Atrial fibrillation (AF) is the most common sustained arrhythmia in the United States, with a nation-wide prevalence projected to reach 12.1 million by 2030.¹ AF is a major driver of adverse clinical outcomes, including stroke, dementia, myocardial infarction, sudden cardiac death, heart failure, chronic kidney disease, and all-cause death.¹ Moreover, AF places a substantial strain on health care costs, accounting for approximately $28 billion annually in expenditures. Contemporary management recognizes AF as a chronic, systemic condition in which outcomes are greatly influenced by modifiable risk factors.

Reflecting the growing evidence, the 2023 ACC/AHA/Multisociety Guideline for the Diagnosis and Management of AF places lifestyle and risk factor modification (LRFM) at the center of AF care, with recommendations targeting obesity, physical inactivity, alcohol use, smoking, and hypertension.¹ This update highlights the importance of LRFM, which requires sustained, longitudinal behavior change, an area in which traditional care models have fallen short due to the time-intensive nature of counseling and limited infrastructure to support ongoing patient follow-up.² As such, AF is particularly well suited to digital health approaches, particularly those augmented by artificial intelligence (AI) and wearable devices, which may bridge the gap between the demands of LRFM and current health care delivery. However, realizing this potential will depend on parallel transformation in health system infrastructure and care delivery.

The Evidence for Digital Health

Digital health, including wearable devices, remote monitoring platforms, and personalized medication tools, offers the opportunity to operationalize a true medical neighborhood for AF, extending care beyond episodic clinic visits to deliver continuous and coordinated management, with shared accountability between patients and care teams.² This medical neighborhood can support the sustained engagement required for behavioral risk factor modification and offer a scalable solution to the resource-intensive risk management clinics that have been successful in reducing morbidity of AF.²

Importantly, the feasibility of digital health infrastructure is established. Data from large-scale studies such as the Apple Heart Study have demonstrated the ability of wearable devices to accurately detect AF, highlighting the potential for continuous, real-world monitoring.³ In the mAFA-II (Mobile Health [mHealth] Technology for Improved Screening, Patient Involvement and Optimizing Integrated Care in Atrial Fibrillation II), a cluster-randomized trial, a mobile health-guided intervention incorporating virtual AF management including stroke prevention and symptom and comorbidity/risk factor modification reduced clinical events, including death and hospitalization, by 60%, with benefits observed even among older adults.⁴ Similarly, the mTECH Afib (Patient Centered mobile health TECHnology Enabled Atrial Fibrillation Management) trial results demonstrated that a 12-week virtual AF management program incorporating a smartphone app, smart devices (including Apple Watch [Apple, Inc.]), and virtual coaching was both feasible and accepted by patients, supporting the practicality of digital care delivery.⁵

The results of the AF-HEART (Risk Factor Management of Atrial Fibrillation Using mHealth: The Atrial Fibrillation – Helping Address Care with Remote Technology) study, a randomized trial using wearable smart device monitoring of rhythm, weight, and blood pressure alongside telehealth visits with a dietician, demonstrated improvements in quality of life (QoL) and weight loss.⁵ As emphasized recently by Isakadze et al., digital health technologies may address persistent implementation gaps in LRFM by enabling scalable, longitudinal care delivery.⁵

Artificial Intelligence in Health

AI represents a critical evolution in this paradigm, underpinning the development of a smart health ecosystem. By integrating data streams from wearables and mobile applications, AI has the potential to provide personalized coaching, monitor adherence, and identify opportunities for early risk factor modification.^5,6 Evidence supporting AI-enabled digital health interventions in AF is expanding, although it remains in a translational phase. In the CHAT-AF (Coordinating Health Care With Artificial Intelligence–Supported Technology for Patients With Atrial Fibrillation) trial, an AI-driven telephone-based engagement platform delivering education, monitoring, and longitudinal support to patients living with AF was associated with improvements in patient-reported QoL.⁶ However, the current landscape remains fragmented, with substantial heterogeneity in intervention design, cost, clinical rigor, and accessibility.^5,7 Although agentic AI holds potential for scaling digitally enabled LRFM, future efforts are needed to design, implement, and rigorously evaluate AI-based tools, including agentic systems, for patients with AF.

