mcdonald's obesity case study

Fast Food and Obesity: A Study in a Local McDonald’s

How the fast food revolution established obesity as a primary concern in america., by iliana nguyen | staff writer | sq vol. 9 (2011-2012).

After smoking, obesity is the leading cause of mortality in the United States. This does not come as a surprise since obesity rates are higher than ever, with a staggering 33.8% of adults who are obese. Obesity statistics for children living in the U.S. are alarming as well, with approximately 17% (or 12.5 million) of children and adolescents ages 2 to 19 years characterized as obese. Obesity is determined through BMI (body mass index), a number that is calculated based on a person’s weight and height.

Childhood obesity is a serious issue because children who are obese are predisposed to have many health problems and diseases in their childhood and adulthood. Although many factors can contribute to obesity, the underlying cause of excessive weight gain is an imbalance of energy. This occurs when more calories are taken in through diet than expended through energy-consuming activities.

Kerri N. Boutelle, Ph.D., an Associate Professor of Pediatrics and Psychiatry at the University of California, San Diego, conducted a study to evaluate the nutritional quality of fast food purchased for children as well as the reasons for dining at the fast food restaurant.

Dr. Boutelle, who studies childhood obesity and nutrition, explains why she chose to concentrate on youth, stating, “One out of every three children is either overweight or obese. We focus on children because the majority of overweight children will become overweight adults, and we have an opportunity to intervene with children earlier.”

This is the first study published to evaluate the purchasing patterns of fast food for youth. Dr. Boutelle explains, “We were interested in studying fast food purchasing for families because it influences how children eat. We believe that educating parents may influence how they feed their children.”

The number of meals eaten away from home is believed to contribute to extra calorie consumption. Overweight children and adolescents consume more foods away from home compared to other children. Due to its low nutritional value, cheap cost, and large portion size, fast food and consequent consumption habits has been considered one of the factors contributing to the obesity epidemic. Since its introduction to the United States, fast food has risen in popularity to become a prominent role-player in the everyday diet of children. According to studies, about 30% of children report consuming fast food on a typical day.

Over the course of six weeks, 544 families were surveyed during lunch time at the McDonald’s located in the Rady’s Children Hospital in San Diego, California. Researchers approached families with children who looked between the ages of 2 and 18 years.

The families were asked to present their receipts and to complete a short survey after purchasing their food. Families were asked questions to explain their purchases: who each item was for, if items were shared, sizes ordered (small, medium, large), whether drinks were diet or regular, the type of combination meal purchased, if condiments were added, and lastly, if there were any changes made to their order. The age and gender of the person for whom the item was purchased as well as the reasons for dining at the McDonald’s were inquired about.

Answer choices included: convenience, cheap, adults liked the food, children liked the food, a reward for a hospital visit, hungry with no other options, kids wanted the toy, and that it is a usual place for lunch.

To analyze the data, children were divided into three groups based on their ages: 2 to 5 years old, 6 to 11 years old, and 12 to 18 years old. Researchers then determined the nutritional content of the meals purchased for the different age groups as well as how the meals relate to daily nutrition requirements.

On average, these meals contained a calorie count of 646 to 811 calories, which is 36% to 51% of the recommended daily calorie intake for youth. The children’s meals were found to be extremely high in sodium, with 866 to 1100 mg of sodium consumed per child.

Most popular foods purchased for youth were French fries, soda, chicken nuggets, cheeseburgers, and hamburgers. A Happy Meal was purchased by a little more than half (53%) of the 490 youth. While McDonald’s does offer healthier options such as apple dippers, these choices were found to be less popular. The fruit and yogurt parfait, a heart-friendly option, demonstrated to be very unpopular, only purchased by 1.4% of the families. The top reasons for dining at McDonald’s were convenience and that the children enjoyed the food. About 50% of the families used fast food to reward their children for visiting the hospital. Surprisingly, 72% of adults reported that they liked eating the fast food.

Even though this study was conducted at one fast food location, Dr. Boutelle states, “I don’t think the results would have changed in other locations. What may have changed is what kinds of families participated in the study. We had a wide range of families in this study, so I think it applies to a wide variety of families.”

Significance

Childhood obesity is a life threatening condition because it significantly increases the chances of developing many health problems such as type 2 diabetes, stroke, heart attack, hypertension (high blood pressure), and various cancers. Childhood obesity may also lead to social, behavioral, and emotional problems. Obese children may suffer from low self-esteem, depression, and difficulty interacting with peers.

Not only do children need to be aware of what they put in their mouths, but college students need to maintain healthy food habits as well. Dr. Boutelle explains, “College students are at an increased risk for weight gain because it is the first time they are on their own and they get to make all the decisions regarding their food intake. There are many cues in the college environment to overeat, and it is very important for college students to understand the impact of calories from fast food or other sources on their weight.”

Many college students are no stranger to the dreaded “freshman fifteen” pounds gained during their first year of college from unhealthy eating habits. College students are free to eat whenever and whatever they want, even if this means devouring a tub of Ben & Jerry’s on a stressful day.

Also, a majority of college campuses are located near or contain a fast food restaurant where students can buy food at a low cost. Many of these fast food chains are open during late hours, which is perfect for the student that has a midnight craving. Fast food is also ideal for the penny pinching college student who is always on the go–quick food at an affordable cost.

However, college students need to be conscious of what they are putting in their mouths; what is best for their wallet is not necessary the best for their health, especially if it is a Big Mac which is high in sodium, high in calories, and has a low nutritional value. According to a study published in Pediatrics Journal, youth who consume fast food on a typical day consumed greater amounts of saturated fat, sodium, carbohydrate, and sugars and less dietary fiber than those who do not eat fast food.

Consuming fast food also reduces the amount of healthy food options eaten. Children who consume fast food tend to consume more sugar beverages and less milk, as well as fewer fruits and vegetables containing starch. Fruits and non-starchy vegetables are an important part of the daily diet because they contain low energy density and high fiber content, which may protect against excess weight gain. Fiber also plays a major factor in regulating digestion as well as preventing heart disease and colon cancer.

Consuming more than the recommended amount of sodium results in elevated blood pressure, which can increase the risk of many health problems. Blood pressure is the pressure of blood exerted against the walls of the arteries as the heart pumps blood. When the body ingests sodium, this raises osmolarity (the measure of the amount of solutes in a solution) because the concentration of sodium has increased while the volume of fluid in the body remains the same. Vasopressin is secreted, which causes the kidneys to absorb water and increases the blood volume. This causes an increase in blood pressure since the heart has to work harder to push the increased blood volume throughout the body. High blood pressure results in the heart exerting a greater pressure upon the arteries, causing them to stretch and expand to allow blood to flow easier. Overstretching creates weak spots in the artery walls, causing them to become more prone to ruptures, which may possibly lead to a stroke or an aneurysm.

Also, congestive heart failure occurs when the heart muscle becomes worn out from working too hard to pump blood and, as a result, can no longer efficiently pump blood. Fast foods are high in cholesterol as well. Atherosclerosis occurs when the coronary arteries become harder and thicker from a buildup of plaque and cholesterol. This buildup causes blood flow to become severely reduced or completely cut off. If portions of plaque disperse into the bloodstream, oxygen transport to the brain and heart can be disrupted, potentially resulting in a stroke or heart attack. Trans-fats escalate the risk of developing heart disease by increasing LDL cholesterol while lowering HDL cholesterol levels. LDL, known as “bad cholesterol,” is responsible for plaque build-up in arteries. HDL cholesterol, or “good cholesterol,” travels through the bloodstream and removes LDL.

Eating large portions of energy-dense foods also play a factor in the obesity epidemic. Fast food restaurants are notorious for their “value meals,” which offers large portions at a low price. “Supersizing” is a widespread practice at many restaurants, which offers more food, such as a drink and a side dish, for a small additional cost. Recent studies have shown that greater portion size leads to greater intake and extra calories. When watching television or driving down the road, it is quite common to see some sort of advertisement or reference for a popular fast food chain. The fast food industry markets to children in hopes of fostering a fast food habit that will continue into adulthood. Therefore, it is important to realize the impact that fast food has on the daily diet of youth and its involvement in the obesity epidemic. However, does this mean that we should give up on eating at fast food joints entirely? Absolutely not. While hamburgers and chicken nuggets are tempting, some fast food restaurants do offer health conscious items such as the fruit and yogurt parfait or apple dippers. It is critical to keep in mind that moderation and variety are key for a balanced diet. According to the late Dr. Paul Saltman, “What makes food good or bad for you is volume, balance, and interaction. What you eat matters less than how much you eat, when you eat it, and what other foods you’re also eating.”

WRITTEN BY ILIANA NGUYEN. Iliana Nguyen is a Biochemistry and Cell Biology major from John Muir College. She will graduate in 2013.

