The WATCH AF trial: SmartWATCHes for detection of atrial fibrillation
Open accessAbstract
Objectives The WATCH AF (SmartWATCHes for Detection of Atrial Fibrillation) trial compared the diagnostic accuracy to detect atrial fibrillation (AF) by a smartwatch-based algorithm using photoplethysmographic (PPG) signals with cardiologists' diagnosis by electrocardiography (ECG).Background Timely detection of AF is crucial for stroke prevention.Methods In this prospective, 2-center, case-control trial, a PPG pulse wave recording using a commercially available smartwatch was obtained along with Internet-enabled mobile ECG in 672 hospitalized subjects. PPG recordings were analyzed by a novel automated algorithm. Cardiologists' diagnoses were available for 650 subjects, although 142 (21.8%) datasets were not suitable for PPG analysis, among them 101 (15.1%) that were also not interpretable by the automated Internet-enabled mobile ECG algorithm, resulting in a sample size of 508 subjects (mean age 76.4 years, 225 women, 237 with AF) for the main analyses.Results For the PPG algorithm, we found a sensitivity of 93.7% (95% confidence interval [CI]: 89.8% to 96.4%), a specificity of 98.2% (95% CI: 95.8% to 99.4%), and 96.1% accuracy (95% CI: 94.0% to 97.5%) to detect AF.Conclusions The results of the WATCH AF trial suggest that detection of AF using a commercially available smartwatch is in principle feasible, with very high diagnostic accuracy. Applicability of the tested algorithm is currently limited by a high dropout rate as a result of insufficient signal quality. Thus, achieving sufficient signal quality remains challenging, but real-time signal quality checks are expected to improve signal quality. Whether smartwatches may be useful complementary tools for convenient long-term AF screening in selected at-risk patients must be evaluated in larger population-based samples. (SmartWATCHes for Detection of Atrial Fibrillation [WATCH AF]:; NCT02956343).
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The reliability of AI-based screening tools for LVSD is limited by the presence of noise in data collected from wearable and handheld devices.
“but the reliability of such tools is limited by the presence of noise in data collected from wearable and handheld devices12,13.”
From Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices (2023) · cites this paper as [12]
The WATCH AF discussion reports that insufficient wrist-signal quality prevented the smartwatch algorithm from producing an AF diagnosis in 21.8% of subjects, and the Study Limitations section identifies a worsening signal-to-noise ratio as a potential problem. This supports a signal-quality limitation for wearable AF screening, but the paper does not establish the claim for AI-based LVSD screening. Page numbers are not available in the supplied passages. (LLM, conf=0.88)
Experimental — accuracy not yet measuredA wearable device-based atrial fibrillation screening study disqualified 22% of patients due to insufficient signal quality.
“with a wearable device-based atrial fibrillation screening study disqualifying 22% of patients due to insufficient signal quality12.”
From Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices (2023) · cites this paper as [12]
A common way to deal with artifact-corrupted PPGs is to exclude segments of poor signal quality and accelerometer-identified motion.
“A common way to deal with artifact-corrupted PPGs, and thus improve the reliability of arrhythmia detectors, is to exclude segments of poor signal quality [10,27,28] as well as accelerometer-identified motion [11][12][13][27][28][29][30].”
From Modeling of artifacts in the wrist photoplethysmogram: Application to the detection of life-threatening arrhythmias (2021) · cites this paper as [28]
The potential utility of wrist-worn PPG devices for detecting possible AF has been assessed in recent studies.
“The potential utility of wrist-worn PPG devices for detecting possible AF has been assessed in recent studies”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [198]
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- Authors
- Dörr, M., Nohturfft, V., Brasier, N., Bosshard, E., Djurdjevic, A., Gross, S., Raichle, C. J., Rhinisperger, M., Stöckli, R., & Eckstein, J.
- Affiliations
- Universitätsmedizin Greifswald (DE)
- University Hospital of Basel (CH)
- Corresponding author
- Dörr, Marcus
- Venue
- JACC. Clinical electrophysiology
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 5
- Issue
- 2
- Pages
- 199-208
- ISSN
- 2405-500X (ISSN-L)
- OpenAlex
- W2902001060
- Cited by
- 245
- References
- 34
- Citations by year
- 2026: 15 · 2025: 21 · 2024: 32 · 2023: 37 · 2022: 45 · 2021: 47 · 2020: 33 · 2019: 14
- FWCI
- 9.39
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- JACC. Clinical electrophysiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Universitätsspital Basel
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Atrial Fibrillation Management and Outcomes
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- atrial fibrillation, smartwatches, photoplethysmography, atrial fibrillation detection, diagnostic accuracy, stroke prevention, hospitalized patients, signal quality
- MeSH terms
- Wearable Electronic Devices, Aged, Aged, 80 and over, Algorithms, Atrial Fibrillation, Electrocardiography, Female, Humans, Male, Prospective Studies, Sensitivity and Specificity, Reproducibility of Results, Case-Control Studies, Photoplethysmography, Pulse Wave Analysis
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