Center for Advancement and Dissemination of Intervention Optimization (CADIO)

Intervention optimization is an emerging scientific field. In this field ideas from behavioral science, engineering, public health, quantitative and qualitative methods, economics, and decision science are integrated to produce innovative approaches for empirical development and optimization of interventions.

MOST from a Conceptual Perspective

Featured Event

May 2026 Conference on Intervention Optimization

Intervention Optimization: Science and Practice, Present and Future CADIO and the Data Science for Dynamic Intervention Decision-Making Center (d3center) are excited to announce the first academic conference on intervention optimization! Click this post to save the date and sign up for more information. May 18-20, 2026; Maryland

Featured Publication

Intervention Optimization’s 20th Anniversary

From the desk of Director Linda Collins: "In this commentary, I reflect on the evolution of intervention optimization from that first publication to today, and on what the future could hold if the intervention science field continues to adopt the optimization paradigm." Read more in Annals of Behavioral Medicine.

Intervention Optimization

Intervention optimization is a different way of thinking for intervention scientists. Here you’ll understand the rationale behind and the goal(s) of intervention optimization.

CADIO Academy

Increasing capacity of scientists working in the intervention optimization space across a variety of disciplines is central to CADIO’s mission.

Resources

This is your one stop shop for tools and information to navigate through the intervention optimization process.

Events

Watch this space for announcements for upcoming workshops, webinars, and other learning opportunities.

Further Learning

Whether you are looking for additional support as you prepare a grant proposal involving MOST or practical information helpful in managing your optimization trial, this section provides resources for a deeper dive into intervention optimization.

REDCap with Most

The goal of this manual is to show how one might setup a REDCap project to support a research study with multiple conditions, such as factorial experiments common in the Multiphase Optimization Strategy (MOST) framework.

Informal introduction to factorial experimental designs

The purpose of this page is to clarify some concepts, notation, and terminology related to factorial experimental designs, and to compare and contrast factorial experiments to randomized controlled trials (RCTs). A more in-depth introduction can...

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