Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation.

dc.contributor.author

Perez, Marco V

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Mahaffey, Kenneth W

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Hedlin, Haley

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Rumsfeld, John S

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Garcia, Ariadna

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Ferris, Todd

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Balasubramanian, Vidhya

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Russo, Andrea M

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Rajmane, Amol

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Cheung, Lauren

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Hung, Grace

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Lee, Justin

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Kowey, Peter

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Talati, Nisha

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Nag, Divya

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Gummidipundi, Santosh E

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Beatty, Alexis

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Hills, Mellanie True

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Desai, Sumbul

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Granger, Christopher B

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Desai, Manisha

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Turakhia, Mintu P

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Apple Heart Study Investigators

dc.date.accessioned

2019-12-12T21:21:26Z

dc.date.available

2019-12-12T21:21:26Z

dc.date.issued

2019-11

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2019-12-12T21:21:26Z

dc.description.abstract

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.).

dc.identifier.issn

0028-4793

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1533-4406

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https://hdl.handle.net/10161/19577

dc.language

eng

dc.publisher

Massachusetts Medical Society

dc.relation.ispartof

The New England journal of medicine

dc.relation.isversionof

10.1056/nejmoa1901183

dc.subject

Apple Heart Study Investigators

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Humans

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Atrial Fibrillation

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Electrocardiography

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Prospective Studies

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Predictive Value of Tests

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Confidentiality

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Telemedicine

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Algorithms

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Adult

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Aged

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Middle Aged

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Female

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Male

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Mobile Applications

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Wearable Electronic Devices

dc.title

Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation.

dc.type

Journal article

duke.contributor.orcid

Granger, Christopher B|0000-0002-0045-3291

pubs.begin-page

1909

pubs.end-page

1917

pubs.issue

20

pubs.organisational-group

School of Medicine

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Duke

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Nursing

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School of Nursing

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Duke Clinical Research Institute

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Institutes and Centers

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Medicine, Cardiology

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Medicine

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Clinical Science Departments

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Biostatistics & Bioinformatics

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Basic Science Departments

pubs.publication-status

Published

pubs.volume

381

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