Corresponding Author: Gerald C. Hsu, eclaireMD Foundation, USA.
The author has contemplated a specific question:
Why do some type 2 diabetes (T2D) patients choose to face serious complications, including death, rather than change their lifestyle in order to control their diabetic conditions?
He discusses two different clinical cases linking patient’s personality traits and behavior psychology with T2D physiological characteristics. He named this approach as the Progressive Behavior Modification which is a part of the Mentality- Personality Modeling.
T2D patients have faced three major challenges:
- (1) Availability of accurate disease information with either physical evidence or quantitative proof, not just some general qualitative descriptions that may include false or commercial driven news over the internet (knowledge issue).
- (2) Awareness of disease status and overcome “self-denial” by moving to “psychological acceptance” in order to “take effective action”. The most difficult barrier to overcome is to have willpower, determination, and persistence on lifestyle change (psychology issues).
- (3) A non-invasive, effective, and ease of use technology-based tool to accurately predict outcomes and also guide patients (technology issue).
The author collected 17,046 glucose data for 241 days and generated 723 postprandial plasma glucose (PPG) waveforms. He decomposed them first and then further re-integrated them into three distinctive waveforms, i.e. Himalaya, Twin Peak, and Grand Canyon.
In summary, peak glucose values of these three patterns are determined by carbs/sugar intake amount (knowledge and willpower for diet control). The Himalaya pattern is created via physical inactivity (no desire to exercise). The Twin Peak pattern is created via wrong exercise pattern (knowledge) and insufficient post-meal exercise (willpower on exercise), while the Grand Canyon pattern is created via correct exercise style (knowledge) and sufficient amount of exercise (willpower on exercise).
By analyzing distribution percentages of these 3 distinctive patterns and then comparing them against PPG time-series data, each patient’s personality traits and behavior psychology will be revealed clearly and the trend of the glucose movement can also be predicted and re-directed as well.
The patients can modify their behavior one step at a time, i.e. taking a little step on a smaller scale. This is what the author defines as a “progressive” behavior modification.
Patient A started with his PPG at 280 mg/dL in 2010 and moving toward lower left direction (lower PPG) via acquiring correct knowledge and being persistent with his exercise regimen. He then identified the effectiveness of post-meal walking in 2015 but still fought with his craving for carbs/sugar. Finally, he reached to 116 mg/dL level after 2017. Case A demonstrated the patient’s strong willpower and persistence with both diet and exercise.
Patient B started using an AI-based tool to monitor and predict his PPG since 4/21/2018. Initially, he followed the tool’s scientific advice on controlling his diet and exercising after each meal. He quickly brought his PPG level down to 159 mg/dL; however, he was not persistent with his exercise routine and reduced his post-meal walking amount by one-third, resulting with his PPG level going up to 170 mg/dL. Case B showed his psychological weakness of lacking willpower on exercise.
Figure: Comparison of glucose movement patterns due to different behavior psychology of two T2D patients
This paper is more of a forward-thinking article. The author believes that a big glucose data will be easily collected for T2D patients down the line. Therefore, he is trying to lay the necessary groundwork for a future endeavor. Through analyzing those distinctive PPG waveforms, the personality traits and behavior psychological pattern of individual T2D patient can be revealed instantly and clearly. As a result, there will be a lesser need to collect and analyze detailed data of food and exercise for this purpose.