Applying a mathematical model to training adaptation in a distance runner

Eur J Appl Physiol. 2005 Jun;94(3):310-6. doi: 10.1007/s00421-005-1319-2. Epub 2005 Mar 12.


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) (, 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.

MeSH terms

  • Adaptation, Physiological*
  • Adult
  • Affect
  • Differential Threshold
  • Fatigue / etiology
  • Fatigue / physiopathology
  • Humans
  • Male
  • Models, Biological*
  • Oxygen Consumption
  • Physical Education and Training*
  • Physical Endurance / physiology*
  • Physical Fitness
  • Respiratory Mechanics
  • Running / physiology*
  • Running / psychology*
  • Task Performance and Analysis
  • Time Factors