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Experience Sampling and Programmed Intervention Method and System for Planning, Authoring, and Deploying Mobile Health Interventions: Design and Case Reports
Journal of Medical Internet Research ( IF 5.8 ) Pub Date : 2021-07-12 , DOI: 10.2196/24278
Bruna Carolina Rodrigues Cunha 1 , Kamila Rios Da Hora Rodrigues 2 , Isabela Zaine 2 , Elias Adriano Nogueira da Silva 2 , Caio César Viel 3 , Maria Da Graça Campos Pimentel 2
Affiliation  

Background: Health professionals initiating mobile health (mHealth) interventions may choose to adapt apps designed for other activities (eg, peer-to-peer communication) or to employ purpose-built apps specialized in the required intervention, or to exploit apps based on methods such as the experience sampling method (ESM). An alternative approach for professionals would be to create their own apps. While ESM-based methods offer important guidance, current systems do not expose their design at a level that promotes replicating, specializing, or extending their contributions. Thus, a twofold solution is required: a method that directs specialists in planning intervention programs themselves, and a model that guides specialists in adopting existing solutions and advises software developers on building new ones. Objective: The main objectives of this study are to design the Experience Sampling and Programmed Intervention Method (ESPIM), formulated toward supporting specialists in deploying mHealth interventions, and the ESPIM model, which guides health specialists in adopting existing solutions and advises software developers on how to build new ones. Another goal is to conceive and implement a software platform allowing specialists to be users who actually plan, create, and deploy interventions (ESPIM system). Methods: We conducted the design and evaluation of the ESPIM method and model alongside a software system comprising integrated web and mobile apps. A participatory design approach with stakeholders included early software prototype, predesign interviews with 12 health specialists, iterative design sustained by the software as an instance of the method’s conceptual model, support to 8 real case studies, and postdesign interviews. Results: The ESPIM comprises (1) a list of requirements for mHealth experience sampling and intervention-based methods and systems, (2) a 4-dimension planning framework, (3) a 7-step-based process, and (4) an ontology-based conceptual model. The ESPIM system encompasses web and mobile apps. Eight long-term case studies, involving professionals in psychology, gerontology, computer science, speech therapy, and occupational therapy, show that the method allowed specialists to be actual users who plan, create, and deploy interventions via the associated system. Specialists’ target users were parents of children diagnosed with autism spectrum disorder, older persons, graduate and undergraduate students, children (age 8-12), and caregivers of older persons. The specialists reported being able to create and conduct their own studies without modifying their original design. A qualitative evaluation of the ontology-based conceptual model showed its compliance to the functional requirements elicited. Conclusions: The ESPIM method succeeds in supporting specialists in planning, authoring, and deploying mobile-based intervention programs when employed via a software system designed and implemented according to its conceptual model. The ESPIM ontology–based conceptual model exposes the design of systems involving active or passive sampling interventions. Such exposure supports the evaluation, implementation, adaptation, or extension of new or existing systems.

This is the abstract only. Read the full article on the JMIR site. JMIR is the leading open access journal for eHealth and healthcare in the Internet age.


中文翻译:

