Journal of Sports Science and Medicine
Journal of Sports Science and Medicine
ISSN: 1303 - 2968   
Ios-APP Journal of Sports Science and Medicine
Androit-APP Journal of Sports Science and Medicine
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©Journal of Sports Science and Medicine ( 2018 ) 17 , 402 - 408

Research article
Modelling the HRV Response to Training Loads in Elite Rugby Sevens Players
Sean Williams1, , Stephen West1, Dan Howells2, Simon P.T. Kemp2, Andrew A. Flatt3, Keith Stokes1
Author Information
1 Department for Health, University of Bath, Bath, UK
2 Rugby Football Union, Twickenham, UK
3 Department of Health Science & Kinesiology, Georgia Southern University, Savannah, USA

Sean Williams
✉ Department for Health, University of Bath, Bath, BA2 7JY United Kingdom
Email: S.Williams@bath.ac.uk
Publish Date
Received: 16-04-2018
Accepted: 30-05-2018
Published (online): 14-08-2018
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ABSTRACT

A systems modelling approach can be used to describe and optimise responses to training stimuli within individuals. However, the requirement for regular maximal performance testing has precluded the widespread implementation of such modelling approaches in team-sport settings. Heart rate variability (HRV) can be used to measure an athlete’s adaptation to training load, without disrupting the training process. As such, the aim of the current study was to assess whether chronic HRV responses, as a representative marker of training adaptation, could be predicted from the training loads undertaken by elite Rugby Sevens players. Eight international male players were followed prospectively throughout an eight-week pre-season period, with HRV and training loads (session-RPE [sRPE] and high-speed distance [HSD]) recorded daily. The Banister model was used to estimate vagally-mediated chronic HRV responses to training loads over the first four weeks (tuning dataset); these estimates were then used to predict chronic HRV responses in the subsequent four-week period (validation dataset). Across the tuning dataset, high correlations were observed between modelled and recorded HRV for both sRPE (r = 0.66 ± 0.32) and HSD measures (r = 0.69 ± 0.12). Across the sRPE validation dataset, seven of the eight athletes met the criterion for validity (typical error <3% and Pearson r >0.30), compared to one athlete in the HSD validation dataset. The sRPE validation data produced likely lower mean bias values, and most likely higher Pearson correlations, compared to the HSD validation dataset. These data suggest that a systems theory approach can be used to accurately model chronic HRV responses to internal training loads within elite Rugby Sevens players, which may be useful for optimising the training process on an individual basis.

Key words: Cardiac parasympathetic function, monitoring, training load


           Key Points
  • A systems theory approach can be used to describe the variation in chronic HRV responses to training within elite Rugby Sevens players.
  • For the majority of athletes, model parameters can be used to accurately predict future responses to training stimuli.
  • Responses that diverge from the predicted values may serve as a useful flag for the investigation of changes in lifestyle factors.
  • Internal training load measures (sRPE) markedly outperformed external load measures (HSD) in predicting future HRV responses to training stimuli.
 
 
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