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

Author:

Hearn Darren W123,Kerr Zachary Yukio45,Wikstrom Erik A4,Goss Donald L6ORCID,Cameron Kenneth L7,Marshall Stephen W58ORCID,Padua Darin A45

Affiliation:

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

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

3. United States Army , Fort Liberty, NC 28310, USA

4. Department of Exercise and Sport Science, University of North Carolina at Chapel Hill , Chapel Hill, NC 27599-8700, USA

5. Injury Prevention Research Center, University of North Carolina at Chapel Hill , Chapel Hill, NC 27599-7505, USA

6. Department of Physical Therapy, High Point University , High Point, NC 27268, USA

7. John Feagin Jr. Sports Medicine Fellowship, Keller Army Hospital, United States Military Academy , West Point, NY, 10996 USA

8. Department of Epidemiology, University of North Carolina at Chapel Hill , Chapel Hill, NC 27599-7505, USA

Abstract

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.

Funder

Instituto Nacional de Biotecnologia Estrutural e Química Medicinal em Doenças Infecciosas

Publisher

Oxford University Press (OUP)

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