Abstract
IMPORTANCE: Musculoskeletal injuries (MSKIs) are pervasive problems in novice training environments. Evaluation of modifiable and nonmodifiable risk factors of MSKI risk prior to entry into these environments is largely understudied. OBJECTIVE: To provide military leaders, civilian and military clinicians, and physical training instructors with an MSKI risk model for identifying low-, moderate-, and high-risk profiles among individuals starting US Army Basic Combat Training (BCT) or a physical training program. DESIGN, SETTING, AND PARTICIPANTS: In this prospective cohort study, data collection was conducted between August 5, 2017, and April 15, 2023, at 2 US Army BCT sites. The sample consisted of volunteer trainees between the ages of 17 and 41 years. They were followed up from the start of BCT. Data analyses were conducted from April to September 2024. EXPOSURES: Data for the factors potentially associated with MSKI were collected during the first week of BCT and included blood draws, total body dual-energy x-ray absorptiometry, and muscle power test results; surveys of demographics, medical history, physical activity, psychological characteristics, and sleep patterns; and physical fitness results. MAIN OUTCOMES AND MEASURES: MSKIs identified using International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes. Logistic regression-based models estimating the risk of MSKI were generated using 5-fold internal cross-validation for the total cohort, males, and females. Traffic light model examples of low (green), moderate (amber), and high (red) MSKI risk tiers were produced. RESULTS: In this cohort study of 2988 Army trainees (median [IQR] age, 19.0 [19.0-22.0] years; 1880 males [62.9%]), 729 females (49.0%) and 758 males (51.0%) had an ICD-10 code-identified MSKI, and 1067 (35.7%) had more than 1 ICD-10 code-identified MSKI. Factors associated with increased MSKI risk in the total cohort and female- and male-specific MSKI risk models (with areas under the receiver operator characteristic curve of 0.701, 0.678, and 0.661, respectively) encompassed 7 variable categories: demographics; anthropometrics and body composition; nutritional status; medical and health history; history of sports and past or current physical activity or fitness; psychological factors (ie, pain, grit, and hardiness); and sleep parameters. CONCLUSIONS AND RELEVANCE: This cohort study presents a tiered approach to identifying persons at increased MSKI risk before the start of a physical training program. Applying a tiered quantification risk metric and incorporating multifactorial interventions from these findings may play a role in reduced MSKI risk.