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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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“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]
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“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]
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“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]
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“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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Consumer-Led Screening for Atrial Fibrillation
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Does asymptomatic atrial fibrillation exist?
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“95,96 The burden of AF has more recently been appreciated as a factor influencing the impact of AF on clinical endpoints.”
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“This was due to recent discussions regarding the potential of using consumer electronics for atrial fibrillation screening and recording of single-lead ECG, both worldwide [37,38] and locally.”
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“Commonly, studies evaluating AF detector performance tend to exclude poor quality segments [35], [36], [37], thus exag-gerating performance.”
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Digital Technologies in Heart Failure Management
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“Although these devices were highly specific and sensitive in detecting atrial fibrillation, a large percentage (> 20%) of rhythms were deemed non-analyzable in trials [26, 27], and therefore, accuracy is a major concern.”
BackgroundImproved Atrial Fibrillation Detection using CNN-LSTM
Tallapragada, V. V. S., Reddy, D. V., Varma, K. N. V. S., & Sarma, G. S. (2022) · International Conference on Trends in Electronics and Informatics (ICOEI) · doi:10.1109/icoei53556.2022.9776825 · Cited by 5
An augmented reality–based method to assess precordial electrocardiogram leads: a feasibility trial
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Atrial fibrillation and sport: need for monitoring
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A Smartwatch to Identify Atrial Fibrillation
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Toward Patient-Centered AI Fact Labels: Leveraging Extrinsic Trust Cues
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“The written vignettes were shared as text with the participants, except in one case where a participant struggled to read the text due to technical issue; in this instance, it was read aloud.”
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“[17] PPG algorithm with ECG 95 % Specific ECG smart-watch is required for the detection.”
MethodRantula, O., Lipponen, J. A., Halonen, J., Jäntti, H., Rissanen, T. T., Naukkarinen, N. S., Väliaho, E., Santala, O. E., Sedha, J., Martikainen, T. J., et al. (2025) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztaf055 · Cited by 4
“,21–26 Similar to our study, in a specific part of the study by Bonomi et al ., wristband PPG was captured during elective CV in stationary patients, and the reported sensitivities (97–98”
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Vorhofflimmerndiagnostik mittels EKG-fähiger Smartwatches
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“8% of the data was not interpretable due to poor signal quality [5].”
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Wearable health monitoring: wave of the future or waste of time?
Hudock, N. L., Hughes, H. M., Shaheen, N., Ramadan, A., Parikh, K., Anamika, F., & Jain, R. (2024) · Global Cardiology Science and Practice · doi:10.21542/gcsp.2024.21 · Cited by 5
“The Samsung watch was found to be 94% sensitive and 98% specific for detecting AF 32 .”
BackgroundEerdekens, R., Zelis, J. M., ter Horst, H., Crooijmans, C., Veer, M. V. ’., Keulards, D. C. J., Kelm, M., Archer, G. T., Kühne, T., Brueren, G. R. G., et al. (2024) · JMIR mhealth and uhealth · doi:10.2196/53964 · Cited by 3
“Smart phones and health watches, in particular, have found their way into the clinic [9].”
BackgroundScreening for Atrial Fibrillation: Risks, Benefits, and Implications On Future Clinical Practice
Ilyas, M. H., Sharih, A. M., Jido, J. T., Ali, A. R. Z., Khoshnaghsh, A., Nadeem, M., & Zahra, S. A. (2024) · Current Treatment Options in Cardiovascular Medicine · doi:10.1007/s11936-024-01047-1 · Cited by 3
“Although limited in numbers, some studies have demonstrated a high diagnostic accuracy for AF using PPG technology, e.g., smart watches [77–79].”
Eerikäinen, L. M., Bonomi, A., Schipper, F., Dekker, L., Vullings, R., de Morree, H. M., & Aarts, R. M. (2019) · TU/e Research Portal · doi:10.23919/cinc49843.2019.9005802 · Cited by 3
Eerikäinen, L. M., Bonomi, A., Schipper, F., Dekker, L., Vullings, R., de Morree, H. M., & Aarts, R. M. (2019) · Computing in cardiology · doi:10.22489/cinc.2019.086 · Cited by 3
Long term effect of vasectomy on pituitary-gonadal axis.
