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Related Experiment Videos

Modeling of adaptations to physical training by using a recursive least squares algorithm

T Busso1, C Denis, R Bonnefoy

  • 1Laboratorie de Physiologie-Groupement d'Intéret Public Exercice, Faculté de Médecine Saint-Etienne, Saint-Etienne, France. busso@univ-st-etienne.fr

Journal of Applied Physiology (Bethesda, Md. : 1985)
|May 1, 1997
PubMed
Summary

A new time-varying systems model accurately describes physical performance changes during training, outperforming older models. This approach aids in understanding adaptation and fatigue mechanisms.

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Area of Science:

  • Exercise Physiology
  • Systems Biology
  • Biomathematics

Background:

  • Understanding physiological adaptation to training is crucial for optimizing athletic performance.
  • Existing models often use time-invariant parameters, potentially limiting their ability to capture dynamic training responses.

Purpose of the Study:

  • To evaluate the effectiveness of a systems model with time-varying parameters for describing physical performance responses to a structured training program.
  • To compare the performance of this novel model against a traditional time-invariant parameter model.

Main Methods:

  • Two subjects underwent a 14-week cycle ergometer training regimen with varied intensity periods.
  • Training load (input) was quantified, and endurance time (output) was measured multiple times weekly.

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  • Time-varying parameters were fitted using a recursive least squares algorithm; time-invariant parameters used the least squares method.

    • Main Results:

    • The time-varying model achieved higher coefficients of determination (r2=0.875, 0.879) compared to the time-invariant model (r2=0.682, 0.666).
    • Parameter variations in the time-varying model correlated with observed changes in training response.
    • The model effectively accounted for shifts in performance over the training period.

    Conclusions:

    • A systems model with time-varying parameters offers a more accurate representation of training adaptations in physical performance.
    • This dynamic modeling approach is valuable for exploring the complex mechanisms underlying physiological adaptation and fatigue.
    • The findings support the utility of dynamic systems modeling in exercise science research.