Use of substances (i.e., tobacco, alcohol, marijuana, and "hard drugs" such as cocaine, inhalants, etc.) has serious implications for adolescent health and well-being. Research has consistently linked family-based factors such as parental monitoring and parent-youth relationships with the initiation and escalation of substance use in adolescence, and numerous family-based prevention programs have been developed to reduce adolescent substance use by addressing the underlying issues within the family system. Although these programs are widely seen as successful, there are several underlying weaknesses in the literature. First, the research that summarizes the findings from the field has often been based upon conventional narrative reviews of the literature, which are essentially subjective and thus cannot provide an empirical, objective, quantifiable evaluation of program effectiveness while controlling for factors that can influence outcomes, such as sample characteristics, study design, rate of engagement, and measurement tools. Second, there is a lack of research that investigates exactly why family-based prevention programs have been successful, and numerous authors in the field have called for more research on the individual components of these programs and their additive or synergistic effects. Third, recent research has questioned whether family-based factors exert similar effects across the entirety of adolescence (e.g., parental monitoring may be a more effective deterrent of substance use in early rather than late adolescence). Thus, the first aim of this R03 project is to use meta-analyti techniques to evaluate family-based prevention programs for adolescent substance use to determine which program components are most strongly linked to success in reducing substance use and/or improving parenting. Specifically, we will use program design, curricula, and delivery methods to predict effect sizes on measures of parenting and adolescent behavior.
The second aim i s to evaluate whether components of family-based prevention programs for adolescent substance use exhibit additive or synergistic effects. Specifically, we will examine whether individual components predict unique variance in outcomes (i.e., additive effects) or whether they exhibit significant interactions with one another (i.e., synergistic effects). The thid aim is to evaluate the degree to which various sample (i.e., youth age and gender) and study factors (e.g., research design, attrition) moderate the link between program components and outcomes (e.g., training in parental monitoring may be associated with reduced substance use, but more strongly in early adolescence). This R03 project will provide many benefits. First, meta-analysis allows us to combine findings across studies, generate greater statistical power, and obtain results less affected by biases inherent in any given study. Second, our project will provide a stronger understanding of how these programs work and will inform future prevention efforts targeting adolescent substance use. Finally, our project will identify the most favorable balance of components for given populations and circumstances and thus enable designers to optimize program design.
Use of substances (i.e., tobacco, alcohol, marijuana, and hard drugs such as cocaine, inhalants, etc.) has serious implications for adolescent health and well-being, and numerous family-based prevention programs have been developed to reduce adolescent substance use by addressing the underlying issues within the family. We will use meta-analytic techniques to evaluate family-based programs for adolescent substance use to determine which program components are most strongly linked to success in reducing substance use and/or improving parenting. We will also evaluate whether components of family-based programs exhibit additive or synergistic effects, and whether various sample and study factors moderate the link between components and study outcomes. Meta-analysis will provide results less affected by researcher and study biases, and our results will enable researchers to optimize program design for specific populations and circumstances.