Neuropsychological assessment of driving safety risk in older adults with and without neurologic disease

Abstract

Decline in cognitive abilities can be an important contributor to the driving problems encountered by older adults, and neuropsychological assessment may provide a practical approach to evaluating this aspect of driving safety risk. The purpose of the present study was to evaluate several commonly used neuropsychological tests in the assessment of driving safety risk in older adults with and without neurological disease. A further goal of this study was to identify brief combinations of neuropsychological tests that sample performances in key functional domains and thus could be used to efficiently assess driving safety risk. A total of 345 legally licensed and active drivers over the age of 50, with no neurologic disease (N = 185), probable Alzheimer's disease (N = 40), Parkinson's disease (N = 91), or stroke (N = 29), completed vision testing, a battery of 10 neuropsychological tests, and an 18-mile drive on urban and rural roads in an instrumented vehicle. Performances on all neuropsychological tests were significantly correlated with driving safety errors. Confirmatory factor analysis was used to identify 3 key cognitive domains assessed by the tests (speed of processing, visuospatial abilities, and memory), and several brief batteries consisting of one test from each domain showed moderate corrected correlations with driving performance. These findings are consistent with the notion that driving places demands on multiple cognitive abilities that can be affected by aging and age-related neurological disease, and that neuropsychological assessment may provide a practical off-road window into the functional status of these cognitive systems.

Keywords:

• Automobile
• Aging
• Driving
• Assessment
• Neuropsychology

Acknowledgments

This study was supported by awards AG 17717 and AG 15071 from the National Institute on Aging (NIA) and NS 44930 from the National Institute of Neurological Disorders and Stroke (NINDS), which provided salary support to the authors. The authors would like to thank the entire neuroergonomics research team and all participants in the study.

Notes

¹Because full information maximum likelihood output from LISREL is severely limited in providing goodness-of-fit indices, with the exception of root mean square error of approximation (RMSEA), the tables and figures are based on robust maximum likelihood estimates using 231 cases with listwise deletion. However, model-fitting analyses relying on full information maximum likelihood on 345 subjects led to similar inferences regarding factor structure.