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

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.

Testing a Brief Quitline Intervention for Tobacco Cannabis Co-Users: A Randomized Controlled Pilot Study

Background: Tobacco cannabis co-use is common and becoming more prevalent. Frequent and heavy users of cannabis may struggle to quit smoking. Quitlines offer free cessation treatment in the United States and 25% of quitline callers may also be cannabis users. The present paper describes a randomized pilot study of a tailored intervention for cannabis and cigarette co-users. The intervention combines the quitline smoking cessation treatment with a motivational enhancement therapy-based cannabis intervention. Methods: The randomized pilot study was conducted within four state-funded quitlines with quitline coaches as interventionists. 102 quitline callers who were cannabis and cigarette co-users were randomized to receive treatment as usual (TAU) or the new Quitline Check-Up (QLCU) intervention. Outcomes were collected 90 days post-randomization. Primary outcomes included feasibility and acceptability of delivering the QLCU in the quitline setting. Secondary outcomes included 7-day point prevalence tobacco abstinence, past 30-day cannabis use, and Cannabis Use Disorder Identification Test scores. Results: Study participants were heavy cannabis users, averaging 25 days of use in the past 30; nearly 70% used at a level considered hazardous. Fidelity ratings indicated coaches were successful at delivering the intervention. Treatment engagement was high for both groups (TAU m = 3.4 calls; QLCU m = 3.6 calls) as was treatment satisfaction. Intent-to-treat quit rates (with survey non-responders classified as smokers) were 28.6% for the TAU control group and 24.5% for the QLCU group (P = .45). Discussion: Hazardous cannabis use rates were high in this sample of tobacco cannabis co-users calling quitlines to quit smoking. The intervention for co-users was acceptable and feasible to deliver. No improvements in tobacco cessation outcomes were observed. Pragmatic intervention development within a real-world clinical setting can streamline the intervention development process. More research is needed on tobacco cannabis co-users and who can benefit from a tailored intervention. Registered: ClinicalTrials.gov NCT04737772, February 4, 2021.

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.

Should carbohydrate-modified diets be the first option for weight loss in people with impaired glucose metabolism? A scoping review

While the "precision nutrition" movement is at an early stage of development, several investigations have compared low-fat versus carbohydrate (CHO)-modified diets (i.e., low-or-reduced-CHO, low glycemic index/load diets, and high-fiber) in people with normal versus impaired glucose metabolism. The purpose of this scoping review was to summarize evidence in support of the hypothesis that CHO-modified diets are more effective for weight loss among people with impaired glucose metabolism. Fifteen articles were included in this review: seven retrospective analyses of randomized clinical trials and eight prospective randomized clinical trials with prespecified hypotheses related to a diet (low-fat vs. CHO-modified) × phenotype (normal vs. impaired) interaction. Evidence in support of the hypothesis was identified in six of seven retrospective and three of eight prospective studies, which led to a recommendation of CHO-modified diets as a first-line option for people with impaired glucose metabolism. However, the evidence in support of this recommendation is relatively weak, and dietary prescriptions should consider additional contextual information that may influence overall dietary adherence. Additional and rigorous research using innovative randomized experimental approaches is needed for stronger dietary weight loss recommendations based on pretreatment glycemic status.

Empirical Development of a Behavioral Intervention for African American/Black and Latino Persons with Unsuppressed HIV Viral Load Levels: An Application of the Multiphase Optimization Strategy (MOST) Using Cost-Effectiveness as an Optimization Objective

