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.
Highlights
In order to enhance the understanding of the association between training and performance, a systems model was developed to predict performance where training represented the dose and a change in performance represented the response [1]
The specific aim of this study was to determine the contribution of nutrition and psychometric indices to the fatigue component of a performance predicting model, in endurance running
When the training period was split into two sections and remodelled, the ability of the model to explain large portions of the variance in performance improved (Model B: r2 = 0.55, P = 0.02; Model C: r2 = 0.87, P = 0.002; Table 1, Figure 1)
Summary
In order to enhance the understanding of the association between training and performance, a systems model was developed to predict performance where training represented the dose and a change in performance represented the response [1] This dose-response relationship uses the two antagonistic components, fitness and fatigue, to calculate performance-based on training data. The model employs fixed equations incorporating constants, unique to the individual that determines the rate of accumulation and decay of both fitness and fatigue, to determine the performance outcome. The pattern between these hypothesized components becomes a valid representation of the real time course when a significant correlation exists between actual and modeled performance [2]. The fatigue component has been investigated previously, with biological indices such as elevated serum enzyme activity [2], iron [3] and testosterone [6] providing limited success, mostly due to the differences in the timing of the changes in these indices as a result of training
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