Design of an experimental protocol to examine medication non-adherence among young drivers diagnosed with ADHD: A driving simulator study.
Design of an experimental protocol to examine medication non-adherence among young drivers diagnosed with ADHD: A driving simulator study.
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DOI：
10.1016/j.conctc.2018.07.007
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发表时间：
2018-09
影响因子：
1.5
通讯作者：
Gonzalez A
中科院分区：
文献类型：
--
作者：
Lee YC;Ward McIntosh C;Winston F;Power T;Huang P;Ontañón S;Gonzalez A
青少年和年轻人中ADHD的诊断与机动车碰撞的可能性较高有关。一些研究表明ADHD药物的有益效果，但确切的疗效仍在争论中。此外，该年龄段的药物依从性较低，可能会进一步降低疗效。我们的长期目标是通过驾驶员行为建模和监测来检测药物不依从性，从而减少ADHD驾驶员的不安全驾驶。作为第一步，我们开发了所描述的实验室研究协议，以获得可靠的驾驶员行为数据，然后将这些数据用于设计和训练通过机器学习构建的行为模型。本实验研究方案旨在系统地比较患有ADHD的年轻人和非ADHD对照组在两种药物条件下（服用药物前后）的驾驶行为。驾驶模拟器被用来检查驾驶行为和与交通的相互作用。主要结果是两个比较的速度管理（ADHD与非ADHD和用药前与用药后），次要目标涉及利用自我报告的调查了解参与者之间关于ADHD症状，驾驶员知识和安全感的差异。研究方案旨在最大限度地提高参与者的安全性和数据收集效率，因为在两次2小时研究访视中收集了多项指标。被抽样的ADHD驾驶员在人口统计学和社会心理学上相似，但在临床上与非ADHD组不同。总的来说，该方案在参与者招募和保留方面是有效的，允许交错数据收集，并且可以纳入随后的临床试验中，以检查基于机器学习的驾驶员监控干预的有效性。
The diagnosis of ADHD among teens and young adults has been associated with a higher likelihood of motor vehicle crashes. Some studies suggest a beneficial effect of ADHD medication but the exact efficacy is still being debated. Further, medication adherence, which is low in this age group, can further reduce effectiveness. Our long-term objective is to reduce unsafe driving among drivers with ADHD by detecting medication non-adherence through driver behavior modeling and monitoring. As a first step, we developed the described lab study protocol to obtain reliable driver behavior data that will then be used to design and train behavior models built through machine learning. This experimental study protocol was developed to systematically compare driving behaviors under two medication conditions (before and after intake of medication) among young adults with ADHD and a control group of non-ADHD. A driving simulator was used to examine driving behaviors and interactions with traffic. The primary outcome was speed management for two comparisons (ADHD vs. non-ADHD and before vs. after medication), and secondary objectives involved understanding differences among the participants utilizing self-reported surveys about ADHD symptoms, drivers' knowledge, and perception about safety. The study protocol was designed to maximize participant safety and efficiency of data collection, as multiple measures were collected over two 2-h study visits. The sampled ADHD drivers were demographically and psychosocially similar but clinically different from the non-ADHD group. Overall, this protocol was effective in participant recruitment and retention, allowed staggered data collection, and can be incorporated in a subsequent clinical trial that examines the efficacy of a machine-learning based driver monitoring intervention.