Identifying Risk Factors for Injury in Adolescent Soccer Players

Abstract

As more adolescents become involved in athletics, there is a proportional increase in musculoskeletal and concussive injury. Prior history of injury and early return to play may escalate the risk of more severe sequelae. The higher incidence of non-collision injuries among females in soccer may provide insight into factors contributing to an injury besides direct collision. Non-collision injuries may be primed by prior injuries and a pre-existing summative load on the central nervous system, which can be captured through preseason screening. PURPOSE: To determine the predictive value of pre-season screening measures for injury in adolescent soccer athletes. METHODS: A cross-sectional, correlation study of 24 adolescent soccer players age 12-17 years old from a soccer club in Virginia. Data collection took place during preseason baseline screening and postseason injury follow up. Preseason measures collected were height, weight, age, history of concussion and injury, ImPACT®, Functional Movement ScreenTM, and Balance Error Scoring System (BESS) scores. Significant correlations and differences between injured and uninjured players for each baseline measure were determined. Baseline measures were regressed against intraseason injury to determine which variables could best predict injury outcomes. An algorithm was synthesized from the preseason metric data to stratify injury risk based on the most significant predictors. Agreement between the algorithm and actual incidence of injury was then calculated. RESULTS: Five players were injured during the season. There were significant differences in height (p = 0.034) and prior concussion (p = 0.015) between injured and uninjured players. Regression analysis revealed a combination of height, prior concussion, and BESS score correctly predicted injury 95.8% of the time (p = 0.005). The algorithm was 94.7% specific for identifying athletes at risk (+LR = 11.4). There was high agreement between the algorithm and incidence of injury (ICC = 0.751, p = 0.001). CONCLUSION: Adolescents most at risk for injury are taller athletes with a history of concussion and low BESS scores. The algorithm may be utilized during preseason screening to identify at risk athletes and indicate a need for individualized preventative interventions.