Detection of Atrial Fibrillation in a Large Population Using Wearable Devices
- Individuals with a Fitbit wearable-based irregular heart rhythm detection device have a substantial likelihood of having atrial fibrillation (AF) confirmed on a subsequent ECG patch monitor and considerable burden of AF.
- Because wearable-based irregular heart rhythm detections using photoplethysmography sensors operate during periods of inactivity, wearing devices at night may maximize the sensitivity, while detection of AF during periods of active motion remains a challenge.
Can optical photoplethysmography (PPG) sensors with a wearable algorithm detect undiagnosed atrial fibrillation (AF)?
A prospective remote single-arm clinical trial design was used to examine use of a PPG-based algorithm for detecting undiagnosed AF from a range of wrist-worn devices. The PPG-based algorithm required at least 30 continuous minutes of an irregular rhythm to generate an irregular heart rhythm detection (IHRD) during periods of inactivity. Adults aged ≥22 years in the United States without AF, using compatible wearable Fitbit devices and Android or iOS smartphones, were included. Eligible participants with an IHRD, defined as 11 consecutive irregular tachograms, were invited to schedule a telehealth visit and were mailed a 1-week ambulatory electrocardiogram (ECG) patch monitor. The primary outcome was the positive predictive value of the first IHRD during ECG patch monitoring for concurrent AF.
A total of 455,699 participants enrolled (median age 47 years, 71% female, 73% White) between May 6–October 1, 2020. At enrollment, 254,430 (56%) participants used smartwatches and 199,895 (44%) used fitness trackers. The median number of days at risk for an IHRD was 122 (interquartile range [IQR], 110-134 days). The median hours of Fitbit device wear time per day was 23 (IQR, 22-24). IHRD occurred for 4,728 (1%) participants, and 2,070 (4%) participants aged ≥65 years during a median of 122 (IQR, 110-134) days were at risk for IHRD. Among 1,057 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 four did not, resulting in an IHRD positive predictive value of 98.2% (95% confidence interval [CI], 95.5%–99.5%). For participants aged ≥65 years, the IHRD positive predictive value was 97.0% (95% CI, 91.4%–99.4%). The predictive value of the algorithm was similar across age, sex, and CHA2DS2-VASc scores.
The investigators concluded that a 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.
In the current study, the positive predictive value was greater for detection of AF compared to earlier studies. As expected, the sensitivity was lower (~68%). The authors also note two important points. First, the study enrolled participants who owned a wearable device; thus, generalizability to adults who may not be able to afford these devises is limited. It should also be noted that connectivity with such devices can be limited in rural areas. Second, further investigation in more diverse populations is warranted. Given the performance of the algorithm, use of wearables and the associated algorithms show promise for detection of AF in large populations. Understanding the cost-efficacy would be an important next step.
Keywords: Arrhythmias, Cardiac, Atrial Fibrillation, Digital Technology, Electrocardiography, Electrocardiography, Ambulatory, Fitness Trackers, Photoplethysmography, Primary Prevention, Smartphone, Software, Telemedicine, Wearable Electronic Devices
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