Detecting Reset-Window-Compatible Dynamics in Public Continuous Glucose Monitoring Data

This paper demonstrates that temporally structured instability dynamics can be directly observed in publicly available continuous glucose monitoring (CGM) data. Using rolling variance (σ²) and rolling lag-1 autocorrelation (ρ₁) in high-resolution metabolic time series from the CGMacros dataset (PhysioNet), the analysis identifies: progressive instability accumulation local instability peaks coordinated descent phases These descent intervals are operationally consistent with previously described Reset-Window dynamics, but no composite instability index (e.g., Reset Index) is applied. The purpose of this study is methodological: To establish that instability geometry is empirically detectable in human metabolic regulation before any formal composite quantification is introduced. No new data are generated.No model assumptions are imposed.Only observable time-series behaviour is analysed. This demonstration provides a necessary empirical foundation for future quantitative integration of dynamical instability metrics in clinical systems analysis.