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首页> 外文期刊>Journal of psychoactive drugs >Development and Testing of a Smartphone-Based Cognitive/Neuropsychological Evaluation System for Substance Abusers
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Development and Testing of a Smartphone-Based Cognitive/Neuropsychological Evaluation System for Substance Abusers

机译:基于智能手机的物质滥用者认知/神经心理学评估系统的开发和测试

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Introduction: In methamphetamine (MA) users, drug-induced neurocognitive deficits may help to determine treatment, monitor adherence, and predict relapse. To measure these relationships, we developed an iPhone app (Neurophone) to compare lab and field performance of N-Back, Stop Signal, and Stroop tasks that are sensitive to MA-induced deficits. Methods: Twenty healthy controls and 16 MA-dependent participants performed the tasks in-lab using a validated computerized platform and the Neurophone before taking the latter home and performing the tasks twice daily for twoweeks. Results: N-Back task: there were no clear differences in performance between computer-based vs. phone-based in-lab tests and phone-based in-lab vs. phone-based in-field tests. Stop-Signal task: difference in parameters prevented comparison of computer-based and phone-based versions. There was significant difference in phone performance between field and lab. Stroop task: response time measured by the speech recognition engine lacked precision to yield quantifiable results. There was no learning effect over time. On an average, each participant completed 84.3% of the in-field NBack tasks and 90.4% of the in-field Stop Signal tasks (MA-dependent participants: 74.8% and 84.3%; healthy controls: 91.4% and 95.0%, respectively). Participants rated Neurophone easy to use. Conclusion: Cognitive tasks performed in-field using Neurophone have the potential to yield results comparable to those obtained in a laboratory setting. Tasks need to be modified for use as the app's voice recognition system is not yet adequate for timed tests.
机译:简介:在甲基苯丙胺(MA)使用者中,药物引起的神经认知功能障碍可能有助于确定治疗,监测依从性并预测复发。为了测量这些关系,我们开发了一个iPhone应用程序(Neurophone),以比较对MA引起的缺陷敏感的N-Back,停止信号和Stroop任务的实验室和现场性能。方法:20名健康对照者和16名依赖MA的参与者使用经过验证的计算机化平台和Neurophone在实验室内完成了任务,然后将后者带回家并每天两次执行任务,持续两周。结果:N-Back任务:基于计算机的实验室测试与基于电话的实验室测试以及基于电话的实验室与基于电话的现场测试之间在性能上没有明显差异。停止信号任务:参数差异阻止比较基于计算机的版本和基于电话的版本。现场和实验室之间的电话性能存在显着差异。 Stroop任务:语音识别引擎测量的响应时间缺乏产生可量化结果的精度。随着时间的流逝,没有学习效果。平均每个参与者完成了84.3%的现场NBack任务和90.4%的现场停止信号任务(依赖MA的参与者:分别为74.8%和84.3%;健康对照:分别为91.4%和95.0%) 。参与者对Neurophone进行了简单易用的评估。结论:使用Neurophone进行的现场认知任务有可能产生与实验室环境相当的结果。由于该应用的语音识别系统尚不足以进行定时测试,因此需要修改任务以供使用。

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