Moving beyond the treatment package approach to developing behavioral interventions: addressing questions that arose during an application of the Multiphase Optimization Strategy (MOST)

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.

Evaluation of a Pilot Program to Prevent the Misuse of Prescribed Opioids Among Health Care Workers: Repeated Measures Survey Study

BACKGROUND

Overprescription of opioids has led to increased misuse of opioids, resulting in higher rates of overdose. The workplace can play a vital role in an individual's intentions to misuse prescription opioids with injured workers being prescribed opioids, at a rate 3 times the national average. For example, health care workers are at risk for injuries, opioid dispensing, and diversion. Intervening within a context that may contribute to risks for opioid misuse while targeting individual psychosocial factors may be a useful complement to interventions at policy and prescribing levels.

OBJECTIVE

This pilot study assessed the effects of a mobile-friendly opioid misuse intervention prototype tailored for health care workers using the preparation phase of a multiphase optimization strategy design.

METHODS

A total of 33 health care practitioners participated in the pilot intervention, which included 10 brief web-based lessons aimed at impacting psychosocial measures that underlie opioid misuse. The lesson topics included: addiction beliefs, addiction control, Centers for Disease Control and Prevention guidelines and recommendations, beliefs about patient-provider relationships and communication, control in communicating with providers, beliefs about self-monitoring pain and side effects, control in self-monitoring pain and side effects, diversion and disposal beliefs, diversion and disposal control, and a conclusion lesson. Using a treatment-only design, pretest and posttest surveys were collected. A general linear repeated measures ANOVA was used to assess mean differences from pretest to posttest. Descriptive statistics were used to assess participant feedback about the intervention.

RESULTS

After completing the intervention, participants showed significant mean changes with increases in knowledge of opioids (+0.459; P<.001), less favorable attitudes toward opioids (-1.081; P=.001), more positive beliefs about communication with providers (+0.205; P=.01), more positive beliefs about pain management control (+0.969; P<.001), and increased intentions to avoid opioid use (+0.212; P=.03). Of the 33 practitioners who completed the program, most felt positive about the information presented, and almost 70% (23/33) agreed or strongly agreed that other workers in the industry should complete a program like this.

CONCLUSIONS

While attempts to address the opioid crisis have been made through public health policies and prescribing initiatives, opioid misuse continues to rise. Certain industries place workers at greater risk for injury and opioid dispensing, making interventions that target workers in these industries of particular importance. Results from this pilot study show positive impacts on knowledge, attitudes, and beliefs about communicating with providers and pain management control, as well as intentions to avoid opioid misuse. However, the dropout rate and small sample size are severe limitations, and the results lack generalizability. Results will be used to inform program revisions and future optimization trials, with the intention of providing insight for future intervention development and evaluation of mobile-friendly eHealth interventions for employees.

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).

A national study on pregaming motives, frequency, consumption, and negative alcohol consequences among university students in the United States

BACKGROUND

Pregaming, or drinking before going out, is a commonly practiced risky behavior. Drinking motives are well-established predictors of alcohol use and negative alcohol consequences. Given the influence of context on drinking practices, motives specific to pregaming may affect pregaming behaviors and outcomes above and beyond general drinking motives. Thus, we examined how pregaming motives are related to pregaming behaviors and negative alcohol consequences.

METHODS

Using data from two national cross-sectional online studies, the current study included undergraduates who pregamed at least once in the past month (n=10,200, Mage=19.9, women=61%, white=73.6%; 119 U.S. universities). Participants completed assessments of demographics, general drinking motives, pregaming motives, pregaming frequency/consumption, and negative alcohol consequences. Data were analyzed using hierarchical linear models accounting for nesting of participants within sites.

RESULTS

When controlling for demographic factors and general drinking motives, interpersonal enhancement motives and intimate pursuit motives were positively associated with pregaming frequency, pregaming consumption, and negative alcohol consequences. Situational control motives were negatively associated with pregaming consumption and negative alcohol consequences. Barriers to consumption motives were negatively associated with pregaming frequency but positively associated with negative alcohol consequences.

CONCLUSIONS

Students who pregame to make the night more fun or to meet potential dating partners appear to be at particular risk for negative alcohol consequences. Motives may be modifiable, particularly via cognitive/behavioral strategies. Findings suggest that specific motives may be appropriate intervention targets when trying to reduce pregaming behaviors and negative alcohol consequences.

