Bilateral Strength Asymmetries and Unilateral Strength Imbalance: Predicting Ankle Injury When Considered With Higher Body Mass in US Special Forces(a)
CONTEXT: Ankle injury is one of the most common conditions in athletics and military activities. Strength asymmetry (SA) and imbalance may represent a risk factor for injury, but past investigations have produced ambiguous conclusions. Perhaps one explanation for this ambiguity is the fact that these authors used univariate models to predict injury. OBJECTIVE: To evaluate the predictive utility of SA and imbalance calculations for ankle injury in univariate and multivariate prediction models. DESIGN: Prospective cohort study. SETTING: Laboratory. PATIENTS OR OTHER PARTICIPANTS: A total of 140 male US Air Force Special Forces. MAIN OUTCOME MEASURE(S): Baseline testing consisted of body composition, isometric strength, and aerobic and anaerobic capacity. A clinician conducted medical chart reviews 365 days posttesting to document the incidence of ankle injury. Strength asymmetries were calculated based on the equations most prevalent in the literature along with known physiological predictors of injury in the military: age, height, weight, body composition, and aerobic capacity. Simple logistic regression was conducted using each predictor, and backward stepwise logistic regression was conducted with each equation method and the physiological predictors entered initially into the model. RESULTS: Strength asymmetry or imbalance or both, as a univariate predictor, was not able to predict ankle injury 365 days posttesting. Body mass (P = .01) and body mass index (P = .01) significantly predicted ankle injury. Strength asymmetry or imbalance or both significantly predicted ankle injury when considered with body mass (P = .002–.008). CONCLUSIONS: As a univariate predictor, SA did not predict ankle injury. However, SA contributed significantly to predicting ankle injury in a multivariate model using body mass. Interpreting SA and imbalance in the presence of other physiological variables can help elucidate the risk of ankle injury.