JMIR Cardio最新文献

{"title":"Mindfulness-Based Self-Management Program Using a Mobile Application for Patients with Pulmonary Hypertension: A Single-Arm Feasibility Study.","authors":"Yuka Takita, Junko Morishita, Sunre Park, Ayumi Goda, Takumi Inami, Hanako Kikuchi, Takashi Kohno, Masaharu Kataoka, Daisuke Fujisawa","doi":"10.2196/79639","DOIUrl":"https://doi.org/10.2196/79639","url":null,"abstract":"<p><strong>Background: </strong>Mindfulness-based interventions have been applied across various chronic illnesses, but no tailored program exists for individuals with pulmonary hypertension (PH).</p><p><strong>Objective: </strong>This study aimed to develop and evaluate the feasibility of a mindfulness-based self-management program for patients with PH, delivered online to accommodate their limited mobility.</p><p><strong>Methods: </strong>A single-arm pre-post study was conducted using an eight-session, weekly videoconference program incorporating PH self-management education and elements of mindfulness-based cognitive therapy. A mobile application linked to an Apple Watch was used to support symptom monitoring and mindfulness awareness. Outcomes included PH-related symptoms; quality of life (emPHasis-10); depression (PHQ-9); anxiety (GAD-7); resilience (CD-RISC); and loneliness (UCLA Loneliness Scale-short version). Assessments occurred at baseline, week 4, and program completion. Exit interviews explored perceived changes and experiences.</p><p><strong>Results: </strong>Twelve participants (mean age 41.8, SD 10.5 years; range 26-56 years) were enrolled, and nine completed the program (75% retention). Participants valued the online format and Apple Watch integration, while noting a need for optional on-demand sessions. Qualitative analysis identified themes such as increased self-awareness, use of meditation for pain management, and enhanced self-compassion. Quantitative analysis showed significant changes across three time points (baseline, week 4, week 8) for emPHasis-10 (χ²₂=9.742; P = .008) and CD-RISC (χ²₂=7.267; P = .03). Trends toward change were observed for PHQ-9 (χ²₂=4.750; P = .09) and GAD-7 (χ²₂=5.067; P = .08), but week-12 data were limited (n=5). No significant changes in loneliness were observed.</p><p><strong>Conclusions: </strong>The program appeared to support patients with PH in managing symptoms and emotions and suggested potential improvements in quality of life. These preliminary findings warrant evaluation in a future randomized controlled trial.</p><p><strong>Clinicaltrial: </strong>UMIN Clinical Trials Registry UMIN000044075; https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000050319.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145809562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Patient-Held Devices to Measure Variations in Resting Heart Rate and Step Count Prior to Presentation With an Acute Illness: International, Multicenter Flash Mob Feasibility Study.","authors":"Jason G A den Duijn, Ahmed A M Hajjaj, John Kellett, Erika Frischknecht Christensen, Harm R Haak, Mikkel Brabrand, Christian H Nickel, Prabath W B Nanayakkara, Christian P Subbe, Jelmer Alsma","doi":"10.2196/76218","DOIUrl":"10.2196/76218","url":null,"abstract":"<p><strong>Background: </strong>Many patients experience a gradual decline in health before seeking hospital care, with subtle changes in vital signs such as increased heart rate or decreased mobility. Recognizing deviations from baseline vital signs can support clinical decision-making, especially admission decisions. Smart devices (ie, smartphones, smartwatches, and activity trackers) track health metrics like heart rate and step count, offering new opportunities to estimate illness severity and track deterioration early.</p><p><strong>Objective: </strong>This study aimed to assess the feasibility of using heart rate and step count measurements from smart devices (ie, smartphones, smartwatches, and activity trackers) to enhance the evaluation of patients presenting with acute illness in emergency settings.</p><p><strong>Methods: </strong>We conducted an international multicenter prospective observational study using the flash mob study design in 34 hospitals in the Netherlands (n=17), the United Kingdom (n=7), Denmark (n=9), and Switzerland (n=1) in May 2024. Researchers collaborated with patients to complete questionnaires upon an acute care (ie, emergency department, acute medical unit, same day emergency care) visit and extracted physiological data from their smart devices.