To use and engage? Identifying distinct user types in interaction with a smartphone-based intervention
Background: Smartphone users are a heterogeneous group, implying that certain user types might be distinguishable by the way they interact with a smartphone-based intervention. As these user types potentially benefit differently from an intervention, there is a need to identify them to inform future...
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Elsevier
2025-03-01
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| Series: | Computers in Human Behavior Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S245195882500017X |
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| author | Aniek M. Siezenga Esther C.A. Mertens Jean-Louis van Gelder |
| author_facet | Aniek M. Siezenga Esther C.A. Mertens Jean-Louis van Gelder |
| author_sort | Aniek M. Siezenga |
| collection | DOAJ |
| description | Background: Smartphone users are a heterogeneous group, implying that certain user types might be distinguishable by the way they interact with a smartphone-based intervention. As these user types potentially benefit differently from an intervention, there is a need to identify them to inform future research, intervention design, and, eventually, improve intervention effectiveness. To this end, we explored 1) whether user types were distinguishable in terms of how much they used a smartphone-based intervention and experienced app engagement; and whether user types differed in 2) their intervention effects; and 3) user characteristics, i.e., HEXACO personality traits and self-efficacy. Method: Participants were Dutch first-year university students that interacted with the FutureU app aimed at increasing future self-identification. App usage data and engagement survey data were obtained in a randomized controlled trial taking place in 2022 (n = 86). K-means++ cluster analyses were applied to identify user types based on app use and engagement. Linear discriminant analyses, ANCOVAs, and MANOVAs were conducted to assess whether the clusters differed in intervention outcomes and individual characteristics. The analyses were replicated in data obtained in an RCT taking place in 2023 with an updated version of the app (n = 106). Results: Four user types were identified: Low use–Low engagement, Low use–High engagement, High use–Low engagement, High use–High engagement. Overall, intervention effects were strongest for the user types High engagement–High use and High engagement–Low use. No significant differences were observed in user characteristics. Conclusion: User types can vary in their use of and engagement with smartphone-based interventions, and benefit differently from these interventions. App engagement appears to play a more significant role than previously assumed, highlighting a need for further studies on drivers of app engagement. |
| format | Article |
| id | doaj-art-3ab464c50b1e437a9fbf712848b865f5 |
| institution | Kabale University |
| issn | 2451-9588 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computers in Human Behavior Reports |
| spelling | doaj-art-3ab464c50b1e437a9fbf712848b865f52025-02-06T05:12:37ZengElsevierComputers in Human Behavior Reports2451-95882025-03-0117100602To use and engage? Identifying distinct user types in interaction with a smartphone-based interventionAniek M. Siezenga0Esther C.A. Mertens1Jean-Louis van Gelder2Max Planck Institute for the Study of Crime, Security and Law, Günterstalstraße 73, 79100, Freiburg Im Breisgau, Germany; Department of Education and Child Studies, Leiden University, Wassenaarseweg 52, 2333AK, Leiden, the NetherlandsMax Planck Institute for the Study of Crime, Security and Law, Günterstalstraße 73, 79100, Freiburg Im Breisgau, Germany; Netherlands Institute for the Study of Crime and Law Enforcement, De Boelelaan 1077, 1081HV, Amsterdam, the NetherlandsMax Planck Institute for the Study of Crime, Security and Law, Günterstalstraße 73, 79100, Freiburg Im Breisgau, Germany; Department of Education and Child Studies, Leiden University, Wassenaarseweg 52, 2333AK, Leiden, the Netherlands; Corresponding author. Max Planck Institute for the Study of Crime, Security and Law, Günterstalstraße 73, 79100, Freiburg im Breisgau, Germany.Background: Smartphone users are a heterogeneous group, implying that certain user types might be distinguishable by the way they interact with a smartphone-based intervention. As these user types potentially benefit differently from an intervention, there is a need to identify them to inform future research, intervention design, and, eventually, improve intervention effectiveness. To this end, we explored 1) whether user types were distinguishable in terms of how much they used a smartphone-based intervention and experienced app engagement; and whether user types differed in 2) their intervention effects; and 3) user characteristics, i.e., HEXACO personality traits and self-efficacy. Method: Participants were Dutch first-year university students that interacted with the FutureU app aimed at increasing future self-identification. App usage data and engagement survey data were obtained in a randomized controlled trial taking place in 2022 (n = 86). K-means++ cluster analyses were applied to identify user types based on app use and engagement. Linear discriminant analyses, ANCOVAs, and MANOVAs were conducted to assess whether the clusters differed in intervention outcomes and individual characteristics. The analyses were replicated in data obtained in an RCT taking place in 2023 with an updated version of the app (n = 106). Results: Four user types were identified: Low use–Low engagement, Low use–High engagement, High use–Low engagement, High use–High engagement. Overall, intervention effects were strongest for the user types High engagement–High use and High engagement–Low use. No significant differences were observed in user characteristics. Conclusion: User types can vary in their use of and engagement with smartphone-based interventions, and benefit differently from these interventions. App engagement appears to play a more significant role than previously assumed, highlighting a need for further studies on drivers of app engagement.http://www.sciencedirect.com/science/article/pii/S245195882500017XSmartphone-based interventionmHealthUsage dataApp engagementCluster analysisUser types |
| spellingShingle | Aniek M. Siezenga Esther C.A. Mertens Jean-Louis van Gelder To use and engage? Identifying distinct user types in interaction with a smartphone-based intervention Computers in Human Behavior Reports Smartphone-based intervention mHealth Usage data App engagement Cluster analysis User types |
| title | To use and engage? Identifying distinct user types in interaction with a smartphone-based intervention |
| title_full | To use and engage? Identifying distinct user types in interaction with a smartphone-based intervention |
| title_fullStr | To use and engage? Identifying distinct user types in interaction with a smartphone-based intervention |
| title_full_unstemmed | To use and engage? Identifying distinct user types in interaction with a smartphone-based intervention |
| title_short | To use and engage? Identifying distinct user types in interaction with a smartphone-based intervention |
| title_sort | to use and engage identifying distinct user types in interaction with a smartphone based intervention |
| topic | Smartphone-based intervention mHealth Usage data App engagement Cluster analysis User types |
| url | http://www.sciencedirect.com/science/article/pii/S245195882500017X |
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