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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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“A study was conducted by Chen et al. [52] on the detection of atrial fibrillation utilizing a smart wristband that included ECG and photoplethysmography sensors.”
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“79 The detection of AF is always more challenging at higher heart rates, so the effectiveness is often context dependent, presenting a challenge in detecting arrhythmia, which has a low incidence across the general population while older populations who have a higher incidence of AF are less likely…”
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“Finally, the SmartWATCHes for Detection of Atrial Fibrillation study, which analyzed pulse data collected from smartwatches using different parameters of heart rate variability than this study (normalized RMSSD and ShE) also found that pulse data can be accurate for AF diagnosis [20].”
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“The mechanism is based on a light directed at the skin and a photo sensor [23,24,51,52].”
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“In the WATCH AF trial, although PPG-based automated AF detection algorithms using smartwatch’ recordings have high diagnostic accuracy when compared with blinded cardiologists’ assessment of these devices tracings, its applicability may be limited by uninterpretable recordings, which may be present in up to 20% of cas…”
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“Due to inconvenience, invasiveness or cost inefficiency, these screening methods are not applicable to everyone (8).”
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“The photoplethysmography technology has also been incorporated in smartwatches to measure HR and rhythm (Dorr 2019, Guo 2019).58,59 Some have developed Circ Arrhythm Electrophysiol.”
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“Smartwatches empowered by arti fi cial intelligence (AI) algorithms have emerged as a promising tool for early detection of AF (10, 11).”
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“Indeed, subsequent development of non-invasive sensing (photoplethysmography) enabled new and exciting possibilities to track health and fitness in a large number of people (Dörr et al., 2019; Seshadri et al., 2020).”
BackgroundPrecision Medicine Approaches to Cardiac Arrhythmias
Giudicessi, J. R., Ackerman, M. J., Fatkin, D., & Kovacic, J. C. (2021) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2021.03.325 · Cited by 30
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Detection of Arrhythmias Using Smartwatches—A Systematic Literature Review
Bogár, B., Pető, D., Sipos, D., Füredi, G., Keszthelyi, A., Betlehem, J., & Pandur, A. A. (2024) · Healthcare · doi:10.3390/healthcare12090892 · Cited by 30
Cardiac Electronic Devices: Future Directions and Challenges
Kotalczyk, A., Kalarus, Z. F., Wright, D. J., Boriani, G., & Lip, G. Y. H. (2020) · Medical Devices Evidence and Research · doi:10.2147/mder.s245625 · Cited by 29
Saarinen, H. J., Joutsen, A. S., Korpi, K., Halkola, T., Nurmi, M., Hernesniemi, J. A., & Vehkaoja, A. T. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.1100127 · Cited by 27
Eerikäinen, L. M., Bonomi, A. G., Dekker, L. R. C., Vullings, R., & Aarts, R. M. (2020) · Cardiovascular Digital Health Journal · doi:10.1016/j.cvdhj.2020.03.001 · Cited by 24
“The measurements were made before and after cardioversion, before or during catheter ablation or electrophysiological study,(19,31,39) in the emergency care or cardiac ward,(40) in postoperative patients,(22,33) in admitted patients,(20,21,27,28) and in laboratory settings.(34,37,38) The recordings from these settings…”
BackgroundMethodEarly warning of atrial fibrillation using deep learning
Gavidia, M. E., Zhu, H., Montanari, A. N., Fuentes, J., Cheng, C., Dubner, S., Chames, M., Maison‐Blanche, P., Rahman, M. M., Sassi, R., et al. (2024) · Patterns · doi:10.1016/j.patter.2024.100970 · Cited by 31
Fekete, M., Liotta, E. M., Molnár, T., Fülöp, G., & Lehoczki, A. (2024) · GeroScience · doi:10.1007/s11357-024-01290-1 · Cited by 27
State of the art of mobile health technologies use in clinical arrhythmia care
Sridhar, A. R. M., Cheung, J. W., Lampert, R., Silva, J. N. A., Gopinathannair, R., Sotomonte, J. C., Tarakji, K. G., Fellman, M., Chrispin, J., Varma, N., et al. (2024) · Communications Medicine · doi:10.1038/s43856-024-00618-4 · Cited by 30
“Rhythm interpretation is an example of such algorithms and in general, whilst good they are far from perfect 115,116 .”
