Background: Atrial fibrillation (AF) is a leading cause of cardiovascular morbidity and mortality, and early detection is important for stroke prevention. The Apple Heart Study (AHS) demonstrated that wearable devices capable of irregular pulse notifications (IPN) can aid in identifying AF. The rates of subsequent diagnoses of cardiovascular disease (CVD) remain unknown. Hypothesis: We aimed to evaluate how smartwatch-detected IPNs are associated with key cardiovascular diagnoses. We hypothesized that, among AHS participants, receiving IPNs would be associated with increased odds of subsequently reporting major cardiovascular diagnoses compared to participants without IPNs. Methods: Out of 419,297 enrolled AHS participants, 288,533 (69%) individuals who completed the end-of-study survey (EOS) were included in this analysis. The self-reported diagnoses consisted of heart failure (HF), stroke, transient ischemic attack (TIA), myocardial infarction (MI), and pulmonary embolism (PE), considered individually and as a composite (MACE). Logistic regression was used to estimate odds ratios (ORs) for each diagnosis among IPN versus non-IPN participants, adjusted for sex, age, and CHA2DS2-VASc components. Results: The EOS survey was completed by 908 notified participants (42% among the IPN cohort) and 287,625 non-notified participants (69% among the non-IPN cohort). IPN recipients were older, more often male, and had a higher comorbidity burden and CVD risk factors than the total cohort (Table 1). The IPN cohort reported higher rates of HF, MI, stroke, TIA, and PE compared to those who did not receive an IPN (Table 2). The fully adjusted logistic regression analysis showed increased odds of each diagnosis among the IPN group (Figure 1). Conclusions: Participants with an IPN had higher odds of subsequently reporting major adverse cardiovascular diagnoses. These associations may reflect pre-existing conditions, underlying CVD risk, and comorbidities among participants who received an IPN, which has a high positive predictive value (PPV) for detecting AF. Given the elevated odds and high PPV, IPNs may warrant clinical consideration of evaluation for CVD or comorbidities, and future studies should assess the diagnostic and management pathways following IPNs.

INTRODUCTION:Cardiovascular trials often underrepresent non-White participants, women, and older individuals. METHODS:We evaluated the impact of digital recruitment on improving diversity in two large prospective cardiovascular studies: the Apple Heart Study (AHS) and the Project Baseline Health Study (PBHS). While both leveraged digital tools, their strategies differed-AHS used a passive, app-based approach followed by a direct-to-participant outreach effort, while PBHS implemented an enrollment model to reach a prespecified diversity goal. RESULTS:While both methods led to a cohort of participants who were comparably aligned with the US Census, we found that intentional, goal-driven digital outreach, and participant selection targeting demographic representation improved inclusion of historically underrepresented groups. CONCLUSIONS:These findings suggest that digital tools, when paired with intentional strategy, may help support more inclusive, representative cardiovascular research.

BACKGROUND:The association between particulate matter (PM) air pollution and ventricular arrhythmias is not well established. In patients with cardiac implantable electronic devices (CIEDs), publicly available day-level air pollution data provide a unique opportunity to study acute and subacute effects of PM pollution. OBJECTIVE:The purpose of this study was to evaluate the association of air pollution with ventricular arrhythmias, physical activity, and CIED markers of heart failure. METHODS:We performed a retrospective cohort study using the CERTITUDE database (Biotronik SE & Co. KG, Berlin, Germany) of patients with CIEDs. The primary predictors were Air Quality Index (AQI), PM < 10 μm in diameter, and PM < 2.5 μm in diameter (PM2.5). We cross-linked day-level air pollutant levels with patient zip codes. We determined the association of air pollution with CIED parameters using (1) a case-crossover analysis using a conditional logistic regression and (2) a time-varying exposure analysis with the Andersen-Gill model. RESULTS:The study cohort included 28,349 patients (9062 [32%] female; mean age 72.8±11.9 years), of whom 17,448 (61.6%) had pacemakers and 9079 (32%) had defibrillators. AQI and PM2.5 were associated with significant changes in physical activity, heart rate, and thoracic impedance. When limiting to the 8687 patients living in Western US Fire States (California, Oregon, Washington, Arizona, Utah, Nevada, New Mexico, and Colorado), there was a strong association between PM2.5 and premature ventricular contraction burden, with an odds ratio of 7.72 (95% confidence interval 7.48-7.96; P < .0001) for PM2.5 ≥ 13.7. Multiple sensitivity analyses demonstrated the stability of our findings. CONCLUSION:In a large cohort of patients with CIEDs, AQI and PM2.5 had significant associations with premature ventricular contraction burden, physical activity, and heart rate. These data also demonstrate the feasibility of linking environmental data with patient sensor data to evaluate exposure-outcome relationships.