用于规划、创作和部署移动健康干预的经验抽样和程序化干预方法和系统:设计和案例报告

背景:发起移动健康 (mHealth) 干预的卫生专业人员可能会选择调整为其他活动(例如点对点通信)设计的应用程序或使用专门用于所需干预的专用应用程序,或基于方法开发应用程序例如经验抽样法(ESM)。专业人士的另一种方法是创建自己的应用程序。虽然基于 ESM 的方法提供了重要的指导,但当前的系统并未在促进复制、专业化或扩展其贡献的级别上公开其设计。因此,需要双重解决方案:指导专家自己规划干预计划的方法,以及指导专家采用现有解决方案并为软件开发人员构建新解决方案提供建议的模型。客观的:本研究的主要目标是设计经验抽样和程序化干预方法 (ESPIM),旨在支持专家部署 mHealth 干预,以及 ESPIM 模型,该模型指导健康专家采用现有解决方案并就如何构建软件开发人员提供建议新的。另一个目标是构思和实施一个软件平台,让专家成为实际计划、创建和部署干预措施的用户(ESPIM 系统)。方法:我们进行了 ESPIM 方法和模型的设计和评估,以及包含集成网络和移动应用程序的软件系统。与利益相关者的参与式设计方法包括早期软件原型、与 12 位健康专家的预设计访谈、软件支持的迭代设计作为方法概念模型的一个实例,支持 8 个真实案例研究和设计后访谈。结果:ESPIM 包括 (1) 对 mHealth 经验抽样和基于干预的方法和系统的要求列表,(2) 4 维规划框架,(3) 基于 7 步的过程,以及 (4)基于本体的概念模型。ESPIM 系统包含网络和移动应用程序。八个长期案例研究,涉及心理学、老年学、计算机科学、言语治疗和职业治疗的专业人士,表明该方法允许专家成为通过相关系统计划、创建和部署干预措施的实际用户。专家的目标用户是被诊断患有自闭症谱系障碍的儿童的父母、老年人、研究生和本科生、儿童(8-12 岁)和老年人的照顾者。专家报告说,他们能够在不修改原始设计的情况下创建和进行自己的研究。基于本体的概念模型的定性评估表明其符合所引出的功能要求。结论:当通过根据其概念模型设计和实施的软件系统使用时,ESPIM 方法成功地支持专家规划、创作和部署基于移动的干预程序。基于 ESPIM 本体的概念模型揭示了涉及主动或被动采样干预的系统设计。这种暴露支持新系统或现有系统的评估、实施、调整或扩展。专家报告说,他们能够在不修改原始设计的情况下创建和进行自己的研究。基于本体的概念模型的定性评估表明其符合所引出的功能要求。结论:当通过根据其概念模型设计和实施的软件系统使用时,ESPIM 方法成功地支持专家规划、创作和部署基于移动的干预程序。基于 ESPIM 本体的概念模型揭示了涉及主动或被动采样干预的系统设计。这种暴露支持新系统或现有系统的评估、实施、调整或扩展。专家报告说,他们能够在不修改原始设计的情况下创建和进行自己的研究。基于本体的概念模型的定性评估表明其符合所引出的功能要求。结论:当通过根据其概念模型设计和实施的软件系统使用时,ESPIM 方法成功地支持专家规划、创作和部署基于移动的干预程序。基于 ESPIM 本体的概念模型揭示了涉及主动或被动采样干预的系统设计。这种暴露支持新系统或现有系统的评估、实施、调整或扩展。基于本体的概念模型的定性评估表明其符合所引出的功能要求。结论:当通过根据其概念模型设计和实施的软件系统使用时,ESPIM 方法成功地支持专家规划、创作和部署基于移动的干预程序。基于 ESPIM 本体的概念模型揭示了涉及主动或被动采样干预的系统设计。这种暴露支持新系统或现有系统的评估、实施、调整或扩展。基于本体的概念模型的定性评估表明其符合所引出的功能要求。结论:当通过根据其概念模型设计和实施的软件系统使用时,ESPIM 方法成功地支持专家规划、创作和部署基于移动的干预程序。基于 ESPIM 本体的概念模型揭示了涉及主动或被动采样干预的系统设计。这种暴露支持新系统或现有系统的评估、实施、调整或扩展。通过根据其概念模型设计和实施的软件系统部署基于移动的干预程序。基于 ESPIM 本体的概念模型揭示了涉及主动或被动采样干预的系统设计。这种暴露支持新系统或现有系统的评估、实施、调整或扩展。通过根据其概念模型设计和实施的软件系统部署基于移动的干预程序。基于 ESPIM 本体的概念模型揭示了涉及主动或被动采样干预的系统设计。这种暴露支持新系统或现有系统的评估、实施、调整或扩展。

这只是摘要。阅读 JMIR 网站上的完整文章。JMIR 是互联网时代电子健康和医疗保健领域领先的开放获取期刊。
更新日期:2021-07-12
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