Devi, P. K., Joshi, U. M., Sb, M., Naik, V. K., Ps, S., & Sheth, A. R. (1977) · PubMed · Cited by 3
Saint‐Lary, L., Pinel, B., Panh, L., Jimenez, G., Geffrelot, J., Kirova, Y., Camilleri, J., Broggio, D., Bernier, M., Mandin, C., et al. (2025) · JMIR Research Protocols · doi:10.2196/67875 · Cited by 2
“ECG smartwatches for the screening of AF has been demonstrated in several studies (sensitivity 55%-97.3%, specificity 60%-98.2% [ 31,34-36]), in particular for silent or paroxysmal AF, by measuring continuous cardiac frequency and SL-ECG [37-39].”
InfluentialBun, S., Taghji, P., & Deharo, J. (2021) · Annales de Cardiologie et d Angéiologie · doi:10.1016/j.ancard.2020.12.005 · Cited by 2
Liran, O., Banon, T., & Grossman, A. (2021) · European Journal of General Practice · doi:10.1080/13814788.2021.1947237 · Cited by 2
Zaydi, H., & Bakkoury, Z. (2023) · International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) · doi:10.1109/iceccme57830.2023.10253335 · Cited by 2
“Connected watches have been used in clinical settings for specific diseases such as AF [5], tachycardia [6], and all arrhythmias.”
MethodDigitale Kompetenzen in der Rhythmologie
Emrani, M., & Zink, M. D. (2024) · Herzschrittmachertherapie + Elektrophysiologie · doi:10.1007/s00399-024-00990-7 · Cited by 2
Oxygen saturation measurement in cyanotic heart disease with the Apple watch
Weis, A., Leroy, M., Jux, C., Rupp, S., & Backhoff, D. (2024) · Cardiology in the Young · doi:10.1017/s1047951124025216 · Cited by 2
Kitajima, H., Takeda, K., Ishizawa, M., Aihara, K., & Minamino, T. (2025) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0212068 · Cited by 2
Alpar, S., & Tatlıparmak, A. C. (2025) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2025.05.037 · Cited by 2
Digital health solutions in the screening of subclinical atrial fibrillation
König, S., Bollmann, A., & Hindricks, G. (2021) · Herz · doi:10.1007/s00059-021-05041-2 · Cited by 2
Osca, J., de Francisco, M. T. I., Pérez, Ó. C., de Carranza, M. J. S.-T., Rubio, J. A., Palop, C. P., Vidal, J., & Martínez‐Dolz, L. (2020) · Revista Española de Cardiología · doi:10.1016/j.recesp.2020.05.006 · Cited by 2
Place de la médecine connectée dans la détection de la fibrillation atriale
Saoudi, N. C., Latcu, G., Enache, B., Yaïci, K., Zarqane, N., Benhenda, N., & Pathak, A. (2021) · Bulletin de l Académie Nationale de Médecine · doi:10.1016/j.banm.2021.09.014 · Cited by 2
Automated Indicator of Atrial Fibrillations Risk Using Machine Learning
Almazrouei, M., & Al-Rajab, M. M. (2021) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci54926.2021.00254 · Cited by 2
Design of Health Detection System for Elderly Smart Watch Based on Biosignal Acquisition
Zhu, Z., Wang, P., & Wang, F. (2022) · Journal of Sensors · doi:10.1155/2022/6988001 · Cited by 2
Role of Artificial Intelligence in Biomedical Imaging
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The potential for photoplethysmographic (PPG)-based smart devices in atrial fibrillation detection
Harrison, S. L., Lane, D. A., Guo, Y., & Lip, G. Y. H. (2020) · Expert Review of Medical Devices · doi:10.1080/17434440.2020.1740085 · Cited by 2
“Studies have included patients from different settings including hospitals [7,9,11,12], primary care [8] or all adults who own a particular device [10].”
