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10 km running performance predicted by a multiple linear regression model with allometrically adjusted variables.

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Peak treadmill running velocity (PTV) and running economy (RE) effectively predict 10 km running performance. Unadjusted PTV offers a simple yet powerful estimation method for race times.

Keywords:
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Area of Science:

  • Exercise Physiology
  • Sports Science
  • Performance Analytics

Background:

  • Predicting endurance running performance is crucial for training optimization.
  • VO2max, peak treadmill running velocity (PTV), and running economy (RE) are key physiological determinants.
  • Understanding the predictive power of adjusted vs. unadjusted variables is essential.

Purpose of the Study:

  • To evaluate the predictive capability of VO2max, PTV, and RE, both unadjusted and allometrically adjusted, for 10 km running performance.
  • To compare the efficacy of different regression models in estimating 10 km race times.

Main Methods:

  • Eighteen male endurance runners completed incremental and submaximal running tests and a 10 km race.
  • VO2max, PTV, and RE were measured, with both unadjusted and allometrically adjusted values calculated.
  • Independent multiple regression models were used to predict 10 km running time.

Main Results:

  • VO2max (adjusted or unadjusted) did not significantly correlate with 10 km running time.
  • Both adjusted and unadjusted RE and PTV showed significant correlations with 10 km running time (p < 0.01).
  • An allometrically adjusted model (PTV^0.72, RE^0.60) explained 83% of the variance in 10 km time, while an unadjusted PTV model explained 72%.

Conclusions:

  • PTV and RE are strong predictors of 10 km running performance, with allometric adjustments potentially enhancing predictive accuracy.
  • While adjusted models offer higher explained variance, unadjusted PTV provides a practical and uncomplicated method for estimating 10 km race performance.