ArticleFrontiers in psychology2024
Optimizing personalized psychological well-being interventions through digital phenotyping: results from a randomized non-clinical trial.
Article in Frontiers in psychology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Application of user profiling in mental health management: a scoping review.BMC psychology · 2025Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Digital technologies, including smartphones, hold great promise for expanding mental health services and improving access to care. Digital phenotyping, which involves the collection of behavioral and physiological data using smartphones, offers a novel way to understand and monitor mental health. This study examines the feasibility of a psychological well-being program using a telegram-integrated chatbot for digital phenotyping. Methods: A one-month randomized non-clinical trial was conducted with 81 young adults aged 18-35 from Italy and the canton of Ticino, a region in southern Switzerland. Participants were randomized to an experimental group that interacted with a chatbot, or to a control group that received general information on psychological well-being. The chatbot collected real-time data on participants' well-being such as user-chatbot interactions, responses to exercises, and emotional and behavioral metrics. A clustering algorithm created a user profile and content recommendation system to provide personalized exercises based on users' responses. Results: Four distinct clusters of participants emerged, based on factors such as online alerts, social media use, insomnia, attention and energy levels. Participants in the experimental group reported improvements in well-being and found the personalized exercises, recommended by the clustering algorithm useful. Conclusion: The study demonstrates the feasibility of a digital phenotyping-based well-being program using a chatbot. Despite limitations such as a small sample size and short study duration, the findings suggest that digital phenotyping and personalized recommendation systems could improve mental health care. Future research should include larger samples and longer follow-up periods to validate these findings and explore clinical applications.
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