GH-METHODS

Math-Physical Medicine

Diabetes

NO. 002

Using GH-Method: Math-Physical Medicine to investigate the macro-scaled relationship between Weather and Glucose 

NO. 004

Using GH-Method: math-physical medicine to analyze relationship  between Meals and PPG

NO. 006

Using GH-Method: math-physical medicine to predict postprandial plasma glucose

NO. 007

Using GH-Method: math-physical medicine to Predict Fasting Plasma Glucose 

NO. 008

Using GH-Method: math-physical medicine to Control Type-2 Diabetes

NO. 009

Using GH-Method: math-physical medicine to control T2D via metabolism monitoring and glucose predictions

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. 012

Relationship between glucose and blood pressure using GH-Method: math-physical medicine

NO. 013

Using GH-Method: math-physical medicine and signal processing techniques to predict PPG

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. 030

Using GH-Method: math-physical medicine to study Differences between Fasting Plasma Glucose and Postprandial Plasma Glucose 

NO. 031

Using GH-Method: math-physical medicine) to develop Health-Maintaining Tips for Diabetes Travelers 

NO. 032

Using GH-Method: math-physical medicine to investigate the micro-scaled relationship between Ambient Temperature and Glucose

NO. 033

Using GH-Method: math-physical medicine to investigate the relationship between Postprandial Plasma Glucose and Food 

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. 040

Investigating wave and energy of glucose waveforms to control glucose fluctuation via optimized combination of food and exercise (GH-Method: math-physical medicine)

NO. 044

Comparison of Glucose Data and Phenomena from Two Different Measurement Methods  (GH-Method: math-physical medicine) 

NO. 045

Categorizing PPG Waveforms into Three Distinctive Patterns Based on Wave Theory and Energy Theory (Part of GH-Method: math-physical medicine)

NO. 046

Comparison Between FPG & PPG Waveforms Using Wave Theory and Energy Theory (Part of GH-Method: math-physical medicine) 

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. 076

Using Candlestick Charting Techniques to Investigate Glucose Behaviors (GH-Method: Math-Physical Medicine)

NO. 077

Using Time-Series and Forecasting to Manage Type 2 Diabetes Conditions (GH-Method: Math-Physical Medicine)

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. 085

Using GH-Method: Math-Physical Medicine to investigate different contribution margins of FPG vs. PPG on HbA1C

NO. 089

Using GH-Method: Math-Physical Medicine to Conduct the Accuracy Comparison of Two different PPG Prediction Methods, No. 89

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. 097

A simplified yet accurate linear equation of PPG prediction model for T2D patients  (GH-Method: math-physical medicine)

NO. 099A

Application of linear equation-based PPG prediction model for four T2D clinic cases  (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. 101

Sensitivity study of HbA1C range using big data analytics based on 5 different models (GH-Method: math-physical medicine)

NO. 102

Sensor glucose behavior pattern analysis using GH-Method: math-physical medicine methodology

NO. 107

Postprandial Plasma Glucose Behavior based on six breakfasts by using AI Glucometer and GH-Method: math-physical medicine

NO. 108

Changes in relative health state of pancreas beta cells over eleven years using GH-Method: math-physical medicine

NO. 110

Comparison of glucose results from Finger-piercing and Sensor-collected data using GH-Method: math-physical medicine

NO. 114

Analyzing a simple lunch’s predicted PPG results using GH-Method: math-physical medicine

NO. 116

A Case Study on the Investigation and Prediction of A1C Variances Over Six Periods Using GH-Method: math-physical medicine

NO. 118

A1C variance study and PPG  prediction methodology

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. 128

First step in controlling diabetes and its complications: a true understanding of glucoses via GH-Method: math-physical medicine

NO. 129

Effectiveness on diabetes control via medication and lifestyle management 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. 156

A Case Study on Prediction of A1C Variances Over Seven Periods with guidelines Using GH-Method: math-physical medicine

NO. 238

The influences of medication on diabetes control using TIR analysis (GH-Method: Math-physical medicine)

NO. 239

Case study of a type 2 diabetes patient’s time-in-range (15-min) analysis using GH-Method: Math-physical medicine

NO. 240

The analysis of the ADA defined TIR, TAR, and TBR based on 5-minute measurement intervals of the CGM sensor glucose data using 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. 248

Comparison of HbA1C values among Lab-tested, Finger-piercing, CGM-collected  (GH-Method: math-physical medicine)

NO. 249

Predicting Finger PPG by using Sensor PPG waveform and data via regression analysis with three different methods  (GH-Method: math-physical medicine)

NO. 251

The CGM Range Analysis based on 5-minute measurement intervals from the CGM sensor glucose data using GH-Method: Math-physical medicine

NO. 261

Comparison study of PPG characteristics from candlestick model using 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. 276

Characteristic pattern study of glucose waveforms using GH-Method: math-physical medicine 

NO. 277

Different glucose levels produced by coffee versus decaffeinated coffee (GH-Method: Math-physical medicine)

NO. 278

Analyzing CGM sensor glucoses at 5-minute intervals using GH-Method: math-physical medicine

NO. 279

Investigation of HbA1C variances and predictions over eight sub-periods 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. 293

Comparison of glucoses and HbA1C values between finger-piercing and sensor collected at 15-minute and 5-minute intervals using GH-Method: math-physical medicine

NO. 302

A comparison of two clinical cases of quantitative lifestyles medicine using GH-Method: math-physical medicine

NO. 303

A comparison of three glucose measurement results during COVID-19 period using GH-Method: math-physical medicine

NO. 304

An investigation of continuous glucose monitor based PPG waves and results using GH-Method: math-physical medicine

NO. 305

Glucose trend pattern analysis and progressive behavior modification of a T2D patient using GH-Method: math-physical medicine

NO. 312

Segmentation analysis of impact on glucoses via diet, exercise, and weather temperature during COVID-19 quarantine period

NO. 317

A postprandial plasma glucose (PPG) comparison study between pre-COVID-19 and during COVID-19 using GH-Method: math-physical medicine

NO. 318

Glucoses and HbA1C comparison study between pre- COVID-19 and COVID-19 using GH-Method: math-physical medicine

NO. 319

Glucose analyses of TIR, TAR, and TBR between pre-COVID-19 and COVID-19 using GH-Method: math-physical medicine

NO. 320

Accuracy of predicted glucose using both natural intelligence (NI) and artificial intelligence (AI) via GH-Method: math-physical medicine

NO. 321

Postprandial plasma glucose segmentation analysis of influences from diet and exercise between the pre-COVID-19 and COVID-19 periods

NO. 322

Detailed lifestyle comparison study between pre-COVID-19 and COVID-19 periods using GH-Method: math-physical medicine

NO. 325

Segmentation and pattern analyses for three meals of postprandial plasma glucose using GH-Method: math-physical medicine

NO. 326

Segmentation and pattern analyses for three meals of postprandial plasma glucose values and associated carbs/sugar amounts 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