to be completed in spring 2021. Conclusions: The AFib 2gether app emerged from a desire to increase the ability of patients and providers to engage in shared decision making around understanding the risk of stroke and AC therapy. We anticipate that the AFib 2gether mobile app will facilitate patient discussion with their cardiologist and other providers. Additionally, we hope the study will help us identify barriers that providers face when placing patients on AC therapy. We aim to demonstrate the usability and perceived usefulness of the app with a future goal of testing the value of our approach in a larger sample of patients and providers at multiple medical centers across the country. Trial Registration: ClinicalTrials.gov NCT04118270; https://clinicaltrials.gov/ct2/show/NCT04118270 International Registered Report Identifier (IRRID): DERR1-10.2196/21986 UR - https://www.researchprotocols.org/2021/2/e21986 UR - http://dx.doi.org/10.2196/21986 UR - http://www.ncbi.nlm.nih.gov/pubmed/33625361 ID - info:doi/10.2196/21986 ER - TY - JOUR AU - Zhang, Yaqi AU - Han, Yongxia AU - Gao, Peng AU - Mo, Yifu AU - Hao, Shiying AU - Huang, Jia AU - Ye, Fangfan AU - Li, Zhen AU - Zheng, Le AU - Yao, Xiaoming AU - Li, Xiaodong AU - Wang, Xiaofang AU - Huang, Chao-Jung AU - Jin, Bo AU - Zhang, Yani AU - Yang, Gabriel AU - Alfreds, T. Shaun AU - Kanov, Laura AU - Sylvester, G. Karl AU - Widen, Eric AU - Li, Licheng AU - Ling, Xuefeng PY - 2021/2/17 TI - Electronic Health Record?Based Prediction of 1-Year Risk of Incident Cardiac Dysrhythmia: Prospective Case-Finding Algorithm Development and Validation Study JO - JMIR Med Inform SP - e23606 VL - 9 IS - 2 KW - cardiac dysrhythmia KW - prospective case finding KW - risk stratification KW - electronic health records N2 - Background: Cardiac dysrhythmia is currently an extremely common disease. Severe arrhythmias often cause a series of complications, including congestive heart failure, fainting or syncope, stroke, and sudden death. Objective: The aim of this study was to predict incident arrhythmia prospectively within a 1-year period to provide early warning of impending arrhythmia. Methods: Retrospective (1,033,856 individuals enrolled between October 1, 2016, and October 1, 2017) and prospective (1,040,767 individuals enrolled between October 1, 2017, and October 1, 2018) cohorts were constructed from integrated electronic health records in Maine, United States. An ensemble learning workflow was built through multiple machine learning algorithms. Differentiating features, including acute and chronic diseases, procedures, health status, laboratory tests, prescriptions, clinical utilization indicators, and socioeconomic determinants, were compiled for incident arrhythmia assessment. The predictive model was retrospectively trained and calibrated using an isotonic regression method and was prospectively validated. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). Results: The cardiac dysrhythmia case-finding algorithm (retrospective: AUROC 0.854; prospective: AUROC 0.827) stratified the population into 5 risk groups: 53.35% (555,233/1,040,767), 44.83% (466,594/1,040,767), 1.76% (18,290/1,040,767), 0.06% (623/1,040,767), and 0.003% (27/1,040,767) were in the very low-risk, low-risk, medium-risk, high-risk, and very high-risk groups, respectively; 51.85% (14/27) patients in the very high-risk subgroup were confirmed to have incident cardiac dysrhythmia within the subsequent 1 year. Conclusions: Our case-finding algorithm is promising for prospectively predicting 1-year incident cardiac dysrhythmias in a general population, and we believe that our case-finding algorithm can serve as an early warning system to allow statewide population-level screening and surveillance to improve cardiac dysrhythmia care. UR - http://medinform.jmir.org/2021/2/e23606/ UR - http://dx.doi.org/10.2196/23606 UR - http://www.ncbi.nlm.nih.gov/pubmed/33595452 ID - info:doi/10.2196/23606 ER - TY - JOUR AU - Walkey, J. Allan AU - Bashar, K. Syed AU - Hossain, Billal Md AU - Ding, Eric AU - Albuquerque, Daniella AU - Winter, Michael AU - Chon, H. Ki AU - McManus, D. David PY - 2021/2/15 TI - Development and Validation of an Automated Algorithm to Detect Atrial Fibrillation Within Stored Intensive Care Unit Continuous Electrocardiographic Data: Observational Study JO - JMIR Cardio SP - e18840 VL - 5 IS - 1 KW - atrial fibrillation KW - sepsis KW - intensive care unit KW - big data KW - data science N2 - Background: Atrial fibrillation (AF) is the most common arrhythmia during critical illness, representing a sepsis-defining cardiac dysfunction associated with adverse outcomes. Large burdens of premature beats and noisy signal during sepsis may pose unique challenges to automated AF detection. Objective: The objective of this study is to develop and validate an automated algorithm to accurately identify AF within electronic health care data among critically ill patients with sepsis. Methods: This is a retrospective cohort study of patients hospitalized with sepsis identified from Medical Information Mart for Intensive Care (MIMIC III) electronic health data with linked electrocardiographic (ECG) telemetry waveforms. Within 3 separate cohorts of 50 patients, we iteratively developed and validated an automated algorithm that identifies ECG signals, removes noise, and identifies irregular rhythm and premature beats in order to identify AF. We compared the automated algorithm to current methods of AF identification in large databases, including ICD-9 (International Classification of Diseases, 9th edition) codes and hourly nurse annotation of heart rhythm. Methods of AF identification were tested against gold-standard manual ECG review. Results: AF detection algorithms that did not differentiate AF from premature atrial and ventricular beats performed modestly, with 76% (95% CI 61%-87%) accuracy. Performance improved (P=.02) with the addition of premature beat detection (validation set accuracy: 94% [95% CI 83%-99%]). Median time between automated and manual detection of AF onset was 30 minutes (25th-75th percentile 0-208 minutes). The accuracy of ICD-9 codes (68%; P=.002 vs automated algorithm) and nurse charting (80%; P=.02 vs algorithm) was lower than that of the automated algorithm. Conclusions: An automated algorithm using telemetry ECG data can feasibly and accurately detect AF among critically ill patients with sepsis, and represents an improvement in AF detection within large databases. UR - http://cardio.jmir.org/2021/1/e18840/ UR - http://dx.doi.org/10.2196/18840 UR - http://www.ncbi.nlm.nih.gov/pubmed/33587041 ID - info:doi/10.2196/18840 ER - TY - JOUR AU - Huynh, Pauline AU - Shan, Rongzi AU - Osuji, Ngozi AU - Ding, Jie AU - Isakadze, Nino AU - Marvel, A. Francoise AU - Sharma, Garima AU - Martin, S. Seth PY - 2021/2/8 TI - Heart Rate Measurements in Patients with Obstructive Sleep Apnea and Atrial Fibrillation: Prospective Pilot Study Assessing Apple Watch?s Agreement With Telemetry Data JO - JMIR Cardio SP - e18050 VL - 5 IS - 1 KW - mHealth KW - wearables KW - atrial fibrillation KW - obstructive sleep apnea KW - digital health N2 - Background: Patients with obstructive sleep apnea (OSA) are at a higher risk for atrial fibrillation (AF). Consumer wearable heart rate (HR) sensors may be a means for passive HR monitoring in patients with AF. Objective: The aim of this study was to assess the Apple Watch?s agreement with telemetry in measuring HR in patients with OSA in AF. Methods: Patients with OSA in AF were prospectively recruited prior to cardioversion/ablation procedures. HR was sampled every 10 seconds for 60 seconds using telemetry and an Apple Watch concomitantly. Agreement of Apple Watch with telemetry, which is the current gold-standard device for measuring HR, was assessed using mixed effects limits agreement and Lin?s concordance correlation coefficient. Results: A total of 20 patients (mean 66 [SD 6.5] years, 85% [n=17] male) participated in this study, yielding 134 HR observations per device. Modified Bland?Altman plot revealed that the variability of the paired difference of the Apple Watch compared with telemetry increased as the magnitude of HR measurements increased. The Apple Watch produced regression-based 95% limits of agreement of 27.8 ? 