The Trend of Obesity in the United States

In the United States, obesity is a significant public health concern that affects both children and adults. Since 1960, obesity prevalence between 1960-1962 and 2009-2010 had almost tripled from 13 percent to 36 percent, respectively (CDC, 2013). Similarly, between 1970 and 2010, obesity prevalence in children in the country had more than tripled from 5 percent to 17 percent (CDC, 2013). By 2017-2018, the Centers for Disease Control and Prevention (CDC) showed an age adjustment in the obesity prevalence among American adults. During this period, 2017-2018, 42.4 percent of adults had obesity, while the majority was 40.0 percent in younger adults aged between 20 and 39 years (CDC, 2020). In middle-aged adults, 40-59 years old, the prevalence stood at 44.8 percent, while in adults 60 years and above, the majority was 42.8 percent (CDC, 2020). By breaking down the statistics to gender, CDC showed no significant distinction between men and women.

The differences were significantly less in the established age groups from the gender-related predominance of obesity in men and women. As CDC (2020) shows, 40.3 percent compared to 39.7 percent among adults aged between 20 and 39 years. 46.4 percent of men and 43.3 percent of women were obese from age group 40-59 years and 42.2 percent and 43.3 percent of men and women, respectively, aged 60years and above (CDC, 2020). Like in adults, the seriousness of childhood obesity has been associated with the risk it poses to their health. According to the CDC (2020), by 2018, obesity predominance in adolescents and children, 2 and 19 years, was 19.3 percent, translating to close to 14.4 million individuals. By breaking down the prevalence into the different age groups, the CDC (2020) shows that in children between 2 and 5 years, the predominance was 13.4 percent. Children between 6 and 11 years represented 20.3 percent, while those between 12 and 19 years represented 21.2 percent (CDC, 2020). Moreover, the likelihood of becoming obese was associated with particular ethnic groups.

By comparing the figure of children and juveniles with obesity in the U.S., it was recognized that different cultural groups had different predominance variations. The Hispanic juveniles and children embodied the highest proportion of children and young persons in the nation, with 25.6 percent (CDC, 2020). They were followed by non-Hispanic Black children with 24.2 percent (CDC, 2020). The non-Hispanic white and non-Hispanic Asian children were found to have prevalence rates of 16.1 and 8.7 percent, respectively (CDC, 2020). Overall, obesity prevalence was highest in low-income groups and lowest in high-income groups.

One eating behavior that contributes to the growing number of obese populations in the United States is the frequency with which the American population consumes away-from-home foods. By definition, away-from-home foods are single ready-to-use items and complete meals purchased at prepared-food counters at grocery stores and restaurants, among other food outlets (Yu, 2016). Recently, both fast-food consumption and obesity or overweight prevalence have been on the rise. In the last three decades, Mohammadbeigi et al. (2018) show the percentage of caloric intake has risen fivefold. The global increase in obesity predominance has resulted in a dramatic rise in severe health problems.

Restaurants have been at the forefront of the rise in fast food consumption due to taste and flavored foods, menu choices, convenience, and cost of their foods. Close to 30 percent of children and more than 50 percent of adolescents, particularly those in colleges and universities, consume fast foods daily (Nixon, 2016). Furthermore, in the U.S, over 33 percent of adults and 17 percent of children and teenagers are obese due to fast food consumption (Mohammadbeigi et al., 2018). Studies show that substantial changes in food habits and increased food consumption are the two significant obesity pandemic factors besides poor diet among adolescents and children (Hall, 2018). Away-from-home foods have been associated with low fibers and high fats, and when consumed frequently, they result in poor diet quality and increase obesity risk in both adults and children.

Obesity is a complicated health issue that results from a combination of individual and causes factors like genetics and behavior. Behavior-related factors constitute inactivity, physical activity, medication use, and dietary patterns, among other factors (Welcome, 2017). Further, contributing factors comprise education and skills, material and food activity environment, and promotion and food marketing. The seriousness of obesity, making it a public health concern, is associated with poorer mental health outcomes and a decline in life quality (Olson et al., 2017). The Obesity Medicine Association describes obesity as “a persistent, multifactorial, degenerational, neurobehavioral sickness, where an augment in body fat endorses adipose tissue dysfunction and irregular fat mass bodily forces, resulting in adverse metabolic, psychological, and biomechanical wellbeing outcomes” (Welcome, 2017). Quantitatively, obesity can be categorized into three; body mass index, abdominal circumference, and body fat percentage. In the U.S. and the world over, obesity along with heart ailment, diabetes, some cancer kinds, and stroke are the piloting origins of death.

The toll associated with obesity on health care costs throughout the U.S. is enormous. In 2010, for example, it was estimated that health care costs attributed to obesity were between $147 and $2010 billion in indirect and direct health care costs (Olson et al., 2017). Further, the indirect and direct costs associated with obesity prevalence in the U.S. stifle organizations and businesses that stimulate growth and jobs in the cities throughout the country. In ten cities with the highest obesity rates in the U.S., direct costs attributed to obesity are roughly $50 million per 100,000 residents (National League of Cities, n.d). Additionally, should the obesity rates remain at 2010 levels, the medical expenditures associated with obesity in the country would amount to $549.5 billion by 2030 (National League of Cities, n.d). Obesity interferes with production since it may cause employees to work at reduced capacities.

