Background:Field expedient screening tools that can identify individuals at an elevated risk for injury are needed to minimize time loss in American football players. Previous research has suggested that poor dynamic balance may be associated with an elevated risk for injury in athletes; however, this has yet to be examined in college football players.Hypothesis:To determine if dynamic balance deficits are associated with an elevated risk of injury in collegiate football players. It was hypothesized that football players with lower performance and increased asymmetry in dynamic balance would be at an elevated risk for sustaining a noncontact lower extremity injury.Study Design:Prospective cohort study.Methods:Fifty-nine collegiate American football players volunteered for this study. Demographic information, injury history, and dynamic balance testing performance were collected, and noncontact lower extremity injuries were recorded over the course of the season. Receiver operator characteristic curves were calculated based on performance on the Star Excursion Balance Test (SEBT), including composite score and asymmetry, to determine the population-specific risk cut-off point. Relative risk was then calculated based on these variables, as well as previous injury.Results:A cut-off point of 89.6% composite score on the SEBT optimized the sensitivity (100%) and specificity (71.7%). A college football player who scored below 89.6% was 3.5 times more likely to get injured.Conclusion:Poor performance on the SEBT may be related to an increased risk for sustaining a noncontact lower extremity injury over the course of a competitive American football season.Clinical Relevance:College football players should be screened preseason using the SEBT to identify those at an elevated risk for injury based upon dynamic balance performance to implement injury mitigation strategies to this specific subgroup of athletes.

In athletics, efficient screening tools are sought to curb the rising number of noncontact injuries and associated health care costs. The authors hypothesized that an injury prediction algorithm that incorporates movement screening performance, demographic information, and injury history can accurately categorize risk of noncontact lower extremity (LE) injury. One hundred eighty-three collegiate athletes were screened during the preseason. The test scores and demographic information were entered into an injury prediction algorithm that weighted the evidence-based risk factors. Athletes were then prospectively followed for noncontact LE injury. Subsequent analysis collapsed the groupings into two risk categories: Low (normal and slight) and High (moderate and substantial). Using these groups and noncontact LE injuries, relative risk (RR), sensitivity, specificity, and likelihood ratios were calculated. Forty-two subjects sustained a noncontact LE injury over the course of the study. Athletes identified as High Risk (n = 63) were at a greater risk of noncontact LE injury (27/63) during the season [RR: 3.4 95% confidence interval 2.0 to 6.0]. These results suggest that an injury prediction algorithm composed of performance on efficient, low-cost, field-ready tests can help identify individuals at elevated risk of noncontact LE injury.

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