GH-METHODS

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

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

Abstract
COVID-19 (the virus) is the most serious epidemic in recent history that can impact many different aspects for every human being in this world.  For a special group of senior-age patients (the patients) with chronic diseases, especially diabetes, their mortality risk would be much higher than any age groups affected by the virus.  As a precaution to reduce the spread of the virus, wearing masks, keeping social distance, and washing hands should be followed.  In addition, it is important to know their overall health status, especially existing medical conditions, metabolism status, and immunity strength to assist with the background preparation in order to fight against this vicious virus.  This preparation task is used to prevent them from serious complications caused by other chronic diseases, such as CVD, stroke, or other inter-connected medical emergencies that could led to hospitalization with patients inflicted with the virus.

Based on the above descriptions, the author decided to take a closer look on his own body’s strength.  By measuring metabolism and immunity along with his health status via medical conditions, especially glucose, and lifestyle details on a continuous basis in order to remind or alert himself from time to time. The final reports, from his self-conducted health examination used in this article, are similar to the medical examination report generated from either a medical office, hospital, or laboratory. However, the self-conducted report includes additional information on his lifestyle details, which ultimately affect his various medical conditions.

The numerical results in all areas show that his health performance during Covid-19 are better than his pre-Covid-19 period.

Generally speaking, the author is extremely satisfied with the outcome of the performance results from this simple data segmentation analysis based on his glucoses, lifestyles, and metabolism. This report offers him a peaceful mind and a re-confirmation regarding his diabetes and overall health state. As a matter of fact, to date, during this 9-month virus period, it has been his “best-performance” duration over the past 25 years since he was diagnosed with type 2 diabetes in 1995.

His research methodology of GH-Method: math-physical medicine has been proven again, while offering a useful general tool to deal with many medical problems or healthcare issues.

Introduction
COVID-19 (the virus) is the most serious epidemic in recent history that can impact many different aspects for every human being in this world.  For a special group of senior-age patients (the patients) with chronic diseases, especially diabetes, their mortality risk would be much higher than any age groups affected by the virus.  As a precaution to reduce the spread of the virus, wearing masks, keeping social distance, and washing hands should be followed.  In addition, it is important to know their overall health status, especially existing medical conditions, metabolism status, and immunity strength to assist with the background preparation in order to fight against this vicious virus.  This preparation task is used to prevent them from serious complications caused by other chronic diseases, such as CVD, stroke, or other inter-connected medical emergencies that could led to hospitalization with patients inflicted with the virus.

Based on the above descriptions, the author decided to take a closer look on his own body’s strength.  By measuring metabolism and immunity along with his health status via medical conditions, especially glucose, and lifestyle details on a continuous basis in order to remind or alert himself from time to time. The final reports, from his self-conducted health examination used in this article, are similar to the medical examination report generated from either a medical office, hospital, or laboratory. However, the self-conducted report includes additional information on his lifestyle details, which ultimately affect his various medical conditions.

Methods
1. Background
To learn more about the GH-Method: math-physical medicine (MPM) research methodology, readers can review his article, Biomedical research methodology based on GH-Method: math-physical medicine (No. 54 and No. 310), to understand his MPM analysis method.

2. Data collection
Since May 2020, he has kept the same research format during the past 9 months, while writing and publishing a few medical articles. This examination is to compare his current virus period of 255 days, from 1/19/2020 to 9/29/2020, against his previously defined “fixed pre-virus period” of 623 days, from 5/5/2018 to 1/18/2020.  In this particular report, there are two general categories, glucose and others (metabolism and lifestyles), which contain a total of 20 items for this comparison.  The reason of selecting the starting date of 5/5/2018 is that he started to measure his daily glucoses via a continuous glucose monitor (CGM) sensor collected glucose at 240 data per day in addition to his traditional finger-piercing collected glucose at 4 data per day.  The selection of the ending date as 1/18/2020 coincides with his self-quarantined period in the US on 1/19/2018 due to his prior horrific experience with the 2003 SARS event that started in East Asia, mainly China and Taiwan.

It should be emphasized that he has conducted a segmentation analysis of his collected finger glucose and sensor glucose by dividing his glucose data into two separated sub-groups.  The “low” group is glucoses within 70 mg/dL to 120 mg/dL for the normal range.  The “high” group is glucoses within 121 mg/dL to 200 mg/dL for the pre-diabetes and diabetes range.  By comparing the data distribution percentages between the low group and high group, along with the pre-virus and virus periods, he can determine if his situation is getting better or worse.