Masculine Drinking Norms and Alcohol Use in a National Sample of NCAA Male Student-Athletes

Objectives

Participation in sports can increase young adults' risk for heavy alcohol use and related consequences. Among student-athletes, more men report heavy drinking than women. These gender differences may reflect men's expression of masculinity which can encompass excessive consumption. While a growing body of research indicates that general masculine norms are positively associated with alcohol use and consequences among men, the extent to which alcohol-specific masculine norms can increase student-athletes' risk for elevated drinking and related outcomes is not yet known. Thus, we examined how masculine drinking norms are associated with alcohol use and related consequences while accounting for demographics and multiple dimensions of general masculine norms.

Methods

1,825 NCAA student-athletes (White=79%, Mage=20.1/SDage=1.3; 50 colleges/universities) completed a confidential online survey which included questions regarding masculine drinking norms of excess and control and conformity to general masculine norms.

Results

We created latent constructs and tested a path model in SEM. Results indicated that, after accounting for demographics and multiple dimensions of general masculine norms, the masculine drinking norm of excess was positively associated with alcohol use and consequences. Conversely, control was negatively related to alcohol use but unrelated to consequences. Compared to control and other dimensions of general masculine norms, excess was most strongly related to alcohol use and consequences.

Conclusions

A move from assessing general masculine norms toward alcohol-specific masculine norms can further researchers' and practitioners' knowledge of masculine norms and their link to drinking behaviors, and enhance the application of masculine norms in alcohol intervention and prevention programs.

Relationships between T-lymphocytes and physical function in adults with chronic lymphocytic leukemia: Results from the HEALTH4CLL pilot study

OBJECTIVE

Examine physical function and T-cell phenotype in patients with chronic lymphocytic leukemia (CLL) before and after a physical activity (PA) intervention.

METHODS

Physical function measures and blood samples were collected from CLL patients (Rai stage 0-4, 50% receiving targeted therapy, N = 24) enrolled in a 16-week intervention of at-home aerobic and/or resistance exercise. Flow cytometry characterized T-cells in cryopreserved peripheral blood cells. Wilcoxon signed-rank test compared physical function and T-cell phenotype at baseline and 16-weeks; Kendall's Tau assessed associations between variables.

RESULTS

Godin leisure-time PA score increased from baseline to 16-weeks (mean difference: 14.61, p < .01) and fatigue decreased (mean difference: 6.71, p < .001). At baseline, lower fatigue correlated with a lower proportion of CD8+ T-cells (τ = 0.32, p = .03) and cardiorespiratory fitness (CRF) inversely correlated with the percentage of PD-1+CD8+ T-cells (τ -0.31, p = .03). At 16-weeks, CRF inversely correlated with the proportion of PD-1+CD4+ T-cells (τ -0.34, p = .02). Reduced fatigue at 16-weeks correlated with an increased CD4:CD8 ratio (τ = 0.36, p = .02) and lower percentage of HLA-DR+PD-1+CD4+ T-cells (τ = -0.37, p = .01).

CONCLUSIONS

This intervention increased leisure-time PA and decreased fatigue in CLL patients. These changes correlated with an increased CD4:CD8 T-cell ratio and reduced proportion of T-cells subsets previously associated with poor outcomes in CLL patients.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02194387.

Optimising psychological treatment for pain after breast cancer: a factorial design study protocol in Denmark

INTRODUCTION

One in five breast cancer (BC) survivors are affected by persistent pain years after completing primary treatment. While the efficacy of psychological interventions for BC-related pain has been documented in several meta-analyses, reported effect sizes are generally modest, pointing to a need for optimisation. Guided by the Multiphase Optimization Strategy, the present study aims to optimise psychological treatment for BC-related pain by identifying active treatment components in a full factorial design.

METHODS AND ANALYSIS

The study uses a 2×3 factorial design, randomising 192 women with BC-related pain (18-75 years) to eight experimental conditions. The eight conditions consist of three contemporary cognitive-behavioural therapy components, namely: (1) mindful attention, (2) decentring, and (3) values and committed action. Each component is delivered in two sessions, and each participant will receive either zero, two, four or six sessions. Participants receiving two or three treatment components will be randomised to receive them in varying order. Assessments will be conducted at baseline (T1), session by session, every day for 6 days following the first session in each treatment component, at post-intervention (T2) and at 12-week follow-up (T3). Primary outcomes are pain intensity (Numerical Rating Scale) and pain interference (Brief Pain Inventory interference subscale) from T1 to T2. Secondary outcomes are pain burden, pain quality, pain frequency, pain catastrophising, psychological distress, well-being and fear of cancer recurrence. Possible mediators include mindful attention, decentring, and pain acceptance and activity engagement. Possible moderators are treatment expectancy, treatment adherence, satisfaction with treatment and therapeutic alliance.

