FIELD-EXPEDIENT SCREENING AND INJURY RISK ALGORITHM CATEGORIES AS PREDICTORS OF NONCONTACT LOWER EXTREMITY INJURY

BACKGROUND & OBJECTIVE

Imagine you could movement screen and test athletes, then allocate them to categories based on their risk of injury. Then imagine those categories were right in associating increased risk with prevalence. Wouldn't that be powerful? That was the objective of Mike Lehr and his team. They had evaluations ready to go and a software program tweaked to provide weighting of certain known risk factors. All they needed to do was test it, categorise them according to an algorithm based on evidence-based risk factors and follow the individuals to see if the algorithm was valid. Let's see what washed out.

METHODS

183 athletes across many sports were included. Others were excluded if they were currently injured or had been so in the previous six months. Any subsequent lower extremity injuries were logged into a software. All athletes were screened using the Functional Movement Screen (FMS) and Lower Quarter Y-Balance Test (LQ-YBT). This study aimed to test the validity of an algorithm developed by two of the authors using evidence-based risk factors. The risk categories generated were normal, slight, moderate and substantial based on specific weighting of evidence-based risk factors - demographic information, previous injury history, Lower Quarter Y-Balance Test (YBT-LQ) score, Functional Movement Screen (FMS) score, and presence of pain. Injury data was collected, and an unadjusted relative risk assigned, compared to the normal risk athletes as a reference.

RESULTS

42 athletes were subsequently injured. Athletes categorized as moderate or substantial risk were 8.9 and 17.6 times, respectively, more likely to sustain an injury when compared to those categorized as having a normal risk for lower extremity injury. Given the

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