The DOI matches its Crossref record; 20 of the 21 references it cites were found in a registry or an index.
Clinical Validation of 5 Direct-to-Consumer Wearable Smart Devices to Detect Atrial Fibrillation
Open accessAbstract
Background Multiple smart devices capable to detect atrial fibrillation (AF) are presently available. Sensitivity and specificity for the detection of AF may differ between available smart devices, and this has not yet been adequately investigated. Objectives The aim was to assess the accuracy of 5 smart devices in identifying AF compared with a physician-interpreted 12-lead electrocardiogram as the reference standard in a real-world cohort of patients. Methods We consecutively enrolled patients presenting to a cardiology service at a tertiary referral center in a prospective, diagnostic study. Results We prospectively analyzed 201 patients (31% women, median age 66.7 years). AF was present in 62 (31%) patients. Sensitivity and specificity for the detection of AF were comparable between devices: 85% and 75% for the Apple Watch 6, 85% and 75% for the Samsung Galaxy Watch 3, 58% and 75% for the Withings Scanwatch, 66% and 79% for the Fitbit Sense, and 79% and 69% for the AliveCor KardiaMobile, respectively. The rate of inconclusive tracings (the algorithm was unable to determine the heart rhythm) was 18%, 17%, 24%, 21%, and 26% for the Apple Watch 6, Samsung Galaxy Watch 3, Withings Scan Watch, Fitbit Sense, and AliveCor KardiaMobile (P < 0.01 for pairwise comparison), respectively. By manual review of inconclusive tracings, the rhythm could be determined in 955 (99%) of 969 single-lead electrocardiograms. Regarding patient acceptance, the Apple Watch was ranked first (39% of participants). Conclusions In this clinical validation of 5 direct-to-consumer smart devices, we found differences in the amount of inconclusive tracings diminishing sensitivity and specificity of the smart devices. In a clinical setting, manual review of tracings is required in about one-fourth of cases.
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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 [13]
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“Studies have shown high rates of poor signal quality among data recorded by wearable devices, and significant variation in quality across different manufacturers13.”
From Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices (2023) · cites this paper as [13]
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Cites
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Prevalence, incidence and lifetime risk of atrial fibrillation: the Rotterdam study
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Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation
Perez, M. V., Mahaffey, K. W., Hedlin, H., Rumsfeld, J. S., Garcia, A., Ferris, T., Balasubramanian, V., Russo, A. M., Rajmane, A., Cheung, L., et al. (2019) · New England Journal of Medicine · doi:10.1056/nejmoa1901183 · Cited by 1,917
Smart wearable devices in cardiovascular care: Where we are and how to move forward
Bayoumy, K., Gaber, M., Elshafeey, A., Mhaimeed, O., Dineen, E. H., Marvel, F. A., Martin, S. S., Muse, E. D., Turakhia, M. P., Tarakji, K. G., et al. (2021) · Nature Reviews Cardiology · doi:10.1038/s41569-021-00522-7 · Cited by 829
Halcox, J. P. J., Wareham, K., Cardew, A., Gilmore, M., Barry, J. P., Phillips, C., & Gravenor, M. B. (2017) · Circulation · doi:10.1161/circulationaha.117.030583 · Cited by 580
Smartwatch Algorithm for Automated Detection of Atrial Fibrillation
Bumgarner, J. M., Lambert, C. T., Hussein, A. A., Cantillon, D. J., Baranowski, B., Wolski, K., Lindsay, B. D., Wazni, O. M., & Tarakji, K. G. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.03.003 · Cited by 446
Accuracy of wrist-worn heart rate monitors
Wang, R., Blackburn, G., Desai, M., Phelan, D., Gillinov, L., Houghtaling, P., & Gillinov, M. (2017) · JAMA Cardiology · doi:10.1001/jamacardio.2016.3340 · Cited by 386
Lakkireddy, D., Chung, M. K., Gopinathannair, R., Patton, K. K., Gluckman, T. J., Turagam, M. K., Cheung, J. W., Patel, P. J., Sotomonte, J. C., Lampert, R. J., et al. (2020) · Heart Rhythm · doi:10.1016/j.hrthm.2020.03.028 · Cited by 262
The WATCH AF trial: SmartWATCHes for detection of atrial fibrillation
Dörr, 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.2018.10.006 · Cited by 245
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Gelder, I. C. V., Rienstra, M., Bunting, K. V., Casado-Arroyo, R., Caso, V., Crijns, H. J. G. M., Potter, T. J. R. D., Dwight, J., Guasti, L., Hanke, T., et al. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehae176 · Cited by 3,594
Current and Future Use of Artificial Intelligence in Electrocardiography
Martínez‐Sellés, M., & Breysse, M. M. (2023) · Journal of Cardiovascular Development and Disease · doi:10.3390/jcdd10040175 · Cited by 163
Neri, L., Oberdier, M. T., van Abeelen, K. C. J., Menghini, L., Tumarkin, E., Tripathi, H., Jaipalli, S., Orro, A., Paolocci, N., Gallelli, I., et al. (2023) · Sensors · doi:10.3390/s23104805 · Cited by 148
Consumer Wearable Health and Fitness Technology in Cardiovascular Medicine
Petek, B. J., Al‐Alusi, M. A., Moulson, N., Grant, A. J., Besson, C., Guseh, J. S., Wasfy, M. M., Grémeaux, V., Churchill, T. W., & Baggish, A. L. (2023) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2023.04.054 · Cited by 113
Khunte, A., Sangha, V., Oikonomou, E. K., Dhingra, L. S., Aminorroaya, A., Mortazavi, B. J., Coppi, A., Brandt, C. A., Krumholz, H. M., & Khera, R. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00869-w · Cited by 95
Metadata
- Authors
- Mannhart, D., Lischer, M., Knecht, S., du Fay de Lavallaz, J., Strebel, I., Serban, T., Vögeli, D., Schaer, B., Osswald, S., Mueller, C., Kühne, M., Sticherling, C., & Badertscher, P.
- Affiliations
- University of Basel (CH)
- Corresponding author
- Badertscher, Patrick
- ORCID
- Venue
- JACC: Clinical Electrophysiology
- Publisher
- Elsevier BV
- Published
- 2023-02
- Volume
- 9
- Issue
- 2
- Pages
- 232-242
- ISSN
- 2405-500X (ISSN-L)
- OpenAlex
- W4317376731
- Cited by
- 175
- References
- 21
- Citations by year
- 2026: 41 · 2025: 63 · 2024: 52 · 2023: 19
- FWCI
- 34.75
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- JACC. Clinical electrophysiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Beckman Coulter Foundation
- Boston Scientific Corporation
- MicroPort
- Pfizer
- Freiwillige Akademische Gesellschaft
- AstraZeneca
- Knowles Teacher Initiative
- Bayer
- Universitätsspital Basel
- Biotronik
- Universität Basel
- Sphingotec GmbH
- Abbott Laboratories
- Medtronic
- Daiichi-Sankyo
- Biosense Webster
- Schweizerische Herzstiftung
- Foundation for Cardiovascular Research
- Siemens
- Boehringer Ingelheim
- Amgen
- Singulex
- Roche
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Johnson and Johnson
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Atrial Fibrillation Management and Outcomes
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- atrial fibrillation, wearable smart devices, Apple Watch, single-lead ECG, device accuracy, sensitivity and specificity, patient acceptance
- MeSH terms
- Wearable Electronic Devices, Aged, Algorithms, Atrial Fibrillation, Electrocardiography, Female, Humans, Male, Prospective Studies
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