scholarly journals A Postprandial Plasma Glucose (PPG) Comparison Study between Pre-COVID-19 and During COVID-19 Using GHMethod: Math-Physical Medicine (No. 317)

2020 ◽  
Vol 3 (2) ◽  
pp. 1-9
Author(s):  
Gerald C. Hsu
Keyword(s):  
Plasma Glucose ◽  
Comparison Study ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose

A biophysical investigation on the relationships between the inputs of meal quantity percentage, carbs/sugar amount, and exercise, along with the outputs of postprandial plasma glucose, and body weight using GH-Method: Math-physical medicine (No. 295)

2020 ◽  
Keyword(s):  
Body Weight ◽  
Plasma Glucose ◽  
Influential Factors ◽  
Research Note ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
The Relationship

This research note describes the author’s investigation on differences among his three meals, which include breakfast, lunch, and dinner, in terms of their influential factors and their respective PPG data and waveforms. He further described the relationship between his body weight and meal quantity percentage for his normal portion. During this period, from 5/5/2018 to 7/14/2020, he collected detailed information of his 2,403 meals and ~64,000 glucose data.

scholarly journals Coefficient of GH.F-Modulus in the Linear Elastic Fasting Plasma Glucose Behavior Study Based on Health Data of Three Diabetes Patients Using the GH-Method: Math-Physical Medicine, Part 4 (No. 356)

2020 ◽  
pp. 1-7
Author(s):  
Gerald C Hsu ◽  
Keyword(s):  
Plasma Glucose ◽  
Fasting Plasma Glucose ◽  
Health Data ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Linear Elastic ◽  
Fasting Plasma ◽  
Behavior Study ◽  
Diabetes Patients

This article is Part 4 of the author’s linear elastic glucose behavior study, which focuses on fasting plasma glucose (FPG) component. It is the continuation of his previous three studies, Parts 1, 2, and 3, on linear elastic postprandial plasma glucose (PPG) behaviors

scholarly journals Accuracy of Predicted Glucose using both Natural Intelligence and Artificial Intelligence via GH-Method: Math-Physical Medicine (No. 320)

2021 ◽  
pp. 1-4
Author(s):  
Gerald C Hsu ◽  
Keyword(s):  
Artificial Intelligence ◽  
Plasma Glucose ◽  
Fasting Plasma Glucose ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Daily Average ◽  
Fasting Plasma ◽  
Average Glucose

This paper describes the accuracy of using natural intelligence (NI) and artificial intelligence (AI) methods to predict three glucoses, fasting plasma glucose (FPG), postprandial plasma glucose (PPG), and daily average glucose, in comparison with the actual measured PPG by using the finger-piercing (Finger) method. The entire glucose database contains 7,652 glucoses (4 glucose data per day) over 1,913 days from 6/1/2015 through 8/27/2020

scholarly journals A Neuroscientific Study of the Brain Stimulator and Simulation Model to Predict Breakfast PPG Using GH-Method: Math-Physical Medicine

2020 ◽  
Vol 3 (3) ◽  
Keyword(s):  
Simulation Model ◽  
Plasma Glucose ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Segmentation Analysis ◽  
The Brain

To prove his hypothesis in this paper, the author interprets the brain stimulator and its associated simulation model of predicted breakfast postprandial plasma glucose (PPG) via a food or meal segmentation analysis and Sensor PPG waveform characteristics study.

scholarly journals Applying Segmentation Pattern Analysis to Investigate Postprandial Plasma Glucose Characteristics and Behaviors of the Carbs/Sugar Intake Amounts In Different Nations (GH Method: Math-Physical Medicine)

2020 ◽  
Vol 5 (1) ◽  
Keyword(s):  
Plasma Glucose ◽  
Pattern Analysis ◽  
Communication Model ◽  
Physical Medicine ◽  
Internal Organs ◽  
Postprandial Plasma Glucose ◽  
Sugar Intake ◽  
Liver And Pancreas ◽  
Segmentation Pattern ◽  
The Brain

In this paper, the author presents the results of his national segmentation pattern analysis of the sensor PPG data based on both high-carb and low-carb intake amounts. It also verified his earlier findings on the communication model between the brain and internal organs such as the stomach, liver, and pancreas.

scholarly journals Using GH-Method: Math-Physical Medicine to Conduct Segmentation Analysis to Investigate the Impact of Weather Temperatures on Glucose (Both FPG And PPG)

