The Feasibility and Utility of Harnessing Digital Health to Understand Clinical Trajectories in Medication Treatment for Opioid Use Disorder: D-TECT Study Design and Methodological Considerations.
The Feasibility and Utility of Harnessing Digital Health to Understand Clinical Trajectories in Medication Treatment for Opioid Use Disorder: D-TECT Study Design and Methodological Considerations.
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DOI：
10.3389/fpsyt.2022.871916
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发表时间：
2022
影响因子：
4.7
通讯作者：
Campbell, Cynthia I.
中科院分区：
文献类型：
--
作者：
Marsch, Lisa A.;Chen, Ching-Hua;Adams, Sara R.;Asyyed, Asma;Does, Monique B.;Hassanpour, Saeed;Hichborn, Emily;Jackson-Morris, Melanie;Jacobson, Nicholas C.;Jones, Heather K.;Kotz, David;Lambert-Harris, Chantal A.;Li, Zhiguo;McLeman, Bethany;Mishra, Varun;Stanger, Catherine;Subramaniam, Geetha;Wu, Weiyi;Campbell, Cynthia I.
关键词：
近年来，在美国各地，阿片类药物使用障碍 (OUD) 的患病率和阿片类药物过量的发生率急剧上升。存在几种有效的 OUD (MOUD) 药物，并且已被证明可以挽救生命。大量研究已经确定了在药物滥用障碍治疗期间预测消耗和持续药物使用的一系列因素。然而，这些文献中的大部分研究了 MOUD 治疗结果的一小部分潜在调节因素或中介因素，并可能导致对治疗不依从性的过度简单化解释。数字健康方法为从 MOUD 治疗中的个体获取密集的、纵向的生态有效数据提供了巨大的希望，以扩展我们对影响治疗参与和结果的因素的理解。本文描述了由国家药物滥用研究所 (NIDA) 国家药物滥用治疗临床试验网络支持的一项新颖研究的方案（包括研究设计和方法学考虑）。本研究 (D-TECT) 主要旨在评估在门诊 MOUD 治疗患者中收集生态瞬时评估 (EMA)、智能手机和智能手表传感器数据以及社交媒体数据的可行性。其次，它试图检查 EMA、数字传感和社交媒体数据（分别并相互比较）在预测 MOUD 治疗保留、阿片类药物使用事件和药物依从性（如电子健康记录 (EHR) 和 EMA 数据中捕获的）方面的效用。据我们所知，这是第一个包含所有三个数字衍生数据来源（EMA、数字传感和社交媒体）的项目，用于了解 MOUD 治疗患者的临床轨迹。这些多个数据流将使我们能够了解从这些不同数据源收集数字数据的相对和组合效用。 EHR 数据的纳入使我们能够专注于数字健康数据在预测客观测量的临床结果方面的效用。结果可能有助于阐明数字数据源和 OUD 治疗结果之间的新关系。它还可以通过评估个人日常生活与其 MOUD 治疗反应之间的动态相互作用来为增强临床试验结果测量的方法提供信息。标识符：NCT04535583。
Across the U.S., the prevalence of opioid use disorder (OUD) and the rates of opioid overdoses have risen precipitously in recent years. Several effective medications for OUD (MOUD) exist and have been shown to be life-saving. A large volume of research has identified a confluence of factors that predict attrition and continued substance use during substance use disorder treatment. However, much of this literature has examined a small set of potential moderators or mediators of outcomes in MOUD treatment and may lead to over-simplified accounts of treatment non-adherence. Digital health methodologies offer great promise for capturing intensive, longitudinal ecologically-valid data from individuals in MOUD treatment to extend our understanding of factors that impact treatment engagement and outcomes. This paper describes the protocol (including the study design and methodological considerations) from a novel study supported by the National Drug Abuse Treatment Clinical Trials Network at the National Institute on Drug Abuse (NIDA). This study (D-TECT) primarily seeks to evaluate the feasibility of collecting ecological momentary assessment (EMA), smartphone and smartwatch sensor data, and social media data among patients in outpatient MOUD treatment. It secondarily seeks to examine the utility of EMA, digital sensing, and social media data (separately and compared to one another) in predicting MOUD treatment retention, opioid use events, and medication adherence [as captured in electronic health records (EHR) and EMA data]. To our knowledge, this is the first project to include all three sources of digitally derived data (EMA, digital sensing, and social media) in understanding the clinical trajectories of patients in MOUD treatment. These multiple data streams will allow us to understand the relative and combined utility of collecting digital data from these diverse data sources. The inclusion of EHR data allows us to focus on the utility of digital health data in predicting objectively measured clinical outcomes. Results may be useful in elucidating novel relations between digital data sources and OUD treatment outcomes. It may also inform approaches to enhancing outcomes measurement in clinical trials by allowing for the assessment of dynamic interactions between individuals' daily lives and their MOUD treatment response. Identifier: NCT04535583.
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影响因子：
120.7
作者：
JOHNSON, RE;JAFFE, JH;FUDALA, PJ
通讯作者：
FUDALA, PJ
DOI：
10.1038/s41386-018-0247-x
发表时间：
2019-03
期刊：
Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
影响因子：
--
作者：
Hassanpour S;Tomita N;DeLise T;Crosier B;Marsch LA
通讯作者：
Marsch LA
影响因子：
11.3
作者：
BUYSSE, DJ;REYNOLDS, CF;KUPFER, DJ
通讯作者：
KUPFER, DJ
影响因子：
3.1
作者：
Hartman, Lauren;Barnes, Erin;Files, Daniel Clark
通讯作者：
Files, Daniel Clark
影响因子：
15.2
作者：
Epstein, David H.;Tyburski, Matthew;Preston, Kenzie L.
通讯作者：
Preston, Kenzie L.