ArticleHealth psychology : official journal of the Division of Health Psychology, American Psychological Association2024
Using decision analysis for intervention value efficiency to select optimized interventions in the multiphase optimization strategy.
Article in Health psychology : official journal of the Division of Health Psychology, American Psychological Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advancing translational research in digital cardiac rehabilitation: The preparation phase of the Multiphase Optimization Strategy.Translational behavioral medicine · 2025Pooled it
- Optimizing Digital Cardiac Rehabilitation Using the Multiphase Optimization Strategy: Mixed Methods Feasibility Study.JMIR formative research · 2026Trial
- Effects of behavioral intervention components to increase COVID-19 testing for African American/Black and Latine frontline essential workers not up-to-date on COVID-19 vaccination: Results of an optimization randomized controlled trial.Journal of behavioral medicine · 2025Trial
- Assessing multidimensional fidelity in a pilot optimization trial: A process evaluation of four intervention components supporting medication adherence in women with breast cancer.Translational behavioral medicine · 2025Trial
- Power Calculation in 2Prevention science : the official journal of the Society for Prevention Research · 2026Article
- Identifying the most effective components of a physical activity intervention for adults with knee replacement: the MOST Energized! study protocol.BMC musculoskeletal disorders · 2026Article
- Integrating implementation science and intervention optimization.Implementation science : IS · 2025Article
- Evidence-Based Design of Prescription Medication Information: An Updated Scoping Review.Drug safety · 2025Article
- Optimizing a mobile just-in-time adaptive intervention (JITAI) for weight loss in young adults: Rationale and design of the AGILE factorial randomized trial.Contemporary clinical trials · 2025Article
- Optimizing diabetes management interventions for Black and Hispanic adults using the multiphase optimization strategy: Protocol for a randomized mixed methods factorial trial.Contemporary clinical trials · 2025Article
- Twenty years of intervention optimization.Annals of behavioral medicine : a publication of the Society of Behavioral Medicine · 2025Article
- 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.Translational behavioral medicine · 2024Article
- Intervention Optimization: A Paradigm Shift and Its Potential Implications for Clinical Psychology.Annual review of clinical psychology · 2024Review
- Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectiveOptimizing 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.
methodWe define various sets of hypothetical decision-maker preferences, and we apply DAIVE to identify optimized interventions appropriate to each case.
resultsWe 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.
conclusionsWe 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).
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