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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：
9980350
负责人：
John J. Curtin
金额：
$ 68.12万
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/9980350
关键词：
Abstinence Acoustics Advertising Affect Alcohol consumption Alcohol or Other Drugs use 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 environment 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 Pharmaceutical Preparations Phenotype Physical activity Positioning Attribute Predictive Analytics Probability Psychopathology Publishing Race Recovery Relapse Risk Risk Factors Sampling Services Severities Signal Transduction Social Environment Social Network 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 model rural setting sensor signal processing skills sleep quality sobriety social media stressor 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.

项目总结