Absenteeism is the number of days an employee is absent from work due to illness. On the other hand, presenteeism is the number of days employees choose to be present at work despite feeling ill. With obesity, studies have shown that absenteeism is higher among obese employees, and a higher illness-absence rate was majorly associated with central abdominal fatness (Sanchez et al., 2015). Employees with obesity, compared to their normal-weight counterparts, were reported to have either increased or a neutral absenteeism level. On the other hand, presenteeism among obese employees was higher (Sanchez et al., 2015). Severely and moderately obese workers encounter limitations in terms of time needed to complete their tasks and their ability to perform physical jobs (Sanchez et al., 2015). However, mildly obese employees have limited or no health-related losses in productivity.

In the United States, the burden attributed to obesity and obesity-related health complications is not borne equally. With this, discrepancies in obesity prevalence in the country heavily rely on geographic location, race or ethnicity, and socioeconomic status. In the U.S., nationwide rural populations exhibit higher obesity prevalence and obesity-related consequences like type 2 diabetes (Petersen et al., 2019). The rates of morbidity and mortality associated with chronic health complications are higher in rural populations than their suburban or urban population counterparts.

Racial or ethnic disparities contribute to higher rates of obesity prevalence in the U.S. In Hispanic and African American populations, rapid weight gains have been linked to possibilities of increased future health complications. Further, obesity does not impact all ethnic groups equally, and among Blacks and Hispanics, overall obesity rates are all-time high in the U.S. (Byrd et al., 2018). As shown concerning rural populations, heightened obesity rates increase morbidity and mortality rates in non-white people. In lower-income populations, individuals with higher socioeconomic status are more likely to be obese. Conversely, in high-income populations, the higher socioeconomic status meant less likelihood of becoming obese (Petersen et al., 2019). In higher-income societies, the problem of malnutrition, which makes most people in low-income neighborhoods go for fast foods, is replaced with overconsumption.

The likelihood of consuming healthy food is unaffected by its proximity and less healthy food in restaurants. However, before arriving at this conclusion, Hunter et al. (2019) set out to uncover the possibility of whether placing a single less healthy food further away influenced the potential of its consumption. In their study, Hunter et al. (2019) found that the proximity effect did not seem to impact cognitive resources moderately. Further, they found out that by placing healthier foods closer to consumers, there were no high possibilities of consuming more nutritious foods (Hollands et al., 2016). However, the proximity of consuming less healthy foods was influenced by proximity, and further, there was a non-statistically significant decline when they were placed far at the table. Therefore, in restaurants, the rates of consuming fast foods increase with their proximity at the table.

The regularity with which people eat in restaurants has been found to grow significantly. In the last two decades, researchers have shown that modern restaurant design trends favor open spaces and hard surfaces compared to their compartmentalized, carpeted, acoustical ceiling-tiled, and linen-wrapped forebears counterparts (Freese et al., 2016). With unrestricted kitchen proliferation, modern restaurant trends have a slight separation between the dining room and the kitchen. Reports on the new noisy standard show that in the last ten years, restaurants have been incorporating new noise levels in their businesses to influence consumer behaviors, and food intake has increased (Nazli et al., 2020). With restaurants getting noisier, a growing number of bar owners and restaurateurs realize that increasing noise in their businesses results in increased turnover, mainly by simply turning up background music.

Public health policies on menu labeling have been focused on caloric food content. Nonetheless, Fernandes et al. (2016) show that restaurants can influence their consumer’s caloric intake based on their menu design. Calorie labeling in menus has been found to have a synonymous effect with which they make consumers make their frequent food choices. However, there are unsupported assumptions that through calorie labeling, people choose reduced-calorie foods, with the results being healthier food preferences (Mbogori & Freeland, 2021). Further, besides calorie content, dietary factors and food healthfulness related to obesity comprise synergetic interactions and food patterns between food-related components and nutrients.

On the outcome of menu classification, food choice arrangements, except the calorie-only technique, report effects that show to be clashing. In menu design, it has been shown that with every nutritional, contextual, food information, and traffic-light labeling, it is possible to determine how consumers purchase their foods based on designated colors (Fernandes et al., 2016). High, medium, or low food calorie levels are also a crucial determinant of how healthy consumers take their food. Colors in menu designs are also significant indicators of food healthfulness trends based on food ingredients.

Faster tempo music has been associated with getting people in and out of restaurants faster during lunch and dinner since the type of music played at meals time determines how consumers drink and eat. The conclusion by a 2008 study by Guéguen et al. showed that tended to consume more foods and drinks when they were exposed to loud music. Further, the research found soft drinks to be a consumer at a higher rate when the accompanying background music had a faster tempo. The rate of consumers eating more food was associated with popular music playing in the background and when its volume was loud (Guéguen et al., 2008). That translates to the knowledge that the rate of fast food consumption, resulting in obesity, was directly correlated to music’s tempo in restaurants. Similarly, lighting influences food consumption in restaurants in that it has been established that dimmed lighting, compared to bright lighting, affects food intake (Verain et al., 2021). By creating a pleasant atmosphere, soft or dim lighting increases comfort and prolongs the duration with which consumers eat.

