Laboratory for the Study of Movement, Department of Orthopedics and Traumatology, School of Medicine, University of São Paulo, São Paulo 05403-010, Brazil.
Graduate Program in Aging Science, São Judas Tadeu University (USJT), São Paulo 03166-000, Brazil.
Int J Environ Res Public Health. 2023 Feb 27;20(5):4212. doi: 10.3390/ijerph20054212.
The ability to drive depends on the motor, visual, and cognitive functions, which are necessary to integrate information and respond appropriately to different situations that occur in traffic. The study aimed to evaluate older drivers in a driving simulator and identify motor, cognitive and visual variables that interfere with safe driving through a cluster analysis, and identify the main predictors of traffic crashes. We analyzed the data of older drivers (n = 100, mean age of 72.5 ± 5.7 years) recruited in a hospital in São Paulo, Brazil. The assessments were divided into three domains: motor, visual, and cognitive. The K-Means algorithm was used to identify clusters of individuals with similar characteristics that may be associated with the risk of a traffic crash. The Random Forest algorithm was used to predict road crash in older drivers and identify the predictors (main risk factors) related to the outcome (number of crashes). The analysis identified two clusters, one with 59 participants and another with 41 drivers. There were no differences in the mean of crashes (1.7 vs. 1.8) and infractions (2.6 vs. 2.0) by cluster. However, the drivers allocated in Cluster 1, when compared to Cluster 2, had higher age, driving time, and braking time ( < 0.05). The random forest performed well (r = 0.98, R = 0.81) in predicting road crash. Advanced age and the functional reach test were the factors representing the highest risk of road crash. There were no differences in the number of crashes and infractions per cluster. However, the Random Forest model performed well in predicting the number of crashes.
驾驶能力取决于运动、视觉和认知功能，这些功能是整合信息并对交通中发生的不同情况做出适当反应所必需的。本研究旨在通过聚类分析评估驾驶模拟器中的老年驾驶员，并确定可能干扰安全驾驶的运动、认知和视觉变量，并确定交通碰撞的主要预测因素。我们分析了巴西圣保罗一家医院招募的 100 名老年驾驶员（平均年龄 72.5 ± 5.7 岁）的数据。评估分为三个领域：运动、视觉和认知。使用 K-Means 算法识别具有相似特征的个体聚类，这些特征可能与交通事故风险相关。使用随机森林算法预测老年驾驶员的道路碰撞，并识别与结果（碰撞次数）相关的预测因素（主要危险因素）。分析确定了两个聚类，一个包含 59 名参与者，另一个包含 41 名驾驶员。聚类之间的平均碰撞次数（1.7 与 1.8）和违规次数（2.6 与 2.0）没有差异。然而，与聚类 2 相比，分配到聚类 1 的驾驶员年龄、驾驶时间和制动时间更高（<0.05）。随机森林在预测道路碰撞方面表现良好（r = 0.98，R = 0.81）。高龄和功能伸展测试是代表道路碰撞风险最高的因素。聚类之间的碰撞次数和违规次数没有差异。然而，随机森林模型在预测碰撞次数方面表现良好。
