Math-Physical Medicine

NO. 270

Relative energy generated by glucoses during COVID-19 quarantine period (GH-Method: math-physical medicine)

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

In this paper, the author describes his research results on the impact and damage on human organs via different relative energy generated by glucose using the frequency domain analysis technique.  This report investigates specifically during his COVID-19 quarantine period.

He has collected his glucose data via a continuous glucose monitor (CGM) sensor device from 5/5/2018 through 6/4/2020 at ~76.5 glucose data per day.  During this 2+ year period (761 days), he has compiled a total of 58,217 glucose data.  In this particular analysis, he has specifically isolated the recent COVID-19 quarantine period of 137 days (1/19/2020 – 6/4/2020) with 10,481 glucose data to compare against the CGM non-virus period of 623 days each.

Initially, he collected and displayed his “time-domain” data result, which represents the horizontal x-axis as time (in day) and the vertical y-axis as glucose (in mg/dL) – similar to an EKG chart for the heart.  Next, he utilized a mathematical algorithm based on “Fourier Transform” technique to convert these time domain data into frequency domain data.  In the frequency domain chart, the x-axis becomes frequency, instead of time, and the y-axis becomes the amplitude scale associated with distinctive frequency, instead of the glucose scale.  In one of his published paper (Reference 1), he proved that this frequency domain’s y-axis value actually indicates the “relative” energy associated with that particular glucose frequency on x-axis.  Based on this frequency domain data chart, he could then segregate the total data of frequency domain into three frequency sub-ranges of low, medium, and high.

In the glucose related frequency domain diagram, we can see that its waveform (or curve) pattern is a symmetric salad bowl shape with the high edge on each rim.  Let us focus on the left half portion of the chart.  The far left side indicates the lower-frequency range, those much higher glucose energy values associated with lower frequencies of glucose components (lower happening times of glucose), while the center portion specifies the higher-frequency range, those much lower glucose energy values associated with higher frequencies of glucose components (higher happening times of glucose).

The above descriptions are directly applied from his learned knowledge of both physics and mathematics.  Now, let us look into the biomedical side.

There are two related concepts applied in this article:

  • He chose the commonly defined value of diabetes as >140 mg/dL for this analysis.
  • The values of TAR (glucose >180), TIR (120 < glucose <180), and TBR (glucose <120); where TAR, TIR, and TBR are defined by American Diabetes Association (ADA) Guidelines (Reference 2). However, following ADA guidelines, he has chosen 120 mg/dL instead of 70 mg/dL for his lower bound and 180 mg/dL for his upper bound for this particular study.

Here, the author wants to concentrate on the glucose range above 120 mg/dL, i.e. the commonly defined value of pre-diabetes, since he had severe type 2 diabetes (T2D) for over twenty years with his average sensor glucose levels around 130 mg/dL in recent years.  Furthermore, his recent glucose results indicate that his ADA defined TBR (< 70 mg/dL) is only ~5%.  This means that his risk of insulin shock is extremely low, which removes this concern.  Therefore, he has chosen 120 mg/dL as his lower bound, instead of 70 mg/dL as suggested by ADA, for this TBR analysis.

In this paper, for clarity, his chosen definitions of values for TAR, TIR, and TBR are re-listed:

  • TAR (time above range): >180
  • TIR (time in range): >120 and <180
  • TBR (time below range): <120

The frequency in time domain can be converted into frequency domain, and then further segregate them into three frequency ranges as low, medium, and high.  The actual numerical value of boundaries for each frequency range varies because it is based on the actual glucose fluctuation in each glucose waveform to meet specific objectives of a research project.  After integrating energy theory from mechanical engineering with wave theory from geophysics and communication engineering together, each frequency range is further associated with certain energy level.  After that, we can then integrate waves and energies with biomedical concerns to discover the potential different levels of damage or impact on the human organs.

In order to accomplish his research objectives, he has to modify and enhance his software programs in order to be able to calculate the relative energy level of any user-defined frequency range which are based on glucose data he collected.

Figure 1 displays certain prominent features in a process workflow using both time-domain analysis example and frequency-domain analysis example.

Figure 1: Example of time-domain and frequency-domain

Readers can delve deeper into the data table in Figure 2, to find out more detailed hidden information.  It shows a table of his summarized glucose and energy data for both CGM non-virus period of 623 days (5/5/18-1/18/20) and COVID-19 period of 137 days (1/19/20-6/4/20).  

Figure 2: Data table for both 623-days CGM non-virus period (5/5/18-1/18/20) and COVID-19 quarantined period (1/19/20-6/4/20)

Figure 3 demonstrates his status of glucose control during COVID-19 period.  It illustrates the bar chart of these two periods with percentages in three glucose ranges, i.e. TAR, TIR, and TBR.  

