Understanding Health Behavior Technology Engagement: Pathway to Measuring Digital Behavior Change Interventions
Understanding Health Behavior Technology Engagement: Pathway to Measuring Digital Behavior Change Interventions
复制标题
了解健康行为技术参与：衡量数字行为改变干预措施的途径
DOI：
10.2196/preprints.14052
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发表时间：
2019
影响因子：
2.2
通讯作者：
J. Turgiss
中科院分区：
文献类型：
--
作者：
H. Cole;Nnamdi Ezeanochie;J. Turgiss
数字行为改变干预（DBCI）的研究人员和实践者对“参与”一词的定义各不相同，而且常常是不一致的，因此导致DBCI测量和评估缺乏准确性。本文的目的是为各种类型的用户参与提出离散的定义，并解释为什么测量这些参与类型的精度是确保干预对健康行为调节有效的必要条件。此外，本文还提出了如何在实践中衡量敬业度的框架和实际步骤，并将其用于DBCI设计和评估。DBCI的主要目的是影响用户目标健康行为的改变，这可能最终改善健康结果。利用现有文献和基于实践的DBCI知识，该框架概念化了必须在DBCI中测量的两种主要参与类别。这两类分别是健康行为参与度，简称“大E”，以及DBCI参与度，简称“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的干预功能（即简单的登录、游戏和社交互动）的交互，并使用户体验具有吸引力；(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，大E的实现取决于小E。如果用户不与DBCI功能交互并享受用户体验，那么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还依赖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造了小e，而小e又旨在改进大e。提议的框架包括一个支持DBCI测量的模型，该模型描述了参与的类别，并详细说明了小e的特征如何产生大e。该框架可应用于DBCI以支持各种健康行为和结果，并可用于识别干预功效和有效性方面的差距。
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.
影响因子：
3.6
作者：
Riley, William T.;Rivera, Daniel E.;Atienza, Audie A.;Nilsen, Wendy;Allison, Susannah M.;Mermelstein, Robin
通讯作者：
Mermelstein, Robin