Session: Applying Engineering-Inspired Methodological Approaches to Optimize Behavioral Interventions (Society for Prevention Research 22nd Annual Meeting)

Prevention scientists, policy makers, and funding agencies are demanding effective, efficient interventions that have a meaningful public health impact. One way to achieve these goals is to apply optimization methods from the field of engineering to the development of behavioral interventions. Such methods attempt to efficiently use resources to achieve clearly defined criteria. The goal of the papers in this symposium is to demonstrate how optimization strategies can be applied to a range of behavioral outcomes (i.e., gestational weight gain, smoking cessation, and substance use) and to a range of populations (i.e., overweight and obese pregnant women, adult smokers, and college student-athletes). Because applying any methodological innovation is not without its challenges, each presenter will discuss challenges that their team encountered as they work to develop optimized interventions and how they addressed these challenges.

Paper 1 focuses on optimizing a gestational weight gain intervention for overweight and obese pregnant women. The authors use dynamical modeling and control systems engineering to develop an individually-tailored (adaptive) behavioral intervention for managing gestational weight gain. They discuss lessons learned during Phase 1 of their study, which examines the feasibility of delivering varied dosages of intervention components (e.g., education, goal-setting, self-monitoring).

Paper 2 focuses on optimizing an online alcohol and other drug use prevention program for college student-athletes. The authors use the Multiphase Optimization Strategy (MOST) to identify which intervention components (i.e., lessons targeting social norms, expectancies, and harm prevention strategies) are not yet meeting a pre-determined criterion and thus need to be revised. They present results from their second component selection experiment and discuss how these results informed their next set of revisions.

Paper 3 focuses on optimizing a smoking cessation intervention targeting smokers who want to quit. The authors also use MOST to identify which intervention components (e.g., using a nicotine patch, receiving different types of counseling) significantly impact cessation phase-specific outcomes (e.g., cessation rates, post-quit cravings, adherence rates). They present results from two studies and describe how these results informed the development of an optimized smoking cessation treatment package that includes only effective components that work well in real-world healthcare settings.

Finally, the discussant will comment on the papers and review the benefits of using optimization strategies to improve the efficiency and public health impact of behavioral interventions.