Why Should Intervention Science Adopt Ideas from Engineering?
Because the methods used in intervention science today have been developed for purposes of intervention evaluation rather than optimization, smoking cessation intervention scientists currently do not have clear methodological guidelines about how to conduct optimization research. We suggest turning to research methods developed for fields such as engineering, manufacturing, and product development (e.g. (11
)) to provide a blueprint for efficient and programmatic smoking cessation research. At first glance it may seem that engineering has little in common with clinical intervention development, but reflection reveals several parallels. In manufacturing and product development, theory often suggests many different component parts or materials that could be used, which could then be combined to form many alternative “draft” versions of the product, or prototypes, that could be built and tested. Because building and testing each of these alternative prototypes would be expensive in terms of personnel and materials and could slow progress, engineering methodologists have developed a more efficient strategy. The strategy is based on the premise that out of the large array of potential prototypes, there is a much smaller number that are the best and most promising. For this reason, rather than building and testing all possible prototypes, engineers typically first conduct experiments to search efficiently and systematically through the various components (and versions of those components) that are candidates to be included in the product, with the objective of identifying the most promising set without having to assemble and test each prototype. After the search for promising components has been completed in an initial phase, and fine-tuning has taken place if necessary, in subsequent phases a prototype is built out of the components that have been identified as promising. This prototype is then subjected to a full test. Taking a programmatic and sequenced experimental approach enables product development to make faster progress while husbanding research resources.
In much the same way, the phase-based framework of tobacco cessation, along with various tobacco dependence theories, suggests many different intervention components. For example, different intervention components may be intended to motivate a smoker to attempt to quit, increase initial abstinence, increase cessation program participation, increase patient adherence, prolong abstinence, or promote re-quitting. The many intervention components suggested by theory could be combined to form hundreds or even thousands of plausible alternative smoking cessation intervention packages. We propose that by adopting a programmatic and sequenced experimental approach similar to that used in engineering, it is possible to search efficiently and systematically to identify the most promising smoking cessation intervention components and levels of components, assemble these components and levels into an optimal treatment package, and then subject only the optimal treatment package to a full efficacy or effectiveness trial. We believe that just as it has hastened product development in other fields, a phased experimental approach will hasten progress in building better smoking cessation interventions, while making the most of research resources.
Two Basic Principles from Engineering
Based on our examination of engineering methods, we have identified two basic principles that translate readily to methodology for intervention science. According to the first, the resource management
principle, available research resources must be managed strategically so as to gain the most information and the most reliable information, and thereby move science forward fastest. The resource management principle has implications for how research should be conducted. In engineering, research designs are chosen by prioritizing which information is most important to gain, and then targeting resources accordingly (12
). There is an emphasis on randomized experimentation, because such designs produce the most reliable scientific information, usually most efficiently (e.g. (13
)). According to the resource management principle, an investigator using MOST should seek an experimental design that addresses the highest-priority research questions in the most efficient way. The choice of a specific experimental design depends upon the research questions to be investigated.
The second principle, the continuous optimization principle, states that a new cycle of research should begin as soon as the previous round of development research is concluded, in order to build on previous work and make further incremental improvements. In the manufacturing and product development fields there is rarely an expectation that once a product has been developed and is ready to be marketed, the job is done. Instead, research generally begins anew, devoted to adding or refining features so that the new product does more than its predecessor; to developing a product that does what its predecessor did but more effectively, efficiently, conveniently or cheaply; or to responding to changes in the environment or in the needs, desires and preferences of the customer base. The new cycle is informed both by the research conducted as part of previous cycles, and by hypothesis-generating secondary data analyses.
Building on Engineering Principles: An Overview of MOST
), which has some conceptual roots in the phased approach to intervention development and evaluation proposed by the United Kingdom’s Medical Research Council (14
) applies the resource management and continuous optimization principles to improve the efficiency of smoking cessation research and the effectiveness and cost-effectiveness of smoking cessation interventions. MOST is a framework for building and optimizing multicomponent behavioral interventions. We wish to stress that by “framework” we mean a general approach rather than an off-the-shelf procedure. How MOST is conducted in any specific application may vary greatly depending upon the motivating research questions, public health area, available resources, and other aspects of the situation. In this article we offer an expansion, clarification and elaboration of MOST.
is a flow chart outlining MOST. As the figure shows, MOST consists of a sequence of steps aimed at the systematic optimization of a multicomponent intervention. The sequence of steps begins with a theoretical model, which informs the identification and selection of intervention components to be examined. Next, the intervention components are examined via randomized experimentation. This experimentation is aimed at gathering information that will be useful in making decisions about each of the intervention components. For example, a decision may concern whether to include a particular component in an intervention, or which intensity level of the component should be used (e.g., one vs. five sessions of counseling). In the next step additional experimental or nonexperimental work may be done for the purpose of refinement and fine-tuning. As is consistent with the resource management principle, the designs of any randomized experiments are carefully chosen with a high priority on efficiency and economy. The purpose is to gather information that will be used to make decisions about which intervention components and component levels will be included in the intervention. Once the information is gathered, it is used to assemble a beta (i.e., draft) version of the intervention. The efficacy or effectiveness of this draft intervention is then confirmed in an evaluation by means of a standard randomized controlled trial (RCT). If efficacy/effectiveness is confirmed, the intervention is released. Consistent with the continuous optimization principle, a new cycle of MOST, aimed at further improvements, would begin immediately.
Figure 1 Flow chart of the Multiphase Optimization Strategy (MOST). A rectangle represents action. A rhombus represents information that may be input and/or output. A diamond represents a decision. The round-cornered rectangle represents the product, in this the (more ...)
In the next section we walk through the steps of MOST illustrated in , using as an example our research applying the MOST framework to optimize the effectiveness of a clinic-based smoking cessation intervention. We hope that this example may motivate some readers to consider how MOST could be applied in their intervention research.