ETHICS AND DISSEMINATION

Ethical approval for the present study was received from the Central Denmark Region Committee on Health Research Ethics (no: 1-10-72-309-40). Findings will be made available to the study funders, care providers, patient organisations and other researchers at international conferences, and published in international, peer-reviewed journals.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov Registry (NCT05444101).

Successful ingredients of effective Collaborative Care programs in low- and middle-income countries: A rapid review

Integrating mental health care in primary healthcare settings is a compelling strategy to address the mental health treatment gap in low- and middle-income countries (LMICs). Collaborative Care is the integrated care model with the most evidence supporting its effectiveness, but most research has been conducted in high-income countries. Efforts to implement this complex multi-component model at scale in LMICs will be enhanced by understanding the model components that have been effective in LMIC settings. Following Cochrane Rapid Reviews Methods Group recommendations, we conducted a rapid review to identify studies of the effectiveness of Collaborative Care for priority adult mental disorders of mhGAP (mood and anxiety disorders, psychosis, substance use disorders and epilepsy) in outpatient medical settings in LMICs. Article screening and data extraction were performed using Covidence software. Data extraction by two authors utilized a checklist of key components of effective interventions. Information was aggregated to examine how frequently the components were applied. Our search yielded 25 articles describing 20 Collaborative Care models that treated depression, anxiety, schizophrenia, alcohol use disorder or epilepsy in nine different LMICs. Fourteen of these models demonstrated statistically significantly improved clinical outcomes compared to comparison groups. Successful models shared key structural and process-of-care elements: a multi-disciplinary care team with structured communication; standardized protocols for evidence-based treatments; systematic identification of mental disorders, and a stepped-care approach to treatment intensification. There was substantial heterogeneity across studies with respect to the specifics of model components, and clear evidence of the importance of tailoring the model to the local context. This review provides evidence that Collaborative Care is effective across a range of mental disorders in LMICs. More work is needed to demonstrate population-level and longer-term outcomes, and to identify strategies that will support successful and sustained implementation in routine clinical settings.

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.

A Proof-of-Concept Pilot Test of a Behavioral Intervention to Improve Adherence to Dietary Recommendations for Cancer Prevention

OBJECTIVES

Prevention programs that can help adults improve the quality of their diets to reduce cancer risk are needed. This Phase IIa study prospectively tested a mHealth intervention designed to improve adherence to dietary quality guidelines for cancer prevention.

METHODS

All participants (N = 62) received nutrition education and a self-regulation skills curriculum, with a primary target of changing grocery shopping behavior. Using a randomized, factorial design, the study varied whether each of the following 4 components were added to the 20-week intervention: (1) location-triggered app messaging, delivered when individuals arrived at grocery stores, (2) reflections on benefits of change, delivered with extra coaching time and tailored app messages, (3) coach monitoring, in which food purchases were digitally monitored by a coach, and (4) involvement of a household member in the intervention.

RESULTS

Benchmarks were successfully met for recruitment, retention, and treatment acceptability. Across conditions, there were significant reductions in highly processed food intake (P < .001, η2 = .48), red and processed meat intake (P < .001, η2 = .20), and sugar-sweetened beverage intake (P = .008, η2 = .13) from pre-to post-treatment. Analyses examining whether each intervention component influenced change across time found that participants who received coach monitoring increased their intake of fruits, vegetables, and fiber, whereas those with no coach monitoring had less improvement (P = .01, η2 = .14). The improvement in red and processed meat was stronger among participants with household support ON, at a marginally significant level, than those with household support OFF (P = .056, η2 = .07).

CONCLUSION

This study showed feasibility, acceptability, and preliminary signals of efficacy of a remotely delivered intervention to facilitate adherence to dietary guidelines for cancer prevention and that coach monitoring and household support may be especially effective strategies. A fully powered clinical trial is warranted to test an optimized version of the intervention that includes nutrition education, self-regulation skills training, coach monitoring, and household member involvement.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04947150.

