research paper on fast food advertising

• Open access
• Published: 27 July 2023

The impact of fast food marketing on brand preferences and fast food intake of youth aged 10–17 across six countries

• Mariangela Bagnato 1 ,
• Marie-Hélène Roy-Gagnon 1 ,
• Lana Vanderlee 2 ,
• Christine White 3 ,
• David Hammond 3 &
• Monique Potvin Kent 1  

BMC Public Health volume  23 , Article number:  1436 ( 2023 ) Cite this article

8482 Accesses

1 Altmetric

Metrics details

Consumption of fast food, which is associated with poor diet, weight gain and the development of noncommunicable diseases, is high amongst youth. Fast food marketing, a modifiable determinant of excess weight and obesity, affects youth’s food-related behaviours. This study aimed to examine the relationship between exposure to fast food marketing and the fast food brand preferences and intake amongst youth aged 10–17 across six countries.

Data from 9,695 youth respondents living in Australia, Canada, Chile, Mexico, the United Kingdom (UK) and the United States (US) were analyzed from the 2019 International Food Policy Study (IFPS) Youth Survey. Survey measures assessed exposure to fast food marketing and brand-specific marketing, and preference for these brands and fast food intake. Regression models adjusted for age, sex, income adequacy and ethnicity were used to examine the associations.

Exposure to fast food marketing was positively associated with brand preferences and intake consistently across most countries. Overall, preference for McDonald’s (OR:1.97; 95% CI:1.52, 2.56), KFC (OR:1.61; 95% CI:1.24, 2.09) and Subway (OR:1.73; 95% CI:1.34, 2.24) were highest when exposed to general fast food marketing ≥ 2x/week compared to never. Preference for McDonald’s (OR:2.32; 95% CI:1.92, 2.79), KFC (OR:2.28; 95% CI:1.95, 2.68) and Subway (OR:2.75; 95% CI:2.32, 3.27) were also higher when exposed to marketing for each brand compared to not. Fast food intake was highest in Chile (IRR:1.90; 95% CI:1.45, 2.48), the UK (IRR:1.40; 95% CI:1.20, 1.63), Canada (IRR:1.32; 95% CI:1.19, 1.48), Mexico (IRR:1.26; 95% CI:1.05, 1.53) and the US (IRR:1.21; 95% CI:1.05, 1.41) when exposed to general fast food marketing ≥ 2x/week compared to never and was higher across most countries when exposed to brand-specific marketing compared to not. Respondents classified as ethnic minorities were more likely to report consuming fast food than ethnic majorities, and females were less likely to report consuming fast food than males.

Conclusions

Exposure to fast food marketing is consistently and positively associated with brand preferences and fast food intake in all six countries. Our results highlight the need for strict government regulation to reduce exposure of unhealthy food marketing to youth in all six countries.

Peer Review reports

Introduction

The burden caused by noncommunicable diseases (NCDs), such as cardiovascular disease, cancer and diabetes, is on the rise globally. In 2019, 20% of adolescent deaths worldwide occurred as a result of NCDs and it has been estimated that 70% of premature deaths in adults are linked to behaviours that developed during childhood and adolescence [ 1 ]. Diet, physical activity and lifestyle factors are modifiable precursors to obesity and excess weight that are an ongoing threat to health and the development of NCDs internationally [ 2 ]. Between 1975 and 2016, the prevalence of obesity and overweight amongst children and adolescents between the ages of 5 and 19 worldwide increased from 4 to 18%, alongside the intake of ultra-processed foods, high in sugar, saturated fats and sodium amongst youth [ 3 , 4 ]. In Canada, youth aged 2–18 years consume over 50% of their total daily energy from ultra-processed food, elevating short- and long-term risks to health, including excess weight and obesity, mortality, and the development of noncommunicable diseases [ 5 , 6 ].

Fast food accounts for a large share of food consumed by youth as on average, over 15% of daily calories consumed by North American youth come from such foods [ 7 , 8 ]. Due to the poor nutrient quality of fast food, intake of these foods is associated with poor dietary quality and weight gain, and may compromise nutrient requirements necessary for proper growth [ 9 , 10 ].

The food environment has been recognized as a determinant of obesity and the marketing of unhealthy foods and beverages to children has been identified as a cause of poor diet and excess weight in youth [ 11 , 12 , 13 ]. Youth are valuable advertising targets for the food and beverage industry, as promoting sales in this highly impressionable age group may help to create life-long brand loyalty [ 14 , 15 , 16 , 17 ]. Youth are exposed to food and beverage marketing (herein referred to as food marketing) daily in a variety of media and settings, which have the power to influence consumption and future health outcomes [ 10 , 11 , 12 , 13 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Research from high-income countries found that the majority of advertisements on youth-oriented media promote unhealthy products and fast food in particular accounts for the largest exposure [ 19 , 23 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ]. Expenditure data also shows that expenditures on youth-oriented advertising across all media is high and overall, the majority of advertising spend is devoted to unhealthy products, with fast food advertising dominating expenditures [ 22 , 33 ]. This emphasis on fast food marketing is notable as youth spend a lot of time viewing various media and hold autonomous buying power [ 14 , 15 , 16 , 17 ]. In response to the ongoing concern caused by industry marketing practices and its negative impacts on youth health, in 2010, the World Health Organization recommended that its members develop restrictions to limit the marketing of foods high in fats, sugars and sodium (HFSS) to children [ 34 ]. Globally, food marketing restrictions are either non-existent, self-regulated by the food and beverage and/or advertising industries (e.g., Canada [excluding Quebec], Australia and the United States [ 35 , 36 , 37 , 38 ]) or government regulated (e.g., United Kingdom, Chile and Mexico [ 39 , 40 , 41 ]).

The logic model of unhealthy food promotion effects predicts that preferences and consumption of unhealthy foods are direct effects of food marketing exposure that eventually lead to long-term post consumption effects such as weight gain and diet-related disease, warranting investigation into its influence on youth [ 42 ]. Currently, research evaluating the impact of unhealthy food marketing on preferences and intake of youth globally is limited, as the few studies identified do not investigate more than one country, are focused on exposure from a specific media channel (mostly television), use a wide variety of data collection methods, rely on data collected from parents, and/or have a narrow age range and small sample size [ 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 ].

No previous studies have tested the association between youth’s self-reported exposure to and preference for specific fast food brands, nor does any investigate fast food marketing exposure, fast food restaurant brand preferences and fast food intake in this population simultaneously. Given that fast food is the most marketed food category to youth across most media [ 19 , 22 , 26 , 30 , 33 ], further investigation of its effects on youth is warranted. The purpose of this study was to examine the relationship between exposure to fast food marketing and the fast food brand preferences and intake of youth in six upper and middle income countries and to explore the relationship between sociodemographic characteristics and fast food preferences and intake.

Data were from the 2019 International Food Policy Study (IFPS) Youth Survey, an annual repeat cross-sectional survey conducted in six countries: Australia, Canada, Chile, Mexico, UK and the US. Data were collected via self-completed web-based surveys conducted in November–December 2019 with youth aged 10–17 years. Respondents were recruited through parents/guardians enrolled in the Nielsen Consumer Insights Global Panel and their partners’ panels and invitation links were sent to panelists within each country. Those who confirmed they had a child aged 10–17 living in their household were asked for permission for their child to complete the survey, with quotas for age and sex groups in the UK and US. After eligibility screening, all potential respondents were provided with information about the study and asked to provide assent. Surveys were conducted in English in Australia and the UK; Spanish in Chile and Mexico; English or French in Canada; and English or Spanish in the US. Members of the research team who were native speakers in each language reviewed the French and Spanish translations independently. Brand marketing exposure and preference were assessed for McDonalds, KFC and Subway as these brands are among the global leaders in fast food service and have chains in each of the 6 countries [ 56 ]. The median survey time was 24 min [ 57 ].

The child’s parent/guardian received remuneration in accordance with their panel’s usual incentive structure (e.g., points-based or monetary rewards, etc.). A full description of the study methods can be found elsewhere [ 57 ].

Independent Measures: Self-reported exposure to fast food marketing

Self-reported exposure to fast food marketing was assessed using two measures: general exposure to all instances of fast food marketing and exposure to only brand-specific fast food marketing. First, general exposure to fast food marketing was assessed using the following measure: “In the last 30 days, how often did you see or hear advertisements for these kinds of food or drinks? Ads for fast food from a restaurant”. The 6-item Likert scale for general exposure to fast food marketing was recategorized into the following: “never” (“never”), “  ≤  1x/week” (“less than once a week”, “once a week”), and “  ≥  2x/week” (“a few times a week”, “every day”, “more than once a day”). Second, self-reported exposure to McDonald’s, KFC and Subway marketing specifically, was assessed using the corresponding brand’s logo displayed with the following measure: “ Have you seen an advertisement for this restaurant in the last 30 days?” ( “yes”, “no”, “don’t know” or “refuse to answer” ) .

Outcome Measures: Self-reported fast-food intake and fast food brand preference

7-item emoji-based Likert scale used for the measurement of fast food brand preference

Sociodemographic measures

The sociodemographic measures included in this study were the respondent’s age, sex at birth, perceived income adequacy and ethnicity. Age was collected as a continuous variable. Sex at birth was collected as either “male” or “female”. Income adequacy was collected using the following measure: “ Does your family have enough money to pay for things your family needs?” (“not enough money”, “barely enough money”, “enough money”, “more than enough money”, “don’t know” or “refuse to answer”). Perceived income adequacy was recategorized into a binary variable for either “enough money” ( “enough money” and “more than enough money” ) or “not enough money” ( “not enough money” and “barely enough money” ). Ethnicity was assessed using census measures from each country and re-coded to either “majority” or “minority” to derive comparable measures across countries.

Data analysis

The analytic sample included 11,108 respondents. A sub-sample of 9,695 respondents were included in the current analysis after excluding those with missing and/or incomplete data (i.e., “don’t know”, “refuse to answer” or left their answer selection blank) on sociodemographic characteristics, predictor variables and outcome variables (1,413 respondents; 12.7%). Sensitivity analyses indicate that excluded respondents were not different demographically to the final analytical sample. Data were weighted with post-stratification sample weights constructed using a raking algorithm with population estimates from the census in each country based on age group, sex, region in all countries, and ethnicity (except in Canada, where ethnicity wasn’t considered in the sample weights). All estimates reported throughout are weighted. Statistical analyses were conducted using SAS Studio OnDemand for Academics (SAS Institute Inc., 2021).

