GH-METHODS
Math-Physical Medicine
Diabetes
NO. 010
A Case Study of Analyzing and Predicting A1C Changes Using Math-Physics Medicine
NO. 011
Using GH-Method: math-physical medicine (MPM) to study the relationship between weight and glucose
NO. 019
Comparison of Characteristics between FPG and PPG via both Time-Series and Frequency Domain Analyses of GH-Method: math-physical medicine
NO. 020
Revaluation of FPG Prediction & Major Contributing Factor Using Frequency Domain Analysis of GH-Method: math-physical medicine
NO. 021
Re-evaluation of FPG & PPG Predictions and their Major Contributing Factors Using Frequency Domain Analysis of GH-Method: math-physical medicine
NO. 022
Re-evaluation of Both FPG and PPG Predictions and Their Major Contributing Factors Using Frequency Domain Analysis of GH-Method: math-physical medicine
NO. 023
A Clinical Case of Using GH-Method: math-physical medicine to Control Metabolic Conditions and Complications via Lifestyle Management
NO. 026
A Case Study of Investigation and Prediction of A1C Variances Using GH-Method: math-physical medicine
NO. 036
Using GH-Method: math-physical medicine and energy theory to re-examine the relationship with food, exercise, and postprandial plasma glucose
NO. 038
Using GH-Method: math-physical medicine and Wave/Energy Theories to identify practical tips of diet & exercise associated with three distinctive PPG waveform patterns
NO. 049
Comparison of two fasting plasma glucose measurements including finger-piercing and continuous sensor monitoring (GH-Method: Math-Physical Medicine)
NO. 050
Comparison of two postprandial plasma glucose measurements including finger-piercing and continuous sensor monitoring (GH-Method: Math-Physical Medicine)
NO. 057
Clinical case of using GH-Method: math-physical medicine to control type 2 diabetes patient via both lifestyle management and effective medications
NO. 058
A clinical case of using GH-Method: math-physical medicine to control Type-2 Diabetes patient via both lifestyle management and effective medication
NO. 059
Three clinical cases of T2D control using GH-Method: math-physical medicine via both lifestyle management and effective medications
NO. 062
Four clinical cases using GH-Method: math-physical medicine to control type 2 diabetes via both lifestyle management and effective medications
NO. 063
Summary of Comparison between finger-piercing measured glucoses and sensor collected glucoses
NO. 065
A Case Study of Investigation and Prediction of A1C Variances Over 5 Periods Using GH-Method: math-physical medicine
NO. 066
Using GH-Method: Math-Physical Medicine to Conduct Segmentation Analysis to Identify the Dividing Line of Weight vs. Fasting Plasma Glucose
NO. 067
Using GH-Method: math-physical medicine to Conduct Segmentation Analysis to Investigate the Impact of both Weight and Weather Temperatures on Fasting Plasma Glucose (FPG)
NO. 068
Using GH-Method: math-physical medicine to Conduct Segmentation Analysis to Investigate the Impact of both Weight and Weather Temperatures on Fasting Plasma Glucose (FPG)
NO. 069
Using GH-Method: Math-Physical Medicine to Conduct Segmentation Analysis to Investigate the Impact of Low-Carbs and High-Carbs Diets on Postprandial Plasma Glucose (PPG)
NO. 070
Using GH-Method: Math-Physical Medicine to Conduct Segmentation Analysis to Investigate the Impact of Different Intensity of Exercise on Postprandial Plasma Glucose (PPG)
NO. 071
Using GH-Method: Math-Physical Medicine to develop a simple yet practical guidelines of carbs/sugar intake amount and post-meal walking steps in order to control Postprandial Plasma Glucose (PPG)
NO. 079
Using Spatial Analysis and Forecasting to Manage Type 2 Diabetes Conditions (GH-Method: Math-Physical Medicine)
NO. 081
Using Candlestick Charting, Segmentation Pattern Analysis, and GH-Method: Math-Physical Medicine to calculate Effective Glucoses based on Finger-Piercing Glucose Measurement
NO. 082
Using GH-Method: Math-Physical Medicine, Fourier Transform, and Frequency Segmentation Pattern Analysis to Investigate Relative Energy Associated with Glucose
NO. 084
Using three analysis methods: Time-Series, Spatial, and Frequency-Domain to analyze and forecast key diabetes biomedical variables (GH-Method: Math-Physical Medicine)
NO. 090
Using a Modified Candlestick Charting Technique, OHCA Model, and the Synthesized PPG Waveform to Develop Two Simple Formulas for PPG Prediction (GH-Method: Math-Physical Medicine)
NO. 091A
Four clinic cases regarding estimation of PPG upper-bound values from sensor measurements to include both different stages of initial condition of type 2 diabetes and hyperglycemia control efforts (GH-Method: Math-Physical Medicine)
NO. 091B
Four clinic cases regarding estimation of PPG upper-bound values from sensor measurements (OHCA model) to include both different stages of initial condition of type 2 diabetes and hyperglycemia control efforts (GH-Method: Math-Physical Medicine)
NO. 092
Using four clinical cases to examine the accuracy of predicted PPG via AI Glucometer tool (GH-Method: Math-Physical Medicine)
NO. 099B
Application of linear equation-based PPG prediction model for four T2D clinical cases (GH-Method: math-physical medicine)
NO. 100
Using two clinical glucose measurement datasets (finger glucose as lower bound and sensor glucose as upper bound) to establish a rational range of HbA1C values for T2D patients (GH-Method: math-physical medicine)
NO. 119
Understanding physical characteristics of PPG via sensor data segmentation analysis (GH-Method: math-physical medicine)