White Paper: Open Digital Health - accelerating transparent and scalable health promotion and treatment

In this White Paper, we outline recommendations from the perspective of health psychology and behavioural science, addressing three research gaps: (1) What methods in the health psychology research toolkit can be best used for developing and evaluating digital health tools? (2) What are the most feasible strategies to reuse digital health tools across populations and settings? (3) What are the main advantages and challenges of sharing (openly publishing) data, code, intervention content and design features of digital health tools? We provide actionable suggestions for researchers joining the continuously growing Open Digital Health movement, poised to revolutionise health psychology research and practice in the coming years. This White Paper is positioned in the current context of the COVID-19 pandemic, exploring how digital health tools have rapidly gained popularity in 2020-2022, when world-wide health promotion and treatment efforts rapidly shifted from face-to-face to remote delivery. This statement is written by the Directors of the not-for-profit Open Digital Health initiative (n = 6), Experts attending the European Health Psychology Society Synergy Expert Meeting (n = 17), and the initiative consultant, following a two-day meeting (19-20th August 2021).

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.

A randomized iterative approach to optimizing an online substance use intervention for collegiate athletes

BACKGROUND

Rates of drug use among collegiate athletes are high, yet there are few evidence-based interventions for this population. myPlaybook, an online intervention for collegiate athletes, targets multiple predictors of drug use (i.e., norms, positive and negative expectancies about use, and harm prevention intentions).

PURPOSE

We aimed to optimize modules from myPlaybook.

METHOD

We evaluated modules through three sequential randomized factorial trials, using the Multiphase Optimization Strategy framework. We recruited and randomized 54 (Trial 1), 47 (Trial 2), and 42 (Trial 3) schools and invited all first-year and transfer collegiate athletes to participate. Athletes completed a baseline survey, their randomly assigned modules, and immediate posttest and 30-day follow-up surveys. Across trials, 3,244 (48.8% female), 2,837 (51.9% female), and 2,193 (51.4% female) athletes participated. In Trial 1, we evaluated and revised less effective modules (defined as d < 0.3-0.4 for targeted outcomes). In Trial 2, we re-evaluated and revised less effective modules. In Trial 3, we re-evaluated the revised modules.

RESULTS

Trial 1: All effects were d < 0.15, so we revised modules to target proximal outcomes (i.e., the hypothesized mediating variables in our conceptual model), rather than specific drug use behaviors. Trial 2: Most effects were d < 0.3, so we revised all modules. Trial 3: The norms module improved descriptive and injunctive norms (all d >0.35). The expectancies module improved alcohol positive expectancies (d = 0.3). The other modules were not effective.

CONCLUSIONS

After three trials, two myPlaybook modules substantially improved proximal outcomes, increasing the likelihood that the combined intervention will have a meaningful clinical impact on collegiate athletes' drug use.

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.

Applying the Multiphase Optimization Strategy for the Development of Optimized Interventions in Palliative Care

Recent systematic reviews and meta-analyses have reported positive benefit of multicomponent "bundled" palliative care interventions for patients and family caregivers while highlighting limitations in determining key elements and mechanisms of improvement. Traditional research approaches, such as the randomized controlled trial (RCT), typically treat interventions as "bundled" treatment packages, making it difficult to assess definitively which aspects of an intervention can be reduced or replaced or whether there are synergistic or antagonistic interactions between intervention components. Progressing toward palliative care interventions that are effective, efficient, and scalable will require new strategies and novel approaches. One such approach is the Multiphase Optimization Strategy (MOST), a framework informed by engineering principles, that uses a systematic process to empirically identify an intervention comprised of components that positively contribute to desired outcomes under real-life constraints. This article provides a brief overview and application of MOST and factorial trial design in palliative care research, including our insights from conducting a pilot factorial trial of an early palliative care intervention to enhance the decision support skills of advanced cancer family caregivers (Project CASCADE).

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.

Experimental and quasi-experimental designs in implementation research

Implementation science is focused on maximizing the adoption, appropriate use, and sustainability of effective clinical practices in real world clinical settings. Many implementation science questions can be feasibly answered by fully experimental designs, typically in the form of randomized controlled trials (RCTs). Implementation-focused RCTs, however, usually differ from traditional efficacy- or effectiveness-oriented RCTs on key parameters. Other implementation science questions are more suited to quasi-experimental designs, which are intended to estimate the effect of an intervention in the absence of randomization. These designs include pre-post designs with a non-equivalent control group, interrupted time series (ITS), and stepped wedges, the last of which require all participants to receive the intervention, but in a staggered fashion. In this article we review the use of experimental designs in implementation science, including recent methodological advances for implementation studies. We also review the use of quasi-experimental designs in implementation science, and discuss the strengths and weaknesses of these approaches. This article is therefore meant to be a practical guide for researchers who are interested in selecting the most appropriate study design to answer relevant implementation science questions, and thereby increase the rate at which effective clinical practices are adopted, spread, and sustained.

