Related Experiment Video
Updated: Jun 23, 2025

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
Published on: January 31, 2014
Optimization of implementation strategies using the Multiphase Optimization STratgey (MOST) framework: Practical
Jacob Szeszulski1, Kate Guastaferro2
1Department of Nutrition, Institute for Advancing Health Through Agriculture (IHA), Texas A&M AgriLife Research, Dallas, TX, USA.
Abstract:
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.
More Related Videos
13:54A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
Published on: August 18, 2023
06:24Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
Published on: December 15, 2017
Related Concept Videos
Factorial Design
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Time and frequency -Domain Interpretation of Phase-lead Control
The design of phase-lead control involves the strategic placement of poles and zeros to balance steady-state error and system...
Strategies for Assessing and Addressing Confounding
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Optimizing Chromatographic Separations
Band broadening refers to spreading solute bands as they travel through the column. This broadening can impact resolution. Plate height (H) represents the length required for one theoretical plate. A lower plate height corresponds to...
Problem-Solving