Use of wearables for monitoring cardiometabolic health: A systematic review
Lee, M. A., Song, M., Bessette, H. C., Davis, M. C., Tyner, T. E., & Reid, A. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105218 · Cited by 31
Paliakaitė, B., Petrėnas, A., Sološenko, A., & Marozas, V. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102421 · Cited by 24
Screening and management of atrial fibrillation in primary care
Ponamgi, S., Siontis, K. C., Rushlow, D. R., Graff‐Radford, J., Montori, V. M., & Noseworthy, P. A. (2021) · BMJ · doi:10.1136/bmj.n379 · Cited by 27
Belani, S., Wahood, W., Hardigan, P. C., Placzek, A. N., & Ely, S. W. (2021) · Cureus · doi:10.7759/cureus.20362 · Cited by 24
“This resulted in 28 articles that underwent full-text analysis, of which nine met the predefined eligibility criteria and were included in the qualitative and quantitative synthesis (Figure 1) [20-28].”
Mohagheghian, F., Han, D., Ghetia, O., Chen, D., Peitzsch, A. G., Nishita, N., Ding, E., Otabil, E. M., Noorishirazi, K., Hamel, A. P., et al. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121611 · Cited by 24
Dickson, E. L., Ding, E., Saczynski, J. S., Han, D., Moonis, M., Fitzgibbons, T. P., BARTON, B. A., Chon, K. H., & McManus, D. D. (2021) · Cardiovascular Digital Health Journal · doi:10.1016/j.cvdhj.2021.07.002 · Cited by 23
Improving Heart Rate Estimation on Consumer Grade Wrist-Worn Device Using Post-Calibration Approach
Choksatchawathi, T., Ponglertnapakorn, P., Ditthapron, A., Leelaarporn, P., Wisutthisen, T., Piriyajitakonkij, M., & Wilaiprasitporn, T. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.2979191 · Cited by 25
“Recently, many successful diagnostic processes of cardiac arrhythmia such as atrial fibrillation have adapted smart wearable devices to aid HR monitoring and further health deterioration prevention [8].”
BackgroundSmart watch applications in atrial fibrillation detection: Current state and future directions
Vyas, R., Jain, S., Thakre, A., Thotamgari, S. R., Raina, S., Brar, V., Sengupta, P. P., & Agrawal, P. K. (2024) · Journal of Cardiovascular Electrophysiology · doi:10.1111/jce.16451 · Cited by 22
Väliaho, E., Kuoppa, P., Lipponen, J. A., Hartikainen, J. E. K., Jäntti, H., Rissanen, T. T., Kolk, I., Pohjantähti‐Maaroos, H., Castrén, M. K., Halonen, J., et al. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.654555 · Cited by 21
Fabritz, L., Connolly, D. L., Czarnecki, E., Dudek, D., Guasch, E., Haase, D., Huebner, T. A., Złahoda-Huzior, A., Jolly, K. B., Kirchhof, P. F., et al. (2022) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztac067 · Cited by 23
“Combined with validated analysis algorithms,(18,19) this can be applied to monitor for arrhythmias.(19) Wearable-based screening for atrial arrhythmias is feasible when company-owned data are analysed in relatively young, early adopters.”
BackgroundSalvi, M., Acharya, M. R., Seoni, S., Faust, O., Tan, R., Barua, P. D., García, S., Molinari, F., & Acharya, U. R. (2024) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1530 · Cited by 26
“To detect AF, E. Chen et al. (2020) and Dörr et al. (2019) applied AI algorithms to analyze combined PPG and single-lead ECG sensor signals from wearable wristband devices.”
MethodThe Digital Neurologic Examination
Cohen, A. B., & Nahed, B. V. (2021) · Digital Biomarkers · doi:10.1159/000515577 · Cited by 22
Non-Contact Atrial Fibrillation Detection From Face Videos by Learning Systolic Peaks
Sun, Z., Junttila, J. M., Tulppo, M. P., Seppänen, T., & Li, X. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3193117 · Cited by 21
Lakkireddy, D. (2020) · Journal of Atrial Fibrillation · doi:10.4022/jafib.20200446 · Cited by 20
Continuous heart rhythm monitoring using mobile photoplethysmography in ambulatory patients
Hochstadt, A., Havakuk, O., Chorin, E., Schwartz, A. L., Merdler, I., Laufer, M., Lubman, N., Ghantous, E., Viskin, S., & Rosso, R. (2020) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2020.04.017 · Cited by 18
Reissenberger, P., Serfözö, P. D., Piper, D., Juchler, N., Glanzmann, S., Gram, J., Hensler, K., Tonidandel, H., Börlin, E., D’Souza, M., et al. (2023) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztad039 · Cited by 18
“The algorithm is clinically validated, CE marked, and certified as medical device (Class IIa).(11,15) Study participants were equipped with a wrist-worn PPG sensor integrated in a smartwatch or a bracelet (CardioWatch 287-1, Corsano Health B.”