0.3 × average HR ? 15.0 to 27.8 ? 0.3 × average HR + 15.0 beats per minute (bpm) with a mean bias of 27.8 ? 0.33 × average HR bpm. Lin?s concordance correlation coefficient was 0.88 (95% CI 0.85-0.91), suggesting acceptable agreement between the Apple Watch and telemetry. Conclusions: In patients with OSA in AF, the Apple Watch provided acceptable agreement with HR measurements by telemetry. Further studies with larger sample populations and wider range of HR are needed to confirm these findings. UR - http://cardio.jmir.org/2021/1/e18050/ UR - http://dx.doi.org/10.2196/18050 UR - http://www.ncbi.nlm.nih.gov/pubmed/33555260 ID - info:doi/10.2196/18050 ER - TY - JOUR AU - Goni, Leticia AU - de la O, Víctor AU - Barrio-López, Teresa M. AU - Ramos, Pablo AU - Tercedor, Luis AU - Ibañez-Criado, Luis Jose AU - Castellanos, Eduardo AU - Ibañez Criado, Alicia AU - Macias Ruiz, Rosa AU - García-Bolao, Ignacio AU - Almendral, Jesus AU - Martínez-González, Ángel Miguel AU - Ruiz-Canela, Miguel PY - 2020/12/7 TI - A Remote Nutritional Intervention to Change the Dietary Habits of Patients Undergoing Ablation of Atrial Fibrillation: Randomized Controlled Trial JO - J Med Internet Res SP - e21436 VL - 22 IS - 12 KW - atrial fibrillation KW - secondary prevention KW - remote intervention KW - Mediterranean diet KW - olive oil N2 - Background: The Prevention With Mediterranean Diet (PREDIMED) trial supported the effectiveness of a nutritional intervention conducted by a dietitian to prevent cardiovascular disease. However, the effect of a remote intervention to follow the Mediterranean diet has been less explored. Objective: This study aims to assess the effectiveness of a remotely provided Mediterranean diet?based nutritional intervention in obtaining favorable dietary changes in the context of a secondary prevention trial of atrial fibrillation (AF). Methods: The PREvention of recurrent arrhythmias with Mediterranean diet (PREDIMAR) study is a 2-year multicenter, randomized, controlled, single-blinded trial to assess the effect of the Mediterranean diet enriched with extra virgin olive oil (EVOO) on the prevention of atrial tachyarrhythmia recurrence after catheter ablation. Participants in sinus rhythm after ablation were randomly assigned to an intervention group (Mediterranean diet enriched with EVOO) or a control group (usual clinical care). The remote nutritional intervention included phone contacts (1 per 3 months) and web-based interventions with provision of dietary recommendations, and participants had access to a web page, a mobile app, and printed resources. The information is divided into 6 areas: Recommended foods, Menus, News and Online resources, Practical tips, Mediterranean diet classroom, and Your personal experience. At baseline and at 1-year and 2-year follow-up, the 14-item Mediterranean Diet Adherence Screener (MEDAS) questionnaire and a semiquantitative food frequency questionnaire were collected by a dietitian by phone. Results: A total of 720 subjects were randomized (365 to the intervention group, 355 to the control group). Up to September 2020, 560 subjects completed the first year (560/574, retention rate 95.6%) and 304 completed the second year (304/322, retention rate 94.4%) of the intervention. After 24 months of follow-up, increased adherence to the Mediterranean diet was observed in both groups, but the improvement was significantly higher in the intervention group than in the control group (net between-group difference: 1.8 points in the MEDAS questionnaire (95% CI 1.4-2.2; P<.001). Compared with the control group, the Mediterranean diet intervention group showed a significant increase in the consumption of fruits (P<.001), olive oil (P<.001), whole grain cereals (P=.002), pulses (P<.001), nuts (P<.001), white fish (P<.001), fatty fish (P<.001), and white meat (P=.007), and a significant reduction in refined cereals (P<.001), red and processed meat (P<.001), and sweets (P<.001) at 2 years of intervention. In terms of nutrients, the intervention group significantly increased their intake of omega-3 (P<.001) and fiber (P<.001), and they decreased their intake of carbohydrates (P=.02) and saturated fatty acids (P<.001) compared with the control group. Conclusions: The remote nutritional intervention using a website and phone calls seems to be effective in increasing adherence to the Mediterranean diet pattern among AF patients treated with catheter ablation. Trial Registration: ClinicalTrials.gov NCT03053843; https://www.clinicaltrials.gov/ct2/show/NCT03053843 UR - http://www.jmir.org/2020/12/e21436/ UR - http://dx.doi.org/10.2196/21436 UR - http://www.ncbi.nlm.nih.gov/pubmed/33284131 ID - info:doi/10.2196/21436 ER - TY - JOUR AU - de Lusignan, Simon AU - Hobbs, Richard F. D. AU - Liyanage, Harshana AU - Ferreira, Filipa AU - Tripathy, Manasa AU - Munro, Neil AU - Feher, Michael AU - Joy, Mark PY - 2020/11/9 TI - Improving the Management of Atrial Fibrillation in General Practice: Protocol for a Mixed Methods Study JO - JMIR Res Protoc SP - e21259 VL - 9 IS - 11 KW - atrial fibrillation KW - medical record systems, computerized KW - general practice KW - cross-sectional studies KW - qualitative research N2 - Background: Atrial fibrillation (AF) is one of the commonest arrhythmias observed in general practice. The thromboembolic complications of AF include transient ischemic attack, stroke, and pulmonary embolism. Early recognition of AF can lead to early intervention with managing the risks of these complications. Objective: The primary aim of this study is to investigate if patients are managed in general practice according to current national guidelines. In addition, the study will evaluate the impact of direct oral anticoagulant use with respect to AF complications in a real-world dataset. The secondary aims of the study are to develop a dashboard that will allow monitoring the management of AF in general practice and evaluate the usability of the dashboard. Methods: The study was conducted in 2 phases. The initial phase was a quantitative analysis of routinely collected primary care data from the Oxford Royal College of General Practitioners Research and Surveillance Center (RCGP RSC) sentinel network database. AF cases from 2009 to 2019 were identified. The study investigated the impact of the use of anticoagulants on complications of AF over this time period. We used this dataset to examine how AF was managed in primary care during the last decade. The second phase involved development of an online dashboard for monitoring management of AF in general practice. We conducted a usability evaluation for the dashboard to