</p><p><strong>Results: </strong>Among patients with an acute care visit (n=1137), 40% (n=452) had a smart device with health data. These patients tended to be from a higher educational level and in relatively good health. Only half had retrievable heart rate or step count data, resulting in a usable data set for 20% (n=209) of the total study population. Analysis showed a significant increase in heart rate (P<.001) and a decrease in step count (P<.001) in the days preceding their hospital visit. Both heart rate (P=.04) and step count (P=.04) on the day before presentation were significantly associated with disposition.</p><p><strong>Conclusions: </strong>Our study demonstrates the feasibility of using a patient's personal smart device to monitor vital signs in the days leading up to an acute care visit. In a selected patient group, significant changes in heart rate and step count were observed prior to hospital presentation, suggesting that disposition may be predicted using data collected from the patient's own device. High-risk patient groups, who might benefit the most from digital health monitoring, are currently underrepresented among device users.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"9 ","pages":"e76218"},"PeriodicalIF":2.2,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12705129/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145762836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Outcomes of Team-Based Digital Monitoring of Patients With Multiple Chronic Conditions: Semiparametric Event Study.","authors":"Ross Graham, Itzik Fadlon, Parag Agnihotri, Christopher A Longhurst, Ming Tai-Seale","doi":"10.2196/75170","DOIUrl":"10.2196/75170","url":null,"abstract":"<p><strong>Background: </strong>Remote patient monitoring (RPM) has emerged as an effective strategy for controlling hypertension by enabling patients to collect and transmit blood pressure (BP) data outside the clinic and supporting proactive care team interventions. While its benefits for hypertension management are well established, less is known about its effectiveness in patients with multiple chronic conditions (MCC), who experience higher morbidity, mortality, and costs.</p><p><strong>Objective: </strong>This study aimed to evaluate the impact of an electronic health record (EHR)-integrated, team-based RPM program on patients with hypertension, alone or co-occurring with ischemic heart disease, type 2 diabetes, or both. This study aimed to determine whether referral to the program was associated with reductions in systolic blood pressure (SBP) across these patient groups.</p><p><strong>Methods: </strong>We analyzed EHR data from patients referred by their primary care physicians to the University of California San Diego Health's Digital Health Program between October 2020 and July 2022. Eligible patients had hypertension, either alone or accompanied by at least 1 coexisting condition, such as ischemic heart disease or type 2 diabetes. Participants received a Bluetooth-enabled BP cuff and ongoing support from a multidisciplinary team, including nurse care managers and a pharmacist. A semiparametric event study design was used to estimate changes in SBP over 24 months, comparing prereferral and postreferral outcomes. To understand the program's impact, outcomes were analyzed for the full cohort of all referred patients and then scaled to reflect the average change in SBP among the program participants.</p><p><strong>Results: </strong>Among patients who had been referred to the program, those with hypertension only experienced an average reduction of 9.70 (SE 0.80) mm Hg in SBP by the end of the analysis horizon of 1 year. Patients with hypertension and either diabetes or ischemic heart disease experienced a reduction of 6.61 (SE 1.12) mm Hg, and those with all 3 conditions experienced a reduction of 6.60 (SE 1.72) mm Hg. The average reductions in SBP among active participants were 16.83 mm Hg, 13.22 mm Hg, and 16.01 mm Hg, respectively.</p><p><strong>Conclusions: </strong>A team-based, EHR-integrated RPM program was associated with clinically meaningful SBP reductions among patients with MCC. The program leveraged existing EHR workflows for referral and monitoring and provided technical and clinical support to patients. These findings suggest that EHR-integrated RPM services can achieve substantial improvements in BP in high-risk populations. As reimbursement for RPM expands, such models represent a promising strategy for addressing hypertension and the disproportionate burden of MCC at the population level.