We used results from an optimization randomized controlled trial which tested five behavioral intervention components to support HIV antiretroviral adherence/HIV viral suppression, grounded in the multiphase optimization strategy and using a fractional factorial design to identify intervention components with cost-effectiveness sufficiently favorable for scalability. Results were incorporated into a validated HIV computer simulation to simulate longer-term effects of combinations of components on health and costs. We simulated the 32 corresponding long-term trajectories for viral load suppression, health related quality of life (HRQoL), and costs. The components were designed to be culturally and structurally salient. They were: motivational interviewing counseling sessions (MI), pre-adherence skill building (SB), peer mentorship (PM), focused support groups (SG), and patient navigation (short version [NS], long version [NL]. All participants also received health education on HIV treatment. We examined four scenarios: one-time intervention with and without discounting and continuous interventions with and without discounting. In all four scenarios, interventions that comprise or include SB and NL (and including health education) were cost effective (< $100,000/quality-adjusted life year). Further, with consideration of HRQoL impact, maximal intervention became cost-effective enough to be scalable. Thus, a fractional factorial experiment coupled with cost-effectiveness analysis is a promising approach to optimize multi-component interventions for scalability. The present study can guide service planning efforts for HIV care settings and health departments.

Using Multiphase Optimization Strategy and Human-Centered Design to Create an Integrated Model of Palliative Care Skills in Home-Based Physical Therapy for Advanced Heart Failure

Background: Many older adults with advanced heart failure receive home health rehabilitation after hospitalization. Yet, integration of palliative care skills into rehabilitation is limited. Objective: Describe using the Multiphase Optimization Strategy (MOST) framework with human-centered design principles to engage clinical partners in the Preparation phase of palliative physical therapy intervention development. Design: We convened a home-based physical therapy advisory team (four clinicians, three clinical leaders) to identify physical therapist needs and preferences for incorporating palliative care skills in rehabilitation and design an intervention prototype. Results: Between 2022 and 2023, we held five advisory team meetings. Initial feedback on palliative care skill preferences and training needs directly informed refinement of our conceptual model and skills in the intervention prototype. Later feedback focused on reviewing and revising intervention content, delivery strategy, and training considerations. Conclusion: Incorporating human-centered design principles within the MOST provided a useful framework to partner with clinical colleagues in intervention design.

Opportunities in the Postpartum Period to Reduce Cardiovascular Disease Risk After Adverse Pregnancy Outcomes: A Scientific Statement From the American Heart Association

Adverse pregnancy outcomes are common among pregnant individuals and are associated with long-term risk of cardiovascular disease. Individuals with adverse pregnancy outcomes also have an increased incidence of cardiovascular disease risk factors after delivery. Despite this, evidence-based approaches to managing these patients after pregnancy to reduce cardiovascular disease risk are lacking. In this scientific statement, we review the current evidence on interpregnancy and postpartum preventive strategies, blood pressure management, and lifestyle interventions for optimizing cardiovascular disease using the American Heart Association Life's Essential 8 framework. Clinical, health system, and community-level interventions can be used to engage postpartum individuals and to reach populations who experience the highest burden of adverse pregnancy outcomes and cardiovascular disease. Future trials are needed to improve screening of subclinical cardiovascular disease in individuals with a history of adverse pregnancy outcomes, before the onset of symptomatic disease. Interventions in the fourth trimester, defined as the 12 weeks after delivery, have great potential to improve cardiovascular health across the life course.

Using decision analysis for intervention value efficiency to select optimized interventions in the multiphase optimization strategy

OBJECTIVE

Optimizing multicomponent behavioral and biobehavioral interventions presents a complex decision problem. To arrive at an intervention that is both effective and readily implementable, it may be necessary to weigh effectiveness against implementability when deciding which components to select for inclusion. Different components may have differential effectiveness on an array of outcome variables. Moreover, different decision-makers will approach this problem with different objectives and preferences. Recent advances in decision-making methodology in the multiphase optimization strategy (MOST) have opened new possibilities for intervention scientists to optimize interventions based on a wide variety of decision-maker preferences, including those that involve multiple outcome variables. In this study, we introduce decision analysis for intervention value efficiency (DAIVE), a decision-making framework for use in MOST that incorporates these new decision-making methods. We apply DAIVE to select optimized interventions based on empirical data from a factorial optimization trial.