identify usability issues and performed enhancements to improve usability. Results: We received funding for both phases in January 2019 and received approval from the RCGP RSC research committee in March 2019. We completed data extraction for phase 1 in May 2019 and completed analysis in December 2019. We completed building the AF dashboard in May 2019. We started recruiting participants for phase 1 in May 2019 and concluded data collection in July 2019. We completed data analysis for phase 2 in October 2019. The results are expected to be published in the second half of 2020. As of October 2020, the publications reporting the results are under review. Conclusions: Results of this study will provide an insight into the current trends in management of AF using real-world data from the Oxford RCGP RSC database. We anticipate that the outcomes of this study will be used to guide the development and implementation of an audit-based intervention tool to assist practitioners in identifying and managing AF in primary care. International Registered Report Identifier (IRRID): RR1-10.2196/21259 UR - https://www.researchprotocols.org/2020/11/e21259 UR - http://dx.doi.org/10.2196/21259 UR - http://www.ncbi.nlm.nih.gov/pubmed/33164903 ID - info:doi/10.2196/21259 ER - TY - JOUR AU - Jiang, Jiang AU - Gu, Xiang AU - Cheng, Chen-Di AU - Li, Hong-Xiao AU - Sun, Xiao-Lin AU - Duan, Ruo-Yu AU - Zhu, Ye AU - Sun, Lei AU - Chen, Fu-Kun AU - Bao, Zheng-Yu AU - Zhang, Yi AU - Shen, Jian-Hua PY - 2020/10/21 TI - The Hospital-Community-Family?Based Telemedicine (HCFT-AF) Program for Integrative Management of Patients With Atrial Fibrillation: Pilot Feasibility Study JO - JMIR Mhealth Uhealth SP - e22137 VL - 8 IS - 10 KW - atrial fibrillation KW - integrative management KW - telemedicine KW - self-management KW - feasibility study N2 - Background: The potential effectiveness of integrated management in further improving the prognosis of patients with atrial fibrillation has been demonstrated; however, the best strategy for implementation remains to be discovered. Objective: The aim of this study was to ascertain the feasibility of implementing integrated atrial fibrillation care via the Hospital-Community-Family?Based Telemedicine (HCFT-AF) program. Methods: In this single-arm, pre-post design pilot study, a multidisciplinary teamwork, supported by efficient infrastructures, provided patients with integrated atrial fibrillation care following the Atrial fibrillation Better Care (ABC) pathway. Eligible patients were continuously recruited and followed up for at least 4 months. The patients? drug adherence, and atrial fibrillation?relevant lifestyles and behaviors were assessed at baseline and at 4 months. The acceptability, feasibility, and usability of the HCFT-AF technology devices and engagement with the HCFT-AF program were assessed at 4 months. Results: A total of 73 patients (mean age, 68.42 years; 52% male) were enrolled in November 2019 with a median follow up of 132 days (IQR 125?138 days). The patients? drug adherence significantly improved after the 4-month intervention (P<.001). The vast majority (94%, 64/68) of indicated patients received anticoagulant therapy at 4 months, and none of them received antiplatelet therapy unless there was an additional indication. The atrial fibrillation?relevant lifestyles and behaviors ameliorated to varying degrees at the end of the study. In general, the majority of patients provided good feedback on the HCFT-AF intervention. More than three-quarters (76%, 54/71) of patients used the software or website more than once a week and accomplished clinic visits as scheduled. Conclusions: The atrial fibrillation?integrated care model described in this study is associated with improved drug adherence, standardized therapy rate, and lifestyles of patients, which highlights the possibility to better deliver integrated atrial fibrillation management. Trial Registration: Clinicaltrials.gov NCT04127799; https://clinicaltrials.gov/ct2/show/NCT04127799 UR - http://mhealth.jmir.org/2020/10/e22137/ UR - http://dx.doi.org/10.2196/22137 UR - http://www.ncbi.nlm.nih.gov/pubmed/33084588 ID - info:doi/10.2196/22137 ER - TY - JOUR AU - Giebel, D. Godwin PY - 2020/10/6 TI - Use of mHealth Devices to Screen for Atrial Fibrillation: Cost-Effectiveness Analysis JO - JMIR Mhealth Uhealth SP - e20496 VL - 8 IS - 10 KW - mHealth KW - atrial fibrillation KW - screening devices KW - strokes KW - cost-effectiveness KW - photoplethysmography N2 - Background: With an estimated prevalence of around 3% and an about 2.5-fold increased risk of stroke, atrial fibrillation (AF) is a serious threat for patients and a high economic burden for health care systems all over the world. Patients with AF could benefit from screening through mobile health (mHealth) devices. Thus, an early diagnosis is possible with mHealth devices, and the risk for stroke can be markedly reduced by using anticoagulation therapy. Objective: The aim of this work was to assess the cost-effectiveness of algorithm-based screening for AF with the aid of photoplethysmography wrist-worn mHealth devices. Even if prevented strokes and prevented deaths from stroke are the most relevant patient outcomes, direct costs were defined as the primary outcome. Methods: A Monte Carlo simulation was conducted based on a developed state-transition model; 30,000 patients for each CHA2DS2-VASc (Congestive heart failure, Hypertension, Age?75 years, Diabetes mellitus, Stroke, Vascular disease, Age 65-74 years, Sex category [female]) score from 1 to 9 were simulated. The first simulation served to estimate the economic burden of AF without the use of mHealth devices. The second simulation served to simulate the economic burden of AF with the use of mHealth devices. Afterwards, the groups were compared in terms of costs, prevented strokes, and deaths from strokes. Results: The CHA2DS2-VASc score as well as the electrocardiography (ECG) confirmation rate had the biggest impact on costs as well as number of strokes. The higher the risk score, the lower were the costs per prevented stroke. Higher ECG confirmation rates intensified this effect. The effect was not seen in groups with lower risk scores. Over 10 years, the use of mHealth (assuming a 75% ECG confirmation rate) resulted in additional costs (?1=US $1.12) of ?441, ?567, ?536, ?520, ?606, ?625, ?623, ?692, and ?847 per patient for a CHA2DS2-VASc score of 1 to 9, respectively. The number of prevented strokes tended to be higher in groups with high risk for stroke. Higher ECG confirmation rates led to higher numbers of prevented strokes. The use of mHealth (assuming a 75% ECG confirmation rate) resulted in 25 (7), ?68 (?54), 98 (?5), 266 (182), 346 (271), 642 (440), 722 (599), 1111 (815), and 1116 (928) prevented strokes (fatal) for CHA2DS2-VASc score of 1 to 9, respectively. Higher device accuracy in terms of sensitivity led to even more prevented fatal strokes. Conclusions: The use of mHealth devices to screen for AF leads to increased costs but also a reduction in the incidence of stroke. In particular, in patients with high CHA2DS2-VASc scores, the risk for stroke and death from stroke can be markedly reduced. UR - http://mhealth.jmir.org/2020/10/e20496/ UR - http://dx.doi.org/10.2196/20496 UR - http://www.ncbi.nlm.nih.gov/pubmed/33021489 ID - info:doi/10.2196/20496 ER - TY - JOUR AU - Cher, Piang Boon AU - Kembhavi, Gayatri AU - Toh, Yee Kai AU - Audimulam, Jananie AU - Chia, Aloysius Wei-Yan AU - Vrijhoef, JM Hubertus AU - Lim, Wei Yee AU - Lim, Wei Toon PY - 2020/9/17 TI - Understanding the Attitudes of Clinicians and Patients Toward a Self-Management eHealth Tool for Atrial Fibrillation: Qualitative Study JO - JMIR Hum Factors SP - e15492 VL - 7 IS - 3 KW - mHealth KW - qualitative research KW - atrial fibrillation KW - self-management KW - chronic disease KW - mobile phone N2 - Background: Atrial fibrillation (AF) is the most common heart rhythm disorder and poses a growing disease burden worldwide because of an aging population. A multidisciplinary approach with an emphasis on patient education and self-management has been demonstrated to improve outcomes for AF through the engagement of patients in their own care. Although electronic tools (e-tools) such as apps have been proposed to provide patient education and facilitate self-management, there have been few studies to guide the development of these tools for patients with AF. Objective: This study aims to explore the perceptions of patients and health care providers (HCPs) and their attitudes toward the use of e-tools for the self-management of AF. It also seeks to elicit the factors that contribute to these attitudes. Methods: Semistructured qualitative interviews with HCPs and patients were conducted to understand the interpretations and expectations of an e-tool that would be used for the self-management of AF. Interview data were analyzed using an exploratory thematic analysis approach to uncover emergent themes and infer ideas of preferred features in a device. A modified technology acceptance model was developed as a framework to help interpret these findings. Data from the HCPs and patients were compared and contrasted. Results: Both patients and HCPs thought that an e-tool would be useful in the self-management of AF. Although both groups favored educational content and monitoring of blood pressure, patients expressed more passivity toward self-care and an ambivalence toward the use of technology to monitor their medical condition. This appears to be related to factors such as a patient?s age, social support, and their attitudes toward technology. Instead, they favored using the app to contact their HCPs. Conclusions: This study provides insights into significant differences in the attitudes of patients and HCPs toward the use of e-tools for self-care against their priorities. Understanding patients? motivations and their needs are key to ensuring higher acceptance of such tools. UR - http://humanfactors.jmir.org/2020/3/e15492/ UR - http://dx.doi.org/10.2196/15492 UR - http://www.ncbi.nlm.nih.gov/pubmed/32940611 ID - info:doi/10.2196/15492 ER - TY - JOUR AU - Guhl, Emily AU - Althouse, D. Andrew AU - Pusateri, M. Alexandra AU - Kimani, Everlyne AU - Paasche-Orlow, K. Michael AU - Bickmore, W. Timothy AU - Magnani, W. Jared PY - 2020/9/4 TI - The Atrial Fibrillation Health Literacy Information Technology Trial: Pilot Trial of a Mobile Health App for Atrial Fibrillation JO - JMIR Cardio SP - e17162 VL - 4 IS - 1 KW - atrial fibrillation KW - health-related quality of life KW - medication adherence KW - health literacy KW - mobile phone N2 - Background: Atrial fibrillation (AF) is a common arrhythmia that adversely affects health-related quality of life (HRQoL). We conducted a pilot trial of individuals with AF using a smartphone to provide a relational agent as well as rhythm monitoring. We employed our pilot to measure acceptability and adherence and to assess its effectiveness in improving HRQoL and adherence. Objective: This study aims to measure acceptability and adherence and to assess its effectiveness to improve HRQoL and adherence. Methods: Participants were recruited from ambulatory clinics and randomized to a 30-day intervention or usual care. We collected baseline characteristics and conducted baseline and 30-day assessments of HRQoL using the Atrial Fibrillation Effect on Quality of Life (AFEQT) measure and self-reported adherence to anticoagulation. The intervention consisted of a smartphone-based relational agent, which simulates face-to-face counseling and delivered content on AF education, adherence, and symptom monitoring with prompted rhythm monitoring. We compared differences in AFEQT and adherence at 30 days, adjusted for baseline values. We quantified participants? use and acceptability of the intervention. Results: A total of 120 participants were recruited and randomized (59 to control and 61 to intervention) to the pilot trial (mean age 72.1 years, SD 9.10; 62/120, 51.7% women). The control group had a 95% follow-up, and the intervention group had a 93% follow-up. The intervention group demonstrated significantly higher improvement in total AFEQT scores (adjusted mean difference 4.5; 95% CI 0.6-8.3; P=.03) and in daily activity (adjusted mean difference 7.1; 95% CI 1.8-12.4; P=.009) compared with the control between baseline and 30 days. The intervention group showed significantly improved self-reported adherence to anticoagulation therapy at 30 days (intervention 3.5%; control 23.2%; adjusted difference 16.6%; 95% CI 2.8%-30.4%; P<.001). Qualitative assessments of acceptability identified that participants found the relational agent useful, informative, and trustworthy. Conclusions: Individuals randomized to a 30-day smartphone intervention with a relational agent and rhythm monitoring showed significant improvement in HRQoL and adherence. Participants had favorable acceptability of the intervention with both objective use and qualitative assessments of acceptability. UR - http://cardio.jmir.org/2020/1/e17162/ UR - http://dx.doi.org/10.2196/17162 UR - http://www.ncbi.nlm.nih.gov/pubmed/32886070 ID - info:doi/10.2196/17162 ER - TY - JOUR AU - Andersen, Osman Tariq AU - Langstrup, Henriette AU - Lomborg, Stine PY - 2020/7/20 TI - Experiences With Wearable Activity Data During Self-Care by Chronic Heart Patients: Qualitative Study JO - J Med Internet Res SP - e15873 VL - 22 IS - 7 KW - consumer health information KW - wearable electronic devices KW - self-care KW - chronic illness KW - patient experiences N2 - Background: Most commercial activity trackers are developed as consumer devices and not as clinical devices. The aim is to monitor and motivate sport activities, healthy living, and similar wellness purposes, and the devices are not designed to support care management in a clinical context. There are great expectations for using wearable sensor devices in health care settings, and the separate realms of wellness tracking and disease self-monitoring are increasingly becoming blurred. However, patients? experiences with activity tracking technologies designed for use outside the clinical context have received little academic attention. Objective: This study aimed to contribute to understanding how patients with a chronic disease experience activity data from consumer self-tracking devices related to self-care and their chronic illness. Our research question was: ?How do patients with heart disease experience activity data in relation to self-care and chronic illness?? Methods: We conducted a qualitative interview study with patients with chronic heart disease (n=27) who had an implanted cardioverter-defibrillator. Patients were invited to wear a FitBit Alta HR wearable activity tracker for 3-12 months and provide their perspectives on their experiences with step, sleep, and heart rate data. The average age was 57.2 years (25 men and 