School of Science and Technology, University of New England, Australia; Institute for Sport Physical Activity and Leisure, Leeds Beckett University, United Kingdom.
School of Science and Technology, University of New England, Australia.
J Sci Med Sport. 2019 Jun;22(6):653-660. doi: 10.1016/j.jsams.2018.11.028. Epub 2018 Dec 18.
This study aimed to: (a) identify the association between external-workloads and injury-risk in the subsequent week; and (b) understand the effectiveness of workload variables in establishing injury-risk.
Retrospective cohort study.
Workload and injury data (soft-tissue) were collected from forty-eight professional male rugby league players. Load variables included duration (min), total distance (m), relative distance (mmin), high speed distance ([m]>20kmh), very-high speed distance ([m]>25kmh), acceleration and deceleration efforts (count) and PlayerLoad (Arbitrary Unit: AU). Cumulative two-, three- and four-weekly loads; Acute:Chronic Workload Ratio (ACWR); Mean-Standard Deviation Workload Ratio (MSWR) and strain values were calculated and divided into three equally-sized bins (low, moderate and high). Generalised Estimating Equations analysed relationships between workload variables and injury probability in the subsequent week.
Injury-risk increased alongside increases in the ACWR for duration, total distance and PlayerLoad. Conversely, injury-risk decreased (Area Under Curve: 0.569-0.585) with increases in the four-weekly duration, total distance, accelerations, decelerations and PlayerLoad. For relative distance, high four-weekly workloads (high: >60mmin) demonstrated a positive association with injury-risk, whilst high two-weekly loads (high: >82 mmin) were negatively associated.
A range of external workload metrics and summary statistics demonstrate either positive or negative associations with injury-risk status. Such findings provide the framework for the development of decision-support systems in which external workload metrics (e.g. total or high speed distance) can be uniquely and routinely monitored across a range of summary statistics (i.e. cumulative weekly loads and ACWR) in order to optimise player performance and welfare.
本研究旨在：(a) 确定外在工作量与随后一周内受伤风险之间的关联；(b) 了解工作量变量在确定受伤风险方面的有效性。
回顾性队列研究。
从四十八名职业男性橄榄球联赛运动员中收集了工作量和受伤数据（软组织）。负荷变量包括持续时间（分钟）、总距离（米）、相对距离（mmin）、高速距离（>20kmh）、超高速度距离（>25kmh）、加速度和减速度努力（计数）和 PlayerLoad（任意单位：AU）。计算并将每周的累积两、三、四周的负荷；急性：慢性工作负荷比（ACWR）；均值-标准差工作负荷比（MSWR）和应变值分为三个相等大小的区间（低、中、高）。广义估计方程分析了工作量变量与随后一周内受伤概率之间的关系。
随着 ACWR 中持续时间、总距离和 PlayerLoad 的增加，受伤风险增加。相反，随着四周持续时间、总距离、加速度、减速度和 PlayerLoad 的增加，受伤风险降低（曲线下面积：0.569-0.585）。对于相对距离，高四周工作量（高：>60mmin）与受伤风险呈正相关，而高两周工作量（高：>82 mmin）则呈负相关。
一系列外在工作量指标和汇总统计数据显示与受伤风险状况呈正相关或负相关。这些发现为开发决策支持系统提供了框架，在这些系统中，可以根据一系列汇总统计数据（即每周累积负荷和 ACWR）独特且常规地监测外在工作量指标（例如总距离或高速距离），以优化运动员的表现和福利。
