Detection of Atrial Fibrillation in a Large Population Using Wearable Devices: The Fitbit Heart Study. Issue 19 (23rd September 2022)

• Record Type: Journal Article
• Authors: Lubitz, Steven A.; Faranesh, Anthony Z.; Selvaggi, Caitlin; Atlas, Steven J.; McManus, David D.; Singer, Daniel E.; Pagoto, Sherry; McConnell, Michael V.; Pantelopoulos, Alexandros; Foulkes, Andrea S.
• Is Part Of: Circulation. Volume 146:Issue 19(2022)
• Page Start: 1415
• Page End: 1424
• Publication Date: 2022-09-23
• Subjects: atrial fibrillation -- diagnostic screening programs -- digital technology -- photoplethysmography -- wearable electronic devices; Blood -- Circulation -- Periodicals; Cardiovascular system -- Periodicals; Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; Blood Circulation; Cardiovascular System; Vascular Diseases; 616.1
• DOI: 10.1161/CIRCULATIONAHA.122.060291
• Languages: English
• ISSNs: 0009-7322

Abstract

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: We performed a prospective remote clinical trial to examine a novel PPG-based algorithm for detecting undiagnosed AF from a range of wrist-worn devices. Adults aged ≥22 years in the United States without AF, using compatible wearable Fitbit devices and Android or iOS smartphones, were included. PPG data were analyzed using a novel algorithm that examines overlapping 5-minute pulse windows (tachograms). Eligible participants with an irregular heart rhythm detection (IHRD), defined as 11 consecutive irregular tachograms, were invited to schedule a telehealth visit and were mailed a 1-week ambulatory ECG patch monitor. The primary outcome was the positive predictive value of the first IHRD during ECG patch monitoring for concurrent AF. Results: A total of 455 699 participants enrolled (median age 47 years, 71% female, 73% White) between May 6 and October 1, 2020. IHRDs occurred for 4728 (1%) participants, and 2070 (4%) participants aged ≥65 years during a median of 122 (interquartile range, 110–134) days at risk for an IHRD. Among 1057 participants with an IHRD notification and subsequent analyzable ECG patch monitor, AF was present in 340 (32.2%). Of the 225 participants with another IHRD during ECG patch monitoring, 221 had concurrent AF on the ECG and 4 did not, resulting in an IHRD positive predictive value of 98.2% (95% CI, 95.5%–99.5%). For participants aged ≥65 years, the IHRD positive predictive value was 97.0% (95% CI, 91.4%–99.4%). Conclusions: A novel PPG software algorithm for wearable Fitbit devices exhibited a high positive predictive value for concurrent AF and identified participants likely to have AF on subsequent ECG patch monitoring. Wearable devices may facilitate identifying individuals with undiagnosed AF. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT04380415.