Applying Multiple Regression Analyses and Linear Elastic Glucose Theory to Analyze and Compare the Predicted and Measured Finger-Pierced Postprandial Plasma Glucose using Carbs/Sugar Intake Amount and Post-Meal Walking Steps as Inputs over 2016-2017 Period for a Diabetes Patient Based on GH-Method: Math-Physical Medicine (No. 544)

Abstract

Gerald C Hsu

In the author’s previous research reports, he mainly applied physics theories, engineering models, mathematical equations, computer big data analytics and artificial intelligence (AI) techniques, as well as some statistical approaches. However, the majority of medical research papers he has read thus far are primarily based on statistics. As a result, in this article, he selected some basic statistical tools, such as correlation, variance, p-values, and multiple regression analyses, to study the predicted finger-piercing postprandial plasma glucose (PPG) as the output (dependent variable) by using his carbs/sugar intake grams and post-meal walking steps as inputs (independent variables). Since 1/1/2018, the author has been utilizing a finger-pierced device to collect and store his glucose data on the iPhone and Amazon cloud server. He has accumulated 4 glucose data per day over the past 10 years, along with entering his carbs/sugar intake grams and post-meal walking steps after each meal into the database.

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