Relationship between multiple predictor variables and normal knee torque production.
The purpose of this study was to develop predictive models relating isokinetic knee testing performance to anthropometric and demographic variables. The subjects were 134 healthy volunteers (70 female, 64 male) between the ages of 10 and 80 years. The investigators measured subjects' peak knee flexion and extension torque production at two angular velocities. Stepwise regression analyses were used to examine the relationship between each torque-dependent variable and the following potential predictor variables: age, sex, side of lower extremity dominance, height, weight, percentage of body fat, and thigh girth. The investigators generated two sets of models designed to predict preinjury knee strength. Clinicians can use one set of models by assessing predictor variables before or immediately following injury. The second set of models involves the assessment of predictor variables postinjury, excluding an assessment of percentage of body fat and thigh girth. The results indicated that peak knee torque production can be predicted with statistically significant accuracy (multiple R = .78-.87). The predictive models generated in this study can be used to establish muscle strength goals for patient rehabilitative programs.