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Food is one of the most frequently promoted commodities, and promoted foods are overwhelmingly unhealthy. Marketing normalizes unhealthy foods, creates a positive brand image, and encourages overconsumption. Limited research is available to describe the extent of food marketing to children on web-based media, and measuring actual exposure is challenging.
This study aims to monitor the extent of children’s exposure to web-based media food marketing as an essential step in increasing the accountability of industry and governments to protect children.
Children aged 13-17 years were recruited from October 2018 to March 2019. Children recorded their mobile device screen for 2 weekdays and 1 weekend day any time they visited relevant web-based platforms. After each day, the participants uploaded the video files to a secure server. Promoted products were defined using the World Health Organization European Region nutrient profile model.
The sample of 95 children uploaded 267.8 hours of video data. Children saw a median of 17.4 food promotions each hour on the internet. Considering the usual time spent on the internet on mobile devices, children would be exposed to a median of 168.4 food promotions on the web on mobile devices per week, 99.5 of which would not be permitted to be marketed based on nutrient profiling criteria. Most promotions (2613/4446, 58.77%) were peer endorsed and derived from third-party sources.
Exposure to brand content that is seemingly endorsed by peers or web-based communities likely heightens the effects of marketing on children. Regulations to protect children from this marketing must extend beyond paid advertising to paid content in posts generated through web-based communities and influencers.
Protecting children from the impacts of unhealthy food and beverage marketing has been repeatedly identified at the highest levels of global policy agenda setting as a priority intervention for childhood obesity prevention. The report from the World Health Organization (WHO) from the Commission on Ending Childhood Obesity explicitly urged governments to regulate the marketing of unhealthy food to protect children from obesity and related noncommunicable diseases [
For more than a decade, evidence on children’s exposures to, and the impacts of, food marketing has identified the increasing prominence of digital or web-based media marketing [
Almost all Australian adolescents aged 13-18 years (94%) have their own mobile screen devices, and three-fourths have a social media account [
With the advancement of the internet as a social and participatory space, marketers have been able to target and engage users with personal communications, infiltrate web-based communities with brand content, and enable peer endorsement of brand messages [
Research evidence on the impact of web-based food marketing on young people has predominately focused on digital games, specifically
Monitoring children’s exposure to food marketing is necessary for engaging policy makers and civil society on the issue, holding industries accountable for their marketing practices, and measuring the effectiveness of any regulations and compliance [
This study aims to quantify and describe children’s exposure to food and beverage marketing during their time spent on the internet, including the types of foods and beverages promoted and the platforms from where exposures were derived. We also sought to describe the nature of promotions, including the extent to which these were found in paid advertising space, on food companies’ own sites and pages, or transmitted through web-based social networks. The approach used to capture marketing exposures also allowed us to identify the extent of children’s engagement or interaction with food promotions. We hypothesized that children would be exposed to a high volume of unhealthy food and beverage marketing in their usual web-based interactions, which exceeded the number of promotions that they see for healthy choices, and that a large proportion of marketing would be peer endorsed and skewed toward third-party sources, such as shared content and blogs. Children aged 13-17 years were selected for the study, as they were deemed to have sufficient cognitive capacities to undertake the web-based survey and monitoring aspects of this project and to comprehend the ethical and privacy considerations of participating. Adolescents are also key social media users and targets for web-based food marketing [
The study was approved by the University of Wollongong Human Research Ethics Committee (HREC 2018/158). Children were recruited through the national adolescent survey panel of the market research agency McNair yellowSquares. This panel comprises parents of young people across Australia who have agreed to be contacted to participate in research studies (approximately 15,000 panel members). Panel members with children aged 13-17 years were invited to indicate their interest in participating in this study. Interested parents and children were sent the participant information sheets and consent forms to both sign and return. Participants were then asked to complete a short prestudy questionnaire that assessed their eligibility to participate, along with collecting information on their usual time spent on the internet on a mobile device and also on desktop and laptop computers, split by weekdays and weekend days. To be included in the study, children needed to have at least one social media account, log on to social media at least once per day, and have access to a mobile device (phone or tablet) that was compatible with the screen recording apps or settings. Only one child per family was chosen for participation. Participants were recruited in 2 rounds to avoid the school holiday period—October to November 2018 and February to March 2019. A sample size of approximately 150 children was sought from a national population estimate of approximately 1.4 million adolescents [
The study required children to record and upload data on their internet use on mobile devices and complete pre- and poststudy questionnaires. The main study was preceded by a pilot study of 26 children. The pilot led to major changes in the recruitment strategy (eg, increasing compensation for participant time), participant tracking and reminders, data coding, and improvements to the data upload server.