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"9 ","pages":"e75170"},"PeriodicalIF":2.4,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12685231/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145708213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Telecardiology Activities in Hospital and University Cardiology Facilities in Italy: Survey Study.","authors":"Manuela Bocchino, Elvira Agazio, Cecilia Damiano, Giuseppe Di Lorenzo, Fabio De Paolis, Duilio Luca Bacocco, Fabrizio Ammirati, Alessio Nardini, Marco Silano","doi":"10.2196/73747","DOIUrl":"10.2196/73747","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Telemedicine enables the provision of health services at a distance using information and communication technologies and includes different types of services: telemonitoring, remote control, virtual visit or televisit, telereferral, teleassistance, medical teleconsultation, health professionals' teleconsultation, and telerehabilitation. Continuous monitoring, early care, and greater therapeutic adherence could be benefits of telemedicine in the management of cardiovascular diseases. There are not many studies in the literature investigating the use of telemedicine in cardiology in Italy.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The aim of this study is to illustrate the results of a survey on telemedicine services in cardiology conducted by the Department of Cardiovascular, Endocrine-Metabolic Diseases and Aging of the Italian National Institute of Health.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The Telehealth Quality of Care Tool (TQoCT) from the World Health Organization (WHO) was used as the model. A survey was disseminated by the National Association of Doctors and Hospital Cardiologists (ANMCO) from June 2024 to October 2024 through a link provided to hospital and university cardiology operative units identified through the 8th Census of Cardiological Structures in Italy. The facilities were contacted by email or telephone. The survey was built using Microsoft Forms and composed of 52 questions divided into 6 sections. The analysis was carried out for the whole national territory and by geographical area.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of the 443 hospitals contacted, the response rate was 56.7% (251/443). Overall, 78.9% (198/251) of facilities reported telemedicine initiatives providing telemonitoring (128/198, 64.6%), telereferrals (104/198, 52.5%), medical teleconsultations (93/198, 47%), televisits (82/198, 41.4%), health professionals' teleconsultations (64/198, 32.3%), and telerehabilitation (10/198, 5.1%). The most frequently followed cardiovascular conditions were heart failure, ischemic heart disease, and cardiac arrhythmias, especially atrial fibrillation. Of the facilities, 51% (101/198) used deliberations, procedures, protocols, or informed consent for their activities, and 46% (91/198) of the reported services were paid. Lack of dedicated staff, complexity in organizational terms, and lack of technological equipment in the structure were the principal obstacles for health professionals; lack of familiarity with technology was the principal obstacle for patients.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;There are still organizational and clinical limitations to resolve to make telemedicine in cardiology an integral part of medical practice. The true challenge of telecardiology is likely the integration of available technology with precise, concrete, and simplified organizational models. As a tool, technology is fundamental only if it is accessible and adequate. However, it must be integrated with new paths built accor","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"9 ","pages":"e73747"},"PeriodicalIF":2.2,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12680089/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145687488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Impact of Digital Intervention Messages Targeting Users With High Blood Pressure Events: Retrospective Real-World Study.","authors":"Yifat Fundoiano-Hershcovitz, Inbar Breuer Asher, Marilyn D Ritholz, David L Horwitz, Omar Manejwala, Claudia Levi, Pavel Goldstein","doi":"10.2196/76275","DOIUrl":"https://doi.org/10.2196/76275","url":null,"abstract":"<p><strong>Background: </strong>Effective hypertension management, particularly through self-care strategies, remains a significant public health challenge. Despite widespread awareness, only approximately 1 in 5 adults achieves adequate blood pressure (BP) control. There is a growing need for scalable digital health interventions that enhance awareness, support behavioral change, and improve clinical outcomes. However, real-world evidence evaluating the impact of such interventions on BP levels and their underlying mechanisms is limited.</p><p><strong>Objective: </strong>This study aimed to evaluate the effectiveness of a digital intervention using data-driven nudges on monthly average BP levels. Specifically, we assessed changes in BP before and after the intervention and examined whether these changes differed compared to a control group in a high BP cohort and a normal BP cohort.