Abstract
Since 2012, the author has collected his body weight and finger-piercing glucose values each day. Next, he accumulated medical conditions data including blood pressure (BP), heart rate (HR), blood lipids along with lifestyle details of diet, exercise, sleep, stress, water intake and daily routine details. Based on these collected big data, he organized them into two main categories. The first is medical conditions (MC) with 4 categories: weight, glucose, BP, and lipids. The second is lifestyle details (LD) with 6 categories: food, exercise, water intake, sleep, stress, and daily routines. Furthermore, he summarized and calculated the two separate individual category scores of MC and LD. As we know, diabetes in combination with hypertension, hyperlipidemia, and obesity would cause many chronic disease complications, including but not limited to cardiovascular disease (CVD) or chronic heart disease (CHD). In this article, the author applies the viscoelasticity and viscoplasticity theories to conduct his research to discover some hidden behavior or relationship between the CVD risk probability (as an output or strain) versus either MC score or LD score (as inputs or stresses). The hidden behaviors and relationships between the output biomarker of CVD Risk and the two input biomarkers, MC score and LD score, are time-dependent which change from time to time.
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