Viscoelastic or Viscoplastic Glucose Theory (VGT 38): Applying VEGT or VPGT to Study Fasting Plasma Glucose (FPG) Versus Sleep, Weight, and HbA1C to Predict FPG using Viscoelastic Perturbation Model over 13 SemiAnnual Periods from Y15H2 to Y21H2 Based on the GH-Method: Math-Physical Medicine (No. 619)

Abstract

Gerald C Hsu

Since 2012, the author has been collecting his body weight and finger-piercing glucose values each day. In addition, he accumulates his medical conditions data including blood pressure (BP), heart rate(HR), and blood lipids along with lifestyle details of diet, exercise, sleep, stress, water intake and daily routine details. Based on the collected big data, he organized them into two main groups. The first group is the medical conditions (MC) with 4 categories: weight, glucose, BP, and lipids. The second group is the lifestyle details (LD) with 6 categories: food, exercise, water intake, sleep, stress, and daily routines. For this study, he collects his daily data and then calculates a unique score for each of the 10 categories, including weight (m1), sleep score (m7), and HbA1C. In this article, the author applies the viscoelasticity and viscoplasticity theories to conduct his research to discover some hidden behavior or relationship between fasting plasma glucose or FPG (as an output or strain) versus sleep, weight, or HbA1C (as inputs or stresses). The hidden behaviors and relationships between the output biomarker for FPG and the three selected input biomarkers, sleep, weight, and A1C, are time-dependent which change from time to time.

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