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：
10427354
负责人：
John J. Curtin
金额：
$ 68.33万
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 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10427354
关键词：
Abstinence Acoustics Advertising Affect Alcohol consumption Alcohols Behavior Caring Cellular Phone Characteristics Clinical Communication Communities Data Sources Diagnosis Distal Drug usage Ecological momentary assessment Enrollment 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 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 base care providers care systems chronic pain clinical care comorbidity cost craving digital disorder later incidence prevention drug abstinence experience information gathering innovation mHealth machine learning method meetings microphone mobile application opioid use opioid use disorder overdose death peer personalized care predictive modeling prevent real time model recruit relapse risk risk prediction risk prediction model rural setting sensor signal processing skills sleep quality sobriety social media stressor substance use substance user suburb

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.

项目摘要阿片类药物使用障碍越来越普遍，导致患者及其其成本造成毁灭性的后果和成本家庭，朋友和社区。阿片类药物和其他药物使用障碍（SUD）的可用治疗方法不是成功维持清醒。绝大多数具有SUD继电器的人在一年内。至关重要的是，他们常常无法检测救济风险的动态，日常变化，并且无法充分利用他们开发的技能，或者通过继续护理利用支持。该项目的广泛目标是开发和提供高度背景化的，失效风险预测模型，以预测阿片类药物的日常概率和其他毒品使用的概率，并在追求戒毒的人们中消失。这种失去风险预测模型将在成瘾的健康增强支持系统（A-CHESS）移动应用程序中提供，该应用已建立为最先进的MHealth系统，用于为酒精和物质使用障碍提供持续的护理服务。为了实现这些广泛的目标，潜水员样本的480名参与者患有阿片类药物使用障碍，他们正在追求戒酒将被招募。这些参与者将恢复12个月，并进行观察在侵犯后的一周，直到18个月后的参与者发生后至18个月样本。建立良好的盘式，静态继电器风险信号（例如成瘾的严重程度，合并症）将是以进气口测量。一系列更近端，时变的阿片类药物（和其他药物使用）也将是风险信号通过参与者的智能手机收集。这些信号包括每两个月的自我报告调查，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，一时的位置（通过智能手机GP和位置服务），体育活动（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其锚定在知名人士，位置，日期和时代的人际关系环境中，进一步增加了。支持或发现参与者的禁欲工作。机器学习方法将用于基于这些上下文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）风险预测模型。这些失误风险预测模型将为参与者提供特定的日常概率预测，以预测阿片类药物戒烟者中使用阿片类药物（或其他药物）使用。这些失误风险预测模型将在项目完成时正式添加到A-Chess Chess Chans Care Mobile应用程序中，以供临床护理。这些项目目标将A-Bead定位为预防和恢复支持，信息和风险监控，并不断地为患者提供。与传统的持续护理相比，A-Chess将提供个性化的护理，并在最需要的时刻提供和实施。实时风险预测具有实质性的承诺，可以通过适应使用这些持续护理服务来鼓励持续恢复。