Optimization of behavioral interventions: three real-world applications [abstract] Abstract uri icon

abstract

  • Recently there an emerging new approach, inspired by engineering principles, to the development of multicomponent behavioral interventions. This approach requires interventions to first be optimized to meet a specific criterion (e.g., only include active components, implement for less than some specified amount of money, select the best set of tailoring variables for an adaptive intervention) before they are evaluated. The talks in this symposium showcase three different applications to optimization of behavioral interventions and discuss the lessons learned to-date. The first talk details the study design and initial results from the optimization of a multicomponent intervention to identify a cost-effective weight loss intervention. This talk highlights the flexibility of a factorial design for optimization. The second talk provides the rationale for using a sequential multiple assignment randomized trial, or SMART, to optimize an adaptive intervention for weight loss management. This study seeks to identify the best time to intervene with non-responders and the relative efficacy of two treatments to address self-regulation challenges. The third talk describes the application of control engineering principles to optimize an intensive adaptive intervention to efficiently manage gestational weight gain. This study demonstrates how dynamical systems modeling of weight gain related to energy intake, physical activity, and planned/self-regulatory behaviors can be used to adapt intervention dosages to pregnant women. As a whole, this symposium will demonstrate how this emerging approach is currently being used in real-world settings to optimize behavioral interventions. These studies were funded by three different institutes, suggesting increased interest in optimizing behavioral interventions across different public health outcomes. The discussant, who has extensive expertise in behavioral interventions, will provide insight from NIH about the funding climate for using this emerging approach.

publication date

  • 2016