Dataset for: Athlete Burnout and Health: Testing Longitudinal Mediation via Biomarkers

DOI

Abstract (prior to peer review): Athletes often face pressure in competitive sports, which can cause physical and mental health problems including athlete burnout. Burnout is becoming increasingly prevalent in athletes and not only affects mental health but heightens the risk for further physical and mental health consequences. While studies have supported this link, few of these examinations have assessed the possible explanatory mechanisms. Therefore, this study aimed to test whether biomarkers of key physiological systems may mediate the relationship between athlete burnout and mental and physical health outcomes over time. We recruited 64 competitive athletes who completed measures of athlete burnout, physical symptoms, depressive symptoms, and insomnia as well as provided saliva samples for the analysis of biomarkers (testosterone, dehydroepiandrosterone-sulphate [DHEA-S], secretory Immunoglobulin A [sIgA]) at three timepoints over six months. At the between level, burnout was associated with all questionnaire measures, while testosterone was associated with physical symptoms. At the within level, burnout predicted depressive symptoms and sIgA predicted insomnia. Exploratory analyses with a Bayesian approach further showed burnout to predict reductions in testosterone, DHEA-S and sIgA. In contrast to our hypotheses, we found no indirect effects linking burnout with potential health consequences via changes in biomarkers. Thus, burnout appears to affect physical and mental health through predominantly direct links.

Identifier
DOI https://doi.org/10.23668/psycharchives.15944
Metadata Access https://api.datacite.org/dois/10.23668/psycharchives.15944
Provenance
Creator Glandorf, Hanna; Madigan, Daniel J.; Kavanagh, Owen; Mallinson-Howard, Sarah H.; Isoard-Gautheur, Sandrine; Gustafsson, Henrik
Publisher PsychArchives
Contributor Leibniz Institut Für Psychologie (ZPID)
Publication Year 2025
OpenAccess true
Representation
Language English
Resource Type Dataset
Discipline Social Sciences