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
Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States.
Center for Computational Health, International Business Machines (IBM) Research, Yorktown Heights, NY, United States.
Front Psychiatry. 2022 Apr 29;13:871916. doi: 10.3389/fpsyt.2022.871916. eCollection 2022.
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.
近年来，在美国，阿片类药物使用障碍（OUD）的患病率和阿片类药物过量使用率急剧上升。有几种治疗OUD的有效药物（MOUD），已被证明能挽救生命。大量研究已经确定了一系列因素，这些因素可预测物质使用障碍治疗期间的治疗中断和持续物质使用情况。然而，这些文献大多只研究了MOUD治疗结果的一小部分潜在调节因素或中介因素，可能会导致对治疗不依从的描述过于简单。数字健康方法有望从接受MOUD治疗的个体中获取密集的、纵向的生态有效数据，以扩展我们对影响治疗参与度和结果的因素的理解。
本文描述了一项由美国国立药物滥用研究所（NIDA）的国家药物滥用治疗临床试验网络支持的新研究的方案（包括研究设计和方法学考量）。这项研究（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，它旨在研究EMA、数字传感和社交媒体数据（分别以及相互比较）在预测MOUD治疗保留率、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数据中所记录]方面的效用。据我们所知，这是第一个在理解MOUD治疗患者临床轨迹时纳入所有三种数字衍生数据来源（EMA、数字传感和社交媒体）的项目。这些多个数据流将使我们能够理解从这些不同数据源收集数字数据的相对效用和综合效用。纳入EHR数据使我们能够关注数字健康数据在预测客观测量临床结果方面的效用。
结果可能有助于阐明数字数据源与OUD治疗结果之间的新关系。它还可能为通过评估个体日常生活与其MOUD治疗反应之间的动态相互作用来加强临床试验结果测量的方法提供信息。
标识符：NCT04535583。
