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一项用于检查被诊断患有注意力缺陷多动障碍的年轻驾驶员用药依从性的实验方案设计:一项驾驶模拟器研究。

Design of an experimental protocol to examine medication non-adherence among young drivers diagnosed with ADHD: A driving simulator study.

作者信息

Lee Yi-Ching, Ward McIntosh Chelsea, Winston Flaura, Power Thomas, Huang Patty, Ontañón Santiago, Gonzalez Avelino

机构信息

George Mason University, USA.

Children's Hospital of Philadelphia, USA.

出版信息

Contemp Clin Trials Commun. 2018 Jul 25;11:149-155. doi: 10.1016/j.conctc.2018.07.007. eCollection 2018 Sep.

Abstract

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.

摘要

青少年和青年成人被诊断患有注意力缺陷多动障碍(ADHD)与机动车碰撞事故的较高可能性相关。一些研究表明ADHD药物有有益效果,但确切疗效仍在争论中。此外,该年龄组的药物依从性较低,这会进一步降低疗效。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情况,从而减少患有ADHD的驾驶员的不安全驾驶行为。作为第一步,我们制定了所描述的实验室研究方案,以获取可靠的驾驶员行为数据,这些数据随后将用于设计和训练通过机器学习构建的行为模型。制定该实验研究方案是为了系统比较患有ADHD的青年成人和非ADHD对照组在两种药物条件下(服药前和服药后)的驾驶行为。使用驾驶模拟器来检查驾驶行为以及与交通的互动情况。主要结果是针对两项比较(ADHD与非ADHD以及服药前与服药后)的速度管理,次要目标包括利用关于ADHD症状、驾驶员知识和安全认知的自我报告调查来了解参与者之间的差异。该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,因为在两次为期2小时的研究访视中收集了多项测量数据。抽样的患有ADHD的驾驶员在人口统计学和社会心理方面与非ADHD组相似,但在临床方面有所不同。总体而言,该方案在参与者招募和留存方面是有效的,允许交错收集数据,并且可纳入后续临床试验,该试验将检验基于机器学习的驾驶员监测干预措施的疗效。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8611/6082792/64a3145ceefa/gr1.jpg

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