Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation
Open accessAbstract
BACKGROUND: Optical sensors on wearable devices can detect irregular pulses. The ability of a smartwatch application (app) to identify atrial fibrillation during typical use is unknown. METHODS: Participants without atrial fibrillation (as reported by the participants themselves) used a smartphone (Apple iPhone) app to consent to monitoring. If a smartwatch-based irregular pulse notification algorithm identified possible atrial fibrillation, a telemedicine visit was initiated and an electrocardiography (ECG) patch was mailed to the participant, to be worn for up to 7 days. Surveys were administered 90 days after notification of the irregular pulse and at the end of the study. The main objectives were to estimate the proportion of notified participants with atrial fibrillation shown on an ECG patch and the positive predictive value of irregular pulse intervals with a targeted confidence interval width of 0.10. RESULTS: We recruited 419,297 participants over 8 months. Over a median of 117 days of monitoring, 2161 participants (0.52%) received notifications of irregular pulse. Among the 450 participants who returned ECG patches containing data that could be analyzed - which had been applied, on average, 13 days after notification - atrial fibrillation was present in 34% (97.5% confidence interval [CI], 29 to 39) overall and in 35% (97.5% CI, 27 to 43) of participants 65 years of age or older. Among participants who were notified of an irregular pulse, the positive predictive value was 0.84 (95% CI, 0.76 to 0.92) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular pulse notification and 0.71 (97.5% CI, 0.69 to 0.74) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular tachogram. Of 1376 notified participants who returned a 90-day survey, 57% contacted health care providers outside the study. There were no reports of serious app-related adverse events. CONCLUSIONS: The probability of receiving an irregular pulse notification was low. Among participants who received notification of an irregular pulse, 34% had atrial fibrillation on subsequent ECG patch readings and 84% of notifications were concordant with atrial fibrillation. This siteless (no on-site visits were required for the participants), pragmatic study design provides a foundation for large-scale pragmatic studies in which outcomes or adherence can be reliably assessed with user-owned devices. (Funded by Apple; Apple Heart Study ClinicalTrials.gov number, NCT03335800.).
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Arrhythmia detection algorithms such as the irregular pulse notification in the Apple Heart Study remained robust, with sensitivity for atrial fibrillation unaffected by skin tone across a large-scale population.
“Importantly, arrhythmia detection algorithms such as the irregular pulse notification in the Apple Heart Study (Perez et al., 2019) remained robust, with sensitivity for atrial fibrillation unaffected by skin tone across a large-scale population [15].”
From Photoplethysmography in Diverse Skin Tones: Evaluating Bias in Smartwatch Health Monitoring (2025) · cites this paper as [15]
The Discussion reports that the Apple Heart Study enrolled 419,297 participants and that 84% of subsequent notifications among participants who returned an ECG patch were concordant with atrial fibrillation. But it explicitly says the study was not designed to measure sensitivity, and our copy reports no analysis showing sensitivity was unaffected by skin tone. (LLM, conf=0.90)
Experimental — accuracy not yet measuredRecent large-scale longitudinal studies have shown promise in leveraging wearable sensor data for health monitoring and prediction.
“While recent large-scale longitudinal studies have shown promise in leveraging wearable sensor data for health monitoring and prediction, they also introduce new challenges for data modelling.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [18]
PPG tachograms, especially when augmented by single-lead ECG, can identify arrhythmias.
“PPG tachograms, especially when augmented by single-lead ECG, can also identify arrhythmias19.”
From Smart wearable devices in cardiovascular care: Where we are and how to move forward (2021) · cites this paper as [19]
Atrial fibrillation screening through PPG has shown variable accuracy depending on the algorithms and devices used.
“AF screening through PPG has shown variable accuracy depending on the algorithms and devices used19,51,52”
From Smart wearable devices in cardiovascular care: Where we are and how to move forward (2021) · cites this paper as [19]
In the Apple Heart study, the positive predictive value for AF detection was 84% for the irregular notification algorithm and 71% for individual tachograms.
“The positive predictive value for AF detection was 84% and 71% for the irregular notification algorithm and individual tachograms, respectively19.”
From Smart wearable devices in cardiovascular care: Where we are and how to move forward (2021) · cites this paper as [19]
A common practice in PPG artifact identification is to involve an accelerometer.
“a common practice is to involve an accelerometer for artifact identification [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 [11]
In a community study, a smartwatch was used to identify possible AF in 1-min PPG recordings, and the positive predictive value of notifications was found to be 0.84, indicating that wearables may have utility for identifying possible AF.
“The positive predictive value (PPV) of notifications was found to be 0.84, indicating that wearables may have utility for identifying possible AF.”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [29]
In [29], an irregular pulse notification was generated if an irregular pulse was identified in at least five out of six PPG recordings in a 48-h period.
