As the number of older drivers grows, it is increasingly important to accurately identify at-risk drivers. This study tested clinical assessments predictive of real-time driving performance. Selected assessment tools considered important in the identification of at-risk older drivers represented the domains of vision, cognition, motor performance, and driving knowledge. Participants were administered the battery of assessments followed by an on-road test. A univariate analysis was conducted to identify significant factors (< .05) to be included in a multivariate regression model. Assessments identified as independently associated with driving performance in the regression model included: FACTTM Contrast sensitivity slide-B, Rapid Pace Walk, UFOV« rating, and MMSE total score. The domains of vision, cognitive, and motor performance were represented in the predictive model. Due to the dynamic nature of the driving task, it is not likely that a single assessment tool will identify at risk drivers. By standardizing the selection of clinical assessments used in driving evaluations, practitioners should be able to provide services more efficiently, more objectively, and more accurately to identify at-risk drivers. (A) Reprinted with permission from Elsevier.
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