Each participant was asked to video record their mobile device screen for 2 weekdays and 1 weekend day anytime they went onto relevant web-based platforms or apps. Relevant platforms include social media websites or apps, video sharing websites or apps, or browsing on the internet. They were asked not to record their screen when they were using any banking platform, using personal messaging (eg, SMS, Facebook Messenger, WhatsApp, or personal messaging on Snapchat or Instagram), making phone calls, or browsing through photos in their device’s gallery. Participants nominated which days they would record within a 2-week period of entry into the study. They were sent 3 SMS text messages on nominated days as a reminder to record their screens.
Participants were provided with detailed written instructions and an instructional video to complete the study screen recording and upload tasks. The recording process varied across mobile device operating systems. For Android devices, participants were asked to download an app called the
Each participant was sent a unique log-in link to the McNair yellowSquares web-based database to upload the data. This was a bespoke platform for uploading files, completing questionnaires, tracking participants’ study progress, and communicating any data issues. After each day of recording, the participants were instructed to upload their video files to the database. Participants were encouraged to edit videos using the video editing function on their device and to remove any footage they did not want the researchers to view. Given the size of the video files and the number of uploads being attempted simultaneously, upload to the database experienced issues with slow uploads and file corruption (inoperable files). Consequently, midway through data collection, new participants were instructed to submit their videos using WhatsApp. WhatsApp uses end-to-end encryption and does not store messages on its own servers.
The participants’ video uploads were monitored daily during the data collection period. Data were deemed to be acceptable if the total duration of uploaded videos for the day was at least 30% of the reported usual time on the web on mobile devices (for weekdays and weekend days separately; from the prestudy questionnaire). When participants failed to reach this threshold of recording, they were contacted by email and phone, given further instruction, and asked to complete a replacement day. Participants received Aus $50 (US $38), paid into their research panel account, if they completed all 3 days of data recording. They received Aus $20 (US $15) if they only completed 1 or 2 days of recording. Participants were included in the final sample if they had at least one acceptable weekday and one weekend day.
Although there was minimal risk involved in participation, some of the main ethical concerns in the project were related to potential risks to privacy and confidentiality. Measures were taken to protect the privacy of the participants and to ensure data security.
At the end of the study, all video data were transferred to CloudStor, a secure cloud storage server. Each video was watched at least twice by 1 person from a pool of 3 trained research assistants. In the first viewing of the video, all food and beverage promotions (including food and beverage products, retailers, and services) were identified and coded. The second viewing focused on recording the length of time spent on different platforms. Only branded food promotions were captured, including branded products and packages, brand logos, and brand characters. To be included, promotions needed to be shown onscreen for a minimum of 1 second and at least half of the brand name or logo needed to be visible.
The coding frame captured both the frequency and duration (seconds) of promotions onscreen, the nature of these promotions, and any participant engagement. Promotions were classified according to the platform (app or website) on which they occurred and the extent to which participants engaged with the promotion by
Promoted products were defined using the WHO Regional Office for Europe nutrient profile model [
Interrater reliability was assessed with each research assistant independently coding the video data of the same 6 participants (15 days). The intraclass correlation coefficient was calculated for absolute agreement between the raters, giving an intraclass correlation coefficient of 0.97, indicating excellent reliability. Reliability results were discussed among the research team, and all issues were resolved before continuing. Reliability testing helped to refine the coding rules about the threshold of time, and the visibility of the brand, onscreen for the promotion to be counted.
Frequency of food and beverage promotions in sample recordings (N=4446).
