PRE-CONFERENCE WORKSHOP V: INTRODUCTION TO MULTIPHASE OPTIMIZATION STRATEGY (MOST) FOR BUILDING MORE EFFECTIVE, EFFICIENT, ECONOMICAL, AND SCALABLE BEHAVIORAL AND BIOBEHAVIORIAL INTERVENTIONS

The majority of behavioral and biobehavioral interventions in use today have been evaluated as a treatment package using a two-arm randomized controlled trial (RCT). This approach is an excellent way to determine whether an intervention is effective. However, the treatment package approach is less helpful in providing empirical information that can be used to optimize the intervention to achieve improved effectiveness and efficiency while maintaining a desired level of economy, and/or scalability. In this workshop an innovative methodological framework for optimizing behavioral interventions, the multiphase optimization strategy (MOST), will be presented. MOST is based on ideas inspired by engineering methods, which stress careful management of research resources and ongoing improvement of products. MOST is a comprehensive strategy that includes three phases: preparation, optimization, and evaluation. MOST can be used to build a new intervention or to improve an existing intervention. Using MOST it is possible to engineer an intervention targeting a particular effect size, level of cost-effectiveness, or any other criterion.

This workshop will provide an introduction to MOST. Ongoing intervention development studies using the MOST approach will be used as illustrative examples. A substantial amount of time will be devoted to experimental design, which is an important tool in MOST. In particular, factorial experiments and fractional factorial experiments will be discussed. Time will be set aside for a couple of small group activities and open discussion of how the concepts presented can be applied in the research of workshop attendees. Learning objective 1: compare intervention evaluation and intervention optimization

• Learning objective 2: understand how to select an experimental design from a resource management perspective
• Learning objective 3: develop clearly stated optimization criteria