PhysioLLM: Supporting Personalized Health Insights with Wearables and Large Language Models
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916304172089344 |
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| author | Fang, Cathy Mengying Danry, Valdemar Whitmore, Nathan Bao, Andria Hutchison, Andrew Pierce, Cayden Maes, Pattie |
| author_facet | Fang, Cathy Mengying Danry, Valdemar Whitmore, Nathan Bao, Andria Hutchison, Andrew Pierce, Cayden Maes, Pattie |
| contents | We present PhysioLLM, an interactive system that leverages large language models (LLMs) to provide personalized health understanding and exploration by integrating physiological data from wearables with contextual information. Unlike commercial health apps for wearables, our system offers a comprehensive statistical analysis component that discovers correlations and trends in user data, allowing users to ask questions in natural language and receive generated personalized insights, and guides them to develop actionable goals. As a case study, we focus on improving sleep quality, given its measurability through physiological data and its importance to general well-being. Through a user study with 24 Fitbit watch users, we demonstrate that PhysioLLM outperforms both the Fitbit App alone and a generic LLM chatbot in facilitating a deeper, personalized understanding of health data and supporting actionable steps toward personal health goals. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_19283 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PhysioLLM: Supporting Personalized Health Insights with Wearables and Large Language Models Fang, Cathy Mengying Danry, Valdemar Whitmore, Nathan Bao, Andria Hutchison, Andrew Pierce, Cayden Maes, Pattie Human-Computer Interaction We present PhysioLLM, an interactive system that leverages large language models (LLMs) to provide personalized health understanding and exploration by integrating physiological data from wearables with contextual information. Unlike commercial health apps for wearables, our system offers a comprehensive statistical analysis component that discovers correlations and trends in user data, allowing users to ask questions in natural language and receive generated personalized insights, and guides them to develop actionable goals. As a case study, we focus on improving sleep quality, given its measurability through physiological data and its importance to general well-being. Through a user study with 24 Fitbit watch users, we demonstrate that PhysioLLM outperforms both the Fitbit App alone and a generic LLM chatbot in facilitating a deeper, personalized understanding of health data and supporting actionable steps toward personal health goals. |
| title | PhysioLLM: Supporting Personalized Health Insights with Wearables and Large Language Models |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2406.19283 |