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Background: Brief motivational interventions (BMIs) are one of the most effective individually focused alcohol intervention strategies for college students. Despite the central theoretical role of motivation for change in BMIs, it is unclear whether BMIs increase motivation to change drinking behavior. We conducted a two‐step meta‐analysis of individual participant data (IPD) to examine whether BMIs increase motivation for change. N = 5903;59% women, 72% White) from Project INTEGRATE. The BMIs included individually delivered motivational interviewing with personalized feedback (MI + PF), stand‐alone personalized feedback (PF), and group‐based motivational interviewing (GMI). Methods: We included 15 trials of BMI (N = 5903;59% women, 72% White) from Project INTEGRATE. The BMIs included individually‐delivered motivational interviewing with personalized feedback (MI + PF), stand‐alone personalized feedback (PF), and group‐based motivational interviewing (GMI). Different measures and responses used in the original trials were harmonized. Effect size estimates were derived from a model that adjusted for baseline motivation and demographic variables for each trial (step 1) and subsequently combined in a random‐effects meta‐analysis (step 2). Results: The overall intervention effect of BMIs on motivation for change was not statistically significant (standard mean difference [SMD]: 0.026, 95% CI: [−0.001, 0.053], p = 0.06, k = 19 comparisons). Of the three subtypes of BMIs, GMI, which tended to provide motivation‐targeted content, had a statistically significant intervention effect on motivation, compared with controls (SMD: 0.055, 95% CI: [0.007, 0.103], p = 0.025, k = 5). By contrast, there was no evidence that MI + PF (SMD = 0.04, 95% CI: [−0.02, 0.10], k = 6, p = 0.20) nor PF increased motivation (SMD = 0.005, 95% CI: [−0.028, 0.039], k = 8, p = 0.75), compared with controls. Post hoc meta‐regression analysis suggested that motivation sharply decreased each month within the first 3 months postintervention (b = −0.050, z = −2.80, p = 0.005 for k = 14). Conclusions: Although BMIs provide motivational content and normative feedback and are assumed to motivate behavior change, the results do not wholly support the hypothesis that BMIs improve motivation for change. Changing motivation is difficult to assess during and following interventions, but it is still a theoretically important clinical endpoint. Further, the evidence cautiously suggests that changing motivation may be achievable, especially if motivation‐targeted content components are provided. [ABSTRACT FROM AUTHOR]

Background. Beginning in 2010, the US Department of Health and Human Services (HHS) funded more than 40 evaluations of adolescent pregnancy prevention interventions. The government's emphasis on rigor and transparency, along with a requirement that grantees collect standardized behavioral outcomes, ensured that findings could be meaningfully compared across evaluations. Objectives. We used random and mixed-effects meta-analysis to analyze the findings generated by these evaluations to learn whether program elements, program implementation features, and participant demographics were associated with effects on adolescent sexual risk behavior. Search Methods. We screened all 43 independent evaluation reports, some of which included multiple studies, funded by HHS and completed before October 1, 2016. HHS released, and our team considered, all such studies regardless of favorability or statistical significance. Selection Criteria. Of these studies, we included those that used a randomized or high-quality quasi-experimental research design. We excluded studies that did not use statistical matching or provide pretest equivalence data on a measure of sexual behavior or a close proxy. We also excluded studies that compared 2 pregnancy prevention interventions without a control group. A total of 44 studies from 39 reports, comprising 51 150 youths, met the inclusion criteria. Data Collection and Analysis. Two researchers extracted data from each study by using standard systematic reviewing and meta-analysis procedures. In addition, study authors provided individual participant data for a subset of 34 studies. We used mixed-effects meta-regressions with aggregate data to examine whether program or participant characteristics were associated with program effects on adolescent sexual risk behaviors and consequences. To examine whether individual-level participant characteristics such as age, gender, and race/ethnicity were associated with program effects, we used a 1-stage meta-regression approach combining participant-level data (48 635 youths) with aggregate data from the 10 studies for which participant-level data were not available. Main Results. Across all 44 studies, we found small but statistically insignificant mean effects favoring the programs and little variability around those means. Only 2 program characteristics showed statistically reliable relationships with program effects. First, gender-specific (girl-only) programs yielded a statistically significant average effect size (P <.05). Second, programs with individualized service delivery were more effective than programs delivering services to youths in small groups (P <.05). We found no other statistically significant associations between program effects and program or participant characteristics, or evaluation methods. Nor was there a statistically significant difference in the mean effect sizes for programs with previous evidence of effectiveness and previously untested programs. Conclusions. Although several individual studies reported positive impacts, the average effects were small and there was minimal variation in effect sizes across studies on all of the outcomes assessed. Thus, we were unable to confidently identify which individual program characteristics were associated with effects. However, these studies examined relatively short-term effects and it is an open question whether some programs, perhaps with distinctive characteristics, will show longer-term effects as more of the adolescent participants become sexually active. Public Health Implications. The success of a small number of individualized interventions designed specifically for girls in changing behavioral outcomes suggests the need to reexamine the assumptions that underlie coed group approaches. However, given the almost total absence of similar programs targeting male adolescents, it is likely to be some time before evidence to support or reject such an approach for boys is available. [ABSTRACT FROM AUTHOR]

Dependent effect sizes are ubiquitous in meta-analysis. Using Monte Carlo simulation, we compared the performance of 2 methods for meta-regression with dependent effect sizes-robust variance estimation (RVE) and 3-level modeling-with the standard meta-analytic method for independent effect sizes. We further compared bias-reduced linearization and jackknife estimators as small-sample adjustments for RVE and Wald-type and likelihood ratio tests for 3-level models. The bias in the slope estimates, width of the confidence intervals around those estimates, and empirical type I error and statistical power rates of the hypothesis tests from these different methods were compared for mixed-effects meta-regression analysis with one moderator either at the study or at the effect size level. All methods yielded nearly unbiased slope estimates under most scenarios, but as expected, the standard method ignoring dependency provided inflated type I error rates when testing the significance of the moderators. Robust variance estimation methods yielded not only the best results in terms of type I error rate but also the widest confidence intervals and the lowest power rates, especially when using the jackknife adjustments. Three-level models showed a promising performance with a moderate to large number of studies, especially with the likelihood ratio test, and yielded narrower confidence intervals around the slope and higher power rates than those obtained with the RVE approach. All methods performed better when the moderator was at the effect size level, the number of studies was moderate to large, and the between-studies variance was small. Our results can help meta-analysts deal with dependency in their data. [ABSTRACT FROM AUTHOR]

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