Andrew A Flatt1,2, Michael R Esco3,4, Fabio Y Nakamura5, Daniel J Plews6,7,8. 1. Department of Kinesiology, Exercise Physiology Laboratory, University of Alabama, Tuscaloosa, AL, USA - aflatt@crimson.ua.edu. 2. Human Performance Laboratory, Auburn University at Montgomery, Montgomery, AL, USA - aflatt@crimson.ua.edu. 3. Department of Kinesiology, Exercise Physiology Laboratory, University of Alabama, Tuscaloosa, AL, USA. 4. Human Performance Laboratory, Auburn University at Montgomery, Montgomery, AL, USA. 5. Department of Physical Education, State University of Londrina, Londrina, Brazil. 6. High Performance Sport New Zealand, Auckland, New Zealand. 7. Department of Sport and Recreation, Waikato University, Hamilton, New Zealand. 8. Sports Performance Research Institute New Zealand (SPRINZ), Auckland University of Technology, Auckland, New Zealand.
Abstract
BACKGROUND: Heart rate variability (HRV) is an objective physiological marker that may be useful for monitoring training status in athletes. However, research aiming to interpret daily HRV changes in female athletes is limited. The objectives of this study were: 1) to assess daily HRV (i.e., log-transformed root mean square of successive R-R interval differences, lnRMSSD) trends both as a team and intra-individually in response to varying training load (TL); and 2) to determine relationships between lnRMSSD fluctuation (coefficient of variation, lnRMSSDcv) and psychometric and fitness parameters in collegiate female soccer players (N.=10). METHODS: Ultra-short, Smartphone-derived lnRMSSD and psychometrics were evaluated daily throughout 2 consecutive weeks of high and low TL. After the training period, fitness parameters were assessed. RESULTS: When compared to baseline, reductions in lnRMSSD ranged from unclear to very likely moderate during the high TL week (effect size ±90% confidence limits [ES±90% CL] =-0.21±0.74 to -0.64±0.78, respectively) while lnRMSSD reductions were unclear during the low TL week (ES±90% CL=-0.03±0.73 to -0.35±0.75, respectively). A large difference in TL between weeks was observed (ES±90% CL=1.37±0.80). Higher lnRMSSDcv was associated with greater perceived fatigue and lower fitness (r [upper and lower 90% CL]=-0.55 [-0.84, -0.003] large, -0.65 [-0.89, -0.15] large). CONCLUSIONS: Athletes with lower fitness or higher perceived fatigue demonstrated greater reductions in lnRMSSD throughout training. This information can be useful when interpreting individual lnRMSSD responses throughout training for managing player fatigue.
BACKGROUND: Heart rate variability (HRV) is an objective physiological marker that may be useful for monitoring training status in athletes. However, research aiming to interpret daily HRV changes in female athletes is limited. The objectives of this study were: 1) to assess daily HRV (i.e., log-transformed root mean square of successive R-R interval differences, lnRMSSD) trends both as a team and intra-individually in response to varying training load (TL); and 2) to determine relationships between lnRMSSD fluctuation (coefficient of variation, lnRMSSDcv) and psychometric and fitness parameters in collegiate female soccer players (N.=10). METHODS: Ultra-short, Smartphone-derived lnRMSSD and psychometrics were evaluated daily throughout 2 consecutive weeks of high and low TL. After the training period, fitness parameters were assessed. RESULTS: When compared to baseline, reductions in lnRMSSD ranged from unclear to very likely moderate during the high TL week (effect size ±90% confidence limits [ES±90% CL] =-0.21±0.74 to -0.64±0.78, respectively) while lnRMSSD reductions were unclear during the low TL week (ES±90% CL=-0.03±0.73 to -0.35±0.75, respectively). A large difference in TL between weeks was observed (ES±90% CL=1.37±0.80). Higher lnRMSSDcv was associated with greater perceived fatigue and lower fitness (r [upper and lower 90% CL]=-0.55 [-0.84, -0.003] large, -0.65 [-0.89, -0.15] large). CONCLUSIONS: Athletes with lower fitness or higher perceived fatigue demonstrated greater reductions in lnRMSSD throughout training. This information can be useful when interpreting individual lnRMSSD responses throughout training for managing player fatigue.
Authors: Jonathan Hu; Jonathan D Browne; Jaxon T Baum; Anthony Robinson; Michael T Arnold; Sean P Reid; Eric V Neufeld; Brett A Dolezal Journal: Int J Exerc Sci Date: 2020-12-01
Authors: Ward C Dobbs; Michael V Fedewa; Hayley V MacDonald; Clifton J Holmes; Zackary S Cicone; Daniel J Plews; Michael R Esco Journal: Sports Med Date: 2019-03 Impact factor: 11.136
Authors: Gregory J Grosicki; Meral N Culver; Nathan K McMillan; Brett L Cross; Alexander H K Montoye; Bryan L Riemann; Andrew A Flatt Journal: Clin Auton Res Date: 2022-08-23 Impact factor: 5.625
Authors: Sara R Sherman; Clifton J Holmes; Alexander P Demos; Tori Stone; Bjoern Hornikel; Hayley V MacDonald; Michael V Fedewa; Michael R Esco Journal: Int J Sports Physiol Perform Date: 2021-11-10 Impact factor: 4.211
Authors: Michael S Blake; Nathaniel R Johnson; Kara A Trautman; James W Grier; Sherri N Stastny; Kyle J Hackney Journal: Int J Exerc Sci Date: 2020-02-01
Authors: Rafaela B Mascarin; Vitor L De Andrade; Ricardo A Barbieri; João P Loures; Carlos A Kalva-Filho; Marcelo Papoti Journal: Front Physiol Date: 2018-07-11 Impact factor: 4.566
Authors: Lea C Rundfeldt; Martina A Maggioni; Robert H Coker; Hanns-Christian Gunga; Alain Riveros-Rivera; Adriane Schalt; Mathias Steinach Journal: Front Physiol Date: 2018-02-12 Impact factor: 4.566
Authors: Chin-Hwai Hung; Filipe Manuel Clemente; Pedro Bezerra; Yi-Wen Chiu; Chia-Hua Chien; Zachary Crowley-McHattan; Yung-Sheng Chen Journal: Int J Environ Res Public Health Date: 2020-06-07 Impact factor: 3.390