Background For over two decades, brief motivational interventions (BMIs) have been implemented on college campuses to reduce heavy drinking and related negative consequences. Such interventions include in-person motivational interviews (MIs), often incorporating personalized feedback (PF), and stand-alone PF interventions delivered via mail, computer, or the Web. Both narrative and meta-analytic reviews using aggregate data from published studies suggest at least short-term efficacy of BMIs, although overall effect sizes have been small. Method The present study was an individual participant-level data (IPD) meta-analysis of 17 randomized clinical trials evaluating BMIs. Unlike typical meta-analysis based on summary data, IPD meta-analysis allows for an analysis that correctly accommodates the sampling, sample characteristics, and distributions of the pooled data. In particular, highly skewed distributions with many zeroes are typical for drinking outcomes, but have not been adequately accounted for in existing studies. Data are from Project INTEGRATE, one of the largest IPD meta-analysis projects to date in alcohol intervention research, representing 6,713 individuals each with two to five repeated measures up to 12 months post-baseline. Results We used Bayesian multilevel over-dispersed Poisson hurdle models to estimate intervention effects on drinks per week and peak drinking, and Gaussian models for alcohol problems. Estimates of overall intervention effects were very small and not statistically significant for any of the outcomes. We further conducted post hoc comparisons of three intervention types (Individual MI with PF, PF only, and Group MI) vs. control. There was a small, statistically significant reduction in alcohol problems among participants who received an individual MI with PF. Short-term and long-term results were similar. Conclusions The present study questions the efficacy and magnitude of effects of BMIs for college drinking prevention and intervention and suggests a need for the development of more effective intervention strategies.

This paper provides an overview of a study that synthesizes multiple, independently collected alcohol intervention studies for college students into a single, multisite longitudinal data set. This research embraced innovative analytic strategies (i.e., integrative data analysis or meta-analysis using individual participant-level data), with the overall goal of answering research questions that are difficult to address in individual studies such as moderation analysis, while providing a built-in replication for the reported efficacy of brief motivational interventions for college students. Data were pooled across 24 intervention studies, of which 21 included a comparison or control condition and all included one or more treatment conditions. This yielded a sample of 12,630 participants (42% men; 58% first-year or incoming students). The majority of the sample identified as White (74%), with 12% Asian, 7% Hispanic, 2% Black, and 5% other/mixed ethnic groups. Participants were assessed two or more times from baseline up to 12 months, with varying assessment schedules across studies. This paper describes how we combined individual participant-level data from multiple studies, and discusses the steps taken to develop commensurate measures across studies via harmonization and newly developed Markov chain Monte Carlo algorithms for two-parameter logistic item response theory models and a generalized partial credit model. This innovative approach has intriguing promises, but significant barriers exist. To lower the barriers, there is a need to increase overlap in measures and timing of follow-up assessments across studies, better define treatment and control groups, and improve transparency and documentation in future single, intervention studies.

Brief motivational interventions (BMIs) that aim to reduce alcohol use and related problems have been widely implemented in college settings. BMIs share common principles, but vary in specific content. Thus far, the variation in content has not been thoroughly understood in relation to intervention outcomes. The present study addressed this gap by examining variation in breadth of BMI content (i.e., total number of components covered), the extent to which content was personalized to participants, and the interaction between breadth and personalization in relation to treatment outcomes. Data (N = 6,047 participants across 31 separate BMI conditions) came from an integrative data analysis (IDA) study featuring individual-level data from a broad sample of 24 BMI studies. Participants were assessed at baseline and at least one follow-up point, conducted up to 12 months post baseline. Structural equation modeling revealed a significant interaction effect between breadth and personalization of BMI content on alcohol use and related problems at the long-term follow-up (6-12 months) but not at the short-term follow-up (1-3 months). Results indicated that “more is better” for reducing both alcohol use and related problems when BMIs were highly personalized to participants. For less personalized BMIs, coverage of more components was associated with increases in both alcohol use and problems. Findings point to the importance of strategically designing BMIs to maximize their impact on drinking outcomes in college students.

ABSTRACT. Objective:The gender gap in alcohol use has been narrowing among young adults, while race differences in alcohol problems change throughout the life course, with Whites experiencing more problems before middle adulthood and Blacks experiencing more after. Yet, there is a paucity of research on the intricate relationship among gender, race, alcohol use, and alcohol problems in emerging adults. The present study addressed this gap in the literature. Method: The sample included White (n = 14,772) and Black (n = 458) college students from multiple colleges across the United States (59% female; 51% freshmen; M age = 20 years). Results: With alcohol use levels adjusted for, women were more likely to report consequences related to damage to self and dependencelike symptoms than men. There were no signifi cant race differences in either the type or the number of alcohol problems. Further, there was no Race × Alcohol Use interaction in relation to alcohol problems. We found a statistically signifi cant interaction between gender and alcohol use in predicting alcohol problems, suggesting that, at higher levels of drinking, the risk for women to experience alcohol problems was signifi cantly greater than that for men. Conclusions: The reverse race gap in alcohol use and problems may not surface until young adulthood or may not be relevant for those who attend college. College interventions should help both Black and White students reduce problems associated with drinking and focus on limiting harm among female students. (J.

The present paper proposes a hierarchical, multi-unidimensional two-parameter logistic item response theory (2PL-MUIRT) model extended for a large number of groups. The proposed model was motivated by a large-scale integrative data analysis (IDA) study which combined data (N = 24,336) from 24 independent alcohol intervention studies. IDA projects face unique challenges that are different from those encountered in individual studies, such as the need to establish a common scoring metric across studies and to handle missingness in the pooled data. To address these challenges, we developed a Markov chain Monte Carlo (MCMC) algorithm for a hierarchical 2PL-MUIRT model for multiple groups in which not only were the item parameters and latent traits estimated, but the means and covariance structures for multiple dimensions were also estimated across different groups. Compared to a few existing MCMC algorithms for multidimensional IRT models that constrain the item parameters to facilitate estimation of the covariance matrix, we adapted an MCMC algorithm so that we could directly estimate the correlation matrix for the anchor group without any constraints on the item parameters. The feasibility of the MCMC algorithm and the validity of the basic calibration procedure were examined using a simulation study. Results showed that model parameters could be adequately recovered, and estimated latent trait scores closely approximated true latent trait scores. The algorithm was then applied to analyze real data (69 items across 20 studies for 22,608 participants). The posterior predictive model check showed that the model fit all items well, and the correlations between the MCMC scores and original scores were overall quite high. An additional simulation study demonstrated robustness of the MCMC procedures in the context of the high proportion of missingness in data. The Bayesian hierarchical IRT model using the MCMC algorithms developed in the current study has the potential to be widely implemented for IDA studies or multi-site studies, and can be further refined to meet more complicated needs in applied research.