Developing mHealth Interventions with Implementation in Mind: Application of the Multiphase Optimization Strategy (MOST) Preparation Phase to Diabetes Prevention Programming (Preprint)

UNSTRUCTURED With thousands of mHealth solutions on the market, patients and healthcare providers struggle to identify which solution to use/prescribe. The lack of evidence-based mHealth solutions may be due to limited research on intervention development and continued use of traditional research methods for mHealth evaluation. The Multiphase Optimization Strategy (MOST) is a framework which aids in developing interventions which are economical, affordable, scalable, and effective (EASE). MOST Phase I highlights the importance of formative intervention development, a stage often overlooked and rarely published. The aim of MOST Phase I is to identify candidate intervention components, create a conceptual model, and define the optimization objective. While MOST sets these three targets, the framework itself does not provide robust guidance on how to conduct quality research within Phase I, and what steps can be taken to identify potential intervention components, develop the conceptual model, and achieve intervention EASE with the implementation context in mind. To advance the applicability of MOST within the field of implementation science, this paper provides an account of the methods used to develop an mHealth intervention. Specifically, we provide a comprehensive example of how to achieve the goals of MOST Phase I by outlining the formative development of an mHealth prompting intervention within a diabetes prevention program. Additionally, recommendations are proposed for future researchers to conduct formative research on mHealth interventions with implementation in mind. Given its considerable reach, mHealth has the potential to positively impact public health by decreasing implementation costs and improving accessibility. MOST is well-suited for the efficient development and optimization of mHealth interventions. By using an implementation-focused lens and outlining the steps in developing an mHealth intervention using MOST Phase I, this work can may guide future intervention developers towards maximizing the impact of mHealth outside of the research laboratory.

The preparation phase in the multiphase optimization strategy (MOST): a systematic review and introduction of a reporting checklist

Multicomponent behavioral interventions developed using the multiphase optimization strategy (MOST) framework offer important advantages over alternative intervention development models by focusing on outcomes within constraints relevant for effective dissemination. MOST consists of three phases: preparation, optimization, and evaluation. The preparation phase is critical to establishing the foundation for the optimization and evaluation phases; thus, detailed reporting is critical to enhancing rigor and reproducibility. A systematic review of published research using the MOST framework was conducted. A structured framework was used to describe and summarize the use of MOST terminology (i.e., preparation phase and optimization objective) and the presentation of preparation work, the conceptual model, and the optimization. Fifty-eight articles were reviewed and the majority focused on either describing the methodology or presenting results of an optimization trial (n = 38, 66%). Although almost all articles identified intervention components (96%), there was considerable variability in the degree to which authors fully described other elements of MOST. In particular, there was less consistency in use of MOST terminology. Reporting on the MOST preparation phase is varied, and there is a need for increased focus on explicit articulation of key design elements and rationale of the preparation phase. The proposed checklist for reporting MOST studies would significantly advance the use of this emerging methodology and improve implementation and dissemination of MOST. Accurate reporting is essential to reproducibility and rigor of scientific trials as it ensures future research fully understands not only the methodology, but the rationale for intervention and optimization decisions.

One view of the next decade of research on behavioral and biobehavioral approaches to cancer prevention and control: intervention optimization

As a new decade begins, we propose that the time is right to reexamine current methods and procedures and look for opportunities to accelerate progress in cancer prevention and control. In this article we offer our view of the next decade of research on behavioral and biobehavioral interventions for cancer prevention and control. We begin by discussing and questioning several implicit conventions. We then briefly introduce an alternative research framework: the multiphase optimization strategy (MOST). MOST, a principled framework for intervention development, optimization, and evaluation, stresses not only intervention effectiveness, but also intervention affordability, scalability, and efficiency. We review some current limitations of MOST along with future directions for methodological work in this area, and suggest some changes in the scientific environment we believe would permit wider adoption of intervention optimization. We propose that wider adoption of intervention optimization would have a positive impact on development and successful implementation of interventions for cancer prevention and control and on intervention science more broadly, including accumulation of a coherent base of knowledge about what works and what does not; establishment of an empirical basis for adaptation of interventions to different settings with different levels and types of resources; and, in the long run, acceleration of progress from Stage 0 to Stage V in the National Institutes of Health Model of Stages of Intervention Development.

