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Innovative Approaches to Modeling Alcohol Relapse

酒精复吸建模的创新方法

基本信息

批准号：
6744174
负责人：
Gordon ALAN Marlatt
金额：
$ 10.98万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2003
资助国家：
美国
起止时间：
2003-05-01 至 2006-04-30
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): The proposed research will use existing data from the Relapse Replication and Extension Project (RREP) to increase our understanding of the relapse process in individuals who have received treatment for an alcohol use disorder. This dataset includes pre- and post-treatment assessment of drinking behavior, background characteristics, and relapse precipitants in a group of 563 research participants, who were recruited from 15 treatment sites that offered a variety of alcoholism treatment approaches. Using a multi-deterministic framework, the relationship between background variables, risk and protective factors, and situational determinants will be examined with respect to post-treatment drinking outcomes. It is well-documented that a lack of effective coping responses increases relapse risk; and self-efficacy, outcome expectancies, and the abstinence violation effect, are cognitive factors that have been identified as important mediators in the return to heavy drinking following treatment. However, the relationship between these variables in the relapse process has not been clearly established. Focusing on the process of relapse, rather than evaluating relapse as a dichotomous event, the proposed research will examine trajectories of drinking behavior during the first year after treatment for an alcohol use disorder. Specifically, the first goal of the research is to further delineate the relationship between coping behavior, background characteristics, and cognitive factors in the prediction of these trajectories using advanced statistical models, including both catastrophe models and general growth mixture models. The second goal of the proposed research is to test the utility and appropriateness of these advanced modeling techniques in the prediction of post-treatment drinking outcomes. Gaining a better understanding of the relapse process has been a primary goal of addictive behaviors research for the past 30 years. The abundance of treatment outcome data for addictive behaviors makes the proposed research an important and cost-effective contribution to the advancement of our understanding of the relapse process.

描述(由申请人提供)：拟议的研究将使用复发复制和扩展项目(RREP)的现有数据，以增加我们对接受酒精使用障碍治疗的个人的复发过程的了解。这一数据集包括对563名研究参与者治疗前和治疗后的饮酒行为、背景特征和复发沉淀物的评估，这些研究参与者来自15个提供各种酒精中毒治疗方法的治疗地点。使用多重决定论框架，将研究治疗后饮酒结果的背景变量、风险和保护因素以及情景决定因素之间的关系。众所周知，缺乏有效的应对措施会增加复发风险；自我效能、结果预期和违反禁欲的效果是认知因素，已被确定为治疗后恢复大量饮酒的重要中介因素。然而，复发过程中这些变量之间的关系还没有明确确立。将重点放在复发的过程上，而不是将复发评估为二分事件，拟议的研究将检查酒精使用障碍治疗后第一年的饮酒行为。具体地说，研究的第一个目标是进一步描绘应对行为与应对行为之间的关系，使用这些轨迹预测的背景特征和认知因素高级统计模型，包括巨灾模型和一般增长混合模型模特们。拟议研究的第二个目标是测试以下各项的实用性和适当性这些先进的建模技术在预测治疗后饮酒结果方面的应用。更好地了解复发过程一直是成瘾的主要目标过去30年的行为研究。大量成瘾行为的治疗结果数据使这项拟议的研究成为促进我们对复发过程的理解的重要且具有成本效益的贡献。