METHOD

We define various sets of hypothetical decision-maker preferences, and we apply DAIVE to identify optimized interventions appropriate to each case.

RESULTS

We demonstrate how DAIVE can be used to make decisions about the composition of optimized interventions and how the choice of optimized intervention can differ according to decision-maker preferences and objectives.

CONCLUSIONS

We offer recommendations for intervention scientists who want to apply DAIVE to select optimized interventions based on data from their own factorial optimization trials. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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.

Optimization of an Information Leaflet to Influence Medication Beliefs in Women With Breast Cancer: A Randomized Factorial Experiment

BACKGROUND

Adherence to adjuvant endocrine therapy (AET) is low in women with breast cancer. Negative beliefs about the necessity of AET and high concerns are barriers to adherence.

PURPOSE

To use the multiphase optimization strategy to optimize the content of an information leaflet intervention, to change AET beliefs.

METHODS

We conducted an online screening experiment using a 25 factorial design to optimize the leaflet. The leaflet had five components, each with two levels: (i) diagrams about AET mechanisms (on/off); (ii) infographics displaying AET benefits (enhanced/basic); (iii) AET side effects (enhanced/basic); (iv) answers to AET concerns (on/off); (v) breast cancer survivor (patient) input: quotes and photographs (on/off). Healthy adult women (n = 1,604), recruited via a market research company, were randomized to 1 of 32 experimental conditions, which determined the levels of components received. Participants completed the Beliefs about Medicines Questionnaire before and after viewing the leaflet.

RESULTS

There was a significant main effect of patient input on beliefs about medication (β = 0.063, p < .001). There was one significant synergistic two-way interaction between diagrams and benefits (β = 0.047, p = .006), and one antagonistic two-way interaction between diagrams and side effects (β = -0.029, p = .093). There was a synergistic three-way interaction between diagrams, concerns, and patient input (β = 0.029, p = .085), and an antagonistic four-way interaction between diagrams, benefits, side effects, and concerns (β = -0.038, p = .024). In a stepped approach, we screened in four components and screened out the side effects component.

CONCLUSIONS

The optimized leaflet did not contain enhanced AET side effect information. Factorial experiments are efficient and effective for refining the content of information leaflet interventions.

Guidance on selecting a translational framework for intervention development: Optimizing interventions for impact

Intervention development frameworks offer the behavioral sciences a systematic and rigorous empirical process to guide the translation of basic science into practice in pursuit of desirable public health and clinical outcomes. The multiple frameworks that have emerged share a goal of optimization during intervention development and can increase the likelihood of arriving at an effective and disseminable intervention. Yet, the process of optimizing an intervention differs functionally and conceptually across frameworks, creating confusion and conflicting guidance on when and how to optimize. This paper seeks to facilitate the use of translational intervention development frameworks by providing a blueprint for selecting and using a framework by considering the process of optimization as conceptualized by each. First, we operationalize optimization and contextualize its role in intervention development. Next, we provide brief overviews of three translational intervention development frameworks (ORBIT, MRC, and MOST), identifying areas of overlap and divergence thereby aligning core concepts across the frameworks to improve translation. We offer considerations and concrete use cases for investigators seeking to identify and use a framework in their intervention development research. We push forward an agenda of a norm to use and specify frameworks in behavioral science to support a more rapid translational pipeline.

Acceptability, fidelity and trial experience of four intervention components to support medication adherence in women with breast cancer: A process evaluation protocol for a pilot fractional factorial trial

Background

The Refining and Optimising a behavioural intervention to Support Endocrine Therapy Adherence (ROSETA) programme has developed four intervention components aiming to improve medication adherence in women with early-stage breast cancer. These are (a) text messages, (b) information leaflet, (c) Acceptance and Commitment Therapy-based guided self-help (ACT), (d) side-effect management website. Guided by the Multiphase Optimisation Strategy, our pilot trial will use a fractional factorial design to evaluate the feasibility of undertaking a larger optimisation trial. The pilot will include a process evaluation to maximise learning regarding the fidelity and acceptability of the intervention components before proceeding with a larger trial. The trial process evaluation has three aims: to assess the (1) fidelity and (2) acceptability of the intervention components; and (3) to understand participant's trial experience, and barriers and facilitators to recruitment and retention.

