Abstract
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.
| Original language | English (US) |
|---|---|
| Article number | 103187 |
| Journal | Journal of Biomedical Informatics |
| Volume | 94 |
| DOIs | |
| State | Published - Jun 1 2019 |
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Keywords
- Anxiety
- Depression
- Digital interventions
- Engagement
- Log data
- Visualization
ASJC Scopus subject areas
- Computer Science Applications
- Health Informatics
Cite this
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A multi-faceted approach to characterizing user behavior and experience in a digital mental health intervention. / Chen, Annie T.; Wu, Shuyang; Tomasino, Kathryn Noth; Lattie, Emily Gardiner; Mohr, David C.
In: Journal of Biomedical Informatics, Vol. 94, 103187, 01.06.2019.Research output: Contribution to journal › Article
TY - JOUR
T1 - A multi-faceted approach to characterizing user behavior and experience in a digital mental health intervention
AU - Chen, Annie T.
AU - Wu, Shuyang
AU - Tomasino, Kathryn Noth
AU - Lattie, Emily Gardiner
AU - Mohr, David C
PY - 2019/6/1
Y1 - 2019/6/1
N2 - 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.
AB - 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.
KW - Anxiety
KW - Depression
KW - Digital interventions
KW - Engagement
KW - Log data
KW - Visualization
UR - http://www.scopus.com/inward/record.url?scp=85065074463&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85065074463&partnerID=8YFLogxK
U2 - 10.1016/j.jbi.2019.103187
DO - 10.1016/j.jbi.2019.103187
M3 - Article
C2 - 31026595
AN - SCOPUS:85065074463
VL - 94
JO - Journal of Biomedical Informatics
JF - Journal of Biomedical Informatics
SN - 1532-0464
M1 - 103187
ER -