MindScape Study: Integrating LLM and Behavioral Sensing for Personalized AI-Driven Journaling Experiences
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3699761.pdf
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19.57 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Nepal, Subigya
Pillai, Arvind
Campbell, William
Massachi, Talie
Heinz, Michael
Kunwar, Ashmita
Choi, Eunsol Soul
Xu, Xuhai "Orson"
Kuc, Joanna
Huckins, Jeremy
Date Issued
November 21, 2024
Journal
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Publisher
ACM
Citation
Nepal, Subigya, Pillai, Arvind, Campbell, William, Massachi, Talie, Heinz, Michael et al. 2024. "MindScape Study: Integrating LLM and Behavioral Sensing for Personalized AI-Driven Journaling Experiences." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 8 (4).
Version
Final published version
Abstract
Mental health concerns are prevalent among college students, highlighting the need for effective interventions that promote self-awareness and holistic well-being. MindScape pioneers a novel approach to AI-powered journaling by integrating passively collected behavioral patterns such as conversational engagement, sleep, and location with Large Language Models (LLMs). This integration creates a highly personalized and context-aware journaling experience, enhancing self-awareness and well-being by embedding behavioral intelligence into AI. We present an 8-week exploratory study with 20 college students, demonstrating the MindScape app's efficacy in enhancing positive affect (7%), reducing negative affect (11%), loneliness (6%), and anxiety and depression, with a significant week-over-week decrease in PHQ-4 scores (-0.25 coefficient), alongside improvements in mindfulness (7%) and self-reflection (6%). The study highlights the advantages of contextual AI journaling, with participants particularly appreciating the tailored prompts and insights provided by the MindScape app. Our analysis also includes a comparison of responses to AI-driven contextual versus generic prompts, participant feedback insights, and proposed strategies for leveraging contextual AI journaling to improve well-being on college campuses. By showcasing the potential of contextual AI journaling to support mental health, we provide a foundation for further investigation into the effects of contextual AI journaling on mental health and well-being.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1145/3699761
