Contextualized daily prediction of lapse risk in opioid use disorder by digital phenotyping

项目摘要 阿片类药物使用障碍日益普遍，导致患者及其家属的毁灭性后果和成本。 家人朋友和社区阿片类药物和其他物质使用障碍（SUD）的现有治疗方法不包括 成功地保持了清醒绝大多数SUD患者在一年内复发。关键的是，他们往往不能 检测其复发风险的动态、逐日变化，没有充分利用其发展的技能或利用通过持续护理提供的支持。该项目的主要目标是开发和提供一个高度情境化的失效风险预测模型，用于预测追求戒毒的人中阿片类药物和其他药物使用失效的日常概率。该失效风险预测模型将在成瘾综合健康增强支持系统（A-CHESS）移动的应用程序中提供，该应用程序由RCT建立，作为最先进的mHealth系统，为酒精和物质使用障碍提供持续护理服务。 为了实现这些广泛的目标，480名患有阿片类药物使用障碍的参与者的多样化样本正在寻求 禁欲将被招募。这些参与者将被跟踪12个月的恢复，观察 最早发生在禁欲后一周，最晚发生在禁欲后18个月。 sample.明确的远端静态复发风险信号（例如，成瘾严重程度，共病精神病理学）将是 测量摄入量。一系列更近端的、随时间变化的阿片类药物（和其他药物使用）失效风险信号也将被 通过参与者的智能手机收集。这些信号包括每两个月一次的自我报告调查、每日生态 瞬时评估，每日视频恢复“签到”，语音电话和短信日志，短信 内容、每时每刻的位置（通过智能手机GPS和定位服务）、身体活动（通过智能手机 传感器），以及使用移动的A-CHESS恢复支持应用程序。这些风险信号的预测能力将 通过将它们锚定在已知的人、地点、日期和时间的人际背景中， 支持或减损参与者的禁欲努力。机器学习方法将用于训练，验证和测试基于这些情境化静态和动态风险信号的阿片类药物（和其他药物）失效风险预测模型。 这些失效风险预测模型将为阿片类药物戒断个体中阿片类药物（或其他药物）使用失效提供参与者特定的逐日概率预测。这些失效风险预测模型将在项目完成后正式添加到A-CHESS持续护理移动的应用程序中，用于临床护理。这些项目目标使A-CHESS能够持续为患者提供复发预防和恢复支持，信息和风险监测。与传统的持续护理相比，A-CHESS将提供个性化的护理，并在最需要的时候提供和实施。综合的实时风险预测具有很大的希望，通过适应性地使用这些持续护理服务来鼓励持续康复。

PROJECT SUMMARY Opioid use disorder is increasingly widespread, leading to devastating consequences and costs for patients and their families, friends, and communities. Available treatments for opioid and other substance use disorders (SUD) are not successful at sustaining sobriety. The vast majority of people with SUD relapse within a year. Critically, they often fail to detect dynamic, day-by-day changes in their risk for relapse and do not adequately employ skills they developed or take advantage of support available through continuing care. The broad goals of this project are to develop and deliver a highly contextualized, lapse risk prediction models for forecasting day-by-day probability of opioid and other drug use lapse among people pursuing drug abstinence. This lapse risk prediction model will be delivered within the Addiction-Comprehensive Health Enhancement Support System (A-CHESS) mobile app, which has been established by RCT as a state-of-the-art mHealth system for providing continuing care services for alcohol and substance use disorders. To accomplish these broad goals, a diverse sample of 480 participants with opioid use disorder who are pursing abstinence will be recruited. These participants will be followed for 12 months of their recovery, with observations occurring as early as one week post-abstinence and as late as 18 months post-abstinence across participants in the sample. Well-established distal, static relapse risk signals (e.g., addiction severity, comorbid psychopathology) will be measured on intake. A range of more proximal, time-varying opioid (and other drug use) lapse risk signals will also be collected via participants’ smartphones. These signals include self-report surveys every two months, daily ecological momentary assessments, daily video recovery “check-ins”, voice phone call and text message logs, text message content, moment-by-moment location (via smartphone GPS and location services), physical activity (via smartphone sensors), and usage of the mobile A-CHESS Recovery Support app. The predictive power of these risk signals will be further increased by anchoring them within an inter-personal context of known people, locations, dates, and times that support or detract from participants’ abstinence efforts. Machine learning methods will be used to train, validate, and test opioid (and other drug) lapse risk prediction models based on these contextualized static and dynamic risk signals. These lapse risk prediction models will provide participant specific, day-by-day probabilistic forecast of a lapse to opioid (or other drug) use among opioid abstinent individuals. These lapse risk prediction models will be formally added to the A-CHESS continuing care mobile app at the completion of the project for use in clinical care. These project goals position A-CHESS to make relapse prevention and recovery support, information, and risk monitoring available to patients continuously. Compared to conventional continuing care, A-CHESS will provide personalized care and be available and implemented during moments of greatest need. Integrated real-time risk prediction holds substantial promise to encourage sustained recovery through adaptive use of these continuing care services.