Optimization Methods and Implementation Science: An Opportunity for Behavioral and Biobehavioral Interventions

Adaptive Designs in Implementation Science and Practice: Their Promise and the Need for Greater Understanding and Improved Communication

The promise of adaptation and adaptive designs in implementation science has been hindered by the lack of clarity and precision in defining what it means to adapt, especially regarding the distinction between adaptive study designs and adaptive implementation strategies. To ensure a common language for science and practice, authors reviewed the implementation science literature and found that the term adaptive was used to describe interventions, implementation strategies, and trial designs. To provide clarity and offer recommendations for reporting and strengthening study design, we propose a taxonomy that describes fixed versus adaptive implementation strategies and implementation trial designs. To improve impact, (a) futureimplementation studies should prespecify implementation strategy core functions that in turn can be taught to and replicated by health system/community partners, (b) funders should support exploratory studies that refine and specify implementation strategies, and (c) investigators should systematically address design requirements and ethical considerations (e.g., randomization, blinding/masking) with health system/community partners.

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).

Family-focused intervention to promote adolescent mental health and well-being in Moldova and North Macedonia (FLOURISH): feasibility study protocol

INTRODUCTION

Family-Focused Adolescent & Lifelong Health Promotion (FLOURISH) project will adapt, implement and evaluate a programme to support adolescent mental health and well-being through strategies, such as strengthening parenting practices, adolescent-caregiver relationships, adolescent and parent socioemotional skills, and social support.

METHODS AND ANALYSIS

The project will focus on adolescents aged 10-14 years and their caregivers in North Macedonia and Moldova. The countries were selected based on implementation readiness of two organisations and a need for accessible evidence-informed services to help mitigate health risks due to economic, social and political challenges. Parenting for Lifelong Health (PLH) for Parents and Teens is a family-based programme developed for low-resource settings. PLH has been adapted with input from advisory groups. The programme includes additional components to strengthen impacts on adolescents: adolescent mental health tools, based on UNICEF's Helping Adolescents Thrive, adolescent peer support and participation booster. This pilot is first of three study phases. The pilot will be a feasibility testing of the adapted intervention and the assessment and implementation procedures to determine further refinements. The pilot will examine if the adapted programme is acceptable for adolescents, their families and providers, explore contextual factors relevant to embedding this programme into longer-term scale-up and investigate whether the programme can be delivered with fidelity and participation; whether the participants report changes in adolescent emotional and behavioural problems, well-being and other outcomes; and whether the study tools are feasible and appropriate. Pre-post adolescent and caregiver questionnaires will provide outcome data. Process evaluation will include attendance and fidelity data, and focus groups. We will examine delivery cost and resource requirements.

ETHICS AND DISSEMINATION

The study was approved at the University of Klagenfurt (Austria), Medical Faculty at St. Cyril and Methodius University (North Macedonia) and National Committee of Ethical Expertise for Clinical Trials (Moldova). Through stakeholder engagement and dissemination, FLOURISH will advance scale-up of open-source family interventions.

TRIAL REGISTRATION NUMBER

Trial registration: ID101095528; project page: https://www.flourish-study.org/about.html; https://www.linkedin.com/company/flourish-study/.

A structured approach to applying systems analysis methods for examining implementation mechanisms

BACKGROUND

It is challenging to identify and understand the specific mechanisms through which an implementation strategy affects implementation outcomes, as implementation happens in the context of complex, multi-level systems. These systems and the mechanisms within each level have their own dynamic environments that change frequently. For instance, sequencing may matter in that a mechanism may only be activated indirectly by a strategy through another mechanism. The dosage or strength of a mechanism may vary over time or across different health care system levels. To elucidate the mechanisms relevant to successful implementation amidst this complexity, systems analysis methods are needed to model and manage complexity.

METHODS

The fields of systems engineering and systems science offer methods-which we refer to as systems analysis methods-to help explain the interdependent relationships between and within systems, as well as dynamic changes to systems over time. When applied to studying implementation mechanisms, systems analysis methods can help (i) better identify and manage unknown conditions that may or may not activate mechanisms (both expected mechanisms targeted by a strategy and unexpected mechanisms that the methods help detect) and (ii) flexibly guide strategy adaptations to address contextual influences that emerge after the strategy is selected and used.

RESULTS

In this paper, we delineate a structured approach to applying systems analysis methods for examining implementation mechanisms. The approach includes explicit steps for selecting, tailoring, and evaluating an implementation strategy regarding the mechanisms that the strategy is initially hypothesized to activate, as well as additional mechanisms that are identified through the steps. We illustrate the approach using a case example. We then discuss the strengths and limitations of this approach, as well as when these steps might be most appropriate, and suggest work to further the contributions of systems analysis methods to implementation mechanisms research.