As AI-enabled wearable technologies become more widely adopted, algorithmic black boxes, limited transparency, and underrepresentation of diverse populations may inadvertently amplify existing health inequities.⁷ Addressing these challenges will require deliberate, multidisciplinary design with rigorous validation to ensure that emerging digital health technologies do not perpetuate a digital divide, particularly among older adults and socioeconomically disadvantaged groups.^5,7

Equally salient is the challenge of clinical integration. An example of integration of remote monitoring into the electronic health record (EHR) was demonstrated in the EXTEND (EXpanding Technology-Enabled, Nurse-Delivered Chronic Disease Care) trial. In this randomized study, device-based monitoring was coupled with infrastructure that transmitted data directly into the EHR dashboard, enabling seamless data exchange, clinician engagement, and self-monitoring for those with complex chronic conditions.⁸ However, integration of remotely collected health data into the EHR remains evolving. Without interoperability for AF-related data, the expanding ecosystem of digital health technologies risks fragmenting care into siloed data streams while increasing administrative burden on clinicians.⁹

Ultimately, realizing the promise of AI-enabled digital health in AF management via sustained LRFM will require both innovation and effective implementation, with an emphasis on embedding tools into equitable and interoperable care models. If thoughtfully designed and evaluated, these technologies have the potential to transform AF care from episodic, reactive management to a proactive and longitudinal model capable of sustainably delivering LRFM at scale (Figure 1).

Figure 1: Operationalization of LRFM for AF: AI-Enabled Digital Health Technology

AF = atrial fibrillation; AI = artificial intelligence; EHR = electronic health record; HTN = hypertension; LRFM = lifestyle and risk factor modification; QoL = quality of life.

References

1. Writing Committee Members, Joglar JA, Chung MK, et al. 2023 ACC/AHA/ACCP/HRS guideline for the diagnosis and management of atrial fibrillation: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol. 2024;83(1):109-279. doi:10.1016/j.jacc.2023.08.017
2. Bhat A, Khanna S, Chen HHL, et al. Integrated care in atrial fibrillation: a road map to the future. Circ Cardiovasc Qual Outcomes. 2021;14(3):e007411. doi:10.1161/CIRCOUTCOMES.120.007411
3. Garcia A, Balasubramanian V, Lee J, et al. Lessons learned in the Apple Heart Study and implications for the data management of future digital clinical trials. J Biopharm Stat. 2022;32(3):496-510. doi:10.1080/10543406.2022.2080698
4. Guo Y, Romiti GF, Proietti M, et al. Mobile health technology integrated care in older atrial fibrillation patients: a subgroup analysis of the mAFA-II randomised clinical trial. Age Ageing. 2022;51(11):afac245. doi:10.1093/ageing/afac245
5. Isakadze N, Armoundas A, Sanders P, et al. Digital health tools for patient-centered lifestyle and risk factor modification in atrial fibrillation management. Heart Rhythm. Published online April 20, 2026. doi:10.1016/j.hrthm.2026.04.002
6. Trivedi R, Laranjo L, Marschner S, et al. Conversational AI phone calls to support patients with atrial fibrillation: randomized controlled trial. JMIR Cardio. 2025;9:e64326. Published 2025 Aug 19. doi:10.2196/64326
7. Jain SS, Elias P, Poterucha T, et al. Artificial intelligence in cardiovascular care-part 2: applications: JACC review topic of the week. J Am Coll Cardiol. 2024;83(24):2487-2496. doi:10.1016/j.jacc.2024.03.401
8. Shaw RJ, Montgomery K, Fiander C, et al. Mobile monitoring-enabled telehealth for patients with complex chronic illnesses. Stud Health Technol Inform. 2024;310:194-198. doi:10.3233/SHTI230954
9. Cheung CC, Gay H, Mendenhall GS. Managing data overload: AI, wearables, and apps. Heart Rhythm. 2024;21(10):e274-e276. doi:10.1016/j.hrthm.2024.08.008