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Running head: ASSOCIATION BETWEEN EXERCISE AND OBESITY The Association Between exercise and Obesity Incidence in Kentucky Eden Wolde Professor; Jaclyn K

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Running head: ASSOCIATION BETWEEN EXERCISE AND OBESITY The Association Between exercise and Obesity Incidence in Kentucky Eden Wolde Professor; Jaclyn K. McDowell 1 ASSOCIATION BETWEEN EXERCISE AND OBESITY 2 The Association Between exercise and Obesity Incidence in Kentucky Background Obesity affects over one-third of U.S. adults, thus a major public health concern. It is also a precursor to other chronic illnesses, including heart disease, diabetes, cancer, and even death, hence a key determinant of public health (Gray et al., 2018). The consensus in the current evidence is that physical inactivity is the primary driver of obesity (Jones, Basilio, Brophy, McCammon, & Hickner, 2009; Lv et al., 2017; Pojskic & Eslami, 2018; Purnell, 2018; Sisson, Krampe, Anundson, & Castle, 2016). The manifestation of this relationship may be mediated by environmental, demographic, and regional factors (Gray et al., 2018). This aspect explains why most studies have focused on physical activity in combination with other precipitators. Lv et al. (2017), for instance, in their review of the current evidence on lifestyle interventions for obesity, deduced that a combination of diet, behavioral interventions, and a regular exercise regime lasting at least one year was greatly linked to a healthy weight. This aspect was also demonstrated in an earlier investigation by Jones et al. (2009) that explored the nature of the inactivity-obesity relationship as it relates to hormones and appetite. The authors established that long-term exercise leads to an increase in total plasma peptide YY concentration in tandem with a decline in body fat (Jones et al., 2009). Sisson et al. (2016), in their review of evidence on the link between obesogenic behaviors and child obesity, found that most studies examined multilevel interventions, comprising exercise, diet, and screen time. However, physical activity explained a significant percentage of the observed overweight problems. As demonstrated above, few studies have examined the linear relationship between physical inactivity and obesity at a population level. Therefore, the present study aims to establish the nature of the relationship between physical inactivity and obesity levels specific to the residents ASSOCIATION BETWEEN EXERCISE AND OBESITY 3 of Kentucky. The study hypothesizes that obesity incidence rates are lower in counties with higher percentages of people who do not exercise. The findings, therefore, will answer the following research question and hypotheses: RQ0 Do people who exercise have a less likelihood of becoming obese? H0 There is a linear relationship between physical inactivity and the prevalence of obesity in Kentucky. H1 Obesity incidence rate (obesity_percent) is lower in counties with the least percentage of people who do not exercise (noexercise_percent). Methodology The study utilizes a quantitative ecological design to evaluate the predictive relationship between physical inactivity and obesity in Kentucky. The non-experimental approach is suitable for population-based studies seeking to establish risk-modifying factors and health outcomes at the population level (Sedgwick, 2014). They are appropriate where the unit of observation is a community or population. Exposures and disease rates are averaged for each of the series of populations, and their relations established using standard bio-statistical methods. In the current study, the units of observation are the 120 counties of Kentucky. A linear regression technique is used to evaluate the association between county-level physical inactivity and the prevalence of obesity. This study used the KY Ecological Dataset to establish whether there exists a significant relationship between physical exercise and obesity levels in Kentucky. It provides age-adjusted, county-level percentages for obesity and physical exercise in all 120 counties in the State. The exposure variable will comprise the noexercise_percent, defined as the percentage of state residents that do not get the recommended amount of physical activity. Similarly, the outcome variable will be obesity percent, defined as the percentage of the Kentucky population with a BMI ASSOCIATION BETWEEN EXERCISE AND OBESITY 4 of 30kg/m2 and above (Purnell, 2018). Both measures for county-level obesity and physical inactivity take the form of continuous variables, hence appropriate for regression analysis. A simple linear regression model was used to estimate the association between physical inactivity and the prevalence of obesity. Stratification was not emphasized, as I was interested in the explicit relationship between obesity and inactivity. The ANOVA analysis was used to test the hypothesis: H0 There is a linear relationship between physical inactivity and the prevalence of obesity in Kentucky. Similarly, the regression coefficients