Optimization of implementation strategies using the Multiphase Optimization STratgey (MOST) framework: Practical guidance using the factorial design.

The Multiphase Optimization STrategy (MOST) is a framework that uses three phases-preparation, optimization, and evaluation-to develop multicomponent interventions that achieve intervention EASE by strategically balancing Effectiveness, Affordability, Scalability, and Efficiency. In implementation science, optimization of the intervention requires focus on the implementation strategies-things that we do to deliver the intervention-and implementation outcomes. MOST has been primarily used to optimize the components of the intervention related to behavioral or health outcomes. However, innovative opportunities to optimize discrete (i.e. single strategy) and multifaceted (i.e. multiple strategies) implementation strategies exist and can be done independently, or in conjunction with, intervention optimization. This article details four scenarios where the MOST framework and the factorial design can be used in the optimization of implementation strategies: (i) the development of new multifaceted implementation strategies; (ii) evaluating interactions between program components and a discrete or multifaceted implementation strategies; (iii) evaluating the independent effects of several discrete strategies that have been previously evaluated as a multifaceted implementation strategy; and (iv) modification of a discrete or multifaceted implementation strategy for the local context. We supply hypothetical school-based physical activity examples to illustrate these four scenarios, and we provide hypothetical data that can help readers make informed decisions derived from their trial data. This manuscript offers a blueprint for implementation scientists such that not only is the field using MOST to optimize the effectiveness of an intervention on a behavioral or health outcome, but also that the implementation of that intervention is optimized.

The Multiphase Optimization STrategy (MOST) is a method used to create interventions that work well, are cost-effective, and can be used widely. Normally, MOST focuses on making interventions better at improving health or behaviors. This article demonstrates that MOST can also improve how interventions are implemented and provide four examples: (i) the development of a new multipart implementation plan; (ii) evaluating how different parts of an intervention and its implementation plan work together; (iii) evaluating how different parts of a multipart implementation plan work alone and in combination; and (iv) modification of an implementation plan for local context. This article is meant to help scientists who work on putting interventions into practice. It shows how MOST can make interventions better and make sure they are used well in different places. By focusing on both the intervention and the implementation plan, we can do a better job of using interventions that have been proven to work in real life.

Decision-making in the multiphase optimization strategy: Applying decision analysis for intervention value efficiency to optimize an information leaflet to promote key antecedents of medication adherence.

Advances in the multiphase optimization strategy (MOST) have suggested a new approach, decision analysis for intervention value efficiency (DAIVE), for selecting an optimized intervention based on the results of a factorial optimization trial. The new approach opens possibilities to select optimized interventions based on multiple valued outcomes. We applied DAIVE to identify an optimized information leaflet intended to support eventual adherence to adjuvant endocrine therapy for women with breast cancer. We used empirical performance data for five candidate leaflet components on three hypothesized antecedents of adherence: beliefs about the medication, objective knowledge about AET, and satisfaction with medication information. Using data from a 25 factorial trial (n = 1603), we applied the following steps: (i) We used Bayesian factorial analysis of variance to estimate main and interaction effects for the five factors on the three outcomes. (ii) We used posterior distributions for main and interaction effects to estimate expected outcomes for each leaflet version (32 total). (iii) We scaled and combined outcomes using a linear value function with predetermined weights indicating the relative importance of outcomes. (iv) We identified the leaflet that maximized the value function as the optimized leaflet, and we systematically varied outcome weights to explore robustness. The optimized leaflet included two candidate components, side-effects, and patient input, set to their higher levels. Selection was generally robust to weight variations consistent with the initial preferences for three outcomes. DAIVE enables selection of optimized interventions with the best-expected performance on multiple outcomes.

