An Application of the Complier Average Causal Effect Analysis to Examine the Effects of a Family Intervention in Reducing Illicit Drug Use among High‐Risk Hispanic Adolescents

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/107494/1/famp12068.pd

Two-Year Impact of Prevention Programs on Adolescent Depression: an Integrative Data Analysis Approach

This paper presents the first findings of an integrative data analysis of individual-level data from 19 adolescent depression prevention trials (n = 5210) involving nine distinct interventions across 2 years post-randomization. In separate papers, several interventions have been found to decrease the risk of depressive disorders or elevated depressive/internalizing symptoms among youth. One type of intervention specifically targets youth without a depressive disorder who are at risk due to elevated depressive symptoms and/or having a parent with a depressive disorder. A second type of intervention targets two broad domains: prevention of problem behaviors, which we define as drug use/abuse, sexual risk behaviors, conduct disorder, or other externalizing problems, and general mental health. Most of these latter interventions improve parenting or family factors. We examined the shared and unique effects of these interventions by level of baseline youth depressive symptoms, sociodemographic characteristics of the youth (age, sex, parent education, and family income), type of intervention, and mode of intervention delivery to the youth, parent(s), or both. We harmonized eight different measures of depression utilized across these trials and used growth models to evaluate intervention impact over 2 years. We found a significant overall effect of these interventions on reducing depressive symptoms over 2 years and a stronger impact among those interventions that targeted depression specifically rather than problem behaviors or general mental health, especially when baseline symptoms were high. Implications for improving population-level impact are discussed

Is Fun For Wellness Engaging? Evaluation of User Experience of an Online Intervention to Promote Well-Being and Physical Activity

Online well-being interventions demonstrate great promise in terms of both engagement and outcomes. Fun For Wellness (FFW) is a novel online intervention grounded in self-efficacy theory and intended to improve multidimensional well-being and physical activity through multi-modal methods. These strategies include capability-enhancing opportunities, learning experiences such as games, video vignettes, and self-assessments. RCT studies have suggested that FFW is efficacious in improving subjective and domain-specific well-being, and effective in improving mental health, physical health, physical activity, and self-efficacy in United States. adults who are overweight and in the general population. The present study uses qualitative and quantitative user experience data collected during two RCT trials to understand and evaluate engagement with FFW, its drivers, and its outcomes. Results suggest that FFW is enjoyable, moderately engaging, and easy to use; and contributes to positive outcomes including skill development and enhanced confidence, for both overweight individuals and the general adult population. Drivers of engagement appear to include rewards, gamification, scenario-based learning, visual tracking for self-monitoring, ease of use and simple communications, and the entertaining, interactive nature of program activities. Findings indicate that there are opportunities to streamline and simplify the experience. These results can help improve FFW and contribute to the science of engagement with online interventions designed to improve well-being

The Implications of Centering in a Three-Level Multilevel Model

Hierarchical data are becoming increasingly complex, often involving more than two levels. This study investigated the implications of centering within context (CWC) and grand mean centering (CGM) in three distinct three-level models. The goals were to (1) determine equivalencies in the means and variances across the centering options, (2) identify the algebraic relationships between the three-level contextual models, and (3) clarify the interpretation of the estimated parameters. Artificial datasets were used for illustration. Centering decisions in multilevel models are closely tied to substantive hypotheses and require researchers to be clear and cautious about their choices. This work is designed to assist the researcher in making centering decisions for analysis of three-level hierarchical data.</p

Preventing alcohol use among Hispanic adolescents through a family-based intervention: The role of parent alcohol misuse

Early-life alcohol use raises the risk of poor long-term alcohol and other health outcomes. U.S. Hispanics are less likely to access treatment if they develop alcohol abuse or dependence, making preventive interventions critical. is a family-based intervention effective in preventing drug and sexual risk behavior among Hispanic youth. The effects of this intervention specifically on youth alcohol use have been less consistent and may be affected by parental factors. The intervention is primarily delivered to parents to ultimately reduce youth risk behaviors, applying research on protective parenting and family influences, such as parental monitoring and positive communication. This study conducted secondary data analysis of an effectiveness randomized controlled trial of the intervention, examining parent moderators of intervention effects on adolescent alcohol use. A total of 746 Hispanic families with 12-16-year-old adolescents were randomized to intervention or control. Logistic regression analyses confirmed no evidence of intervention effectiveness in reducing 90-day adolescent alcohol use at 30-month follow-up. However, there was evidence that parent misuse moderated intervention effects on adolescent alcohol use. Among youth whose parents reported any episode of alcohol misuse in their lifetime, the intervention was associated with lower odds of youth alcohol use at 30 months compared to youth in the control condition. Potential reasons and intervention implications are reviewed, including how parent alcohol use experiences might raise awareness of youth risks and motivate involvement or protective behaviors. Understanding intervention moderators can help shape, target, and adapt interventions to enhance their effectiveness and reach. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Multilevel models to identify contextual effects on individual group member outcomes: a family example

