Department of Neurology, University of Iowa, Iowa City, IA 52242, USA.
Psychol Aging. 2012 Sep;27(3):550-9. doi: 10.1037/a0026359. Epub 2011 Dec 19.
This study aimed to develop predictive models for real-life driving outcomes in older drivers. Demographics, driving history, on-road driving errors, and performance on visual, motor, and neuropsychological test scores at baseline were assessed in 100 older drivers (ages 65-89 years [72.7]). These variables were used to predict time to driving cessation, first moving violation, or crash. Using Cox proportional hazards regression models, significant individual predictors for driving cessation were greater age and poorer scores on Near Visual Acuity, Contrast Sensitivity, Useful Field of View, Judgment of Line Orientation, Trail Making Test-Part A, Benton Visual Retention Test, Grooved Pegboard, and a composite index of overall cognitive ability. Greater weekly mileage, higher education, and "serious" on-road errors predicted moving violations. Poorer scores from Trail Making Test-Part B or Trail Making Test (B-A) and serious on-road errors predicted crashes. Multivariate models using "off-road" predictors revealed (a) age and Contrast Sensitivity as best predictors for driving cessation; (b) education, weekly mileage, and Auditory Verbal Learning Task-Recall for moving violations; and (c) education, number of crashes over the past year, Auditory Verbal Learning Task-Recall, and Trail Making Test (B-A) for crashes. Diminished visual, motor, and cognitive abilities in older drivers can be easily and noninvasively monitored with standardized off-road tests, and performances on these measures predict involvement in motor vehicle crashes and driving cessation, even in the absence of a neurological disorder.
本研究旨在为老年驾驶员的实际驾驶结果开发预测模型。评估了 100 名老年驾驶员(年龄 65-89 岁[72.7])的人口统计学、驾驶史、道路驾驶错误以及基线时视觉、运动和神经心理测试成绩。这些变量用于预测驾驶停止、首次违规或事故的时间。使用 Cox 比例风险回归模型,驾驶停止的显著个体预测因子为年龄较大和以下测试项目的得分较差:近视力、对比敏感度、有用视野、线定向判断、连线测试 A、Benton 视觉保持测试、滚花钉板和整体认知能力综合指数。每周行驶里程较高、受教育程度较高和“严重”道路错误预测违规行为。连线测试 B 或(B-A)的较差得分和严重道路错误预测事故。使用“道路外”预测因子的多元模型显示:(a)年龄和对比敏感度是驾驶停止的最佳预测因子;(b)教育、每周行驶里程和听觉言语学习任务-回忆是违规行为的最佳预测因子;(c)教育、过去一年的事故次数、听觉言语学习任务-回忆和连线测试(B-A)是事故的最佳预测因子。使用标准化的“道路外”测试可以轻松且无创地监测老年驾驶员的视觉、运动和认知能力下降,并且这些措施的表现可预测机动车事故和驾驶停止的发生,即使没有神经障碍。