Year: 2025
Type: article
Abstract: Accurate comparison of wearables requires contextual equivalenceWe read, with great interest, the recent article by Dial et al. (2025) titled "Validation of nocturnal resting heart rate and heart rate variability in consumer wearables."The authors present valuable data from 13 healthy adults across 536 nights of sleep to explore agreement between wearable-and chest-strap-derived measures of resting heart rate (RHR) and heart rate variability (HRV).As the use of wearable technology continues to rise, with many devices now entering health care settings, the need for transparent and methodologically sound validation studies is increasingly important (Ginsburg et al., 2024).RHR and HRV are widely used non-invasive indices of cardiac autonomic function and are well-established predictors of both short-term training adaptation (Plews et al., 2013) and long-term health risks including cardiovascularspecific and all-cause mortality (Fang et al., 2020;Zhang et al., 2016).This work therefore represents a valuable step towards understanding how several of the most widely used consumer wearable devices perform in capturing these key parameters during sleep.Fundamental to accurate comparison of wearables is contextual equivalence, which considers alignment between device and reference on (i) metric definition (e.g., HRV as RMSSD vs. SDNN), (ii) temporal sampling window (e.g., first 4 h vs. whole night or stage-specific), and (iii) the averaging or weighting procedure applied to those samples.The authors acknowledge that each wearable computes RHR and HRV using different parameters, with varying frequencies, requirements, and processes (Dial et al., 2025).In light of these differences, Dial et al. adjusted their analysis for the Polar Grit X Pro (Polar, Kempele, Finland) by matching reference data to that reported by the device (i.e., during the first 4 h of sleep) and excluded the Garmin Fenix 6 (Garmin, Kansas City, MO, USA) from RHR analysis owing to its reporting of values from the lowest 30-min average in a 24-h period.While the authors acknowledge that WHOOP 4.0 (WHOOP Inc., Boston, MA, USA) calculates HRV using a dynamic average during sleep weighted towards the last phase of slow wave sleep, they compared WHOOP-derived RHR and
Source: Physiological Reports
DOI: 10.14814/phy2.70710
Authors: Gregory J. Grosicki, David M. Presby
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,050
OpenAlex ID: W4417304489