Use of a Smart Watch for Early Detection of Paroxysmal Atrial Fibrillation: Validation Study

Abstract

Background Wearable devices with photoplethysmography (PPG) technology can be useful for detecting paroxysmal atrial fibrillation (AF), which often goes uncaptured despite being a leading cause of stroke. Objective This study is the first part of a 2-phase study that aimed at developing a method for immediate detection of paroxysmal AF using PPG-integrated wearable devices. In this study, the diagnostic performance of 2 major smart watches, Apple Watch Series 3 and Fitbit (FBT) Charge HR Wireless Activity Wristband, each equipped with a PPG sensor, was compared, and the pulse rate data outputted from those devices were analyzed for precision and accuracy in reference to the heart rate data from electrocardiography (ECG) during AF. Methods A total of 40 subjects from patients who underwent cardiac surgery at a single center between September 2017 and March 2018 were monitored for postoperative AF using telemetric ECG and PPG devices. AF was diagnosed using a 12-lead ECG by qualified physicians. Each subject was given a pair of smart watches, Apple Watch and FBT, for simultaneous pulse rate monitoring. The heart rate of all subjects was also recorded on the telemetry system. Time series pulse rate trends and heart rate trends were created and analyzed for trend pattern similarities. Those trend data were then used to determine the accuracy of PPG-based pulse rate measurements in reference to ECG-based heart rate measurements during AF. Results Of the 20 AF events in group FBT, 6 (30%) showed a moderate or higher correlation (cross-correlation function>0.40) between pulse rate trend patterns and heart rate trend patterns. Of the 16 AF events in group Apple Watch (workout [W] mode), 12 (75%) showed a moderate or higher correlation between the 2 trend patterns. Linear regression analyses also showed a significant correlation between the pulse rates and the heart rates during AF in the subjects with Apple Watch. This correlation was not observed with FBT. The regression formula for Apple Watch W mode and FBT was X=14.203 + 0.841Y and X=58.225 + 0.228Y, respectively (where X denotes the mean of all average pulse rates during AF and Y denotes the mean of all corresponding average heart rates during AF), and the coefficient of determination (R2) was 0.685 and 0.057, respectively (P<.001 and .29, respectively). Conclusions In this validation study, the detection precision of AF and measurement accuracy during AF were both better with Apple Watch W mode than with FBT.

Validation of an algorithm for continuous monitoring of atrial fibrillation using a consumer smartwatch

“…5,6 Mobile health technology, specifically noninvasive wearables with photoplethysmography (PPG), has been a promising avenue for the detection of AF and may be an effective and practical approach for large-scale screening given their growing wide-spread use. [7][8][9][10][11][12] Although consumer-grade on-demand electrocardiogram (ECG) devices have been developed and used to detect symptomatic AF, they have minimal utility for detecting asymptomatic AF. We first reported a machine learning algorithm to detect AF from heart rate measurements derived from a commercial smartwatch, 10 and a similar approach has been used to trigger an on-demand ECG.…”

Intelligence-Based Spine Care Model: A New Era of Research and Clinical Decision-Making

“… 98 This is not to say consumer wearables are without the potential for clinical utility, as the Apple Watch has been shown to accurately detect paroxysmal atrial fibrillation. 99 Wearable devices can also accurately assess sleep. 100 As there is a link between quality of sleep and pain, 101 including low back pain, if these devices positively influence sleep patterns, they may be useful in the management of such pain.…”

Remote and at-home data collection: Considerations for the NIH HEALthy Brain and Cognitive Development (HBCD) study

“…Studies have begun to evaluate the validity of these devices, with comparisons to clinical gold standards and methods revealing differences across devices. For example, Apple Watch has been found to have clinically acceptable measures of heart rate, heart rate variability, sleep/wake ( Wallen et al, 2016 ; Walch et al, 2019 ), and incorporates an FDA-approved ECG method for atrial fibrillation detection ( Inui et al, 2020 ); but overestimates measures of energy expenditure relative to indirect calorimetry. Other wearables, such as those from Fitbit, Samsung, and Oura, likewise show similar performance for heart rate and energy expenditure.…”