Citation

Galambos SA, Terry PC, Moyle GM, Locke SA, Lane AM. Br. J. Sports Med. 2005; 39(6): 351-354.

Affiliation

Queensland Academy of Sport, Centre of Excellence for Applied Sports Science Research, Brisbane, Australia. sandor.galambos@srq.qld.gov.au

Copyright

(Copyright © 2005, BMJ Publishing Group)

DOI

10.1136/bjsm.2005.018440

PMID

15911606

PMCID

PMC1725228

Abstract

OBJECTIVES: To establish injury rates among a population of elite athletes, to provide normative data for psychological variables hypothesised to be predictive of sport injuries, and to establish relations between measures of mood, perceived life stress, and injury characteristics as a precursor to introducing a psychological intervention to ameliorate the injury problem. METHODS: As part of annual screening procedures, athletes at the Queensland Academy of Sport report medical and psychological status. Data from 845 screenings (433 female and 412 male athletes) were reviewed. Population specific tables of normative data were established for the Brunel mood scale and the perceived stress scale. RESULTS: About 67% of athletes were injured each year, and about 18% were injured at the time of screening. Fifty percent of variance in stress scores could be predicted from mood scores, especially for vigour, depression, and tension. Mood and stress scores collectively had significant utility in predicting injury characteristics. Injury status (current, healed, no injury) was correctly classified with 39% accuracy, and back pain with 48% accuracy. Among a subset of 233 uninjured athletes (116 female and 117 male), five mood dimensions (anger, confusion, fatigue, tension, depression) were significantly related to orthopaedic incidents over the preceding 12 months, with each mood dimension explaining 6-7% of the variance. No sex differences in these relations were found. CONCLUSIONS: The findings support suggestions that psychological measures have utility in predicting athletic injury, although the relatively modest explained variance highlights the need to also include underlying physiological indicators of allostatic load, such as stress hormones, in predictive models.