“In [29], an irregular pulse notification was generated if an irregular pulse was identified in at least five out of six PPG recordings in a 48-h period [29].”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [29]
PPG signal quality is highest during sleep when movement and ambient light are minimal.
“Initial work indicates that PPG signal quality is highest during sleep when movement and ambient light are minimal, supporting the approach of only acquiring PPG signals during periods of low activity [29].”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [29]
The Apple Heart Study showed that wearable devices may detect atrial fibrillation.
“The Apple Heart Study [15] is another example that was a breakthrough for showing that wearable devices may detect atrial fibrillation (AF) and foster a discussion of potentials and limitations with regard to health care providers, researchers, and members of the media and economy [16,17].”
From The impact of wearable technologies in health research: scoping review (2022) · cites this paper as [15]
Fitbit has approximately 476 published studies and 449 studies registered on ClinicalTrials.gov, and the Apple Watch Heart Study recruited 419,927 people to track irregular heart rhythms.
“For example, Fitbit alone has approximately 476 published studies and 449 studies registered on ClinicalTrials.gov [4] and the Apple Watch Heart Study recruited 419 927 people to track irregular heart rhythms [20].”
From Limiting racial disparities and bias for wearable devices in health science research (2020) · cites this paper as [20]
Researchers are increasingly using consumer wearables or wrist-worn smartwatches and fitness monitors for digital measurement of cardiovascular health and psychophysiological stress because these devices allow continuous, scalable, unobtrusive big data collection of cardiac activity in real-world conditions with large samples.
“Researchers are increasingly using consumer wearables or wrist-worn smartwatches and fitness monitors for the digital measurement of cardiovascular health and psychophysiological stress as these devices allow for continuous, scalable, and unobtrusive, “big data” collection of overall cardiac activity in real-world conditions with large samples25–30.”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [27]
If a study is interested in heart rate arrhythmias using wearable assessments, extreme values may be of vital interest and should not be removed or altered.
“However, if a study were interested in HR arrythmias using wearable assessments (such as atrial fibrillation), then these extreme values may be of vital interest and should not be removed or altered27.”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [27]
Deep neural networks (DNNs) have emerged as a powerful tool for learning features and predictions from raw data in cardiology.
“Deep neural networks (DNNs) have emerged as a powerful tool for learning features and predictions from raw data in cardiology.12”
From Unlocking Tomorrow’s Health Care: Expanding the Clinical Scope of Wearables by Applying Artificial Intelligence (2024) · cites this paper as [12]
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- Authors
- Perez, M. V., Mahaffey, K. W., Hedlin, H., Rumsfeld, J. S., Garcia, A., Ferris, T., Balasubramanian, V., Russo, A. M., Rajmane, A., Cheung, L., Hung, G., Lee, J., Kowey, P., Talati, N., Nag, D., Gummidipundi, S. E., Beatty, A., Hills, M. T., Desai, S., Granger, C. B., Desai, M., & Turakhia, M. P.
- Affiliations
- Stanford Medicine (US)
- From the Division of Cardiovascular Medicine (M.V.P.), Stanford Center for Clinical Research (K.W.M., A.R., N.T.), the Quantitative Sciences Unit (H.H., A.G., V.B., J.L., S.E.G., M.D.), Information Resources and Technology (T.F., G.H.), Department of Medicine (S.D.), and the Center for Digital Health (M.P.T.), Stanford University, Stanford, Apple, Cupertino (L.C., D.N., A.B., S.D.), and the Veterans Affairs Palo Alto Health Care System, Palo Alto (M.P.T.) — all in California; the University of...
- Cooper Medical School of Rowan University (US)
- Lankenau Heart Institute (US)
- Duke University (US)
- VA Palo Alto Health Care System (US)
- ORCID
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2019-11-14
- Volume
- 381
- Issue
- 20
- Pages
- 1909-1917
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- PMC
- PMC8112605
- OpenAlex
- W2987985090
- Cited by
- 1,917
- References
- 12
- Citations by year
- 2026: 263 · 2025: 300 · 2024: 259 · 2023: 276 · 2022: 271 · 2021: 304 · 2020: 228 · 2019: 12 · 2016: 1
- FWCI
- 71.74
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- New England Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Stanford University
- 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, wearable devices, optical pulse detection, positive predictive value, ECG patch, telemedicine, mobile application, health care utilization, older adults, adverse events, participant satisfaction, participant burden, participant retention, participant recruitment
- MeSH terms
- Wearable Electronic Devices, Adult, Aged, Algorithms, Atrial Fibrillation, Confidentiality, Electrocardiography, Female, Humans, Male, Middle Aged, Predictive Value of Tests, Prospective Studies, Telemedicine, Mobile Applications
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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