Atherosklerose: Open Access

Abstrakt

Traveling impact on glucose, metabolism, exercise, and daily life routines based on 8 years of big data using GH-Method: math-physical medicine (No. 335)

Gerald C Hsu

In this article, the author conducted an impact study of his average glucose, overall metabolism, and two key lifestyle details based on a period of 8 years (2,938 days) from 1/1/2012 to 9/19/2020. He separated his 242 trips during this period into 134 short travel trips (<3 hours of flying time) and 108 long travel trips (>3 hours of flying time). He has purposely chosen glucose, metabolism, exercise, and daily life routines for this special investigation because he has had Type-2 Diabetes (T2D) for over 25 years and his main medical research work is based on lifestyle and metabolism. Among the lifestyle details, he has selected exercise and daily life routines due to the fact they are extremely difficult to maintain during travel days.

Here is a summary of the results in the format of glucose/Metabolism Index (MI) score/Walking steps/Daily life routines score with the respective baselines or “break-even” scores of 120 mg/dL, 73.5%, 15,000 steps, 0.7 or 70%.

Glucose/MI score/Walking steps/Daily life routine score:

Long:127/79.7%/12,316/99.95%

Short:126/76.6%/15,784/84.96%

Total:124/69.7%/16,124/73.00%

It should be pointed out that all scores in these four categories are above the baselines or break-even scores. This means that his glucose level, metabolism index, exercise, and daily routine regularity are “unhealthy”.

By using a designated set of baselines, we can further calculate how much excessive percentage of these four parameters above the “baseline” conditions such as glucose, MI, walking, daily life routines that are associated with long trips, short trips, and total period.

Glucose/MI score/Walking steps/Daily life routine score:

Long:106%/108%/177%/143%

Short:105%/103%/142%/121%

Total:103%/95%/140%/104%

To compare the results of these three periods, the total period has the best performance of all four parameters. This is due to the fact that the total period (2,938 days) contains more non-traveling days, stable environment, and healthier days (92% of total days). Scores of short and long trips (total 242 traveling days, 8% of total days) are higher than the score of total period, specifically 8% to 13% higher from glucose and MI, and 37% to 39% higher from exercise and daily life routines. Furthermore, the long trips scores are higher than the short trips scores by 1% to 5% for glucose and MI, and 22% to 35% for exercise and daily life routines. It should be noted that the author adopts the traditional medical principles, where the lower score indicates a better condition. Therefore, when traveling, his diabetes conditions contribute to higher glucoses along with elevated MI percentage, which confirms additional damage to his overall health. Due to interruptions to his meals, exercise schedule, sleep pattern, and his daily life routines cause the four markers to become higher or unhealthier during air travel days. Long trips are worse than short trips because the long air travel time covers at least two meals without post-meal exercise and typically crosses over multiple time zones, resulting in jet lag.

The observed conclusions from above may be intuitive for some patients and for most doctors; however, this study utilized a big data analytics approach based on several hundred thousand data over an 8-year period, the segmentation analysis method, along with a complex mathematical model of metabolism to offer some concrete evidence with a high precision and quantitative numerical proof to readers.