ArticleJMIR formative research2022
Atrial Fibrillation Detection With an Analog Smartwatch: Prospective Clinical Study and Algorithm Validation.
Article in JMIR formative research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04351386 (Validation Study of the Watch HWA09 and Its Softwares ECG-SW1 and PPG-SW1 for the Detection of Atrial Fibrillation), which is not on this map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Validation Study of the Watch HWA09 and Its Softwares ECG-SW1 and PPG-SW1 for the Detection of Atrial Fibrillation
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Wearable Devices for Remote Monitoring of Chronic Diseases: Systematic Review.JMIR mHealth and uHealth · 2026Pooled it
- Accuracy and Systematic Biases of Heart Rate Measurements by Consumer-Grade Fitness Trackers in Postoperative Patients: Prospective Clinical Trial.Journal of medical Internet research · 2022Trial
- Consumer Smartwatch Technology in Health and Performance Research: Validity, Limitations, and Real-World Applications.Sensors (Basel, Switzerland) · 2026Review
- Clinical evaluation of a spot-check ECG for the diagnosis of atrial fibrillation.Heart rhythm O2 · 2026Article
- Accuracy of Smartwatches in the Detection of Atrial Fibrillation: A Systematic Review and Diagnostic Meta-Analysis.JACC. Advances · 2025Article
- Noninvasive biometric monitoring technologies for patients with heart failure.Heart failure reviews · 2025Review
- Accuracy and interpretability of smartwatch electrocardiogram for early detection of atrial fibrillation: A systematic review and meta-analysis.Journal of arrhythmia · 2025Article
- Wearable Devices for Arrhythmia Detection: Advancements and Clinical Implications.Sensors (Basel, Switzerland) · 2025Review
- Comparison of diagnostic accuracy of electrocardiogram-based versus photoplethysmography-based smartwatches for atrial fibrillation detection: A Systematic Review and Meta-Analysis.Annals of medicine and surgery (2012) · 2025Article
- Continuous smartwatch monitoring after atrial fibrillation ablation: feasibility of burden estimation and association with quality of life.Frontiers in cardiovascular medicine · 2025Article
- Accuracy of Artificial Intelligence-Based Technologies for the Diagnosis of Atrial Fibrillation: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2023Review
- Electrocardiogram Devices for Home Use: Technological and Clinical Scoping Review.JMIR cardio · 2023Article
- Searching for atrial fibrillation post stroke: is it time for digital devices?Frontiers in cardiovascular medicine · 2023Review
- Digital health technology to support care and improve outcomes of chronic kidney disease patients: as a case illustration, the Withings toolkit health sensing tools.Frontiers in nephrology · 2023Review
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11 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundAtrial fibrillation affects approximately 4% of the world's population and is one of the major causes of stroke, heart failure, sudden death, and cardiovascular morbidity. It can be difficult to diagnose when asymptomatic or in the paroxysmal stage, and its natural history is not well understood. New wearables and connected devices offer an opportunity to improve on this situation.
objectiveWe aimed to validate an algorithm for the automatic detection of atrial fibrillation from a single-lead electrocardiogram taken with a smartwatch.
methodsEligible patients were recruited from 4 sites in Paris, France. Electrocardiograms (12-lead reference and single lead) were captured simultaneously. The electrocardiograms were reviewed by independent, blinded board-certified cardiologists. The sensitivity and specificity of the algorithm to detect atrial fibrillation and normal sinus rhythm were calculated. The quality of single-lead electrocardiograms (visibility and polarity of waves, interval durations, heart rate) was assessed in comparison with the gold standard (12-lead electrocardiogram).
resultsA total of 262 patients (atrial fibrillation: n=100, age: mean 74.3 years, SD 12.3; normal sinus rhythm: n=113, age: 61.8 years, SD 14.3; other arrhythmia: n=45, 66.9 years, SD 15.2; unreadable electrocardiograms: n=4) were included in the final analysis; 6.9% (18/262) were classified as Noise by the algorithm. Excluding other arrhythmias and Noise, the sensitivity for atrial fibrillation detection was 0.963 (95% CI lower bound 0.894), and the specificity was 1.000 (95% CI lower bound 0.967). Visibility and polarity accuracies were similar (1-lead electrocardiogram: P waves: 96.9%, QRS complexes: 99.2%, T waves: 91.2%; 12-lead electrocardiogram: P waves: 100%, QRS complexes: 98.8%, T waves: 99.5%). P-wave visibility accuracy was 99% (99/100) for patients with atrial fibrillation and 95.7% (155/162) for patients with normal sinus rhythm, other arrhythmias, and unreadable electrocardiograms. The absolute values of the mean differences in PR duration and QRS width were <3 ms, and more than 97% were <40 ms. The mean difference between the heart rates from the 1-lead electrocardiogram calculated by the algorithm and those calculated by cardiologists was 0.55 bpm.
conclusionsThe algorithm demonstrated great diagnostic performance for atrial fibrillation detection. The smartwatch's single-lead electrocardiogram also demonstrated good quality for physician use in daily routine care.
trial registrationClinicalTrials.gov NCT04351386; http://clinicaltrials.gov/ct2/show/NCT04351386.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.