Intervention optimization involves using data from an optimization trial to select the combination of intervention components that are expected to successfully balance effectiveness (i.e. improving an outcome in the desired direction) with efficiency (i.e. producing a good outcome without wasting resources). Recently, a new method for selecting optimized interventions has been proposed that has a number of advantages, including the ability to use empirical information about more than one outcome variable of interest. Here, we applied this new method to identify an optimized information leaflet designed to support eventual medication adherence in women with breast cancer, using empirical information about three outcome variables that are thought to be important for later medication adherence: beliefs about the medication, objective knowledge about the medication, and satisfaction with the leaflet information. When we let beliefs about the medication be most important; knowledge about the medication to be half as important as beliefs; and satisfaction with information to be half as important as knowledge, the optimized leaflet included enhanced information about side-effects and photos and quotes from women with breast cancer. This decision remained generally the same when we systematically varied the weights used to give outcomes their relative importance.

Intervention Optimization: A Paradigm Shift and Its Potential Implications for Clinical Psychology.

To build a coherent knowledge base about what psychological intervention strategies work, develop interventions that have positive societal impact, and maintain and increase this impact over time, it is necessary to replace the classical treatment package research paradigm. The multiphase optimization strategy (MOST) is an alternative paradigm that integrates ideas from behavioral science, engineering, implementation science, economics, and decision science. MOST enables optimization of interventions to strategically balance effectiveness, affordability, scalability, and efficiency. In this review we provide an overview of MOST, discuss several experimental designs that can be used in intervention optimization, consider how the investigator can use experimental results to select components for inclusion in the optimized intervention, discuss the application of MOST in implementation science, and list future issues in this rapidly evolving field. We highlight the feasibility of adopting this new research paradigm as well as its potential to hasten the progress of psychological intervention science.

Optimizing Interventions for Equitability: Some Initial Ideas.

Interventions (including behavioral, biobehavioral, biomedical, and social-structural interventions) hold tremendous potential not only to improve public health overall but also to reduce health disparities and promote health equity. In this study, we introduce one way in which interventions can be optimized for health equity in a principled fashion using the multiphase optimization strategy (MOST). Specifically, we define intervention equitability as the extent to which the health benefits provided by an intervention are distributed evenly versus concentrated among those who are already advantaged, and we suggest that, if intervention equitability is acknowledged to be a priority, then equitability should be a key criterion that is balanced with other criteria (effectiveness overall, as well as affordability, scalability, and/or efficiency) in intervention optimization. Using a hypothetical case study and simulated data, we show how MOST can be applied to achieve a strategic balance that incorporates equitability. We also show how the composition of an optimized intervention can differ when equitability is considered versus when it is not. We conclude with a vision for next steps to build on this initial foray into optimizing interventions for equitability.

PATHway: Intervention optimization of a prevention program for adolescents at-risk for depression in the primary care setting.

With as many as 13% of adolescents diagnosed with depressive disorders each year, prevention of depressive disorders has become a key priority for the National Institute of Mental Health (NIMH). Currently, we have no widely available interventions to prevent these disorders. To address this need, we developed a multi-health system collaboration to develop and evaluate the primary care based technology "behavioral vaccine," Competent Adulthood Transition with Cognitive-Behavioral Humanistic and Interpersonal Therapy (CATCH-IT). The full CATCH-IT program demonstrated evidence of efficacy in prevention of depressive episodes in clinical trials. However, CATCH-IT became larger and more complex across trials, creating issues with adherence and scalability. We will use a multiphase optimization strategy approach to optimize CATCH-IT. The theoretically grounded components of CATCH-IT include: behavioral activation, cognitive-behavioral therapy, interpersonal psychotherapy, and parent program. We will use a 4-factor (2x2x2x2) fully crossed factorial design with N = 16 cells (25 per cell, after allowing 15% dropout) to evaluate the contribution of each component. Eligible at-risk youth will be high school students 13 through 18 years old, with subsyndromal symptoms of depression. The study design will enable us to eliminate non-contributing components while preserving efficacy and to optimize CATCH-IT by strengthening tolerability and scalability by reducing resource use. By reducing resource use, we anticipate satisfaction and acceptability will also increase, preparing the way for an implementation trial.