2020 ◽  
Vol 5 (3) ◽  
Keyword(s):  
Type 2 Diabetes ◽  
Big Data ◽  
Plasma Glucose ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Segmentation Analysis ◽  
Metabolic Conditions ◽  
The Impact

This paper is based on big data collected from a period of 1,420daysfrom 6/1/2015 to 4/21/2019 with a total of 4,260 data, including highest ambient temperature (weather) of each day in degree Fahrenheit (°F), fasting plasma glucose (FPG) and postprandial plasma glucose (PPG) in mg/dL. The dataset is provided by the author, who uses his own type 2 diabetes metabolic conditions control, as a case study via the “math-physical medicine” approach of a non-traditional methodology in medical research.

scholarly journals Investigation the importance of metabolism on body weight and glucose for 8.5 years using GH-Method: math-physical medicine (No. 300)

2020 ◽  
Vol 5 (5) ◽  
Keyword(s):  
Time Series ◽  
Body Weight ◽  
Time Series Analysis ◽  
Plasma Glucose ◽  
Correlation Coefficients ◽  
Annual Data ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Series Analysis ◽  
Daily Data

The author uses GH-method: math-physical medicine (MPM) approach to investigate three sets of correlation between: (1) Weight vs. Metabolism Index (2) Glucose vs. Metabolism Index (3) Weight vs. Glucose - Weight is measured in early mornings and Glucose consists of daily average glucose, including both fasting plasma glucose (FPG) and three postprandial plasma glucose (PPG). He utilized time-series analysis on both his “daily data” and his “annual data” for comparison. His selected study period was 8.5 years (3,124 days) from 1/1/2012 through 7/23/2020. The reason for this specific time period was due to his weight (M1) and glucose (M2) data collection starting on 1/1/2012 along with the calculation of his metabolism index (MI) values. It is clear that, through his sophisticated math-physical medicine of metabolism and then statistical method of time-series analysis, all of these three biomarkers, weight, glucose, and metabolism index are proven to be highly correlated to each other. The following order ranking of correlation coefficients remained to be true between daily data and annual data: M1&MI > M2&MI > M1&M2 Daily: 84% > 72% > 61% Annual: 91% > 81% > 67% In other words, if you manage your metabolism (4 medical conditions and 6 lifestyle details) by controlling your disease conditions and monitoring your lifestyle details, your body weight and glucose will reduce accordingly. The author’s analyses is based on his personal biomarkers of two million data within 8.5 years (3,124 days) has further proven a simple and clean conclusion that has already been observed by many clinical physicians and healthcare professionals from their patients.

scholarly journals Investigation of the triangular dual-correlations among weight, glucose, and blood pressure for 6.5 years using GH-method: math-physical medicine (No. 299)

2020 ◽  
Vol 5 (3) ◽  
Keyword(s):  
Blood Pressure ◽  
Time Series ◽  
Spatial Analysis ◽  
Plasma Glucose ◽  
Blood Pressure Reduction ◽  
Strong Relationship ◽  
Annual Data ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Daily Data

The author uses the math-physical medicine approach to investigate three sets of correlation between: (1) Weight vs. Glucose - Weight is measured in early mornings and Glucose consisting of daily average glucose, including both fasting plasma glucose (FPG) and three postprandial plasma glucose (PPG). (2) Weight vs. blood pressure (BP) - BP is measured in early mornings. (3) Glucose vs. BP. He utilized both time-series and spatial analysis of his “daily data” in comparison with his “annual data”. His selected study period is 6.5 years (2,394 days) from 1/1/2014 through 7/23/2020. The reason he chose this specific time period is due to his blood pressure (BP) data collection starting on 1/1/2014, while both weight and glucose data were collected since 1/1/2012. It is clear that, through statistical methods of time-series and spatial analysis, all of these three biomarkers, weight, glucose, and BP are correlated to each other. However, the following order ranking of correlation coefficients remain to be true between daily data and annual data: M1&M2 > M2&M3 > M1&M3 Daily: 81% > 54% > 50% Annual: 89% > 83% > 76% By reducing your body weight, your glucose values will then be lower. This strong relationship is the most obvious correlation. Similarly, your blood pressure will be lower when your glucose value is low. Finally, weight loss helps your blood pressure reduction as well. The author’s statistical analyses are based on his 100,000+ personal biomarker’s data within the last 6.5 years (2,394 days) has further proven a simple and clean conclusion that has already been observed by many clinical physicians and healthcare professionals from their patients.

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