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Study Background
This study will use the BMI tool to predict and check for obesity and overweight among children and adults (Rafiq & Jeppesen, 2018). BMI is calculated through the division of total weight and height. It is said to be directly connected to one’s amount of fat within the body. Consequently, a high level of fat within the body is associated with numerous issues of health (Rafiq & Jeppesen, 2018). In adults aged 20 years and beyond, a healthy bodyweight ranges lies between 18.50 and 24.90 kgs, abnormal body weight starts from 25 to 29.9, obesity starts from 30 to less than 40, and severe obesity starts from 40 going forward (Rundle et al., 2020). BMI calculator is used to predict BMI classifications for teens and children as follows; teens and children categorized in lower than fifth percentage scale are underweight (Zhang, et al., 2019). Those in the fifth percentage scale and lower than eighty-fifth percentage scale have a normal body mass while those in the eighty-fifth percentage scale and lower than the ninety-fifth percentage scale are overweight (Zhang, et al., 2019). Lastly, those in the ninety-fifth percentage scale and beyond are obese.
An observational analysis is a qualitative type of research used for the observation of continuing behaviors within natural conditions (Dean, 2019). This technique will be used to study the connection between two main variables of the study; obesity and diabetes mellitus- type 2 (T2DM) (Dean, 2019). Generally, the data set will be extracted from a population size of 500 diabetic patients of which about 50 patients with BMIs of more than 30 will be selected. Primarily, the observational analysis will be used to investigate the exposure to obesity and outcome (T2DM) to enable comprehension of the relationship between obesity and increased risk of T2DM.
Description of Data
The data will be collected and recorded according to variables such as; age, sex, BMI, haemoglobin glycosylated (HgB A1C), and treated or not treated (Pandey, 2020). The data variables can be divided into two types of variables namely, numeric and categorical variables. The numeric type of variables is made up of continuous variables like age and discrete variables like HgB A1C (Pandey, 2020). The categorical type of variables is made up of nominal variables like labels, names, sex, and ordinal variables like whether the patient is treated or not treated.
In terms of age, the patients selected to become part of the study have to be 19 years or more (Pandey, 2020). The participants will also be classified into either male or female. Based on BMI, only those participants with a BMI of 30 or more will be selected (Zhang, et al., 2019). As for HgB A1C, values below 5.7 are of normal health while those between 5.7 and 6.4 indicate prediabetes and high chances of developing diabetes, and those values from 6.5 and beyond indicate the presence of T2DM (Rafiq & Jeppesen, 2018). This study is focused on identifying those that are type 2 diabetic, therefore, those with HgB A1C of more than 6.5 percent will be selected. Additionally, a combination of patients who have been treated and those not treated before will be included to get comparative results (Dean, 2019). Currently, the data is in Excel and is still not complete because it is yet to be processed for analysis. This is not a big issue for the study because importing the data from Excel to SPSS for analysis will not be a problematic process.
Description of Variables. Like other studies, this study will have independent, dependent, and confounding variables (Pandey, 2020). An independent variable is a cause of the issue being studied. A dependent variable is perceived as the outcome of the independent variable (Pandey, 2020). A confounding variable is believed to influence both the independent and dependent variables (Pandey, 2020). Obesity is the independent variable of this study because it is the causative factor. Diabetes is the dependent variable of the study as it is the outcome of the independent variable (obesity) (Pandey, 2020). Three variables that are interesting in this study include; annual income, ethnicity, and degree of education.
All these variables will be used as confounding variables due to the following reasons.
First, the annual income of the patient is an economic determinant that influences the food needs of the patient and choices of food diet by the individual (Pandey, 2020). It is said that the higher the income, the higher the expenditure on food. Therefore, the inclusion of the annual income variable into the analysis will enable the study of the influence of annual income on dietary behaviors and diabetes. Second, ethnicity is a social factor that also influences the food needs and choices of an individual (Butler, 2017). Inclusion of ethnicity variable will enable the evaluation of diabetic prevalence rates across various ethnic backgrounds. Lastly, the degree of education is a physical factor that influences the food choices and needs of an individual (Butler, 2017). For instance, individuals with lower education are more likely to eat unhealthy diets than individuals with higher levels of education. Therefore, the inclusion of this variable will enable the evaluation of diabetic prevalence rates based on education levels.
All the data collected in terms of variables will be recorded and analyzed using statistical methods with the help of SPSS software (Miller, 2017). This analytical tool enables the performance of descriptive analysis and representation of data through visualizations that are clear and easy to understand (Miller, 2017). In the end, the results are expected to affirm a direct relationship between obesity and T2DM based on the patient’s age, sex, BMI, annual income, ethnicity, and level of education.
References
Butler, A. M. (2017). Social determinants of health and racial/ethnic disparities in type 2 diabetes in youth.Current diabetes reports, 17(8), 1-4. Web.
Dean, B. A. (2019). Observational research in work-integrated learning. Web.
Miller, R. L. (2017). SPSS for social scientists. Macmillan International Higher Education.
Pandey, S. (2020). Types of variables in medical research.Journal of the Practice of Cardiovascular Sciences, 6(1), 4-4. Web.
Rafiq, S., & Jeppesen, P. B. (2018). Body mass index, vitamin D, and type 2 diabetes: A systematic review and meta-analysis. Nutrients, 10(9), 1182. Web.
Rundle, A. G., Factor-Litvak, P., Suglia, S. F., Susser, E. S., Kezios, K. L., Lovasi, G. S.,… & Link, B. G. (2020). Tracking of obesity in childhood into adulthood: Effects on body mass index and fat mass index at age 50.Childhood Obesity, 16(3), 226-233. Web.
Zhang, T., Whelton, P. K., Xi, B., Krousel‐Wood, M., Bazzano, L., He, J.,… & Li, S. (2019). Rate of change in body mass index at different ages during childhood and adult obesity risk.Pediatric obesity, 14(7), e12513. Web.
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