Personalised Recommendations in Mental Health Apps: The Impact of Autonomy and Data Sharing
This research addresses the challenge of designing effective personalized recommendations for mental health app users, though it is incremental as it builds on existing work in digital interventions.
The study investigated how autonomy versus personalized guidance affects user preferences and engagement in mental health apps, finding that while users reported preferring personalized guidance, behavioral data showed higher engagement with a blend of autonomy and recommendations, and data source (questionnaires vs. mobile sensors) did not impact actual app use.
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.