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首页> 外文期刊>Contemporary Clinical Trials Communications >Design of an experimental protocol to examine medication non-adherence among young drivers diagnosed with ADHD: A driving simulator study
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Design of an experimental protocol to examine medication non-adherence among young drivers diagnosed with ADHD: A driving simulator study

机译:设计实验协议以检查诊断为多动症的年轻驾驶员中的药物不依从性:驾驶模拟器研究

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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.
机译:青少年和年轻人多动症的诊断与机动车碰撞的可能性更高有关。一些研究表明多动症药物的有益作用,但确切的疗效仍在争论中。此外,在该年龄组中药物依从性较低,会进一步降低有效性。我们的长期目标是通过驾驶员行为建模和监控来检测药物不遵守情况,从而减少多动症驾驶员的不安全驾驶。第一步,我们开发了描述的实验室研究协议以获得可靠的驾驶员行为数据,然后将其用于设计和训练通过机器学习构建的行为模型。开发该实验研究方案的目的是,系统地比较患有ADHD的年轻人和非ADHD的对照组在两种药物条件下(服药前后)的驾驶行为。驾驶模拟器用于检查驾驶行为以及与交通的互动。主要结果是对速度进行了两次比较(ADHD与非ADHD以及服药前与服药后)的比较,次要目标是利用自我报告的关于ADHD症状,驾驶员的知识以及对安全性的看法,了解参与者之间的差异。研究方案旨在最大程度地提高参与者的安全性和数据收集效率,因为在两次2小时的研究访问中收集了多种措施。抽样的ADHD驾驶员在人口统计学和社会心理上相似,但在临床上与非ADHD组不同。总体而言,该协议在参与者招募和保留方面是有效的,允许交错数据收集,并且可以并入随后的临床试验中,该临床试验检查基于机器学习的驾驶员监视干预的功效。

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