scholarly journals Segmentation and pattern analyses for three meals of postprandial plasma glucose values and associated carbs/sugar amounts using GH-Method: math-physical medicine (No. 326)

2020 ◽  
Vol 4 (4) ◽  
Keyword(s):  
Plasma Glucose ◽  
Exercise Program ◽  
Management Program ◽  
Physical Medicine ◽  
Lifestyle Management ◽  
Postprandial Plasma Glucose ◽  
Sugar Intake ◽  
Glucose Levels ◽  
The Past ◽  
Segmentation Analysis

The author describes the results of segmentation and pattern analyses of postprandial plasma glucose levels (PPG) and carbs/sugar intake amount (carbs), which are associated with his three daily meals. In this paper, there are three consistent ranges of low, medium, and high for PPG values and carbs/sugar amounts that are used for each meal but with different units. One of the final objectives for this analysis is to calculate the most reasonable and effective conversion ratio between measured PPG in mg/dL and carbs/sugar intake amount in grams, by discovering how much PPG amount would be generated from 1 gram of carbs/sugar intake. This investigation utilized the PPG data and carbs/sugar amount collected during a period of 2+ years from 5/5/2018 to 9/6/2020 with a breakdown of 855 days, including 2,565 meals, 33,345 glucose data, and 33,345 carbs/sugar data. By using the segmentation analysis of his 33,345 PPG data and 2,565 carbs/sugar data, the author has conducted a pattern recognition and segmentation analysis from his PPG profiles with its associated carbs/sugar intake of his food and meals in the past 855 days. Since 12/8/2015, he ceased taking any diabetes medications. In other words, his diabetes control is 100% dependent on his lifestyle management program with no chemical intervention from any medications. Subsequently, he has maintained a stringent exercise program after each meal; therefore, the development of his simplified PPG prediction model, excluding the exercise factor, can be expressed solely with carbs/sugar intake amount.

Segmentation and Pattern Analyses for Three Meals of Postprandial Plasma Glucose Values and Associated Carbs/Sugar Amounts Using GH-Method: Math-Physical Medicine (No. 326)

2020 ◽  
Vol 2 (2) ◽  
pp. 1-4
Author(s):  
Gerald C Hsu ◽  
Keyword(s):  
Plasma Glucose ◽  
Research Work ◽  
Exercise Program ◽  
Management Program ◽  
Conversion Ratio ◽  
Lifestyle Management ◽  
Postprandial Plasma Glucose ◽  
Sugar Intake ◽  
Glucose Levels ◽  
Segmentation Analysis

The author describes the results of segmentation and pattern analyses of postprandial plasma glucose levels (PPG) and carbs/sugar intake amount (carbs), which are associated with his three daily meals. In this paper, there are three consistent ranges of low, medium, and high for PPG values and carbs/sugar amounts that are used for each meal but with different units. One of the final objectives for this analysis is to calculate the most reasonable and effective conversion ratio between measured PPG in mg/dL and carbs/sugar intake amount in grams, by discovering how much PPG amount would be generated from 1 gram of carbs/sugar intake. This investigation utilized the PPG data and carbs/sugar amount collected during a period of 2+ years from 5/5/2018 to 9/6/2020 with a breakdown of 855 days, including 2,565 meals, 33,345 glucose data, and 33,345 carbs/sugar data. By using the segmentation analysis of his 33,345 PPG data and 2,565 carbs/sugar data, the author has conducted a pattern recognition and segmentation analysis from his PPG profiles with its associated carbs/sugar intake of his food and meals in the past 855 days. Since 12/8/2015, he ceased taking any diabetes medications. In other words, his diabetes control is 100% dependent on his lifestyle management program with no chemical intervention from any medications. Subsequently, he has maintained a stringent exercise program after each meal; therefore, the development of his simplified PPG prediction model, excluding the exercise factor, can be expressed solely with carbs/sugar intake amount. Predicted PPG = (baseline glucose) + (conversion ratio * carbs/sugar amount) In his research work, he found the reasonable and effective conversion ratio between PPG and carbs that ranges from 1.8 mg/dL per gram to 2.5 mg/dL per gram. This simple equation could assist many type 2 diabetes (T2D) patients in controlling their diabetes via carbs/sugar intake amount. During this particular time period, his PPG control via a stringent lifestyle management without medication is highly successful. His estimated mathematically derived HbA1C values should be between 5.56% to 6.05%, which is a satisfactory HbA1C level for a 73-year-old male with a 25-year history of severe diabetes. It should be mentioned that he had an average daily glucose of 280 mg/dL and HbA1C of 11% in 2010. This segmented pattern analyses based on his PPG data and carbs/sugar intake amount offer a useful tool for analyzing other types of biomarkers in a deeper investigation with a wider entry point of research.