Ordinal logistic or negative binomial regression models were used to model the associations as appropriate. Each model was adjusted for age, sex, perceived income adequacy and ethnicity. Statistical significance for all models was set at an alpha level < 0.05, and significance was determined using a p -value < 0.05 or a 95% confidence interval. Two-way interaction terms were tested between country and each of the sociodemographic variables. Significant interactions were noted for the associations between youth’s self-reported general exposure to fast food brand-specific marketing and self-reported fast food intake (p < 0.05), and the association between youth’s self-reported exposure to brand-specific marketing and self-reported fast food intake (p < 0.05). Since some significant interactions were found, all results were stratified by country.

Weighted sample characteristics of youth respondents aged 10–17 in all six countries are presented in Table 1 . Proportional differences in sociodemographic characteristics were noted across all countries. Overall, there was a higher proportion of adolescents aged 13–17 in all countries, the US had a higher proportion of minority respondents than other countries, and Canada had a higher proportion of respondents who perceived their family to have enough money compared to the other countries. In terms of general exposure to all fast food marketing, between 58–75% of respondents reported exposure ≥ 2x/week, with the greatest exposure reported in Mexico (75.3% of respondents) and the least exposure reported in the UK (58.7%), whereas between 17–26% of respondents reported exposure ≤ 1x/week with the greatest exposure reported in the UK (26.4%) and the least exposure reported in the US (17.3%).

Association between youth’s self-reported general exposure to all fast food marketing and fast food brand preference

General exposure to all fast food marketing and preference for mcdonald’s.

Overall, the odds of preferring McDonald’s were significantly higher in the UK and the US and significantly lower in Mexico and Chile compared to Canada (Table 2 ). In terms of general exposure to fast food marketing, overall, respondents reportedly preferred McDonald’s most when exposed to general fast food marketing ≥ 2x/week ( OR: 1.97; 95% CI: 1.52, 2.56) and ≤ 1x/week (OR: 1.57; 95% CI: 1.17, 2.10) compared to never being exposed to this marketing. Additionally, the odds of preferring McDonald’s decreased with increasing age.

By country, the odds of preferring McDonald’s when exposed to general fast food marketing ≥ 2x/week compared to never being exposed in a week were greatest in the US, followed by the UK, Canada and Australia (Table 3 ).

General exposure to all fast food marketing and preference for KFC

Compared to Canada, overall, respondents from all countries were more likely to prefer KFC more, with the odds being highest in Mexico, followed by Australia, the US, the UK, and Chile (Table 2 ). Females were also less likely to prefer KFC than males by a factor of 0.72 ( 95% CI: 0.62, 0.84). In terms of general exposure to fast food marketing, the likelihood of preferring KFC was highest when respondents reportedly viewed this type of marketing ≥ 2x/week ( OR: 1.61; 95% CI: 1.24, 2.09) and ≤ 1x/week ( OR: 1.54; 95% CI: 1.15, 2.07) compared to not at all.

By country, the odds of preferring KFC when exposed to general fast food marketing ≥ 2x/week compared to not being exposed to this marketing at all were highest in Chile, followed by Australia and the UK (Table 3 ). In terms of sociodemographic characteristics, female respondents in Australia and Canada had a significantly lower preference for KFC compared to males, and in Canada, individuals who identified as a minority ethnicity preferred KFC significantly more than those who identified as a majority ethnicity.

General exposure to all fast food marketing and preference for Subway

Overall, compared to Canada, the likelihood of preferring Subway was significantly lower in most countries, with the lowest odds in Chile, followed by Mexico, Australia and the UK (Table 2 ). When respondents were exposed to general fast food marketing, the odds of preferring Subway was highest when exposed ≥ 2x/week ( OR: 1.73; 95% CI: 1.34, 2.24) and ≤ 1x/week ( OR: 1.46; 95% CI: 1.09, 1.97) compared to not being exposed at all.

By country, in Mexico and the UK, the odds of preferring Subway were 2.8 times ( 95% CI: 1.33, 5.91) and 1.99 times greater ( 95% CI: 1.10, 3.61), respectively, when exposed to general fast food marketing ≥ 2x/week compared to never being exposed to this marketing in a week (Table 3 ). With respect to sociodemographic characteristics, in the UK, females were 1.57 times more likely ( 95% CI: 1.02, 2.41) to prefer Subway than males, and in Chile, those who reported perceiving their family to have enough money were 1.93 times more likely ( 95% CI: 1.20, 3.11) to prefer Subway than those who perceived their family to not have enough money.

Association between youth’s self-reported exposure to McDonald’s, Subway and KFC marketing and respective fast food brand preference

Exposure to only mcdonald’s marketing and preference for mcdonald’s.

In all countries, more respondents reported being exposed to McDonald’s marketing than not (Table 1 ). Mexico had the greatest number of exposed respondents (84% of respondents), and the UK had the smallest number of exposed respondents (66%).

Similar to the models above, overall, the odds of preferring McDonald’s were significantly higher in the UK and the US and significantly lower in Chile and Mexico compared to Canada (Table 4 ). When exposed to McDonald’s marketing, the odds of respondents preferring McDonald’s were 2.32 times higher ( 95% CI: 1.92, 2.79), compared to not being exposed. In terms of age, preference for McDonald’s decreased with increasing age.

By country, the odds of preferring McDonald’s were greater when exposed to McDonald’s marketing as opposed to not being exposed, with the highest odds being in Chile, followed by Australia, Mexico, the US, Canada and the UK. (Table 5 ).

Exposure to only KFC marketing and preference for KFC

In most countries, more respondents reported being exposed to KFC marketing than not (Table 1 ). Mexico had the greatest number of exposed respondents (83.9% of respondents), and the UK had the smallest number of exposed respondents (44.4%). Both the UK and Canada had more respondents who reported not being exposed to KFC marketing than being exposed (55.6% and 51.6%, respectively).

Similar to the previous models, compared to Canada, the odds of preferring KFC were significantly higher in all countries, with the highest odds of preference being in Australia, followed by Mexico, the UK, the US and Chile (Table 4 ). In terms of sex, females were less likely to prefer KFC than males. When reportedly viewing KFC marketing compared to not, the odds of preferring KFC were higher by a factor of 2.28 ( 95% CI: 1.95, 2.68).

By country, the odds of preferring KFC was higher in all countries when exposed to KFC marketing compared to not being exposed, with the greatest odds of preference in Canada, followed by Australia, the UK, Mexico, the US and Chile (Table 5 ). Females reportedly preferred KFC significantly less than males in Australia and Canada.

Exposure to only Subway marketing and preference for Subway

In the US, Canada and Mexico, more respondents reported being exposed to Subway marketing than not (70.1%, 68.8% and 61.9%, respectively) (Table 1 ). In the UK, Australia and Chile, more respondents reported not being exposed to Subway marketing than being exposed (65.8%, 52.5% and 50.8%, respectively).

Overall, the odds of preferring Subway were significantly lower in Chile, Mexico and Australia compared to Canada (Table 4 ). Additionally, respondents who reported being exposed to Subway marketing were significantly more likely to prefer Subway compared to those who were not exposed to this marketing ( OR: 2.75; 95% CI: 2.32, 3.27).

By country, the odds of preferring Subway in all countries was greater when exposed to Subway marketing compared to not being exposed, with the highest odds in the US, followed by the UK, Chile, Mexico, Australia and Canada (Table 5 ). In Chile, those who perceived their families to have enough money were more likely to prefer Subway than those who did not.

Association between youth’s self-reported general exposure to all fast food marketing and fast food intake

In most countries, the odds of fast food intake were highest when exposed to general fast food marketing ≥ 2x/week compared to reportedly never being exposed, with the highest odds being in Chile, followed by the UK, Canada, Mexico and the US (Table 6 ). In terms of sociodemographic variables, in four countries, the odds of reported intake were significantly lower for females than males. Additionally, in almost all countries, the odds of reported fast food intake were significantly higher for those who identified as a minority compared to those who identified as a majority.

Association between youth’s self-reported exposure to only McDonald’s, KFC or Subway marketing and fast food intake

Fast food intake and exposure to only mcdonald’s marketing.

In almost all countries, the odds of reported fast food intake were higher for those who were reportedly exposed to McDonald’s marketing compared to those who were not exposed, with the highest odds being in Chile, followed by Canada, the UK, the US and Mexico (Table 7 ). With respect to sociodemographic characteristics, in the UK, Australia, Canada and Chile, the odds of reportedly consuming fast food were significantly lower for females than males. With regard to ethnicity, in almost all countries, the odds of reportedly eating fast food was significantly higher amongst those who identified as a minority in their country as opposed to a majority.

Fast food intake and exposure to only KFC marketing

In almost all countries, the odds of reportedly consuming fast food were higher for those who were reportedly exposed to KFC marketing compared to those who were not, with the highest odds being in Canada, followed by the UK, the US, Chile and Mexico (Table 7 ). In terms of sex, in four countries, females reportedly ate fast food significantly less than males. In almost all countries, the odds of consuming fast food were higher amongst those who identified as a minority compared to those who identified as a majority.

Fast food intake and exposure to only Subway marketing

In all countries, the odds of reportedly eating fast food was significantly higher when exposed to Subway marketing as opposed to not being exposed, with the highest odds being in Chile, followed by the UK, Mexico, the US, Canada and Australia (Table 7 ). In terms of sex, in four countries, females reportedly ate fast food significantly less than males. The odds of consuming fast food were also significantly higher for those who identified as a minority compared to those who identified as a majority in almost all countries.

Overall, positive associations were found between exposure to fast food marketing and fast food brand preferences and intake. Preference for specific fast food brands was generally highest across countries when exposed to general fast food marketing ≥ 2x/week and ≤ 1x/week compared to those who were not exposed, and also higher among those who self-reported exposure to marketing for each respective brand compared to those who did not, and this relationship was consistent across all countries. In terms of fast food intake, reported consumption was generally highest across countries when exposed to general fast food marketing ≥ 2x/week and ≤ 1x/week compared to those who were not exposed. Across almost all countries, reported consumption of fast food was higher amongst those who were exposed to marketing for McDonald’s, KFC and Subway as opposed to those who were not. With respect to sociodemographic characteristics, across most countries overall, respondents who identified as a minority ethnicity were more likely to consume fast food than those of a majority ethnicity, and females were less likely to reportedly consume fast food than males.