NO. 124
A case study of the impact on glucose, particularly postprandial plasma glucose based on the 14-day sensor device reliability (using GH-Method: math-physical medicine)
NO. 134
Using waveform characteristics analysis and energy theory to analyze a meal’s PPG waveforms (GH-Method: math-physical medicine)
NO. 144
Geographical segmentation analysis of Sensor PPG data by nations (GH Method: math-physical medicine)
NO. 145
Geographical segmentation analysis of Sensor PPG data by nations (GH Method: math-physical medicine)
NO. 146
Geographical segmentation analysis of Sensor PPG data by nations and eating places (GH Method: math-physical medicine)
NO. 149
Geographical segmentation analysis of Sensor PPG data by nations and eating places (GH Method: math-physical medicine)
NO. 150
Applying segmentation pattern analysis to investigate postprandial plasma glucose characteristics and behaviors of the carbs/sugar intake amounts in different eating places (GH Method: math-physical medicine)
NO. 151
Geographical segmentation analysis of Sensor PPG data by nations and eating places (GH Method: math-physical medicine)
NO. 155
HbA1C conversion using ADA formula of eAG (based on both finger glucose and sensor glucose) and comparison with eclaireMD mathematically developed A1C model results (GH-Method: math-physical medicine)
NO. 245
Predicting Finger PPG by using Sensor PPG waveform and data via regression analysis with two different methods, matching time and matching glucose (GH-Method: math-physical medicine)
NO. 246
Segmentation analysis of sensor glucoses and their associated energy (GH-Method: math-physical medicine)
NO. 261A
Medical research of PPG characteristics using candlestick model from Wall Street (GH-Method: Math-Physical Medicine)
NO. 262
A Case Study on the Prediction of A1C Variances over Seven Periods with guidelines Using GH-Method: math-physical medicine
NO. 281
Differences between 5-minute and 15-minute measurement time intervals of the CGM sensor glucoses device using GH-Method: math-physical medicine
NO. 288
Diabetes control and metabolism maintenance during COVID-19 period in comparison to three other periods using GH-Method: math-physical medicine
NO. 329
A Case Study on the Prediction of A1C Variances over Eight Periods using GH-Method: math-physical medicine
NO. 336
Influences from medication and lifestyle on glucose and metabolism during two equal length of 4-years but different sub-periods, one with-medication and the other without-medication using GH-Method: math-physical medicine
NO. 337
A detailed illustration on the prediction accuracy of postprandial plasma glucose value using a sample lunch meal via GH-Method: math-physical medicine
NO. 338
Analysis of glucose conditions and other related key factors between pre-virus period and COVID-19 period using GH-Method: math-physical medicine
NO. 341
Extended comparison study among lab-tested HbA1C, ADA’s eAG HbA1C conversion model, and eclaireMD mathematical HbA1C prediction model based on the lower bound and higher bound of measured glucoses using GH-Method: math-physical medicine
NO. 343
Continuous Glucose Monitoring Sensor postprandial plasma glucose waveform study of breakfasts at McDonald’s using the GH-Method: math-physical medicine
NO. 344
A case report of observational comparison sensor study of US cooked meals postprandial plasma glucose and worldwide fasting plasma glucose between pre-Virus and Virus periods using GH-Method: math-physical medicine
NO. 345
Application of linear equations to predict sensor and finger based postprandial plasma glucoses and daily glucoses during pre-Covid-19, Covid-19, and total periods using GH-Method: math-physical medicine
NO. 348
A Case Study on the Prediction of A1C Variances over Seven Periods with guidelines Using GH-Method: math-physical medicine
NO. 353
Lifestyle medicine practice to diagnose the relationship between sleep patterns and glucoses of three type 2 diabetes patients using GH-Method: math-physical medicine
NO. 354
Applying linear elastic glucose behavior theory and AI auto-correction to predict A1C Variances over the ninth period using GH-Method: math-physical medicine
NO. 368
The differences in characteristics and energy levels associated with different glucose frequency components of 5-minute and 15-minute measurement time intervals from the continuous glucose monitor device using GH-Method: math-physical medicine
NO. 373
Study of triglyceride and glucose index biomarker (TyG) for diabetes control through improvement on insulin resistance using GH-Method: math-physical medicine
NO. 374
Using a newly redefined biomarker, triglyceride and glucose index biomarker (New TyG), as an alternative tool for diabetes patients to control their insulin resistance conditions based on GH-Method: math-physical medicine
NO. 376
A study on the relationships between body weight versus triglyceride, fasting plasma glucose, along with triglyceride and glucose index biomarker based on GH-Method: math-physical medicine
NO. 380
A summary report of two biomarkers for triglyceride and glucose index (TyG) and accuracy sensitivity analysis in estimating the insulin resistance status based on GH-Method: math-physical medicine
NO. 393
Time In Range, Time Above Range, and Time Below Range analyses of the sensor collected glucoses for a period of approximately 3 years covering the pre-virus and COVID-19 periods using GH-Method: math-physical medicine
NO. 400
Analyzing postprandial plasma glucose wave fluctuations using GH-Method: math-physical medicine