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...

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Vydané v:Journal of arrhythmia Ročník 41; číslo 3; s. e70087 - n/a
Hlavní autori: Iqhrammullah, Muhammad, Abdullah, Asnawi, Hermansyah, Ichwansyah, Fahmi, Rani, Hafnidar A., Alina, Meulu, Simanjuntak, Artha M. T., Rampengan, Derren D. C. H., Al‐Gunaid, Seba Talat, Gusti, Naufal, Damarkusuma, Arditya, Wikurendra, Edza Aria
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Japan John Wiley & Sons, Inc 01.06.2025
John Wiley and Sons Inc
Wiley
Predmet:
Accuracy
Algorithms
arrhythmia
Atrial fibrillation
Body mass index
Cardiac arrhythmia
Cardiovascular disease
ECG
Electrocardiogram
Electrocardiography
Keywords
Medical personnel
Meta-analysis
Mortality
Original
Patients
smartwatch
Smartwatches
Systematic review
Wearable computers
smartwatch
ECG
atrial fibrillation
electrocardiogram
arrhythmia
ISSN:1880-4276, 1883-2148
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Popis
Shrnutí: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). The smartwatch ECG algorithm achieved 86% sensitivity and 94% specificity with an sAUC of 96%, while manual readings by trained personnel reached 96% sensitivity, 95% specificity, and an sAUC of 98% in a brand‐specific subgroup analysis. These findings demonstrate that the smartwatch ECG detects AFib, although 3% of results are unreadable.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1880-4276
1883-2148
DOI:10.1002/joa3.70087