Colby Marcus J, Dawson Brian, Peeling Peter, Heasman Jarryd, Rogalski Brent, Drew Michael K, Stares Jordan, Zouhal Hassane, Lester Leanne
School of Sport Science, Exercise and Health, The University of Western Australia, Australia; West Coast Eagles Football Club, Australia.
School of Sport Science, Exercise and Health, The University of Western Australia, Australia; West Coast Eagles Football Club, Australia.
J Sci Med Sport. 2017 Dec;20(12):1068-1074. doi: 10.1016/j.jsams.2017.05.010. Epub 2017 May 25.
To assess the association between workload, subjective wellness, musculoskeletal screening measures and non-contact injury risk in elite Australian footballers.
Prospective cohort study.
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).
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).
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.
评估澳大利亚精英足球运动员的工作量、主观健康状况、肌肉骨骼筛查指标与非接触性损伤风险之间的关联。
前瞻性队列研究。
在来自一家俱乐部的70名球员的4个赛季中，计算了每周的累积工作量（急性；1周，慢性；2周、3周、4周）以及感知运动用力评分（sRPE）、GPS得出的距离和冲刺距离的急性：慢性工作量比值（ACWR：1周负荷/平均4周负荷）。还记录了健康状况、筛查和非接触性损伤数据。单变量和多变量回归模型确定了损伤发病率比值（IRR），同时考虑了交互作用/调节效应。受试者工作特征曲线确定了模型的预测准确性（曲线下面积：AUC）。
极低的累积慢性（2周、3周、4周）工作量与随后一周最大的损伤风险相关（单变量IRR=1.71 - 2.16，95%CI=1.10 - 4.52）。在多变量分析中，低慢性负荷与非常高距离（调整后IRR=2.60，95%CI=1.07 - 6.34）或低sRPE ACWR（调整后IRR=2.52，95%CI=1.01 - 6.29）之间的相互作用与损伤风险增加相关。在过去7天主观报告有旧的下肢疼痛和大量非足球活动（多变量调整后IRR=2.01 - 2.25，95%CI=1.02 - 4.95）以及比赛经验（>9年）（多变量调整后IRR=2.05，95%CI=1.03 - 4.06）也与损伤风险增加相关，但筛查数据无关。多变量模型的预测能力明显优于单变量模型（AUC=0.70，95%CI 0.64 - 0.75；AUC范围=0.51 - 0.60）。
慢性负荷是工作量与损伤关系中的一个重要调节因素。低慢性负荷加上低或非常高的ACWR与损伤风险增加相关。