Using the Multiphase Optimization Strategy (MOST) framework to test intervention delivery strategies: a study protocol

Background

Delivery of behavioral interventions is complex, as the majority of interventions consist of multiple components used either simultaneously, sequentially, or both. The importance of clearly delineating delivery strategies within these complex interventions—and furthermore understanding the impact of each strategy on effectiveness—has recently emerged as an important facet of intervention research. Yet, few methodologies exist to prospectively test the effectiveness of delivery strategies and how they impact implementation. In the current paper, we describe a study protocol for a large randomized controlled trial in which we will use the Multiphase Optimization Strategy (MOST), a novel framework developed to optimize interventions, i.e., to test the effectiveness of intervention delivery strategies using a factorial design. We apply this framework to delivery of Family Navigation (FN), an evidence-based care management strategy designed to reduce disparities and improve access to behavioral health services, and test four components related to its implementation.

Methods/design

The MOST framework contains three distinct phases: Preparation, Optimization, and Evaluation. The Preparation phase for this study occurred previously. The current study consists of the Optimization and Evaluation phases. Children aged 3-to-12 years old who are detected as “at-risk” for behavioral health disorders (n = 304) at a large, urban federally qualified community health center will be referred to a Family Partner—a bicultural, bilingual member of the community with training in behavioral health and systems navigation—who will perform FN. Families will then be randomized to one of 16 possible combinations of FN delivery strategies (2 × 2 × 2× 2 factorial design). The primary outcome measure will be achieving a family-centered goal related to behavioral health services within 90 days of randomization. Implementation data on the fidelity, acceptability, feasibility, and cost of each strategy will also be collected. Results from the primary and secondary outcomes will be reviewed by our team of stakeholders to optimize FN delivery for implementation and dissemination based on effectiveness, efficiency, and cost.

Discussion

In this protocol paper, we describe how the MOST framework can be used to improve intervention delivery. These methods will be useful for future studies testing intervention delivery strategies and their impact on implementation.

Trial registration

ClinicalTrials.gov, NCT03569449. Registered on 26 June 2018.

Research Agenda of the Oncology Nursing Society: 2019-2022

PROBLEM STATEMENT

To define the Oncology Nursing Society Research Agenda for 2019-2022.

DESIGN

Multimethod, consensus-building approach by members of the Research Agenda Project Team.

DATA SOURCES

Expert opinion, literature review, surveys, interviews, focus groups, town hall, and review of research priorities from other cancer care organizations and funding agencies.

ANALYSIS

Content analysis and descriptive statistics were used to synthesize research priority themes that emerged.

FINDINGS

Three priority areas for scientific development were identified.

IMPLICATIONS FOR NURSING

The Research Agenda can be used to focus oncology nurses' research, scholarship, leadership, and health policy efforts to advance quality cancer care, inform research funding priorities, and align initiatives and resources across the ONS enterprise.

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.

Testing e-mail content to encourage physicians to access an audit and feedback tool: a factorial randomized experiment

Background

In Ontario, an online audit and feedback tool that provides primary care physicians with detailed information about patients who are overdue for cancer screening is underused. In the present study, we aimed to examine the effect of messages operationalizing 3 behaviour change techniques on access to the audit and feedback tool and on cancer screening rates.

Methods

During May-September 2017, a pragmatic 2×2×2 factorial experiment tested 3 behaviour change techniques: anticipated regret, material incentive, and problem-solving. Outcomes were assessed using routinely collected administrative data. A qualitative process evaluation explored how and why the e-mail messages did or did not support Screening Activity Report access.

Results

Of 5449 primary care physicians randomly allocated to 1 of 8 e-mail messages, fewer than half opened the messages and fewer than 1 in 10 clicked through the messages. Messages with problem-solving content were associated with a 12.9% relative reduction in access to the tool (risk ratio: 0.871; 95% confidence interval: 0.791 to 0.958; p = 0.005), but a 0.3% increase in cervical cancer screening (rate ratio: 1.003; 95% confidence interval: 1.001 to 1.006; p = 0.003). If true, that association would represent 7568 more patients being screened. No other significant effects were observed.

Conclusions

For audit and feedback to work, recipients must engage with the data; for e-mail messages to prompt activity, recipients must open and review the message content. This large factorial experiment demonstrated that small changes in the content of such e-mail messages might influence clinical behaviour. Future research should focus on strategies to make cancer screening more user-centred.

Secondary effects of myPlaybook on college athletes' avoidance of drinking games or pregaming as a protective behavior strategy: A multisite randomized controlled study

RATIONALE

Student-athletes are at risk for engaging in drinking games and pregaming. Research suggests that brief motivational and alcohol education intervention approaches designed to reduce harmful drinking behaviors may not be effective in lowering students' participation in drinking games or pregaming.

