Identification of Risk Factors Prospectively Associated With Musculoskeletal Injury in a Warrior Athlete Population. (November 2020)

• Record Type: Journal Article
• Authors: Teyhen, Deydre S.; Shaffer, Scott W.; Goffar, Stephen L.; Kiesel, Kyle; Butler, Robert J.; Rhon, Daniel I.; Plisky, Phillip J.
• Is Part Of: Sports health. Volume 12:Number 6(2020)
• Page Start: 564
• Page End: 572
• Publication Date: 2020-11
• Subjects: injury prediction -- musculoskeletal injury -- intrinsic risk factors -- movement tests; Sports medicine -- Periodicals; Athletic Injuries -- Periodicals; Physical Education and Training -- Periodicals; Musculoskeletal Physiological Phenomena -- Periodicals; Médecine du sport -- Périodiques; 617.1027
• DOI: 10.1177/1941738120902991
• Languages: English
• ISSNs: 1941-7381

Abstract

Background: Musculoskeletal injuries are a primary source of disability. Understanding how risk factors predict injury is necessary to individualize and enhance injury reduction programs. Hypothesis: Because of the multifactorial nature of musculoskeletal injuries, multiple risk factors will provide a useful method of categorizing warrior athletes based on injury risk. Study Design: Prospective observational cohort study. Level of Evidence: Level 2. Methods: 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 2 ) 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 t 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. Results: 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 R 2 = 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. Conclusion: 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. Clinical Relevance: 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.