Personalised Recommendations in Mental Health Apps: The Impact of Autonomy and Data Sharing

要旨

The recent growth of digital interventions for mental well-being prompts a call-to-arms to explore the delivery of personalised recommendations from a user's perspective. In a randomised placebo study with a two-way factorial design, we analysed the difference between an autonomous user experience as opposed to personalised guidance, with respect to both users’ preference and their actual usage of a mental well-being app. Furthermore, we explored users’ preference in sharing their data for receiving personalised recommendations, by juxtaposing questionnaires and mobile sensor data. Interestingly, self-reported results indicate the preference for personalised guidance, whereas behavioural data suggests that a blend of autonomous choice and recommended activities results in higher engagement. Additionally, although users reported a strong preference of filling out questionnaires instead of sharing their mobile data, the data source did not have any impact on the actual app use. We discuss the implications of our findings and provide takeaways for designers of mental well-being applications.

著者
Svenja Pieritz
Alpha, Barcelona, Spain
Mohammed Khwaja
Imperial College London, London, United Kingdom
Aldo A. Faisal
Imperial College London, London, Greater London, United Kingdom
Aleksandar Matic
Koa Health, Barcelona, Spain
DOI

10.1145/3411764.3445523

論文URL

https://doi.org/10.1145/3411764.3445523

動画

会議: CHI 2021

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2021.acm.org/)

セッション: Mental Health

[B] Paper Room 07, 2021-05-12 01:00:00~2021-05-12 03:00:00 / [A] Paper Room 07, 2021-05-11 17:00:00~2021-05-11 19:00:00 / [C] Paper Room 07, 2021-05-12 09:00:00~2021-05-12 11:00:00
Paper Room 07
14 件の発表
2021-05-12 01:00:00
2021-05-12 03:00:00
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