CONCLUSIONS

Our approach to applying systems analysis methods can encourage more mechanisms research efforts to consider these methods and in turn fuel both (i) rigorous comparisons of these methods to alternative mechanisms research approaches and (ii) an active discourse across the field to better delineate when these methods are appropriate for advancing mechanisms-related knowledge.

Optimising engagement in a digital parenting intervention to prevent violence against adolescents in Tanzania: protocol for a cluster randomised factorial trial

BACKGROUND

Violence against adolescents is a universal reality, with severe individual and societal costs. There is a critical need for scalable and effective violence prevention strategies such as parenting programmes, particularly in low- and middle-income countries where rates of maltreatment are highest. Digital interventions may be a scalable and cost-effective alternative to in-person delivery, yet maximising caregiver engagement is a substantial challenge. This trial employs a cluster randomised factorial experiment and a novel mixed-methods analytic approach to assess the effectiveness, cost-effectiveness, and feasibility of intervention components designed to optimise engagement in an open-source parenting app, ParentApp for Teens. The app is based on the evidence-based Parenting for Lifelong Health for Teens programme, developed collaboratively by academic institutions in the Global South and North, the WHO, and UNICEF.

METHODS/DESIGN

Sixteen neighbourhoods, i.e., clusters, will be randomised to one of eight experimental conditions which consist of any combination of three components (Support: self-guided/moderated WhatsApp groups; App Design: sequential workshops/non-sequential modules; Digital Literacy Training: on/off). The study will be conducted in low-income communities in Tanzania, targeting socioeconomically vulnerable caregivers of adolescents aged 10 to 17 years (16 clusters, 8 conditions, 640 caregivers, 80 per condition). The primary objective of this trial is to estimate the main effects of the three components on engagement. Secondary objectives are to explore the interactions between components, the effects of the components on caregiver behavioural outcomes, moderators and mediators of programme engagement and impact, and the cost-effectiveness of components. The study will also assess enablers and barriers to engagement qualitatively via interviews with a subset of low, medium, and high engaging participants. We will combine quantitative and qualitative data to develop an optimised ParentApp for Teens delivery package.

DISCUSSION

This is the first known cluster randomised factorial trial for the optimisation of engagement in a digital parenting intervention in a low- and middle-income country. Findings will be used to inform the evaluation of the optimised app in a subsequent randomised controlled trial.

TRIAL REGISTRATION

Pan African Clinical Trial Registry, PACTR202210657553944. Registered 11 October 2022, https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=24051 .

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).

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.

Strengthening patients' triage in community pharmacies: A cluster randomised controlled trial to evaluate the clinical impact of a minor ailment service

Background

Self-perceived minor ailments might conceal other health conditions if patients are not appropriately assisted by health care professionals. The aim of the study was to evaluate the patient-related outcomes of a community pharmacy Minor Ailment Service (MAS) compared to usual pharmacist care (UC).

Methods

A cluster randomised controlled trial was conducted over six months in community pharmacy in the province of Valencia (Spain). Patients seeking care or requesting a product for a minor ailments considered in the study (dermatological problems, gastrointestinal disturbance, pain and upper respiratory tract related symptoms) were included. The intervention consisted of a standardised pharmacist-patient consultation guided by a web-based program using co-developed management protocols and patients’ educational material. Patients were followed up by phone ten days later. Primary clinical outcomes were appropriate medical referral and modification of direct product request. Secondary outcomes were symptom resolution and reconsultation rates.

Results

A total of 808 patients (323 MAS and 485 UC) were recruited in 27 pharmacies of 21 municipalities. Patients visiting MAS pharmacies had higher odds for being referred to a physician (OR = 2.343, CI95% = [1.146–4.792]) and higher reconsultation rates (OR = 1.833, CI95% = [1.151–2.919]) compared to UC. No significant differences between groups were observed for modification of direct product request and symptom resolution.

Conclusions

The use of management protocols through the MAS strengthened the identification of referral criteria such as red flags in patients suffering minor ailments. These patients with symptoms of minor ailments possibly due to more severe illness were to be referred and evaluated by physicians. Results reinforce that MAS increases safety for those patients consulting in community pharmacy for minor ailments.

Trial registration

Trial registration number:ISRCTN17235323. Retrospectively registered 07/05/2021, https://www.isrctn.com/ISRCTN17235323.

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.