Mapping the co-evolution of Craving, Affect, Stressors, and Access to Alcohol (CASA) using responsive EMA

使用响应式 EMA 绘制渴望、情感、压力源和酒精获取 (CASA) 的共同演化

• 批准号: 10196353
• 负责人: Bilal Khan
• 金额: $ 26.24万
• 依托单位: UNIVERSITY OF NEBRASKA LINCOLN
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-10 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10196353
• 关键词: Address; Adherence; Adult; Affect; Aftercare; Alaska; Alcohol abuse; Alcohol consumption; Alcohols; Attention; Behavioral; Buffers; Caring; Cellular Phone; Chronic; Cities; Cognitive; Communities; Computer software; Data; Data Analyses; Data Collection; Data Set; Development; Diagnosis; Dimensions; Drug usage; Drug user; Ecological momentary assessment; Enrollment; Environmental Impact; Environmental Risk Factor; Event; Evidence based intervention; Evolution; Exposure to; Failure; Female; Foundations; Frequencies; Future; Goals; Grain; Great Plains; Hand; Homelessness; Housing; Incentives; Independent Living; Individual; Intervention; Interview; Knowledge; Link; Location; Machine Learning; Mental Health; Methodology; Modality; Modeling; Monitor; Moods; National Institute on Alcohol Abuse and Alcoholism; New York City; Outcome; Participant; Pathway interactions; Patients; Pattern; Persons; Phase; Pilot Projects; Population; Poverty; Prevalence; Process; Protocols documentation; Public Health; Puerto Rico; Recovery; Relapse; Research; Research Personnel; Residential Facilities; Residential Treatment; Risk; Risk Behaviors; Risk Factors; Role; Rural; Social support; Source; Stress; Suicide prevention; System; Technology; Teenagers; Testing; Time; Transcend; Work; alcohol abuse therapy; alcohol availability; alcohol outlet density; alcohol use disorder; base; cohort; contextual factors; cost; cost effective; craving; data access; data modeling; design; disorder later incidence prevention; distilled alcoholic beverage; drinking; environmental stressor; experience; high risk; improved; innovation; inpatient service; mHealth; machine learning algorithm; meetings; member; mobile computing; negative affect; next generation; novel; precision medicine; predictive modeling; problem drinker; programs; protective factors; psychologic; psychosocial; public health research; recruit; relapse prediction; relapse risk; response; service intervention; sobriety; social; spatiotemporal; stressor; substance use; substance use treatment; success; treatment program; treatment services

ABSTRACT Recovering from Alcohol Use Disorders (AUD) following treatment is difficult, with 43-83% relapsing post- discharge. These chronically high rates arise, in part, because of a gap in our knowledge about the co-evolution of in the moment risk mechanisms associated with relapse among those recovering from AUD, including Craving, Negative Affect, Stressors and Access to alcohol (CASA). These four factors co-evolve among important protective factors that buffer relapse risk (e.g., improved sober support and psychosocial function associated with post-treatment community AUD recovery programs), and context-based risk factors such as near-at-hand places where alcohol is available. The immediate goal of this project is to develop the means to assess and understand the impact of these environmental/contextual factors for recovering alcoholics, towards a longer term research objective of developing effective just-in-time interventions that mitigate the risk of relapse. In this project, we will leverage novel assessment methodologies via a responsive ecological momentary assessment (rEMA) system developed by our team. This app-based system allows researchers to incorporate geospatial, time- specific and event-specific criteria for enhanced monitoring of (and just-in-time delivery of interventions to) recovering patients in post-treatment settings. Consistent with PA-18-620 from NIAAA, this project aims to build foundational evidence and refine the data collection/analysis strategy needed to achieve our long term objective, via the following specific aims: Aim 1) Pilot the use of our rEMA platform to capture fine-grained longitudinal data on Craving, Affect, Stressors and Access to alcohol among 60 recovering alcoholics across 30 days, to evaluate CASA construct validity within rEMA data and assess the extent and impact of data missingness; Aim 2) Test incentive modalities that will inform us regarding best practices that maximize adherence to the rEMA protocol; Aim 3) Develop analytic strategies based machine-learning models of rEMA data, to forecast imminent risk and quantify causal pathways among CASA variables. To accomplish these aims, we will recruit and enroll adults (n=60; 50% female) in non-hospitalized residential AUD treatments in the Central Plains. Using smartphone assessment with GPS monitoring technology, we will collect longitudinal behavioral and experiential data from each participant over 30 days. Analysis of these data will advance our understanding of co-evolving contextual/social-environmental factors that impact the success/failure of the recovery effort, while establishing foundational evidence from which time/event-specific smartphone-based just-in-time interventions may be developed. The final phase of the project includes the creation of an advisory and dissemination board composed of local and national-level stakeholders, AUD treatment practitioners and other researchers that will guide the development of such an intervention. Included in this group are the directors of two non-hospitalized residential substance treatment facilities that function as community partners for the project through all project stages.

摘要 治疗后从酒精使用障碍（AUD）中恢复是困难的，43-83%的治疗后复发。 放电这些长期高比率的出现，部分是因为我们对共同进化的知识存在差距 目前与从AUD中恢复的患者复发相关的风险机制，包括渴望， 消极影响，压力和酒精（CASA）。这四个因素在重要的 缓冲复发风险的保护性因素（例如，改善清醒支持和相关的心理社会功能 治疗后社区AUD恢复计划），以及基于背景的风险因素，如附近 有酒的地方。该项目的近期目标是制定评估和 了解这些环境/背景因素对康复酗酒者的影响，从长远来看， 研究目标是制定有效的及时干预措施，减轻复发的风险。在这个项目中， 我们将通过响应性生态瞬时评估（reEMA）利用新的评估方法 我们团队开发的系统。这个基于应用程序的系统允许研究人员将地理空间，时间- 加强监测（和及时提供干预措施）的具体和针对具体事件的标准 在治疗后的环境中恢复患者。与NIAAA的PA-18-620一致，该项目旨在建立 基础证据和完善数据收集/分析策略，以实现我们的长期目标， 通过以下具体目标：目标1）试验使用我们的rEMA平台捕获细粒度纵向数据 在30天内，对60名正在康复的酗酒者进行了渴望、情感、压力源和酒精获取的研究，以评估 CASA在rEMA数据中构建有效性，并评估数据缺失的程度和影响;目的2）测试 激励模式，将告知我们最佳做法，最大限度地遵守rEMA协议; 目标3）开发基于rEMA数据的机器学习模型的分析策略，以预测即将发生的风险， 量化CASA变量之间的因果路径。为了实现这些目标，我们将招募和招收成年人 （n=60; 50%女性）在中原地区接受非住院AUD治疗。使用智能手机 评估与GPS监测技术，我们将收集纵向行为和经验数据， 每个参与者超过30天。对这些数据的分析将促进我们对共同进化的理解 影响恢复工作成败的背景/社会环境因素，同时确定 基于时间/事件特定智能手机的即时干预措施可能 开发该项目的最后阶段包括设立一个咨询和传播委员会， 地方和国家一级的利益相关者，AUD治疗从业人员和其他研究人员，将指导 发展这样的干预。这一组包括两个非住院的住院部的主任。 在项目的所有阶段充当项目社区合作伙伴的物质处理设施。