Smart Wearable Devices For Early Detection Of Cardiovascular Diseases: A Comprehensive Systematic Review

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, necessitating improved strategies for early detection and prevention. Smart wearable devices have emerged as promising tools in this context, capable of continuously monitoring physiological signals to identify asymptomatic or early-stage CVDs. Objective: This systematic review synthesizes evidence on the effectiveness of smart wearables in the early detection of CVDs. Methods: Following PRISMA guidelines, we searched multiple databases (2010–2025) for studies evaluating wearable devices in detecting cardiovascular conditions. Key data on device type, monitored parameters, target disease, diagnostic performance, and outcomes were extracted. Results: We included numerous studies (n ≈ 100) covering arrhythmia detection (especially atrial fibrillation, AF), ischemic heart disease, and heart failure monitoring. Wearable ECG and photoplethysmography-based devices demonstrated high accuracy for arrhythmia detection (sensitivity and specificity often ~90–95%), exemplified by large trials like the Apple Heart Study (419,000 participants) which showed low false-positive notification rates and confirmed AF in one-third of alerted individuals. Early evidence suggests wearables can also detect ischemic changes and predict heart failure decompensation days in advance. Conclusion: Smart wearables show considerable potential for early CVD detection, enabling timely intervention. They have demonstrated reliable performance in identifying arrhythmias and other cardiac abnormalities outside clinical settings. However, challenges remain in data quality, user adherence, and integration into healthcare workflows. Further large-scale studies and technological refinements are needed to fully realize wearables’ clinical impact in reducing CVD morbidity and mortality.