This article is Part 16 of the author’s linear elastic glucose behavior study. It focuses on a deeper investigation of GH.p-modulus over the period from 8/5/2018 through 11/27/2020 using both finger-piercing measured glucoses (finger) and continuous glucose monitor (CGM) sensor collected glucoses (sensor). The author plans to conduct additional studies on linear elastic glucose behavior theory in order to obtain a solid and better understanding on the glucose coefficient of GH.p-modulus. Here is the step-by-step explanation for the predicted postprandial plasma glucose (PPG) equation using linear elastic glucose theory as described in References 9 through 22: (1) Baseline PPG equals to 97% of fasting plasma glucose (FPG) value, or 97% * (weight * GH.f-Modulus). (2) Baseline PPG plus increased amount of PPG due to food, i.e., plus (carbs/sugar intake amount * GH.p-Modulus). (3) Baseline PPG plus increased PPG due to food, and then subtracts reduction amount of PPG due to exercise, i.e., minus (post-meal walking k-steps * 5). (4) The Predicted PPG equals to Baseline PPG plus the food influences, and then subtracts the exercise influences. The linear elastic glucose equation is: Predicted PPG = (0.97 * GH.f-modulus * Weight) + (GH.p-modulus * Carbs&sugar) - (post-meal walking k-steps * 5) Where, (1) Incremental PPG = Predicted PPG - Baseline PPG + Exercise impact (2) GH.f-modulus = FPG / Weight (3) GH.p-modulus = Incremental PPG / Carbs intake Therefore, GH.p-modulus = (PPG - (0.97 * FPG) + (post-meal walking k-steps * 5)) / (Carbs&Sugar intake) The study in this article calculates and analyzes the glucose coefficient of GH.p-modulus values over the period from 8/5/2018 through 11/27/2020 using both finger glucoses and sensor glucoses. The calculated GH.p-modulus values are 2.0 for finger glucoses, and 3.3 for sensor glucoses. This paper investigates the likely situations of the author’s health conditions and lifestyle details based on two different glucose measuring methods. These two GH.p-modulus values have a relatively small and insignificant variance of 1.2, which is between 2.0 and 3.2. Actually, any number located between the range of 1.8 to 3.3, even if it skews toward the higher side of this scale, can be used as an application to the GH.p-modulus for PPG prediction. This study utilizes a step-by-step illustration, moving from the difference between PPG and FPG, going through the Incremental PPG, and then arriving at the Predicted PPG. In the described steps, the most important variable of the linear elastic glucose behaviors is the coefficient of GH.p-modulus (similar to Young’s modules in theory of engineering elasticity). That is why the author has conducted a massive amount of research on linear elastic glucose behaviors theory in order to acquire a good and solid understanding for the GH.p-modulus.