scholarly journals A simple formula based on postprandial plasma glucose prediction using 5,640 meals data via GH-Method: math-physical medicine (No. 301)

2020 ◽  
Vol 5 (5) ◽  
Keyword(s):  
Plasma Glucose ◽  
Research Work ◽  
Physical Medicine ◽  
Prediction Formula ◽  
Postprandial Plasma Glucose ◽  
Sugar Intake ◽  
The Past ◽  
The Status ◽  
Glucose Prediction

This article is based on the continuation of the author’s research work, a simple and practical, yet highly accurate postprandial plasma glucose (PPG) prediction formula for type 2 diabetes (T2D) patients. His methodology is the developed GH-Method: math-physical medicine (MPM) which has been utilized repeatedly in the past decade. The predicted PPG formula-based on the status of fasting plasma glucose (FPG), carbs/sugar intake amount, and postmeal walking steps are as follows:

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 of Two Glucose Coefficients of GH.f-Modulus and GH.p-Modulus based on the Data of Three Clinical Cases During the COVID-19 Period using linear Elastic Glucose Theory of GHMethod: Math-Physical Medicine, Part 8 (No. 360)

2021 ◽  
pp. 1-6
Author(s):  
Gerald C Hsu ◽  
Keyword(s):  
Plasma Glucose ◽  
Fasting Plasma Glucose ◽  
Health Conditions ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Sugar Intake ◽  
Linear Elastic ◽  
Behavior Study ◽  
Obesity And Diabetes ◽  
Near Future

This article is Part 8 of the author’s linear elastic glucose behavior study which focuses on the deeper understanding of these two newly defined glucose coefficients, GH.f-modulus and GH.p-modulus. Findings have shown the sensitive relationships with health conditions such as obesity and diabetes and certain lifestyle components, e.g. carbs/sugar intake amount and post-meal walking steps. Hopefully in the near future, he will be able to develop a reasonable and applicable numerical ranges of these two glucose coefficients. He used his measured glucose data and predicted glucose models for both fasting plasma glucose (FPG) and postprandial plasma glucose (PPG) for three patients with chronic diseases over the COVID-19 quarantined period, from 3/1/2020 to 11/10/2020.

Postprandial plasma glucose segmentation analysis of influences from diet and exercise between the preCOVID-19 and COVID-19 periods (No. 321)

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
GERALD C. HSU
Keyword(s):  
Plasma Glucose ◽  
Input Data ◽  
Postprandial Plasma Glucose ◽  
Sugar Intake ◽  
Segmentation Analysis ◽  
Diet And Exercise ◽  
High Range

In this paper, the author describes the special segmentation analysis of impact on his post-prandial plasma glucose (PPG) via diet and exercise during the pre-COVID-19, from 5/5/2018 - 1/18/2019, and COVID-19, from 1/19/2020 - 8/28/2020, periods. He segmented his carbs/sugar intake amount and post-meal walking step into two ranges, low range and high range, for two periods, respectively. Initially, he calculates their associated PPG values for each range of input data, then he compares these two segmented outcomes against the total results from the combined periods.

scholarly journals A simple formula based on postprandial plasma glucose prediction using 5,640 meals data via GH-Method: math-physical medicine (No. 301)

2020 ◽  
Vol 5 (5) ◽  
Keyword(s):  
Plasma Glucose ◽  
Research Work ◽  
The Other ◽  
Diabetes Research ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Sugar Intake ◽  
Glucose Testing ◽  
The Status ◽  
Food Nutrition