METHOD

We evaluated the effects of myPlaybook (a student-athlete-specific web-based alcohol intervention) on student-athletes' avoidance of drinking games and pregaming over a 4-month period. Seventy-three NCAA member institutions were randomly assigned to the treatment condition or a no-intervention control. Student-athletes at these schools (N = 2449) completed assessments at baseline, 1-, and 4-months post-intervention. At each assessment, participants indicated how often they used each of several harm prevention strategies when they drank in the past month including "avoided drinking games" and "avoided drinking before going out (i.e., pregaming or pre-drinking)."

RESULTS

Controlling for gender and race/ethnicity, treatment condition was not associated with change in avoidance of drinking games and pregaming between baseline and either follow-up. Athletic season did not moderate treatment effects on avoidance of either behavior. We found no evidence that myPlaybook, a general alcohol-reduction intervention, is efficacious in influencing student-athletes' avoidance of drinking games or pregaming as a protective strategy.

CONCLUSIONS

Findings from the present study as well as other research suggest that general alcohol-focused interventions may not have secondary effects on reducing students' participation in drinking games and pregaming and as such, more specific targeted interventions should be investigated.

Designing and conducting proof-of-concept chronic pain analgesic clinical trials

Introduction:

The evolution of pain treatment is dependent on successful development and testing of interventions. Proof-of-concept (POC) studies bridge the gap between identification of a novel target and evaluation of the candidate intervention's efficacy within a pain model or the intended clinical pain population.

Methods:

This narrative review describes and evaluates clinical trial phases, specific POC pain trials, and approaches to patient profiling.

Results:

We describe common POC trial designs and their value and challenges, a mechanism-based approach, and statistical issues for consideration.

Conclusion:

Proof-of-concept trials provide initial evidence for target use in a specific population, the most appropriate dosing strategy, and duration of treatment. A significant goal in designing an informative and efficient POC study is to ensure that the study is safe and sufficiently sensitive to detect a preliminary efficacy signal (ie, a potentially valuable therapy). Proof-of-concept studies help avoid resources wasted on targets/molecules that are not likely to succeed. As such, the design of a successful POC trial requires careful consideration of the research objective, patient population, the particular intervention, and outcome(s) of interest. These trials provide the basis for future, larger-scale studies confirming efficacy, tolerability, side effects, and other associated risks.

Optimization of a technology-supported physical activity intervention for breast cancer survivors: Fit2Thrive study protocol

Fit2Thrive is a theory-guided physical activity promotion trial using the Multiphase Optimization Strategy (MOST) to test efficacy for improving physical activity of five technology-supported physical activity promotion intervention components among breast cancer survivors. This trial will recruit 256 inactive breast cancer survivors nationwide. All participants will receive the core intervention which includes a Fitbit and standard self-monitoring Fit2Thrive smartphone application which will be downloaded to their personal phone. Women will be randomized to one of 32 conditions in a factorial design involving five factors with two levels: support calls (No vs. Yes), app type (standard vs. deluxe), text messaging (No vs. Yes), online gym (No vs. Yes) and Fitbit Buddy (No vs. Yes). The proposed trial examines the effects of the components on physical activity at 12 and 24weeks. Results will support the selection of a final package of intervention components that has been optimized to maximize physical activity and is subject to an upper limit of cost. The optimized intervention will be tested in a future trial. Fit2Thrive is the first trial to use the MOST framework to develop and test a physical activity promotion intervention in breast cancer survivors and will lead to an improved understanding of how to effectively change survivors' physical activity. These findings could result in more scalable, effective physical activity interventions for breast cancer survivors, and, ultimately, improve health and disease outcomes.

Testing Behavior Change Techniques to Encourage Primary Care Physicians to Access Cancer Screening Audit and Feedback Reports: Protocol for a Factorial Randomized Experiment of Email Content

Background

Cancer Care Ontario’s Screening Activity Report (SAR) is an online audit and feedback tool designed to help primary care physicians in Ontario, Canada, identify patients who are overdue for cancer screening or have abnormal results requiring follow-up. Use of the SAR is associated with increased screening rates. To encourage SAR use, Cancer Care Ontario sends monthly emails to registered primary care physicians announcing that updated data are available. However, analytics reveal that 50% of email recipients do not open the email and less than 7% click the embedded link to log in to their report.

