Abstract
Commercial off-the shelf (COTS) wearable devices continue development at unprecedented rates. An unfortunate consequence of their rapid commercialization is the lack of independent, third-party accuracy verification for reported physiological metrics of interest, such as heart rate (HR) and heart rate variability (HRV). To address these shortcomings, the present study examined the accuracy of seven COTS devices in assessing resting-state HR and root mean square of successive differences (rMSSD). Five healthy young adults generated 148 total trials, each of which compared COTS devices against a validation standard, multi-lead electrocardiogram (mECG). All devices accurately reported mean HR, according to absolute percent error summary statistics, although the highest mean absolute percent error (MAPE) was observed for CameraHRV (17.26%). The next highest MAPE for HR was nearly 15% less (HRV4Training, 2.34%). When measuring rMSSD, MAPE was again the highest for CameraHRV [112.36%, concordance correlation coefficient (CCC): 0.04], while the lowest MAPEs observed were from HRV4Training (4.10%; CCC: 0.98) and OURA (6.84%; CCC: 0.91). Our findings support extant literature that exposes varying degrees of veracity among COTS devices. To thoroughly address questionable claims from manufacturers, elucidate the accuracy of data parameters, and maximize the real-world applicative value of emerging devices, future research must continually evaluate COTS devices.
Highlights
The proliferating market for consumer off-the-shelf (COTS) wearables – Forbes forecasted the wearable market to evolve into a $27 billion industry by 2022 – has created an opportunity for consumers to systematically monitor their own health on a regular basis (Bunn et al, 2018; Lamkin, 2018)
The present study aimed to assess the validity of various COTS devices when measuring rMSSD, a common heart rate variability (HRV) metric consistently reported across the wearables market (Bent et al, 2020)
Examples include daily and/or nightly HRV assessments to ascertain individual levels of readiness and fatigue as well as average heart rates during sleep (Shaffer and Ginsberg, 2017; Bent et al, 2020). These models have the capability to provide objective, actionable insight to personalize stress mitigation strategies through accurate obtention and analysis of HRV and HR related metrics. These metrics provide significant insight into performance and recovery ramifications across athletic and elite performing populations that are used in training and workload outcomes
Summary
The proliferating market for consumer off-the-shelf (COTS) wearables – Forbes forecasted the wearable market to evolve into a $27 billion industry by 2022 – has created an opportunity for consumers to systematically monitor their own health on a regular basis (Bunn et al, 2018; Lamkin, 2018). As the wearables market continually becomes more competitive, options for wearables include fashion commodities such as smart watches and rings, as well as clothing textiles offering dual-purposes beyond visual appeal that, in many cases, provide users with a plethora of COTS Device Accuracy for HRV health-related data (Waugh et al, 2018; Aroganam et al, 2019; Depner et al, 2020) These data present a unique opportunity to the end-user that affords them the ability to garner actionable insights related to their personal health, which may include stress states and recovery, as well as physical, cognitive, and psychomotor performance (De Arriba-Pérez et al, 2016; Cardinale and Varley, 2017; Bunn et al, 2018; Aroganam et al, 2019). Fluctuations in ANS elements are often quantified by assessing an individual’s heart rate variability (HRV) (Shaffer and Ginsberg, 2017)
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