The study findings suggest that the likelihood of preferring a fast food brand and consuming fast food increased with both exposure to brand-specific and general fast food marketing. These findings are consistent with previous epidemiological evidence assessing the association between food marketing that is not food category specific and health behaviours including youth’s intake and preferences, and also consistent with similarly designed cross-sectional observational studies among adults and younger age groups and specific food categories [ 43 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 ]. Our findings build on this current body of knowledge by providing evidence for these associations for fast food specifically, which is important since it is the most marketed of all food categories [ 19 , 22 , 26 , 30 , 33 ]. This study also found that the odds of preferring a brand were higher overall across models when variables included recall of brand-specific fast food marketing, as opposed to more general exposure to fast food marketing. This may indicate that fast food brand-specific marketing has a greater effect on youth’s preferences for the respective brand compared to general fast food marketing, which would be consistent with data from other fields of research investigating the association between cigarette brand-specific marketing and brand preferences amongst adolescents and young adults [ 66 , 67 ]. This stronger association may also be due to improved recall of instances of brand-specific marketing (compared to general instances of fast food marketing), as well as the type of questions asked (e.g., brand-specific marketing exposure was measured using a response of “yes” or “no” , compared to general marketing exposure which was assessed using a 6-item Likert scale). To help address this, the 6-item scale was re-categorized into a 3-item scale, but the associations amongst the brand-specific measure remained stronger. Although the results were largely consistent across countries, we cannot fully conclude from this study alone that these associations are causal, due to the self-reported, cross-sectional nature of the data. For example, the association between marketing exposure and food intake could be bidirectional in nature: it is possible that greater intake of certain fast food brands may also lead to increased exposure/attention to brand-specific marketing. However, our results are supported by existing epidemiological data and will also help to strengthen existing evidence on associations between exposure to unhealthy food marketing and increased preference and consumption [ 68 ].

Overall, the country-stratified results were fairly consistent across countries. As mentioned previously, the policy environments restricting unhealthy food marketing to children differ in stringency across the countries investigated, but yet, exposures are still high and the relationships between these exposures and eating behaviours are consistently strong across countries. Although most existing policies apply to children under the age of 14 and this study investigated those 10–17 years old, these findings still indicate that fast food marketing exposure is affecting the eating behaviours of youth and that current regulatory policies need to be strengthened to raise age thresholds beyond children, adopt more specific and uniform definitions for what is considered child marketing and implement more stringent HFSS thresholds.

This comprehensive survey also allowed for exploration of sociodemographic differences within the measured associations. Overall, females in most countries were less likely to report consumption of fast food than males, which is congruent with previous research measuring fast food intake [ 69 , 70 , 71 ]. An explanation for this consistent finding could be that female youth are more likely to engage in diet-related practices and are more attentive to their body image [ 72 , 73 ]. It may also be possible that males are targeted by industry marketing practices more often than females, as males are reportedly featured more frequently in food marketing, which could lead to greater persuasion towards consuming the product [ 74 ]. We also found that individuals classified as ethnic minorities were more likely to report the consumption of fast food than ethnic majorities. Recent data has suggested that Black and Hispanic youth in the US are being disproportionally exposed to more unhealthy food marketing, which brings concern as socioeconomic status is associated with ethnic minority status in countries like the US, and those with a lower socioeconomic status are more likely to exhibit poorer health outcomes [ 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 ]. Thus, the marketing unhealthy foods may be exacerbating poor health outcomes in already at-risk populations. Implementing stringent regulations to protect youth from exposure to unhealthy food marketing may help to reduce these differences [ 77 ].

Strengths and limitations

To our knowledge, this is the first study to examine associations between specific fast food brand marketing exposure and youth-reported intake and preferences. This study employs consistent measures across a large sample size with a wide age range and includes respondents from a variety of ethnicities and socioeconomic backgrounds in six different countries, which allows for greater generalizability and between country comparisons. Post-stratification weights were also used to provide a more representative sample, which also increases the generalizability of our findings. Additionally, as the exposure measures did not specifically focus on marketing in particular types of media, this allowed us to report our associations based on a wide range of exposures.

Interpretation of the findings should consider potential limitations of self-reported data. In addition to being subject to recall bias and reverse causation, the self-reported exposure variables do not examine the power, ad content, frequency, and extent to which it targets the individual. Past research has shown that certain marketing techniques affect one’s recall of the advertisement, which could have altered their ability to remember marketing exposures [ 83 ]. While the self-reported fast food intake variable technically includes food intake from settings beyond fast food places (i.e., restaurants, food stands or vending machines), these other sources can arguably also be considered fast food-like, due to the ease of purchase and poor nutrient content of most foods sold from these sources. Additionally, it is possible that what respondents encompassed under ‘fast food advertising’ may have been interpreted differently by individuals, introducing additional bias. Aside from its limitations, self-reported measures are also valuable in that they are more feasible to collect. Objective measures are often more difficult to gather, as they are more resource-intense and do not necessarily accurately represent day-to-day choices [ 68 ]. Furthermore, existing evidence suggests that self-reported exposure measures are correlated with objective exposure measures [ 84 , 85 ]. The increased feasibility of self-reported measures also allows for more frequent monitoring and the ability to collect and compare data across multiple countries simultaneously.

Additionally, recruitment was completed using nonprobability-based sampling, meaning these findings may not be representative of national estimates. However, data were weighted by age group, sex, region, and ethnicity (except in Canada), which should mitigate this even if it did not completely remove the effect.

This study did not analyze these data by marketing policy jurisdiction, due to the complexities and differences in the policy inclusions/exclusions across the 6 countries and the cross-sectional nature of the data that cannot adjust for secular trends, as well as the sample not including children under the age of 10.

Overall, we found positive associations between exposure to fast food marketing and the brand preferences and reported intake of youth across all six countries. Regardless of the policy landscape surrounding restricting unhealthy food marketing to children, it is evident that exposure to fast food marketing is negatively influencing youth’s preference for and intake of these foods, as evidence has suggested that the odds of becoming overweight or developing obesity increases with fast food consumption [ 86 ]. The results demonstrate that current efforts to limit marketing to children and youth are not effective. As such, more comprehensive and stringent government regulation restricting fast food marketing to youth in all media may help reduce preferences and consumption of fast food. Including adolescents in these restrictions is also important, as they hold independent purchasing power, are easily influenced, spend a lot of time watching screens and have a high consumption of fast food products [ 24 , 25 , 71 , 87 ]. Future research should examine if and how these modelled associations differ by child and adolescent age groups. This research could provide preliminary evidence on the likely influence of marketing exposure on older youth on whom there is little research [ 64 ] and to investigate whether existing policies protecting children under 13 years old are effective in reducing exposure to fast food marketing and its consequences, such as brand preferences and intake.

Availability of data and materials

The data that support the findings from this study are available from the corresponding author under reasonable request.

Abbreviations

Noncommunicable diseases

High in fat, sugar and sodium

International Food Policy Study

Kentucky Fried Chicken

Confidence interval

Incidence rate ratio

Unicef Data. https://data.unicef.org/topic/child-health/noncommunicable-diseases/#:~:text=As%20NCDs%20are%20driven%20primarily,negative%20health%20outcomes%20in%20adulthood (2021). Accessed 20 Jul 2022.

The World Health Organization. https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases (2021). Accessed 20 Jul 2022.

The World Health Organization. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight (2021). Accessed 20 Jul 2022.

Marino M, Puppo F, Del Bo’ C, Vinelli V, Riso P, Porrini M, et al. A systematic review of worldwide consumption of ultra-processed foods: Findings and criticisms. Nutrients. 2021;13(8):2778.

Article   PubMed   PubMed Central   Google Scholar  

Polsky J, Moubarac J-C, Garriguet D. Consumption of ultra-processed foods in Canada. Statistics Canada, Catalogue no 82–003-X. 2020.

Chang K, Khandpur N, Neri D, Touvier M, Huybrechts I, Millett C, et al. Association between childhood consumption of ultraprocessed food and adiposity trajectories in the Avon Longitudinal Study of parents and children birth cohort. JAMA Pediatrics. 2021;175(9):e211573.

Fryar CD, Carroll MD, Afful J. Prevalence of overweight, obesity, and severe obesity among children and adolescents aged 2–19 years: United States, 1963–1965 through 2017–2018. NCHS Health E-Stats, Centers for Disease Control and Prevention. 2020. www.cdc.gov/nchs/data/hestat/obesity-child-17-18/overweight-obesity-child-H.pdf . Accessed 22 Jul 2021.

Polsky J, Garriguet D. Eating away from home in Canada: impact on dietary intake. Statistics Canada, Catalogue no 82–003-X. 2021.

Ashdown-Franks G, Vancampfort D, Firth J, Smith L, Sabiston CM, Stubbs B, et al. Association of leisure-time sedentary behavior with fast food and carbonated soft drink consumption among 133,555 adolescents aged 12–15 years in 44 low- and middle-income countries. Int J Behav Nutr Phys Act. 2019;16(1):35.

Das JK, Salam RA, Thornburg KL, Prentice AM, Campisi S, Lassi ZS, et al. Nutrition in adolescents: Physiology, metabolism, and nutritional needs. Ann N Y Acad Sci. 2017;1393(1):21–33.

Article   PubMed   Google Scholar  

Glanz K, Sallis JF, Saelens BE, Frank LD. Healthy Nutrition Environments: Concepts and measures. Ann N Y Acad Sci. 2005;19(5):330–3.

Google Scholar  

Swinburn BA, Sacks G, Hall KD, McPherson K, Finegood DT, Moodie ML, et al. The global obesity pandemic: Shaped by global drivers and local environments. Lancet. 2011;378(9793):804–14.

McGinnis JM, Gootman J, Kraak VI, editors. Food Marketing to Children and Youth: Threat or Opportunity Washington. DC: The National Academies Press; 2006.

Story M, French S. Food Advertising and Marketing Directed at Children and Adolescents in the US. Int J Behav Nutr Phys Act. 2004;1(1):3.

Moore ES. Children and the changing world of advertising. J Bus Ethics. 2004;52(2):161–7.

Article   Google Scholar  

Palan KM, Gentina E, Muratore I. Adolescent consumption autonomy: A cross-cultural examination. J Bus Res. 2010;63(12):1342–8.

Bassett R, Chapman GE, Beagan BL. Autonomy and control: The co-construction of Adolescent Food Choice. Appetite. 2008;50(2–3):325–32.

Czoli CD, Pauzé E, Potvin KM. Exposure to food and beverage advertising on television among Canadian adolescents, 2011 to 2016. Nutrients. 2020;12(2):428.

Potvin Kent M, Pauzé E, Roy E-A, de Billy N, Czoli C. Children and adolescents' exposure to food and beverage marketing in social media apps. Pediatr Obes. 2019;14(6):e12508.

Huang D, Brien A, Omari L, Culpin A, Smith M, Egli V. Bus stops near schools advertising junk food and sugary drinks. Nutrients. 2020;12(4):1192.

No E, Kelly B, Devi A, Swinburn B, Vandevijvere S. Food references and marketing in popular magazines for Children and Adolescents in New Zealand: A content analysis. Appetite. 2014;83:75–81.

Powell LM, Harris JL, Fox T. Food marketing expenditures aimed at youth. Am J Prev Med. 2013;45(4):453–61.

Potvin Kent M, Pauzé E. The frequency and healthfulness of food and beverages advertised on adolescents’ preferred web sites in Canada. J Adolesc Health. 2018;63(1):102–7.

