Prediction of mild anatomical leg length discrepancy based on gait kinematics and linear regression model

Leg length discrepancy (LLD) can be related to different pathologies, due to an inadequate distribution of mechanical loads, as well as gait kinematics asymmetries resulted from LLD.

To validate a model to predict anatomical LLD (ALLD) based on gait kinematics.

Gait of 39 participants with different lower limb pathologies and mild discrepancy were collected. Pelvic, hip, knee and ankle kinematics were measured with a 3D motion analysis system and ALLD, femur discrepancy (FD) and tibia discrepancy (TD) were measured by a computerized digital radiograph. Three multiple linear regression models were used to identify the ability of kinematic variables to predict ALLD (model 1), FD (model 2) and TD (model 3).

Difference between peak knee and hip flexion of the long and short lower limb was selected by models 1 (p < 0.001) and 2 (p < 0.001). Hip adduction was selected as a predictor only by model 1 (p = 0.05). Peak pelvic obliquity and ankle dorsiflexion were not selected by any model and model 3 did not retain any dependent variable (p > 0.05). Regression models predicted mild ALLD with moderate accuracy based on hip and knee kinematics during gait, but not ankle strategies. Excessive hip flexion of the longer limb possibly occurs to reduce the limb to equalize the LLD, and discrepancies of the femur and tibia affects gait cycle in a different way.

This study showed that kinematic variables during gait could be used as a screening tool to identify patients with ALLD, reducing unnecessary x-ray exposure and assisting rehabilitation programs.