• Boutelle, K., Email Correspondence, 1, Nov. 2011
• Boutelle, PhD, Kerri N., Hanaah Fannin, BS, Ron S. New eld, MD, and Lisa Harnack PhD. “Nutritional Quality of Lunch Meal Purchased for Children at a Fast-Food Restaurant.” Childhood Obesity 7.4 (2011): 316-21. Print. Bowman, PhD, Shanthy A., Steven L. Gortmaker, PhD, Cara B. Ebbeling, PhD, Mark A. Pereira, PhD, and David S. Ludwig, MD, PhD. “Effects of Fast-Food Consumption on Energy Intake and Diet Quality Among Children in a National Household Survey.” Pediatrics 113.1 (2044): 112-18. Print.
• Gifford Sawyer, Michael, Taylor Harchak, Melissa Wake, and John Lynch. “Four Year Prospective Study of BMI and Mental Health Problems in Young Children.” Pediatrics 128.4 (2011): 677-84. Print.
• Rolls, PhD, Barbara J. “ The Supersizing of America Portion Size and the Obesity Epidemic.” Nutrition Today 38.2 (2003): 42-53. Print.
• Saltman, Paul, Joel Gurin, and Ira Mothner.  The University of California San Diego Nutrition Book. Boston: Little, Brown, 1993. Print.
• Schlosser, Eric. Fast Food Nation: the Dark Side of the All-American Meal. Boston: Houghton Mi in, 2001. Print.
• Silverthorn, Ph.D, Dee Unglaub. Human Physiology. 4th ed. San Francisco: Pearon Benjamin Cummings, 2009. Print.
• www.heart.org. Web. 20 Nov. 2011. < http://www.heart.org >.

Thanks for the terrific article

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Notify me of follow-up comments by email.

Notify me of new posts by email.

This site uses Akismet to reduce spam. Learn how your comment data is processed .

McDonald’s Company: Marketing and Obesity Case Study

• To find inspiration for your paper and overcome writer’s block
• As a source of information (ensure proper referencing)
• As a template for you assignment

As cases of obesity among children and young people increase throughout the world, a focus has turned on the need to reduce the amount of junk foods available in the market. One way of doing this is to ensure that marketers reduce the amount of junk foods in the market, especially products targeting children in various nations. Another important method adopted by various governments across the world involves prohibiting direct advertisements that attract children’s attention. However, none of these strategies has shown evidence of reducing cases of obesity by a significant margin. On the other hand, global fast food marketers have faced accusations for their contribution to the obesity problem. In fact, reports indicate that more than 1 billion people are overweight with more than 400 million considered obese (Cateora, Philip, Gilly and Graham 425).

Despite the campaigns to reduce the amount of junk foods in the market and discourage people from taking such foods, the trend is increasing. Reports show that the rate of obesity is on the rise. Mcdonald’s, the global marketer of fast foods, has become the major subject of discussion in reference to the rising cases of obesity. The company’s massive and aggressive advertisement, multiple locations in the world and food content have attracted massive debate. In many parts of the world, governments have established measures to control the problem of obesity, but in most cases, McDonald’s seems to be the prime target. Does the company need to change its marketing models, branding, food content and advertisement to fit with the modern health demands?

In Europe, McDonald’s and other companies in the fast food industry face an uphill task in convincing the market to continue taking their food products. While the companies are increasing their presence in almost every city and town, governments, medical and health bodies, as well as other enthusiasts, are increasingly targeting McDonald’s and similar corporations. In Europe, for example, various governments have attempted to ban advertisement of junk food, especially those that target children (Cateora, Philip, Gilly and Graham 427). Others have forced such companies as McDonald’s to reduce the amount of fats in food they sell to children or reduce their advertisements targeting children. In Scandinavian countries, for instance, people are made to perceive the effects of fast foods as equal to the harm of tobacco.

The media in Europe and America is awash with blames targeting McDonald’s and other related companies for their contribution to health problems. For instance, the UK film “Super Size Me” accuses McDonald’s of perpetrating and increasing the incidences of obesity problem in the country. These and other attacks on the company have forced it to initiate strategies to convince the market that its food is healthy. For instance, McDonald’s has initiated different campaigns to advertise new and healthy products, such as low-fat foods and low sugar drinks. In addition, it has initiated campaigns to encourage people to “lead active lives” for avoiding overweight and obesity. In addition, it has reduced the resources used to target children in its ads for junk foods.

Despite this, such efforts have not saved the company from global accusations for increased rates of obesity and related health conditions. For instance, the impact of Prince Williams of the UK when he accused the company of obesity in his Middle East tour is negative to the company. Such accusations may force the company to face growth problems in the future, especially if it fails to develop better strategies to convince the world that it does not contribute to the health problem. Therefore, McDonald’s needs an overhaul of its business strategies to sustain growth.

Works Cited

Cateora, Philip, Mary Gilly and John Graham. International marketing . New York: McGraw-Hill, 2010. Print

• McDonald’s in the Context of Obesity Problem
• McDonald’s Integrated Communication Strategy
• The "Waist Banned" Article - Taxes on Junk Food
• The Online University Broadcasting Station: Rewarding Volunteers
• The Use of ISO 9000:2008 by Dubai Metro
• Dubai Airport Free Zone: Business Models
• Disney Company Organizational Climate: Michael Eisner
• Marketing Persuasive Messages: Molo Yoghurt and Yu Mobile
• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2020, May 26). McDonald’s Company: Marketing and Obesity. https://ivypanda.com/essays/mcdonalds-company-marketing-and-obesity/

"McDonald’s Company: Marketing and Obesity." IvyPanda , 26 May 2020, ivypanda.com/essays/mcdonalds-company-marketing-and-obesity/.

IvyPanda . (2020) 'McDonald’s Company: Marketing and Obesity'. 26 May.

IvyPanda . 2020. "McDonald’s Company: Marketing and Obesity." May 26, 2020. https://ivypanda.com/essays/mcdonalds-company-marketing-and-obesity/.

1. IvyPanda . "McDonald’s Company: Marketing and Obesity." May 26, 2020. https://ivypanda.com/essays/mcdonalds-company-marketing-and-obesity/.

Bibliography

IvyPanda . "McDonald’s Company: Marketing and Obesity." May 26, 2020. https://ivypanda.com/essays/mcdonalds-company-marketing-and-obesity/.

• DOI: 10.2991/assehr.k.211209.241
• Corpus ID: 245797913

Adolescent Obesity: Case Study of McDonald’s Happy Meal

• Yuxuan Guo , Xiang Lan , +1 author Yizhen Song
• Published in Proceedings of the 3rd… 2021
• Business, Medicine
• Proceedings of the 2021 3rd International Conference on Economic Management and Cultural Industry (ICEMCI 2021)

Figures from this paper

21 References

Young children are the main victims of fast food induced obesity in brazil, the fast food and obesity link: consumption patterns and severity of obesity, the risk to the american fast-food industry of obesity litigation, fast food consumption and its associations with obesity and hypertension among children: results from the baseline data of the childhood obesity study in china mega-cities, worldwide relation between the number of mcdonald's restaurants and the prevalence of obesity, health consequences of obesity in youth: childhood predictors of adult disease., no toy for you the healthy food incentives ordinance: paternalism or consumer protection, changes in the nutritional quality of fast-food items marketed at restaurants, 2010 v. 2013, voluntary kids’ meal beverage standards: are they sufficient to ensure healthier restaurant practices and consumer choices, for personal use. only reproduce with permission from the lancet publishing group. international epidemic of childhood obesity childhood obesity: public-health crisis, common sense cure, related papers.

Showing 1 through 3 of 0 Related Papers

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List

Young children are the main victims of fast food induced obesity in Brazil

Paul-georges reuter.

SAMU 93—UF Recherche-Enseignement-Qualité, Université Paris, Sorbonne Cité, Inserm U942, Hôpital Avicenne, AP-HP, Bobigny, France

Lucas Afonso Barbosa Saraiva

Lisa weisslinger, carla de stefano, frédéric adnet, frédéric lapostolle, associated data.

The exhaustive list of all McDonald restaurants in Brazil was obtained from the AggData website ( https://www.aggdata.com/ ). The search terms used were "Brazil" and "McDonald's". I confirm that others can recreate the findings of our study using only data purchased from the AggData website without additional data.

Introduction

Obesity and overweight strongly contribute to increasing cardiovascular morbidity and mortality, and are becoming a worldwide health issue. The prevalence of obesity has increased dramatically in Latin America. Child obesity is a major issue. Fast food is strongly suspected of contributing to this epidemic of obesity, although there is a lack of evidence.

We studied the correlation between the number of McDonald restaurants and overweight and obesity prevalence by region stratified by gender and age. Data on prevalences were obtained within national studies conducted by the Brazilian Ministry of Health. Three age sub-groups were analyzed: 5 to 9-year-olds, 10 to 19-year-olds and over 19-year-olds.

There was a very strong positive correlation between overweight rates and the number of McDonald restaurants for both males and females between 5 and 9 years old (R 2 respectively = 0.92 and 0.84) and a strong positive correlation for females between 10 and 19 years old (R 2 = 0.68).

