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Data from the Fitbit Heart study presented at AHA 2021 suggest a novel algorithm for detecting irregular heart rhythms had a positive predictive value of 98% for detecting atrial fibrillation in patients with undiagnosed atrial fibrillation.
Two years after the Apple Heart Study made waves at AHA 2019, results of the Fitbit Heart study demonstrate the potential of the Fitbit and other wearable devices for the detection of undiagnosed atrial fibrillation.
Presented at the American Heart Association (AHA) 2021 Scientific Sessions, results of the study suggest a novel software algorithm compatible with an array of smartwatches and wearable fitness trackers has the potential to identify cases of atrial fibrillation with a positive predictive value of 98% in a population of more than 450,000.
“These results show that wearables have the ability to identify undiagnosed atrial fibrillation with high reliability,” said Steven A. Lubitz, MD, MPH, associate professor of medicine at Harvard Medical School and cardiologist at the Massachusetts General Hospital, in a statement. “Since so many consumers use wearables, it is possible that algorithms such as the one we studied could be applied widely to help identify undiagnosed atrial fibrillation, allowing patients to obtain care before devastating complications such as a disabling stroke may occur.”
As the popularity of wearable devices has ballooned and technology continues to evolve, these devices offer a unique opportunity to assess patients for atrial fibrillation on a large scale. With this in mind, Fitbit sponsored the current study to assess whether a novel software algorithm with frequent overlapping photoplethysmography (PPG) pulse tachogram sampling reliably detect atrial fibrillation and flutter.
A prospective single-arm remote clinical trial enrolling US adults aged 22 years and older, the primary end point of interest was the positive predictive value (PPV) of the first irregular heart rhythm detection during ECG monitoring, which was defined as the portion of subjects with an irregular heart rhythm detection and 30 seconds or more of concurrent atrial fibrillation confirmed on the ECG. The secondary end point of interest was the proportion of 5-minute pulse tachograms within the first irregular heart rhythm detection during the ECG that corresponded with 30 seconds or more of atrial fibrillation confirmed on the ECG.
With patients recruited via email, Fitbit app notifications, social media, and other channels, investigators enrolled a population of 455,000 adult smartwatch or fitness tracker users in the US for inclusion in the trial. The mean age of participants was 47 years, 71% were female, 73% were white, and irregular heart rhythms were detected among 4728 (1%). Investigators noted 2070 of these events occurred among patients aged 65 years or older.
Of the 4728 irregular heart rhythm detections to occur in the trial, 1057 individuals underwent subsequent ECG monitoring. Of the 1057 who underwent ECG patch monitoring, atrial fibrillation was detached in 32.2% (n=340). Additionally, an irregular heart rhythm detection occurred during ECG monitoring in 225 individuals, including 221 with had concurrent atrial fibrillation on the ECG, which corresponded to a positive predictive value of 98.2% (95%CI, 95.5-99.5).
Further analysis of patients aged 65 and older indicated the positive predictive value was 97.0% (95% CI, 91.4-99.4). Investigators also pointed out 98.1% of the 5-minute pulse tachograms corresponded to atrial fibrillation on the ECG during the first irregular heart rhythm detection during the ECG monitoring period.
“Most of the episodes of undiagnosed atrial fibrillation detected occurred during sleep, and we suspect that these episodes were asymptomatic. Since the algorithm is most active when wearers are physically inactive, the wearable should be worn during sleep for the greatest benefits,” Lubitz added.
This study, “Detection of Atrial Fibrillation in a Large Population using Wearable Devices: the Fitbit Heart Study,” was presented at AHA 2021.