2 women), and patients used the tracker for 4-49 weeks (mean 26.1 weeks). Semistructured interviews (n=66) were conducted with patients 2?3 times and were analyzed iteratively in workshops using thematic analysis and abductive reasoning logic. Results: Of the 27 patients, 18 related the heart rate, sleep, and step count data directly to their heart disease. Wearable activity trackers actualized patients? experiences across 3 dimensions with a spectrum of contrasting experiences: (1) knowing, which spanned gaining insight and evoking doubts; (2) feeling, which spanned being reassured and becoming anxious; and (3) evaluating, which spanned promoting improvements and exposing failure. Conclusions: Patients? experiences could reside more on one end of the spectrum, could reside across all 3 dimensions, or could combine contrasting positions and even move across the spectrum over time. Activity data from wearable devices may be a resource for self-care; however, the data may simultaneously constrain and create uncertainty, fear, and anxiety. By showing how patients experience self-tracking data across dimensions of knowing, feeling, and evaluating, we point toward the richness and complexity of these data experiences in the context of chronic illness and self-care. UR - https://www.jmir.org/2020/7/e15873 UR - http://dx.doi.org/10.2196/15873 UR - http://www.ncbi.nlm.nih.gov/pubmed/32706663 ID - info:doi/10.2196/15873 ER - TY - JOUR AU - Ferguson, Caleb AU - Inglis, C. Sally AU - Breen, P. Paul AU - Gargiulo, D. Gaetano AU - Byiers, Victoria AU - Macdonald, S. Peter AU - Hickman, D. Louise PY - 2020/6/18 TI - Clinician Perspectives on the Design and Application of Wearable Cardiac Technologies for Older Adults: Qualitative Study JO - JMIR Aging SP - e17299 VL - 3 IS - 1 KW - technology KW - arrhythmia KW - monitoring KW - older people KW - cardiology KW - qualitative KW - wearable N2 - 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. UR - http://aging.jmir.org/2020/1/e17299/ UR - http://dx.doi.org/10.2196/17299 UR - http://www.ncbi.nlm.nih.gov/pubmed/32554377 ID - info:doi/10.2196/17299 ER - TY - JOUR AU - Kwon, Soonil AU - Hong, Joonki AU - Choi, Eue-Keun AU - Lee, Byunghwan AU - Baik, Changhyun AU - Lee, Euijae AU - Jeong, Eui-Rim AU - Koo, Bon-Kwon AU - Oh, Seil AU - Yi, Yung PY - 2020/5/21 TI - Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study JO - J Med Internet Res SP - e16443 VL - 22 IS - 5 KW - atrial fibrillation KW - deep learning KW - diagnosis KW - photoplethysmography KW - wearable electronic devices N2 - 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 UR - http://www.jmir.org/2020/5/e16443/ UR - http://dx.doi.org/10.2196/16443 UR - http://www.ncbi.nlm.nih.gov/pubmed/32348254 ID - info:doi/10.2196/16443 ER - TY - JOUR AU - Biersteker, E. Tom AU - Boogers, J. Mark AU - de Lind van Wijngaarden, AF Robert AU - Groenwold, HH Rolf AU - Trines, A. Serge AU - van Alem, P. Anouk AU - Kirchhof, JHJ Charles AU - van Hof, Nicolette AU - Klautz, JM Robert AU - Schalij, J. Martin AU - Treskes, W. Roderick PY - 2020/4/21 TI - Use of Smart Technology for the Early Diagnosis of Complications After Cardiac Surgery: The Box 2.0 Study Protocol JO - JMIR Res Protoc SP - e16326 VL - 9 IS - 4 KW - mHealth KW - cardiac surgery KW - atrial fibrillation KW - postoperative care KW - ambulatory electrocardiography N2 - Background: Atrial fibrillation (AF), sternal wound infection, and cardiac decompensation are complications that can occur after cardiac surgery. Early detection of these complications is clinically relevant, as early treatment is associated with better clinical outcomes. Remote monitoring with the use of a smartphone (mobile health [mHealth]) might improve the early detection of complications after cardiac surgery. Objective: The primary aim of this study is to compare the detection rate of AF diagnosed with an mHealth solution to the detection rate of AF diagnosed with standard care. Secondary objectives include detection of sternal wound infection and cardiac decompensation, as well as assessment of quality of life, patient satisfaction, and cost-effectiveness. Methods: The Box 2.0 is a study with a prospective intervention group and a historical control group for comparison. Patients undergoing cardiac surgery at Leiden University Medical Center are eligible for enrollment. In this study, 365 historical patients will be used as controls and 365 other participants will be asked to receive either The Box 2.0 intervention consisting of seven home measurement devices along with a video consultation 2 weeks after discharge or standard cardiac care for 3 months. Patient information will be analyzed according to the intention-to-treat principle. The Box 2.0 devices include a blood pressure monitor, thermometer, weight scale, step count watch, single-lead electrocardiogram (ECG) device, 12-lead ECG device, and pulse oximeter. Results: The study started in November 2018. The primary outcome of this study is the detection rate of AF in both groups. Quality of life is measured with the five-level EuroQol five-dimension (EQ-5D-5L) questionnaire. Cost-effectiveness is calculated from a society perspective using prices from Dutch costing guidelines and quality of life data from the study. In the historical cohort, 93.9% (336/358) completed the EQ-5D-5L and patient satisfaction questionnaires 3 months after cardiac surgery. Conclusions: The rationale and design of a study to investigate mHealth devices in postoperative cardiac surgery patients are presented. The first results are expected in September 2020. Trial Registration: ClinicalTrials.gov NCT03690492; http://clinicaltrials.gov/show/NCT03690492 International Registered Report Identifier (IRRID): DERR1-10.2196/16326 UR - http://www.researchprotocols.org/2020/4/e16326/ UR - http://dx.doi.org/10.2196/16326 UR - http://www.ncbi.nlm.nih.gov/pubmed/32314974 ID - info:doi/10.2196/16326 ER - TY - JOUR AU - Inui, Tomohiko AU - Kohno, Hiroki AU - Kawasaki, Yohei AU - Matsuura, Kaoru AU - Ueda, Hideki AU - Tamura, Yusaku AU - Watanabe, Michiko AU - Inage, Yuichi AU - Yakita, Yasunori AU - Wakabayashi, Yutaka AU - Matsumiya, Goro PY - 2020/1/22 TI - Use of a Smart Watch for Early Detection of Paroxysmal Atrial Fibrillation: Validation Study JO - JMIR Cardio SP - e14857 VL - 4 IS - 1 KW - Apple Watch KW - Fitbit Charge HR KW - paroxysmal atrial fibrillation KW - photoplethysmography KW - mobile health KW - heart rate KW - validation KW - wrist-banded devices N2 - 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. UR - http://cardio.jmir.org/2020/1/e14857/ UR - http://dx.doi.org/10.2196/14857 UR - http://www.ncbi.nlm.nih.gov/pubmed/32012044 ID - info:doi/10.2196/14857 ER - TY - JOUR AU - Giannola, Gabriele AU - Torcivia, Riccardo AU - Airò Farulla, Riccardo AU - Cipolla, Tommaso PY - 2019/12/18 TI - Outsourcing the Remote Management of Cardiac Implantable Electronic Devices: Medical Care Quality Improvement Project JO - JMIR Cardio SP - e9815 VL - 3 IS - 2 KW - remote monitoring KW - telemonitoring KW - cardiac implantable electronic devices KW - implantable defibrillators KW - pacemaker KW - implantable cardioverter defibrillator KW - triage outsourcing KW - follow-up N2 - Background: Remote management is partially replacing routine follow-up in patients implanted with cardiac implantable electronic devices (CIEDs). Although it reduces clinical staff time compared with standard in-office follow-up, a new