</p><p><strong>Methods: </strong>In this retrospective, real-world cohort study, we analyzed two user cohorts from a digital health platform: (1) individuals with high BP readings and (2) individuals with normal BP readings. Participants who received a digital intervention were propensity score-matched to users who did not receive the intervention, based on demographic and clinical variables. Monthly average BP and the proportion of high readings were assessed 3 months before and after the intervention. A piecewise mixed-effects model was used to evaluate BP trajectories, and simple slope analysis assessed the interaction between the outcomes and the groups, as well as the moderating effect of lifestyle activities on systolic blood pressure (SBP).</p><p><strong>Results: </strong>In total, 408 users were included in the study. In the high BP cohort (n=296), the intervention group showed a significant decrease in the monthly average SBP after the intervention (B=-2.09; P<.001), while the control group showed a smaller reduction (B=-1.06; P=.007). Additionally, users reporting higher lifestyle activity levels experienced a greater reduction in SBP (B=-5.27; P<.001). In the normal BP cohort (n=112), the intervention group maintained stable BP levels after the intervention (B=-0.39; P=.27), while the control group exhibited a significant increase in BP levels (B=0.69; P=.03).</p><p><strong>Conclusions: </strong>Data-driven nudges delivered via a digital health platform were associated with improved BP outcomes among individuals with high BP levels and helped maintain BP stability among those with normal BP levels. These findings reinforce the integration of personalized digital interventions into hypertension management and highlight the potential role of positive messaging, behavioral engagement, and user empowerment in improving long-term outcomes.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"9 ","pages":"e76275"},"PeriodicalIF":2.2,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12655891/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145633715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physicians' Use of Electronic Health Record Data Elements and Decision Support Tools in Heart Failure Management: User-Centered Cross-Sectional Survey Study.","authors":"Mohamed S Ali, Bruna Oewel, Kaitlyn M Greer, Sabah Ganai, Mark W Newman, Kelly Murdoch-Kitt, Scott L Hummel, Michael P Dorsch","doi":"10.2196/79239","DOIUrl":"10.2196/79239","url":null,"abstract":"<p><strong>Background: </strong>The management of heart failure (HF) requires complex, data-driven decision-making. Although electronic health record (EHR) systems and clinical decision support (CDS) tools can streamline access to essential clinical information, it remains unclear which EHR elements and tools cardiologists and general medicine physicians prioritize when caring for patients with HF.</p><p><strong>Objective: </strong>This study aims to identify these elements and tools to improve the user interface design of future EHR applications.</p><p><strong>Methods: </strong>This study used a user-centered design research approach to understand physician workflows and decision-making needs in HF care. A cross-sectional online survey was administered to 302 physicians, comprising 150 cardiologists (including 15 HF specialists) and 152 general medicine physicians. Respondents reported their use of EHR variables (eg, medication lists, laboratory results, diagnostic tests, problem lists, clinical notes) for decision-making in HF care, as well as their time spent in the EHR before, during, and after patient visits along with their use of predictive models and patient-reported outcome questionnaire. Descriptive analyses, χ2 tests, and t tests were conducted to compare groups, with statistical significance set at P<.05.</p><p><strong>Results: </strong>A total of 302 health care providers participated in the survey, nearly evenly split between cardiologists (49.7%, 150/302) and general medicine physicians (50.3%, 152/302). Both groups consistently relied on medication lists, vital signs, laboratory results, diagnostic tests, problem lists, and clinical notes for HF decision-making. Cardiologists placed greater emphasis on diagnostic tests for inpatient HF care (mean [SD] overall frequency, 4.66 [0.50] vs 4.44 [0.64]; P=.012) and outpatient HF care (mean [SD] overall frequency, 4.67 [0.55] vs 4.35 [0.71], P<.001). In contrast, general medicine physicians relied more on problem lists for inpatient HF care (mean [SD] overall frequency, 4.63 [0.58] vs 4.43 [0.72], P=.034), with no significant difference in the outpatient setting (P>.05). Both groups underutilized standardized questionnaires and predictive models, with only 20.1% (29/144) of cardiologists and 4.5% (6/133) of general medicine physicians using standardized questionnaires (P<.001).