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

通过数字表型分析对阿片类药物使用障碍的失效风险进行情境化每日预测

基本信息

批准号：
10642766
负责人：
John J. Curtin
金额：
$ 68.56万
依托单位：
UNIVERSITY OF WISCONSIN-MADISON
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10642766
关键词：
Abstinence Acoustics Advertising Affect Alcohols Behavior Caring Cellular Phone Characteristics Clinical Communication Communities Data Sources Diagnosis Distal Drug Combinations Drug usage Ecological momentary assessment Equilibrium Face Facebook Family Foundations Frequencies Friends Gender Goals Health Home Individual Intake Location Measures Medical Mental disorders Methods Mobile Health Application Modeling Monitor Narcotics Anonymous Natural Language Processing Opioid Paper Participant Patient Recruitments Patient Self-Report Patients Pattern Persons Pharmaceutical Preparations Phenotype Physical activity Positioning Attribute Predictive Analytics Probability Psychopathology Publishing Race Recovery Recovery Support Relapse Risk Risk Factors Sampling Services Severities Signal Transduction Social Environment Social Network Stimulant Substance Use Disorder Support System Surveys System Telephone Testing Text Messaging Theoretical model Time Training Visual Voice addiction alcohol testing alcohol use disorder care providers care systems chronic pain clinical care comorbidity cost craving digital drug abstinence experience information gathering innovation mHealth machine learning method meetings microphone mobile application opioid use opioid use disorder overdose death participant enrollment peer personalized care predictive modeling prolonged abstinence real time model recruit relapse prevention relapse risk risk prediction risk prediction model rural setting sensor signal processing skills sleep quality sobriety social media stressor suburb sustained recovery

项目摘要

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.

项目概要阿片类药物使用障碍日益普遍，给患者及其家属带来毁灭性的后果和成本家人、朋友和社区。阿片类药物和其他物质使用障碍 (SUD) 的可用治疗方法尚未成功地保持清醒。绝大多数 SUD 患者会在一年内复发。关键的是，他们常常无法检测到复发风险的动态、日常变化，并且没有充分利用他们开发的技能或利用通过持续护理提供的支持。该项目的总体目标是开发和提供高度情境化的戒断风险预测模型，用于预测戒毒人群中阿片类药物和其他药物戒断的日常概率。这种失误风险预测模型将在成瘾综合健康增强支持系统 (A-CHESS) 移动应用程序中提供，该应用程序由 RCT 建立，作为最先进的移动医疗系统，为酒精和药物滥用障碍提供持续护理服务。为了实现这些广泛的目标，我们对 480 名患有阿片类药物使用障碍的参与者进行了多样化抽样调查，他们正在寻求禁欲者将被招募。这些参与者将接受为期 12 个月的康复跟踪，并进行观察参与者最早在禁欲后 1 周、最晚在禁欲后 18 个月发生样本。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）将被根据摄入量进行测量。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进行一次自我报告调查、每日生态即时评估、每日视频恢复“签到”、语音电话和短信日志、短信内容、即时位置（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），以及移动 A-CHESS 恢复支持应用程序的使用。这些风险信号的预测能力将是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，进一步增加支持或削弱参与者的禁欲努力。机器学习方法将用于基于这些情境化的静态和动态风险信号来训练、验证和测试阿片类药物（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参与者提供阿片类药物戒断个体中阿片类药物（或其他药物）使用失效的每日概率预测。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康复支持、信息和风险监控。与传统的持续护理相比，A-CHESS 将提供个性化护理，并在最需要的时刻提供和实施。综合实时风险预测有望通过适应性使用这些持续护理服务来鼓励持续康复。