Literature DB >> 15765236

Applying a mathematical model to training adaptation in a distance runner.

Rachel Elise Wood1, Scott Hayter, David Rowbottom, Ian Stewart.   

Abstract

This study investigated physiological and psychological correlates of the positive and negative components of a systems model in a well-trained male middle-distance runner. In the systems model, performance at any given point in time is seen as the difference between two antagonistic components, fitness and fatigue, which represent the positive and negative adaptation to training, respectively. Each component comprises a set of parameters unique to the individual, which were estimated by fitting model-predicted performance to performance measured weekly throughout a 12-week training period. The model fitness component was correlated with extrapolated VO(2max) (ml.kg(-1).min(-1)), running economy (RE) (VO(2) at 17 km.h(-1)), and running speed (km.h(-1)) at ventilatory threshold (VTRS). The model fatigue component was correlated with the fatigue subset of the profile of mood states (POMS). The fit between model and actual performance was significant (r(2)=0.92, P< 0.01). In the case of fitness, both VTRS (r=0.94, P=0.0001) and RE (r=-0.61, P=0.04) were significantly correlated with the model fitness component. There was also a moderate correlation between the fatigue subset of the POMS and the fatigue component (r=0.75, p< 0.05). In summary, this is the first time VTRS and the POMS have been used in an attempt to validate the model components. The findings of the present study support previous validation attempts using biochemical and hormonal markers of fitness and fatigue.

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Year:  2005        PMID: 15765236     DOI: 10.1007/s00421-005-1319-2

Source DB:  PubMed          Journal:  Eur J Appl Physiol        ISSN: 1439-6319            Impact factor:   3.078


  21 in total

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Journal:  Int J Sports Med       Date:  1994-02       Impact factor: 3.118

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  7 in total

1.  A mathematical model for quantifying training.

Authors:  Philip R Hayes; Mike D Quinn
Journal:  Eur J Appl Physiol       Date:  2009-05-26       Impact factor: 3.078

Review 2.  The quantification of training load, the training response and the effect on performance.

Authors:  Jill Borresen; Michael Ian Lambert
Journal:  Sports Med       Date:  2009       Impact factor: 11.136

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

Authors:  Sean Williams; Stephen West; Dan Howells; Simon P T Kemp; Andrew A Flatt; Keith Stokes
Journal:  J Sports Sci Med       Date:  2018-08-14       Impact factor: 2.988

4.  Relations between psychometric profiles and cardiovascular autonomic regulation in physical education students.

Authors:  Frédéric Nuissier; Didier Chapelot; Cécile Vallet; Aurélien Pichon
Journal:  Eur J Appl Physiol       Date:  2007-01-12       Impact factor: 3.078

5.  A comparison of methods for quantifying training load: relationships between modelled and actual training responses.

Authors:  L K Wallace; K M Slattery; Aaron J Coutts
Journal:  Eur J Appl Physiol       Date:  2013-10-09       Impact factor: 3.078

Review 6.  Monitoring the athlete training response: subjective self-reported measures trump commonly used objective measures: a systematic review.

Authors:  Anna E Saw; Luana C Main; Paul B Gastin
Journal:  Br J Sports Med       Date:  2015-09-09       Impact factor: 13.800

7.  A quantitative method for estimating the adaptedness in a physiological study.

Authors:  Vladimir N Melnikov
Journal:  Theor Biol Med Model       Date:  2019-09-03       Impact factor: 2.432

  7 in total

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