An Inferential Model for Understanding the Effects of Demographic and Gait Factors and Their Interactions on the...

The gait index (GI), a valuable metric to assess human gait, incorporates clinically relevant parameters such as walking speed, knee angle, stride length, and stance-to-swing phase ratio. This index offers insights into an individual's gait pattern, aiding in the identification of subtle gait abnormalities and enabling continuous monitoring of gait changes over time. Building upon this foundation, the present study investigated the influence of specific gait parameters and demographic factors on the gait index, alongside their interaction effects. Analyzing data from 120 healthy individuals using beta regression models, we uncovered significant predictors and interaction effects shaping the Index. Our comparative assessment between Variable Dispersion Beta Regression (VDBR) and Fixed Dispersion Beta Regression (FDBR) models revealed VDBR's superiority over FPBR in capturing gait data heterogeneity. Our analysis revealed that while aging was correlated with decreased GI, gender and BMI exhibited limited individual impact. However, gait-specific predictors such as knee angle, stride length, walking speed, and stance-to-swing phase ratio significantly contributed to GI variability. Additionally, significant interaction effects were identified between knee angle and height normalized stride length, age and knee angle, and age and walking speed, highlighting the complex interplay between demographic and gait-related factors. These findings underscore the multifaceted nature of gait dynamics and offer valuable insights for clinicians, aiding in precise gait pattern assessment and informing the development of gait-related clinical practice, preventive care strategies, and rehabilitation programs. Overall, our research contributes to enhancing mobility and functionality in individuals with gait degradation by identifying significant predictors and interaction effects.