American Academy of Child & Adolescent Psychiatry: Screen Time and Children. https://www.aacap.org/AACAP/Families_and_Youth/Facts_for_Families/FFF-Guide/Children-And-Watching-TV-054.aspx (2020). Accessed 24 Jul 2022.

Centers for Disease Control and Prevention: Screen Time vs. Lean Time Infographic. https://www.cdc.gov/nccdphp/dnpao/multimedia/infographics/getmoving.html (2018). Accessed 24 Jul 2022.

Pauzé E, Potvin Kent M. Children’s measured exposure to food and beverage advertising on television in Toronto (Canada), May 2011–May 2019. Can J Public Health. 2021;112(6):1008–19.

Kelly B, Vandevijvere S, Ng SH, Adams J, Allemandi L, Bahena-Espina L, et al. Global benchmarking of children’s exposure to television advertising of unhealthy foods and beverages across 22 countries. Obes Rev. 2019;20(S2):116–28.

British Heart Foundation: The 21st century gingerbread house: how companies are marketing junk food to children online. https://www.bhf.org.uk/-/media/files/publications/policy-documents/the-21st-century-gingerbread-house.pdf (2011). Accessed 24 Jul 2022.

Lingas EO, Dorfman L, Bukofzer E. Nutrition content of food and beverage products on web sites popular with children. Am J Public Health. 2009;99(S3):S587–92.

Ustjanauskas AE, Harris JL, Schwartz MB. Food and beverage advertising on children’s web sites. Pediatr Obes. 2013;9(5):362–72.

Potvin Kent M, Pauzé E. The effectiveness of self-regulation in limiting the advertising of unhealthy foods and beverages on children’s preferred websites in Canada. Public Health Nutr. 2018;21(9):1608–17.

Potvin Kent M, Dubois L, Wanless A. A nutritional comparison of foods and beverages marketed to children in two advertising policy environments. Obesity. 2012;20(9):1829–37.

Article   CAS   PubMed   Google Scholar  

Potvin Kent M, Pauzé E, Bagnato M, Soares Guimarães J, Pinto A, Remedios L, et al. Advertising expenditures across media on food and beverage products heavily advertised on youth-appealing television stations in Canada. Applied Physiology, Nutrition, and Metabolism. 2022.

The World Health Organization: Set of Recommendations on the Marketing of Foods and Non-Alcoholic Beverages to Children. Resolution of the Sixty-third World Health Assembly WHA63.14 Marketing of Food and Non-Alcoholic Beverages to Children. https://www.who.int/publications/i/item/9789241500210 (2010). Accessed 25 Jul 2022.

Ad Standards: About the CAI. https://adstandards.ca/about/childrens-advertising-initiative/about-the-cai/ (2022). Accessed 25 Jul 2022.

Ad Standards: The Broadcast Code for Advertising to Children. https://adstandards.ca/preclearance/advertising-preclearance/childrens/childrens-code/ (2022). Accessed 25 Jul 2022.

Obesity Policy Coalition: Food Advertising Regulation in Australia. https://www.opc.org.au/downloads/policy-briefs/food-advertising-regulation-in-australia.pdf (2018). Accessed 25 Jul 2022.

BBB National Programs: CFBAI Nutrition Criteria. https://bbbprograms.org/programs/all-programs/cfbai/cfbainutritioncriteria . Accessed 25 Jul 2022.

Conway L. Advertising to Children. House of Commons Library. 2022. https://researchbriefings.files.parliament.uk/documents/CBP-8198/CBP-8198.pdf . Accessed 25 Jul 2022.

Corvalán C, Reyes M, Garmendia ML, Uauy R. Structural responses to the obesity and non-communicable diseases epidemic: Update on the Chilean Law of Food Labelling and advertising. Obes Rev. 2018;20(3):367–74.

Taillie LS, Busey E, Stoltze FM, Dillman Carpentier FR. Governmental policies to reduce unhealthy food marketing to children. Nutr Rev. 2019;77(11):787–816.

Kelly B, King MPL, Chapman MK, Boyland E, Bauman AE, Baur LA. A hierarchy of unhealthy food promotion effects: Identifying methodological approaches and knowledge Gaps. Am J Public Health. 2015;105(4):e86–95.

Andreyeva T, Kelly IR, Harris JL. Exposure to food advertising on television: Associations with Children’s fast food and soft drink consumption and obesity. Econ Hum Biol. 2011;9(3):221–33.

Giese H, König LM, Tăut D, Ollila H, Băban A, Absetz P, et al. Exploring the association between television advertising of healthy and unhealthy foods, self-control, and food intake in three European countries. Appl Psychol Health Well Being. 2014;7(1):41–62.

Kelly B, Freeman B, King L, Chapman K, Baur LA, Gill T. Television advertising, not viewing, is associated with negative dietary patterns in children. Pediatr Obes. 2015;11(2):158–60.

Longacre MR, Drake KM, Titus LJ, Cleveland LP, Langeloh G, Hendricks K, et al. A toy story: Association between Young Children’s knowledge of fast food toy premiums and their fast food consumption. Appetite. 2016;96:473–80.

Critchlow N, Newberry Le Vay J, MacKintosh A, Hooper L, Thomas C, Vohra J. Adolescents’ reactions to adverts for fast-food and confectionery brands that are high in fat, salt, and/or sugar (HFSS), and possible implications for future research and regulation: Findings from a cross-sectional survey of 11–19 year olds in the United Kingdom. nt J Environ Res Public Health. 2020;17(5):1689.

Ponce-Blandón JA, Pabón-Carrasco M, Romero-Castillo R, Romero-Martín M, Jiménez-Picón N. Lomas-Campos Mde. Effects of advertising on food consumption preferences in children. Nutrients. 2020;12(11):3337.

Coon KA, Goldberg J, Rogers BL, Tucker KL. Relationships between use of television during meals and children's food consumption patterns. Pediatrics. 2001;107(1):E7.

Horgan KB, Choate M, Brownell KD. Television food advertising. In: Singer DG, Singer JL, editors. Handbook of Children and Media. Thousand Oaks, CA: Sage Publications; 2001. p. 447–61.

Taras H, Sallis J, Patterson T, Nader P, Nelson J. Television's influence on children's diet and physical activity. J Dev Behav Pediatr. 1989;10(4):176–80.

Borzekowski DLG, Robinson TN. The 30-second effect. J Am Diet Assoc. 2001;101(1):42–6.

Gorn GJ, Goldberg ME. Behavioral evidence of the effects of televised food messages on children. J Consum Res. 1982;9(2):200.

Balfour Jeffrey D, McLellarn RW, Fox DT. The development of children’s eating habits: The role of television commercials. Health Educ Q. 1982;9(2–3):78–93.

Kelly B, King L, Baur L, Rayner M, Lobstein T, Monteiro C, et al. Monitoring Food and non-alcoholic beverage promotions to children. Obes Rev. 2013;14:59–69.

Euromonitor International. Consumer Foodservice: World brand shares – Limited-Service Restaurants. Euromonitor Passport. 2018. https://www-portal-euromonitor-com.proxy.bib.uottawa.ca/portal/statisticsevolution/index . Accessed 27 Jul 2022.

Hammond D, White CM, Rynard VL, Vanderlee L. International Food Policy Study: Technical Report – 2019 Youth Survey. University of Waterloo. 2021. www.foodpolicystudy.com/methods . Accessed 27 Jul 2022.

Boyland E, McGale L, Maden M, Hounsome J, Boland A, Angus K, et al. Association of Food and Nonalcoholic Beverage Marketing with children and adolescents’ eating behaviors and health. JAMA Pediatrics. 2022;176(7):e221037.

Folkvord F, van ‘t Riet J. The persuasive effect of advergames promoting unhealthy foods among children: A meta-analysis. Appetite. 2018;129:245–51.

Russell SJ, Croker H, Viner RM. The effect of screen advertising on children’s dietary intake: A systematic review and meta-analysis. Obes Rev. 2018;20(4):554–68.

Sadeghirad B, Duhaney T, Motaghipisheh S, Campbell NR, Johnston BC. Influence of unhealthy food and beverage marketing on children’s dietary intake and preference: A systematic review and meta-analysis of Randomized Trials. Obes Rev. 2016;17(10):945–59.

Boyland EJ, Nolan S, Kelly B, Tudur-Smith C, Jones A, Halford JC, et al. Advertising as a cue to consume: A systematic review and meta-analysis of the effects of acute exposure to unhealthy food and nonalcoholic beverage advertising on intake in children and adults. Am J Clin Nutr. 2016;103(2):519–33.

Qutteina Y, De Backer C, Smits T. Media Food Marketing and eating outcomes among pre-adolescents and adolescents: A systematic review and meta-analysis. Obes Rev. 2019;20(12):1708–19.

Gascoyne C, Scully M, Wakefield M, Morley B. Food and drink marketing on social media and dietary intake in Australian adolescents: Findings from a cross-sectional survey. Appetite. 2021;166:105431.

Scully M, Wakefield M, Niven P, Chapman K, Crawford D, Pratt IS, et al. Association between food marketing exposure and adolescents’ food choices and eating behaviors. Appetite. 2012;58(1):1–5.

Arnett JJ, Terhanian G. Adolescents’ responses to cigarette advertisements: Links between exposure, liking, and the appeal of Smoking. Tob Control. 1998;7(2):129–33.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Moran MB, Soneji S, Tan AS, Choi K. Associations between exposure and receptivity to branded cigarette advertising and subsequent Brand Preference Among Us Young Adults. Nicotine Tob Res. 2019;22(6):1030–5.

Article   PubMed Central   Google Scholar  

Norman J, Kelly B, Boyland E, McMahon A-T. The impact of marketing and advertising on food behaviours: Evaluating the evidence for a causal relationship. Curr Nutr Rep. 2016;5(3):139–49.

Paeratakul S, Ferdinand DP, Champagne CM, Ryan DH, Bray GA. Fast-food consumption among us adults and children: Dietary and nutrient intake profile. J Am Diet Assoc. 2003;103(10):1332–8.

Bowman SA, Gortmaker SL, Ebbeling CB, Pereira MA, Ludwig DS. Effects of fast-food consumption on energy intake and diet quality among children in a national household survey. Pediatrics. 2004;113(1):112–8.

Lillico HG, Hammond D, Manske S, Murnaghan D. The prevalence of eating behaviors among Canadian youth using cross-sectional school-based surveys. BMC Public Health. 2014;14(1):1–21.

Deshmukh-Taskar PR, Nicklas TA, O’Neil CE, Keast DR, Radcliffe JD, Cho S. The relationship of breakfast skipping and type of breakfast consumption with nutrient intake and weight status in children and adolescents: The National Health and Nutrition Examination Survey 1999–2006. J Am Diet Assoc. 2010;110(6):869–78.