</p><p><strong>Conclusions: </strong>Both physician groups depend on medication lists, laboratory results, diagnostic tests, and problem lists. Cardiologists prioritize diagnostic tests, whereas general medicine physicians more often use problem lists. Low use of questionnaires and predictive models highlights the need for better integration of these tools. Future EHR design interface should tailor functionalities to accommodate these differing priorities and optimize HF care.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"9 ","pages":"e79239"},"PeriodicalIF":2.2,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12617960/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145523564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Web-Based Platform for the Chilean Cardiac Surgery Registry: Algorithm Development and Validation Study.","authors":"Sergio Guinez-Molinos, Enrique Seguel, Jaime Gonzalez, Benjamin Castillo","doi":"10.2196/70147","DOIUrl":"10.2196/70147","url":null,"abstract":"<p><strong>Background: </strong>Cardiac surgeries in Chile lack a national registry for systematic data collection and analysis, limiting insights into procedural outcomes and patient demographics. In response to this gap, we developed a web-based platform to support the documentation of high-complexity cardiac surgeries.</p><p><strong>Objective: </strong>This study aimed to design, develop, and implement a cardiac surgery data collection and analysis platform that conforms to international standards to support clinical decision-making and research initiatives.</p><p><strong>Methods: </strong>A web-based platform was developed using the model-view-controller architecture, incorporating input from health care professionals and based on the fourth European Association for Cardio-Thoracic Surgery adult cardiac surgical database report. The platform captures more than 160 clinical variables across 15 categories, spanning preoperative, intraoperative, and postoperative stages.</p><p><strong>Results: </strong>The most significant outcome of this study is the development of the first online platform for documenting cardiac surgeries in Chile. Since its implementation in 2014, the platform has documented more than 4800 cardiac surgeries, establishing it as the largest database for a single institution in Latin America. The platform offers real-time access to data, supports planning and resource allocation, and enables the systematic evaluation of clinical outcomes. Integrating the European System for Cardiac Operative Risk Evaluation II risk model enables a standardized assessment of mortality risk.</p><p><strong>Conclusions: </strong>The platform contributes to the collection of cardiac surgery data in Chile, enabling evidence-based clinical decision-making and informed public health planning. It has documented cardiac surgeries for 10 years and has become the official registry tool for cardiac surgeries. By 2026, its application will be extended to 2 more centers, with the expectation that it will soon become the national database of cardiac surgeries. Future developments should improve scalability, interoperability, and data analysis to establish a national registry and further align Chilean cardiac surgery practices with international standards.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"9 ","pages":"e70147"},"PeriodicalIF":2.2,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12604825/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145495595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Photoplethysmography-Based Machine Learning Approaches for Atrial Fibrillation Burden: Algorithm Development and Validation.","authors":"Hong Wang, Binbin Liu, Hui Zhang, Zheqi Zhang, Zhigeng Jin, Hao Wang, Yu-Tao Guo","doi":"10.2196/78075","DOIUrl":"10.2196/78075","url":null,"abstract":"<p><strong>Background: </strong>Atrial fibrillation (AF) burden is associated with cardiovascular events such as stroke and heart failure. Recent advancements in photoplethysmography (PPG) technology have provided new insights into noninvasive and convenient AF burden detection.</p><p><strong>Objective: </strong>This study aimed to establish an AF burden model based on smartwatch-monitored PPG technology to track the progression of AF.