Objective

The goal of the study is to determine whether rewritten emails result in increased log-ins. This manuscript describes how different user- and theory-informed messages intended to improve the impact of the monthly emails will be experimentally tested and how a process evaluation will explore why and how any effects observed were (or were not) achieved.

Methods

A user-centered approach was used to rewrite the content of the monthly email, including messages operationalizing 3 behavior change techniques: anticipated regret, material incentive (behavior), and problem solving. A pragmatic, 2x2x2 factorial experiment within a multiphase optimization strategy will test the redesigned emails with an embedded qualitative process evaluation to understand how and why the emails may or may not have worked. Trial outcomes will be ascertained using routinely collected administrative data. Physicians will be recruited for semistructured interviews using convenience and snowball sampling.

Results

As of April 2017, 5576 primary care physicians across the province of Ontario, Canada, had voluntarily registered for the SAR, and in so doing, signed up to receive the monthly email updates. From May to August 2017 participants received the redesigned monthly emails with content specific to their allocated experimental condition prompting use of the SAR. We have not yet begun analyses.

Conclusions

This study will inform how to communicate effectively with primary care providers by email and identify which behavior change techniques tested are most effective at encouraging engagement with an audit and feedback report.

Trial Registration

ClinicalTrials.gov NCT03124316; https://clinicaltrials.gov/ct2/show/NCT03124316 (Archived by WebCite at http://www.webcitation.org/6w2MqDWGu)

An Overview of Research and Evaluation Designs for Dissemination and Implementation

The wide variety of dissemination and implementation designs now being used to evaluate and improve health systems and outcomes warrants review of the scope, features, and limitations of these designs. This article is one product of a design workgroup that was formed in 2013 by the National Institutes of Health to address dissemination and implementation research, and whose members represented diverse methodologic backgrounds, content focus areas, and health sectors. These experts integrated their collective knowledge on dissemination and implementation designs with searches of published evaluations strategies. This article emphasizes randomized and nonrandomized designs for the traditional translational research continuum or pipeline, which builds on existing efficacy and effectiveness trials to examine how one or more evidence-based clinical/prevention interventions are adopted, scaled up, and sustained in community or service delivery systems. We also mention other designs, including hybrid designs that combine effectiveness and implementation research, quality improvement designs for local knowledge, and designs that use simulation modeling.

Applying and advancing behavior change theories and techniques in the context of a digital health revolution: proposals for more effectively realizing untapped potential

As more behavioral health interventions move from traditional to digital platforms, the application of evidence-based theories and techniques may be doubly advantageous. First, it can expedite digital health intervention development, improving efficacy, and increasing reach. Second, moving behavioral health interventions to digital platforms presents researchers with novel (potentially paradigm shifting) opportunities for advancing theories and techniques. In particular, the potential for technology to revolutionize theory refinement is made possible by leveraging the proliferation of "real-time" objective measurement and "big data" commonly generated and stored by digital platforms. Much more could be done to realize this potential. This paper offers proposals for better leveraging the potential advantages of digital health platforms, and reviews three of the cutting edge methods for doing so: optimization designs, dynamic systems modeling, and social network analysis.

Multilevel Interventions Targeting Obesity: Research Recommendations for Vulnerable Populations

INTRODUCTION

The origins of obesity are complex and multifaceted. To be successful, an intervention aiming to prevent or treat obesity may need to address multiple layers of biological, social, and environmental influences.

METHODS

NIH recognizes the importance of identifying effective strategies to combat obesity, particularly in high-risk and disadvantaged populations with heightened susceptibility to obesity and subsequent metabolic sequelae. To move this work forward, the National Heart, Lung, and Blood Institute, in collaboration with the NIH Office of Behavioral and Social Science Research and NIH Office of Disease Prevention convened a working group to inform research on multilevel obesity interventions in vulnerable populations. The working group reviewed relevant aspects of intervention planning, recruitment, retention, implementation, evaluation, and analysis, and then made recommendations.

RESULTS

Recruitment and retention techniques used in multilevel research must be culturally appropriate and suited to both individuals and organizations. Adequate time and resources for preliminary work are essential. Collaborative projects can benefit from complementary areas of expertise and shared investigations rigorously pretesting specific aspects of approaches. Study designs need to accommodate the social and environmental levels under study, and include appropriate attention given to statistical power. Projects should monitor implementation in the multiple venues and include a priori estimation of the magnitude of change expected within and across levels.

CONCLUSIONS

The complexity and challenges of delivering interventions at several levels of the social-ecologic model require careful planning and implementation, but hold promise for successful reduction of obesity in vulnerable populations.

