A new over-the-counter glucose monitor approved for young children has sparked debate among paediatricians. While mar… #Childmentalhealth #Childhoodobesity #Continuousglucosemonitoring #Medicaldeviceregulation #Paediatrichealth

In the ever-evolving landscape of technology, the micro-chip stands as a testament to human ingenuity and the relentless pursuit of…

Background: Continuous glucose monitoring (CGM) generates high-frequency time-series data, creating challenges for efficient storage, transmission, and analysis. Objective: This study aimed to develop and evaluate a CGM-specific compression method that achieves high compression ratios while preserving signal fidelity and clinically relevant glycemic metrics. Methods: We introduce a content-based encoding approach (PN+) that represents CGM profiles using physiologically salient landmarks: glucose peaks and nadirs and a small set of optimally selected support points. Reconstruction is performed using piecewise cubic Hermite interpolation. PN+ was evaluated against peaks and nadirs only, uniform downsampling, piecewise aggregate approximation, and autoencoder-based compression. Performance was assessed across multiple compression ratios using 2 complementary datasets: 40,000 synthetic CGM profiles and real-world CGM data from a randomized crossover trial (558 days from 30 patients). Performance was evaluated using compression ratio, mean absolute error, and between original and reconstructed CGM-derived clinical metrics. Results: At a compression ratio of 13 (22 points per 24-hour profile), PN+ achieved substantially lower reconstruction error than comparator methods (mean absolute error=0.77 vs 2.75-3.45) and consistently higher values across glycemic metrics. Improvements were most pronounced for excursion-sensitive measures such as mean amplitude of glycemic excursions, where PN+ reduced error by more than 4-fold compared with downsampling, piecewise aggregate approximation, and autoencoders. These performance advantages were preserved in heterogeneous real-world data. Encoding and decoding required less than 0.2 seconds per profile, supporting practical scalability. Conclusions: PN+ enables robust CGM data compression by explicitly preserving physiologically meaningful glucose dynamics. The method outperforms generic compression techniques in reconstructing clinically relevant metrics while maintaining low computational overhead, making it well suited for large-scale CGM storage, interoperability, and downstream analytics.

The FDA has named Dexcom as the first company to take part in its TEMPO pilot, which aims to bring digital health tech to patients more quickly.