A Biopsychosocial Model for Understanding Training Load, Fatigue, and Musculoskeletal Sport Injury in University Athletes: A Scoping Review

Abstract

Abstract McClean, ZJ, Pasanen, K, Lun, V, Charest, J, Herzog, W, Werthner, P, Black, A, Vleuten, RV, Lacoste, E, and Jordan, MJ. A biopsychosocial model for understanding training load, fatigue, and musculoskeletal sport injury in university athletes: A scoping review. J Strength Cond Res 38(6): 1177–1188, 2024—The impact of musculoskeletal (MSK) injury on athlete health and performance has been studied extensively in youth sport and elite sport. Current research examining the relationship between training load, injury, and fatigue in university athletes is sparse. Furthermore, a range of contextual factors that influence the training load-fatigue-injury relationship exist, necessitating an integrative biopsychosocial model to address primary and secondary injury prevention research. The objectives of this review were (a) to review the scientific literature examining the relationship between training load, fatigue, and MSK injury in university athletes and (b) to use this review in conjunction with a transdisciplinary research team to identify biopsychosocial factors that influence MSK injury and develop an updated, holistic biopsychosocial model to inform injury prevention research and practice in university sport. Ten articles were identified for inclusion in this review. Key findings were an absence of injury surveillance methodology and contextual factors that can influence the training load-fatigue-MSK injury relationship. We highlight the inclusion of academic load, social load, and mental health load as key variables contributing to a multifactorial, gendered environmental, scientific inquiry on sport injury and reinjury in university sport. An integrative biopsychosocial model for MSK injury in university sport is presented that can be used to study the biological, psychological, and social factors that modulate injury and reinjury risk in university athletes. Finally, we provide an example of how causal inference can be used to maximize the utility of longitudinally collected observational data that is characteristic of sport performance research in university sport.