## GH-METHODS

Math-Physical Medicine

### NO. 073

The impact of plantar fasciitis on glucose and A1C (GH-Method: Math-Physical Medicine)

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

Introduction
The author is investigating the impact of plantar fasciitis on postprandial plasma glucose (PPG) and HbA1C based on 522 data collected for a period of 174 days from 10/28/2018 to 4/27/2019 with post-meal walking steps, carbs/sugar intake grams, and measured PPG. The dataset is provided by the author, who uses his own type 2 diabetes (T2D) metabolic conditions control, as a case study via the “math-physical medicine” approach of a non-traditional methodology in medical research.

Math-physical medicine (MPM) starts with the observation of the human body’s physical phenomena (not biological or chemical characteristics), collecting elements of the disease related data (preferring big data), utilizing applicable engineering modeling techniques, developing appropriate mathematical equations (not just statistical analysis), and finally predicting the direction of the development and control mechanism of the disease.

Method
In 2018, this patient walked 18,458 steps daily (~7.7 miles or ~12.3 km), where each heel took 9,229 impact force per day.  During the second half of 2018 while he was traveling, he wore a pair of leisure shoes instead of his customized shoes with protective insoles.  Towards the end of 2018, his plantar fasciitis condition returned.  As a result, he could not walk as much as usual due to the sharp pain in his heels.  By March of 2019, he noticed his increased values of PPG and HbA1C.  The author then utilized his MPM approach and his previous published papers to analyze his personalized data.

Results
During the plantar fasciitis period, his post-meal waking steps reduced by 1,800 steps from 4,800 to 3,000.  Based on the author’s exercise formula (see Figures 1 & 2), every block of one thousand steps decreased approximately 5 mg/dL of PPG.  The 1,800 steps reduction would increase PPG value by 9 mg/dL.  This estimation is quite comparable with the actual increase amount of PPG of 10 mg/dL from 112 to 122.  The minor difference of 1 mg/dL is resulted from a variance of carbs/sugar intake amount.

###### Figure 2: Plantar Fasciitis impact on PPG and A1C

Conclusion
This special clinical case demonstrates the inter-connectivity among different parts of the human body.  Even having plantar fasciitis could increase a patient’s glucose and A1C significantly.  It also shows the importance of lifestyle management, including both diet and exercise, to diabetes patients.  Finally, the MPM approach can be used to analyze many biomedical phenomena as long as the “related and clean” data are collected and organized efficiently.

References

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. Retrieved from http://www.kosmospublishers.com/wp-content/uploads/ 2018/06/JEAD-101-1.pdf
2. 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.
3. Hsu, Gerald C. (2018). Using Signal Processing Techniques to Predict PPG for T2D. International Journal of Diabetes & Metabolic Disorders, 3(2),1-3. Retrieved from https://www.opastonline.com/wp-content/uploads/2018/06/using-signal-processing-techniques-to-predict-ppg-for-t2d-ijdmd-18.pdf
4. 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. Retrieved from http://cmepub.com/pdfs/using-mathphysical-medicine-and-artificial-intelligence-technology-to-manage-lifestyle-and-control-metabolic-conditions-of-t2d-412.pdf
5. Hsu, Gerald C. (2018). A Clinic Case of Using Math-Physical Medicine to Study the Probability of Having a Heart Attack or Stroke Based on Combination of Metabolic Conditions, Lifestyle, and Metabolism Index. Journal of Clinical Review & Case Reports, 3(5), 1-2. Retrieved from https://www.opastonline.com/wp-content/uploads/2018/07/a-clinic-case-of-using-math-physical-medicine-to-study-the-probability-of-having-a-heart-attack-or-stroke-based-on-combination-of-metabolic-conditions-lifestyle-and-metabolism-index-jcrc-2018.pdf