A multi-faceted approach to characterizing user behavior and experience in a digital mental health intervention
A multi-faceted approach to characterizing user behavior and experience in a digital mental health intervention
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DOI：
10.1016/j.jbi.2019.103187
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发表时间：
2019-06-01
影响因子：
4.5
通讯作者：
Mohr, David C.
中科院分区：
文献类型：
--
作者：
Chen, Annie T.;Wu, Shuyang;Mohr, David C.
数字干预为支持与健康相关的行为改变提供了巨大的希望。然而，我们还需要了解人们如何应对这些挑战。在这项研究中，我们提出了一种新的混合方法来分析数字干预措施收集的复杂而丰富的数据。我们对IntelliCare进行了二次分析，参与者可以在八周内尝试14种不同的心理健康应用程序。我们分析的目标是描述用户的应用使用行为和体验，并植根于参与度的理论概念，即使用和用户体验。在第一个目标中，我们采用聚类分析来确定参与者的子组，这些子组在使用特定应用程序的频率方面具有相似性，然后采用其他参与措施来比较聚类。我们确定了四个具有不同应用程序使用模式的集群：低使用率，高使用率，日常功能用户和日常用户。每个集群都以其应用程序使用的总体频率或参与者使用的主要应用程序来区分。在第二个目标中，我们开发了一种计算机辅助文本分析和可视化方法-消息突出显示-以方便集群的比较。最后，我们使用参与者的信息进行了定性分析，以更好地理解聚类分析中突出应用程序的变化和可用性机制。我们的新方法将文本和视觉分析与更传统的定性分析技术相结合，可用于生成有关数字健康环境中用户行为和体验的见解，用于后续个性化，并确定干预技术的改进领域。
Digital interventions offer great promise for supporting health-related behavior change. However, there is much that we have yet to learn about how people respond to them. In this study, we present a novel mixed-methods approach to analysis of the complex and rich data that digital interventions collect. We perform secondary analysis of IntelliCare, an intervention in which participants are able to try 14 different mental health apps over the course of eight weeks. The goal of our analysis is to characterize users' app use behavior and experiences, and is rooted in theoretical conceptualizations of engagement as both usage and user experience. In the first aim, we employ cluster analysis to identify subgroups of participants that share similarities in terms of the frequency of their usage of particular apps, and then employ other engagement measures to compare the clusters. We identified four clusters with different app usage patterns: Low Usage, High Usage, Daily Feats Users, and Day to Day users. Each cluster was distinguished by its overall frequency of app use, or the main app that participants used. In the second aim, we developed a computer-assisted text analysis and visualization method - message highlighting - to facilitate comparison of the clusters. Last, we performed a qualitative analysis using participant messages to better understand the mechanisms of change and usability of salient apps from the cluster analysis. Our novel approach, integrating text and visual analytics with more traditional qualitative analysis techniques, can be used to generate insights concerning the behavior and experience of users in digital health contexts, for subsequent personalization and to identify areas for improvement of intervention technologies.