BACKGROUND:Dronedarone and sotalol are antiarrhythmic drugs (AADs) recommended in similar populations per atrial fibrillation (AF) guidelines; however, comparative safety data are limited. OBJECTIVES:The goal of this study was to assess the safety of dronedarone vs sotalol for treatment of AF in AAD-naive patients. METHODS:This was a prespecified noninterventional meta-analysis of 4 retrospective observational cohort studies from 4 databases (Optum Clinformatics Data Mart, Merative MarketScan, Veterans Health Administration Electronic Health Record, and the Swedish National Patient Register) conducted by using one master protocol. Each analysis emulated the target trial using an active comparator (dronedarone vs sotalol), new user design with an as-treated approach. Primary outcomes were tested hierarchically for dronedarone vs sotalol: first for statistical significance of cardiovascular (CV) hospitalization, and then for statistical significance of ventricular arrhythmias. Propensity score matching (PSM) was used for confounding control, and negative control outcomes were used to assess residual confounding. Outcomes were evaluated by using Cox proportional hazards regression; meta-analysis was performed by using fixed effects models. RESULTS:The dronedarone and sotalol cohorts were well balanced within databases before and after PSM (after PSM mean age range: 62.5-70.9 years; mean CHA2DS2-VASc score range: 1.81-3.15). Negative control outcomes exhibited little-to-no evidence of residual confounding. Meta-analysis found significantly lower rates of CV hospitalization (pooled HR: 0.91; 95% CI: 0.85-0.97) and ventricular arrhythmias (pooled HR: 0.77; 95% CI: 0.69-0.85) with dronedarone vs sotalol. CONCLUSIONS:In this retrospective meta-analysis, dronedarone exhibited significantly lower rates of CV hospitalization and ventricular arrhythmias compared with sotalol. These findings provide real-world evidence to support selection of the most appropriate first-line AAD for rhythm control in patients with AF.

Importance Consumer wearable technologies have wide applications, including some that have US Food and Drug Administration clearance for health-related notifications. While wearable technologies may have premarket testing, validation, and safety evaluation as part of a regulatory authorization process, information on their postmarket use remains limited. The Stanford Center for Digital Health organized 2 pan-stakeholder think tank meetings to develop an organizing concept for empirical research on the postmarket evaluation of consumer-facing wearables. Observations The postmarket evaluation of consumer wearables involves broad consideration of an individual consumer's journey from acquisition, intended and unintended use of the wearable, and access to health care resources on receipt of a notification. For individuals who do access the health care system, a wearable's downstream effects can be studied through appropriate clinical evaluation, delivery of guideline-directed treatments, shared decision-making in areas of clinical equipoise, and analysis of clinical end points and patient harms. Effective postmarket research draws from denominators appropriate to the clinical question, with clearly defined parameters for success and failure. Generalizability related to data completeness and reliability should also be considered. As patients increasingly integrate wearables into their health monitoring, cross-platform data sharing with a focus on privacy and data quality can drive patient-centered innovation and identify opportunities to bridge gaps in medical care. Relevance The think tank identified priorities in postmarket research, comprising the journey from consumer to patient and accounting for patient, clinician, health care delivery system, and societal impacts of consumer wearables. Overall, this approach serves not only to organize the study of consumer wearables but also to act as a guidepost for using real-world data in postmarket research.

