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

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, p0.05), 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.【摘要】 背景：与年龄相关的认知衰退会损害驾驶所需的核心认知能力。 目的：本研究旨在衡量不同驾驶场景下，与高交通违章频次相关的主要认知功能。 方法：招募34名年龄介于65至90岁的老年人，使用驾驶模拟器 (Driving Simulator) 开展4种场景的测试（交叉路口、超车、雨天、故障任务），并接受成套认知测试，涵盖记忆、注意力、视空间能力及认知筛查类项目。受试者被分为两组：高风险驾驶组（High-risk driving, HR，罚分排名前20%）与正常风险驾驶组（Normal-risk driving, NR）。采用非参数组间比较与回归分析开展统计检验。 结果：高风险驾驶组的驾驶总罚分显著高于正常风险组（高风险组中位数为29，范围9~44；正常风险组中位数为61，范围47~97，p<0.05），两组效应量较小（科恩d值 (Cohen’s d) 均为0.3）。 结论：言语学习得分或许比认知筛查测验更能预测驾驶风险。高风险驾驶员在交叉路口、超车及雨天测试中的道路交通罚分也显著更高。