The Multiphase Optimization Strategy for Engineering Effective Tobacco Use Interventions

Abstract

The Multiphase Optimization Strategy (MOST) is a new methodological approach for building, optimizing, and evaluating multicomponent interventions. Conceptually rooted in engineering, MOST emphasizes efficiency and careful management of resources to move intervention science forward steadily and incrementally. MOST can be used to guide the evaluation of research evidence, develop an optimal intervention (the best set of intervention components), and enhance the translation of research findings, particularly Type II translation. This article uses an ongoing study to illustrate the application of MOST in the evaluation of diverse intervention components derived from the phase-based framework reviewed in the companion article by Baker et al. (1). The article also discusses considerations, challenges, and potential benefits associated with using MOST and similar principled approaches to improving intervention efficacy, effectiveness and cost-effectiveness. The applicability of this methodology may extend beyond smoking cessation to the development of behavioral interventions for other chronic health challenges.

Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research

“…SMARTs are part of the Multiphase Optimization Strategy (MOST; [66]), a framework for constructing effective multicomponent behavioral interventions. To appreciate this, consider that: (a) AIs, which SMARTs aim to construct, are multicomponent interventions (e.g., different components may be provided at different time points to different individuals); (b) factorial designs (along with other approaches) are often used in the optimization stage of MOST to develop more potent interventions [65,67] and, as stated earlier, SMART is a type of factorial design [17]; and (c) using a SMART to construct the best AI prior to evaluating it in an RCT is consistent with the resource management principle of MOST, which states that "available research resources must be managed strategically so as to gain the most information [to] move science forward fastest" [66].…”

“…To appreciate this, consider that: (a) AIs, which SMARTs aim to construct, are multicomponent interventions (e.g., different components may be provided at different time points to different individuals); (b) factorial designs (along with other approaches) are often used in the optimization stage of MOST to develop more potent interventions [65,67] and, as stated earlier, SMART is a type of factorial design [17]; and (c) using a SMART to construct the best AI prior to evaluating it in an RCT is consistent with the resource management principle of MOST, which states that "available research resources must be managed strategically so as to gain the most information [to] move science forward fastest" [66]. For example, concerning (c), it would be less cost-effective-both in terms of actual dollars spent and, importantly, the value of the scientific information gained (see Section "Overview of data analyses for the common scientific aims in a SMART" of [1]-to conduct one RCT to evaluate the ORIGINAL RESEARCH effect of short versus long duration initial IBT, followed by a second RCT (among nonresponders to the IBT duration which appears best on average based on the results of the first RCT) to evaluate the effect ACT vs IBT+MR, followed by a third RCT to evaluate the effect of the adaptive intervention as a whole (for both responders and nonresponders) versus a suitable control.…”

“…A key idea to understanding why standard data analysis methods are possible to address these aims is that a SMART is a form of a factorial experimental design [17]. Consistent with the analysis of factorial experiments for the development of behavioral interventions [65][66][67], the analyses associated with these two "main effect" aims pool together different groups of participants from the multiple subgroups A-F shown on the right margin of Fig. 2.…”

Electronic health records as a tool for recruitment of participants' clinical effectiveness research: lessons learned from tobacco cessation

“…This NCIfunded clinical trial aims to develop a set of treatments for health systems that are highly effective for virtually every smoker seeking healthcare, regardless of their current readiness to quit smoking. The research will recruit 1,700 smokers into three separate, well-powered (β>0.80) factorial experiments [28] that will compare multiple smoking cessation interventions in patients recruited from 10-12 primary care or family practice clinics in southern Wisconsin. Cessation treatments will be delivered on site or in person and by telephonic means.…”

Development of StopAdvisor

“…This paper describes the development of a theory-based, interactive smoking cessation website, StopAdvisor, designed to be attractive and effective across the social spectrum. We used an open-source web-development platform, LifeGuide (www.lifeguideonline.org), as a starting point for a process of optimisation of behaviour change technology [5]. To our knowledge, this is the first attempt to be fully transparent about the content and development of an internet-based behaviour change intervention.…”