Applying Multiple Regression Analysis and Linear Elastic Glucose Theory to Analyze and Compare the Predicted Postprandial Plasma Glucose Using Carbs/Sugar Intake Amount and Post-Meal Walking Steps as Inputs over an Approximate 2-Year COVID-19 Quarantine Period from a type 2 Diabetes Patient Based on GH-Method: Math-Physical Medicine (No. 543)

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. The majority of medical research scientists’ published 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 postprandial plasma glucose (PPG) as the dependent variable using his carbs/sugar intake grams and post-meal walking steps as inputs (independent variables). Since 5/8/2018, the author has been utilizing a continuous glucose monitoring (CGM) sensor device on his upper arm that collected and recorded the complete glucose data continuously at 15-minute time intervals on his iPhone. He accumulated 96 glucoses per day over the past ~3.5 years. After each meal, he collects 13 PPG data, accumulating 39 PPG values per day, along with entering his carbs/sugar intake grams and post-meal walking steps.

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