BackgroundMethodInfluentialPenkert, H., Härtl, J., Hapfelmeier, A., Egert-Schwender, S., Heimsch, E., Friedenberg, S., Müller, A., Hahn, F., Martens, E., & Wunderlich, S. (2025) · Frontiers in Neurology · doi:10.3389/fneur.2025.1661087 · Cited by 2
Armas, R. E. D., & Singh, J. P. (2026) · Current Cardiology Reports · doi:10.1007/s11886-026-02358-4 · Cited by 2
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Smartwatch helps detects lung cancer: Using personal technology to advance healthcare
Cole, A. P. C., Kar, A. J., Nimako, K., & Smelt, J. (2022) · JRSM Open · doi:10.1177/20542704211068651 · Cited by 1
AI and The Cardiologist-When Mind, Heart and Machine Unite
D’Costa, A., & Zatale, A. (2022) · Communications in computer and information science · doi:10.1007/978-3-030-95502-1_10 · Cited by 1
Smart Watches: Capturing Our Hearts
Higgins John, P., Cerra, Z. A., & Apicella, M. (2022) · Journal of Cardiothoracic Surgery and Therapeutics · doi:10.36959/582/441 · Cited by 1
Pfeiffer, B. F., & de Almeida, C. P. (2022) · Research Society and Development · doi:10.33448/rsd-v11i16.37774 · Cited by 1
Explainable detection of atrial fibrillation using deep convolutional neural network with UCMFB
Rao, B. M., & Kumar, A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15123-4 · Cited by 1
“Various techniques for early-stage detection of AF pathology were developed and validated in recent years [17, 32, 47, 52, 57].”
MethodAlzahrani, A., Pyevich, V., & Law, I. H. (2024) · JACC Case Reports · doi:10.1016/j.jaccas.2024.102314 · Cited by 1
Wearables in der Schlaganfallmedizin
Baumgartner, C., Baumgartner, J., Pirker-Kees, A., & Rumpl, E. (2021) · Klinische Neurophysiologie · doi:10.1055/a-1254-9616 · Cited by 1
“[37] Dörr M, Nohturfft V, Brasier N et al.”
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A model to enhance the atrial fibrillations’ risk detection using deep learning
Almazrouei, M., & Al-Rajab, M. M. (2022) · Periodicals of Engineering and Natural Sciences (PEN) · doi:10.21533/pen.v10.i3.650 · Cited by 1
Zhang, A. A., Sia, C., & Poh, K. K. (2025) · Singapore Medical Journal · doi:10.4103/singaporemedj.smj-2025-189 · Cited by 1
“[19,20] Mobile devices have also been demonstrated to detect ventricular tachycardia and fibrillation with an accuracy of up to 96.3%.”
InfluentialDörr, M., Nohturfft, V., Brasier, N., Bosshard, E., Djurdjevic, A., Groß, S., Raichle, C. J., Rhinisperger, M., Stöckli, R., & Eckstein, J. (2019) · JACC. Clinical electrophysiology · doi:10.1016/j.jacep.2019.03.001 · Cited by 1
Cardiac Health Assessment using a wearable device before and after TAVIPreprint
Eerdekens, R., Zelis, J. M., ter Horst, H., Crooijmans, C., Veer, M. V. ’., Keulards, D. C. J., Kelm, M., Archer, G. T., Kühne, T., Brueren, G. R. G., et al. (2023) · medRxiv · doi:10.1101/2023.03.22.23287604 · Cited by 1
“Smart phones and health watches, in particular, have found their way into the clinic.(6) These devices can detect atrial fibrillation(7) and predict the wearer’s 5-year risk of dying.”
Screening for atrial fibrillation and treatment with anticoagulant to prevent stroke
Ghazal, F. (2020) · Figshare
Metadata
- 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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