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25th European Nutrition and Dietetics Conference

Rome, Italy

Gerald C. Hsu

EclaireMD Foundation, USA

Title: T2D nursing and healthcare guide based on GH-Method: Math-physical medicine to develop simple but accurate conversion factors from annualized quantitative analysis of relationships among PPG, food and exercise


Biography: Gerald C. Hsu


Introduction: The author presents a simple, yet highly accurate estimation of conversion ratios between Postprandial Plasma Glucose (PPG) versus carbs/sugar intake amount and post-meal waking steps.

Methods: The author collected 12,249 data of PPG, carbs/sugar grams and post-meal walking steps for 1,361 days (6/1/2015 - 2/21/2019).  He used his developed AI-tool to get predicted PPG values.  Furthermore, he applied his acquired knowledge from his 9-year development process of the GH-Method: math-physical medicine, which contains impacts on PPG from both food nutrition (his 8-million food database) and exercise.  Finally, he developed two simple conversion equations of PPG vs. Carbs/Sugar intake and PPG vs. post-meal walking which involved practical engineering methods of trial-and-error and curve fitting.  He has named this simplified, yet highly accurate technique, as the Natural Intelligence (NI) approach. 

Results: As shown in Figures 1 and 2, the PPG and its key formation factors, food and exercise, are waves which fluctuate violently.  He has summarized these two graphs into Table 1 with the following observed facts:


  1. Although all waves fluctuate violently, their annual averaged values are mostly within a narrow (healthy) range.  This means that he has already learned how to control his PPG effectively through lifestyle management.
  2. His averaged measured values are:  PPG is ~118 mg/dL, carbs/sugar per meal is ~15 grams, post-meal walking is >4,000 steps.
  3. His developed 2 equations:  Each gram of carbs/sugar generated 2.4 mg/dL of PPG and each thousand steps of walking reduces 5 mg/dL of PPG.
  4. Using these two simple but effective conversion equations, both of his AI-based and NI-based PPG predictions have achieved ~100% accuracy in comparison with 4,083 finger-piercing measured PPG data. 

Conclusion: The author has developed a simple and practical, yet highly accurate PPG prediction method to provide as a nursing tool and healthcare guide to T2D patients.  Once patients PPG (~70% to 85% of A1C formation) is under control, their A1C will most likely follow suit. 

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