Decreasing the public health burden of eating disorders: Commentary Schleider et al. (2023).

Eating disorders (EDs) are common, disabling, and costly; yet, less than 20% of those with EDs receive treatment. EDs have also skyrocketed in the COVID-19 pandemic, with access to care worse than ever, further solidifying the need to not only make EDs a priority but also embrace new approaches to address this major public health problem. Schleider et al. argue for the single-session intervention (SSI) as one such option and outline an agenda that would aid in building the evidence base and realizing the promise of SSIs for EDs. This commentary details three additional key issues that need to be addressed in order to realize the full potential of SSIs and related approaches and ultimately decrease the public health burden of EDs. These include conducting work to optimize interventions for greatest effectiveness, recognizing the value and working to massively increase reach of interventions like SSIs that can scale and meet diverse needs, and engaging in the work needed to address structural barriers to widespread dissemination of these approaches. Through this agenda, we will do more than embrace a single-session "mindset" and will catalyze the work needed to disseminate SSIs and related approaches at massive scale and maximize their impact.

Considering depression as a secondary outcome in the optimization of physical activity interventions for breast cancer survivors in the PACES trial: a factorial randomized controlled trial.

BACKGROUND: Depressive symptoms result in considerable burden for breast cancer survivors. Increased physical activity may reduce these burdens but existing evidence from physical activity interventions in equivocal. Furthermore, physical activity intervention strategies may differentially impact depressive symptoms, which should be considered in designing and optimizing behavioral interventions for breast cancer survivors. METHODS: The Physical Activity for Cancer Survivors (PACES) trial enrolled 336 participants breast cancer survivors, who were 3 months to 10 years post-treatment, and insufficiently active (< 150 min of moderate-to-vigorous physical activity per week). Participants were randomly assigned to a combination of 4 intervention strategies in a full-factorial design: 1) supervised exercise sessions, 2) facility access, 3) Active Living Every Day, and 4) Fitbit self-monitoring. Depressive symptoms were assessed at baseline, mid-intervention (3 months), and post-intervention (6 months) using the Quick Inventory for Depressive Symptoms. Change in depressive symptoms were analyzed using a linear mixed-effects model. RESULTS: Results from the linear mixed-effects model indicated that depressive symptoms decreased significantly across the entire study sample over the 6-month intervention (F = 4.09, p = 0.044). A significant ALED x time interaction indicated participants who received the ALED intervention experienced greater reductions in depressive symptoms (F = 5.29, p = 0.022). No other intervention strategy significantly impacted depressive symptoms. CONCLUSIONS: The ALED intervention consists of strategies (i.e., goal setting, social support) that may have a beneficial impact on depressive symptoms above and beyond the effect of increased physical activity. Our findings highlight the need to consider secondary outcomes when designing and optimizing physical activity interventions. TRIAL REGISTRATION: ClinicalTrials.gov NCT03060941. Posted February 23, 2017.

Multiphase optimization strategy: How to build more effective, affordable, scalable and efficient social and behavioural oral health interventions.

This commentary introduces the field of social behavioural oral health interventions to the multiphase optimization strategy (MOST). MOST is a principled framework for the development, optimization and evaluation of multicomponent interventions. Drawing from the fields of engineering, behavioural science, economics, decision science and public health, intervention optimization requires a strategic balance of effectiveness with affordability, scalability and efficiency. We argue that interventions developed using MOST are more likely to maximize the public health impact of social behavioural oral health interventions.

Understanding the barriers and facilitators to using self-sampling packs for sexually transmitted infections and blood-borne viruses: Thematic analyses for intervention optimization.

