Health AI Central
Share:
Peer-Reviewed Publication
Circulation2022;146(19):1415-1424.November 8, 2022Journal Article

Detection of Atrial Fibrillation in a Large Population Using Wearable Devices: The Fitbit Heart Study.

Steven A Lubitz1,2, Anthony Z Faranesh3, Caitlin Selvaggi4, Steven J Atlas5,2, David D McManus6, Daniel E Singer5,2, Sherry Pagoto7, Michael V McConnell3,8, Alexandros Pantelopoulos3, Andrea S Foulkes4,2
1Cardiac Arrhythmia Service and Cardiovascular Research Center (S.A.L.), Massachusetts General Hospital, Boston, MA.
2Harvard Medical School, Boston, MA (S.A.L., S.J.A., D.E.S., A.S.F.).
3Fitbit LLC (Google LLC), San Francisco, CA (A.Z.F., M.V.M., A.P.).
4Biostatistics Center (C.S., A.S.F.), Massachusetts General Hospital, Boston, MA.
5Division of General Internal Medicine (S.J.A., D.E.S.), Massachusetts General Hospital, Boston, MA.
6Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester (D.D.M.).
7Department of Allied Health Sciences, University of Connecticut, Storrs (S.P.).
8Division of Cardiovascular Medicine, Stanford University School of Medicine, CA (M.V.M.).
PubMed PMC DOI PDF

Abstract

BACKGROUND: Morbidity from undiagnosed atrial fibrillation (AF) may be preventable with early detection. Many consumer wearables contain optical photoplethysmography (PPG) sensors to measure pulse rate. PPG-based software algorithms that detect irregular heart rhythms may identify undiagnosed AF in large populations using wearables, but minimizing false-positive detections is essential. METHODS:…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free AccountSign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.