## GH-METHODS

Math-Physical Medicine

### NO. 004

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

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

Introduction
The author has spent 8.5-years doing research on diabetes.  Food is the most difficult factor in controlling PPG.  His approach is to identify the quantitative relationship between PPG and food/meals, such as location, type, and nutrition contents.

Method
He developed a SmartPhoto software to collect his meal data and stored them into a relational database with machine learning and artificial intelligence (AI) capabilities.  The established database include attributes, such as nation, meal location, food type, dish name,  nutritional ingredients, and measured glucose value.  This system can estimate his carbs/sugar intake amount and post-meal walking amount, and then predict his PPG value combined with other secondary influential factors, such as weather, stress, sleep disturbance, illness, glucose measurement time delay, etc.

Results
He selected a period of 1,309 days (6/1/2015-12/31/2018) with 3,992 meals and ~150,000 data for his analysis.  He made 102 trips to more than a dozen nations during this period.  The summary results are listed by both nation and meal location and then sorted by PPG value with the format of (average PPG mg/dL, carbs/sugar gram).

During this period (6/1/2015 – 12/31/2018), he had 3,992 meals and snacks/fruits with an averaged PPG of  118.4 mg/dL and Carbs/Sugar intake of 15.0 gram (see Figure 1).

By Nation (Figure 1):
In summary, he had 53% of meals within the USA and 47% in other nations.

• Airlines: cross nations (134.6, 25.0g) Other
• Nations (122.1, 19.7g)
• Taiwan (120.8, 15.6g)
• USA (117.0, 13.4g)
• Japan (117.8, 16.5g)