Purpose: Race has rarely been the focus of biomechanics investigations, despite affecting the incidence of musculoskeletal injury and disease. Existing racial differences in movement mechanics could drive disease development and help identify factors contributing to racial health disparities. This study aimed to 1) Identify racial differences in walking, running, and landing mechanics between African Americans and white Americans and 2) Determine whether racial differences can be explained by anthropometric, strength, and health status factors.
Methods: Venous blood samples, anthropometric measures, lower extremity strength, and a health status assessment were collected for 92 participants (18-30y) in an IRB approved study. After measuring self-selected walking speed, 3D motion capture and force plate data were recorded during 7 trials in the following conditions: regular walking (1.35m/s), fast walking (1.6m/s), running (3.2m/s), and drop vertical jump (31cm box height). Fundamental gait measures and running and landing measures associated with overuse and impact injury risk were extracted using Visual3D and custom Matlab scripts. Multivariate and post-hoc univariate ANOVA models were fit to determine main and interaction effects of gender and race (JMP Pro 15, α=0.05) after which data was separated by gender. Stepwise linear regression models evaluated whether anthropometric, strength, and health status factors explained racial effects.
Results: Several racial differences in walking, running, and landing mechanics were observed in both men and women, but differed between genders. Effect sizes of observed racial differences indicate the potential for both statistical and clinical significance. Although several racial differences during all tasks were explained by anthropometric, strength, and health status factors in women, none were explained by these factors in men. In women, explanatory factors were a combination of innate and modifiable.
Conclusion: Future steps should include the development of racially diverse databases and the identification of potential factors to target in interventions aimed at reducing racial health disparities