Methodologies for optimizing behavioral interventions: introduction to special section

An extensive literature has established the effectiveness of various behavioral interventions for a range of conditions [e.g., 1–3], but this literature often fails to isolate the intervention components that are more or less effective. Therefore, despite numerous controlled trials of various interventions for a given problem, the field has little guidance on how to improve upon previously studied interventions, adapt them to specific populations, contexts, or delivery mechanisms, or streamline them to facilitate use in real-world settings with constrained resources. Behavioral intervention research cannot become a cumulative science that builds upon prior research until intervention studies can answer not only if the intervention changed behavior, but also how it changed behavior, and which intervention components were most effective in changing behavior. To illustrate the current state of behavioral intervention research, Fig. 1 shows a common meta-analysis forest plot, in this case, the effects of group interventions versus self-help for smoking cessation [4]. Like many systematic reviews of behavioral interventions, this one finds considerable heterogeneity of treatment effects across studies. This heterogeneity can be the result of a variety of factors including differences between studies in sample characteristics, delivery mechanisms and context, and/or differences in intervention components, including the nature, dose, and sequence of these components. In this illustrative meta-analysis, different intervention components were used across the included studies, and treatments were delivered in different dosages by different providers in different contexts. A researcher wanting to develop an improved intervention for group-based smoking cessation would receive little guidance on how to do so from the studies in this meta-analysis.