There was a very strong positive correlation between obesity rates and the number of McDonald restaurants for males between 5 and 9 years old (R 2 = 0.95). This positive correlation was strong for both males and females between 10 and 19 years old (R 2 respectively = 0.77 and 0.63).

Other correlations were not significant.

A strong correlation between the prevalence of overweight and obesity and the number of McDonald restaurants was found for Brazilian children and was most important within the group of youngest children. These results should be taken into consideration by education and prevention campaigns.

Obesity and overweight strongly contribute to increasing cardiovascular morbidity and mortality, and are becoming a worldwide health issue.[ 1 ] Both the number of countries and the proportion of population affected are continuously increasing. In 2016, over 39% of adults were overweight and over 13% had obesity according to the World Health Organization (WHO).[ 2 ] However, countries and continents aren’t affected equally. Most affected are western countries. In the United States, one third of the population is affected by obesity. In contrast, in Africa, 13% of adults are overweight and 8% have obesity.

The prevalence of obesity has increased dramatically in Latin America. In Brazil, obesity rates increased from 11.6% in 2006 to 17.4% in 2012.[ 3 ] Two populations are particularly affected, women and children.[ 4 , 5 ] Obesity in children is a major issue and is well known to be a predictive factor of adult obesity.[ 6 ] It is responsible for specific diseases such as type 2 diabetes mellitus, hypertension, nonalcoholic fatty liver disease, obstructive sleep apnea, and dyslipidemia.[ 6 ]

Modern ways of life and especially changes in our eating habits, as well as sedentary lifestyles, have largely contributed to increasing obesity. Fast food is strongly suspected of contributing to this epidemic of obesity. We recently reported a strong correlation between a country’s number of McDonald restaurants and overall obesity rates.[ 7 ] Number of McDonald restaurants can be considered a strong indicator of change in local population lifestyles. Children are the main target for these kinds of restaurants. There is no specific evidence of the relation between fast food and childhood obesity.

Due to the crucial issue of child obesity in Brazil, we decided to study the correlation between fast food restaurant presence and obesity–overweight rates in the Brazilian population.

Brazil is geopolitically divided into five macro-regions, each of which has its own economical and sociocultural pattern. These divisions were used as the unit of comparison in our study. The “Pesquisa de Orçamentos Familiares” is a study of nutritional and economical facts of the population that was performed by the Brazilian Ministry of Health ( https://biblioteca.ibge.gov.br/visualizacao/livros/liv50063.pdf ). Their results were presented by age groups of 5 years. The last version (2008–2009) of this document was used as a reference for obesity rates in each region. Obesity and overweight were defined according to World Health Organization references ( http://www.who.int/growthref/who2007_bmi_for_age/en/ ). Obesity was defined as more than two standard deviations over expected weight for patient’s age and overweight as more than one standard deviation over expected weight for patient’s age. These thresholds are equivalent to respectively BMI > 30 and 25kg/m2 in adult.

Results were stratified by gender and age groups. We studied three age sub-groups, 5 to 9-year-olds, 10 to 19-year-olds and over 19-year-olds.

The exhaustive list of all McDonald restaurants in Brazil was obtained from the AggData website ( https://www.aggdata.com/ ). The Brazilian Demographic Census provided the population per region. With these two data, we calculated the ratio of “McDonald restaurants per million inhabitants per region”.

We then studied the correlation between McDonald restaurant presence and population affected by obesity or overweight in each region. For each analysis, we calculated the determination coefficient (i.e. R squared). It was considered very strong when superior to 0.8 and strong when superior to 0.6 [ 8 ] In other cases correlation was considered not significant. We used R software (v3.1.0).

Total population was 199,492,433 inhabitants, ranging from 14,702,592 to 84,046,162 depending on the region. Population of children aged 5 to 9 years old were 16,009,509 (8%), ranging from 7% to 11% depending on the region. The total number of McDonald restaurants in Brazil was 786, ranging from 14 to 528 depending on the region. The number of McDonald restaurants per million inhabitants was 3.9, ranging from 0.8 to 6.3 depending on the region. Details are in Table 1 . Overweight and obesity rates per million inhabitants were at their maximum in the population of over 19 year-olds in both males and females.

• Overweight

The correlation between overweight and the number of McDonald restaurants was very strong for both males and females between 5 and 9 years old (R 2 respectively = 0.92 and 0.84) ( Fig 1 ). The correlation was strong for females between 10 and 19 years old (R 2 = 0.68) ( Fig 2 ). Other correlations were considered not significant ( Fig 3 ).

• Obesity

The correlation between obesity rates and the number of McDonald restaurants was very strong for males between 5 and 9 years old (R 2 = 0.95) ( Fig 1 ). The correlation was strong for both males and females between 10 and 19 years old (R 2 respectively = 0.77 and 0.63) ( Fig 2 ). Other correlations were considered not significant (Figs ​ (Figs1 1 – 3 ).

A strong or very strong correlation between the prevalence of Brazilian children affected by overweight or obesity and the number of McDonald restaurants was found in many subgroups of gender and age class. The highest correlation—i.e. a very strong correlation—was observed for the youngest children. We also noted that this correlation seemed stronger for males than for females. These results should be taken into consideration by education and prevention campaigns.

The correlation between prevalence of overweight or obesity and the number of McDonald restaurants per region in Brazil that we found was weaker than the correlation that we previously reported on a global scale.[ 7 ] In a worldwide analysis we found a linear, very strong (R 2 = 0,95) correlation between overweight and number of McDonald restaurants. In this last study, the conditions for highlighting such a correlation were favorable as the analysis covered 75% of the world population and especially because, in this large sample, the prevalence of people affected by overweight in the population ranged from 2% (Viet-Nam) to 32% (United States) and the number of McDonald's restaurants per million inhabitants from 0 (Viet-Nam) to 45 (United States). In contrast, in Brazil, the prevalence of population affected by obesity ranged from 3% (females 10 to 19 years in the Norte) to 21% (males 5 to 9 years in the Sudeste) and the number of McDonald restaurants per million inhabitants ranged from 0.8 to 6.3. Although the correlation we found here is less statistically strong, the trends all go in the same direction, whichever age category or gender is considered. The appearance of these curves (Figs ​ (Figs1 1 – 3 ) leaves little room for doubt as to the existence, in Brazil, of a correlation between the number of McDonald restaurants per million inhabitants and the prevalence of overweight and obesity. The proximity of fast food restaurants seems to be a factor associated with overweight and obesity, in adults, as well as in children.[ 7 , 9 ]

The strongest correlations were for boys from 5 to 9 years old (R 2 respectively = 0.93 and 0.95) and for overweight, for girls from 5 to 9 years old (R 2 = 0.84). This observation is crucial for several reasons. It is clear that children who are affected by overweight and especially obesity have an increased risk of being affected by obesity in adulthood. In fact, the risk for a child with obesity of having obesity in adulthood is multiplied, depending on the gender, by 5 to 9.[ 10 ] More generally, childhood obesity is associated with increased medical complications—especially cardiovascular—in adulthood.[ 11 ] Childhood diet determines the tastes and eating habits of the teenager and the adult. This ultimately contributes, directly or indirectly, to developing obesity or an ability to control one’s weight. Finally, because children, and more specifically young children, are a preferred commercial target for McDonald's, the correlation was lower for older children and was missing in children over 19 years. It should be noted that this last sub-group was not exposed to McDonald restaurants in the 1990s.[ 12 ]

This particularly sensitive subject led certain American jurisdictions to only authorize offering a gift with a meal on the condition that the meal conform to certain nutritional values.[ 13 ] Interestingly, a recent study revealed that children will chose the healthiest menu if it comes with a gift.[ 13 ] Various educational tools such as the simple question ‘‘what would Batman eat?” have been suggested.[ 14 ]

Obesity is already identified as a major health issue in Brazil.[ 15 ] In Brazil, contrary to what is observed in Western countries like the United States, the female population is predominantly affected by obesity. This may change. Indeed, the prevalence of obesity in males was significantly higher than that of obesity in females in all regions before the age of 19. The greater the gap between male and female obesity rates, the greater the general obesity rates. Identifying subgroups of population (by age, gender, geographical area) that are most at risk of having overweight or obesity is crucial to optimizing prevention campaigns. The results of this study contribute to this. Taking charge of the '' epidemic '' of overweight and obesity is a very real public health challenge worldwide.[ 16 ] Moreover, the interaction between obesity risk factors in children is complex, as shown by a recent Brazilian study.[ 17 ]

Such results do not mean that McDonald restaurants are the first responsible for obesity. The establishment and attendance of McDonald restaurants are in fact indicators, among others, of a change in lifestyles, and diet is only one of its components. Thus, analysis carried out with other criteria such as the consumption of sodas, time spent watching television or playing video games or, conversely, the practice of sport, would certainly obtain fairly similar results.[ 18 , 19 ] Many behavioral factors interfere with diet and weight.[ 20 ] In Brazil, the amount of McDonald restaurants has grown exponentially over the past 25 years.[ 12 ] The number of televisions and refrigerators in households followed the same trend.[ 12 ] In our study disparities were observed between rural and urban areas.[ 12 ] It is important to note however, that Brazil is already pointing the finger at McDonald’s…[ 21 ]

Limitations of the study

Our results may suffer some criticism. Our analysis focused exclusively on the McDonald's chain. Other fast-food chains could also play a role in the results of this study, as well as other types of food ignored here. However the latter is unlikely, as the correlation between fast food consumption and childhood obesity has been demonstrated worldwide.[ 22 ] Causes of overweight and obesity remain multiple and complex.[ 23 ] The confounding factors could not be taken into account in this study. Moreover, the validity of our results in other countries cannot be affirmed, although once more, the universality of the correlation has already been demonstrated.[ 7 ] Because this correlation was established in a single country study that only analyzed five regions and had a limited range in number of restaurants and in prevalence of obesity and overweight, it is more likely to support a strong relationship. Analysis in other countries will have to confirm this correlation. The reference values used for overweight and obesity are from 2008–09. The impact of the dated data on our interpretation could only be penalizing as the prevalence of overweight and obesity increases, similarly to an epidemic.

