Contribution of Nutrition and Psychometric Factors to the Fatigue Component of a Performance Prediction Model in Endurance Running

Emily Baker¹, Colin Solomon^{2, 3,} and Ian Stewart¹

¹Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia

²School of Life Sciences, Queensland University of Technology, Brisbane, Australia

³School of Health and Sport Sciences, University of the Sunshine Coast, Sippy Downs, Australia

Cite this paper:
Emily Baker, Colin Solomon and Ian Stewart. Contribution of Nutrition and Psychometric Factors to the Fatigue Component of a Performance Prediction Model in Endurance Running. Journal of Physical Activity Research. 2020; 5(2):91-99. doi: 10.12691/jpar-5-2-5

Abstract

Mathematical models can be used to predict exercise performance, but the specific factors contributing to the fatigue component of these models are unknown. This study was designed to determine the contribution of nutrition and psychometric factors to the fatigue component of a performance prediction model for endurance running. It was hypothesized that there would be a positive correlation between both nutritional intake and psychometric factors, and the modeled fatigue. One experienced male marathon and ultra-marathon runner was monitored during 18-weeks of training, involving a weekly performance test (mean ± SD; distance = 10508 ± 113 m), nutritional diaries, and psychometric questionnaires (POMS and RESTQ-Sport). A dose-response based model incorporating two antagonistic components, fitness and fatigue, and training data, was used to calculate modeled performance, which was correlated against actual performance. The performance fit was low (r² = 0.24, P = 0.05) when modelled for the total 122 day period, however the fit was increased when the model was divided into two separate training periods (days 1 - 66: r² = 0.55, P = 0.02; days 66 - 122: r² = 0.87, P = 0.002). There were significant (P <0.01) positive correlations between modelled fatigue and the nutritional data (Fat r² = 0.78), POMS (Vigour r² = 0.92), and RESTQ-Sport (General Recovery r² = 0.74; Sports Recovery r² = 0.71; Global Recovery r² = 0.78). The results indicate a high correlation between nutritional intake and scores on the psychometric questionnaires, and the fatigue parameter of the model. Therefore, these factors should be measured and used in models of fatigue.

Keywords:
fatigue performance endurance model

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