@article{10.11648/j.aap.20220702.12,
  author = {Kaela Hierholzer and Robert Briggs and Michael Tolston and Nicholas Mackowski and Jason Eckerle and Maegan O’Connor and Kristyn Barrett and Roger Smith and Adam Strang},
  title = {Heart Rate Variability: A Longitudinal Comparison of Commercial Devices for Individual and Group Stress-Response},
  journal = {Advances in Applied Physiology},
  volume = {7},
  number = {2},
  pages = {26-33},
  doi = {10.11648/j.aap.20220702.12},
  url = {https://doi.org/10.11648/j.aap.20220702.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.aap.20220702.12},
  abstract = {The collection of heart rate variability (HRV) for health and performance observations have become prominent. However, each wearable device has proprietary algorithms that govern methods and timing of HRV capture and subsequent analysis. The purpose of this study was to evaluate HRV metrics taken from three, commonly used commercial wearables, and identify reliability and relationships to one another over time. Methods: Twenty-five subjects (18 males; 7 females) with ages ranging from 23 to 41 years (32.70 ± 4.65 years) were included in this study. These subjects were participants in a 12-week exercise intervention study. Each subject was equipped with a Whoop Strap (v2.0), the Garmin Fenix 5 Smartwatch and chest strap, and the Omegawave chest strap and sensor. Statistical Analysis: Between and within-subject correlations were calculated as well as average correlations, descriptive and inferential statistics, and the resultant z-score, which was transformed back into a correlation. Intraclass correlation coefficients (ICC) were calculated. Finally, linear mixed models were used to evaluate trends in HRV. Results: Within-subject correlations (0.24 ± 0.27) were lower than between-subjects correlations (0.54 ± 0.43), t (35) = -4.02, p p = 0.020) and a negative effect of day of the week (p = 0.030). Day of the week was the only significant predictor of Whoop recovery score (p Conclusion: The correlations of HRV values remain more consistent when assessed at similar times of the day, rather than being device dependent. Regardless of which wearable device is considered, HRV measures should be collected at a specific time each day for the best reliability. When creating an individualized or group exercise program, the human performance specialist should be aware that fatigue may become increasingly evident during the course of each week (e.g. individuals demonstrably fatigued by Friday may exhibit physiological indicators of relative recovery by Monday).},
 year = {2022}
}

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TY  - JOUR
T1  - Heart Rate Variability: A Longitudinal Comparison of Commercial Devices for Individual and Group Stress-Response
AU  - Kaela Hierholzer
AU  - Robert Briggs
AU  - Michael Tolston
AU  - Nicholas Mackowski
AU  - Jason Eckerle
AU  - Maegan O’Connor
AU  - Kristyn Barrett
AU  - Roger Smith
AU  - Adam Strang
Y1  - 2022/10/11
PY  - 2022
N1  - https://doi.org/10.11648/j.aap.20220702.12
DO  - 10.11648/j.aap.20220702.12
T2  - Advances in Applied Physiology
JF  - Advances in Applied Physiology
JO  - Advances in Applied Physiology
SP  - 26
EP  - 33
PB  - Science Publishing Group
SN  - 2471-9714
UR  - https://doi.org/10.11648/j.aap.20220702.12
AB  - The collection of heart rate variability (HRV) for health and performance observations have become prominent. However, each wearable device has proprietary algorithms that govern methods and timing of HRV capture and subsequent analysis. The purpose of this study was to evaluate HRV metrics taken from three, commonly used commercial wearables, and identify reliability and relationships to one another over time. Methods: Twenty-five subjects (18 males; 7 females) with ages ranging from 23 to 41 years (32.70 ± 4.65 years) were included in this study. These subjects were participants in a 12-week exercise intervention study. Each subject was equipped with a Whoop Strap (v2.0), the Garmin Fenix 5 Smartwatch and chest strap, and the Omegawave chest strap and sensor. Statistical Analysis: Between and within-subject correlations were calculated as well as average correlations, descriptive and inferential statistics, and the resultant z-score, which was transformed back into a correlation. Intraclass correlation coefficients (ICC) were calculated. Finally, linear mixed models were used to evaluate trends in HRV. Results: Within-subject correlations (0.24 ± 0.27) were lower than between-subjects correlations (0.54 ± 0.43), t (35) = -4.02, p p = 0.020) and a negative effect of day of the week (p = 0.030). Day of the week was the only significant predictor of Whoop recovery score (p Conclusion: The correlations of HRV values remain more consistent when assessed at similar times of the day, rather than being device dependent. Regardless of which wearable device is considered, HRV measures should be collected at a specific time each day for the best reliability. When creating an individualized or group exercise program, the human performance specialist should be aware that fatigue may become increasingly evident during the course of each week (e.g. individuals demonstrably fatigued by Friday may exhibit physiological indicators of relative recovery by Monday).
VL  - 7
IS  - 2
ER  - 

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