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Cognitive predictors of unsafe driving in older drivers: a meta-analysis

  • J. L. Mathias (a1) and L. K. Lucas (a1)


Background: Older drivers are at a higher risk of being involved in a motor vehicle accident. However, on-road assessments of all older drivers are impractical, highlighting the need to screen for potentially unsafe drivers. This study undertook a meta-analysis of research examining the cognitive predictors of driving ability in older drivers in order to provide an evidence-based method for screening drivers.

Methods: Comprehensive searches were undertaken of the PubMed, PsycINFO, CINAHL, and Health-Source Nursing electronic databases between 1980 and 2007 in order to identify studies that examined cognitive differences between drivers aged over 55 years who either passed or failed a driving assessment. Twenty-one studies were eligible for inclusion. Weighted Cohen's d effect sizes, percentage overlap statistics, Fail-safe Ns and 95% CIs were calculated for all cognitive tests.

Results: The best predictors of on-road driving were the Ergovision and Useful Field of View (UFOV) tests, a complex RT task, Paper Folding task, Dot Counting, WMS Visual Reproduction, and Computerized Visual Attention Task. Simulator driving performance was best predicted by the Benton Line Orientation Task, Clock Drawing, a Driver Scanning task, the UFOV, WAIS Picture Arrangement and MMSE. Finally, the Trail Making Test, Stroop, UFOV, WAIS Block Design, and Automated Psychophysical Test were good predictors of driving problems.

Conclusions: There are a variety of tests that appear suitable for screening older drivers, the exact choice of which depends on the “gold standard” for determining driving ability (on-road driving, driving simulator, driving problems) and whether a computerized or paper-and-pencil task is required.


Corresponding author

Correspondence should be addressed to: Dr J. L. Mathias, School of Psychology, University of Adelaide, Adelaide, South Australia 5005, Australia. Phone: +61 8 83035266; Fax: +61 8 83033770 Email:


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*Denotes a study that was included in the meta-analysis.

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