This article is based on the continuation of the author’s research work, a simple and practical, yet highly accurate postprandial plasma glucose (PPG) prediction formula for type 2 diabetes (T2D) patients. His methodology is the developed GH-Method: math-physical medicine (MPM) which has been utilized repeatedly in the past decade. The predicted PPG formula-based on the status of fasting plasma glucose (FPG), carbs/sugar intake amount, and postmeal walking steps are as follows: Predicted PPG = 0.97 * FPG + (carbs/sugar grams * 1.8) - (post-meal walking steps in thousand * 5) The conclusive results have the order of values m1 / m2 /m3 / prediction accuracy %. Case A: 1.8 / 5.0 / 0.97 / 99.8% Case B: 2.0 / 5.0 / 0.945 / 99.9% Case C: 2.2 / 5.0 / 0.92 / 99.9% Exercise is important, contributing ~3% higher than food, is easily achieved compared to the required knowledge of diet. As a result, the author spent four years to study food nutrition. Most T2D patients are seniors; therefore, he suggests that walking is the best form of exercise. However, the most difficult part of exercise is the behavior psychology related to the issue of “discipline and persistence”. T2D patients need to walk between 2,000 to 4,000 steps after each meal. The author walks an average of 4,300 steps after each meal. On the other hand, diet (carbs/sugar amount and nutritional balance) requires much more and deeper knowledge of food nutrition in order to control diabetes. Therefore, the author developed an AI-based tool to assist T2D patients. For non-tech patients, the following simple guidelines can assist with meal intake: Starchy food: Eat an amount half of your fist or hand at most Colorful vegetables: Eat an amount limited to one fist or hand. Green vegetables: Eat an amount limited to 2.5 fists or hands. Please note: you must combine two types of vegetable together in order to get the total intake limitation. The author highly recommends the patients to measure their FPG at least several times a quarter, in order to get a quarterly average FPG value. The other three PPG values can then utilize the formula-based predicted PPG to control their overall diabetes conditions. The described method mentioned above in regard to the predicted PPG formula along with the post-meal walking exercise and carbs/sugar intake amount can help patients control their diabetes without painful and troublesome fingerpiercing glucose measurements. The author has been measuring his glucoses for 8.5 years (3,126 days) with fingerpiercing glucose testing combined with his 10-years of diabetes research work. He hopes this article can provide useful guidelines to other diabetes patients to take back their lives from this dreadful chronic disease.

scholarly journals Postprandial Plasma Glucose Segmentation Analysis of Influences from Diet and Exercise between the Pre-COVID-19 and COVID-19 Periods (No. 321)

2020 ◽  
Vol 3 (4) ◽  
Keyword(s):  
Plasma Glucose ◽  
Input Data ◽  
Postprandial Plasma Glucose ◽  
Sugar Intake ◽  
Segmentation Analysis ◽  
Diet And Exercise ◽  
High Range

In this paper, the author describes the special segmentation analysis of impact on his post-prandial plasma glucose (PPG) via diet and exercise during the pre-COVID-19, from 5/5/2018 - 1/18/2019, and COVID-19, from 1/19/2020 - 8/28/2020, periods. He segmented his carbs/sugar intake amount and post-meal walking step into two ranges, low range and high range, for two periods, respectively. Initially, he calculates their associated PPG values for each range of input data, then he compares these two segmented outcomes against the total results from the combined periods.

scholarly journals A1C Variance Study and PPG Prediction Methodology over Six Periods Using GH-Method: Math-Physical Medicine

2020 ◽  
Vol 5 (4) ◽  
Keyword(s):  
Plasma Glucose ◽  
Hemoglobin A1c ◽  
Prediction Models ◽  
Big Data Analytics ◽  
Physical Medicine ◽  
Lifestyle Management ◽  
Postprandial Plasma Glucose ◽  
Index Weight ◽  
Engineering Modeling

In this case study, the author analyzed, predicted, and interpreted a type 2 diabetes (T2D) patient’s hemoglobin A1C variances based on six periods data utilizing the GH-Method: math-physical medicine approach by applying mathematics, physics, engineering modeling, and computer science (big data analytics and AI). He believes in “prediction” and has developed five models, including metabolism index, weight, fasting plasma glucose (FPG), postprandial plasma glucose (PPG), and hemoglobin A1C. All prediction models have reached to 95% to 99% accuracy. His focus is on preventive medicine, especially on diabetes control via lifestyle management.

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