A personalized and automated real-time meal detection algorithm based on continuous glucose monitoring and heart rate data for individuals with post-bariatric hypoglycemia.

Introduction: Continuous glucose monitoring (CGM) sensors are increasingly used to identify and manage post-bariatric hypoglycemia (PBH) and to support decision support systems (DSSs) for proactive glucose management. Despite meal timing being key information for these systems, automated, wearable-based meal detection remains an unmet clinical need. Methods: We present a real-time meal detection algorithm for individuals with PBH that combines CGM and heart rate (HR) signals. The algorithm is a heuristic decision-tree model based on four individualized features extracted from CGM (rate of change, glucose relative excursion, glucose peak value) and HR (peak value). It was developed and tested using a dataset of 40 PBH patients monitored for up to 50 days with a Dexcom G6 CGM and a Garmin Venu Sq smartwatch, and its performance was evaluated in both controlled and free-living conditions, benchmarked against state-of-the-art CGM-only meal detection methods for the PBH population. Results: The algorithm achieved 100% recall in the controlled setting and, in free-living conditions, an average precision of 85% and recall of 78%. It also reduced false positives compared with CGM-only algorithms (one every 2.3 days vs. one every 1.3 days). Discussion: Eliminating the need for manual meal announcement, the proposed algorithm overcomes a key barrier to fully automated glucose management, reducing patient burden while maintaining reliable detection performance even in unstructured, free-living conditions. These results support the integration of the algorithm into DSSs for PBH and other populations, where timely and accurate meal detection is critical.