A strong correlation between overweight and obesity rates and the number of McDonald restaurants has been found in the population of Brazilian children. This correlation was at its maximum for the youngest children and for males. Fast-food consumption must be maintained as a main target in the battle against childhood obesity.

Competing interest statement

All authors have completed the Unified Competing Interest form (available on request from the corresponding author) and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years, no other relationships or activities that could appear to have influenced the submitted work.

Funding Statement

The authors received no specific funding for this work.

Data Availability

• Open access
• Published: 04 November 2019

Evaluation of the impact of calorie labeling on McDonald’s restaurant menus: a natural experiment

• Joshua Petimar   ORCID: orcid.org/0000-0002-3025-5931 1 ,
• Maricelle Ramirez 2 , 3 ,
• Sheryl L. Rifas-Shiman 2 ,
• Stephanie Linakis 2 ,
• Jewel Mullen 4 ,
• Christina A. Roberto 5 &
• Jason P. Block 2  

International Journal of Behavioral Nutrition and Physical Activity volume  16 , Article number:  99 ( 2019 ) Cite this article

15k Accesses

27 Citations

34 Altmetric

Metrics details

The long-term effect of calorie labeling on fast-food purchases is unclear. McDonald’s voluntarily labeled its menus with calories in 2012, providing an opportunity to evaluate this initiative on purchases.

From 2010 to 2014, we collected receipts from and administered questionnaires to 2971 adults, 2164 adolescents, and 447 parents/guardians of school-age children during repeated visits to 82 restaurants, including McDonald’s and five control chains that did not label menus over the study period in four New England cities. In 2018, we analyzed the data by using difference-in-differences analyses to estimate associations of calorie labeling with calories purchased (actual and estimated) and predicted probability of noticing calorie information on menus.

Calorie labeling at McDonald’s was not associated with changes in calories purchased in adults (change = − 19 cal pre- vs. post-labeling at McDonald’s compared to control chains, 95% CI: − 112, 75), adolescents (change = − 49 cal, 95% CI: − 136, 38), or children (change = 13 cal, 95% CI: − 108, 135). Calorie labeling generally increased the predicted probability of noticing calorie information, but did not improve estimation of calories purchased.

Conclusions

Calorie labeling at McDonald’s was not associated with changes in calories purchased in adults, adolescents, or children. Although participants were more likely to notice calories on menus post-labeling, there was no improvement in ability to accurately estimate calories purchased.

In May 2018, restaurant chains with 20 or more locations in the United States were mandated to label their menus with calorie information to comply with the menu labeling provision of the 2010 Patient Protection and Affordable Care Act (ACA) [ 1 ]. Policymakers adopted this requirement to increase awareness of the calorie content of prepared food purchased outside the home, especially restaurant food, where calories are underestimated by restaurant patrons [ 2 , 3 , 4 ]. The federal policy also preempted city and state laws requiring calorie labeling, establishing uniform requirements for chain food establishments across the country [ 5 ]. The downstream goal of the law is to enhance diet quality by changing consumer behavior and encouraging food retailers to offer lower calorie items. In part, due to several delays in implementation of the law [ 5 ], many large restaurant chains began voluntarily posting calories on their menus before it was required, including McDonald’s, which began labeling in September 2012.

Despite the popularity of calorie labeling [ 6 ] and the federal requirement, the effectiveness of this policy for reducing calories purchased in restaurants is unclear. Although some previous observational and experimental studies have found that calorie labeling reduces calories purchased [ 7 , 8 , 9 , 10 , 11 ], other studies have found no difference, especially those conducted in real-world settings, primarily at fast-food restaurants [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. Importantly, many studies that previously examined this association lacked appropriate comparison groups [ 20 ]. Few studies in adolescents and children have had large enough samples to examine this association overall or in subgroups [ 14 , 17 , 21 ].

To address these gaps, we conducted a study to evaluate McDonald’s calorie labeling on customers’ actual and estimated calorie content of purchased foods, compared to customers of five fast-food chains that did not implement labeling over the study period. We examined this separately in adults, adolescents, and children.

Study area and restaurant selection

We capitalized on a natural experiment to examine calorie purchases before and after voluntary calorie labeling at McDonald’s in 2012 compared to a group of control restaurants: Burger King, Subway, KFC (except for adolescents), Wendy’s, and Dunkin Donuts (only for adolescents). These were chosen as control restaurants because they are similar to McDonald’s in popularity, price point, and types of meals served. Further, all have a wide range of menu offerings in terms of calories, allowing us to detect changes in overall trends in calorie purchases independent of labeling. Other details regarding the process and rationale for restaurant selection have been described previously [ 2 ]. Our study area included Boston and Springfield, Massachusetts; Hartford, Connecticut; and Providence, Rhode Island, four large New England cities with substantial racial and socioeconomic diversity. For adults and children, we randomly selected three McDonald’s, three Burger Kings, two Subways, one KFC, and one Wendy’s in each city in 2010. These restaurant chains were chosen because they had at least two locations in each city and offered dinnertime meals. Restaurants that had closed or whose management refused participation in 2010 were replaced by randomly selecting another restaurant of the same chain in the same city. We revisited the same restaurants every year from 2011 to 2014 except when management refused to participate, in which case we selected the nearest restaurant of the same chain. We sampled adults and children in 48 restaurants, 37 of which were included in both the pre-intervention (i.e. 2010–2012) and post-intervention periods (i.e. 2013–2014). Data collection in 2012 was limited and only done to supplement collection in restaurants that were added in 2011. We collected participant surveys and receipts in the evenings from April to August in each year of data collection.

The restaurant selection procedure was similar for adolescent participants, except we chose restaurant chains with at least two sites within one mile of a high school (three McDonald’s, two Burger Kings, two Subways, two Dunkin Donuts, and one Wendy’s in each city) and enrolled participants in the early afternoon during the school year and at lunchtime over the summer. We believed this sampling strategy would help us recruit adolescents, especially after school when they may be unaccompanied by adults, thereby minimizing the influence of parent and guardian preferences. We excluded restaurants poorly attended by adolescents; this resulted in more exclusions in the pre-intervention period for adolescents ( n  = 11) than we had for adults and children ( n  = 5). As a result, we visited more restaurants to recruit the adolescent sample than we did for adults and children. Overall, we sampled adolescents from 53 restaurants, 37 of which were included in both the pre- and post-intervention periods. We collected surveys from June to August in each year of data collection; in Boston only, we collected a separate after-school sample from April through June in each year of data collection. This study was approved by the institutional review board of Harvard Pilgrim Health Care.

Participant enrollment

We invited all adults (≥18 years), adolescents (11–20 years), and parents or legal guardians of children (3–15 years) to participate. While there was an overlap in age eligibility for the groups, we enrolled them at different times of day, making it unlikely that any participants were included in both samples. As described previously [ 2 ], we approached restaurant customers as they entered the restaurant and requested they return the receipt and complete a questionnaire upon exiting in exchange for a $2 incentive. After participants returned their receipt, we administered a questionnaire asking them to identify which items on the receipt were purchased for their personal consumption (or their child’s for the children sample). With the questionnaire, we further assessed details that were not clear from the receipt, such as whether items were shared, the use of sauces/condiments, the addition of cheese, the type of salad dressing, and specific beverage choices. We also asked participants to estimate their meal’s calorie content and assessed participant characteristics. We gathered all information directly from adults and adolescents; for children, all questions were directed to their parent or legal guardian. Although adults and children were enrolled at the same restaurants and times, we did not include parents or legal guardians in the adult sample if their accompanying child was enrolled (we preferentially enrolled children when an adult was accompanying a child). We administered questionnaires in English, but a Spanish language version of the recruitment script was available to facilitate recruitment of Spanish speakers. The overall response rate was approximately 42% for adults (40% pre-intervention and 45% post-intervention), 46% for adolescents (43% pre-intervention and 51% post-intervention), and 44% for children (44% both pre- and post-intervention).

