Modelling the HRV Response to Training Loads in Elite Rugby Sevens Players

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...

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Main Author: Sean Williams, Stephen West, Dan Howells, Simon P.T. Kemp, Andrew A. Flatt, Keith Stokes
Format: Article
Language:English
Published: University of Uludag 2018-09-01
Series:Journal of Sports Science and Medicine
Subjects:
Online Access:https://www.jssm.org/hf.php?id=jssm-17-402.xml
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spelling doaj-5ff406ed4ba648b8a8d66f182210b91f2020-11-25T02:19:39ZengUniversity of UludagJournal of Sports Science and Medicine1303-29682018-09-01173402408Modelling the HRV Response to Training Loads in Elite Rugby Sevens PlayersSean Williams, Stephen West, Dan Howells, Simon P.T. Kemp, Andrew A. Flatt, Keith Stokes0Department for Health, University of Bath, Bath, UKA 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.https://www.jssm.org/hf.php?id=jssm-17-402.xmlCardiac parasympathetic functionmonitoringtraining load
collection DOAJ
language English
format Article
sources DOAJ
author Sean Williams, Stephen West, Dan Howells, Simon P.T. Kemp, Andrew A. Flatt, Keith Stokes
spellingShingle Sean Williams, Stephen West, Dan Howells, Simon P.T. Kemp, Andrew A. Flatt, Keith Stokes
Modelling the HRV Response to Training Loads in Elite Rugby Sevens Players
Journal of Sports Science and Medicine
Cardiac parasympathetic function
monitoring
training load
author_facet Sean Williams, Stephen West, Dan Howells, Simon P.T. Kemp, Andrew A. Flatt, Keith Stokes
author_sort Sean Williams, Stephen West, Dan Howells, Simon P.T. Kemp, Andrew A. Flatt, Keith Stokes
title Modelling the HRV Response to Training Loads in Elite Rugby Sevens Players
title_short Modelling the HRV Response to Training Loads in Elite Rugby Sevens Players
title_full Modelling the HRV Response to Training Loads in Elite Rugby Sevens Players
title_fullStr Modelling the HRV Response to Training Loads in Elite Rugby Sevens Players
title_full_unstemmed Modelling the HRV Response to Training Loads in Elite Rugby Sevens Players
title_sort modelling the hrv response to training loads in elite rugby sevens players
publisher University of Uludag
series Journal of Sports Science and Medicine
issn 1303-2968
publishDate 2018-09-01
description 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.
topic Cardiac parasympathetic function
monitoring
training load
url https://www.jssm.org/hf.php?id=jssm-17-402.xml
work_keys_str_mv AT seanwilliamsstephenwestdanhowellssimonptkempandrewaflattkeithstokes modellingthehrvresponsetotrainingloadsineliterugbysevensplayers
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