Neumark-Sztainer D, Wall M, Larson NI, Eisenberg ME, Loth K. Dieting and disordered eating behaviors from adolescence to young adulthood: Findings from a 10-year longitudinal study. J Am Diet Assoc. 2011;111(7):1004–11.

Castronuovo L, Guarnieri L, Tiscornia MV, Allemandi L. Food Marketing and gender among children and adolescents: A scoping review. Nutr J. 2021;20(1):52.

Powell LM, Szczypka G, Chaloupka FJ. Adolescent exposure to food advertising on television. Am J Prev Med. 2007;33(4):S251–6.

UConn Rudd Center for Food Policy & Obesity: Fast food advertising: Billions in spending, continued high exposure by youth. https://media.ruddcenter.uconn.edu/PDFs/FACTS2021.pdf (2021). Accessed 01 Aug 2022.

Backholer K, Gupta A, Zorbas C, Bennett R, Huse O, Chung A, et al. Differential exposure to, and potential impact of, unhealthy advertising to children by socio‐economic and ethnic groups: A systematic review of the evidence. Obesity Reviews. 2020;22(3):e13144.

Kunkel D, Mastro D, Ortiz M, McKinley C. Food marketing to children on U.S. Spanish-language television. J Health Commun. 2013;18(9):1084–96.

Bell RA, Cassady D, Culp J, Alcalay R. Frequency and types of foods advertised on Saturday morning and weekday afternoon english- and Spanish-language American television programs. J Nutr Educ Behav. 2009;41(6):406–13.

Braveman PA, Cubbin C, Egerter S, Williams DR, Pamuk E. Socioeconomic disparities in health in the United States: What the patterns tell us. Am J Public Health. 2010;100(S1):S186–96.

Williams DR, Mohammed SA, Leavell J, Collins C. Race, socioeconomic status, and health: Complexities, ongoing challenges, and research opportunities. Ann N Y Acad Sci. 2010;1186(1):69–101.

Williams DR, Priest N, Anderson NB. Understanding associations among race, socioeconomic status, and Health: Patterns and prospects. Health Psychol. 2016;35(4):407–11.

Harris JL, Brownell KD, Bargh JA. The Food Marketing Defense Model: Integrating Psychological Research to protect youth and inform public policy. Soc Issues Policy Rev. 2009;3(1):211–71.

Southwell BG, Barmada CH, Hornik RC, Maklan DM. Can we measure encoded exposure? validation evidence from a national campaign. J Health Commun. 2002;7(5):445–53.

Feighery E, Henriksen L, Wang Y, Schleicher N, Fortmann S. An evaluation of four measures of adolescents’ exposure to cigarette marketing in stores. Nicotine Tob Res. 2006;8(6):751–9.

Fulkerson JA, Farbakhsh K, Lytle L, Hearst MO, Dengel DR, Pasch KE, et al. Away-from-home family dinner sources and associations with weight status, body composition, and related biomarkers of chronic disease among adolescents and their parents. J Am Diet Assoc. 2011;111(12):1892–7.

Statistics Canada. https://www150.statcan.gc.ca/n1/pub/82-003-x/2021008/article/00003/c-g/c-g01-eng.htm . Accessed 02 Aug 2022.

Download references

Acknowledgements

Not applicable.

All methods were performed in accordance with the relevant guidelines and regulations for this journal.

Funding for this project was provided by an International Health Grant from the Public Health Agency of Canada (PHAC), with additional support from a Canadian Institutes of Health Research (CIHR) Project Grant (PJT-162167).

Author information

Authors and affiliations.

School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada

Mariangela Bagnato, Marie-Hélène Roy-Gagnon & Monique Potvin Kent

École de Nutrition, Centre Nutrition, Santé Et Société (Centre NUTRISS), and Institut Sur La Nutrition Et Les Aliments Fonctionnels (INAF), Université Laval, Quebec City, Canada

Lana Vanderlee

School of Public Health Sciences, University of Waterloo, Waterloo, Canada

Christine White & David Hammond

You can also search for this author in PubMed   Google Scholar

Contributions

MB and MPK designed the study and MRG, MPK, CW, DH and LV oversaw the analysis. MB conducted the analysis and drafted the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Monique Potvin Kent .

Ethics declarations

Ethics approval and consent to participate.

The study was reviewed by and received ethics clearance through a University of Waterloo Research Ethics Committee (ORE# 41477). Additional clearance for secondary analyses were received through a University of Ottawa Research Ethics Committee (H-06-20-5908).

Parents/guardians with a potentially eligible child were provided information about the study and asked to provide informed consent for their child to participate. Only one child per household was invited to participate. The child was subsequently screened directly to confirm eligibility based on age and sex. Children aged 10 to 17 years were eligible to participate, with quotas for age and sex groups in the UK and US. Eligible children were provided with information about the study and were asked to provide assent before beginning the survey.

Consent for publication

Competing interests, additional information, publisher’s note.

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

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . 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 in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Bagnato, M., Roy-Gagnon, MH., Vanderlee, L. et al. The impact of fast food marketing on brand preferences and fast food intake of youth aged 10–17 across six countries. BMC Public Health 23 , 1436 (2023). https://doi.org/10.1186/s12889-023-16158-w

Download citation

Received : 07 February 2023

Accepted : 20 June 2023

Published : 27 July 2023

DOI : https://doi.org/10.1186/s12889-023-16158-w

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

• Advertising to youth
• Food environment
• Preferences

BMC Public Health

ISSN: 1471-2458

• Submission enquiries: [email protected]
• General enquiries: [email protected]

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
• v.2013; 2013

Experimental Evidence on the Impact of Food Advertising on Children's Knowledge about and Preferences for Healthful Food

Lucia a. reisch.

1 Copenhagen Business School, Porcelaenshaven 18, 2000 Frederiksberg, Denmark

Wencke Gwozdz

Gianvincenzo barba.

2 National Research Council, Institute of Food Sciences, Via Roma, 52 A/C, 83100 Avellino, Italy

Stefaan De Henauw

3 Ghent University, De Pintelaan 185 Blok. A-2, 9000 Ghent, Belgium

Natalia Lascorz

4 University of Zaragoza, Domingo Miral s/n, 50009 Zaragoza, Spain

Iris Pigeot

5 University of Bremen, Achterstraße 30, 28359 Bremen, Germany

To understand the rising prevalence of childhood obesity in affluent societies, it is necessary to take into account the growing obesity infrastructure, which over past decades has developed into an obesogenic environment. This study examines the effects of one of the constituent factors of consumer societies and a potential contributory factor to childhood obesity: commercial food communication targeted to children. Specifically, it investigates the impact of TV advertising on children's food knowledge and food preferences and correlates these findings with their weight status. Evaluations of traditional information- and education-based interventions suggest that they may not sustainably change food patterns. Based on prior consumer research, we propose five hypotheses, which we then test using a subsample from the IDEFICS study, a large-scale pan-European intervention study on childhood obesity. The results indicate that advertising has divergent effects on children's food knowledge and preferences and that food knowledge is unrelated to food preferences. This finding has important implications for both future research and public policy.

1. Background and Aim of the Study

In consumer societies, modern diets based on unhealthy fast foods, convenience foods, energy dense snacks, and soft drinks, the abundance and omnipresence of food, and sedentary lifestyles and electronic recreation that minimises physical activity have led to serious weight control problems. A particularly severe trend impacting future health levels are the high, and in most countries still rising, levels of overweight and obesity in infants and children [ 1 ]. According to statistics provided by the World Health Organization [ 2 ], the Organisation for Economic Co-operation and Development [ 3 ], and the International Obesity Task Force (IOTF) ( http://www.iaso.org/iotf/obesity/ ), the problem is increasing and steadily affecting many low- and middle-income countries. Globally, the number of overweight children under the age of five was estimated in 2010 to be over 42 million; close to 35 million of them living in developing countries. About 60% of children who are overweight before puberty will be overweight in early adulthood [ 4 ].

On an individual level, childhood obesity is strongly associated with risk factors for type 2 diabetes, cardiovascular disease, underachievement in school, and lower self-esteem. On a social level, it jeopardises societies' sustainability through the erosion of social cohesion, equity, and fairness. In the developed world, obesity is closely connected with low socioeconomic status (SES); that is, membership in groups for whom access to and availability and affordability of healthier food choices and physical activity is particularly limited [ 5 ]. There is also evidence that cumulative exposure to television food advertising—which is higher in lower SES groups—is linked to adult fast-food consumption [ 6 ].

Beyond individual and social problems, rising obesity rates impact healthcare systems and labour markets and also carry environmental costs: modern diets in consumer societies, high in processed foods and animal protein, have a particularly negative ecological footprint—a long neglected fact that has given rise to a debate on “globesity” [ 7 ]. Put simply, halting and reversing current childhood obesity trends is not simply an imperative for public health policies but rather is increasingly understood as a broader societal challenge that has become an explicit goal of sustainability strategies worldwide [ 8 ]. As a result, addressing obesity among children and adolescents has become a top public health priority—particularly in the USA, which has one of the highest incidences of obesity worldwide [ 9 ].

1.1. Drivers and Impact of Childhood Obesity

In light of these challenges, researchers and policy makers have been focusing on the key drivers and barriers for healthy diets and healthy lives in childhood. Based on scientific evidence on the importance of the immediate “choice context” of the socialisation environment in which children acquire their food knowledge, develop preferences, and actually make food choices, the need to create “junk-free environments” for children has gained increasing support from health professionals, consumer advocates, and concerned political circles [ 10 ]. Attempts to steer children's preferences and food choices in a healthier direction, however, have limited success in an “obesogenic environment” [ 11 ], one that promotes unhealthy foodstuffs and offers limited incentives for healthy, active lifestyles.

Although this “infrastructure of obesity” comprises many levels, is highly complex, and includes many interacting factors (see Butland et al.'s [ 12 ] influential 2007 Foresight report on tackling obesity in Britain), the key influential factors at work for children might, from a human ecological perspective, be roughly grouped by environmental type. Such ecological models, which consider individual behaviour in the context of multiple environments, offer a promising approach to obesity prevention [ 13 – 16 ]. In this paper, we focus on variables from the following three types of environments.