Behavioral scientists have documented that in particular colors in restaurants, eating habits among consumers change. The scientists have gone ahead to show that when wall colors are red, the likelihood of increasing consumer appetite increases, and that is the main reason why a majority of restaurants and eateries paint their walls red (Cankul et al., 2021). With the red color comes an increase in consumer eating, increasing the likelihood of pace, eating amount, and type of food consumers take in restaurants. The blue color tends to calm consumers and the rate with which consumers take their food slowly. The calm colors in restaurants slow the pace consumers take their food with since they give clients the feeling of being full (Cankul et al., 202). That is the reason restaurants have warm colors on their walls.


Byrd, A. S., Toth, A. T., & Stanford, F. C. (2018). Racial disparities in obesity treatment. Current obesity reports, 7(2), 130–138. Web.

Cankul, D., Ari, O. P., & Okumus, B. (2021). The current practices of food and beverage photography and styling in the food business. Journal of Hospitality and Tourism Technology, 12, 2, 287-306.

Centers for Disease Control and Prevention. (2013). Obesity – United States, 1999-2010. Web.

Centers for Disease Control and Prevention. (2020). Prevalence of childhood obesity in the United States. Web.

Centers for Disease Control and Prevention. (2020). Prevalence of obesity and severe obesity among adults: United States, 2017-2018. Web.

Fernandes, A. C., Oliveira, R. C., Proena, R. P., Curioni, C. C., Rodrigues, V. M., & Fiates, G. M. (2016). Influence of menu labeling on food choices in real-life settings: a systematic review. Nutrition Reviews Washington-, 74, 8, 534-548.

Freese, J., Ruiz-Nez, B., Heynck, R., Schwarz, S., Pruimboom, L., Renner, R., & Ltzerich, H. (March 02, 2016). To restore health, “do we have to go back to the future?” The impact of a 4-day paleolithic lifestyle change on human metabolism – a pilot study. Journal of Evolution and Health, 1, 1.

Guéguen N, Jacob C, Le Guellec H, Morineau T, Lourel M. (2008). Sound level of environmental music and drinking behavior: a field experiment with beer drinkers. Alcoholism: Clinical and Experimental Research, 32, 1-4.

Hall K. D. (2018). Did the food environment cause the obesity epidemic?. Obesity, 26(1), 11–13. Web.

Hollands G. J., Marteau T. M., & Fletcher P. C. (2016). Non-conscious processes in changing health-related behaviour: A conceptual analysis and framework. Health Psychology Review, 10(4), 381–394.

Hunter, J. A., Hollands, G. J., Pilling, M., & Marteau, T. M. (2019). Impact of proximity of healthier versus less healthy foods on intake: A lab-based experiment. Appetite, 133, 147–155. Web.

Mbogori, T. N., & Freeland, K. B. (2021). Calorie labelling on the menu: Extent and disparities in use at sit down restaurants in the United States. International Journal of Community Medicine and Public Health, 8(7), 3215.

Mohammadbeigi, A., Asgarian, A., Moshir, E., Heidari, H., Afrashteh, S., Khazaei, S., & Ansari, H. (2018). Fast-food consumption and overweight/obesity prevalence in students and its association with general and abdominal obesity. Journal of Preventive Medicine and Hygiene, 59(3), E236–E240. Web.

National League of Cities. (n.d). Economic costs of obesity. Web.

Nazli, C. D., Noor, A. M., Raha, S., Zunaibi, A., Musli, N. Y., & Zaiton, H. (May 01, 2020). Conceptual framework on noise ranking classification in eatery places for human psycho-acoustics preferences towards acoustic comfort. IOP Conference Series Materials Science and Engineering, 849, 1.

Nixon H, & Doud L. (2016). Do fast food restaurants cluster around high schools? A geospatial analysis of proximity of fast-food restaurants to high schools and the connection to childhood obesity rates. Journal of Agriculture, Food Systems, and Community Development, 2(1), 181-194.

Olson, S. (2017). The challenge of treating obesity and overweight: Proceedings of a workshop. National Academies Press.

Petersen R, Pan L, Blanck HM. (2019). Racial and ethnic disparities in adult obesity in the United States: CDC’s tracking to inform state and local action. Preventing Chronic Disease, 16. Web.

Sanchez, B. A., Vargas, K. G., & Gomero-Cuadra, R. (2015). Work productivity among adults with varied body mass index: Results from a Canadian population-based survey. Journal of Epidemiology and Global Health, 5, 2, 191-199.

Verain, M. C. D., Bouwman, E. P., Galama, J., & Reinders, M. J. (2021). Healthy eating strategies: Individually different or context-dependent? Appetite, 168, 2022-1.

Welcome, A. (2017). Definition of obesity. Web.

Yu Y. (2016). Four decades of obesity trends among non-Hispanic whites and Blacks in the United States: Analyzing the influences of educational inequalities in obesity and population improvements in education. PloS One, 11(11), e0167193. Web.

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