A multi-faceted approach to characterizing user behavior and experience in a digital mental health intervention

• Published in: Journal of biomedical informatics 2019-06, Vol.94, p.103187-103187, Article 103187
• Main Authors: Chen, Annie T., Wu, Shuyang, Tomasino, Kathryn N., Lattie, Emily G., Mohr, David C.
• Format: Article
• Language: English
• Subjects: Anxiety; Attitude to Computers; Depression; Digital interventions; Engagement; Humans; Log data; Mental Disorders - therapy; Mental Health; Mobile Applications; Telemedicine; Visualization
• ISSN: 1532-0464; 1532-0480; 1532-0480
• DOI: 10.1016/j.jbi.2019.103187

[Display omitted] •Log data in digital interventions can be leveraged to understand user engagement.•Users with different engagement patterns can be identified through app usage.•Text and visual analytics can be used to characterize engagement.•Qualitative analysis can provide insight on mechanisms of change and usability. 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.