The current research intends to expand pre-season physical examination testing implemented for the purposes of injury prediction in collegiate athletes to include advanced body composition testing. To predict injury using subjective and objective clinical measures and body composition testing. It was hypothesized that dual-energy x-ray absorptiometry (DXA) body composition scans would supplement physical performance and patient reported outcome measures to predict if an athlete would sustain an in-season injury. A total of 94 participants (84 non-injured, 10 injured) from a collegiate institution were analyzed. Demographic information, Knee Injury and Osteoarthritis Outcome Score (KOOS), Foot and Ankle Disability Index (FADI) questionnaire scores, single leg hop for distance (SLH), and star excursion balance test (SEBT) anterior reach performance scores, and DXA scans were collected for each participant before the execution of this study. Injuries to the knee and ankle were recorded, including side of the body and injury type. Seven variables were available for analysis, utilizing a forward stepwise binary logistic regression analysis to determine a significant model of the best combination of predictors. The logistic regression analysis generated a model (R2= 0.26, p<0.001) that predicted the probability of a lower extremity injury, utilizing two variables in the equation. These two variables were history of injury and lean difference between each leg with adjusted odds ratios of 5.1 (p=0.06, 95%CI=0.91, 29) and 5.5 (p=0.15, 95%CI=0.54, 56) respectively. The DXA had identified abnormalities between the injured and non-injured regions of an athlete, however no variable was statistically significant. Further research is encouraged.

Semester/Year of Award

Fall 2018


Dr. Aaron D. Sciascia

Department/Professional Affiliation

Department of Exercise & Sport Science

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Restricted Access Thesis

Degree Name

Honors Scholars


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