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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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 [13]
The Discussion says technical artefacts, including noise, affected the devices’ automated interpretation of single-lead ECGs. The study evaluates atrial fibrillation detection, however, not AI-based screening for left ventricular systolic dysfunction (LVSD), so it does not establish that limitation for LVSD tools. (LLM, conf=0.86)
Experimental — accuracy not yet measuredStudies have shown high rates of poor signal quality among data recorded by wearable devices, and significant variation in quality across different manufacturers.
“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]
The Discussion reports that 17%–26% of recordings were labeled inconclusive by device algorithms and says these rates differed significantly across devices. But inconclusive does not necessarily mean poor signal quality: the Results say cardiologists could interpret 95% of those tracings, and only 1% of 825 recordings were noninterpretable because of low quality or noise. The paper therefore supports variation in algorithm outcomes, not high rates of poor-quality signals across manufacturers. (LLM, conf=0.87)
Experimental — accuracy not yet measured
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- 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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