Linear Regression Analysis Results of the CGM Sensor PPG Comparison Between Predicted PPG Data Using the Candlestick Model and the LEGT Model Against the Measured Sensor PPG Data During a 2-year COVID-19 Quarantine Period for a Type 2 Diabetes Patient Based on GH-Method: MathPhysical Medicine (No. 541)

Abstract

Gerald C Hsu

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 the 2020-2021 COVID-19 quarantine period, he maintained a strict daily routine, without any travel, allowing him to reach 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 postmeal walking exercise (average of 4,300 steps). These lifestyle improvements helped reduce his PPG waveform amplitudes, including the four associated PPG data of candlestick (aka K-line) model: opening, maximum, minimum, and closing.

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