Math-Physical Medicine

NO. 072

Using GH-Method: math-physical medicine, mentality-personality modeling, and segmentation pattern analysis to compare two clinic cases about linkage between T2D patient’s psychological behavior and physiological characteristics

Corresponding Author: Gerald C. Hsu, eclaireMD Foundation, USA.

The author has contemplated a specific question:

Why do some type 2 diabetes (T2D) patients choose to face serious complications, including death, rather than change their lifestyle in order to control their diabetic conditions?

This paper utilized segmentation pattern analysis to analyze two different clinic cases linking T2D patient’s personality traits and psychological behavior with diabetes physiological characteristics.

T2D patients have faced three major challenges:

  1. Availability of accurate disease information with either physical evidence or quantitative proof, not just some general qualitative descriptions that may include false or commercial driven news over the internet (knowledge issue).
  2. Awareness of disease status and overcome self-denial by moving to “psychological acceptance” in order to take effective action.  The most difficult barrier to overcome is to have willpower, determination, and persistence on lifestyle change (psychology issue).
  3. A non-invasive, effective, and ease of use technology-based tool to accurately predict outcomes and also guide patients (technology issue).

The author collected 4,320 data of carbs, sugar, walking, and finger-piercing PPG for 360 days (5/5/2918 – 4/29/2019) for Case A.  The total data size of Case B is 58% of Case A.  Patients of both cases are over 70 years old male with more than 20 years history of type 2 diabetes (T2D).  During this collected data period, Case A has not taken any diabetes medication while Case B has taken 1000 mg of metformin daily.

The author applied a segmentation pattern analysis to investigate individual PPG behaviors with the following different segments.

  • Carbs/Sugar intake amount: 60 grams per day (~15 grams per meal) to separate low-carbs diet and high-carbs diet.
  • Every thousand block of post-meal walking steps to separate walking steps into 5 levels (“level x where x = 1, 2, 3, 4, 5)
  • <120 mg/dL of PPG value as Under-controlled Diabetes; 120 – 140 mg/dL as Pre-diabetes; >140 mg/dL as Diabetes.

(1) Personality regarding persistence via data completeness percentages:

  • Case A: 100% means he has not missed any data input for monitoring and analysis over 360 days and 1,080 meals.
  • Case B: 40% on diet and 76% on exercise and 58% on total data completeness means that his persistence is less strong.

(2) Resistance against craving for food via diet segmentation:

  • Case A: averaged 14.4 grams of carbs/sugar per meal with 74% low-carbs diet and 26% high-carbs diet.
  • Case B: averaged 13.1 grams of carbs/sugar per meal with 93% low-carbs diet and 7% high-carbs diet.

This shows that Case B has stronger will power against food seduction.

(3) Will-power and Persistence via repetitive post-meal walking:

  • Case A:  averaged 4,338 post-meal walking steps (76% at level 5) shows his strong will-power and persistence on exercise to reduce his PPG.
  • Case B: averaged 1,820 steps (92% at level 3) shows his weaker will-power and persistence on exercise.

(4) Diabetes control effectiveness via average PPG values:

  • Case A: 118 mg/dL (under-controlled diabetes via lifestyle management)
  • Case B: 156 mg/dL (diabetes condition not under well controlled  even with medication).
Figure 1: 90-days moving averaged charts of Case A vs. Case B of PPG,
Diet, and Exercise
Figure 2: Data Comparison of Case A vs. Case B of Data Completeness,
Diet, and Exercise

This paper has applied data segmentation analysis and data pattern recognition method to conduct a comparison study between two severe T2D patients.  Through this analysis, the personality traits and behavior psychological pattern of individual T2D patient can be revealed instantly and clearly. As a result, a more practical guidance of “progressive behavior modification” can then be provided to specific T2D patient.


  1. Hsu, Gerald C. (2018). Using Math-Physical Medicine to Control T2D via Metabolism Monitoring and Glucose Predictions. Journal of Endocrinology and Diabetes, 1(1), 1-6.
  2.  Hsu, Gerald C. (2018). Using Signal Processing Techniques to Predict PPG for T2D. International Journal of Diabetes & Metabolic Disorders, 3(2),1-3.
  3. Hsu, Gerald C. (2018). Using Math-Physical Medicine and Artificial Intelligence Technology to Manage Lifestyle and Control Metabolic Conditions of T2D. International Journal of Diabetes & Its Complications, 2(3),1-7.
  4. Hsu, Gerald C. (2018, June). Using Math-Physical Medicine to Analyze Metabolism and Improve Health Conditions. Video presented at the meeting of the 3rd International Conference on Endocrinology and Metabolic Syndrome 2018, Amsterdam, Netherlands.
  5. Hsu, Gerald C. (2018). Using Math-Physical Medicine to Study the Risk Probability of having a Heart Attack or Stroke Based on Three Approaches, Medical Conditions, Lifestyle Management Details, and Metabolic Index. EC Cardiology, 5(12), 1-9.