Simulated Car Crashes and Crash Predictors in Drivers With Alzheimer Disease

Abstract

BACKGROUND: Alzheimer disease (AD) is the most common cause of dementia and can impair cognitive abilities crucial to the task of driving. Rational decisions about whether such impaired individuals should continue to drive require objective assessments of driver performance. OBJECTIVE: To measure relevant performance factors using high-fidelity driving simulation. DESIGN: We examined the effect of AD on driver collision avoidance using the Iowa Driving Simulator, which provided a high-fidelity, closely controlled environment in which to observe serious errors by at-risk drivers. We determined how such unsafe events are predicted by visual and cognitive factors sensitive to decline in aging and AD. SETTING: The University of Iowa Hospitals and Clinics, Iowa City, and the Iowa Driving Simulator. PARTICIPANTS: Thirty-nine licensed drivers: 21 with AD and 18 controls without dementia. MAIN OUTCOME MEASURES: We determined the number of crashes and related performance errors and analyzed how these occurrences were predicted by visual and cognitive factors. RESULTS: Six participants (29%) with AD experienced crashes vs 0 of 18 control participants (P = .022). Drivers with AD were more than twice as likely to experience close calls (P = .042). Plots of critical control factors in the moments preceding a crash revealed patterns of driver in-attention and error. Strong predictors of crashes included visuospatial impairment, reduction in the useful field of view, and reduced perception of 3-dimensional structure-from-motion. CONCLUSIONS: High-fidelity driving simulation provides a unique new source of performance parameters to standardize the assessment of driver fitness. Detailed observations of crashes and other safety errors provide unbiased evidence to aid in the difficult clinical decision of whether older or medically impaired individuals should continue to drive. The findings are complementary to evidence currently being gathered using techniques from epidemiology and cognitive neuroscience.