• Social Environment . Children are embedded in families, neighbourhoods, peer groups, schools, and child care facilities in which others influence their food preferences and practices by transposing their social norms and attitudes, food likes and dislikes, and consumption practices and affect their food habits through exposure and learning processes. These social groups also act as “communication buffers” between the children and the advertising and media messages that group members filter and evaluate.
• Physical Environment . Children are directly exposed to a physical environment that offers or limits opportunities for physical activity (e.g., neighbourhood bikeability and walkability), access to healthful foods (e.g., accessibility and availability of healthy food in schools), and access to media (e.g., a TV in the child's own room). Such an environment thus provides both drivers and barriers for actors—from parents to community and school officials—to build “choice architectures” for more health-promoting environments.
• Media Environment . The media environment and in particular commercial communication (e.g., food advertising and all kinds of stealth marketing) have been shown to shape food-related knowledge, attitudes, preferences, and practices both directly and indirectly. On a political level, regulation and self-regulation of advertising towards children are instruments that actively shape the media environment and potentially limit its influence on children's food preferences. A key moderating variable is children's advertising literacy or “ad smartness”, which increases with cognitive development and hence children's age.

This present study, although it acknowledges the multitude of influential factors and the interactions within these three environments, focuses on only a few key factors, whose selection was driven by one widely accepted and empirically based assumption: children's exposure to highly sophisticated advertising messages, including less blunt forms of subtle “stealth” marketing techniques, together with ubiquitous food availability that encourages the consumption of calorie-dense food products of low nutritional value, is a major cause of children's unhealthy dietary choices [ 17 , 18 ]. The question, therefore, is not whether food marketing to children works, but how it affects them. A better understanding of this process is a precondition for developing effective consumer policy tools to protect children from overexposure and imprinting. To enhance such understanding, this paper analyses the associations between TV food advertising and children's food knowledge, food preferences, diets, and weight status. Specifically, it draws on data for a subsample of the IDEFICS study [ 19 ], 229 elementary school children aged between 6 to 9 years from five European countries.

Before outlining our research design, we briefly sketch the key results of prior research on the impact of TV advertising on children's food knowledge and food preferences. Because the recent scientific literature offers comprehensive overviews on the state of the art in this field (e.g., [ 20 – 23 ]), we focus on the key variables in our study and their reported interrelations with each other and with advertising. Against this background, we develop our theoretical model and formulate the research hypotheses that guide our empirical study. After describing our methodology and analyses, we discuss our results as they relate to our hypotheses and conclude by outlining the policy implications of our findings.

1.2. The Impact of Food Advertising on Food Knowledge and Preferences

Children in Europe and the USA are heavily exposed to mass media, watching over two and a half hours of television daily on average (e.g., [ 24 ]). Depending on the children's age and taking into account multiuse of media, recent reports show an average media exposition of 8-to-18-year olds in the USA of more than seven hours per day [ 25 ]. Because ad-free noncommercial children's TV channels like those in Germany and Sweden are the exception, these hours of viewing bombard children with advertising [ 26 ]. As a result, in the USA, foods consumed in front of the TV account for about 20–25% of children's daily energy intake [ 27 ]. In the EU, the Audiovisual Media Directive limits product placement and commercial sponsoring during children's programmes while still leaving member states adequate leeway in audiovisual media regulation; nevertheless, limits are stricter in some EU countries than in others [ 28 ]. No such regulation exists in the USA, however, where children aged between 2 and 11 are exposed to about 25,000 commercials per year, some during adult programming like soap operas or cooking shows [ 29 ]. In the USA, 20% of these commercials are for food products, 98% of them high in sugar, fat, and/or sodium [ 20 , 28 ]. The same holds true for Europe where the “big five”—sugared breakfast cereals, soft drinks, confectionary, savoury snacks, and fast food outlets—represent the majority of advertised food [ 22 ]. There is ample empirical evidence that such advertising content often leads to unhealthier food choices [ 30 ]. In fact, research identifies a direct causal effect of exposure to food advertising on children's diet ; in particular, an increase in snack [ 31 ] and overall calorie consumption [ 17 ], an immediately lower intake of fruits and vegetables [ 32 ], and higher rates of obesity [ 33 ].

There is also empirical evidence that food advertising affects knowledge about (un)healthy nutrition: commercials for unhealthy foods relate directly to lower levels of nutritional knowledge (e.g., [ 34 ]). Advertising, therefore, seemingly overrides knowledge already acquired from other sources that promote healthier choices. In fact, effective advertising messages, rather than requiring active processing and understanding, imprint positive associations on children's brains that can be triggered in decision situations [ 35 ]. Nevertheless, evaluations based on comprehensive literature reviews [ 22 , 36 ] conclude that the overall direct effect of advertising on children's food knowledge and preferences is modest rather than strong.

Empirical consumer research also shows that consumer knowledge does not necessarily lead to preferences for healthier food and that even if such preferences develop, they do not automatically guide behaviour. Thus, although most children and their families generally know what a healthy diet involves, their food choices often do not mirror this knowledge [ 37 ]. In fact, research indicates that accurate beliefs about food healthfulness are not associated with either food preferences or food consumption in children [ 38 ]. There is also evidence that the food choices of both children and their families are determined far more by attitudes and preferences than by acquired knowledge and that children are highly susceptible to the influence of peers in other social contexts [ 39 ]. Yet despite such evidence, prevention and intervention programmes usually take the educational approach [ 40 ].

Children's food preferences are also influenced by their immediate environment, particularly exposure to and familiarity with food stuffs, and by role models. Yet, according to the empirical literature [ 41 ], food advertising can influence children's preferences either way—healthier or unhealthier preferences [ 42 ]. Children also imitate their parents' (and other adult caretakers') food styles and learn by observation, meaning that they prefer eating fruits and vegetables if their parents do so. Their food preferences can also be influenced by sheer exposure to specific foods (the “I like what I know” phenomenon) [ 43 ].

2. The Study

2.1. the human ecological model and key variables.

This study investigates the association between food advertising and children's food knowledge, food preferences, diet, and weight status, as summarised in Figure 1 . In line with the theory of human ecological development (“ecological model”, [ 44 ]) and based on the literature sketched above, we select as our key variables potentially influential factors from the children's social, physical, and media environment; namely, food-related norms, attitudes, and lifestyles at home; the children's access to TV and consumption of TV commercials; and the children's level of advertising literacy. We also examine the relation between food knowledge, preferences, diet, and weight status.

The role of commercial communication and food knowledge, preferences, diet, and weight status.

2.2. Food-Related Norms and Attitudes

We measure the food and media setting at home by parents' general attitude towards advertising [ 45 ] and hypothesise that the more sceptical parents are about food advertising, the less susceptible their children are to the effects of advertising on food knowledge, preferences, diet and weight status:

• H1: the more sceptical the parental attitude towards advertising, the better their children's food knowledge, the healthier their food preferences and diet, and the lower their weight status.

We also take into account the suggestion put forward in the consumer socialisation literature that if parents discuss and reflect on the aims of advertising with their children—for example, while watching TV together—they can help their offspring develop the “advertising literacy” [ 32 ] that is part of an effective advertising defence model:

• H2: the fact that parents discuss the TV programmes/ads watched with their children influences these children's food knowledge, food preferences, diet, and consequently, weight status.

2.3. Access to and Consumption of Television Advertising

The potential impact of TV advertising is influenced by three variables: children's access to media, their penchants for TV programmes that carry more or less advertising, and their actual exposure. Unrestricted access increases hours of actual media exposure and influences the time of exposure to advertising, factors that are further augmented by children having a television in their own bedrooms [ 46 ]. The country of residence and the type of programme watched also influence exposure to advertising. Assuming the so-called “mere exposure effect”, therefore—that is, that mere (and also incidental) exposure to advertising affects children's food knowledge and preferences—and accepting that advertising has the power to shape preferences [ 41 ], food knowledge should be lower [ 34 ] and preferences should be unhealthier when exposure is high. Such high exposure has consequences for both diet and weight status:

• H3: unrestricted access and thus more exposure to advertising leads to lower food knowledge, unhealthier preferences, diets, and an unhealthier weight status.

2.4. Advertising Literacy

Children's handling of advertising depends on their advertising literacy—their knowledge about the goals and mechanisms of advertising—as well as on their attitudes towards advertising. In this context, knowledge refers to children's perceptions, including suspiciousness, of advertising's credibility and usefulness, whereas attitudes reflect the entertainment value that the advertisements hold for children [ 47 ]. Hence, following Livingstone and Helsper [ 32 ], we propose the following hypothesis:

• H4: children's advertising literacy is related to their food knowledge, preferences, diets, and weight status; hence, higher advertising literacy is associated with better food knowledge, healthier preferences and diets, and lower weight.

2.5. The Relation between Food Knowledge, Preferences, Diet, and Weight Status

This study assumes a sequential relation between food knowledge, preferences, diet, and consequently weight status; that is, better food knowledge leads to healthier food preferences, which in turn lead to healthier food choices that are mirrored in a healthy weight status. As regards the effect of food knowledge on preferences, there is empirical evidence that in children accurate beliefs about food healthiness are not associated with food preferences or consumption [ 38 ]. Obviously, in the light of this finding, the widely held assumption that increased knowledge of healthy nutrition leads to healthier choices is a “misperception” [ 31 , p. 223]. We therefore offer an alternative hypothesis:

• H5: better food knowledge does not necessarily imply healthier food preferences (a), food preferences have no direct effect on dietary choice (b), and the latter has no significant effect on weight status (c).

3. Data and Methodology

3.1. sample and survey.

The data used for our analyses were obtained in the context of the IDEFICS study, a prospective cohort study that began with a baseline survey in 2007/2008 and continued with a follow-up survey two years later [ 19 ]. The total IDEFICS cohort consists of 16,225 children aged 2 to 10 years from eight European countries. One unique feature of this study is that it employs a large number of objective measurements and supplements the questionnaire data with a large amount of laboratory data. For example, in the IDEFICS baseline survey, run between September 2007 and May 2008, parents described their children's lifestyle, television consumption habits, diets, parental attitudes and sociodemographic circumstances in a detailed self-administered questionnaire. A thorough physical examination was also conducted on all children in the sample to determine their amount of body fat, weight, height, and other health indicators [ 19 ]. To gather more specific information on the children's food knowledge and preferences as well as on their advertising literacy, between April and June 2009, we developed instruments (choice experiments and a questionnaire) and collected additional data for a subsample in five countries. Only children that participated in the experiments and the questionnaire are included in the present analysis. The resulting sample size is 229 children aged between 6 and 9 years (average age = 7.83; standard deviation (SD = .77)), 122 (53.3%) of whom are female. The participants are distributed as follows across the five countries: Belgium, 60 (26.2%), Estonia, 25 (10.9%), Germany, 48 (21.0%), Italy, 47 (20.5%), and Spain, 49 (21.4%).

3.2. Food Knowledge and Preferences: A Choice Experiment

The data on children's food knowledge and preferences were gathered via a choice experiment (see Gwozdz and Reisch [ 48 ]) based on Kopelman et al. [ 37 ] but adapted to our research question and settings. The primary stimuli were two brochures showing 10 matched pairs of food cards; one picturing relatively healthy food, the other relatively unhealthy food. As shown in Table 1 , these matched pairs belong to the same respective food category (e.g., “juice”). (To minimise unintended influences, it would have been preferable to have the children choose between real food products instead of pictures; however, because the experiments were carried out in five different countries in which the same products were not available or known to the children, doing so was not an option. Rather, to ensure comparability between the countries, we chose the pictures as feasible alternative.)