PURPOSE: Self-sampling packs for sexually transmitted infections (STIs) and blood-borne viruses (BBVs) are widely offered. There are ongoing problems with reach and sample return rates. The packs have arisen without formal intervention development. This paper illustrates initial steps of an intervention optimization process to improve the packs. METHODS: Eleven focus groups and seven interviews were conducted with convenience samples of patients recruited from sexual health clinics and members of the public (n = 56). To enable intervention optimization, firstly, we conducted an inductive appraisal of the behavioural system of using the pack to understand meaningful constituent behavioural domains. Subsequently, we conducted a thematic analysis of barriers and facilitators to enacting each sequential behavioural domain in preparation for future behaviour change wheel analysis. RESULTS: Overall, we found that self-sampling packs were acceptable. Participants understood their overall logic and value as a pragmatic intervention that simultaneously facilitated and reduced barriers to individuals being tested for STIs and BBVs. However, at the level of each behavioural domain (e.g., reading leaflets, returning samples) problems with the pack were identified, as well as a series of potential optimizations, which might widen the reach of self-sampling and increase the return of viable samples. CONCLUSIONS: This paper provides an example of a pragmatic approach to optimizing an intervention already widely offered globally. The paper demonstrates the added value health psychological approaches offer; conceptualizing interventions in behavioural terms, pinpointing granular behavioural problems amenable for systematic further improvement.

An iterative, interdisciplinary, collaborative framework for developing and evaluating digital behavior change interventions.

The rapid expansion of technology promises to transform the behavior science field by revolutionizing the ways in which individuals can monitor and improve their health behaviors. To fully live into this promise, the behavior science field must address distinct challenges, including: building interventions that are not only scientifically sound but also engaging; using evaluation methods to precisely assess intervention components for intervention optimization; and building personalized interventions that acknowledge and adapt to the dynamic ecosystem of individual and contextual variables that impact behavior change. The purpose of this paper is to provide a framework to address these challenges by leveraging behavior science, human-centered design, and data science expertise throughout the cycle of developing and evaluating digital behavior change interventions (DBCIs). To define this framework, we reviewed current models and practices for intervention development and evaluation, as well as technology industry models for product development. The framework promotes an iterative process, aiming to maximize outcomes by incorporating faster and more frequent testing cycles into the lifecycle of a DBCI. Within the framework provided, we describe each phase, from development to evaluation, to discuss the optimal practices, necessary stakeholders, and proposed evaluation methods. The proposed framework may inform practices in both academia and industry, as well as highlight the need to offer collaborative platforms to ensure successful partnerships that can lead to more effective DBCIs that reach broad and diverse populations.

Randomized factorial experiment of components of the SmokeFree Baby smartphone application to aid smoking cessation in pregnancy.

Smartphone applications (apps) might be able to reach pregnant smokers who do not engage with face-to-face support. However, we do not know how far pregnant smokers will engage with smoking cessation apps or what components are likely to be effective. This study aimed to assess pregnant smokers' engagement with the SmokeFree Baby app (v1) and to assess the short-term efficacy of selected components ("modules") for smoking abstinence. Positive outcomes would provide a basis for further development and evaluation. SmokeFree Baby was developed drawing on behavior change theories and relevant evidence. Pregnant smokers (18+) who were interested in quitting and set a quit date were recruited. Following multiphase optimization development principles, participants (N = 565) were randomly allocated to one of 32 (2 × 2 × 2 × 2 × 2) experimental groups in a full factorial design to evaluate five modules (each in minimal and full version: identity, health information, stress management, face-to-face support, and behavioral substitution). Measures of engagement included duration and frequency of engagement with the app. Smoking abstinence was measured by self-reported number of smoke-free days up to 4 weeks from the quit date. Participants engaged with the app for a mean of 4.5 days (SD = 8.5) and logged in a mean of 2.9 times (SD = 3.1). Main effects of the modules on the number of smoke-free days were not statistically significant (identity: p = .782, health information: p = .905, stress management: p = .103, face-to-face support: p = .397, behavioral substitution: p = .945). Despite systematic development and usability testing, engagement with SmokeFree Baby (v1) was low and the app did not appear to increase smoking abstinence during pregnancy.