</p><p><strong>Methods: </strong>This prospective pilot study (January 2024 to January 2025) at the Chinese PLA General Hospital enrolled patients with paroxysmal AF. Participants underwent simultaneous rhythm monitoring using smartwatch PPG and 24-hour Holter electrocardiogram monitoring (the gold standard). Five PPG-derived AF burden metrics were defined: (1) ratio of AF episode duration to total monitoring time (M1), (2) ratio of AF episode frequency to total measurements (M2), (3) AF episode density (M3), (4) AF episode variability (M4), and (5) proportion of rapid ventricular rate in AF episodes (>120 beats per minute; M5). Smartwatch PPG signals were collected once per minute. Sensitivity, specificity, accuracy, precision, and F1 score were used to evaluate the PPG algorithm's AF detection capability through comparison with the gold standard (24-hour Holter monitoring). The mean absolute error (MAE) and Spearman rank correlation coefficient (rs) were used to assess the correlation between the PPG-based AF burden metrics and the gold standard.</p><p><strong>Results: </strong>A total of 145 participants with paroxysmal AF (n=96, 66.2% male; mean age 63.28, SD 14.23 years) were included. Compared to the gold standard, the PPG-based AF burden model demonstrated a sensitivity of 91.5% (95% CI 87.9%-95.1%), specificity of 97.2% (95% CI 95.9%-98.5%), precision of 92.9% (95% CI 88.6%-97.3%), accuracy of 93.3% (95% CI 88.2%-98.5%), and F1 score of 90.5% (95% CI 86.3%-94.7%). The AF burden model exhibited strong discriminatory power in the test cohort (area under the curve=89.5%, 95% CI 89.4%-89.7%). For M1, the MAE for the model of AF episode duration as a proportion of total monitoring time was 0.0400 (P=.008), with a correlation coefficient (rs) of 0.8788 (P<.001). For M4, the MAE for the AF episode variability model was 3.9967 (P<.001), with a correlation coefficient (rs) of 0.7876 (P<.001). The MAE for the average real variability model was 4.6436 (P<.001), with a correlation coefficient (rs) of 0.8127 (P<.001). The MAE for the average AF change model was 0.3893 (P=.27), with a correlation coefficient (rs) of 0.7246 (P<.001).</p><p><strong>Conclusions: </strong>The PPG-based AF burden model demonstrated high concordance with the gold standard of 24-hour Holter monitoring in tracking AF episode duration and variability, providing new perspectives for exploring AF progression dynamics.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"9 ","pages":"e78075"},"PeriodicalIF":2.2,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12599996/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145488646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient Preferences for Using Remote Care Technology in Heart Failure: Discrete Choice Experiment.","authors":"Ahmed Al-Naher, Jennifer Downing, Dyfrig Hughes, Munir Pirmohamed","doi":"10.2196/68022","DOIUrl":"10.2196/68022","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Remote care technology has been used to bridge the gap between health care in a clinical setting and in the community, all the more essential post-COVID. Patients with chronic conditions may benefit from interventions that could provide more continuous and frequent monitoring of their disease process and support self-management. A common barrier, however, is the lack of engagement with technological interventions or devices that provide care remotely, which could lead to loss of resources invested and reduced quality of care.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This discrete choice experiment elicits the preferences of patients with heart failure with regard to potential remote care technologies that they would be willing to engage with and, in turn, creates a hierarchy of factors that can affect engagement for use within future technology design.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A survey was created using discrete choice design and with input from a patient and public involvement group. It was distributed online via social media to patients with heart failure and to patient support groups. The attributes used for the experiment were based on a previous systematic review looking at factors that affect engagement in remote care and which generated five central themes, each of which was assigned to an attribute directly: communication (increasing interaction between patients and health care staff/carers/other patients), clinical care (improving the quality of care compared to established practice), education (providing tailored information to help with self-care and reduce uncertainty), ease of use (the technical aspects of the intervention are easy to handle without issues), and convenience (the intervention fits well around the patient's lifestyle and requires minimal effort). Each of the five themes had two levels, positive and negative. The survey presented participants with multiple forced-choice two-alternative scenarios of remote care, which allowed them to trade attributes according to their preference. The results were analyzed using binary logit to obtain preference weights for each attribute.