Temporal Discontinuity in Continuous Glucose Monitoring: How Midnight Segmentation Generates Systematic Measurement Artifact

Continuous glucose monitoring (CGM) generates dense physiological time-series data sampled every 5 min across 24-h periods. Standard analytical approaches impose calendar-based temporal boundaries-treating midnight as a natural segmentation point-despite glucose homeostasis operating through continuous circadian oscillators that recognize no such delimiter. This structural misalignment introduces a systematic measurement artifact: biologically continuous overnight patterns are bisected at 00:00, artificially inflating glycemic variability estimates and obscuring individual circadian phase relationships. We analyzed approximately 60 days of CGM data, comparing three binning strategies: (1) 24 linear hour-bins (conventional), (2) 36 linear 40-min bins (resolution control), and (3) 36 angular 10° bins (circular topology). Shannon entropy with variance-weighted probabilities quantified information content. Bootstrap resampling (1000 iterations) and null topology permutation (random within-day time-permutation, 1000 iterations) distinguished genuine temporal structure from mathematical artifact. Circular representation demonstrated 12.1% higher information entropy compared to linear binning at matched resolution (3.56 vs. 3.18 bits, P r = 0.31 vs. r = 0.87, P P < 0.001), with 1.97-fold variance differential between evening (21:20-00:00) and midday (11:20-14:00) zones. Midnight segmentation introduces quantifiable information loss through temporal discontinuity. Circular time representation-mapping 24-h cycles onto angular coordinates using established directional statistics-eliminates this artifact while preserving temporal information. Current glycemic variability metrics (coefficient of variation, time-in-range) calculated within midnight-bounded periods inherit discontinuity artifacts, potentially misclassifying normal circadian oscillations as pathological variability. Adoption of circular frameworks would align CGM analytics with chronobiological principles and enable individual circadian phenotyping without data manipulation. This represents methodological infrastructure requiring prospective validation for clinical utility.