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Comparative Effectiveness of Alcohol Treatments

酒精治疗的比较效果

基本信息

批准号：
8307194
负责人：
GEORGIY BOBASHEV
金额：
$ 4.97万
依托单位：
RESEARCH TRIANGLE INSTITUTE
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-09-30 至 2013-09-29
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8307194
关键词：
Accounting Address Alcohol abuse Alcohol consumption Alcohol dependence Alcoholic beverage heavy drinker Area Automobile Driving Awareness Behavioral Biological Characteristics Chronic Disease Clinical Clinical Research Clinical Trials Clinical Trials Design Complex Consultations Data Data Set Decision Making Dependence Development Disease Etiology Exclusion Criteria Future Genetic Health Personnel Heterogeneity Individual Intervention Lead Literature Methodological Studies Methodology Methods Modeling Multivariate Analysis Naltrexone Nature North Carolina Observational Study Outcome Patients Pattern Pharmacological Treatment Physicians Population Probability Psychiatrist Randomized Clinical Trials Records Recurrence Relapse Research Research Personnel Review Literature Sample Size Series Simulate Stress Subgroup Suggestion Survival Analysis Testing Time Treatment Effectiveness Treatment outcome Uncertainty Universities Update Veterans abstracting acamprosate addiction alcohol abuse therapy alternative treatment base binge drinker clinical practice comparative effectiveness data mining disorder later incidence prevention drinking empowered evidence base experience improved innovation markov model motivational enhancement therapy multidisciplinary predictive modeling prevent response sobriety social sound substance abuse treatment success tool treatment program treatment response trend

项目摘要

Abstract The objective of this methodological study is to develop and systematically test predictive models of an individual's alcohol consumption. The ability to forecast alcohol consumption in patients can potentially help individualized treatment programs. For binge drinkers, predicting the timing of their next episode provides additional awareness that could help to control, or even to prevent, the entire episode. For heavy drinkers, predictive models can forecast the windows of treatment opportunity when the patient is most sober and responsive to intervention. For those who are attempting to quit drinking, forecasting the next relapse episode could be used to trigger motivational interviewing or other timely interventions that will help to prevent the relapse. In this project, we will analyze two datasets containing long time series' (i.e., 2 years and 6 months) of individual daily records of alcohol use, stress, and other factors. We will adapt innovative predictive models developed to forecast future alcohol consumption, as well as identify and explain a variety of daily use patterns. The data on individual alcohol consumption is very limited, and this is the first study aimed at building forecasting models using such data. This study offers the field a "next step," with an innovative analysis approach that can possibly offer clinical implications for relapse prevention, increased treatment efficiency, and enhanced understanding of the factors driving the variety of daily patterns of use.

摘要本方法学研究的目的是开发和系统地测试一个预测模型，个人的酒精消费。预测患者饮酒量的能力可能会有所帮助个性化的治疗方案。对于酗酒者来说，预测他们下一次发作的时间提供了额外的意识，可以帮助控制，甚至防止，整个事件。对于酗酒者来说，预测模型可以预测患者最清醒时的治疗机会窗口，响应干预。对于那些试图戒酒的人来说，预测下一次复发的情况可以用来触发动机访谈或其他及时干预，这将有助于防止复发在这个项目中，我们将分析两个包含长时间序列的数据集（即，2岁零6个月）个人每日饮酒记录，压力和其他因素。我们将采用创新的预测模型它被开发来预测未来的酒精消费量，以及识别和解释各种日常使用，模式.关于个人饮酒量的数据非常有限，这是第一项旨在建立使用这些数据的预测模型。这项研究为该领域提供了“下一步”，并进行了创新分析这种方法可能为预防复发、提高治疗效率和增强了对各种日常使用模式驱动因素的理解。