Methods

The process evaluation will use multiple methods. Fidelity of the intervention components will be assessed using self-reported questionnaire data, trial data on intervention component adherence, and observations of the ACT sessions. Acceptability of the intervention components and trial experience will be explored using an acceptability questionnaire and interviews with patients and trial therapists. Trial experience will be assessed using a questionnaire and interviews with participants, while barriers and facilitators to recruitment and retention will be assessed using a questionnaire completed by research nurses and participant interviews. The pilot trial opened for recruitment on 20th May 2022 and was open at the time of submission.

Conclusions

This process evaluation will provide information regarding whether the intervention components can be delivered with fidelity within a national healthcare setting and are acceptable to participants. We will also better understand participant experience in a pilot trial with a fractional factorial design, and any barriers and facilitators to recruitment and retention.

Registration

ISRCTN registry ( ISRCTN10487576, 16/12/2021).

Refining and optimising a behavioural intervention to support endocrine therapy adherence (ROSETA) in UK women with breast cancer: protocol for a pilot fractional factorial trial

INTRODUCTION

Women with breast cancer who do not adhere to adjuvant endocrine therapy (AET) have increased risks of mortality and recurrence. There are multiple barriers to AET adherence, including medication side-effects, beliefs about medication, memory and psychological distress. We developed four intervention components, each targeting a different barrier. This pilot trial is part of the preparation phase of the Multiphase Optimisation Strategy, and aims to establish key trial parameters, establish intervention component adherence, establish availability and feasibility of outcome and process data, estimate variability in planned outcome measures and estimate cost of developing and delivering each intervention component.

METHODS AND ANALYSIS

The four intervention components are as follows: short message service text reminders (target: memory); a written information leaflet (target: medication beliefs); a guided self-help Acceptance and Commitment Therapy programme (target: psychological flexibility to reduce distress) and a self-management website (target: side-effect management). To evaluate the feasibility of recruitment, acceptability of the intervention components and the availability of outcome data, we will conduct a multisite, exploratory pilot trial using a 2^4-1 fractional factorial design, with a nested process evaluation. We will randomise 80 women with early-stage breast cancer who have been prescribed AET to one of eight experimental conditions. This will determine the combination of intervention components they receive, ranging from zero to four, with all conditions receiving usual care. Key outcomes of interest include medication adherence and quality of life. Progression to the optimisation phase will be based on predefined criteria for consent rates, patient adherence to intervention components and availability of medication adherence data.

ETHICS AND DISSEMINATION

The study was reviewed by the Wales Research Authority Research Ethics Committee 3 (21/WA/0322). Written informed consent will be obtained from all patients before randomisation. The results of this trial will be disseminated in a peer-reviewed journal.

TRIAL REGISTRATION NUMBER

ISRTCN10487576.

Quantifying implementation strategy and dissemination channel preferences and experiences for pain management in primary care: a novel implementer-reported outcome

BACKGROUND

Precision implementation science requires methods to evaluate and select implementation strategies. This study developed and evaluated a novel measure of concordance between current and preferred dissemination channels (DC) and implementation strategies (IS) to guide efforts to improve the adoption of evidence-based management strategies for chronic pain.

METHODS

We conducted a one-time electronic survey of Pennsylvania primary care practitioners (PCPs) about current vs. preferred chronic pain management DC and IS use. Survey items were selected based on preliminary data, the Model for Dissemination of Research, and the Evidence-Based Recommendations for Implementing Change taxonomy of implementation strategies. We used Cohen's kappa (κ) to assess the agreement between participant-level current and preferred DC/IS. We calculated % preferred minus % experienced for each DC/IS and assessed the equality of proportions to determine whether this difference significantly departed from zero. We categorized DC and IS based on the degree of use and preference, to evaluate alignment.

