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Predicting self-reported illness for professional team-sport athletes

Thornton, H.R., Delaney, J.A., Duthie, G.M., Scott, B.R., Chivers, W.J., Sanctuary, C.E. and Dascombe, B.J. (2016) Predicting self-reported illness for professional team-sport athletes. International Journal of Sports Physiology and Performance, 11 (4). pp. 543-550.

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To identify contributing factors to the incidence of illness for professional team-sport athletes, using training load (TL), self-reported illness, and well-being data. Methods: Thirty-two professional rugby league players (26.0 ± 4.8 y, 99.1 ± 9.6 kg, 1.84 ± 0.06 m) were recruited from the same club. Players participated in prescribed training and responded to a series of questionnaires to determine the presence of self-reported illness and markers of well-being. Internal TL was determined using the session rating of perceived exertion. These data were collected over 29 wk, across the preparatory and competition macrocycles. Results: The predictive models developed recognized increases in internal TL (strain values of >2282 AU, weekly TL >2786 AU, and monotony >0.78 AU) to best predict when athletes are at increased risk of self-reported illness. In addition, a reduction in overall well-being (<7.25 AU) in the presence of increased internal TL, as previously stated, was highlighted as a contributor to self-reported-illness occurrence. Conclusions: These results indicate that self-report data can be successfully used to provide a novel understanding of the interactions between competition-associated stressors experienced by professional team-sport athletes and their susceptibility to illness. This may help coaching staff more effectively monitor players during the season and potentially implement preventive measures to reduce the likelihood of illnesses occurring.

Publication Type: Journal Article
Murdoch Affiliation: School of Psychology and Exercise Science
Publisher: Human Kinetics
Copyright: © 2016 Human Kinetics, Inc.
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