Development of Linear Regression Models to Estimate the Margin of Stability Based on Spatio-Temporal Gait Parameters

Spatio-temporal gait parameters such as step width, cadence, stride length, and walking speed contribute to dynamic stability. Several studies have investigated the role of gait parameters in maintaining balance. However, in these studies, subjects were instructed to alter their gait. This intentional alteration has the potential to create error in the results, as subjects are not walking with a natural and comfortable gait. In consideration of this, the sample chosen in this study consisted of patients who had undergone a knee replacement. Such individuals naturally have gait parameters that differ from normal subjects. The primary objective of this study was to develop regression models that predict and measure gait stability in both the anterior-posterior and medio-lateral directions based on gait parameters. The maximum deviation of the extrapolated center of mass from the border of the base of support was the measure of gait stability. A forward stepwise multiple regression analysis was conducted to develop both models. In testing the goodness of fit of models, the values of coefficient of determination, standard error of estimates, and root mean square error were calculated. Both models showed sufficient values of goodness of fit. To improve walking stability and minimize falls, fall-prone people should walk with an adequate base-of-support area, and with lower cadence and speed. The results of this study contribute to an understanding of gait patterns and their relationship to walking stability and to how gait strategies might be taught in physical therapy programs to minimize the risk of falls.