RESULTS

The current sample included 101 Pennsylvania PCPs primarily in urban (94.06%), non-academic (90.10%) settings who self-identified as mostly female (66.34%) and white (85.15%). The greatest difference between preferred and experienced DCs, or "need," was identified by participants as workshops, clinical experts, seminars, and researchers. Similarly, participants reported the greatest IS gaps as multidisciplinary chronic pain workgroups, targeted support for clinicians, and a chronic pain clinical champion. Participating PCPs had moderate DC concordance (kappa = 0.45, 95% CI = 0.38-0.52) and low IS concordance (kappa = 0.18, 95% CI = 0.13-0.23). DC and IS concordance were both greater than that expected by chance. We further identified well-aligned DC and IS, including professional organizations, briefs, and guidelines.

CONCLUSION

We identified a novel implementer-reported outcome of dissemination channel and implementation strategy concordance that allows implementation scientists to quantify the magnitude of the gap between the current and preferred experience of implementers. This quantitative measure can help with the selection and evaluation of dissemination channels and implementation strategies. Future research should leverage this measure to understand the degree to which preference concordance influences clinical outcomes and performance.

mHealth Interventions to Promote a Healthy Diet and Physical Activity among Cancer Survivors: A Systematic Review of Randomized Controlled Trials

Simple Summary

The number of cancer survivors has increased dramatically in the past several decades. Research indicates that health behaviors, including having a healthy diet and engaging in regular exercise, may improve the treatment outcomes and quality of life of cancer survivors. Behavioral interventions using web and mobile technology may be feasible and acceptable approaches to modify physical activity and dietary behaviors. This review summarizes the feasibility, acceptability and estimated effects of physical activity and dietary interventions using web and mobile technology from the published studies.

Abstract

Background: Technology-based interventions are increasingly used to improve physical activity (PA) and diet. Methods: We conducted a systematic review of randomized controlled trials (RCTs) published up to August 2021 that tested mobile health (mHealth) PA and/or dietary interventions among cancer survivors and reported on the feasibility, satisfaction, behavioral change, and/or quality of life (QOL) outcomes. Results: In total, 61 articles were identified on PubMed, and 23 of those met the inclusion criteria. The most common cancers were breast (n = 1000), prostate (n = 713), and colorectal (n = 650). Participants were predominantly White (median: 84%, interquartile range (IQR): 20%) and college-educated (58%). The interventions varied, but the most common combination of components (six studies) was a website/mobile app with an activity tracker and coaching. In terms of duration, 70% (n = 16) of the interventions lasted 12 weeks. The median total tracker wear was 87% of the study days (IQR: 6%) and the median text-message reply rate was 73% (IQR 4%). Most participants (median: 87%; IQR: 16%) were satisfied with at least one intervention component. Eleven out of 18 studies examining behavioral change reported significant between-group differences and six out of 11 studies examining QoL reported significant improvements. Conclusions: mHealth interventions are a promising approach to improving the PA and diets of cancer survivors. Research in racially/ethnically and socioeconomically diverse populations is needed.