Our primary outcome was total calories purchased for each participant, which was calculated by linking items purchased for participants’ consumption to nutrition information from restaurant websites (collected in July of each year of the study) and summing the total calories purchased for each participant. Estimated total calories purchased was a secondary outcome because we wanted to determine if labeling helped consumers understand the overall calorie content of their meals (even if they did not purchase fewer calories). We also included whether participants noticed calorie information on menus as a secondary outcome. Both of these were assessed on questionnaires.

We measured participant characteristics that we hypothesized might affect response to labeling on questionnaires, including age, sex, race/ethnicity (“White,” “Black,” “Hispanic,” “Asian” and/or “Other”), and self-reported weight and height, which we used to calculate body mass index (BMI). We also asked participants how important price, taste, convenience, and the total number of calories were when deciding which items to order at the restaurant (“not at all”, “a little”, or “a lot”).

Statistical analysis

We conducted analyses separately in adults, adolescents, and children. We excluded participants whose estimated or actual calorie intake exceeded 5000 cal (< 1% in all samples) and those who had missing data on any of the covariates in our main model ( n  = 179 adults [6%], n  = 115 adolescents [5%], n  = 72 children [14%]).

For our primary analyses examining the association between calorie labeling and calories purchased after labeling in 2012, we fit multivariable generalized estimating equations (GEE) that included indicator variables for group (McDonald’s vs. other) and period (pre- vs. post-labeling), an interaction term between group and period (β interaction ), which estimated the effect of calorie labeling, and covariates whose distributions appeared to change slightly over time differently between groups: age, sex, race/ethnicity, BMI, city, and restaurant chain. In adolescents and children, we adjusted for BMI-for-age-and-sex z-score, calculated from national reference data [ 22 ], instead of BMI. We included these covariates because if the population composition of the two intervention groups changed differently over time, and were related to calorie purchases, this could bias the association between labeling and calorie purchases. To account for correlation between purchases in the same restaurant, we included restaurant location as a random effect. We additionally examined differences in calories purchased between the post- and pre-intervention periods within each intervention group.

In secondary analyses, we examined differences in the predicted probability of underestimating the calorie content of purchased meals, as well as whether participants noticed calorie information on menus. For each analysis, we excluded individuals missing data on the respective outcome (across samples: 7–12% missing calorie underestimation; < 1–2% missing noticing calorie information). We ran multivariable GEEs, adjusting for the same covariates as in our primary analysis, and obtained standardized predicted probabilities of each outcome [ 23 , 24 ] within each group and period. We then found the difference in mean standardized predicted probabilities between the post- and pre-intervention periods for each intervention group, calculated the difference-in-differences, and obtained 95% confidence intervals (CI) from 1000 bootstrapped samples. In sensitivity analyses, we considered underestimation of calories purchased as a continuous measure, rather than a binary measure. We additionally calculated the proportion of customers who reported using calorie labels to make purchasing decisions among those who said they noticed the calorie labels in McDonald’s in the post-labeling period.

One important assumption of difference-in-differences analyses is that the pre-trend values for the outcomes are similar in the intervention and control groups. To test this, we ran the primary models described above with observations from 2010 and 2011 only and evaluated the interactions between intervention group and time (there were too few participants enrolled in 2012 to include in this analysis). We did not detect any significant interactions (P-interaction > 0.20 for all), and therefore had no evidence that the pre-intervention trends in the outcomes differed between intervention and control groups.

We conducted sensitivity analyses where we repeated all primary and secondary analyses additionally adjusting for importance of calories, convenience, price, and taste in participant food choices, as well as whether participants properly estimated recommended daily calorie intake. We also reexamined associations between calorie labeling and calories purchased after excluding McDonald’s customers who did not report seeing calorie labeling after implementation. Lastly, we conducted exploratory subgroup analyses in which we repeated our primary analysis within strata of participant sex (male/female), weight status (obesity/no obesity), and race/ethnicity (Black/Hispanic/White); we could not explore these in purchases made for children due to low sample size.

All statistical analyses were conducted in SAS version 9.4 (Cary, NC). We calculated 2-sided 95% CIs for all statistical tests.

The study population after exclusions (Table  1 ) included 2971 adults (31% McDonald’s customers; mean age, 37.6 years [SD, 15.9]; 43% female), 2164 adolescents (41% McDonald’s customers; mean age, 16.3 years [2.7]; 48% female), and 447 children (41% McDonald’s customers; mean age, 8.2 years [3.1]; 51% female). Most participants were non-White (60 to 84%) across all samples. Among adults, compared to control chains, McDonald’s customers were more likely to be female and Black; school-age children at McDonald’s were also more likely to be Black. Among adolescents, a higher proportion of McDonald’s customers were in Boston compared to the control restaurants due to the additional sample during the school year. The mean calories purchased was 807 (range: 0–4000) for adults, 746 (range: 0–2980) for adolescents, and 694 (range: 90–2170) for children. About half of participants across age groups responded that calories influenced their meal selection to some degree, and most answered that that recommended daily calorie intake was between 1000 and 3000 cal.

In multivariable-adjusted models, the calorie content of meals declined by 80 cal in McDonald’s (95% CI: − 155, − 4) and by 60 cal in control restaurants (95% CI: − 116, − 5) in the post- compared to the pre-intervention period among adults. In difference-in-differences models comparing McDonald’s to control restaurants before vs. after labeling, there was no change in calories purchased associated with calorie labeling (β interaction  = − 19 cal, 95% CI: − 112, 75) (Table  2 ). We similarly observed decreases in calories purchased among children in both McDonald’s and control restaurants, but again no change in calories purchased associated with labeling (β interaction  = 13 cal, 95% CI: − 108, 135). In adolescents, we observed neither a decline in calories purchased over time nor a change associated with labeling; the β interaction was the largest for this group though (− 49, 95% CI -136, 38). Results were similar when adjusting for additional covariates in sensitivity analyses (Additional file  1 : Table S1) and when restricting the analysis to McDonald’s customers who reported seeing calorie information on menus (Additional file 1 : Table S2).

We did not observe any changes in predicted probability of underestimating calories purchased in either McDonald’s (predicted change = 1, 95% CI: − 6, 8) or control restaurants (predicted change = − 1, 95% CI: − 5, 4) in the post- compared to the pre-intervention period among adults, and, in differences-in-differences models, there was no association of calorie labeling on ability to accurately estimate calories purchased (β interaction  = 1, 95% CI: -7, 9). Difference-in-differences were similarly null among adolescent purchases and purchases made for children (Table  3 ). Results with continuous underestimation of calories were consistent with those using the binary measure (Additional file 1 : Table S3). In adults, as expected, calorie labeling was associated with a substantial increase in predicted probability of noticing calorie information on McDonald’s menus compared to control restaurant menus (β interaction  = 28, 95% CI: 21, 36). These results were similar among adolescents, but were weaker among parents/guardians of children (β interaction  = 13, 95% CI: − 7, 31). Results for both underestimation of calories and noticing of calorie information on menus were similar when adjusting for additional covariates, though we could not run these analyses in children due to small sample sizes (Additional file 1 : Table S4). We found that 28% of adults, 15% of adolescents, and 24% of parents/guardians of children who noticed labels said that they used the label to decide what to purchase. Among all participants in McDonald’s in the post-labeling period, 13% of adults, 6% of adolescents, and 8% of children noticed and used the labels.

When repeating our primary analysis stratified by participant characteristics (Table  4 ), we observed fewer calories purchased by adolescents with obesity in McDonald’s compared to control restaurants (β interaction  = − 246 cal, 95% CI: -500, 9). Calorie labeling was also associated with reduced calories purchased by Black (β interaction  = − 128 cal, 95% CI: − 237, − 19) and White adolescents (β interaction  = − 141 cal, 95% CI: − 291, 10), but not among Hispanics or in either sex. We did not observe associations of calorie labeling by these characteristics in adults.

In this natural experiment of adults, adolescents, and children, we found that McDonald’s voluntary calorie labeling was not associated with large overall changes in calorie content of meals purchased compared to unlabeled fast-food restaurant meals. Exploratory analyses revealed that calorie labeling was associated with fewer calories purchased among adolescents with obesity as well as Black and White adolescents. Although there were no associations of calorie labeling in our primary analyses, adult purchases and purchases made for children in both intervention and control groups had fewer calories over time. Moreover, although there was a substantially higher predicted probability of noticing calorie information in McDonald’s after labeling compared to control restaurants among adults and adolescents, calorie labeling was not associated with improvements in estimation of calories purchased.