Using factorial mediation analysis to better understand the effects of interventions

To improve understanding of how interventions work or why they do not work, there is need for methods of testing hypotheses about the causal mechanisms underlying the individual and combined effects of the components that make up interventions. Factorial mediation analysis, i.e., mediation analysis applied to data from a factorial optimization trial, enables testing such hypotheses. In this commentary, we demonstrate how factorial mediation analysis can contribute detailed information about an intervention’s causal mechanisms. We briefly review the multiphase optimization strategy (MOST) and the factorial experiment. We use an empirical example from a 25 factorial optimization trial to demonstrate how factorial mediation analysis opens possibilities for better understanding the individual and combined effects of intervention components. Factorial mediation analysis has important potential to advance theory about interventions and to inform intervention improvements.

Engineering a Mobile Platform to Promote Sleep in the Pediatric Primary Care Setting

Study Objectives Pediatricians lack tools to support families at home for the promotion of childhood sleep. We are using the Multiphase Optimization Strategy (MOST) framework to guide the development of a mobile health platform for childhood sleep promotion. The objective of this study is to demonstrate feasibility of a mobile health platform towards treating children with insufficient sleep. Methods Children aged 10-12y were enrolled (Study #1: N=30; Study #2: N=43). Participants wore a sleep tracker to measure sleep duration. Data were retrieved by a mobile health platform, programmed to send introductory messages during run-in (2 weeks) and goal achievement messages during intervention (7 weeks) periods. In study #1, participants were randomized to control, gain-framed incentive or loss-framed incentive arms. In study #2, participants were randomized to control, loss-framed incentive, normative feedback or loss-framed incentive plus normative feedback arms. Results In study #1, 1,514 nights of data were captured (69%) and sleep duration during the intervention was higher by an average of 21 (95% CI: -8, 51) and 34 (95% CI: 7, 61) minutes per night for the gain-framed and loss-framed arms, respectively, compared to controls. In study #2, 2,689 nights of data were captured (81%), with no major differences in average sleep duration between the control and the loss-framed or normative feedback arms. Conclusion We have developed and deployed a mobile health platform that can capture sleep data and remotely communicate with families. Promising candidate intervention components will be further investigated under the optimization phase of the MOST framework.

Enhancing behavioral intervention science: using community-based participatory research principles with the multiphase optimization strategy

Innovative methodological frameworks are needed in intervention science to increase efficiency, potency, and community adoption of behavioral health interventions, as it currently takes 17 years and millions of dollars to test and disseminate interventions. The multiphase optimization strategy (MOST) for developing behavioral interventions was designed to optimize efficiency, efficacy, and sustainability, while community-based participatory research (CBPR) engages community members in all research steps. Classical approaches for developing behavioral interventions include testing against control interventions in randomized controlled trials. MOST adds an optimization phase to assess performance of individual intervention components and their interactions on outcomes. This information is used to engineer interventions that meet specific optimization criteria focused on effectiveness, cost, or time. Combining CBPR and MOST facilitates development of behavioral interventions that effectively address complex health challenges, are acceptable to communities, and sustainable by maximizing resources, building community capacity and acceptance. Herein, we present a case study to illustrate the value of combining MOST and CBPR to optimize a multilevel intervention for reducing substance misuse among formerly incarcerated men, for under $250 per person. This integration merged experiential and cutting-edge scientific knowledge and methods, built community capacity, and promoted the development of efficient interventions. Integrating CBPR and MOST principles yielded a framework of intervention development/testing that is more efficient, faster, cheaper, and rigorous than traditional stage models. Combining MOST and CBPR addressed significant intervention science gaps and speeds up testing and implementation of interventions.