The conceptual model depicts the relationship between intervention components and the process to be intervened upon. Information from sources such as behavioral theory, scientific literature, and secondary analyses of existing data is used to form the basis of a conceptual model. This theory- and empirically-derived model is critical for guiding decisions in MOST, in particular, the selection of which intervention components to examine.
What are the features of the conceptual model needed for the Preparation Phase of MOST?
A good conceptual model should specify:
- the determinants of and influences on the health behavior or outcome to be impacted by the behavioral intervention;
- all important mediators and moderators, including which intervention components, if any, are expected to interact with each other;
- what psychosocial and/or biological theories inform which parts of the model–in most cases there will be several theories that inform a conceptual model; and
- which intervention components are aimed at which mediators.
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 detail in Chapter 2 of Collins (2018). For an example, see Gwadz et al. (2017).
The identification of intervention components that are candidates for inclusion in the optimized intervention is informed by the conceptual model. The granularity of a component is not prescribed – in some cases, an entire intervention could be a component; in other cases, a component could be a smaller unit.
Any pilot testing of intervention components (highly recommended) or the optimization trial (also highly recommended) is done in this phase. The purpose of a pilot study in the preparation phase is to ascertain the acceptability or feasibility of a component and/or to refine procedures. The purpose is not to determine the efficacy of a component.
An essential part of the preparation phase is identifying and operationalizing a clear optimization objective (also called the optimization criterion). This is an explicit definition of how effectiveness, affordability, scalability, and efficiency are to be balanced, in other words, how intervention EASE is to be achieved in this particular application of MOST. For example, an investigator may have been informed that a health care system is willing to pay up to, say, $500 per person to implement a smoking cessation intervention. In this case $500 is a constraint, and the investigator seeks to achieve intervention EASE by identifying the set of components and component levels that provides the best expected outcome achievable within this constraint on implementation cost. In other settings there may be constraints on resources such as staff time available or tolerable participant burden. However, an optimization objective does not necessarily include explicit constraints; an investigator may wish to identify the most cost-effective intervention, or may simply wish to eliminate any inactive components to arrive at an efficient intervention without any explicit consideration of cost. Although it is not strictly necessary, it is recommended to identify the optimization objective during the preparation phase, to ensure that any data necessary for applying the optimization objective (e.g., data on how much it costs to implement each component) are collected during the optimization phase. An example of optimization using an upper limit on cost can be found in Spring et al. (2020).