PURPOSE: 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.
PURPOSE: 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.
Authors: Andrew Watson; Eric Post; Kevin Biese; Stephanie Kliethermes; M Alison Brooks; David Bell Journal: Sports Health Date: 2020-07-06 Impact factor: 3.843
Authors: Rafael Luchoro-Parrilla; Pere Lavega-Burgués; Sabrine Damian-Silva; Queralt Prat; Unai Sáez de Ocáriz; Enric Ormo-Ribes; Miguel Pic Journal: Front Psychol Date: 2021-02-25
Authors: Martin Schwellnus; Torbjørn Soligard; Juan-Manuel Alonso; Roald Bahr; Ben Clarsen; H Paul Dijkstra; Tim J Gabbett; Michael Gleeson; Martin Hägglund; Mark R Hutchinson; Christa Janse Van Rensburg; Romain Meeusen; John W Orchard; Babette M Pluim; Martin Raftery; Richard Budgett; Lars Engebretsen Journal: Br J Sports Med Date: 2016-09 Impact factor: 13.800
Authors: Sabrine Damian-Silva; Carles Feixa; Queralt Prat; Rafael Luchoro-Parrilla; Miguel Pic; Aaron Rillo-Albert; Unai Sáez de Ocáriz; Antoni Costes; Pere Lavega-Burgués Journal: Front Psychol Date: 2021-04-01