以人为本的设计方法,实现多阶段优化战略框架中的准备阶段目标。

Implementation research and practice Pub Date : 2022-10-22 eCollection Date: 2022-01-01 DOI:10.1177/26334895221131052
Karey L O'Hara, Lindsey M Knowles, Kate Guastaferro, Aaron R Lyon
{"title":"以人为本的设计方法,实现多阶段优化战略框架中的准备阶段目标。","authors":"Karey L O'Hara, Lindsey M Knowles, Kate Guastaferro, Aaron R Lyon","doi":"10.1177/26334895221131052","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>The multiphase optimization strategy (MOST) is an innovative, principled framework that guides the development of multicomponent interventions. Each phase of MOST (<i>Preparation</i>, <i>Optimization</i>, <i>Evaluation</i>) has explicit goals and a range of appropriate research methods to achieve them. Methods for attaining <i>Optimization</i> and <i>Evaluation</i> phase goals are well-developed. However, methods used in the <i>Preparation</i> phase are often highly researcher-specific, and concrete ways to achieve <i>Preparation</i> phase goals are a priority area for further development.</p><p><strong>Results: </strong>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 <i>Preparation</i> 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.</p><p><strong>Conclusions: </strong>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 <i>Preparation</i> phase of MOST thereby providing a framework for rigorous and efficient intervention development research to bolster the success of intervention optimization.</p><p><strong>Plain language summary: </strong>1. <i>What is already known about the topic?</i> Optimizing behavioral interventions to balance effectiveness with affordability, scalability, and efficiency requires a significant investment in intervention development.2. <i>What does this paper add?</i> This paper provides a structured approach to integrating human-centered design principles into the <i>Preparation</i> phase of the multiphase optimization strategy (MOST).3. <i>What are the implications for practice, research, or policy?</i> The proposed synthesized model provides a framework for rigorous and efficient intervention development research in the <i>Preparation</i> 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.</p>","PeriodicalId":73354,"journal":{"name":"Implementation research and practice","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/d0/4d/10.1177_26334895221131052.PMC9924242.pdf","citationCount":"0","resultStr":"{\"title\":\"Human-centered design methods to achieve preparation phase goals in the multiphase optimization strategy framework.\",\"authors\":\"Karey L O'Hara, Lindsey M Knowles, Kate Guastaferro, Aaron R Lyon\",\"doi\":\"10.1177/26334895221131052\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>The multiphase optimization strategy (MOST) is an innovative, principled framework that guides the development of multicomponent interventions. Each phase of MOST (<i>Preparation</i>, <i>Optimization</i>, <i>Evaluation</i>) has explicit goals and a range of appropriate research methods to achieve them. Methods for attaining <i>Optimization</i> and <i>Evaluation</i> phase goals are well-developed. However, methods used in the <i>Preparation</i> phase are often highly researcher-specific, and concrete ways to achieve <i>Preparation</i> phase goals are a priority area for further development.</p><p><strong>Results: </strong>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 <i>Preparation</i> 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.</p><p><strong>Conclusions: </strong>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 <i>Preparation</i> phase of MOST thereby providing a framework for rigorous and efficient intervention development research to bolster the success of intervention optimization.</p><p><strong>Plain language summary: </strong>1. <i>What is already known about the topic?</i> Optimizing behavioral interventions to balance effectiveness with affordability, scalability, and efficiency requires a significant investment in intervention development.2. <i>What does this paper add?</i> This paper provides a structured approach to integrating human-centered design principles into the <i>Preparation</i> phase of the multiphase optimization strategy (MOST).3. <i>What are the implications for practice, research, or policy?</i> The proposed synthesized model provides a framework for rigorous and efficient intervention development research in the <i>Preparation</i> 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.</p>\",\"PeriodicalId\":73354,\"journal\":{\"name\":\"Implementation research and practice\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/d0/4d/10.1177_26334895221131052.PMC9924242.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Implementation research and practice\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/26334895221131052\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2022/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Implementation research and practice","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/26334895221131052","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

背景:预防和治疗心理健康和药物使用问题的行为和生物行为干预措施对公共健康的影响取决于能否制定方法,从战略上最大限度地提高其有效性、可负担性、可扩展性和效率:方法:多阶段优化策略(MOST)是一个创新的原则性框架,用于指导多成分干预措施的开发。多阶段优化战略的每个阶段(准备、优化、评估)都有明确的目标和一系列适当的研究方法来实现这些目标。实现优化和评估阶段目标的方法已经成熟。然而,在准备阶段使用的方法往往具有很强的研究针对性,因此,实现准备阶段目标的具体方法是需要进一步开发的优先领域:我们提出,发现、设计、构建和测试(DDBT)框架为实现社会变革管理计划准备阶段的目标提供了一个理论驱动、方法丰富的路线图,包括明确概念模型、识别和测试候选干预组件以及定义优化目标。DDBT 框架利用以人为本的设计和实施科学领域的策略来推动其数据收集方法:社会变革管理计划和 DDBT 在概念上有许多共同之处,包括明确关注实施的决定因素,具有迭代性和灵活性,以及设计干预措施以产生最大的公共卫生影响。拟议的 DDBT/MOST 综合方法将 DDBT 纳入 MOST 的准备阶段,从而为严格、高效的干预发展研究提供了一个框架,以促进干预优化的成功。优化行为干预措施,在有效性与可负担性、可扩展性和效率之间取得平衡,需要对干预措施的开发进行大量投资。本文提供了一种结构化方法,将以人为本的设计原则融入多阶段优化策略(MOST)的准备阶段。 3. 对实践、研究或政策有何意义?本文提出的综合模型为多阶段优化策略准备阶段严格、高效的干预开发研究提供了一个框架,这将确保干预优化的成功,并有助于提高心理健康和药物使用干预对公众健康的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

摘要图片

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
0.50
自引率
0.00%
发文量
0
审稿时长
18 weeks
期刊最新文献
Calculating power for multilevel implementation trials in mental health: Meaningful effect sizes, intraclass correlation coefficients, and proportions of variance explained by covariates. Preparation for implementation of evidence-based practices in urban schools: A shared process with implementing partners. Are we being equitable enough? Lessons learned from sites lost in an implementation trial. Examining implementation determinants of a culturally grounded, school-based prevention curriculum in rural Hawai'i: A test development and validation study. Applying the resource management principle to achieve community engagement and experimental rigor in the multiphase optimization strategy framework.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1