Accuracy and interpretability of smartwatch electrocardiogram for early detection of atrial fibrillation: A systematic review and meta‐analysis

Background: The prevalence of atrial fibrillation (AFib) continues to increase globally, posing a significant risk for serious cardiovascular complications, such as ischemic stroke and thromboembolism. Smartwatch single-lead electrocardiogram (ECG) can be a practical and accurate early detection tool for AFib. Objective: The aim of this study was to fill the research gap in evaluating the accuracy and interpretability of smartwatch ECG for early AFib detection. Methods: Data derived from indexed literature in the Scopus, Scilit, PubMed, Google Scholar, Web of Science, IEEE, and Cochrane Library databases (as of June 1, 2024) were systematically screened and extracted. The quantitative synthesis was performed using a two-level mixed-effects logistic regression model, as well as a proportional analysis with Freeman-Tukey double transformation on a restricted maximum-likelihood model. Results: The sensitivity and specificity of smartwatch ECG in algorithmic readings were 86% and 94%, respectively. In manual readings, the sensitivity and specificity reached 96% and 95%, respectively. In a brand-specific subgroup analysis, the algorithmic reading reached a summary area under the curve (sAUC) of 96%, while another brand achieved the highest sAUC of 98% in manual reading. The level of manual interpretability was relatively high with Cohen's Kappa of 0.83, but 3% of ECG results were difficult to read manually. Conclusion: This study shows that smartwatch ECG is able to detect AFib with high accuracy, especially through manual reading by trained medical personnel. PROSPERO Registration: CRD42024548537 (May 29, 2024).