Effect of Psychological Behavior Intervention on the Control of Chronic Diseases Resulting from the Adherence to Lifestyle and Medication Including Geriatric Longevity Study Via Effective Health Age, Using GH-Method: Math-Physical Medicine or Mentality-Personality Modeling (No. 392)

Abstract

Gerald C Hsu

In this article, the author uses his 11 years of collected data from body weight and glucose with several prominent lifestyle details, mainly food portion/carbs & sugar intake amount, and daily/post-meal walking steps to address the reduction trend patterns of weight and glucose. This research project is based on progressive behavioral modifications of his lifestyle which is also a part of his developed Mentality-Personality Modeling (MPM) of psychology. He identified the quantitative linkage between the physiological phenomena of obesity and diabetes and their associated psychological lifestyle behavioral interventions of a patient with both obesity and type 2 diabetes (T2D). The physiological part uses his developed GH-Method: math-physical medicine (MPM) research methodology.

In addition, he utilizes the signal processing technique of wave theory to decompose the postprandial plasma glucose (PPG) wave into three primary sub-waves of food, exercise, and medications. Furthermore, he used his developed linear elastic glucose theory (LEGT) in 2020 to study the impact and adherence of medications on T2D for elderly patients.

To address the common concern of longevity among elderly people, he further applied his metabolism index (MI) model developed in 2014 to calculate the effective health ages of elderly people in comparison with their real biological ages in terms of reducing or prolonging their perspective lifespan.

For this research process, he creates a special geometric presentation model with the meal portion percentage and carbs/sugar intake amount as the x-axis, daily walking steps and post-meal walking steps as the y-axis, and daily weight and glucose data as the z-axis. He then “folds over” the z-axis and superimposes it with the x-y planar space in a “radio wave” format. These radio waves represent the different annual statuses of both weight and glucose. Under this created three-dimensional (3D) presentation on a two-dimensional (2D) planar space, the biomarker improvement patterns and moving trend of biomarkers, such as weight, glucose, and effective health age become ultra-clear. For effective health age, the same “pseudo-3D” geometric presentation model also applies. Instead, we can use medical conditions as the x-axis and lifestyle details as the y-axis.

Over the past 11 years (2010 - 2020), the path of his annual weight and glucose moving patterns started from the upper right corner (subregion E5 in 2010), moving with a downward angle of 30 to 45 degrees, and finally reaching the lower left corner (subregion A1 in 2020).

Through analyzing the distinctive daily weight and glucose trend patterns, the personality traits and related psychological behavior characteristics of this patient with both obesity and T2D can be revealed instantly and clearly. As a result, more practical guidance on progressive behavior modification can be provided to other patients to improve their medical conditions for chronic diseases, where some are caused by obesity.

He was dependent on diabetes medications from 2010 through 2015 with a reduction trend pattern in the types and dosages of medications. Finally, on 12/8/2015, he ceased taking any medications for his chronic disease control. The author’s collected detailed data on diet, exercise, and medications began in 2014. From the decomposed PPG sub-component waves, during 2014-2015, the medication contributed a -21 mg/dL of PPG reduction. Thereafter, the combined contribution of diet and exercise replaced the role of medication.

It should be noted that his collected data covered the period of 2010 through 2020 where his real age was 63 to 73 years old. Therefore, the results from this research note are based on the realistic data of an elderly person.

This longevity study using the effective health age (eclaireMD APP tool) demonstrates that in 2012, his effective health age was 75, which is 9 years older than his real biological age of 66. However, in 2020, his effective health age of 63 became 10 years younger than his real biological age of 73. In other words, over 9 years (2012-2020), he gained 19 years of life expectancy through a stringent lifestyle management program, without adherence to any medication.

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