Optimizing Behavioral Interventions with the MOST Framework

Designing effective behavioral interventions in public health, education, and social sciences requires a systematic, resource-efficient approach. Traditional evaluation methods often test complete packages without knowing which individual components actually work. This text-based course introduces you to the Multiphase Optimization Strategy (MOST), a pioneering methodological framework for building highly effective and scalable interventions. You will transition from evaluating static intervention packages to systematically selecting, testing, and optimizing active components. What you'll learn: - Understand the core principles and three phases of the Multiphase Optimization Strategy (MOST). - Identify and select candidate intervention components based on conceptual models. - Design efficient experimental trials, such as factorial designs, to screen components. - Apply optimization criteria to balance effectiveness, affordability, and scalability. - Integrate modern open science practices, including pre-registration of intervention trials. - Analyze decision-making processes for selecting the final optimized intervention. We begin with foundational definitions and the core philosophy of intervention optimization before moving into experimental designs, component screening, and decision-making frameworks. Through structured reading and written scenarios, you will learn how to apply these methodological steps to your own research domain. This course is designed for beginning researchers, intervention scientists, and graduate students in public health, education, and behavioral sciences who want to learn optimization methodology. No prior experience with the MOST framework is required. Start reading today to build more efficient, impactful, and scalable behavioral interventions.