College of Computing and Informatics, Drexel University, Philadelphia, PA 19104, United States.
College of Computing and Informatics, Drexel University, Philadelphia, PA 19104, United States.
J Safety Res. 2022 Sep;82:233-240. doi: 10.1016/j.jsr.2022.05.014. Epub 2022 Jun 6.
Road injuries remain a persistent public health concern across the world. The task of driving is complicated by mental health conditions, which may affect drivers' executive functioning and cognitive resource allocation. This study examines whether attention-deficit/hyperactivity disorder (ADHD) and depression are associated with unsafe driving behaviors.
Generalized linear mixed models were employed to estimate the association of self-reported ADHD and depression with 18 unsafe driving behavior types found prior to at-fault crashes and near-crashes using a large-scale naturalistic driving dataset. Driver demographics, cognitive traits, environmental factors, and driver random effects were included to reduce confounding and biases.
Controlling for other covariates, people with self-reported ADHD were more likely to have performed improper braking or stopping (OR = 4.89, 95% CI 1.82-13.17) prior to an at-fault crash or near-crash, while those with self-reported depression did not have a significant association with any unsafe driving behavior.
After accounting for demographic, cognitive, and environmental covariates, individuals with ADHD and depression were not prone to purposefully aggressive or reckless driving. Instead, drivers with self-reported ADHD may unintentionally execute unsafe driving behaviors in particular driving scenarios that require a high level of cognitive judgment.
These findings can inform the curriculum design of driver's education programs that help learners with mental health conditions gain practice in certain road scenarios, for example, more practice on preemptively reducing speed instead of making sudden brakes and smooth turning on curved roads for students with ADHD. Furthermore, specific advanced driver assistance systems may prove particularly helpful for drivers with ADHD, such as detection of leading objects and curve speed warning.
道路伤害仍然是全球范围内一个持续存在的公共卫生问题。心理健康状况会使驾驶任务变得复杂，这可能会影响驾驶员的执行功能和认知资源分配。本研究旨在探讨注意力缺陷多动障碍（ADHD）和抑郁症是否与不安全的驾驶行为有关。
采用广义线性混合模型，利用大规模自然驾驶数据集，在事故前和事故前的近事故中，估计自我报告的 ADHD 和抑郁与 18 种不安全驾驶行为类型之间的关联。纳入驾驶员人口统计学特征、认知特征、环境因素和驾驶员随机效应，以减少混杂和偏倚。
在控制其他协变量的情况下，自我报告患有 ADHD 的人在事故前或近事故前更有可能出现不当制动或停车行为（OR=4.89，95%CI 1.82-13.17），而自我报告患有抑郁症的人则与任何不安全的驾驶行为都没有显著关联。
在考虑了人口统计学、认知和环境协变量后，ADHD 和抑郁症患者并不倾向于有意的攻击性或鲁莽驾驶。相反，自我报告患有 ADHD 的驾驶员可能会在特定需要高度认知判断的驾驶场景中无意中执行不安全的驾驶行为。
这些发现可以为驾驶员教育课程的课程设计提供信息，帮助有心理健康状况的学习者在某些道路场景中获得实践经验，例如，对于 ADHD 患者，更多地练习预先减速而不是突然刹车，以及在弯道上平稳转弯。此外，特定的高级驾驶员辅助系统可能对 ADHD 患者特别有帮助，例如检测前方物体和弯道速度警告。
