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Abstract  We sought to extend recent research that explored model-based approaches for combining clinical and gait measures to determine the most sensitive grouping for retrospectively classifying fallers from non-fallers which resulted in a model with 92% sensitivity and 66% specificity and an overall model of 83%. In the present study, the clinical assessment battery was augmented by incorporating more challenging balance items while removing clinical measures characterized by ceiling effects and restricted range. Thirty-two community-dwelling older adults (>70yrs, 16 fallers, 16 non-fallers) completed a battery comprising 76 measures of more challenging clinical measures of mobility and balance, and retained gait (GaitRITE), postural sway and physiological measures. Within each domain, highly collinear and theoretically-redundant measures were removed. Next, a Principal Component Analysis (PCA) identified those clinical and gait variables that accounted for the most unique variance. Finally, a backward stepwise logistic regression was performed on the reduced set of variables from the PCA to develop predictive equations. The current analysis yielded improved specificity of 75%, but slightly lower sensitivity 81%. Interestingly, when the results for the PCA from the previous study were used with the current data, the model classified fallers with 87% sensitivity and 86% specificity and an overall model of 86%. Notably, in all analyses, gait variables were central in identifying fall risk, with single- vs. dual-task difference scores of particular predictive importance. The differences observed between the best-fitting models across the two cohorts implies that modelling methods should accommodate and harness individual differences (e.g., machine learning techniques).

NOVEL INSIGHTS ON THE RELATIVE IMPORTANCE OF CLINICAL AND GAIT MEASURES FOR DETECTING FALL RISK IN OLDER ADULTS