Matched pairs of food cards based in part on Kopelman et al. [ 37 ].

The two-step experimental procedure included a preference test and a knowledge test. In the preference test, the children were asked, “Which food or drinks do you like best?” They then drew a smile (for “true”) or a frown (for “false”) for each matched pair according to their (forced-choice) preference. The knowledge test proceeded in a similar way. Again, the children drew a smile or a frown for each matched pair in reaction to the following question: “What do you think: Which food or drink is the healthier one?” This order was chosen based on pretest results showing that conducting the preference test first would reduce framing effects.

Based on the children's choice experiment scores (i.e., whether they chose healthier or unhealthier foods and drinks from the 10 matched pairs), we built one indicator for food knowledge and another for food preferences. Both indicators range between 0 (no healthy food chosen) and 10 (only healthy food chosen). We also created a dummy variable capturing high knowledge or healthy preferences whenever a score equalled 6 or above, i.e., 1 “score > 6” and 0 “score ≤ 6”) (see [ 37 ]).

3.3. Measurement of Variables

3.3.1. children's diet.

Our first diet measure reflects children's diet quality—including meal frequency, diet composition and variety, fast food consumption, and snack and beverage consumption—as well as family control [ 49 ]. This first dependent variable is a continuous variable that describes diet quality based on the Youth Healthy Eating Index (YHEI) [ 50 ], which ranges from 0 to 80, with a higher score signalling a more healthful diet. The YHEI, which measures food consumption and food-related behavioural patterns, is based on food frequencies, which in the IDEFICS survey are collected using the Children's Eating Habits Questionnaire (CEHQ) [ 51 ]. This latter asks parents for information on their children's food consumption of 43 predefined food categories, excluding foods served at school. The YHEI scores, therefore, measure solely the healthfulness of the diet under parental control. We do, however, also include meal pattern information from the CEHQ, such as frequencies of fast food consumption, breakfast at home or in school, and family dinners.

Based on these data, we are able to replicate 10 of the 13 original YHEI dimensions, which are listed below with nutritional values in brackets

  Food types :

• whole grains (sources of fibre, vitamins, and minerals),
• vegetables (sources of vitamins and minerals),
• fruits (sources of vitamins),
• dairy (sources of calcium),
• snack foods (unnecessary energy),
• soda and drinks (unnecessary energy),
• margarine and butter (sources of fat).

  Food behavioural patterns :

• (8) fried foods outside home (high energy intake),
• (9) eat breakfast (indicator of healthful dietary patterns),
• (10) dinner with the family (indicator of healthful dietary patterns).

The original version of the YHEI also includes the dimensions “meat ratio”, “multivitamin use,” and “visible animal fat”, but these factors are not covered in the IDEFICS data. We calculate the YHEI using the sum of all available subscores for the 10 dimensions, the criteria for which are adapted from Feskanich et al. [ 50 ].

The other two dietary measures mirror the relative intake of sugar and fat and thus also reflect diet quality. Specifically, we calculated the weekly consumption frequencies of each of 12 foods and beverages that are high in sugar content and 17 foods and beverages that are high in fat and divided the weekly sugar and consumption by the individual's total consumed food frequencies (see [ 52 ]).

3.3.2. Children's Weight Status

The last set of dependent variables relate to the children's lagged weight status (in the follow-up survey, i.e., weight status two years after the baseline survey in 2007/2008). The IDEFICS data set provides several anthropometric measurements related to body composition, all measured by trained nurses based on the same standard operating procedures (SOPs) in all countries. Again, we used three different models to capture the weight status, each based on a different dependent variable.

  (i) Model 1 . We consider the body mass index (BMI = weight in kilograms by squared height in meters) as a continuous variable, calculated as usual as a z -score according to the growth charts from the Centers for Disease Control (CDC) [ 53 ].

  (ii) Model 2 . As a second anthropometric measure, we used the corresponding z -scores of waist circumference, based on the growth charts of the International Obesity Task Force (IOTF) [ 54 ].

  (iii) Model 3 . As a third measure, we used body fat mass, which must be calculated based on fat mass, as derived from Bammann et al.'s [ 55 ] “four component model”, together with hip circumference and triceps skinfold.

3.3.3. Parental Norms, Attitudes, and Food Practices

Norms and attitudes reflect the influence of the “setting”; that is, the general parental attitudes toward advertising [ 56 ] and whether parents discuss the TV programmes watched with their child. Hence, parental attitudes towards advertising measure the perceived usefulness and credibility of ads, as well as the expected effects of the ads on their children. Borrowing from Diehl and Daum's [ 56 ] Attitudes Toward TV Food Advertising Aimed at Children scale (AFAC), we ran a factor analysis for identifying dimensions of parental attitudes towards advertising. (We carried out a principal component analysis with varimax rotation resulting in two factors. The eigenvalue is 1.44, the Kaiser-Meyer-Olkin measure is .651, and all factor loadings are above .405. Cronbach's Alpha for Factor 1 is .737 (four items) and for Factor 2 is .510 (three items).) The result was two factors with the following statements for Factor 1: ad usefulness and credibility: (a) TV food advertising is a good source of information for children and parents, (b) TV food advertising assists parents in their efforts to feed their child a healthy and balanced diet, (c) a child clearly understands just how good the product presented in TV advertising is, and (d) TV food advertising informs children and parents about things they would otherwise never learn about. For the second factor on the effect of TV food ads, we include the following three statements: (a) TV food advertising causes children and their parents to spend their money on unnecessary and sometimes even harmful products, (b) TV food advertising is largely responsible for the weight problems and bad teeth of many children, and (c) TV food advertising can hardly have an influence on what children eat and drink (reversed). Reversed items were recoded before the average score was calculated for each of the two dimensions. The question on discussing TV content with children is phrased as follows: “When watching TV, do you discuss the programme/ads with your child?” The variable is coded as a dummy: 0 = “never or sometimes”; 1 = “often or always”.

3.3.4. Exposure to Media and Advertising

Other information for the direct advertising context stems from the IDEFICS baseline survey and comprises data related to the children's TV viewing habits: whether a television is available in the children's bedroom (dummy) and their weekly TV viewing time.

3.3.5. Children's Advertising Knowledge and Attitudes

The questionnaire used to measure children's advertising knowledge and attitudes is based on an instrument developed and validated by Diehl [ 47 ], which covers three dimensions, each measured by three questionnaire items: credibility, children's perception of TV advertisements as a useful source of information; suspiciousness, their questioning of commercial messages; and entertainment, the fun factor of watching commercials. The first dimension, the credibility and usefulness of food advertising, assesses whether children perceive TV advertisement as a useful source of information about foods and drinks. The hypothesis underlying the second dimension, suspiciousness toward food advertisements, is that if children are suspicious of TV food advertising, they will know not to trust any advertising content and will thus question commercial messages. The assumption underpinning the third dimension, the entertainment factor of TV advertising, is that children who are more suspicious and have less trust in the credibility of TV advertisements experience them as less entertaining. This latter implies that once children understand the mechanisms underlying advertising, they no longer enjoy watching them as much as before. To these three dimensions, we add an additional dimension, social desirability, measured on a four-point scale from “disagree fully” (−2) to “disagree” (−1), “agree” (+1), and “agree fully” (+2). Taking into account our respondents' young ages, we present the answer categories as pictograms (“smileys”) instead of words, expressing the respective nuances of (dis)agreement with more or less happy faces. The suitability of this instrument was demonstrated in pretests [ 48 ].

3.3.6. Control Variables

The controls encompass socioeconomic status, indicated by the maximum parental education level (ISCED levels 1–6), child's age, child's sex, and country dummies. Thus, all analyses have been adjusted for these variables where child's age is introduced by three dummy variables: age <8 years, =8 years, and >8 years, the latter acting as the reference category. Child's sex is also expressed in form of a dummy variable (0 is male; 1 is female). For the five countries, we created five dummies, with Belgium acting as the reference group.

3.4. Statistical Analysis

To meet the study goals we use STATA/SE 11 software to carry out a set of ordinary least squares (OLS) or probit regressions in which food knowledge, preferences, diet, and weight status are the dependent variables. In a first step, we estimate the following regression model:

where F is a vector for our measure for food knowledge or preferences and may have either continuous or discrete variables as defined above. DA is a vector of direct advertising context factors, IA is a vector of indirect advertising context factors, C is a vector of child and family characteristics, and D is a vector of country dummy variables (five countries, with Belgium as the reference country). ε is a vector of idiosyncratic error terms, and the β s are the coefficients to be estimated, with β 1 and β 2 being the coefficients of particular relevance in this study. Depending on the nature of F , we use either ordinary least squares or a probit model. Because we assume that knowledge is associated with preferences, we estimate the model on preferences a second time, now including food knowledge as an independent variable (see Figure 1 ).

In the next step, we first exchange the dependent variables knowledge and preferences with diet and then, in a third step, with lagged weight status. We then repeat the analyses. For weight status, we include an additional control variable in the form of a dummy variable indicating whether a child stems from the control or the intervention region in order to consider any possible intervention effect. For each analysis, we estimate two models for each dependent variable.

4.1. Descriptive Statistics

Among the 229 children that participated in the choice experiment, the average score for food knowledge is 7.76 (SD = 1.18), higher than the average score of 4.78 (SD = 2.08) for food preferences, both measured on the same scale. Although 95% of the 229 children scored 6 or higher in the food knowledge experiment, only 33% chose 6 or more healthy foods in the preference experiment. As regards diet, the average YHEI is 49.6 on a scale between 0 and 80, the relative sugar intake is 27.9%, and the relative fat intake is 26.5%. As Table 4 also shows, diet quality and intake varies by country: Estonian and Spanish children show a higher diet quality and less sugar intake than children from the other countries. The lowest fat intake is among children from Italy and Spain. Belgian children, whose diet is comparatively high in relative sugar and fat intake, are the thinnest in the sample, with the lowest BMI, waist circumference, and fat mass. An overview of descriptive statistics of all used variables can be found Table 4 .

Descriptive statistics.

Given the previously discussed assumption of a relation between food knowledge and preferences, we expect that both variables will influence children's diets and that diet in turn will be associated with weight. We therefore ran a correlation analysis for these dependent variables; however, we did not find any statistically significant correlation between food knowledge and food preferences ( r = .109, P = .101). The diet variables themselves (YHEI, proportional sugar, and fat intake) are statistically significantly correlated—a high proportion of sugar or fat in the diet is linked to an unhealthful diet (YHEI) and vice versa—and as might be expected the strongest correlations occur between the weight status variables. Yet the correlation analysis reveals no indication of links between food knowledge and preferences and diet and weight status.

