MindScape Study: Integrating LLM and Behavioral Sensing for Personalized AI-Driven Journaling Experiences
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2024
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| author | Nepal, Subigya Pillai, Arvind Campbell, William Massachi, Talie Heinz, Michael V. Kunwar, Ashmita Choi, Eunsol Soul Xu, Orson Kuc, Joanna Huckins, Jeremy Holden, Jason Preum, Sarah M. Depp, Colin Jacobson, Nicholas Czerwinski, Mary Granholm, Eric Campbell, Andrew T. |
| author_facet | Nepal, Subigya Pillai, Arvind Campbell, William Massachi, Talie Heinz, Michael V. Kunwar, Ashmita Choi, Eunsol Soul Xu, Orson Kuc, Joanna Huckins, Jeremy Holden, Jason Preum, Sarah M. Depp, Colin Jacobson, Nicholas Czerwinski, Mary Granholm, Eric Campbell, Andrew T. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09570 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MindScape Study: Integrating LLM and Behavioral Sensing for Personalized AI-Driven Journaling Experiences Nepal, Subigya Pillai, Arvind Campbell, William Massachi, Talie Heinz, Michael V. Kunwar, Ashmita Choi, Eunsol Soul Xu, Orson Kuc, Joanna Huckins, Jeremy Holden, Jason Preum, Sarah M. Depp, Colin Jacobson, Nicholas Czerwinski, Mary Granholm, Eric Campbell, Andrew T. Human-Computer Interaction Artificial Intelligence H.5.0; H.5.3; H.5.m; J.0 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. |
| title | MindScape Study: Integrating LLM and Behavioral Sensing for Personalized AI-Driven Journaling Experiences |
| topic | Human-Computer Interaction Artificial Intelligence H.5.0; H.5.3; H.5.m; J.0 |
| url | https://arxiv.org/abs/2409.09570 |