Subpopulation Differences in Intervention Efficacy for College Drinkers

描述(申请人提供)：大学生饮酒者干预效果的亚群差异。尽管采取了预防措施，但大学酗酒仍然造成了无数与酒精有关的个人和社会负面后果。短暂的动机干预(BMI)已被证明在减少大学饮酒方面有效；然而，平均而言，报告的干预效果往往很小且持续时间很短。人们通常更感兴趣的是了解谁对干预做出了反应，而不是确定不同亚群的平均影响。这项研究的目的不是表征“典型”大学饮酒者的干预效果，而是表征不同亚群或类型的饮酒者的干预效果。适度性分析检验了可能影响干预效果的假设因素。与传统的适度分析不同，增长混合建模(GMM)通过经验探索数据来识别酒精消费中的同质纵向模式，而不需要预先识别的预测因子。使用转基因技术，过去的研究已经确定了多达五种类型的饮酒者关于自然发生的酒精消费随着时间的推移的轨迹；然而，拟议的项目采取了一种新的方法，通过探索干预后的饮酒模式。拟议的项目包括对3个随机临床试验的二级数据分析，总共1384名大学生饮酒者(509名志愿者，875名因酒精违规而强制饮酒)，他们的饮酒行为在基线、干预后1、6和12个月进行评估。参与者被随机分为BMI组(n=602)、2个计算机化干预措施中的1个(酒精101，n=271；酒精EDU，n=167)或非干预控制条件(n=344)。这四个具体目标是：a)确定大学饮酒者对酒精BMI的短期和长期反应有多少不同的亚群；b)确定相对于计算机干预和每种类型饮酒者的对照的BMI有效性；c)使用人口统计学和心理学解释变量来描述每个饮酒者亚群的特征；以及d)确定饮酒者亚群与高风险饮酒后果的关系。不连续的GMM将被用来确定不同类型的饮酒者在从基线到1个月(短期效果)和从1个月到12个月(长期效果)的干预后的独特反应，以及在被制裁的个人和志愿者中识别饮酒者亚群的相关心理预测因素。这项研究的结果可以用来了解谁最容易接受体重指数干预，指导筛查工作以可靠地检测高危个体，并利用关于最具抵抗力的亚群(S)的信息来指导未来的干预发展。 公共卫生相关性：大学饮酒者干预效果的亚群差异拟议的研究将使用创新和先进的统计模型来确定对旨在减少大学饮酒者饮酒的干预措施有反应和无反应的个人的亚群特征。这项研究的结果可以用来了解谁最容易接受短暂的动机干预，指导筛查努力可靠地发现高危个体，并利用关于最具抵抗力的亚群(S)的信息来指导未来的干预发展。改进的筛查和干预方法对于减少大学生中与酒精有关的个人和社会后果至关重要。

DESCRIPTION (provided by applicant): Subpopulation Differences in Intervention Efficacy for College Drinkers. Despite prevention efforts, college binge drinking continues to account for a myriad of negative personal and social alcohol-related consequences. Brief motivational interventions (BMIs) have proved efficacious in reducing college alcohol consumption; however, averaging across people, reported intervention effects tend to be small and short-lived. It is often of more interest to understand who responds to an intervention rather than determine the average effect across different subpopulations. Instead of characterizing intervention efficacy for a "typical" college drinker, the purpose of this research is to characterize intervention effects across different subpopulations, or types, of drinkers. Moderation analyses test hypothesized factors that may influence intervention efficacy. In contrast to traditional moderation analyses, growth mixture modeling (GMM) empirically explores the data to identify homogenous longitudinal patterns in alcohol consumption without need of pre-identified predictors. Using GMMs, past research has identified up to five types of drinkers regarding naturally-occurring trajectories of alcohol consumption over time; however, the proposed project takes a novel approach by exploring drinking patterns following intervention. The proposed project includes secondary data analysis of 3 randomized clinical trials with a combined total of 1,384 college-student drinkers (509 volunteers, 875 mandated as a result of alcohol violations), whose drinking behaviors were assessed at baseline, and at 1, 6, and 12 months post-intervention. Participants were randomized into a BMI (n = 602), 1 of 2 computerized interventions (Alcohol 101, n = 271; Alcohol EDU, n = 167), or a no-intervention control condition (n = 344). The four specific aims are a) to identify how many different subpopulations of college drinkers exist regarding short- and long-term response to an alcohol BMI; b) to characterize BMI efficacy relative to computerized intervention and a control for each type of drinker; c) to use demographic and psychological explanatory variables to characterize each drinker subpopulation; and d) ascertain how drinker subpopulations relate to high-risk alcohol consequences. Discontinuous GMMs will be used to ascertain how different types of drinkers uniquely respond following intervention from baseline to 1-month (short-term effect) and from 1-month to 12- months (long-term effect), as well as to identify relevant psychological predictors of drinker subpopulations among both sanctioned individuals and volunteers. Results of this research can be used to understand who is most receptive to BMI intervention, to guide screening efforts to reliably detect high-risk individuals, and to use the information about the most resistant subpopulation(s) to inform future intervention development. PUBLIC HEALTH RELEVANCE: Subpopulation Differences in Intervention Efficacy for College Drinkers The proposed research will use innovative and advanced statistical modeling to identify subpopulation characteristics of individuals who respond and do not respond to interventions designed to reduce alcohol consumption among college drinkers. The results of this research can be used to understand who is most receptive to brief motivational intervention, to guide screening efforts to reliably detect high-risk individuals, and to use the information about the most resistant subpopulation(s) to inform future intervention development. Improved screening and intervention methods are critical to reducing personal and social alcohol-related consequences among college populations.