Verbal Learning as a predictor of risks of accidents in elderly drivers

Abstract

ABSTRACT Background: Age-related cognitive decline impacts cognitive abilities essential for driving. Objective: We aimed to measure main cognitive functions associated with a high number of traffic violations in different driving settings. Methods: Thirty-four elderly individuals, aged between 65 and 90 years, were evaluated with a driving simulator in four different settings (Intersection, Overtaking, Rain, and Malfunction tasks) and underwent a battery of cognitive tests, including memory, attention, visuospatial, and cognitive screening tests. Individuals were divided into two groups: High-risk driving (HR, top 20% of penalty points) and normal-risk driving (NR). Non-parametric group comparison and regression analysis were performed. Results: The HR group showed higher total driving penalty score compared to the NR group (median=29, range= 9-44 vs. median=61, range= 47-97, p<0.001). The HR group showed higher penalty scores in the Intersection task (p<0.001) and the Overtaking and Rain tasks (p<0.05 both). The verbal learning score was significantly lower in the HR group (median=33, range=12-57) compared with the NR group (median=38, range=23-57, p<0.05), and it was observed that this score had the best predictive value for worse driving performance in the regression model. General cognitive screening tests (Mini-Mental State Examination and Addenbrooke's Cognitive Evaluation) were similar between the groups (p>0.05), with a small effect size (Cohen’s d=0.3 both). Conclusion: The verbal learning score may be a better predictor of driving risk than cognitive screening tests. High-risk drivers also showed significantly higher traffic driving penalty scores in the Intersection, Overtaking, and Rain tests.