Our main results are consistent with most previous studies conducted in fast-food restaurants using a natural experiment design [ 12 , 13 , 14 , 15 , 16 , 17 , 20 ], which have generally found that calorie labeling is associated with noticing calorie information but not with calories purchased. Many of these studies sampled participants from the same chains as in the present study, using data collected in cities that had implemented calorie labeling (i.e. New York City, Philadelphia, and Seattle) compared to control cities that had not. Only two of these studies investigated this association in children and adolescents specifically, and both did not observe any differences in calories purchased after labeling [ 14 , 17 ]. Although this suggests limited ability of calorie labeling to reduce calories purchased in fast-food settings, one exception is a study conducted by Bollinger et al., which found a significant 15-cal decrease in Starbucks purchases in New York City after labeling (compared to Starbucks locations in Boston and Philadelphia where labeling was not yet implemented) [ 7 ]. This study used > 100 million restaurant transactions, allowing them to detect very small differences in calories purchased. No other study to date, including the present study, has been large enough to detect such small differences in calories purchased. However, microsimulation studies have shown that even small reductions (e.g. 8–11 cal/day) could prevent hundreds of thousands of cases of obesity and cardiometabolic disease [ 25 , 26 ]. Thus, the lack of an association in the present study and in past small studies can be used only as evidence against a moderate or large effect of labeling on calories purchased. They should not be used to rule out a small but meaningful effect of labeling because these studies have generally not been powered to detect such effects. The statistically non-significant reduction of 49 cal among adolescents dining at McDonald’s compared to other chains, pre vs. post, provides further evidence that a small reduction cannot be ruled out in this study.

Although our study included a greater number of child and adolescent participants than previous studies [ 14 , 17 ], we could not completely separate potential parent and guardian influence over choices. Our sampling of adolescents in the early afternoon in restaurants within one mile of a high school may have increased our chances of enrolling adolescents unaccompanied by adults. However, we did not exclude adolescents who were accompanied by an adult. This is also true for children in our sample, who were all accompanied by an adult. In a study of parent-child dyads by Tandon et al., most parents (~ 70%) reported that the child alone selected the child’s meal [ 17 ], whereas Elbel et al. reported that only 31% of children decided on their own what they ate [ 14 ]. Purchases made for children in our study, therefore, likely represent some combination of child and adult preferences. However, Elbel et al. also reported no association between parental involvement and fast-food calorie consumption; it is unclear whether parent/guardian influence would have an effect on changes in calorie purchases made after labeling.

Although calorie labeling was not associated with calories purchased in our study overall, we observed a decrease in calories purchased among adolescents with obesity and Black and White adolescents in exploratory analyses. Previous studies have found that individuals with overweight or obesity are more likely to notice or use calorie labels than individuals with underweight or normal weight [ 27 , 28 , 29 ]; these studies did not examine calorie purchases specifically. Studies examining calorie label awareness or use by race/ethnicity have been inconsistent, with some finding greater benefits of labeling in White populations and others finding greater benefits in non-White populations [ 29 , 30 , 31 ]. Our exploratory analyses do not clarify this question because they suggest similar reductions in calories purchased in Black and White groups. Because we found no associations of calorie labeling with calories purchased among all adolescents, these analyses suggest that this null association may have been driven by participants in the Hispanic, Asian, and Other race/ethnicity groups (the latter two of which we did not conduct subgroup analyses in due to small sample sizes). To our knowledge, this has not been reported previously, though the finding in Black adolescents may be worth further investigation given the higher risk of chronic disease in Black populations [ 32 , 33 , 34 ]. Given our lack of a priori hypotheses about the subgroups and the relatively small number of adolescents in each, these results may also be due to chance.

Enhancing consumers’ knowledge of the calorie content of restaurant meals is one mechanism through which calorie labeling aims to reduce calories purchased. This is important considering that consumers frequently underestimate calories in restaurant meals [ 14 , 38 , 39 ]. Indeed, in the present study, more than 60% of participants in the pre-labeling period underestimated calories purchased across age groups, with mean underestimated calories ranging from 111 (adults in McDonald’s) to 260 (adolescents in control chains). However, we did not find any differences in estimation of calories purchased after labeling implementation. This may be a sign that calorie labels are an insufficient means of communicating nutrition information, further supported by the fact that fewer than 50% of McDonald’s customers noticed calorie information after labeling. Other studies have observed similar findings [ 15 , 16 , 29 ]. Although this suggests the need for enhanced promotion of calorie labels, a potentially bigger problem is that even among the minority of customers who noticed the labels, few (15–28% depending on age) used them to inform their meal choices. This likely explains why we observed no differences in calories purchased when restricting our sample to McDonald’s customers who reported noticing labels after implementation (though this analysis might have introduced other biases [ 40 ]).

These results together imply that promoting labels or increasing their visibility might not be enough to reduce overall calorie purchases in fast-food settings. More widespread rollout of calorie labeling across the U.S. might increase the salience of this information over time. However, many fast-food consumers may just not find this information helpful; about half of participants responded that calories were “not at all” important in their meal selection. Other types of health communication, such as traffic light labels, may better communicate the importance of energy balance for health [ 41 ], and labels might be more effective in other settings like full-service chain restaurants [ 42 , 43 ] or cafeterias [ 10 , 11 , 44 , 45 ].

The natural experiment design we used, which accounted for secular trends in calorie purchases and included a control group with similar menu offerings and clientele as our intervention group, make our results more robust to unmeasured confounding [ 46 ]. This allowed us to draw more valid inferences. Other strengths include our repeated sampling in the same restaurant locations over a five-year period spanning labeling implementation, and a racially and ethnically diverse population.

There are also several limitations to this study. First, although difference-in-differences analyses are generally robust to unmeasured confounding, it is possible that there were some characteristics that changed between groups over time that we did not measure. This could confound our results if these characteristics are related to calorie intake. However, we measured the major characteristics that we felt were most likely to be confounders and adjusted for these variables even though their distributions seemed to differ only slightly between groups over time. Second, even though this study is, to our knowledge, the largest to date of adolescents and children (and one of the largest of adults), we had low power to detect small differences in calories purchased after labeling implementation. Third, although our results are generalizable to individuals of different racial/ethnic groups, participation rates were < 50% across samples, suggesting we may have enrolled a select population among eligible individuals, such as people with lower incomes (i.e. those more receptive to a $2 incentive) and those who primarily do not use drive-thrus (from whom we did not collect data), where calorie labels may be visualized differently than in restaurants. Our results therefore may not be generalizable to all fast-food consumers in the U.S., though our sampling of participants from top-selling chains may enhance this generalizability. Given some differences in menu offerings of these restaurants in different countries, and differences in the demographic composition and health literacy of their clientele, it is unclear how generalizable these results are to customers outside of the U.S. Fourth, individuals’ purchases may have been influenced by participation in the study. However, we expect that any change in purchases due to participation in this study would be similar in McDonald’s and control restaurants and would not greatly affect our main results. When approaching potential participants (prior to their orders), we provided only minimal information about the study, stating that the study was about “food and drink choices at fast-food restaurants.” Fifth, we only assessed purchased, not consumed food. If calorie labeling is associated with reduced intake, but not purchases, of restaurant meals, our results could be underestimated. Last, labeling may have caused some people to forego dining at McDonald’s, but we were not able to capture this information due to the repeated cross-sectional nature of our study. People who decided not to purchase food from McDonald’s due to labeling might have consumed more or fewer calories for that meal at a different location than at McDonald’s. A previous study in Philadelphia found that individuals did not change their frequency of fast-food visits after labeling [ 15 ], but more information on substitution is needed to understand the effect of labeling on overall diet quality.

In summary, we did not observe large differences in actual or estimated calorie content of meals purchased in McDonald’s restaurants after calorie labeling when compared to control restaurants in adults, adolescents, or children. These findings may be partially explained by the still incomplete recognition of calories, even when labeling is present. However, it is also possible that any true effect of calorie labeling is smaller than we could detect with our sample sizes. Although there were no associations of calorie labeling on purchases, the calories purchased by adults and adolescents declined over time, suggesting this might be a secular trend that is driven by myriad factors, one of which could be labeling or the anticipation of labeling.

The recent nationwide implementation of the calorie labeling law offers future opportunities to investigate these effects in larger studies, particularly in full-service restaurants, where the effect of calorie labeling on diet quality may be stronger [ 42 ]. Because of the now widespread implementation of labeling, chains might offer lower calorie options, decreasing calorie content by default [ 47 ], which should also be examined further. The law additionally requires labeling of prepared foods in supermarkets; no studies have investigated labeling in these settings [ 20 ]. Other areas for future research include evaluation of the effects of calorie labeling on overall diet quality, including nutrient and food group composition of purchases [ 48 ]. Lastly, calorie labeling in the presence of other policies for obesity prevention (e.g. beverage taxes) should be investigated because there may be synergistic effects that have a positive impact on diet.

Availability of data and materials

Data are available upon request as long as proper data use agreements are executed and approved by the Harvard Pilgrim Health Care Office of Sponsored Programs and Institutional Review Board.

