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

Area of Science:

• Cardiology
• Biomedical Engineering
• Digital Health

Background:

• Undiagnosed atrial fibrillation (AF) leads to preventable morbidity.
• Consumer wearables with photoplethysmography (PPG) sensors offer potential for widespread pulse rate monitoring.
• PPG-based algorithms for irregular heart rhythm detection could identify undiagnosed AF, but minimizing false positives is crucial.

Purpose of the Study:

• To evaluate a novel PPG-based algorithm for detecting undiagnosed AF using wrist-worn devices.
• To assess the positive predictive value of the algorithm's irregular heart rhythm detection (IHRD) for concurrent AF.

Main Methods:

• Prospective remote clinical trial involving adults (≥22 years) in the US without AF, using compatible Fitbit devices and smartphones.
• Analysis of PPG data using a novel algorithm examining overlapping 5-minute pulse windows (tachograms).
• Participants with IHRD (11 consecutive irregular tachograms) underwent telehealth visits and 1-week ambulatory ECG patch monitoring.

Main Results:

• Over 455,000 participants enrolled; 1% experienced IHRDs.
• Among 1057 participants with analyzable ECG patch monitors after IHRD notification, 32.2% had AF.
• The positive predictive value of IHRD for concurrent AF was 98.2% (95% CI, 95.5%-99.5%), and 97.0% for those ≥65 years.

Conclusions:

• A novel PPG software algorithm for Fitbit devices demonstrated high positive predictive value for detecting AF.
• The algorithm successfully identified participants likely to have AF, confirmed by subsequent ECG monitoring.
• Wearable devices show promise for facilitating the identification of individuals with undiagnosed AF.