Do you see MOST intersecting with implementation science, and if so, how?


We see MOST intersecting with implementation science in at least three ways.

First, one of the main objectives of MOST is to arrive at an intervention that balances effectiveness against affordability, scalability, and efficiency.  This perspective emphasizes developing interventions for immediate scalability, in other words, identifying the best combination of components that can be implemented within whatever constraints may be present on money, time, etc.  This is different from the prevailing approach to intervention development, in which interventions are developed with little attention paid to scalability; evaluated; and only then assessed for implementability and scalability.  We believe that interventions developed using MOST are likely to be closer to implementation-readiness than interventions developed using the classical treatment package approach.

Second, the field of implementation science has acknowledged that interventions sometimes need to be adapted to fit characteristics of local settings.  There are many aspects of an intervention that potentially could be adapted.  We wish to point out that once an optimization trial has been conducted, the results can potentially be used to optimize an intervention for a particular setting, using an optimization objective that suits that setting.  This approach is based on the assumptions that (a) the results of the optimization trial and (b) the conceptual model generalize across the settings under consideration.  To our knowledge this has not been tried yet.  This approach would not deal with every aspect of adaptation, but it would deal with some of them.

Third, MOST can be used to optimize intervention implementation itself. This is likely to be most useful if implementation can be considered a kind of wrap-around intervention, distinct from the core intervention, with its own components and component levels. An optimization trial can be undertaken to assess the performance of individual components of implementation, and then to identify the optimized implementation procedure based on the optimization objective and the results of the trial. Like all intervention optimization, this approach requires a detailed conceptual mode at the outset, in this case a model of the factors resulting in effective implementation. In some cases, important outcomes will be defined at an aggregate level, such as at the hospital, church, medical practice, physician, or classroom teacher level. This may require the use of multilevel optimization trial designs. As is common in all research on aggregate units, achieving the desired level of statistical power may be challenging, but the efficiency of the factorial experimental design may offset this to an extent.

The integration of MOST and implementation science is an active research area.  Happily, the Intervention Optimization Initiative has a lot of contact with  Dr. Donna Shelley’s Global Center for Implementation Science.

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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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