Heart rate variability (HRV) has become popular for assessing improvements in physical fitness, performance, and recovery. The purpose of this study was to assess the ability of HRV metrics to predict strength and cardiovascular performance in a military cohort using data obtained from commercial off-the-shelf (COTS) wearables. (1) Methods: Twenty-four active-duty military personnel (17 males; 7 females), ranging from age 23 to 41 (32.70 ± 4.65), were equipped with a Whoop Strap 3.0, a Garmin Fenix 5, and an Omegawave during a 12-week exercise intervention study. For this experiment researchers focused solely on HRV metrics obtained on scheduled “Gameday” competitions that occurred periodically during the intervention and contained a battery for strength, power, and cardiovascular performance tests. (2) Statistical Analysis: HRV metrics fitted with linear mixed models and applied to a composite strength variable derived following interrogation of performance tests with principal component analysis (PCA). Akaike’s information criterion (AIC) was also used to compare cardiovascular and strength metrics. (3) Results: Results indicated that standard deviation of NN intervals (SDNN)] obtained from Omegawave was the best overall predictor of performance (AIC > 5.00). (4) Conclusion: Our analyses demonstrated that traditional metrics obtained with the Omegawave were the best performance predictors. HRV measured by Omegawave immediately prior to Gameday assessment was inversely related with strength performance, suggesting that a lower HRV was associated with higher performance (p = 0.002). These findings demonstrate the potential influence of timing and raw values utilized on HRV interpretation to predict strength and cardiovascular performance.
| Published in | American Journal of Sports Science (Volume 10, Issue 1) |
| DOI | 10.11648/j.ajss.20221001.13 |
| Page(s) | 14-23 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Heart Rate Variability, Performance, Strength Training
| [1] | Nummela, A., Hynynen, E., Kaikkonen, P., Rusko, H. (2009). Endurance performance and nocturnal HRV indices. International Journal of Sports Medicine, 31 (3), 154-9. |
| [2] | Hedelin, R., Bjerle, P., Henriksson-Larsen, K (2001). Heart rate variability in athletes: relationship with central and peripheral performance. Medicine and Science in Sports and Exercise, 33 (8), 1394-1398. |
| [3] | Vesterinen, V., Häkkinen, K., Hynynen, E., Mikkola, J., Hokka, L., Nummela, A. (2013). Heart rate variability in prediction of individual adaptation to endurance training in recreational endurance runners. Scandinavian Journal of Medicine & Science in Sports, 23 (2), 171-80. |
| [4] | Tomes, C., Schram, B., & Orr, R. (2020). Relationships between heart rate variability, occupational performance, and fitness for tactical personnel: A systematic review. Frontiers in Public Health, 8, 1-16. |
| [5] | Shaffer, F. and Ginsberg, J. P. (2017). An overview of heart rate variability metrics and norms. Frontiers in Public Health, 5 (258). |
| [6] | Billman, G. E. (2011). Heart rate variability - a historical perspective. Frontiers in Physiology, 2 (86). |
| [7] | Garmin (2021). Garmin. Garmin. https://www.garmin.com/en-US/. |
| [8] | Whoop (2021). Whoop. Whoop. https://www.whoop.com/. |
| [9] | Buchheit, M., Chivot, A., Parouty, J., Mercier, D., Haddad, H. A., Laursen, P. B., & Ahmaidi, S. (2010). Monitoring endurance running performance using cardiac parasympathetic function. European Journal of Applied Physiology, 108 (6), 1153-1167. |
| [10] | Thiel, C., Vogt, L., Burklein, M., Rosenhagen, A., Hubscher, M., & Banzer, W. (2011). Functional overreaching during preparation training of elite tennis professionals. Journal of Human Kinetics, 28, 79-89. |
| [11] | Miller, A. E., MacDougall, J. D., Tarnopolsky, M. A., & Sale, D. G. (1993). Gender differences in strength and muscle fiber characteristics. European Journal of Applied Physiology and Occupational Physiology, 66 (3). 254-262. |
| [12] | Baechle, T. R., Earle, R. W. (2008). Essentials of Strength Training and Conditioning. 3rd ed. Human Kinetics: Champaign, IL. |
| [13] | Symonds, M. R. & Moussalli, A. (2011). A brief guide to model selection, multimodel inference and model averaging in behavioral ecology using Akaike’s information criterion. Behavioral Ecology and Sociobiology, 65 (1), 13–21. |
| [14] | Stone, M. H., Stone, M., & Sands, W. A. (2007). Principles and Practice of Resistance Training. Human Kinetics: Champaign, IL. |
| [15] | Breuer, H. W., Skyschally, A., Schulz, R., Martin, C., Wehr, M., & Heusch, G. (1993). Heart rate variability and circulating catecholamine concentrations during steady state exercise in health volunteers. British Heart Journal, 70 (2), 144-149. |
| [16] | French, D. N., Kraemer, W. J., Volek, J. S., Spiering, B. A., Judelson, D. A., Hoffman, J. R., & Maresh, C. M. (2007). Anticipatory responses of catecholamines on muscle force production. Journal of Applied Physiology, 102 (1), https://doi.org/10.1152/japplphysiol.00586.2006. |
| [17] | Gladwell, V. F. & Coote, J. H. (2002). Heart rate at the onset of muscle contraction and during passive muscle stretch in humans: a role for mechanoreceptors. The Journal of Physiology, 540 (3), 1095-1102. |
| [18] | Turner, M. & Jones, M. (2018). Arousal control in sport. Psychology. https://doi.org/10.1093/acrefore/9780190236557.013.155 |
| [19] | Hughes, D. C., Ellefsen, S., & Baar, K. (2018). Adaptations to endurance and strength training. Cold Spring Harbor Perspectives in Medicine, 8 (6). |
APA Style