Food and Drug Administration. Food Labeling; Nutrition Labeling of Standard Menu Items in Restaurants and Similar Retail Food Establishments; Extension of Compliance Date. In: Food and Drug Administration, editor.: Federal Register; 2017. p. 20825–9.

Block JP, Condon SK, Kleinman K, Mullen J, Linakis S, Rifas-Shiman S, et al. Consumers' estimation of calorie content at fast food restaurants: cross sectional observational study. BMJ (Clinical research ed). 2013;346:f2907.

Google Scholar  

Franckle RL, Block JP, Roberto CA. Calorie underestimation when buying high-calorie beverages in fast-food contexts. Am J Public Health. 2016;106(7):1254–5.

Article   PubMed   PubMed Central   Google Scholar  

Wansink B, Chandon P. Meal size, not body size, explains errors in estimating the calorie content of meals. Ann Intern Med. 2006;145(5):326–32.

Article   PubMed   Google Scholar  

Block JP. The calorie-labeling Saga - Federal Preemption and delayed implementation of public health law. N Engl J Med. 2018;379(2):103–5.

Morain S, Mello MM. Survey finds public support for legal interventions directed at health behavior to fight noncommunicable disease. Health Aff (Project Hope). 2013;32(3):486–96.

Article   Google Scholar  

Bollinger B, Leslie P, Sorensen A. Calorie posting in chain restaurants. Am Econ J Econ Pol. 2011;3(1):91–128.

Roberto CA, Larsen PD, Agnew H, Baik J, Brownell KD. Evaluating the impact of menu labeling on food choices and intake. Am J Public Health. 2010;100(2):312–8.

Wisdom J, Downs JS, Loewenstein G. Promoting health choices: information versus convenience. Am Econ J Appl Econ. 2010;2(2):164–78.

Chu YH, Frongillo EA, Jones SJ, Kaye GL. Improving patrons' meal selections through the use of point-of-selection nutrition labels. Am J Public Health. 2009;99(11):2001–5.

VanEpps EM, Downs JS, Loewenstein G. Calorie label formats: using numeric and traffic light calorie labels to reduce lunch calories. J Public Policy Mark. 2016;35:26–36.

Finkelstein EA, Strombotne KL, Chan NL, Krieger J. Mandatory menu labeling in one fast-food chain in King County, Washington. Am J Prev Med. 2011;40(2):122–7.

Elbel B, Kersh R, Brescoll VL, Dixon LB. Calorie labeling and food choices: a first look at the effects on low-income people in New York City. Health Aff (Project Hope). 2009;28(6):w1110–21.

Elbel B, Gyamfi J, Kersh R. Child and adolescent fast-food choice and the influence of calorie labeling: a natural experiment. Int J Obes (2005). 2011;35(4):493–500.

Article   CAS   Google Scholar  

Elbel B, Mijanovich T, Dixon LB, Abrams C, Weitzman B, Kersh R, et al. Calorie labeling, fast food purchasing and restaurant visits. Obesity (Silver Spring, Md). 2013;21(11):2172–9.

Cantor J, Torres A, Abrams C, Elbel B. Five years later: awareness of New York City's calorie labels declined, with no changes in calories purchased. Health Aff (Project Hope). 2015;34(11):1893–900.

Tandon PS, Zhou C, Chan NL, Lozano P, Couch SC, Glanz K, et al. The impact of menu labeling on fast-food purchases for children and parents. Am J Prev Med. 2011;41(4):434–8.

Ellison B, Lusk JL, Davis D. Looking at the label and beyond: the effects of calorie labels, health consciousness, and demographics on caloric intake in restaurants. Int J Behav Nutr Phys Act. 2013;10:21.

Dumanovsky T, Huang CY, Nonas CA, Matte TD, Bassett MT, Silver LD. Changes in energy content of lunchtime purchases from fast food restaurants after introduction of calorie labelling: cross sectional customer surveys. BMJ (Clinical research ed). 2011;343:d4464.

Bleich SN, Economos CD, Spiker ML, Vercammen KA, VanEpps EM, Block JP, et al. A Systematic Review of Calorie Labeling and Modified Calorie Labeling Interventions: Impact on Consumer and Restaurant Behavior. Obesity (Silver Spring, Md). 2017;25(12):2018–44.

Holmes AS, Serrano EL, Machin JE, Duetsch T, Davis GC. Effect of different children's menu labeling designs on family purchases. Appetite. 2013;62:198–202.

Flegal KM, Cole TJ. Construction of LMS Parameters for the Centers for Disease Control and Prevention 2000 Growth Charts; 2013.

Austin PC. Absolute risk reductions, relative risks, relative risk reductions, and numbers needed to treat can be obtained from a logistic regression model. J Clin Epidemiol. 2010;63(1):2–6.

MacDonald SC, McElrath TF, Hernandez-Diaz S. Pregnancy outcomes in women with multiple sclerosis. Am J Epidemiol. 2018.

Gortmaker SL, Wang YC, Long MW, Giles CM, Ward ZJ, Barrett JL, et al. Three interventions that reduce childhood obesity are projected to save more than they cost to implement. Health Aff (Project Hope). 2015;34(11):1932–9.

Basu S, Seligman HK, Gardner C, Bhattacharya J. Ending SNAP subsidies for sugar-sweetened beverages could reduce obesity and type 2 diabetes. Health Aff (Project Hope). 2014;33(6):1032–9.

Article   PubMed Central   Google Scholar  

Wethington H, Maynard LM, Blanck HM. Use of calorie information at fast food and chain restaurants among US youth aged 9–18 years, 2010. J Public Health (Oxford, England). 2013;35(3):354–60.

Lee-Kwan SH, Pan L, Maynard LM, McGuire LC, Park S. Factors associated with self-reported menu-labeling usage among US adults. J Acad Nutr Diet. 2016;116(7):1127–35.

Chen R, Smyser M, Chan N, Ta M, Saelens BE, Krieger J. Changes in awareness and use of calorie information after mandatory menu labeling in restaurants in King County, Washington. Am J Public Health. 2015;105(3):546–53.

Feng W, Fox A. Menu labels, for better, and worse? Exploring socio-economic and race-ethnic differences in menu label use in a national sample. Appetite. 2018;128:223–32.

Lee-Kwan SH, Pan L, Maynard L, Kumar G, Park S. Restaurant menu labeling use among adults--17 states, 2012. MMWR Morb Mortal Wkly Rep. 2014;63(27):581–4.

PubMed   PubMed Central   Google Scholar  

Pool LR, Ning H, Lloyd-Jones DM, Allen NB. Trends in Racial/Ethnic Disparities in Cardiovascular Health Among US Adults From 1999–2012. J Am Heart Assoc. 2017;6(9):e006027.

Spanakis EK, Golden SH. Race/ethnic difference in diabetes and diabetic complications. Curr Diabetes Rep. 2013;13(6):814–23.

O'Keefe EB, Meltzer JP, Bethea TN. Health disparities and cancer: racial disparities in cancer mortality in the United States, 2000-2010. Front Public Health. 2015;3:51.

Rendell SL, Swencionis C. Point-of-purchase calorie labeling has little influence on calories ordered regardless of body mass index. Curr Obes Rep. 2014;3(3):368–75.

Wethington H, Maynard LM, Haltiwanger C, Blanck HM. Use of calorie information at fast-food and chain restaurants among US adults, 2009. J Public Health (Oxford, England). 2014;36(3):490–6.

Bray GA, Kim KK, Wilding JPH. Obesity: a chronic relapsing progressive disease process. A position statement of the world obesity federation. Obes Rev. 2017;18(7):715–23.

Article   CAS   PubMed   Google Scholar  

Taksler GB, Elbel B. Calorie labeling and consumer estimation of calories purchased. Int J Behav Nutr Phys Act. 2014;11:91.

Burton S, Creyer EH, Kees J, Huggins K. Attacking the obesity epidemic: the potential health benefits of providing nutrition information in restaurants. Am J Public Health. 2006;96(9):1669–75.

Hernan MA, Robins JM. Per-protocol analyses of pragmatic trials. N Engl J Med. 2017;377(14):1391–8.

Cecchini M, Warin L. Impact of food labelling systems on food choices and eating behaviours: a systematic review and meta-analysis of randomized studies. Obes Rev. 2016;17(3):201–10.

Auchincloss AH, Mallya GG, Leonberg BL, Ricchezza A, Glanz K, Schwarz DF. Customer responses to mandatory menu labeling at full-service restaurants. Am J Prev Med. 2013;45(6):710–9.

Cawley J, Susskind A, Willage B. The Impact of Information Disclosure on Consumer Behavior: Evidence from a Randomized Field Experiment of Calorie Labels on Restaurant Menus. NEBR Working Paper No 24889. 2018.

Nikolaou CK, Hankey CR, Lean ME. Preventing weight gain with calorie-labeling. Obesity (Silver Spring, Md). 2014;22(11):2277–83.