Food category | Frequency of promotions, n (%) | |
|
108 (2.43) | |
|
Plain breads, rice, noodles, and crackers | 20 (0.45) |
|
Fruits and fruit products without added fats, sugars, or salt; ≥98% fruit juices | 19 (0.43) |
|
Milks and yogurts (≤3 g fat/100 g), cheese (≤15 g fat/100 g), and alternatives | 18 (0.4) |
|
Bottled water | 15 (0.34) |
|
Low sugar or high fiber breakfast cereals (<20 g sugar and >5 g dietary fiber/100 g) | 15 (0.34) |
|
Meat and alternatives, including unsalted nuts, seeds, and their pastes | 10 (0.22) |
|
Vegetables and vegetable products without added fats, sugars, or salt | 4 (0.08) |
|
Low fat or salt meals: frozen or packaged meals (≤6 g saturated fat and <900 mg sodium per serve), soups (<2 g fat/100 g, exclude dehydrated), sandwiches, and mixed salads | 3 (0.07) |
|
Healthy snacks: based on core foods (<600 kJ and <3 g saturated fat and <200 mg sodium per serve) | 3 (0.07) |
|
Oils high in mono- or polyunsaturated fats | 1 (0.02) |
|
2579 (58.01) | |
|
Chocolate and confectionery | 539 (12.12) |
|
Fast food restaurant or delivery service: unhealthy options | 503 (11.31) |
|
Sugar-sweetened beverages | 435 (9.78) |
|
Alcohol | 244 (5.48) |
|
Sweet breads, cakes and biscuits, and high-fat savory biscuits and pastries | 165 (3.71) |
|
Savory snack foods with added salt or fat include chips, extruded snacks, flavored popcorn, and salted or coated nuts | 155 (3.48) |
|
Local restaurant or delivery service: unhealthy options | 142 (3.19) |
|
Supermarket or retailer: unhealthy options | 85 (1.91) |
|
Ice cream and iced confection | 84 (1.88) |
|
Other high-fat or salt products include spreads with added salt, animal fats, high-fat savory sauces (>10 g fat/100 g), and soups (>2 g fat/100 g, dehydrated) | 75 (1.68) |
|
High-sugar or low-fiber breakfast cereals (>20 g sugars or <5 g dietary fiber/100 g) | 34 (0.76) |
|
Full cream milk and yogurts (>3 g fat/100 g) and cheese (>15 g fat/100 g, high-salt cheeses) and alternatives | 32 (0.72) |
|
Flavored or fried instant rice and noodles | 37 (0.83) |
|
Sweet snack foods include sugar-coated dried fruits or nuts and nut- or seed-based bars | 14 (0.31) |
|
Fruit juice or drinks with <98% fruit | 13 (0.31) |
|
Meat and alternatives processed or preserved in salt | 12 (0.27) |
|
High-fat or salt meals: frozen or packaged meals (>6 g saturated fat or >900 mg sodium per serve) | 10 (0.22) |
|
1759 (39.56) | |
|
Fast food restaurant or delivery service: no specific product | 931 (20.94) |
|
Local restaurant or delivery service: no specific product | 365 (8.21) |
|
Supermarket or retailer: no specific product | 207 (4.66) |
|
Local restaurant or delivery service: only healthier options | 111 (2.49) |
|
Tea and coffee | 51 (1.15) |
|
Dietary supplements and sugar-free gum | 26 (0.58) |
|
Fast food restaurant or delivery service: only healthier options | 25 (0.56) |
|
Supermarket or retailer: only healthier options | 22 (0.49) |
|
Recipe additions: include soup cubes, seasonings, and other sauces | 19 (0.43) |
|
Food manufacturer: no specific product | 2 (0.04) |
Participants were sent a unique link to a web-based questionnaire at the start and end of the study. This captured data on their usual time spent on the web on mobile devices and on all devices on weekdays and weekend days, social media use (on which platforms they had accounts, number of people per pages they followed on each account, and number of food brands they followed), number of food or beverage brand apps they had on their device, and number of emails or SMS messages they received each week from food or beverage companies.
Statistical analyses were conducted using SPSS for Windows, version 25 (IBM Corporation). Data were analyzed descriptively, including the types of promotions (
The final sample of 95 children uploaded 272.8 hours of recordings, of which 267.8 hours were relevant (captured web-based use, excluding personal messaging and banking). The study completion rate was 14.8% (95/644). Across the 2 rounds of recruitment, 736 people were disqualified based on the prescreening questionnaire. Furthermore, 429 people declined to participate or did not start the task after qualifying, 95 dropped out during the study, and 25 were excluded as they did not reach the 30% video upload threshold of reported usual time on the web on mobile devices. Across the 280 days of recordings captured, 23% (22/95) reached a threshold of 75%-100% of the usual recorded time spent on the internet, 45% (43/95) captured 50%-74% of the usual time spent on the internet, and 32% (30/95) captured less than 50% of the usual time spent on the internet.
Sample description (n=95).