Two observations are emphasized below:

  • TAR (>180 mg/dL, extremely high glucoses range):  COVID-19 periods 6% is 4% lower than non-virus periods 10%.  
  • TBR (<120 mg/dL, normal glucoses range): COVID-19 periods 49% is 9% higher than non-virus periods 40%.  

These observations have proven that his glucose control during 137-days of COVID-19 period is better than 623-days non-virus period.  

The actual contributing factors that provided better glucose control results during the COVID-19 period are: eating the right food, no dining out at all, consistent and sufficient exercise, normal daily routine with a simple lifestyle, sufficient and good quality of sleep, less stressful daily life by avoiding the bad news of the virus and riots, and no traveling to medical conferences at all.  

On the other hand, Figure 5 shows the relative energy level generated by glucose for these two periods.  It is obvious that the averaged relative energy per day is 107 during the COVID-19 period, while the averaged relative energy is 259 during the CGM non-virus period.  The  GM non-virus period’s averaged energy level is 2.4 times higher than COVID-19 period.  The non-virus period time span of 623 days is 4.55 times longer than the  OVUD-19 time span of 137 days.  This is why he calculated his daily “average” relative energy for comparison.  This observation of energy level shows his non-virus period has created a higher degree of impact on his internal organs than his COVID-19 period.  

Figure 3: Relative energy distribution % of 3 glucose ranges of TAR, TIR, TBR (CGM non-virus period and COVID-19 period)
Figure 4: Averaged relative energy per day between CGM non-virus period

This unusual COVID-19 quarantine period has offered a nice peaceful living environment and routine lifestyle programs for the author in achieving a better glucose control, and creating lower impact or damage to his internal organs, therefore, improving his overall health conditions, in comparison with the CGM non-virus period.

He is not as concerned about the relative energy associated with various glucose components causing damage to his various internal organs during this COVID quarantine period.  By continuously maintaining a strong glucose management, it should remove the fundamental cause of damages to arteries such as CVD and stroke, and to micro-vessels such as CKD and DR. In addition, he has been meticulous with controlling his blood pressure and lipids as well.  These combined efforts should greatly reduce his risk probabilities of having CVD, stroke, CKD, and DR, along with lowering complications for bladder, feet, and the nervous system.  This may also contribute his cancer prevention up to a 45% – 50% level of protection.

The central circular biomedical core of a person’s health is based on the knowledge of medicine along with the disciplined lifestyle management. This core is surrounded by three outside circles.  The most inner circle is the lifestyle management including eating, water intake, exercising with sunlight, sleeping, stress, and daily routine life.  The middle circle is metabolism resulting in both medical conditions and lifestyle.  The outer circle is immunity, where immunity and metabolism are two sides of the same coin.  These four circles constitute a person’s biomedical defense army to fight against external invading enemy troops that include four causes of death: chronic diseases along with their complications (50%), cancer diseases (29%), infectious diseases (11%), and non-diseases related to death (10%, prevention factors are personal safety, luck on avoiding dangerous situations, and psychological health).  The descriptions mentioned above can be seen in Figure 5.

In general, diabetes patients who are disciplined with their lifestyle management would focus on their daily glucose control tasks and try to maintain them at a level below 140 mg/dL, but below 120 mg/dL is even better.  This is a good practice in managing their diabetes; however, the research results illustrate that it is the energy associated with glucoses causing all of the biomedical problems which needs to be focused on.

This paper also demonstrates that the GH-Method: math-physical medicine is a powerful methodology that can be used more widely to discover some hidden truths regarding diseases and health matters.

Figure 5: Lifestyle, Metabolism, Immunity, Diseases, and Causes of Death


  1. Hsu, Gerald C., eclaireMD Foundation, USA. October 2019. No. 120: “The study on the damage to internal organs and the pancreatic beta cells health state due to excessive energy associated with high PPG components and distinctive waveforms using GH-Method: math-physical medicine.”
  2. Hsu, Gerald C., eclaireMD Foundation, USA. March 2020. No. 238: “The influences of medication on diabetes control using TIR analysis (GH-Method: Math-physical medicine).”
  3. Hsu, Gerald C., eclaireMD Foundation, USA. April 2020. No. 246: “Segmentation analysis of sensor glucoses and their associated energy (GH-Method: math-physical medicine).”
  4. Hsu, Gerald C., eclaireMD Foundation, USA. June 2020. No. 267: “Investigation on the impact of different glucose ranges and the damage on human internal organs using frequency domain analyses (GH-Method: math-physical medicine).”