Kaela Hierholzer, Robert Briggs, Michael Tolston, Nicholas Mackowski, Maegan O’Connor, et al. (2022). Heart Rate Variability Metrics from Commercial Devices Predicts Strength and Cardiovascular Performance in a Military Cohort. American Journal of Sports Science, 10(1), 14-23. https://doi.org/10.11648/j.ajss.20221001.13
ACS Style
Kaela Hierholzer; Robert Briggs; Michael Tolston; Nicholas Mackowski; Maegan O’Connor, et al. Heart Rate Variability Metrics from Commercial Devices Predicts Strength and Cardiovascular Performance in a Military Cohort. Am. J. Sports Sci. 2022, 10(1), 14-23. doi: 10.11648/j.ajss.20221001.13
AMA Style
Kaela Hierholzer, Robert Briggs, Michael Tolston, Nicholas Mackowski, Maegan O’Connor, et al. Heart Rate Variability Metrics from Commercial Devices Predicts Strength and Cardiovascular Performance in a Military Cohort. Am J Sports Sci. 2022;10(1):14-23. doi: 10.11648/j.ajss.20221001.13
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Download@article{10.11648/j.ajss.20221001.13,
author = {Kaela Hierholzer and Robert Briggs and Michael Tolston and Nicholas Mackowski and Maegan O’Connor and Kristyn Barrett and Roger Smith and Jason Eckerle and Adam Strang},
title = {Heart Rate Variability Metrics from Commercial Devices Predicts Strength and Cardiovascular Performance in a Military Cohort},
journal = {American Journal of Sports Science},
volume = {10},
number = {1},
pages = {14-23},
doi = {10.11648/j.ajss.20221001.13},
url = {https://doi.org/10.11648/j.ajss.20221001.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajss.20221001.13},
abstract = {Heart rate variability (HRV) has become popular for assessing improvements in physical fitness, performance, and recovery. The purpose of this study was to assess the ability of HRV metrics to predict strength and cardiovascular performance in a military cohort using data obtained from commercial off-the-shelf (COTS) wearables. (1) Methods: Twenty-four active-duty military personnel (17 males; 7 females), ranging from age 23 to 41 (32.70 ± 4.65), were equipped with a Whoop Strap 3.0, a Garmin Fenix 5, and an Omegawave during a 12-week exercise intervention study. For this experiment researchers focused solely on HRV metrics obtained on scheduled “Gameday” competitions that occurred periodically during the intervention and contained a battery for strength, power, and cardiovascular performance tests. (2) Statistical Analysis: HRV metrics fitted with linear mixed models and applied to a composite strength variable derived following interrogation of performance tests with principal component analysis (PCA). Akaike’s information criterion (AIC) was also used to compare cardiovascular and strength metrics. (3) Results: Results indicated that standard deviation of NN intervals (SDNN)] obtained from Omegawave was the best overall predictor of performance (AIC > 5.00). (4) Conclusion: Our analyses demonstrated that traditional metrics obtained with the Omegawave were the best performance predictors. HRV measured by Omegawave immediately prior to Gameday assessment was inversely related with strength performance, suggesting that a lower HRV was associated with higher performance (p = 0.002). These findings demonstrate the potential influence of timing and raw values utilized on HRV interpretation to predict strength and cardiovascular performance.},
year = {2022}
}
TY - JOUR T1 - Heart Rate Variability Metrics from Commercial Devices Predicts Strength and Cardiovascular Performance in a Military Cohort AU - Kaela Hierholzer AU - Robert Briggs AU - Michael Tolston AU - Nicholas Mackowski AU - Maegan O’Connor AU - Kristyn Barrett AU - Roger Smith AU - Jason Eckerle AU - Adam Strang Y1 - 2022/02/25 PY - 2022 N1 - https://doi.org/10.11648/j.ajss.20221001.13 DO - 10.11648/j.ajss.20221001.13 T2 - American Journal of Sports Science JF - American Journal of Sports Science JO - American Journal of Sports Science SP - 14 EP - 23 PB - Science Publishing Group SN - 2330-8540 UR - https://doi.org/10.11648/j.ajss.20221001.13 AB - Heart rate variability (HRV) has become popular for assessing improvements in physical fitness, performance, and recovery. The purpose of this study was to assess the ability of HRV metrics to predict strength and cardiovascular performance in a military cohort using data obtained from commercial off-the-shelf (COTS) wearables. (1) Methods: Twenty-four active-duty military personnel (17 males; 7 females), ranging from age 23 to 41 (32.70 ± 4.65), were equipped with a Whoop Strap 3.0, a Garmin Fenix 5, and an Omegawave during a 12-week exercise intervention study. For this experiment researchers focused solely on HRV metrics obtained on scheduled “Gameday” competitions that occurred periodically during the intervention and contained a battery for strength, power, and cardiovascular performance tests. (2) Statistical Analysis: HRV metrics fitted with linear mixed models and applied to a composite strength variable derived following interrogation of performance tests with principal component analysis (PCA). Akaike’s information criterion (AIC) was also used to compare cardiovascular and strength metrics. (3) Results: Results indicated that standard deviation of NN intervals (SDNN)] obtained from Omegawave was the best overall predictor of performance (AIC > 5.00). (4) Conclusion: Our analyses demonstrated that traditional metrics obtained with the Omegawave were the best performance predictors. HRV measured by Omegawave immediately prior to Gameday assessment was inversely related with strength performance, suggesting that a lower HRV was associated with higher performance (p = 0.002). These findings demonstrate the potential influence of timing and raw values utilized on HRV interpretation to predict strength and cardiovascular performance. VL - 10 IS - 1 ER -
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