were used to affirm the assertion that: H1 Obesity incidence rates (obesity) are lower in counties with the least percentage of people who do not exercise (noexercise_percent). The estimates are reported as county-level prevalence and 95% confidence intervals (CI). The IBM SPSS Statistics software version 25.0 was used to conduct all analyses. Results Table 1 present the descriptive characteristic of study the 120 counties included in the study. The county-level medium income for the overall population was $39,753 (SD: 10,744.21), with $86,324 as the highest and $18,972 as the lowest (See Table 1). The mean percentage for obesity and physical inactivity was 14.58% (SD: 4.67) and 36.36% (SD: 5.54), respectively. The highest county-level prevalence of obesity was 47.46% and the least was 27.12%. In contrast, the highest percentage of physical inactivity was 50.46%, and least was 24.18%. Table 1 Descriptive Statistics Mean Standard Error Median median income 39940.48 980.81 39753.00 obese percent 36.58 0.43 35.85 noexercise_percent 36.36 0.51 36.97 ASSOCIATION BETWEEN EXERCISE AND OBESITY Standard Deviation Kurtosis Minimum Maximum Count 10744.21 2.39 18972.00 86324.00 120.00 4.67 -0.28 27.12 47.46 120.00 5 5.54 0.39 24.18 50.46 120 Table 2 presents outputs from the analysis of variance (ANOVA) used to test the existence of a statistically significant difference between obesity and noexercise_percent variables. The results indicate a significance level of F = 0.000155 (See Table 2), which is also equal to the pvalue = 0.0002 of the regression model (See Table 3). Thus, it disapproves of the null hypothesis by showing that there is a non-zero linear relationship between physical inactivity and obesity rates across different counties in Kentucky. Table 2 Analysis of Variance (ANOVA) Regression Residual Total df 1 118 119 SS 297.9308 2300.77 2598.701 MS 297.9308 19.49806 F 15.28003 Significance F 0.00015517 Table 3 presents results from the linear regression analysis that tested whether physical inactivity predicts an increase in the county-level incidence rates of obesity. The coefficient for inactivity in percentage is 0.2854% (95% CI: 0.1408, 0.4299). For every additional percentage unit in the number of people who do not exercise in a given county, the prevalence of obesity is expected to increase by an average of 0.2854%. With a 95% confidence interval (CI), the true increase in noexercise_percent for a percentage increase in obesity is between 0.1408 and 0.4299 ASSOCIATION BETWEEN EXERCISE AND OBESITY 6 (See Table 3). It indicates that there is a positive linear relationship between physical inactivity and the county obesity prevalence at the 0.05 level of significance. Table 3 Regression Analysis Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 26.2068 2.6851 9.7599 0.0000 20.8895 31.5241 noexercise_percent 0.2854 0.0730 3.9090 0.0002 0.1408 0.4299 Discussion The results indicate that physical inactivity is a significant (p = 0.0002) predictor of obesity in Kentucky. This relationship is defined in the regression model: Obesity (y) = no exercise coefficient (0.2854) × no exercise percent (x) + intercept coefficient (26.2068) + residual value (ε). For instance, with a physical inactivity of 38.95%, the model predicts the prevalence of obesity in Adair County as 0.2854 × 38.95 + 26.2068 + 3.3181 = 40.64%. Compared to Fayette County, which has the least number of people who did not exercise, the predicted percentage rate of obesity is correspondingly lower (obesity = 0.2854 × 24.18 + 26.2068 + -5.9870 = 27.12%). Thus, it affirms the hypothesis that obesity incidence rates are lower in counties with higher percentages of people who exercise. These findings agree with other studies, which have found a strong relationship between physical activity and the risk of becoming obese (Jones et al., 2009; Lv et al., 2017; Pojskic & Eslami, 2018; Purnell, 2018; Sisson et al., 2016). The influence of exercise on obesity may be due to its effect on plasma peptide YY concentration, which regulates food intake (Jones et al., 2009). Equally, the outcome may be explained by the role of exercise in the metabolism of body fat and carbohydrates. ASSOCIATION BETWEEN EXERCISE AND OBESITY 7 The study offers strong evidence about the county-level relationship between a sedentary lifestyle and obesity incidence rates in Kentucky. The results, however, suffer limitations that primarily center on the failure to account for mediators of the inactivity-obesity association. The exposure variable (noexercise_percent) only explains 11% (R Square = 0.1146) of the outcome variable (obesity) (See Table 1). It implies that a significant percentage of the reported weight problems in Kentucky could be explained by predictors other than physical inactivity, including diet and environmental factors (Gray et al., 2018; Lv et al., 2017). The study also established a greater effect when physical activity is undertaken in combination with other strategies, including diet and behavioral interventions. To better test the hypotheses herein, future studies should control