Linear Regression Analysis Results of the CGM Sensor PPG Comparison Between Predicted PPG Data Using the Linear Elastic Glucose Theory (LEGT) and Measured PPG Data During a ~2-year COVID-19 Quarantine Period for a type 2 Diabetes Patient Based on GH-Method: Math-Physical Medicine (No. 540)

Abstract

Since 5/5/2018, the author has been applying 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. As a result, over these 1,272 days, he has compiled a total of 122,112 glucose data and stored them in his database where postprandial plasma glucose (PPG) occupies 45,792 data size and 37.5% of the total glucose database. During 2020-2021 COVID-19 quarantine period, he has a strictly managed routine, without any traveling, which allowed him to have an overall healthy lifestyle. Therefore, all of the 19 influential factors of PPG are mainly control by two primary factors: carbs/sugar intake amount (average at 13.1 gram, low-carb diet) and post-meal waking e excise (average of 4,300 steps). Based on this simplified and healthy lifestyle, he can then easily utilize his developed linear elastic glucose theory (LEGT) model to predict his PPG. In his previous research reports, he has applied physics concepts and theories, engineering models and equations, mathematical concepts and formulas, computer big data and artificial intelligence (AI) techniques, as well as some statistical approaches. The majority of published medical papers he has read are mainly based on statistics. As a result, in this article, he selected one of the basic statistical tools, linear regression analysis, to study the comparison between his predicted PPG using LEGT and CGM sensor measured PPG. In conclusion, the linear regression analysis results have provided similar findings with his previous analysis outcomes using other math-physical tools.