In the “others” category, he includes weight in pounds, carbs/sugar intake amount in grams (for PPG), post-meal walking in hundred steps (for PPG), food and three meals portion quantity in percentage (for weight control), bowel movement amount in percentage (for gastrointestinal system and weight control), and weather temperature in degree Fahrenheit (for both FPG and PPG).  Readers can learn more about MI (metabolism index) and GHSU (general health status unit) from the article listed in Reference 1, because these two are the most important parameters.

Results
In Figure 1, it shows the background data table of this analysis which is generated via a big data analytics from the author’s collected database and his processed data based on his research work from the past decade. Figures 2, 3, and 4 are generated from this data table.

Figure 1: Background data table

The direct glucose comparison between his pre-virus period versus the virus period of a total of 12 components is shown in Figure 2.  From the comparison of bar heights, the virus period is lower than the pre-virus period which indicates his glucose conditions are improved during the virus period. The percentages of low and high glucoses are reversing since the majority of them are below 120 mg/dL.  As a result, during the virus period, it is even more obvious than during the pre-virus period because most of his glucoses are moving into the low range, particularly in the virus period.  

Figure 3 reveals the direct comparison of other 8 components between his pre-virus period versus his virus period.  Similar to the glucose comparison in Figure 2, the data in the virus period are lower than his pre-virus period.  His post-meal walking steps is higher, meaning better, along with his bowel movement percentage being higher.  

Figure 2: Glucose comparison with 12 components
Figure 3: Others comparison with 8 components

Finally, Figure 4 is extracted from the general data table in Figure 1, which shows the differences or improvements, from the pre-virus period to the virus period.  In this table, a minus sign reflects a better or improved situation. With his average weight having no fluctuations, other than walking steps and bowel movement being positive values, all of the remaining 7 components are improved with minus values.  

Figure 4: Improvement % between pre-virus period and virus period

Conclusions
Generally speaking, the author is extremely satisfied with the outcome of the performance results from this simple data segmentation analysis based on his glucoses, lifestyles, and metabolism.  This report offers him a peaceful mind and a re-confirmation regarding his diabetes and overall health state.  As a matter of fact, to date, during this 9-month virus period, it has been his “best-performance” duration over the past 25 years since he was diagnosed with type 2 diabetes in 1995.

His research methodology of GH-Method: math-physical medicine has been proven again, while offering a useful general tool to deal with many medical problems or healthcare issues.

References

  1. Hsu, Gerald C., eclaireMD Foundation, USA; “GH-Method: Methodology of math-physical medicine, No. 54 and No. 310”
  2. Hsu, Gerald C., eclaireMD Foundation, USA; “Controlling type 2 diabetes via artificial intelligence technology (GH-Method: math-physical medicine)  No. 125”
  3. Hsu, Gerald C., eclaireMD Foundation, USA; “Guesstimate probable partial self-recovery of pancreatic beta cells using calculations of annualized glucose data using GH-Method: math-physical medicine (No. 139)
  4. Hsu, Gerald C., eclaireMD Foundation, USA; “Using wave characteristic analysis to study T2D patient’s personality traits and psychological behavior using GH-Method: math-physical medicine, No. 52”
  5. Hsu, Gerald C., eclaireMD Foundation, USA; “Trending pattern analysis and progressive behavior modification of two clinic cases of correlation between patient psychological behavior and physiological characteristics of T2D Using GH-Method: math-physical medicine & mentality-personality modeling, No. 53”
  6. Hsu, Gerald C., eclaireMD Foundation, USA; “Using wave characteristic analysis to study T2D patient’s personality traits and psychological behavior (Based on GH-Method: Math-Physical Medicine), No. 59”
  7. Hsu, Gerald C., eclaireMD Foundation, USA; “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, No.72”
  8. Hsu, Gerald C., eclaireMD Foundation, USA; “Using artificial intelligence technology to overcome some behavioral psychological resistance for diabetes patients on controlling their glucose level using GH-Method: math-physical medicine & mentality-personality modeling, No. 93”
  9. Hsu, Gerald C., eclaireMD Foundation, USA; “A comparison of three glucose measurement results during COVID-19 period using GH-Method: math-physical medicine (No. 303)”
  10. Hsu, Gerald C., eclaireMD Foundation, USA; “Segmentation analysis of impact on glucoses via diet, exercise, and weather temperature during COVID-19 quarantine period, 312”