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 93 completed responses were entered into the analysis. The results of the binary logit created coefficients for each attribute, which equated to the relative preference of the associated themes: clinical care, 2.022; education, 1.252; convenience, 1.245; ease of use, 1.155; communication, 1.040. All calculated coefficients were statistically significant (P&lt;.01).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The results show that, in this cohort of patients with heart failure, the most preferred factor, clinical care, has enough value to be traded for approximately any two other factors. It also shows that the factor of communication is the least preferred attribute. Technology designers can use the associated preference weights to determine the relative increase of value perceived by pati","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"9 ","pages":"e68022"},"PeriodicalIF":2.2,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12588585/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145451903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Serial 12-Lead Electrocardiogram-Based Deep-Learning Model for Hospital Admission Prediction in Emergency Department Cardiac Presentations: Retrospective Cohort Study.","authors":"Arda Altintepe, Kutsev Bengisu Ozyoruk","doi":"10.2196/80569","DOIUrl":"10.2196/80569","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Emergency department (ED) crowding is often attributed to a slow hospitalization process, leading to reduced quality of care. Predicting early disposition in patients presenting with cardiac issues is challenging: most are ultimately discharged, yet those with a cardiac etiology frequently require hospital admission. Existing scores rely on single-time-point data and often underperform when patient risk evolves during the visit.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to develop and validate a real-time deep-learning model that fuses serial 12-lead electrocardiogram (ECG) waveforms with sequential vitals and routinely available clinical data to predict hospital admission early in ED encounters.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a retrospective cohort study using the Medical Information Mart for Intensive Care (MIMIC) IV, MIMIC-IV Emergency Department module, and MIMIC-IV electrocardiogram module databases. Adults presenting with chest pain, dyspnea, syncope, or presyncope and at least 1 ECG within their ED stay were included. Two evaluation cohorts were defined: all stays with ≥1 ECG (n=30,421) and a subset with ≥2 ECGs during the encounter (n=11,273). To predict hospital admission, we first established 2 baseline models: a tabular model (random forest [RF]) trained on structured clinical variables, including demographics, triage acuity, past medical history, medications, and laboratory results, and an ECG-only model that learned directly from raw 12-lead waveforms. We then developed a multimodal deep-learning model that combined ECGs with sequential vital signs as well as the same static tabular features. All models were restricted to data available during the stay up to the time of the last ECG. Performance was assessed with stratified 5-fold cross-validation using identical splits across models.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The multimodal model achieved an area under receiver operating characteristic (AUROC) of 0.911 when trained on all eligible stays. The model predicted disposition after the final ECG was taken, which was a median of 0.3 (IQR 0.2-5.3) hours after triage and 4.6 (IQR 2.7-7.3) hours before ED departure. Baseline models performed worse: the ECG-only model had an AUROC of 0.852, and the tabular RF had an AUROC of 0.886. In the subset requiring at least 2 ECGs within the stay, ECG-only reached an AUROC of 0.859, and RF, with the longer interval to chart tabular data, reached a higher AUROC of 0.911. The multimodal model had an AUROC of 0.924 and outperformed baselines in each cohort (paired DeLong P&lt;.001).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Serial ECGs, when integrated with evolving vitals and routine clinical features, enable accurate, early prediction of ED disposition in patients presenting with cardiac issues. This open-source, reproducible framework highlights the potential of multimodal deep learning to streamline ED flow, prioritize higher risk cases, and detect evo","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"9 ","pages":"e80569"},"PeriodicalIF":2.2,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12533930/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145312886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}