The emergence and rapid adoption of digital health technologies (DHT) present unprecedented opportunities to democratize and reduce disparities in health care by monitoring health and disease at the point of care in all patients. However, limited access to DHT is becoming a major obstacle to realizing these goals. Access to DHT is influenced not only by well-recognized social determinants of health, but also by digital determinants of health, such as digital literacy and the need for broad access to digital infrastructure, as well as commercial and economic factors. Addressing these challenges and designing unbiased systems of care are essential to enable broad access to DHT and to benefit diverse and under-represented communities. Doing so will fill gaps in the clinical evidence base and avoid perpetuating historical biases. In this Review, we propose a personalized framework to improve access to DHT, addressing determinants of access at the individual, interpersonal, community, society, government and industry levels. We frame these issues globally, highlighting how the challenges to DHT access and potential solutions might differ between continents while also emphasizing common themes. We provide perspectives from partners across the spectrum of health care, including clinicians, clinical trialists, and experts from digital health and industry. In this Review, Narayan and colleagues discuss global disparities in access to digital health technologies, with a focus on cardiovascular medicine. The authors summarize the factors that affect access at various levels of society and present solutions that target each of these levels, culminating in a personalized framework to improve access to digital health technologies.

Objective To evaluate differences in study engagement in diverse racial/ethnic groups that have been significantly underrepresented in atrial fibrillation and digital clinical trials. Patients and Methods This was a secondary analysis of participants from the Apple Heart Study, a prospective, siteless, single-arm pragmatic clinical trial from November 29, 2017, to January 31, 2019. Black, Hispanic, Asian, and White participants were monitored using an irregular rhythm notification algorithm designed to detect atrial fibrillation on a smartwatch. Logistic regression was performed to evaluate the relationship between race/ethnicity and completion of the first study visit after an irregular rhythm notification, adjusting for demographic characteristics and comorbidities. Results Of the 419,297 participants, 393,396 (93.8%) individuals self-identified as White, Black, Hispanic, or Asian. Overall, participants were 57% men and had a mean (SD) age of 41 (13) years. Among 2044 (0.52%) participants who received an irregular rhythm notification, non-White participants had lower odds of completing the initial virtual study visit compared with White participants (Black: OR, 0.61; 95% CI, 0.39-0.94; Hispanic: OR, 0.62; 95% CI, 0.40-0.95; Asian: OR, 0.40; 95% CI, 0.23-0.66) after multivariate adjustment. Among those who completed the initial study visit, there was no statistically significant difference in the odds of returning the electrocardiogram patch in the non-White groups compared with that of the White group. Conclusion Despite successful recruitment of racially and ethnically diverse participants, there were differences in subsequent engagement by non-White compared with that by White participants. Equitable representation and engagement of diverse racial and ethnic groups in digital clinical studies requires further study. Trial Registration Clinicaltrials.gov Identifier: NCT03335800

Objective We evaluated the performance of risk models that incorporate ambulatory ECG data and clinical information for prediction of healthcare expenditures related to heart failure (HF) and stroke events in treated and untreated patients.Design and setting A retrospective cohort study of Medicare patients who underwent Zio XT ambulatory monitoring in the USA was conducted between 2014 and 2020.Participants and outcomes 14-day ambulatory ECG data and claims data were evaluated in the study sample which included 89 923 patients in the HF hospitalisation group, 75 870 in the new-onset HF group and 90 159 in the stroke hospitalisation group. Predictive models for new-onset HF, HF hospitalisation and stroke hospitalisation were generated using LASSO Cox regression with ambulatory ECG variables and components of the CHA2DS2-VASc. For each outcome, we scored patients using standardised linear predictors from three composite risk models, and we evaluated the association between risk score and total Medicare cost.Results The following hazard ratios per one SD increase in the new risk score were observed for the model that included all CHA2DS2-VASc components and ECG variables: HF hospitalisation in treated 2.94, 95% CI 2.75 to 3.15; new-onset HF in treated 1.84, 95% CI 1.75 to 1.93; HF hospitalisation in untreated 3.51, 95% CI 3.23 to 3.82; and new-onset HF in untreated 1.92, 95% CI 1.85 to 2.00. Risk scores generated by the model were also predictive of Medicare cost in both treated and untreated patients, with patients in the high-risk category for all outcomes having the greatest Medicare costs during 1 year of follow-up.Conclusions Integrating arrhythmia data from ambulatory ECG monitoring into clinical risk models allows for better prediction of healthcare utilisation and cost in both treated and untreated patients at high risk for HF and stroke events.

