McDonald’s, Television, and Prevalence of Obesity Essay Example
Aim
The rising rates of obesity in both developed and developing economies are a cause for concern. Scientists have connected these rates to lifestyle changes. Therefore, it is vital to establish effective public health strategies that can tackle the increasing prevalence of the obesity epidemic.
According to Alheritiere et al. (2013), the main strategy for addressing obesity should be to make lifestyle changes, including following a low-calorie diet and engaging in more physical activity. The authors argue that factors such as spending too much time watching television and the presence of fast food chains like McDonald's are contributing to the increasing rates of obesity.
With the given context, the aim of this investigation is to determine whether the obesity rates in a country are influenced by two factors: the number of McDonald's restaurants per 1,000 people and the number of televisions per 1,00
...0 people.
Background
Obesity is a health condition characterized by excessive body weight in the form of fat (World Obesity Federation, 2012). There are approximately 1.5 billion overweight or obese adults worldwide, and it is estimated that over 200 million school-age children are also overweight (World Obesity Federation, 2012). The obesity epidemic has been steadily increasing in all OECD countries since its emergence (OECD, 2014).
According to the OECD, around 18% of adults in member states are classified as obese. The countries with the highest obesity rates are New Zealand and the United States, where over a third of adults are obese. Australia and Canada have slightly lower obesity rates, with more than a quarter of adults being obese (OECD, 2014). This widespread problem of obesity has significant impacts on society's health, social interactions, and economy.
Obesity has negative impacts
on health, society, and the economy. It is a major contributor to illness, disability, and premature death. Moreover, obese individuals have a higher risk of developing chronic diseases compared to non-obese individuals. Chronic conditions like type 2 diabetes, high blood pressure, coronary heart disease, cancer, fatty liver, osteoarthritis, gall-bladder disease,and sleep apnea are all linked to obesity (World Obesity Federation, 2012). Furthermore,
obesity results in social ramifications by fostering widespread prejudice and discrimination against those affected.
Stigmatization can have negative effects on an individual's quality of life, leading to depression and low self-esteem. Additionally, societal bias and stigmatization contribute to mental health problems, decreased educational achievements, and limited job prospects (World Obesity Federation, 2012). The economic implications of obesity include both direct costs related to medical care for obese individuals.
The expenses associated with the diagnosis and treatment of obesity, as well as its related health conditions, are substantial. Furthermore, obesity has indirect implications such as decreased productivity, financial loss, sickness, early death, absence from work or school, missed chances, and limited physical activity. Societies also invest in various accommodations like wheelchairs, reinforced beds, and changes to transportation safety standards to support individuals with obesity (World Obesity Federation 2012). Sedentary behaviors and the consumption of high-calorie fast foods have been identified as risk factors for overweightness and obesity (Alheritiere et al.).
Research conducted by Zimmerman & A. Bell (2010) and Alheritiere et al. (2013) indicates that McDonald's has been widely regarded as the ultimate representation of fast food. In a study encompassing 44 countries, it was discovered that there exists a significant link between the quantity of McDonald's establishments and the proportion of individuals suffering from obesity.
According to a
study conducted by Alheritiere et al. (2013), the link between McDonald's and obesity was not established. However, it is widely recognized that watching television greatly contributes to the risk of obesity. This can be observed in the growing prevalence of sedentary lifestyles, as demonstrated by the increasing number of households with multiple TV sets and the amount of time devoted to television viewing. Research has revealed that watching TV decreases metabolic rate and promotes higher consumption of calorie-dense foods as a result of exposure to food advertisements (Zimmerman & Bell, 2010).
The data utilized in this study was gathered from 28 primarily European countries.
The analysis used IBM SPSS Statistics Version 20 to collect data on obesity rates, the number of McDonald's restaurants per 1000 people, and the number of TVs per 1000 people in different Asian and North American countries. A multiple regression analysis was conducted to test the hypothesis, with obesity rate as the dependent variable.
The variables used in the forecast were the number of McDonald’s establishments per 1000 individuals and the number of TVs per 1000 individuals.
Results
The average percentage of obese people among the 28 states was 13.8536 (
South dakota
= 6.45729 ) . The average number of McDonald’s establishments per 1000 individuals among the 28 states was.15557 (
South dakota
= .112533 ) . The number of TVs per 1000 individuals among the 28 states was 475.64689 (
South dakota
= 132.068532 ) . A multiple regression analysis was performed with the number of McDonald’s restaurants per 1000 individuals and the number of TVs per 1000 individuals
as predictor variables and percentage of obesity as the outcome variable.
One-dimensionality of the independent variables was assessed utilizing partial secret plans. Figure 1 and Figure 2 present the relationship between percentage of obesity and number of McDonalds per 1000 individuals, as well as the relationship between percentage of obesity and number of TVs per 1000 individuals. These figures show that while there is no perfect additive relationship, none of the partial regression plots display an obvious nonlinear form. This suggests that the assumption of one-dimensionality in multiple regression is likely not violated.
Figure 3 shows that the points are evenly and randomly dispersed throughout the plot.
This form indicates that both one-dimensionality and homoscedasticity assumptions were satisfied. Figure 3 displays a graph of Standardized Residuals vs. Standardized Predicted Values, revealing that the model's quality was comparable based on the multiple correlation coefficient (R = .425). While this correlation was not strong, it was low but equal.