Hammond D, Lillico HG, Vanderlee L, White CM, Reid JL. The impact of nutrition labeling on menus: a naturalistic cohort study. Am J Health Behav. 2015;39(4):540–8.

Wing C, Simon K, Bello-Gomez RA. Designing difference in difference studies: best practices for public health policy research. Annu Rev Public Health. 2018;39:453–69.

Bruemmer B, Krieger J, Saelens BE, Chan N. Energy, saturated fat, and sodium were lower in entrees at chain restaurants at 18 months compared with 6 months following the implementation of mandatory menu labeling regulation in King County, Washington. J Acad Nutr Diet. 2012;112(8):1169–76.

Wang DD, Li Y, Afshin A, Springmann M, Mozaffarian D, Stampfer MJ, et al. Global improvement in dietary quality could Lead to substantial reduction in premature death. J Nutr. 2019;149(6):1065–74.

Download references

Acknowledgements

The authors would like to thank the participants for their valuable contributions to this study.

This study was supported by a career development award from the National Heart, Lung, and Blood Institute (grant number K23HL111211, PI: Block), a grant from the Healthy Eating Research program of the Robert Wood Johnson Foundation to JPB, and an unrestricted grant from the McLaughlin Family Foundation. JP is supported by T32HL098048. Funding sources had no role in the study design, data collection and analysis, or writing of the manuscript.

Author information

Authors and affiliations.

Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA

Joshua Petimar

Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA

Maricelle Ramirez, Sheryl L. Rifas-Shiman, Stephanie Linakis & Jason P. Block

Brigham and Women’s Hospital, Boston, MA, USA

Maricelle Ramirez

Departments of Population Health and Internal Medicine, The University of Texas at Austin Dell Medical School, Austin, TX, USA

Jewel Mullen

Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Christina A. Roberto

You can also search for this author in PubMed   Google Scholar

Contributions

JP conducted all analyses and wrote the final manuscript; JPB designed the study; MR, SRS, SL, and JPB aided in collecting and curating the data; MR, SRS, SL, JM, CAR, and JPB helped with interpretation of the data, and all authors read and approved the final version for submission.

Corresponding author

Correspondence to Joshua Petimar .

Ethics declarations

Ethics approval and consent to participate.

The study was approved by the institutional review board of Harvard Pilgrim Health Care. Because data collection was anonymous and queried non-sensitive information, the board approved this study without requiring written informed consent from participants.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Additional file 1: table s1..

Multivariable-Adjusted Changes (95% CI) in Calories Purchased by Adults, Adolescents, and Children After Calorie Labeling in McDonald’s Restaurants Compared to Other Fast Food Restaurants After Adjusting for Additional Participant Characteristics. Table S2. Multivariable-Adjusted Changes (95% CI) in Calories Purchased After Calorie Labeling in McDonald’s Restaurants Compared to Other Fast Food Restaurants After Excluding McDonald’s Customers Who Did Not Report Seeing Calories on Menus in the Post Period. Table S3. Multivariable-Adjusted Changes (95% CI) in Underestimation of Calories Purchased (Continuous) by Adults, Adolescents, and Children After Calorie Labeling in McDonald’s Restaurants Compared to Other Fast Food Restaurants. Table S4. Multivariable-Adjusted Percent Changes (95% CI) in Calorie Underestimation and Noticing of Menu Calorie Information by Adults and Adolescents After Calorie Labeling in McDonald’s Restaurants Compared to Other Fast Food Restaurants After Adjusting for Additional Participant Characteristics.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Cite this article.

Petimar, J., Ramirez, M., Rifas-Shiman, S.L. et al. Evaluation of the impact of calorie labeling on McDonald’s restaurant menus: a natural experiment. Int J Behav Nutr Phys Act 16 , 99 (2019). https://doi.org/10.1186/s12966-019-0865-7

Download citation

Received : 02 April 2019

Accepted : 21 October 2019

Published : 04 November 2019

DOI : https://doi.org/10.1186/s12966-019-0865-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Calorie labeling
• Menu labeling
• Nutrition policy
• Natural experiment
• Obesity prevention

International Journal of Behavioral Nutrition and Physical Activity

ISSN: 1479-5868

• Submission enquiries: Access here and click Contact Us
• General enquiries: [email protected]

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

CASE 27 McDonald's and Obesity

Related Papers

American Journal of Preventive Medicine

Vivica Kraak

Journal of Marketing Communications

Sandy Bulmer

Turkish Pizza

Paul Harrison , Kathryn Chalmers

"Integrated Marketing Communication (IMC) is a relatively new concept in marketing and corporate strategy and is arguably the major communication development of the last decade of the 20th century. Until the late 1980s, promotion was mostly confined to advertising, sales promotions, direct marketing and public relations. IMC incorporates every form of communication to customers and includes the price of the product (that might create the perception of quality and exclusiveness), where the product is located or purchased, how attractively it is displayed, advertising across multiple platforms, how the product is labelled and packaged, direct marketing, sponsorship and public relations. It also includes what the company’s employees say about the product via sales pitches, telephone conversations, internal communications, and even what is said at parties. Rather than a single factor, it is the cumulative effect of IMC that works to persuade children to make particular brand choices. Three brands were investigated for this study viz., McDonald’s, Freddo Frog (Cadbury), and Nutri-Grain (Kellogg’s). Of the three brands in the study, McDonald’s have the most sophisticated, extensive, and integrated communication strategy targeted at children. In 2007, McDonald’s spent almost USD2.5 million a day on its marketing activities in the US market. They use a range of tactics to reinforce the brand amongst target markets, including outdoor advertising, sponsorship, menu design, store layout, visual shortcuts (e.g., Heart Foundation tick), characters, online promotions, interactive websites, brand associations and connections (e.g., Blue Ribbon Day, McHappy Day), product placement, and charities. Cadbury uses the visual image of the “friendly frog” (Freddo) as a means of recognition very early in a child’s cognitive development. Promotional characters act as cues that invoke visual brand recognition and are essential due to young children’s limited reading abilities, and they have also been shown to be associated with children’s positive attitudes towards food. The Freddo character is used as an alternative to showing the actual product in much promotion, as a means to reinforce the brand, without showing the chocolate. Freddo also use branded activities in their online interactive games targeted at young children. Nutri-Grain is a cereal targeted specifically at teenage boys, primarily with the use of sports themes. Nutri-Grain is positioned as an energy food for power, strength and performance. Nutri-Grain uses sophisticated visual imagery, and links with sportspeople and events to reinforce the sport theme. The IMC also targets mothers of teenage boys, tapping into anxiety around parenting and adolescence."

… : Department of Rural …

Page 1. Integrating Food Policy with Growing Health and Wellness Concerns: An Analytical Literature Review of the Issues Affecting Government, Industry, and Civil Society A Report to Agriculture and Agri-Food Canada, Food Value Chain Bureau ...

Ellen Goddard

… of Rural Economy. …

Ellen Goddard , Jose Lomeli

Stephen Kline

Arhlene Flowers

Unhealthy foods and drinks are among the top products advertised to young children. Considering the growing childhood obesity epidemic and the soaring number of children accessing the Internet, even online junk food advertising has come under increasing scrutiny. Many countries are in the process of expanding and revising existing regulation to account for the realities of the digital age and to respond to health and other social concerns. This paper focuses on two European countries in particular to examine and compare these processes through the lens of junk food advergames aimed at children. Our questions are: 1) Given the differences in the media landscapes of the UK and Hungary, what types of junk food advergames target children?; and 2) In light of the growing childhood obesity problem faced by both nations, how have government bodies,advocacy groups, and advertisers approached junk food advertising targeting children in general and online advertising including advergames in particular? The United Kingdom represents a country with the highest Internet usage by children and the most developed online advertising market in Europe, while Hungary, a post-communist country, represents an emerging media market where young people have less access to the Internet and buying power but constitute a crucial “entry point” for food advertisers.

Noor Mustafa

FAST FOOD OBESITY 16

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

Emmanuel Marillier

The Journal of Law, …

Richard Daynard

Dr. Vinay Kumar

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

Proceedings of the 2021 3rd International Conference on Economic Management and Cultural Industry (ICEMCI 2021)

Adolescent Obesity: Case Study of McDonald’s Happy Meal

[email protected]

[email protected]

[email protected]

These authors contributed equally.

Adolescent obesity has been deemed a prominent public health problem in the 21 st century, and the over-consumption of fast food is considered one of the essential causes. In this situation, the Happy Meal strategy put forward by McDonald’s has also been accused of luring children to consume those unhealthy fast food. Thereby, this study, based on business ethics theory, attempts to probe this critical issue. It is suggested by the analysis that the Happy Meal strategy, though it has obtained huge financial success, is not an ethical business practice. Hence, an array of suggestions to modify the Happy Meal strategy is proposed in this article. This study would contribute to the business ethics discussions in line with the children’s overweight context.

Download article (PDF)

Cite this article