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Multivariate modelling of subjective and objective monitoring data improve the detection of non-contact injury risk in elite Australian footballers

Abstract : Objectives - To assess the association between workload, subjective wellness, musculoskeletal screening measures and non-contact injury risk in elite Australian footballers. Design - Prospective cohort study. Methods - Across 4 seasons in 70 players from one club, cumulative weekly workloads (acute; 1 week, chronic; 2-, 3-, 4-week) and acute:chronic workload ratio's (ACWR: 1-week load/average 4-weekly load) for session-Rating of Perceived Exertion (sRPE) and GPS-derived distance and sprint distance were calculated. Wellness, screening and non-contact injury data were also documented. Univariate and multivariate regression models determined injury incidence rate ratios (IRR) while accounting for interaction/moderating effects. Receiver operating characteristics determined model predictive accuracy (area under curve: AUC). Results - Very low cumulative chronic (2-, 3-, 4- week) workloads were associated with the greatest injury risk (univariate IRR=1.71-2.16, 95% CI=1.10-4.52) in the subsequent week. In multivariate analysis, the interaction between a low chronic load and a very high distance (adj-IRR=2.60, 95% CI=1.07-6.34) or low sRPE ACWR (adj-IRR=2.52, 95% CI=1.01-6.29) was associated with increased injury risk. Subjectively reporting "yes" (vs. "no") for old lower limb pain and heavy non-football activity in the previous 7 days (multivariate adj-IRR=2.01-2.25, 95% CI=1.02-4.95) and playing experience (>9 years) (multivariate adj-IRR=2.05, 95% CI=1.03-4.06) was also associated with increased injury risk, but screening data were not. Predictive capacity of multivariate models was significantly better than univariate (AUC=0.70, 95% CI 0.64-0.75; AUC range=0.51-0.60). Conclusions - Chronic load is an important moderating factor in the workload-injury relationship. Low chronic loads coupled with low or very high ACWR are associated with increased injury risk.
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Submitted on : Wednesday, January 31, 2018 - 9:41:21 AM
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M.J. Colby, B. Dawson, P. Peeling, J. Heasman, B. Rogalski, et al.. Multivariate modelling of subjective and objective monitoring data improve the detection of non-contact injury risk in elite Australian footballers. Journal of Science and Medicine in Sport, Elsevier, 2017, 20 (12), pp.1068-1074. ⟨10.1016/j.jsams.2017.05.010⟩. ⟨hal-01681441⟩

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