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Related Experiment Videos

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

Rachel Elise Wood1, Scott Hayter, David Rowbottom

  • 1Queensland University of Technology, Victoria Park Road, Kelvin Grove, Queensland 4059, Australia. re.wood@qut.edu.au

European Journal of Applied Physiology
|March 15, 2005
PubMed
Summary

This study validated a systems model of athletic performance by correlating its fitness and fatigue components with physiological measures and mood states in a middle-distance runner.

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Area of Science:

  • Sports Science
  • Exercise Physiology
  • Performance Analytics

Background:

  • Athletic performance is influenced by the interplay of fitness and fatigue.
  • A systems model conceptualizes performance as the difference between these two components.
  • Individualized parameters are crucial for accurately modeling fitness and fatigue.

Purpose of the Study:

  • To investigate physiological and psychological correlates of a systems model's fitness and fatigue components.
  • To validate the model using running economy, VO2max, ventilatory threshold, and mood states.
  • To assess the model's predictive accuracy over a 12-week training period.

Main Methods:

  • A systems model was applied to a trained male middle-distance runner over 12 weeks.

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  • Model components were estimated by fitting predicted performance to weekly measured performance.
  • Correlations were calculated between model components and extrapolated VO2max, running economy, running speed at ventilatory threshold, and Profile of Mood States (POMS).

    • Main Results:

    • The model demonstrated a significant fit with actual performance (r²=0.92).
    • The fitness component strongly correlated with running speed at ventilatory threshold (r=0.94) and running economy (r=-0.61).
    • The fatigue component showed a moderate correlation with the fatigue subset of the POMS (r=0.75).

    Conclusions:

    • Running speed at ventilatory threshold and running economy are valid physiological correlates of the model's fitness component.
    • The Profile of Mood States is a potential psychological correlate for the model's fatigue component.
    • This study provides novel validation for a systems model of athletic performance using physiological and psychological markers.