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

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.

Velocity-Load Jump Testing Predicts Acceleration Performance in Elite Speed Skaters: But Does Movement Specificity Matter?

PURPOSE: In this study, we compared the influence of movement specificity during velocity-load jump testing to predict on-ice acceleration performance in elite speed skaters. METHODS: Elite long-track speed skaters (N = 27) performed velocity-load testing with 3 external loads during unilateral horizontal jumping, lateral jumping, and bilateral vertical countermovement jumping. For the unilateral tests, external load conditions were set to 10 N, 7.5% and 15% of external load relative to body weight. For the countermovement jumping, load conditions were body weight and 30% and 60% of external load relative to body weight. On-ice performance measures were obtained during maximal 50-m accelerations from a standing start, including maximal skating speed, maximal acceleration capacity, and maximum horizontal power. The 100-m split time from a 500-m race was also obtained. Regularized regression models were used to identify the most important predictors of on-ice acceleration performance. In addition to regularized regression coefficients, Pearson correlation coefficients (r) were calculated for all variables retained by the model to assess interrelationships between single predictors and on-ice performance measures. RESULTS: The countermovement jump with 30% of body mass demonstrated the strongest association with maximal skating speed, maximum horizontal power, and 100-m time (regularized regression coefficient = .16-.49, r = .84-.97, P < .001). Horizontal jump with 15% of body mass was the strongest predictor of maximal acceleration capacity performance (regularized regression coefficient = .08, r = .83, P < .001). CONCLUSIONS: The findings of this study suggest that mechanical specificity rather than movement specificity was more relevant for predicting on-ice acceleration performance.

Relationship Between External Training Load and Session Rating of Perceived Exertion Training Impulse in Elite Sprinters.

PURPOSE: To quantify the change in session rating of perceived exertion training impulse (RPE-TRIMP) that may occur in response to increased running distance at 3 running velocity ranges in elite sprinters. METHODS: We monitored training load in elite sprinters (women: n = 7; men: n = 11) using wearable Global Positioning System technology and RPE-TRIMP for a total of 681 individual training sessions during a 22-week competition-preparation period. Internal training load was operationalized by RPE-TRIMP, and external training load was operationalized by distance covered in 3 velocity ranges. A linear mixed-effects model with athlete as a random effect was fit to RPE-TRIMP with total distance covered at ≤69.99% (low-velocity running [LVR]), 70% to 84.99% (high-velocity running [HVR]), and 85% to 100% (very-high-velocity running [VHVR]) of individual maximum velocity. RESULTS: Increased running distance in all 3 velocity ranges (LVR, HVR, and VHVR) resulted in a significant (P < .001) increase in RPE-TRIMP. Coefficients (95% CIs) were .10 (.08-.11) for LVR, .23 (.18-.28) for HVR, and .44 (.35-.53) for VHVR. A 50-m increase in running distance covered in the LVR, HVR, and VHVR velocity ranges was associated with increases in RPE-TRIMP of 5, 11.5, and 22 arbitrary units, respectively. CONCLUSIONS: Internal training load, calculated as RPE-TRIMP, increased with increases in total distance covered in the LVR, HVR, and VHVR velocity ranges (P < .001). RPE-TRIMP can be a practical solution for monitoring global training-session load in elite sprinters.

Longitudinal Monitoring of Biomechanical and Psychological State in Collegiate Female Basketball Athletes Using Principal Component Analysis.

Background: The growth in participation in collegiate athletics has been accompanied by increased sport-related injuries. The complex and multifactorial nature of sports injuries highlights the importance of monitoring athletes prospectively using a novel and integrated biopsychosocial approach, as opposed to contemporary practices that silo these facets of health. Methods: Data collected over two competitive basketball seasons were used in a principal component analysis (PCA) model with the following objectives: (i) investigate whether biomechanical PCs (i.e., on-court and countermovement jump (CMJ) metrics) were correlated with psychological state across a season and (ii) explore whether subject-specific significant fluctuations could be detected using minimum detectable change statistics. Weekly CMJ (force plates) and on-court data (inertial measurement units), as well as psychological state (questionnaire) data, were collected on the female collegiate basketball team for two seasons. Results: While some relationships (n = 2) were identified between biomechanical PCs and psychological state metrics, the magnitude of these associations was weak (r = |0.18-0.19|, p < 0.05), and no other overarching associations were identified at the group level. However, post-hoc case study analysis showed subject-specific relationships that highlight the potential utility of red-flagging meaningful fluctuations from normative biomechanical and psychological patterns. Conclusion: Overall, this work demonstrates the potential of advanced analytical modeling to characterize components of and detect statistically and clinically relevant fluctuations in student-athlete performance, health, and well-being and the need for more tailored and athlete-centered monitoring practices.