for the modifying factors for the physical inactivity-obesity relationship, including environmental quality and behavioral risk factors. ASSOCIATION BETWEEN EXERCISE AND OBESITY 8 References Gray, C. L., Messer, L. C., Rappazzo, K. M., Jagai, J. S., Grabich, S. C., & Lobdell, D. T. (2018). The association between physical inactivity and obesity is modified by five domains of environmental quality in U.S. adults: A cross-sectional study. PLOS ONE, 13(8), e0203301. https://doi.org/10.1371/journal.pone.0203301 Jones, T. E., Basilio, J. L., Brophy, P. M., McCammon, M. R., & Hickner, R. C. (2009). Longterm exercise training in overweight adolescents improves plasma peptide YY and Resistin. Obesity, 17(6), 1189–1195. https://doi.org/10.1038/oby.2009.11 Lv, N., Azar, K. M. J., Rosas, L. G., Wulfovich, S., Xiao, L., & Ma, J. (2017). Behavioral lifestyle interventions for moderate and severe obesity: A systematic review. Preventive Medicine, 100, 180–193. https://doi.org/10.1016/j.ypmed.2017.04.022 Pojskic, H., & Eslami, B. (2018). Relationship between obesity, physical activity, and cardiorespiratory fitness levels in children and adolescents in Bosnia and Herzegovina: An analysis of gender differences. Frontiers in Physiology, 9. https://doi.org/10.3389/fphys.2018.01734 Purnell, J. Q. (2018). Definitions, classification, and epidemiology of obesity. In K. Feingold, B. Anawalt, & A. Boyce (Eds.), Endotext [Internet]. South Dartmouth, MA: MDText.com, Inc. Sedgwick, P. (2014). Ecological studies: Advantages and disadvantages. BMJ, 348(may02 4), g2979–g2979. https://doi.org/10.1136/bmj.g2979 Sisson, S. B., Krampe, M., Anundson, K., & Castle, S. (2016). Obesity prevention obesogenic ASSOCIATION BETWEEN EXERCISE AND OBESITY behavior interventions in child care: A systematic review. Preventive Medicine, 87, 57– 69. https://doi.org/10.1016/j.ypmed.2016.02.016 9 Statistics Your research project should include at least one statistical test that was covered in this course: Student's (independent sample) t-test, paired t-test, x? (chi-square) test, ANOVA, or correlation/simple regression. Others are also possible, if you have the ability and get prior approval from the instructor. Tables, figures Your paper should include at least two (2) relevant tables and/or figures that you have created to characterize your data and present your findings. For most students, the first table will summarize the characteristics of the sample, and the second table (or a figure, such as a scatter plot, if appropriate) will show the relationship between the exposure and outcome variables. You are responsible for creating your own tables and/or figures using the output you get from running your biostatistical tests in Excel or Open Epi. Please refer to the lecture on Wednesday, 4/28 for guidance on how to present your results. This data set contains age-adjusted incidence rates for several cancers in all 120 Kentucky counties. The data set also includes county-level smoking prevalence rates, percentage of adult county residents with a high school education, percentage of the population living below the poverty level, and several other exposure-related variables from the Behavioral Risk Factor Surveillance System (BRFSS), U.S. Census, and more. Examples of the types of hypotheses that can be tested with these data include: • Cancer incidence rates are higher in counties with higher prevalence rates of smoking. • Counties with higher proportion of the adult population with a high school education have lower rates of colorectal cancer. Cross-sectional data set (from National Health and Nutrition Examination Survey.). This data set contains data on a subset of participants in the NHANES survey from 2017-2018. The data set includes several exposure and outcome variables for each study participant-e.g., age, race, gender, body mass index (BMI), smoking, cholesterol levels, blood pressure, asthma, diabetes, and others. Examples of the types of hypotheses that can be tested with these data include: Methods Consult the websites of any data sources you used for your project to describe the data set and how it was collected (paper survey, telephone interview, physical examination?). Information on the data sources for all variables in both data sets are located in the "Data sources" tabs in the excel files located on Canvas. Also describe your study design, the statistical test(s) used to test your selected hypothesis, and any other analytic methods (e.g., stratification). This will comprise the METHODS section. Results Provide tables or figures presenting the results of your analyses, including measures of association and the results of statistical tests. Be sure to include statistical significance levels (i.e., p-values), where appropriate. You must also write a description of these tables or figures for the RESULTS section. Discussion In the DISCUSSION section, provide your interpretation of the results, including an explanation for why you obtained the observed results. Also, discuss the limitations of your study and how studies could be designed to better test your hypothesis.