definition of roles and responsibilities may be needed to review remote transmissions in an effective, efficient, and timely manner. Whether remote triage may be outsourced to an external remote monitoring center (ERMC) is still unclear. Objective: The aim of this health care quality improvement project was to evaluate the feasibility of outsourcing remote triage to an ERMC to improve patient care and health care resource utilization. Methods: Patients (N=153) with implanted CIEDs were followed up for 8 months. An ERMC composed of nurses and physicians reviewed remote transmissions daily following a specific remote monitoring (RM) protocol. A 6-month benchmarking phase where patients? transmissions were managed directly by hospital staff was evaluated as a term of comparison. Results: A total of 654 transmissions were recorded in the RM system and managed by the ERMC team within 2 working days, showing a significant time reduction compared with standard RM management (100% vs 11%, respectively, within 2 days; P<.001). A total of 84.3% (551/654) of the transmissions did not include a prioritized event and did not require escalation to the hospital clinician. High priority was assigned to 2.3% (15/654) of transmissions, which were communicated to the hospital team by email within 1 working day. Nonurgent device status events occurred in 88 cases and were communicated to the hospital within 2 working days. Of these, 11% (10/88) were followed by a hospitalization. Conclusions: The outsourcing of RM management to an ERMC safely provides efficacy and efficiency gains in patients? care compared with a standard in-hospital management. Moreover, the externalization of RM management could be a key tool for saving dedicated staff and facility time with possible positive economic impact. Trial Registration: ClinicalTrials.gov NCT01007474; http://clinicaltrials.gov/ct2/show/NCT01007474 UR - https://cardio.jmir.org/2019/2/e9815 UR - http://dx.doi.org/10.2196/cardio.9815 UR - http://www.ncbi.nlm.nih.gov/pubmed/31845898 ID - info:doi/10.2196/cardio.9815 ER - TY - JOUR AU - Waring, E. Molly AU - Hills, T. Mellanie AU - Lessard, M. Darleen AU - Saczynski, S. Jane AU - Libby, A. Brooke AU - Holovatska, M. Marta AU - Kapoor, Alok AU - Kiefe, I. Catarina AU - McManus, D. David PY - 2019/11/14 TI - Characteristics Associated With Facebook Use and Interest in Digital Disease Support Among Older Adults With Atrial Fibrillation: Cross-Sectional Analysis of Baseline Data From the Systematic Assessment of Geriatric Elements in Atrial Fibrillation (SAGE-AF) Cohort JO - JMIR Cardio SP - e15320 VL - 3 IS - 2 KW - atrial fibrillation KW - social media KW - information seeking behavior N2 - Background: Online support groups for atrial fibrillation (AF) and apps to detect and manage AF exist, but the scientific literature does not describe which patients are interested in digital disease support. Objective: The objective of this study was to describe characteristics associated with Facebook use and interest in digital disease support among older patients with AF who used the internet. Methods: We used baseline data from the Systematic Assessment of Geriatric Elements in Atrial Fibrillation (SAGE-AF), a prospective cohort of older adults (?65 years) with AF at high stroke risk. Participants self-reported demographics, clinical characteristics, and Facebook and technology use. Online patients (internet use in the past 4 weeks) were asked whether they would be interested in participating in an online support AF community. Mobile users (owns smartphone and/or tablet) were asked about interest in communicating with their health care team about their AF-related health using a secure app. Logistic regression models identified crude and multivariable predictors of Facebook use and interest in digital disease support. Results: Online patients (N=816) were aged 74.2 (SD 6.6) years, 47.8% (390/816) were female, and 91.1% (743/816) were non-Hispanic white. Roughly half (52.5%; 428/816) used Facebook. Facebook use was more common among women (adjusted odds ratio [aOR] 2.21, 95% CI 1.66-2.95) and patients with mild to severe depressive symptoms (aOR 1.50, 95% CI 1.08-2.10) and less common among patients aged ?85 years (aOR 0.27, 95% CI 0.15-0.48). Forty percent (40.4%; 330/816) reported interest in an online AF patient community. Interest in an online AF patient community was more common among online patients with some college/trade school or Bachelors/graduate school (aOR 1.70, 95% CI 1.10-2.61 and aOR 1.82, 95% CI 1.13-2.92, respectively), obesity (aOR 1.65, 95% CI 1.08-2.52), online health information seeking at most weekly or multiple times per week (aOR 1.84, 95% CI 1.32-2.56 and aOR 2.78, 95% CI 1.86-4.16, respectively), and daily Facebook use (aOR 1.76, 95% CI 1.26-2.46). Among mobile users, 51.8% (324/626) reported interest in communicating with their health care team via a mobile app. Interest in app-mediated communication was less likely among women (aOR 0.48, 95% CI 0.34-0.68) and more common among online patients who had completed trade school/some college versus high school/General Educational Development (aOR 1.95, 95% CI 1.17-3.22), sought online health information at most weekly or multiple times per week (aOR 1.86, 95% CI 1.27-2.74 and aOR 2.24, 95% CI 1.39-3.62, respectively), and had health-related apps (aOR 3.92, 95% CI 2.62-5.86). Conclusions: Among older adults with AF who use the internet, technology use and demographics are associated with interest in digital disease support. Clinics and health care providers may wish to encourage patients to join an existing online support community for AF and explore opportunities for app-mediated patient-provider communication. UR - http://cardio.jmir.org/2019/2/e15320/ UR - http://dx.doi.org/10.2196/15320 UR - http://www.ncbi.nlm.nih.gov/pubmed/31758791 ID - info:doi/10.2196/15320 ER - TY - JOUR AU - Giebel, Denk Godwin AU - Gissel, Christian PY - 2019/6/16 TI - Accuracy of mHealth Devices for Atrial Fibrillation Screening: Systematic Review JO - JMIR Mhealth Uhealth SP - e13641 VL - 7 IS - 6 KW - mHealth KW - atrial fibrillation KW - wearable KW - app N2 - 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. UR - http://mhealth.jmir.org/2019/6/e13641/ UR - http://dx.doi.org/10.2196/13641 UR - http://www.ncbi.nlm.nih.gov/pubmed/31199337 ID - info:doi/10.2196/13641 ER - TY - JOUR AU - Kwon, Soonil AU - Hong, Joonki AU - Choi, Eue-Keun AU - Lee, Euijae AU - Hostallero, Earl David AU - Kang, Ju Wan AU - Lee, Byunghwan AU - Jeong, Eui-Rim AU - Koo, Bon-Kwon AU - Oh, Seil AU - Yi, Yung PY - 2019/6/6 TI - Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study JO - JMIR Mhealth Uhealth SP - e12770 VL - 7 IS - 6 KW - atrial fibrillation KW - deep learning KW - photoplethysmography KW - pulse oximetry KW - diagnosis N2 - 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. UR - http://mhealth.jmir.org/2019/6/e12770/ UR - http://dx.doi.org/10.2196/12770 UR - http://www.ncbi.nlm.nih.gov/pubmed/31199302 ID - info:doi/10.2196/12770 ER - 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 - Proesmans, Tine AU - Mortelmans, Christophe AU - Van Haelst, Ruth AU - Verbrugge, Frederik AU - Vandervoort, Pieter AU - Vaes, Bert PY - 2019/03/27 TI - Mobile Phone?Based Use of the Photoplethysmography Technique to Detect Atrial Fibrillation in Primary Care: Diagnostic Accuracy Study of the FibriCheck App JO - JMIR Mhealth Uhealth SP - e12284 VL - 7 IS - 3 KW - atrial fibrillation KW - electrocardiography KW - photoplethysmography KW - mobile phone KW - algorithm N2 - 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. UR - http://mhealth.jmir.org/2019/3/e12284/ UR - http://dx.doi.org/10.2196/12284 UR - http://www.ncbi.nlm.nih.gov/pubmed/30916656 ID - info:doi/10.2196/12284 ER - TY - JOUR AU - Fan, Yong-Yan AU - Li, Yan-Guang AU - Li, Jian AU - Cheng, Wen-Kun AU - Shan, Zhao-Liang AU - Wang, Yu-Tang AU - Guo, Yu-Tao PY - 2019/03/05 TI - Diagnostic Performance of a Smart Device With Photoplethysmography Technology for Atrial Fibrillation Detection: Pilot Study (Pre-mAFA II Registry) JO - JMIR Mhealth Uhealth SP - e11437 VL - 7 IS - 3 KW - atrial fibrillation KW - photoplethysmography KW - detection KW - accuracy KW - mobile phone KW - smart band KW - algorithm N2 - 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) UR - http://mhealth.jmir.org/2019/3/e11437/ UR - http://dx.doi.org/10.2196/11437 UR - http://www.ncbi.nlm.nih.gov/pubmed/30835243 ID - info:doi/10.2196/11437 ER - TY - JOUR AU - Li, Christien Ka Hou AU - White, Anne Francesca AU - Tipoe, Timothy AU - Liu, Tong AU - Wong, CS Martin AU - Jesuthasan, Aaron AU - Baranchuk, Adrian AU - Tse, Gary AU - Yan, P. Bryan PY - 2019/02/15 TI - The Current State of Mobile Phone Apps for Monitoring Heart Rate, Heart Rate Variability, and Atrial Fibrillation: Narrative Review JO - JMIR Mhealth Uhealth SP - e11606 VL - 7 IS - 2 KW - mobile phone apps KW - atrial fibrillation KW - heart rate KW - arrhythmia KW - photoplethysmography KW - electrocardiography KW - mobile health N2 - 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. UR - http://mhealth.jmir.org/2019/2/e11606/ UR - http://dx.doi.org/10.2196/11606 UR - http://www.ncbi.nlm.nih.gov/pubmed/30767904 ID - info:doi/10.2196/11606 ER - TY - JOUR AU - Gliner, Vadim AU - Behar, Joachim AU - Yaniv, Yael PY - 2018/05/22 TI - Novel Method to Efficiently Create an mHealth App: Implementation of a Real-Time Electrocardiogram R Peak Detector JO - JMIR Mhealth Uhealth SP - e118 VL - 6 IS - 5 KW - atrial fibrillation KW - arrhythmia KW - heart rate variability KW - MATLAB Mobile KW - mobile device N2 - 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. UR - http://mhealth.jmir.org/2018/5/e118/ UR - http://dx.doi.org/10.2196/mhealth.8429 UR - http://www.ncbi.nlm.nih.gov/pubmed/29789276 ID - info:doi/10.2196/mhealth.8429 ER - TY - JOUR AU - Hirschey, Jaclyn AU - Bane, Sunetra AU - Mansour, Moussa AU - Sperber, Jodi AU - Agboola, Stephen AU - Kvedar, Joseph AU - Jethwani, Kamal PY - 2018/03/15 TI - Evaluating the Usability and Usefulness of a Mobile App for Atrial Fibrillation Using Qualitative Methods: Exploratory Pilot Study JO - JMIR Hum Factors SP - e13 VL - 5 IS - 1 KW - nonvalvular atrial fibrillation KW - medication adherence KW - patient self-care KW - mobile application KW - exploratory research KW - pilot study KW - usability study KW - acceptability study KW - qualitative methods N2 - Background: Atrial fibrillation (AFib) is the most common form of heart arrhythmia and a potent risk factor for stroke. Nonvitamin K antagonist oral anticoagulants (NOACs) are routinely prescribed to manage AFib stroke risk; however, nonadherence to treatment is a concern. Additional tools that support self-care and medication adherence may benefit patients with AFib. Objective: The aim of this study was to evaluate the perceived usability and usefulness of a mobile app designed to support self-care and treatment adherence for AFib patients who are prescribed NOACs. Methods: A mobile app to support AFib patients was previously developed based on early stage interview and usability test data from clinicians and patients. An exploratory pilot study consisting of naturalistic app use, surveys, and semistructured interviews was then conducted to examine patients? perceptions and everyday use of the app. Results: A total of 12 individuals with an existing diagnosis of nonvalvular AFib completed the 4-week study. The average age of participants was 59 years. All participants somewhat or strongly agreed that the app was easy to use, and 92% (11/12) reported being satisfied or very satisfied with the app. Participant feedback identified changes that may improve app usability and usefulness for patients with AFib. Areas of usability improvement were organized by three themes: app navigation, clarity of app instructions and design intent, and software bugs. Perceptions of app usefulness were grouped by three key variables: core needs of the patient segment, patient workflow while managing AFib, and the app?s ability to support the patient?s evolving needs. Conclusions: The results of this study suggest that mobile tools that target self-care and treatment adherence may be helpful to AFib patients, particularly those who are newly diagnosed. Additionally, participant feedback provided insight into the varied needs and health experiences of AFib patients, which may improve the design and targeting of the intervention. Pilot studies that qualitatively examine patient perceptions of usability and usefulness are a valuable and often underutilized method for assessing the real-world acceptability of an intervention. Additional research evaluating the AFib Connect mobile app over a longer period, and including a larger, more diverse sample of AFib patients, will be helpful for understanding whether the app is perceived more broadly to be useful and effective in supporting patient self-care and medication adherence. UR - http://humanfactors.jmir.org/2018/1/e13/ UR - http://dx.doi.org/10.2196/humanfactors.8004 UR - http://www.ncbi.nlm.nih.gov/pubmed/29549073 ID - info:doi/10.2196/humanfactors.8004 ER - TY - JOUR AU - Kehl, Devin AU - Zimmer, Raymond AU - Sudan, Madhuri AU - Kedan, Ilan PY - 2018/03/08 TI - Handheld Ultrasound as a Novel Predictive Tool in Atrial Fibrillation: Prediction of Outcomes Following Electrical Cardioversion JO - JMIR Cardio SP - e7 VL - 2 IS - 1 KW - atrial fibrillation KW - cardioversion KW - recurrence KW - inferior vena cava KW - hand held ultrasound KW - point of care N2 - 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. UR - http://cardio.jmir.org/2018/1/e7/ UR - http://dx.doi.org/10.2196/cardio.9534 UR - http://www.ncbi.nlm.nih.gov/pubmed/31758780 ID - info:doi/10.2196/cardio.9534 ER - TY - JOUR AU - Kropp, Caley AU - Ellis, Jordan AU - Nekkanti, Rajasekhar AU - Sears, Samuel PY - 2018/02/21 TI - Monitoring Patients With Implantable Cardioverter Defibrillators Using Mobile Phone Electrocardiogram: Case Study JO - JMIR Cardio SP - e5 VL - 2 IS - 1 KW - atrial fibrillation KW - ICD KW - ECG KW - mobile phone monitoring KW - mobile health KW - electrophysiology N2 - Background: Preventable poor health outcomes associated with atrial fibrillation continue to make early detection a priority. A one-lead mobile electrocardiogram (mECG) device given to patients with an implantable cardioverter defibrillator (ICD) allowed users to receive real-time ECG readings in 30 seconds. Objective: Three cases were selected from an institutional review board-approved clinical trial aimed at assessing mECG device usage and satisfaction, patient engagement, quality of life (QoL), and cardiac anxiety. These three specific cases were selected to examine a variety of possible patient presentations and user experiences. Methods: Three ICD patients with mobile phones who were being seen in an adult device clinic were asked to