Behavioral, biobehavioral, biomedical, and social-structural interventions appear throughout society. They are important in many areas of public health, such as substance misuse, HIV/AIDS, Hepatitis C, smoking cessation, cancer treatment, weight management, treatment of depression and other mental health problems, and prevention of child maltreatment. They are also important in the enhancement of educational achievement and promotion of human well-being.
Among the challenges faced by scientists is how to use interventions to achieve the greatest societal benefit. Societal benefit is a function of not only the effectiveness of an intervention, but also how many people it reaches. An intervention may be highly effective, but if it reaches no one, its societal benefit is zero. Unfortunately, much time and money has been devoted to development of interventions that ultimately reach very few, or even no, people. As Onken et al. (2014) noted, “so many efficacious behavioral interventions do not make their way down the pipeline through implementation.”
To develop interventions that will “make their way down the pipeline through implementation” it is necessary to acknowledge two inescapable realities:
- Where there are constraints on the resources available for implementation, in general intervention effectiveness will be somewhat less than what it would have been if there were no constraints.
- There are always constraints on implementation resources.
Here the term “resources” is broadly defined to include, for example, the amount a payer (e.g. insurance company or school district) is willing to pay to implement the intervention; the amount of staff or classroom time that can be spared; and the amount of time participants are willing to devote to completing the intervention. Intervention optimization is about maximizing the effectiveness that can be obtained while operating within realistic constraints on resources. The ultimate objective of optimization is arriving at interventions that offer the highest level of societal benefits.
The multiphase optimization strategy (MOST) is an engineering-inspired framework for development, optimization, and evaluation of behavioral and biobehavioral interventions. The purpose of MOST is to arrive at an intervention that achieves intervention EASE by strategically balancing Effectiveness against Affordability (extent to which the intervention is deliverable within budget, and offers a good value); Scalability (extent to which the intervention is implementable in the intended setting with no need for ad hoc modifications); and Efficiency (extent to which the intervention is made up solely of active components, i.e., components that, when included, improve outcomes).
As compared to the classical treatment package approach, in which the only empirical evaluation of an intervention (other than pilot testing) is typically an RCT, MOST is a very different way of thinking. To learn more about MOST and how to use it in your research, explore this web site, and read Collins (2018; available for free download from many university libraries) and the other recommended readings listed here.
Collins, L.M. (2018). Optimization of behavioral, biobehavioral, and biomedical interventions: The multiphase optimization strategy (MOST). New York: Springer.
Onken, L.S., Carroll, K.M., Shoham, V., Cuthbert, B.N., & Riddle, M. (2014). Reenvisioning clinical science: Unifying the discipline to improve public health. Clinical Psychological Science, 2, 22-34.
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.
Establishing a conceptual model and understanding optimization
Sometimes the conceptual model is not a model of a health behavior per se, but a model of maintaining treatment fidelity, promoting adherence or compliance, or the like. The conceptual model is explained in more...
Common misconceptions about factorial experiments
A factorial experiment is essentially an RCT with a lot of experimental conditions, and therefore is extremely difficult to power.
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...
LET’S STAY IN TOUCH
Join the cadio Mailing List
Keep up to date with the latest news, events, online courses, and resources from cadio.