  Health monitoring systems have revolutionized modern healthcare by enabling
the continuous capture of physiological and behavioral data, essential for
preventive measures and early health intervention. While integrating this data
with Large Language Models (LLMs) has shown promise in delivering interactive
health advice, traditional methods like Retrieval-Augmented Generation (RAG)
and fine-tuning often fail to fully utilize the complex, multi-dimensional, and
temporally relevant data from wearable devices. These conventional approaches
typically provide limited actionable and personalized health insights due to
their inadequate capacity to dynamically integrate and interpret diverse health
data streams. In response, this paper introduces a graph-augmented LLM
framework designed to significantly enhance the personalization and clarity of
health insights. Utilizing a hierarchical graph structure, the framework
captures inter and intra-patient relationships, enriching LLM prompts with
dynamic feature importance scores derived from a Random Forest Model. The
effectiveness of this approach is demonstrated through a sleep analysis case
study involving 20 college students during the COVID-19 lockdown, highlighting
the potential of our model to generate actionable and personalized health
insights efficiently. We leverage another LLM to evaluate the insights for
relevance, comprehensiveness, actionability, and personalization, addressing
the critical need for models that process and interpret complex health data
effectively. Our findings show that augmenting prompts with our framework
yields significant improvements in all 4 criteria. Through our framework, we
can elicit well-crafted, more thoughtful responses tailored to a specific
patient.