This manuscript illustrates methods for utilizing measurements of individuals to identify group contextual effects on individual outcomes. Contextual effects can be identified by 1 of 3 methods: (1) divergence of the simple within- and between-group regression coefficients, (2) the presence of a cross-level interaction of the within- and between-group predictor variable, or (3) the effect of discrepancies within the group. These methods can be used to incorporate group context into an individual model and can be utilized for any individual process variable that might be affected by a group context. Example data include measures of hassles and coping adequacy of inner city, poor, African American new mothers, and their family members

A Tutorial on Centering in Cross-Sectional Two-Level Models

The primary purpose of this tutorial is to succinctly review some options for, and consequences of, centering Level 1 predictors in commonly applied cross-sectional two-level models. It is geared toward both practitioners and researchers. A general understanding of multilevel modeling is necessary prior to understanding the subtleties of centering decisions. A review of some high-quality journals within the broad discipline of exercise science provides evidence that multilevel modeling is used relatively infrequently in this field. Therefore, a secondary purpose is to introduce Measurement in Physical Education and Exercise Science readers to some core facets of multilevel modeling within the framework of this tutorial. A relevant dataset is used to demonstrate potential consequences of different centering decisions within a multilevel model. Depending on the model and the data, different centering decisions can exert non-trivial influence on the meaning of some model parameters, results from fitting the model, and subsequent conclusions

Modeling Site Effects in the Design and Analysis of Multi-site Trials

Background: Careful consideration of site effects is important in the analysis of multi-site clinical trials for drug abuse treatment. The statistical choices for modeling these effects have implications for both trial planning and interpretation of findings. Objectives: Three broad approaches for modeling site effects are presented: omitting site from the analysis; modeling site as a fixed effect; and modeling site as a random effect. Both the direct effect of site and the interaction of site and treatment are considered. Methods: The statistical model, and consequences, for each approach are presented along with examples from existing clinical trials. Power analysis calculations provide sample size requirements for adequate statistical power for studies utilizing 6, 8, 10, 12, 14, and 16 treatment sites. Results: Results of the power analyses showed that the total sample required falls rapidly as the number of sites increases in the random effect approach. In the fixed effect approach in which the interaction of site and treatment is considered, the required number of participants per site decreases as the number of sites increases. Conclusions: Ignoring site effects is not a viable option in multi-site clinical trials. There are advantages and disadvantages to the fixed effect and random effect approaches to modeling site effects. Scientific Significance: The distinction between efficacy trials and effectiveness trials is rarely sharp. The choice between random effect and fixed effect statistical modeling can provide different benefits depending on the goals of the study

Screen-based sedentary behaviors and internalizing symptoms across time among U.S. Hispanic adolescents

The pervasive use of technology has raised concerns about its association with adolescent mental health, including internalizing symptoms. Existing studies have not always had consistent findings. Longitudinal research with diverse subgroups is needed. This study examines the relationship between screen-based sedentary (SBS) behaviors and internalizing symptoms among 370 Hispanic adolescents living in Miami, Florida- United States, who were followed for 2 ½ years and assessed at baseline, 6, 18 and 30 months post-baseline between the years 2010 and 2014. Approximately 48% were girls, and 44% were foreign-born, most of these youth being from Cuba. Mean age at baseline was 13.4 years, while at the last time-point it was 15.9 years. Findings show that girls had higher internalizing symptoms and different patterns of screen use compared to boys, including higher phone, email, and text use. SBS behaviors and internalizing symptoms cooccurred at each time-point, and their trajectories were significantly related (r = 0.45, p < .001). Cross-lagged panel analyses found that SBS behaviors were not associated with subsequent internalizing symptoms. Among girls, however, internalizing symptoms were associated with subsequent SBS behaviors during later adolescence, with internalizing symptoms at the 18-month assessment (almost 15 years old) associated with subsequent SBS behaviors at the 30-month assessment (almost 16 years old; β = 0.20, p < .01). Continued research and monitoring of internalizing symptoms and screen use among adolescents is important, especially among girls. This includes assessments that capture quantity, context, and content of screen time