To use and engage? Identifying distinct user types in interaction with a smartphone-based intervention

IF 4.9 Q1 PSYCHOLOGY, EXPERIMENTAL
Aniek M. Siezenga , Esther C.A. Mertens , Jean-Louis van Gelder
{"title":"To use and engage? Identifying distinct user types in interaction with a smartphone-based intervention","authors":"Aniek M. Siezenga ,&nbsp;Esther C.A. Mertens ,&nbsp;Jean-Louis van Gelder","doi":"10.1016/j.chbr.2025.100602","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>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.</div></div><div><h3>Method</h3><div>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 (<em>n</em> = 86). <em>K</em>-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 (<em>n</em> = 106).</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"17 ","pages":"Article 100602"},"PeriodicalIF":4.9000,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers in human behavior reports","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S245195882500017X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0

Abstract

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.
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
7.80
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信