Walter Reed Army Institute of Research (WRAIR), Silver Spring, Maryland.
Department of Physical Therapy, Texas State University, Round Rock, Texas.
Sports Health. 2020 Nov/Dec;12(6):564-572. doi: 10.1177/1941738120902991. Epub 2020 Mar 5.
Musculoskeletal injuries are a primary source of disability. Understanding how risk factors predict injury is necessary to individualize and enhance injury reduction programs.
Because of the multifactorial nature of musculoskeletal injuries, multiple risk factors will provide a useful method of categorizing warrior athletes based on injury risk.
Prospective observational cohort study.
Level 2.
Baseline data were collected on 922 US Army soldiers/warrior athletes (mean age, 24.7 ± 5.2 years; mean body mass index, 26.8 ± 3.4 kg/m) using surveys and physical measures. Injury occurrence and health care utilization were collected for 1 year. Variables were compared in healthy versus injured participants using independent tests or chi-square analysis. Significantly different factors between each group were entered into a logistic regression equation. Receiver operating characteristic curve and accuracy statistics were calculated for regression variables.
Of the 922 warrior athletes, 38.8% suffered a time-loss injury (TLI). Overall, 35 variables had a significant relationship with TLIs. The logistic regression equation, consisting of 11 variables of interest, was significant (adjusted = 0.21; odds ratio, 5.7 [95% CI, 4.1-7.9]; relative risk, 2.5 [95% CI, 2.1-2.9]; area under the curve, 0.73). Individuals with 2 variables had a sensitivity of 0.89, those with 7 or more variables had a specificity of 0.94.
The sum of individual risk factors (prior injury, prior work restrictions, lower perceived recovery from injury, asymmetrical ankle dorsiflexion, decreased or asymmetrical performance on the Lower and Upper Quarter Y-Balance test, pain with movement, slower 2-mile run times, age, and sex) produced a highly sensitive and specific multivariate model for TLI in military servicemembers.
A better understanding of characteristics associated with future injury risk can provide a foundation for prevention programs designed to reduce medical costs and time lost.
肌肉骨骼损伤是残疾的主要原因。了解危险因素如何预测损伤对于针对个体和增强损伤预防计划是必要的。
由于肌肉骨骼损伤的多因素性质,多个危险因素将为基于损伤风险对战士运动员进行分类提供有用的方法。
前瞻性观察队列研究。
2 级。
使用调查和身体测量收集了 922 名美国陆军士兵/战士运动员(平均年龄 24.7±5.2 岁;平均体重指数 26.8±3.4kg/m)的基线数据。收集了 1 年的损伤发生和医疗保健利用情况。使用独立 t 检验或卡方检验比较健康参与者和受伤参与者之间的变量。将每组之间差异显著的因素纳入逻辑回归方程。计算回归变量的受试者工作特征曲线和准确性统计。
在 922 名战士运动员中,38.8%遭受了非战斗性损伤(TLI)。总体而言,35 个变量与 TLI 有显著关系。由 11 个感兴趣的变量组成的逻辑回归方程具有统计学意义(调整后 =0.21;比值比 5.7[95%可信区间,4.1-7.9];相对风险 2.5[95%可信区间,2.1-2.9];曲线下面积 0.73)。具有 2 个变量的个体具有 0.89 的敏感性,具有 7 个或更多变量的个体具有 0.94 的特异性。
个体危险因素(既往损伤、既往工作限制、受伤后恢复感觉较差、踝关节背屈不对称、Lower 和 Upper Quarter Y-Balance 测试中表现下降或不对称、运动时疼痛、2 英里跑速度较慢、年龄和性别)的总和为军事人员的 TLI 产生了一个高度敏感和特异的多变量模型。
更好地了解与未来损伤风险相关的特征可以为旨在降低医疗成本和损失时间的预防计划提供基础。