participate. The participants chosen represented individuals with varying degrees of reported education and patient engagement. Participants were instructed to use the mECG device at least once per day for 30 days. Positive ECGs for atrial fibrillation were evaluated in clinic. At follow-up, information was collected regarding their frequency of use of the mECG device and three psychological outcomes in the domains of patient engagement, QoL, and cardiac anxiety. Results: Each patient used the technology approximately daily or every other day as prescribed. At the 30-day follow-up, usage reports indicated an average of 32 readings per month per participant. At 90-day follow-up, usage reports indicated an average of 34 readings per month per participant. Two of the three participants self-reported a significant improvement in their physical QoL from baseline to completion, while simultaneously self-reporting a significant decrease in their mental QoL. All three participants reported high levels of device acceptance and technology satisfaction. Conclusions: This case study demonstrates that ICD patients with varying degrees of education and patient engagement were relatively active in their use of mECGs. All three participants using the mECG technology reported high technology satisfaction and device acceptance. High sensitivity, specificity, and accuracy of mECG technology may allow routine atrial fibrillation screening at lower costs, in addition to improving patient outcomes. UR - http://cardio.jmir.org/2018/1/e5/ UR - http://dx.doi.org/10.2196/cardio.8710 UR - http://www.ncbi.nlm.nih.gov/pubmed/31758776 ID - info:doi/10.2196/cardio.8710 ER - TY - JOUR PY - 2017// TI - Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review JO - Online J Public Health Inform SP - e7864 VL - 9 IS - 3 UR - UR - http://dx.doi.org/10.5210/ojphi.v9i3.7864 UR - http://www.ncbi.nlm.nih.gov/pubmed/29403578 ID - info:doi/10.5210/ojphi.v9i3.7864 ER - TY - JOUR AU - Chen, Ying-Hsien AU - Hung, Chi-Sheng AU - Huang, Ching-Chang AU - Hung, Yu-Chien AU - Hwang, Juey-Jen AU - Ho, Yi-Lwun PY - 2017/09/26 TI - Atrial Fibrillation Screening in Nonmetropolitan Areas Using a Telehealth Surveillance System With an Embedded Cloud-Computing Algorithm: Prospective Pilot Study JO - JMIR Mhealth Uhealth SP - e135 VL - 5 IS - 9 KW - atrial fibrillation KW - screen KW - cloud-computing algorithm KW - electrocardiography N2 - Background: Atrial fibrillation (AF) is a common form of arrhythmia that is associated with increased risk of stroke and mortality. Detecting AF before the first complication occurs is a recognized priority. No previous studies have examined the feasibility of undertaking AF screening using a telehealth surveillance system with an embedded cloud-computing algorithm; we address this issue in this study. Objective: The objective of this study was to evaluate the feasibility of AF screening in nonmetropolitan areas using a telehealth surveillance system with an embedded cloud-computing algorithm. Methods: We conducted a prospective AF screening study in a nonmetropolitan area using a single-lead electrocardiogram (ECG) recorder. All ECG measurements were reviewed on the telehealth surveillance system and interpreted by the cloud-computing algorithm and a cardiologist. The process of AF screening was evaluated with a satisfaction questionnaire. Results: Between March 11, 2016 and August 31, 2016, 967 ECGs were recorded from 922 residents in nonmetropolitan areas. A total of 22 (2.4%, 22/922) residents with AF were identified by the physician?s ECG interpretation, and only 0.2% (2/967) of ECGs contained significant artifacts. The novel cloud-computing algorithm for AF detection had a sensitivity of 95.5% (95% CI 77.2%-99.9%) and specificity of 97.7% (95% CI 96.5%-98.5%). The overall satisfaction score for the process of AF screening was 92.1%. Conclusions: AF screening in nonmetropolitan areas using a telehealth surveillance system with an embedded cloud-computing algorithm is feasible. UR - https://mhealth.jmir.org/2017/9/e135/ UR - http://dx.doi.org/10.2196/mhealth.8290 UR - http://www.ncbi.nlm.nih.gov/pubmed/28951384 ID - info:doi/10.2196/mhealth.8290 ER - TY - JOUR AU - Desteghe, Lien AU - Kluts, Kiki AU - Vijgen, Johan AU - Koopman, Pieter AU - Dilling-Boer, Dagmara AU - Schurmans, Joris AU - Dendale, Paul AU - Heidbuchel, Hein PY - 2017/07/19 TI - The Health Buddies App as a Novel Tool to Improve Adherence and Knowledge in Atrial Fibrillation Patients: A Pilot Study JO - JMIR Mhealth Uhealth SP - e98 VL - 5 IS - 7 KW - mHealth KW - anticoagulants KW - medication adherence KW - education KW - atrial fibrillation N2 - Background: Atrial fibrillation (AF) constitutes an important risk for stroke, especially in an ageing population. A new app (Health Buddies) was developed as a tool to improve adherence to non-vitamin K antagonist oral anticoagulants (NOACs) in an elderly AF population by providing a virtual contract with their grandchildren, spelling out daily challenges for both. Objective: The aim of this pilot study was to assess the feasibility and usability of the Health Buddies app in AF patients. Methods: Two workshops were conducted to steer app development and to test a first prototype. The feasibility of the finalized app was investigated by assessing the number of eligible AF patients (based on current prescription of NOACs, the presence of grandchildren between 5 and 15 years old, availability of a mobile phone, computer, or tablet), and the proportion of those who were willing to participate. Participants had to use the app for 3 months. The motivation of the patients to use the app was assessed based on the number of logins to the app. Their perception of its usefulness was examined by specific questionnaires. Additionally, the effects on knowledge level about AF and its treatment, and adherence to NOAC intake were investigated. Results: Out of 830 screened AF patients, 410 were taking NOACs and 114 were eligible for inclusion. However, only 3.7% (15/410) of the total NOAC population or 13.2% of the eligible patients (15/114) were willing to participate. The main reasons for not participating were no interest to participate in general or in the concept in particular (29/99, 29%), not feeling comfortable using technology (22/99, 22%), no interest by the grandchildren or their parents (20/99, 20%), or too busy a lifestyle (12/99, 12%). App use significantly decreased towards the end of the study period in both patients (P=.009) and grandchildren (P<.001). NOAC adherence showed a taking adherence and regimen adherence of 88.6% (SD 15.4) and 81.8% (SD 18.7), respectively. Knowledge level increased from 64.6% (SD 14.7) to 70.4% (SD 10.4) after 3 months (P=.09). The app scored positively on clarity, novelty, stimulation, and attractiveness as measured with the user experience questionnaire. Patients evaluated the educational aspect of this app as a capital gain. Conclusions: Only a small proportion of the current AF population seems eligible for the innovative Health Buddies app in its current form. Although the app was positively rated by its users, a large subset of patients was not willing to participate in this study or to use the app. Efforts have to be made to expand the target group in the future. UR - http://mhealth.jmir.org/2017/7/e98/ UR - http://dx.doi.org/10.2196/mhealth.7420 UR - http://www.ncbi.nlm.nih.gov/pubmed/28724512 ID - info:doi/10.2196/mhealth.7420 ER -