{"title":"利用多相优化技术(MOST)框架优化实施战略:使用因子设计的实用指导。","authors":"Jacob Szeszulski, Kate Guastaferro","doi":"10.1093/tbm/ibae035","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":48679,"journal":{"name":"Translational Behavioral Medicine","volume":" ","pages":"505-513"},"PeriodicalIF":3.6000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimization of implementation strategies using the Multiphase Optimization STratgey (MOST) framework: Practical guidance using the factorial design.\",\"authors\":\"Jacob Szeszulski, Kate Guastaferro\",\"doi\":\"10.1093/tbm/ibae035\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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. 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引用次数: 0
摘要
多阶段优化战略(MOST)是一个框架,它利用准备、优化和评估三个阶段来制定多成分干预措施,通过战略性地平衡有效性、可负担性、可扩展性和效率,实现干预的 EASE。在实施科学中,干预措施的优化需要关注实施策略--我们为实施干预措施所做的事情--以及实施结果。社会变革管理计划主要用于优化干预措施中与行为或健康结果相关的部分。然而,优化离散(即单一策略)和多方面(即多种策略)实施策略的创新机会是存在的,可以独立完成,也可以与干预优化一起完成。本文详细介绍了社会变革管理框架和因子设计可用于优化实施策略的四种情况:(i) 制定新的多方面实施策略;(ii) 评估计划组成部分与离散或多方面实施策略之间的相互作用;(iii) 评估先前作为多方面实施策略评估过的几种离散策略的独立效果;(iv) 根据当地情况修改离散或多方面实施策略。我们提供了假定的校本体育活动实例来说明这四种情况,并提供假定数据,帮助读者根据试验数据做出明智的决定。本手稿为实施科学家提供了一个蓝图,使该领域不仅能利用 MOST 优化干预措施对行为或健康结果的效果,还能优化干预措施的实施。
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.
期刊介绍:
Translational Behavioral Medicine publishes content that engages, informs, and catalyzes dialogue about behavioral medicine among the research, practice, and policy communities. TBM began receiving an Impact Factor in 2015 and currently holds an Impact Factor of 2.989.
TBM is one of two journals published by the Society of Behavioral Medicine. The Society of Behavioral Medicine is a multidisciplinary organization of clinicians, educators, and scientists dedicated to promoting the study of the interactions of behavior with biology and the environment, and then applying that knowledge to improve the health and well-being of individuals, families, communities, and populations.