Smartwatch for the Detection of Atrial Fibrillation
Tajrishi, F. Z., Chitsazan, M., Chitsazan, M., Shojaei, F., Gunnam, V., & Chi, G. (2019) · Critical Pathways in Cardiology A Journal of Evidence-Based Medicine · doi:10.1097/hpc.0000000000000192 · Cited by 18
Continuous 24-h Photoplethysmogram Monitoring Enables Detection of Atrial Fibrillation
Väliaho, E., Lipponen, J. A., Kuoppa, P., Martikainen, T. J., Jäntti, H., Rissanen, T. T., Castrén, M. K., Halonen, J., Tarvainen, M. P., Laitinen, T. M., et al. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2021.778775 · Cited by 17
Comparative study of a single lead ECG in a wearable device
Funston, R., Gibbs, A., Diven, J., Francey, J., Easlea, H., Murray, S., Fitzpatrick, M. P., Condon, A., & Mitchell, A. R. J. (2022) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2022.08.004 · Cited by 17
Recomendaciones preventivas cardiovasculares. Actualización PAPPS 2022
Orozco‐Beltrán, D., Cuixart, C. B., Banegas, J. R., Gil-Guillén, V. F., Cuenca, A. M. C., Rioboó, E. M., Baldó, A. J., Vicuña, J., & Pérez, J. N. (2022) · Atención Primaria · doi:10.1016/j.aprim.2022.102444 · Cited by 64
mCardia: A Context-Aware ECG Collection System for Ambulatory Arrhythmia Screening
Kumar, D., Maharjan, R., Maxhuni, A., Domínguez, H., Frølich, A., & Bardram, J. E. (2022) · ACM Transactions on Computing for Healthcare · doi:10.1145/3494581 · Cited by 16
Varma, N., Cygankiewicz, I., Turakhia, M. P., Heidbuchel, H., Hu, Y., Chen, L. Y., Couderc, J.-P., Cronin, E. M., Estep, J. D., Grieten, L., et al. (2021) · Cardiovascular Digital Health Journal · doi:10.1016/j.cvdhj.2020.11.004 · Cited by 16
“The PPG technology has also been incorporated in smartwatches to measure heart rate and rhythm (Dorr 2019, Guo 2019).”
MethodMutke, M. R., Brasier, N., Raichle, C. J., Ravanelli, F., Dörr, M., & Eckstein, J. (2020) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2020.0036 · Cited by 16
“21,22 Both trials were conducted in Switzerland and Germany at two University Hospitals along subjects with a high prevalence of AF.”
Telehealth in the time of Corona: ‘doctor in the house’
Almallah, Z., & Doyle, D. J. (2020) · Internal Medicine Journal · doi:10.1111/imj.15108 · Cited by 14
Carrington, M., Providencia, R. A., Chahal, C. A. A., Ricci, F., Epstein, A. E., Gallina, S., Fedorowski, A., Sutton, R., & Khanji, M. Y. (2022) · EP Europace · doi:10.1093/europace/euac088 · Cited by 15
Cheung, C. C., Gin, K. G., & Andrade, J. G. (2019) · JACC. Clinical electrophysiology · doi:10.1016/j.jacep.2019.02.008 · Cited by 15
Mehawej, J., Tran, K., Filippaios, A., Paul, T. J., Abu, H. O., Ding, E., Mishra, A. K., Dai, Q., Hariri, E. H., Wilson, S. H., et al. (2023) · Annals of Medicine · doi:10.1080/07853890.2022.2159516 · Cited by 14
Enhancing Healthcare Access–Smartphone Apps in Arrhythmia Screening: Viewpoint
Książczyk, M., Dębska-Kozłowska, A., Warchoł, I., & Lubiński, A. (2021) · JMIR mhealth and uhealth · doi:10.2196/23425 · Cited by 14
“7 AliveCor Dörr et al, (WATCH AFj), 2019 [36]”
Varma, N., Cygankiewicz, I., Turakhia, M. P., Heidbüchel, H., Hu, Y., Chen, L. Y., Couderc, J., Cronin, E. M., Estep, J. D., Grieten, L., et al. (2021) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztab001 · Cited by 17
Anesthesia Patient Monitoring 2050
Kück, K., Lofgren, L. R., & Lybbert, C. (2024) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000006660 · Cited by 15
Detection of Atrial Fibrillation Using a Home Blood Pressure Monitor
Balanis, T., & Sanner, B. M. (2021) · Vascular Health and Risk Management · doi:10.2147/vhrm.s317859 · Cited by 15
“They tested the diagnostic value of an AF-detecting home BP device (modified Omron 712C) by 450 subjects, of whom 54 had AF on ECG.13 In this study, the sensitivity was 100%, the specificity 91%, and the diagnostic accuracy 92% for detecting AF.13 Another study by Wiesel et al13 assessed the same in 205 people, of who…”
BackgroundInfluential
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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