Applying Multiple Regression Analyses to Compare the Regression Predicted and Measured Body Weight Using Food Quantity and Sleep Score as Inputs over a 6.5-Year Period for a type 2 Diabetes Patient Based on GH-Method: Math-Physical Medicine (No. 547)

Abstract

In the author’s previous medical research reports, he mainly applied physics theories, engineering models, mathematical equations, computer big data analytics and artificial intelligence (AI) techniques, as well as some statistical approaches to explore and interpret various biophysical phenomena. However, the majority of medical research papers he has read thus far are primarily based on statistics. As a result, in this article, he selects some basic statistical tools, such as correlation, variance, p-values, and multiple regression analyses, to study the predicted body weight as the output (dependent variable) by using his foods quantity and sleep score as inputs (independent variables). Since 5/1/2015, the author has been collecting various data related to his food nutrition (~0.5 million data) and sleep conditions. The Food Details (FD) category includes both food quantity (m9a) and food quality (nutrition, m9b). Previously, he has researched and published a few articles regarding the relationship between body weight and quantity of food consumption. During 11/8/2020 - 11/10/2021, he experimented with intermittent fasting experiments to study their inter-connectivity. In this paper, he will combine sleep score (m7) with food quantity (m9a) and connected them with his body weight in early morning (m1) using the multiple regression analysis method. In this study, he will not repeat the detailed introduction of the regression analysis in the Method section because it is available in many statistics textbook. It should be noted that in regression analysis, the correlation coefficient R should be > 0.5 or 50% to indicate a strong inter-connectivity and the p-value should be <0.05 to be considered as statistically significant.