Gait and balance metrics comparison among different fall risk groups and principal component analysis for fall prediction in older people.

This study focuses on comparing gait and balance parameters across different fall risk levels in community-dwelling older adults and identifying principal components predictive of fall risk. Using the STEADI (Stopping Elderly Accidents, Deaths, and Injuries) toolkit, 300 participants were classified into low, moderate, and high fall risk groups, with gait and balance metrics assessed via pressure-sensitive walkways and force platforms. Principal component analysis (PCA) reduced these metrics into key components, revealing that gait instability, rhythm control, and increased postural sway—particularly center of pressure (CoP) and center of mass (CoM) sway—are significant predictors of high fall risk. Logistic regression incorporating these components achieved a classification accuracy of 77.2%, supporting the integration of multidimensional gait and balance assessments to enhance fall risk stratification and inform targeted prevention strategies in older adults.