Understanding Health Behavior Technology Engagement: Pathway to Measuring Digital Behavior Change Interventions.
Understanding Health Behavior Technology Engagement: Pathway to Measuring Digital Behavior Change Interventions.
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DOI：
10.2196/14052
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发表时间：
2019-10-10
影响因子：
2.2
通讯作者：
Turgiss, Jennifer
中科院分区：
文献类型：
--
作者：
Cole-Lewis, Heather;Ezeanochie, Nnamdi;Turgiss, Jennifer
数字行为改变干预（DBCI）的研究人员和实践者使用不同的，往往是不一致的定义术语“参与”，从而导致DBCI测量和评估缺乏准确性。本文的目的是提出不同类型的用户参与的离散定义，并解释为什么这些参与类型的测量精度是不可或缺的，以确保干预是有效的健康行为调制。此外，本文还提供了如何在实践中衡量参与度并用于为DBCI设计和评估提供信息的框架和实际步骤。DBCI的主要目的是影响用户的目标健康行为的变化，这可能最终改善健康结果。使用现有的文献和实践为基础的知识DBCI，框架概念化的两个主要类别的参与，必须在DBCI测量。这些类别是健康行为参与，称为“大E”，和DBCI参与，称为“小E”。“DBCI参与进一步分为两个子类：（1）用户与旨在鼓励使用频率（即简单登录，游戏和社交互动）并使用户体验具有吸引力的干预功能的交互，以及（2）用户与行为改变干预组件（即行为改变技术）的交互，这些组件影响健康行为的决定因素并随后影响健康行为。在通过数字手段提供的干预中实现大E取决于小E。如果用户不与DBCI功能进行交互并享受用户体验，则接触行为改变干预组件将受到限制，并且不太可能影响导致健康行为参与的行为决定因素（大E）。大E还取决于解决方案中行为改变干预组件的质量和相关性。因此，用户交互和行为改变干预组件的组合创建了小e，而小e又被设计为改进大E。拟议的框架包括一个模型，以支持测量DBCI，描述了参与的类别和细节如何小e产生大E的功能。该框架可应用于DBCI，以支持各种健康行为和结果，并可用于确定干预效果和有效性的差距。
Researchers and practitioners of digital behavior change interventions (DBCI) use varying and, often, incongruent definitions of the term "engagement," thus leading to a lack of precision in DBCI measurement and evaluation. The objective of this paper is to propose discrete definitions for various types of user engagement and to explain why precision in the measurement of these engagement types is integral to ensuring the intervention is effective for health behavior modulation. Additionally, this paper presents a framework and practical steps for how engagement can be measured in practice and used to inform DBCI design and evaluation. The key purpose of a DBCI is to influence change in a target health behavior of a user, which may ultimately improve a health outcome. Using available literature and practice-based knowledge of DBCI, the framework conceptualizes two primary categories of engagement that must be measured in DBCI. The categories are health behavior engagement, referred to as "Big E," and DBCI engagement, referred to as "Little e." DBCI engagement is further bifurcated into two subclasses: (1) user interactions with features of the intervention designed to encourage frequency of use (ie, simple login, games, and social interactions) and make the user experience appealing, and (2) user interactions with behavior change intervention components (ie, behavior change techniques), which influence determinants of health behavior and subsequently influence health behavior. Achievement of Big E in an intervention delivered via digital means is contingent upon Little e. If users do not interact with DBCI features and enjoy the user experience, exposure to behavior change intervention components will be limited and less likely to influence the behavioral determinants that lead to health behavior engagement (Big E). Big E is also dependent upon the quality and relevance of the behavior change intervention components within the solution. Therefore, the combination of user interactions and behavior change intervention components creates Little e, which is, in turn, designed to improve Big E. The proposed framework includes a model to support measurement of DBCI that describes categories of engagement and details how features of Little e produce Big E. This framework can be applied to DBCI to support various health behaviors and outcomes and can be utilized to identify gaps in intervention efficacy and effectiveness.