A comparison of five methods to normalize joint moments during running

Abstract

BackgroundNet joint moments (NJM) are typically normalized for a (combination of) physical body characteristics such as mass, height, and limb length using ratio scaling to account for differences in body characteristics between individuals. Four assumptions must be met when normalizing NJM data this way to ensure valid conclusions. First, the relationship between the non-normalized NJM and participant characteristic should be linear. Second, the regression line between NJM and the characteristic(s) used should pass through the origin. Third, scaling should not significantly perturb the statistical distribution of the data. Fourth, normalizing a NJM should eliminate its correlation with the characteristic(s) normalized for. Research questionThis study assessed these assumptions using data collected among 59 individuals running at 10 km h−1. MethodsStandard inverse dynamics analyses were conducted, and ratios were computed between the sagittal-plane hip, knee and ankle NJM’s and the participant’s mass, height, leg length, mass × height, and mass × leg length. ResultsThe most important finding of this study was that none of the scaling variables fulfilled all assumptions across all joints. However, scaling by mass, mass*height and mass*leg length satisfied the assumptions for the knee joint moment and log-transformed hip joint moment, suggesting these methods generally performed best. SignificanceOur findings suggests that scaling by mass, mass*height and mass*leg length may be considered to normalize joint moments during running. Nevertheless, we urge researchers to check the statistical assumptions to ensure valid conclusions. We provide supplementary code to check the statistical assumptions, and discuss consequences of inappropriate scaling.