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Gait and balance metrics comparison among different fall risk groups and principal component analysis for fall
Lulu Yin1, Hyeri Nam1, Yaru Wei1
1Key Laboratory of Exercise and Health Sciences (Shanghai University of Sport), Ministry of Education, Shanghai, China.
Background:
Falls are a leading cause of morbidity and mortality among older adults, often linked to gait and balance impairments.
Objective:
To compare gait and balance metrics across fall risk levels in community-dwelling older adults and identify principal components predictive of fall risk.
Design:
Retrospective cohort study.
Setting:
General community.
Subjects:
Three hundred older adults were stratified into low, moderate and high fall risk groups using the STEADI toolkit.
Methods:
Gait and balance metrics were compared across groups. Principal component analysis (PCA) reduced dimensionality, and binary logistic regression assessed the predictive value of components.
Results:
High-risk individuals showed slower cadence, shorter step length, wider step width, greater gait variability and increased centre of pressure (CoP) and centre of mass (CoM) sway. PCA identified four gait and seven balance components, explaining 71.62% and 75.88% of variance, respectively. Logistic regression revealed Gait_principal component (PC)2 (instability) (OR = 2.545, P < .001), Gait_PC3 (rhythm control) (OR = 1.659, P = .006), Balance_PC1 (CoP sway during single-leg stance) (OR = 1.628, P = .007), Balance_PC2 (CoM sway velocity variability) (OR = 1.450, P = .032) and Balance_PC4 (CoP sway during double-leg stance, eyes closed) (OR = 1.616, P = .004) as significant predictors. The model achieved 77.2% accuracy, with a sensitivity of 73.1% and a specificity of 79.4%.
Conclusions:
Gait instability, rhythm control and increased postural sway are key predictors of fall risk. Integrating gait and balance metrics enhances fall risk stratification, supporting clinical decision-making.
