Empowering digital health management with on-device large language models for glucose prediction

Long-term management of chronic diseases such as diabetes is increasingly based on wearable technologies, particularly continuous glucose monitoring (CGM), integrated with smartphone-based digital health systems. When combined with artificial intelligence, especially deep learning, these systems offer highly personalized decision support, including glucose prediction. Although large language models (LLMs) have demonstrated strong performance across various healthcare tasks, their integration into day-to-day digital health remains limited, primarily due to privacy concerns associated with transmitting sensitive data to remote servers. Recent advances in lightweight LLMs create new opportunities for secure and local deployment. In this study, we first evaluated the zero-shot glucose prediction performance of eight pretrained lightweight LLMs across multiple model families. None achieved clinically viable outputs, highlighting the need for domain-specific adaptation. To address this, we propose GluLLM, a multimodal adapter-based framework that enhances pretrained LLMs for on-device glucose forecasting. GluLLM integrates CGM data, daily activity logs, and electronic health records using customized encoder and decoder modules while preserving the foundational capabilities of pretrained LLMs. We trained and evaluated GluLLM on the REPLACE-BG dataset, which includes 226 individuals with type 1 diabetes, and validated it on an external cohort comprising 207 individuals with type 2 diabetes or without diabetes. Compared with 15 state-of-the-art deep learning baseline models for time series prediction, GluLLM with a LLaMA 3.2 1B backbone achieved superior prediction performance, demonstrating the lowest root mean square error and the highest sensitivity for hypoglycemia detection in both datasets. Further-more, the deployment of GluLLM on two smartphone platforms demonstrated feasible computational requirements with acceptable CPU and memory usage, highlighting its practical utility for real-world LLM-driven digital health management.