The original arrested development model accounted for 18.1% of the obesity rate discrepancy before adjustment, but after adjusting for confusing variables, the adjusted model only explained 11.5% of the discrepancy. This indicates limited explanatory ability.
| Model Summary B | ||||
| Model | R | R Square | Adjusted R Square | Std.Error of the Estimate< / tbody >
The adjusted model did not fit well with the data as indicated in Table2, where there was no statistically significant prediction for obesity percentage based on this regression model. The number of McDonald's establishments and the number of TVs per 1000 individuals did not significantly impact obesity rate (F(2,25) =2 .760,p=.083). The F-test supports the null hypothesis, which states that there is no additive relationship between the independent variables (McDonald's per 1000 individuals, TVs per 1000 individuals, and percentage of obesity), indicating a low probability of a linear relationship in the regression model. Table 2 displays the F-test statistics for Multiple Linear Regression along with coefficient estimates. These coefficients show how the dependent variable (percentage of obesity) changes when each independent variable varies while keeping other variables constant. According to Table 3, an additional McDonald's establishment per 1000 individuals corresponds to a 34.163 increase in obesity percentage, which is statistically significant (p = 0.027) but leaning towards nonsignificance. Conversely, an increase of one television set per 1000 individuals leads to a decrease of 0.021 in obesity percentage within the country. The statistical significance of the decrease in obesity prevalence (P = .106) was not observed. However, it was found that the number of McDonald's per 1000 individuals (Beta = .595) had a stronger association with obesity percentage compared to the number of TVs per 1000 individuals (Beta = -.425). In simpler terms, having more McDonald's outlets per 1000 individuals has a greater impact on obesity prevalence in a country than having more TVs per 1000 individuals. Thus, the number of McDonald's per 1000 individuals contributes more significantly to the prevalence of obesity than the number of TVs per 1000 individuals. The following table displays the coefficients, unstandardized coefficients, standardized coefficients, T values, significance levels, and confidence intervals for the model. The first row contains the labels for each column. The subsequent rows contain specific data points. You predicted Percentage Obesity =18.428 +34.163(McDonalds) - .021 (TVs) For Australia, Predicted Percentage Obesity =18.428 +34.163(.349) - .021(505.226) =18.428 +11. Therefore, the predicted percentage of obesity in Australia is 19.74%. Although the theoretical model can only explain a small portion (11.5%) of the difference in the obesity rate when considering other factors, it is clear that the predicted value is lower. DiscussionThe prevalence of obesity has become widespread in recent years (Foster-Schubert, 2012), with changes in lifestyle identified as the main reason for the rising rates of obesity (WHO, 2004). Lifestyle interventions targeting weight loss are crucial for preventing obesity. Examining the relationship between the quantity of McDonald's restaurants and TVs per 1000 individuals and the obesity percentage in a country, a survey was conducted. The results suggest that when using these variables as independent factors and obesity percentage as the dependent variable, statistical predictions for obesity percentage were not accurate. Initially, the model accounted for a mere 18.1% of the variability in obesity percentage before factoring in irrelevant elements. Moreover, even after considering confounding variables, the model explained even less variation in obesity percentage. The F-test indicated that the theoretical model did not significantly predict the percentage of obesity. Consistent with previous research (Alheritiere et al., 2013), the number of McDonald's per 1000 individuals in a given country significantly contributed to the model. However, the number of TVs per 1000 individuals did not significantly contribute to the prediction of obesity percentage. The number of McDonald's outlets per 1000 individuals was a stronger predictor of obesity percentage than the number of TVs per 1000 individuals in that country. The reason why the number of TVs did not statistically contribute to the prediction model is unclear. While having a Television in a family does not necessarily indicate a lack of physical exercise, it is important to consider other factors. One such factor is the presence of the FTO gene variation, which increases susceptibility to obesity. If individuals in a family do not have this gene, they may avoid becoming obese even if they watch television instead of exercising. A surprising observation regarding the number of TVs per 1000 individuals and obesity percentage was made. However, this association lacks statistical significance. Initially, it was expected that more TVs would lead to higher obesity rates. This finding could potentially be explained by how televisions within families served as valuable sources of information on preventing obesity. Having access to Televisions can offer individuals more information regarding obesity. On the other hand, the existence of McDonald’s indicates that the country consumes products from this establishment. A substantial number of McDonald's restaurants suggests a high consumption of their calorie-rich products within that country. This may potentially clarify why the presence of McDonald's is a significant predictor of obesity rates in that particular nation. Nevertheless, based on the results of this study, a predictive model comprising of the ratio of McDonald's per 1000 individuals and TVs per 1000 individuals does not statistically forecast obesity percentage. The predictive model indicates that the presence of McDonald's restaurants per 1000 people has a positive impact, as it leads to a statistically significant increase in obesity rates. Conversely, the number of TVs per 1000 people does not have a significant effect on obesity rates. There is only a negligible decrease in obesity rates with an increase in TV ownership per 1000 people. Therefore, it is recommended that efforts to prevent obesity should prioritize regulating fast food chains like McDonald's, which sell high-calorie diets and play a significant role in causing obesity. To prevent the consumption of iron-rich nutrients that may contribute to obesity, it is suggested that a law be implemented requiring these products to have prominent labels indicating their potential for weight gain. Furthermore, in order to effectively promote healthy habits and combat obesity, health campaigns should utilize television as a means of reaching a wider audience. Rather than viewing television as a factor causing obesity, it should be acknowledged as a valuable tool for preventing it. Lastly, additional research is needed to examine the relationship between owning televisions and obesity.
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