ArticleSensors (Basel, Switzerland)2026
Literature-Grounded Simulation of Non-Invasive Glucose Monitoring Using NIR Wearable Sensor Archetypes: Accuracy Metrics, Candidate Confounders, and an Adaptive Calibration Framework.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
Non-invasive continuous glucose monitoring remains an important technological challenge in diabetes management. This study was revised as a literature-grounded simulation and methodological proof-of-concept rather than an experimental clinical validation. The analysis used 320 independent synthetic reference-NIR glucose pairs generated from physiological ranges and performance distributions reported in the cited literature, together with an illustrative 576-point, 48-h synthetic time series. Three sensor archetypes were informed by published optical systems. Descriptive agreement was evaluated using mean absolute relative difference (MARD), Pearson correlation, Bland-Altman analysis, and the supplied Clarke Error Grid (CEG) categories. Candidate physiological and environmental factors were examined using Pearson correlations with 95% confidence intervals, raw
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