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

学员 阿卡克信息准则 医学 物理疗法 人口 比例危险模型 考试(生物学) 身体素质 节奏 体质指数 物理医学与康复 统计 外科 内科学 数学 环境卫生 古生物学 考古 生物 历史
作者
Darren W. Hearn,Zachary Y. Kerr,Erik A. Wikstrom,Donald L. Goss,Kenneth L. Cameron,Stephen W. Marshall,Darin A. Padua
出处
期刊:Military Medicine [Oxford University Press]
卷期号:189 (9-10): e2039-e2046
标识
DOI:10.1093/milmed/usae083
摘要

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.

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