Background: Long-term continuous ambulatory cardiac monitoring (LTCM) is a widely used diagnostic tool for arrhythmia detection, outperforming other modalities. COVID-19 accelerated adoption of home enrollment (HE) for LTCM, which includes mailing devices to patients for self-application and activation, highlighting the need for patient-centered solutions that optimize usability and comfort. HE was recently made available for a next-generation LTCM, which is smaller and lighter than prior designs, with a breathable adhesive, and has demonstrated superior performance. Aims: We assessed wear compliance and ECG signal quality for next generation LTCM devices applied in-clinic by a technician vs. HE. Additionally, we evaluated the impact of a smartphone app on wear compliance and ECG quality. Methods: U.S. adults prescribed the Zio Monitor (iRhythm Technologies, San Francisco, CA) for 14 days between December 2, 2024 - March 16, 2025, were included, corresponding to the initial availability of HE for Zio Monitor. Outcomes compared between in-clinic and HE devices included mean wear time, mean analyzable time (% free from artifact), early wear terminations (≤ 2 days), and actionable arrhythmia yield. Additional analyses evaluated outcomes among patients opting to use a smartphone app (MyZio), which provides onboarding, digitized instructions, and reminders for wear and return, vs. those who did not. Results: Of 304,735 LTCM devices worn, 276,142 (91.6%) were applied in-clinic and 28,593 (9.4%) were HE. Mean age was 61.5±17.9 years; 56.0% were female. App use was higher in the HE group (54% vs 17%, p < 0.0001). Mean wear time and % analyzable time were high and comparable for in-clinic and HE. Early wear terminations were infrequent in both groups and arrhythmia yield was comparable. App use was associated with lower % of early wear terminations and greater analyzable time in both groups (Table). Among prescribed devices, return compliance (activated, worn and returned ≤ 45 days) was higher in app users for both in-clinic (96.0% vs. 93.2%) and HE (90.4% vs. 71.1%) devices. Conclusion: Wear compliance and percent analyzable time for a next-generation LTCM were high and comparable when applied in-clinic by a technician vs. HE, indicating that HE achieves comparable arrhythmia detection while eliminating in-clinic visits and reducing provider burden. Patient apps as medical device adjuncts may further improve enrollment and compliance with home-based or ambulatory diagnostics.

Recent advances in remote monitoring technologies have empowered continuous tracking of physiologic parameters with high accuracy and granularity. However, there are important differences in the hardware, data processing, and feedback streams of individual devices which can impact the use of digital health technologies when evaluating safety in studies. This expert panel is the result of a think tank through a public-private partnership of the Cardiovascular Sciences Research Consortium and the U.S. Food and Drug Administration. The white paper discusses regulatory considerations for remote monitoring through digital health technologies to provide a framework for their use when evaluating safety or adverse events. It also provides practical recommendations and best practices on the implementation of these technologies into clinical trials through a scientific, technical, and operational lens. This manuscript does not constitute regulatory guidance. (JACC Adv. 2025;4:102059) Published by Elsevier on behalf of the American College of Cardiology Foundation. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

This article reviews the use of digital wearable technologies for monitoring of three common cardiovascular conditions: hypertension, heart failure, and atrial fibrillation.