WEARABLE TECHNOLOGIES FOR ATRIAL FIBRILLATION: A NARRATIVE REVIEW OF DIAGNOSTIC ACCURACY, CLINICAL INTEGRATION, AND THE MANAGEMENT OF SILENT ARRHYTHMIAS

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia worldwide and represents a major contributor to stroke, heart failure, and cardiovascular mortality. A significant proportion of AF cases remain asymptomatic, often referred to as "silent AF," which delays diagnosis and increases the risk of severe thromboembolic complications. Conventional diagnostic methods, including standard electrocardiography (ECG) and short-term Holter monitoring, are limited by their episodic nature and reliance on clinical settings, frequently failing to capture intermittent arrhythmic events. In recent years, consumer wearable technologies - particularly smartwatches equipped with photoplethysmography (PPG) sensors and single-lead ECG capabilities - have emerged as promising tools for continuous cardiac rhythm monitoring.

This narrative review synthesizes contemporary evidence on the diagnostic accuracy, clinical integration, and behavioral implications of wearable technologies for AF detection. Literature published between 2018 and 2025 was identified through searches of PubMed/MEDLINE, Scopus, and Google Scholar. Studies evaluating sensing technologies, algorithmic approaches, large-scale population trials, and digital health behavior were included.

Current evidence demonstrates that wearable devices can achieve high concordance with traditional ECG-based diagnostic methods, with many studies reporting sensitivities and specificities exceeding 90%. Passive PPG monitoring combined with user-initiated ECG confirmation enables scalable detection of previously undiagnosed AF in real-world settings. However, challenges remain, including signal artifacts, false-positive alerts, user adherence, and socioeconomic disparities in device access.

Overall, wearable technologies represent a valuable adjunct for early AF detection and long-term rhythm monitoring. Their optimal clinical role lies within hybrid diagnostic pathways that integrate patient-generated data with physician validation and established cardiovascular care frameworks.