Accuracy in Wrist-Worn, Sensor-Based Measurements of Heart Rate and Energy Expenditure in a Diverse Cohort

By Anna Shcherbina, C. Mattsson, Daryl Waggott, Heidi Salisbury, J. Christle, Trevor J. Hastie, Matthew T. Wheeler, Euan A. Ashley. bioRxiv, 2016

604 citations recorded by the source corpus.

Background The ability to measure activity and physiology through wrist-worn devices provides an opportunity for cardiovascular medicine. However, the accuracy of commercial devices is largely unknown. Objective To assess the accuracy of seven commercially available wrist-worn devices in estimating heart rate (HR) and energy expenditure (EE) and to propose a wearable sensor evaluation framework. Methods We evaluated the Apple Watch, Basis Peak, Fitbit Surge, Microsoft Band, Mio Alpha 2, PulseOn, and Samsung Gear S2. Participants wore devices while being simultaneously assessed with continuous telemetry and indirect calorimetry while sitting, walking, running, and cycling. Sixty volunteers (29 male, 31 female, age 38 ± 11 years) of diverse age, height, weight, skin tone, and fitness level were selected. Error in HR and EE was computed for each subject/device/activity combination. Results Devices reported the lowest error for cycling and the highest for walking. Device error was higher for males,greater body mass index, darker skin tone, and walking. Six of the devices achieved a median error for HR below 5% during cycling. No device achieved an error in EE below 20 percent. The Apple Watch achieved the lowest overall error in both HR and EE, while the Samsung Gear S2 reported the highest. Conclusions Most wrist-worn devices adequately measure HR in laboratory-based activities, but poorly estimate EE, suggesting caution in the use of EE measurements as part of health improvement programs. We propose reference standards for the validation of consumer health devices (http://precision.stanford.edu/). Abbreviations (EE) Energy expenditure (HR) Heart rate (GEE) General estimating equation

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Complementary and alternative medicine

View Paper: https://www.semanticscholar.org/paper/9f455261c87bef6e2986a1f520716a9b225f7997

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In Biomedical Engineering

In Cardiology and Cardiovascular Medicine

Citations in the corpus

Wearable Health Technology and Electronic Health Record Integration: Scoping Review and Future Directions (2019, neutral): “For example, the Apple Health and PulseOn Android apps provide heart rate data at 60-second long and 3-second long intervals, respectively; transmission of such large volumes of data will require backend analysis to be processed into a simpler and more usable form [54].”
Back to the Future: Achieving Health Equity Through Health Informatics and Digital Health (2019, background): “One study provided evidence that these devices were within acceptable error range, but this existing bias is unacceptable considering the surge in clinical research studies integrating these wearable technologies [9].”
Investigating sources of inaccuracy in wearable optical heart rate sensors (2020, background): “Our analyses reinforces the growing body of research demonstrating that wearable devices have higher error during activity than at rest.(8,11,35,38,39) We further demonstrate that the directionality of the HR error is dependent on the activity type.”
Deep learning for cardiovascular medicine: a practical primer. (2019, background): “had acceptable accuracy for resting heart rate yet not for ambulatory exercise heart rates nor energy expenditure.(80) Current ESC guidelines provide a Class I recommendation for the opportunistic screen-”
Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables (2018, methodology): “Mio, Misfit, Polar, Withings, and Xiaomi, were the only brands that automatically synchronized fitness data to both of these repositories through these open APIs.”