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美国军事学院学员基础训练中下肢肌肉骨骼损伤风险建模。

Modeling Risk for Lower Extremity Musculoskeletal Injury in U.S. Military Academy Cadet Basic Training.

机构信息

Doctor of Physical Therapy Program, South College, Knoxville, TN 37909, USA.

Human Movement Science Curriculum, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-8700, USA.

出版信息

Mil Med. 2024 Aug 30;189(9-10):e2039-e2046. doi: 10.1093/milmed/usae083.

Abstract

INTRODUCTION

Sport and tactical populations are often impacted by musculoskeletal injury. Many publications have highlighted that risk is correlated with multiple variables. There do not appear to be existing studies that have evaluated a predetermined combination of risk factors that provide a pragmatic model for application in tactical and/or sports settings.

PURPOSE

To develop and test the predictive capability of multivariable risk models of lower extremity musculoskeletal injury during cadet basic training at the U.S.Military Academy.

MATERIALS AND METHODS

Cadets from the class of 2022 served as the study population. Sex and injury history were collected by questionnaire. Body Mass Index (BMI) and aerobic fitness were calculated during testing in the first week of training. Movement screening was performed using the Landing Error Scoring System during week 1 and cadence was collected using an accelerometer worn throughout initial training. Kaplan-Meier survival curves estimated group differences in time to the first musculoskeletal injury during training. Cox regression was used to estimate hazard ratios (HRs) and Akaike Information Criterion (AIC) was used to compare model fit.

RESULTS

Cox modeling using HRs indicated that the following variables were associated with injury risk : Sex, history of injury, Landing Error Scoring System Score Category, and Physical Fitness Test (PT) Run Score. When controlling for sex and history of injury, amodel including aerobic fitness and BMI outperformed the model including movement screening risk and cadence (AIC: 1068.56 vs. 1074.11) and a model containing all variables that were significant in the univariable analysis was the most precise (AIC: 1063.68).

CONCLUSIONS

In addition to variables typically collected in this tactical setting (Injury History, BMI, and aerobic fitness), the inclusion of kinematic testing appears to enhance the precision of the risk identification model and will likely continue to be included in screening cadets at greater risk.

摘要

简介

运动和战术人群经常受到肌肉骨骼损伤的影响。许多出版物强调,风险与多个变量相关。似乎没有现有的研究评估预先确定的风险因素组合,这些组合为在战术和/或运动环境中应用提供了一个实用的模型。

目的

在美国军事学院的学员基础训练中,开发和测试下肢肌肉骨骼损伤的多变量风险模型的预测能力。

材料和方法

2022 届学员作为研究对象。通过问卷调查收集性别和受伤史。在训练的第一周的测试中计算体重指数(BMI)和有氧健身水平。在第 1 周使用着陆错误评分系统进行运动筛查,使用整个初始训练过程中佩戴的加速度计收集步频。Kaplan-Meier 生存曲线估计了训练中首次肌肉骨骼损伤的组间差异。Cox 回归用于估计风险比(HR),Akaike 信息准则(AIC)用于比较模型拟合度。

结果

使用 HR 的 Cox 建模表明,以下变量与受伤风险相关:性别、受伤史、着陆错误评分系统评分类别和体能测试(PT)跑步得分。当控制性别和受伤史时,包含有氧健身和 BMI 的模型优于包含运动筛查风险和步频的模型(AIC:1068.56 对 1074.11),包含单变量分析中所有有意义的变量的模型是最精确的(AIC:1063.68)。

结论

除了在这种战术环境中通常收集的变量(受伤史、BMI 和有氧健身)外,运动学测试的纳入似乎提高了风险识别模型的精度,并且可能会继续包括筛选风险更高的学员。

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