Perceived norms and alcohol use among first-year college student-athletes' different types of friends

OBJECTIVE

To describe first-year college student-athletes' friendship contexts and test whether their perceptions of alcohol use and approval by different types of friends are associated with their own alcohol use.

PARTICIPANTS

First-year student-athletes (N = 2,622) from 47 colleges and universities participating in National Collegiate Athletic Association (NCAA) sports during February-March 2013.

METHODS

Student-athletes completed online surveys during the baseline assessment of an alcohol and other drug prevention program evaluation. Analyses tested whether perceptions of friends' alcohol use (descriptive norms) and perceptions of friends' approval of alcohol use (injunctive norms) predicted their alcohol use.

RESULTS

Both use and approval perceptions by upperclassmen, same-team, and most influential friends significantly predicted alcohol use. By contrast, only perceived use by first-year, nonteam, and less influential friends significantly predicted alcohol use.

CONCLUSIONS

Athletics departments' alcohol policies and prevention programming for first-year student-athletes should address the potential influence of different types of friends on alcohol use.

Evaluating Digital Health Interventions: Key Questions and Approaches

Digital health interventions have enormous potential as scalable tools to improve health and healthcare delivery by improving effectiveness, efficiency, accessibility, safety, and personalization. Achieving these improvements requires a cumulative knowledge base to inform development and deployment of digital health interventions. However, evaluations of digital health interventions present special challenges. This paper aims to examine these challenges and outline an evaluation strategy in terms of the research questions needed to appraise such interventions. As they are at the intersection of biomedical, behavioral, computing, and engineering research, methods drawn from all of these disciplines are required. Relevant research questions include defining the problem and the likely benefit of the digital health intervention, which in turn requires establishing the likely reach and uptake of the intervention, the causal model describing how the intervention will achieve its intended benefit, key components, and how they interact with one another, and estimating overall benefit in terms of effectiveness, cost effectiveness, and harms. Although RCTs are important for evaluation of effectiveness and cost effectiveness, they are best undertaken only when: (1) the intervention and its delivery package are stable; (2) these can be implemented with high fidelity; and (3) there is a reasonable likelihood that the overall benefits will be clinically meaningful (improved outcomes or equivalent outcomes at lower cost). Broadening the portfolio of research questions and evaluation methods will help with developing the necessary knowledge base to inform decisions on policy, practice, and research.

Mobile Interventions Targeting Risky Drinking Among University Students: A Review

Mobile interventions based on text messages, automated telephone programs (interactive voice response (IVR)), and smartphone apps offer a new approach targeting hazardous alcohol use in university students. This review covers seven recent studies involving college or university students that evaluated intervention efficacy in comparison to controls: four using text messages, one using IVR, and two smartphone apps. Only the study evaluating IVR reported positive results for the primary outcome. Two of the text message studies reported positive results on secondary outcomes, while the other two reported no differences in comparison to control groups. For smartphone apps, one study reported positive results on secondary outcomes, while the other showed no differences in comparison to controls for a web-based app and negative results for a native app. Further development of mobile interventions is needed for this at-risk population, both in terms of intervention content and use of robust research designs.

Improved Designs for Cluster Randomized Trials

Studies in which clusters of individuals are randomized to conditions are increasingly common in public health research. However, the designs utilized for such studies are often suboptimal and inefficient. We review strategies to improve the design of cluster randomized trials. We discuss both older but effective design concepts that are underutilized, such as stratification and factorial designs, as well as emergent ideas including fractional factorial designs and cluster randomized crossover studies. We draw examples from the recent literature and provide resources for sample size and power planning. Given the inherent inefficiencies of cluster randomized trials, these design strategies merit wider consideration and can lead to studies that are more cost-effective and potentially more rigorous than traditional approaches.

Federal Mechanisms to Support Intervention Dissemination

This paper examines federal mechanisms that support program developers and researchers in disseminating effective interventions for public benefit. The purpose of this paper is not to discuss the dissemination of intervention research (i.e., how to inform stakeholders about research findings), nor is it intended to discuss the research of intervention dissemination (i.e., what is the best approach to disseminate an intervention). Rather, the paper discusses the challenges specific to finding pathways to disseminate an intervention and describes federal opportunities to support intervention dissemination. Three specific mechanisms are discussed: Federal Registries of Evidence-Based Programs, the Tiered Evidence Grant Programs, and the Small Business Innovative Research (SBIR) and the Small Technology Transfer Research (STTR) programs. The article presents some limitations associated with federal mechanisms for dissemination of effective interventions, but is intended to highlight current and future opportunities they may offer.