As regards the remaining variables, parents perceive advertising on average as having medium credibility ( M = 1.86, SD = .69 on a scale from 0 to 4) and relate food advertising to negative effects on children's health ( M = 2.74, SD = .61). In terms of access to media, analysed in terms of bedroom equipment and TV consumption time, about 38% of the children in the sample have a television in their bedroom; however, there are large differences between countries. The majority of children in Italy (82.6%) and Estonia (64.0%) have a television in their bedroom, followed by about a third of children in Germany (33.3%), but far fewer in Belgium (15.0%) and Spain (14.9%). The children spend an average of 1.32 hours per day watching TV.

The assessment of advertising knowledge and attitude, measured on a scale from −6 to +6, indicates that on average children feel more suspicious ( M = 1.73, SD = 2.96) about advertising than they think it is credible or useful ( M = .86, SD = 3.25) and entertaining ( M = .13, SD = 2.82). In our sample, the most suspicious are the Italian children ( M = 3.87, SD = 2.20) while the least suspicious are the Spanish ( M = .87, SD = 3.17) and Estonian children ( M = .79, SD = 3.05). The Spanish children also perceive advertising as entertaining ( M = .90, SD = 3.20) and believe in its credibility as a source of information ( M = 1.15, SD = 3.28) more than any other national group except for Belgian children ( M = 1.78, SD = 2.97). The Estonian children are the most critical: they are the least entertained by advertising ( M = −1.88, SD = 2.83) and perceive food advertising as the least credible ( M = −1.40, SD = 3.28). Overall, we observe a variation by country; however, because our small sample size precludes any analyses stratified by country, we must rather rely on the inclusion of country dummies as control variables.

4.2. Associations between Advertising and Food Knowledge and Preferences

In this section, we investigate the associations between the variables discussed above: parental norms and attitudes, access and exposure (as well as advertising knowledge), and food knowledge, preferences, diet, and weight status. Table 2 presents the estimates of the food knowledge and preferences regressions, those for the continuous dependent variables in columns 1 and 3 and those for the dependent dummy variables on knowledge and preferences in column 2 and 5. The results reported in columns 4 and 6 are for the models that include food knowledge as an independent variable.

Role of commercials on food knowledge and preferences: OLS/probit estimates.

Robust standard errors in parentheses; control variables are sex and age of child, parental education (ISCED), and country dummies. Reference category for age is 9 years and for countries, Belgium.

* P < .1; ** P < .05; *** P < .01.

OLS: ordinary least squares estimator.

As the table shows, there is an apparent significant relation between parental norms and attitudes: if parental attitudes towards advertising are critical (i.e., if they believe that food advertising has a negative effect on children's dietary behaviour), children's food preferences are more healthful (e.g., β = .271, SD = .149, column 5). Other parental attitudes, however, do not appear to be statistically significant. Access to media, on the one hand, shows no statistically significant effects: it seemingly plays no role in either food knowledge or preferences. Media literacy and food knowledge, on the other hand, are related along the entertainment dimension: children who are entertained by ads show also less healthy food knowledge than others. In terms of advertising, the statistical significance of advertising's credibility on food preferences is especially noteworthy: we find a highly significant negative effect of advertising's credibility on food preferences, meaning that children who are less sceptical of advertising have less healthful food preferences. Not surprisingly, given the prior finding in the correlation analysis of no relation between food knowledge and preferences, the introduction of food knowledge into the preference models (columns 4 and 6) does not improve the models: there is no change in adjusted R 2 and food knowledge is not significantly associated with food preferences.

4.3. Associations between Advertising, Food-Related Lifestyles, and Diet

Table 3 shows the results for the role of commercial communication on diet. On the one hand, we find that diet quality (YHEI) and fat intake are associated with parental norms and attitudes when such attitudes are critical of advertising. That is, in direct contrast to our expectations, the more critical the parents, the less healthful a child's diet and the higher the proportional fat intake. One possible reason for this unexpected finding could be social desirability effect, although a reactionary effect of children to parents' effort to make them eat healthily could also be at work. Such speculation, however, cannot be tested using the available data. On the other hand, we find no direct evidence of an influence of TV consumption on diet, although children with equipment in their bedroom show a higher proportion of sugar intake in their diet (e.g., one TV increases the share of sugar by 3.35%). Advertising literacy, however, is statistically significant in two cases: the more children feel entertained by advertising, the more healthful their diet—which once again stands in contrast to our expectations. The positive association between a higher credibility and usefulness of advertising and the relative high sugar intake, however, is in line with our fourth hypothesis (H4). Our introduction of food knowledge and preferences into the models (columns 2, 4, and 6) does show they have a statistically significant effect on diet and the adjusted R 2 indicates an improvement.

Role of commercials on diet: OLS.

Robust standard errors in parentheses; control variables are sex and age of child, parental education (ISCED), and country dummies. Reference category for age is 9 years and for countries Belgium.

4.4. Associations between Advertising and Children's Weight Status

We also ran regression analyses for estimating the relationship between advertising and children's weight status. The dependent variables are BMI (CDC, z -score), waist circumference (Cole, z -score), and relative body fat (kg/m 2 ). We find no association between weight status and either parental norms and attitudes or the physical environment. We do show that children who are suspicious of ads have a higher BMI (column 2); however, only when diet factors are included. In fact, diet seems to have an influence on weight status; rather, counterintuitively, the proportional sugar intake is statistically significantly in relation to the lagged weight status, indicating that the higher the share of sugar in a diet, the lower the weight status. Neither the diet quality nor the proportional fat intake are statistically significantly associated with lagged weight status.

In sum, our findings are rather mixed. Although some factors of the attitudes and norms environment show effects in the predicted direction on the healthfulness of food preferences and diet (diet quality and proportional fat intake), we find no robust associations between the physical environment and food knowledge, preferences, diet, or weight status. If we substitute TV consumption with audiovisual media (AVM) consumption (TV plus computer, game console consumption time), however, there is a statistically positive association between AVM time and weight status. The media environment (i.e., media literacy), however, seems to have the hypothesised effects on food knowledge and preferences but not on diet and weight status.

5. Discussion and Conclusions

This analysis, based on a subsample from the IDEFICS study, examines the effects of advertising on children's food knowledge and preferences, as well as on dietary choices and weight status. For the sake of focusing on the role of commercial communication, we do neglect possible impacts of genetic as well as lifestyle factors—which may indeed modify appetite, food intake, and preferences—in our analysis. Both types of factors and their influence have been studied within the IDEFICS study and will be published elsewhere. The key findings of our study are that better food knowledge is not seemingly linked to healthier food preferences and diet apparently has no significant effect on weight status. Although we acknowledge that the study is limited in sample size and operationalisation of the variables is based on our own reasoning and hence could be debated, these key findings do stand on robust empirical ground based on the analysis presented in this paper.

We interpret our results in light of the frequent claims that effectively countering harmful food marketing practices requires child awareness and understanding, paired with the ability and motivation to resist [ 31 ]. Many empirical studies, as well as evaluations of health intervention programmes, have indeed shown that providing information and education alone—the major policy strategy of recent decades—fails to successfully decrease advertising's effects on children. One reason that advertising literacy alone does not seem to help is that this knowledge only guides behaviour when it is accessed and used at the same time as the advertising stimulus, something that marketers carefully avoid. In addition, different processes of persuasion operate at different age levels—that is, at different perceptual stages and levels of advertising literacy—which age-specific advertising takes into account [ 32 ]. Yet, although consumer policy efforts to strengthen children's ability to resist food industry lures have been part of many educational programmes on media literacy and consumer competence-building since the 1970s, no effective “food marketing defense model” [ 31 ] has been developed. The findings of this study provide further evidence that any such effort must go beyond informational approaches.

Overall, our findings support the contention that traditional policy strategies, based primarily on informational and educational goals, are insufficient to decrease the effects of advertising on children. Hence, although food smartness and advertising literacy will remain unquestioned goals of young consumers' socialisation, they cannot be expected to adequately guide behaviour in a healthier direction [ 57 ]. A more promising policy approach might lie in the tools of behaviourally informed social regulation suggested in the behavioural economics literature on “nudging” [ 58 ]. From this perspective, parents and caretakers should be aware of their decisive role as “choice architects”; as artisans who guide their children's selections by regularly offering healthful and attractive food and limiting their exposure to television and other sedentary behaviours. Hence, the old WHO motto “making the healthy choice the easy choice” should be reassessed and taken more seriously by everyone responsible for children's diet. Above all, food choices are strongly affected by the “triple A” of food items—availability, affordability, and accessibility—particularly if paired with and supported by social norms [ 59 ]. For instance, customer's food choices can be strongly influenced by the mere promotion of healthful food choices in “smart canteens” that offer the healthier choice as the default option [ 60 ]. This influence is, of course, no news for marketing professionals, but the power of context and the limited cognitive involvement of consumers in habitual consumer behaviours have too long been neglected by policy makers and health professionals alike. These latter particularly must recognise that a junk-free, nonobesogenic environment may be a necessary condition for successfully reducing obesity rates.

Fast-Food Restaurant Advertising on Television and Its Influence on Youth Body Composition

We examine the effects of fast-food restaurant advertising on television on the body composition of adolescents as measured by percentage body fat (PBF) and to assess the sensitivity of these effects to using conventional measures of youth obesity based on body-mass index (BMI). We merge measures of body composition from bioelectrical-impedance analysis (BIA) and dual-energy x-ray absorptiometry (DXA) from the National Health and Nutrition Examination Survey with individual level data from the National Longitudinal Survey of Youth 1997 and data on local fast-food restaurant advertising on television from Competitive Media Reporting. Exposure to fast-food restaurant advertising on television causes statistically significant increases in PBF in adolescents. These results are consistent with those obtained by using BMI-based measures of obesity. The responsiveness to fast-food advertising is greater for PBF than for BMI. Males are more responsive to advertising than females regardless of the measure. A complete advertising ban on fast-food restaurants on television would reduce BMI by 2 percent and PBF by 3 percent. The elimination of the tax deductibility of food advertising costs would still leave a considerable number of youth exposed to fast-food advertising on television but would still result in non-trivial reductions in obesity.

Research for this paper was supported by Grant #65068 from the Robert Wood Johnson Foundation to the National Bureau of Economic Research. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

MARC RIS BibTeΧ

Download Citation Data

More from NBER

In addition to working papers , the NBER disseminates affiliates’ latest findings through a range of free periodicals — the NBER Reporter , the NBER Digest , the Bulletin on Retirement and Disability , the Bulletin on Health , and the Bulletin on Entrepreneurship  — as well as online conference reports , video lectures , and interviews .