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

With thousands of mobile health (mHealth) solutions on the market, patients and health care providers struggle to identify which solution to use and prescribe. The lack of evidence-based mHealth solutions may be because of limited research on intervention development and the continued use of traditional research methods for mHealth evaluation. The Multiphase Optimization Strategy (MOST) is a framework that aids in developing interventions that produce the best-expected outcomes (ie, effectiveness), given constraints imposed on affordability, scalability, and efficiency (also known as achieving intervention EASE). The preparation phase of the MOST highlights the importance of formative intervention development—a stage often overlooked and rarely published. The aim of the preparation phase of the MOST is to identify candidate intervention components, create a conceptual model, and define the optimization objective. Although the MOST sets these 3 targets, no guidance is provided on how to conduct quality research within the preparation phase and what specific 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 the MOST within the field of implementation science, this study provides an account of the methods used to develop an mHealth intervention using the MOST. Specifically, we provide an example of how to achieve the goals of the preparation phase by outlining the formative development of an mHealth-prompting intervention within a diabetes prevention program. In addition, recommendations are proposed for future researchers to consider when conducting formative research on mHealth interventions with implementation in mind. Given its considerable reach, mHealth has the potential to positively affect public health by decreasing implementation costs and improving accessibility. The 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 the preparation phase of the MOST, this study may guide future intervention developers toward maximizing the impact of mHealth outside academia.

Human-centered design methods to achieve preparation phase goals in the multiphase optimization strategy framework

Background

The public health impact of behavioral and biobehavioral interventions to prevent and treat mental health and substance use problems hinges on developing methods to strategically maximize their effectiveness, affordability, scalability, and efficiency.

Methods

The multiphase optimization strategy (MOST) is an innovative, principled framework that guides the development of multicomponent interventions. Each phase of MOST (Preparation, Optimization, Evaluation) has explicit goals and a range of appropriate research methods to achieve them. Methods for attaining Optimization and Evaluation phase goals are well-developed. However, methods used in the Preparation phase are often highly researcher-specific, and concrete ways to achieve Preparation phase goals are a priority area for further development.

Results

We propose that the discover, design, build, and test (DDBT) framework provides a theory-driven and methods-rich roadmap for achieving the goals of the Preparation phase of MOST, including specifying the conceptual model, identifying and testing candidate intervention components, and defining the optimization objective. The DDBT framework capitalizes on strategies from the field of human-centered design and implementation science to drive its data collection methods.

Conclusions

MOST and DDBT share many conceptual features, including an explicit focus on implementation determinants, being iterative and flexible, and designing interventions for the greatest public health impact. The proposed synthesized DDBT/MOST approach integrates DDBT into the Preparation phase of MOST thereby providing a framework for rigorous and efficient intervention development research to bolster the success of intervention optimization.

Plain Language Summary

1. What is already known about the topic? Optimizing behavioral interventions to balance effectiveness with affordability, scalability, and efficiency requires a significant investment in intervention development.2. What does this paper add? This paper provides a structured approach to integrating human-centered design principles into the Preparation phase of the multiphase optimization strategy (MOST).3. What are the implications for practice, research, or policy? The proposed synthesized model provides a framework for rigorous and efficient intervention development research in the Preparation phase of MOST that will ensure the success of intervention optimization and contribute to improving public health impact of mental health and substance use interventions.

Behavioral medicine, cancer control, and NCI: reflections on a fruitful past and auspicious future

Cancer prevention and control has benefited substantially from behavioral medicine research over the last several decades. The National Cancer Institute's (NCI) Division of Cancer Control and Population Sciences, ably led by Barbara Rimer and then Bob Croyle since being established in 1997, has been a primary supporter of this research. NCI has made significant investments in many of the topics featured in this special section and will continue to do so. These include research on basic behavioral processes such as affect as well as optimal approaches to health communication. A key and enduring focus has been the support of behavioral interventions, particularly for tobacco, diet, physical activity, and sun exposure. The success of such interventions will be amplified to the extent that they leverage novel research designs, emerging digital technologies, evidence gleaned from the burgeoning field of implementation science, and lessons learned from greater attention to the impact of health disparities and inequities. Moreover, as the cancer survivor population continues to grow given the rapid development of diagnostic and therapeutic science, it will be even more essential to devote attention to understanding and addressing the health care and other needs of survivors such as cognitive dysfunction and financial toxicity. The field of behavioral medicine should be both applauded for its many contributions to reducing the cancer burden and encouraged to continue developing new research ideas in these critical areas.