Child characteristics | Statistics | ||
Age (years), mean (SD) | 16.2 (1.07) | ||
Usual weekly web-based media use mobile devices (hours), mean (SD) | 12.1 (9.71) | ||
Usual weekly web-based media use all devices (hours), mean (SD) | 28.9 (18.36) | ||
|
|||
|
Male | 32 (34) | |
|
Female | 63 (66) | |
|
|||
|
Low | 15 (16) | |
|
Medium | 26 (27) | |
|
High | 51 (54) | |
|
|||
|
87 (92) | ||
|
69 (73) | ||
|
Snapchat | 68 (72) | |
|
Music streaming apps | 68 (72) | |
|
YouTube | 43 (45) | |
|
29 (31) | ||
|
25 (26) | ||
|
Twitch | 10 (11) | |
|
|||
|
Frequency (n=91), n (%) | 43 (45) | |
|
Number followed, mean (SD) | 0.8 (0.47) | |
|
|||
|
Frequency (n=91), n (%) | 71 (75) | |
|
Number of apps, mean (SD) | 1.9 (1.80) | |
|
|||
|
None | 27 (28) | |
|
1-5 per week | 51 (54) | |
|
6-10 per week | 11 (12) | |
|
11 or more per week | 2 (2) |
Across the sample recordings, there were 4446 food and beverage promotions. Of these 4446 promotions, 2613 (58.77%) were earned media impressions, 732 (16.46%) were on media
The INFORMAS food classification system was used to describe the nature of foods and beverages, as a large number (n=1840) could not be classified using the WHO European nutrient profiling food categories. The highest proportion of promoted foods and beverages was noncore (2579/4446, 58.01%;
Children were exposed to a median of 17.4 food or beverage promotions each hour on the internet for a total duration of 1.3 minutes per hour (IQR 1-2;
Weighted median rates of web-based food and beverage promotions per hour and by weekly exposures on mobile devices.
Rate of promotions | Weighted median rate per hour (IQR) | Weighted median rate on mobile devices per week (IQR) | |||
Total promotion count | 17.4 (10-26) | 168.4 (85-289) | |||
|
|||||
|
Earned media | 9.9 (6-15) | 84.8 (40-177) | ||
|
Paid media | 3.7 (1-8) | 36.1 (12-75) | ||
|
Owned media | 0.6 (0-3) | 5.3 (0-36) | ||
|
|||||
|
Not permitted | 10.0 (5-17) | 99.5 (43-159) | ||
|
Company brand only | 4.4 (2-8) | 37.2 (17-89) | ||
|
Permitted | 0.2 (0-1) | 3.6 (0-8) | ||
|
Not applicable | 0.0 (0-0.4) | 0.0 (0-5) | ||
|
|||||
|
Noncore foods | 10.1 (5-17) | 99.4 (43-159) | ||
|
Miscellaneous | 6.4 (2-10) | 52.9 (24-99) | ||
|
Core foods | 0.0 (0-1) | 0.0 (0-8) | ||
|
|||||
|
Fast food restaurants, no specific product | 1.9 (0.6-4) | 17.1 (6-46) | ||
|
Fast food restaurants, unhealthy products | 1.8 (0.3-4) | 16.5 (5-34) | ||
|
Chocolate and confectionery | 1.5 (0.3-3) | 12.4 (4-29) | ||
|
Sugar-sweetened beverages | 0.9 (0-3) | 11.6 (0-27) |
aUsing World Health Organization for Europe Nutrient Profiling Model.
b2.59% (115/4446) could not be specified because of unavailable nutrition composition information.
cINFORMAS: International Network for Food and Obesity/noncommunicable diseases Research, Monitoring and Action Support.
dUsing International Network for Food and Obesity/noncommunicable diseases Research, Monitoring and Action Support food classification.
Considering children’s reported usual time on the web on mobile devices, children would be exposed to a median of 168.4 food and beverage promotions on the internet on mobile devices per week for a total duration of 13.2 minutes (IQR 7-27). Children would be exposed to a median of 99.5 food promotions per week on their mobile devices that would not be permitted using WHO nutrient profiling criteria. This includes a median of almost 34 promotions per week for fast food restaurants or delivery services (company only or promoting unhealthy choices), 12.4 promotions for chocolate and confectionery, and 11.6 promotions for sugar-sweetened beverages.
The rates of promotions per hour varied by platform (Kruskal-Wallis H7=142.12;
Weighted median rates of web-based food and beverage promotions, by platform. The rates given as a function of time spent on platform, except for food apps, which is given as a function of total time on the web for only those reporting having food apps on phone. Error bars represent IQR. The number of participants visiting each site during the study was 76 for Instagram, 57 for Facebook, 40 for Snapchat, 11 for Pinterest, 22 for Twitter, 58 for YouTube, and 53 for other platforms (apps and websites).
Participant engagement with promotions included
Using negative binomial regression, the only factor that was significantly associated with weekly exposure to food and beverage promotions was the amount of time spent on the internet on mobile devices (B=0.54, SE 0.01;
Negative binomial regression incident rate ratios of the count of weekly exposures to food and beverage promotions on mobile devices.
Independent variable | Incidence rate ratio (95% CI) |
Number of food apps | 1.09 (0.98-1.21) |
Usual weekly time on the web (mobile devices) | 1.06 (1.03-1.08)a |
Any accounts following on social media | 1.02 (0.96-1.08) |
Age | 1.02 (0.85-1.24) |
Food brands following on social media | 0.95 (0.81-1.11) |
a
This study exposes Australian children’s exceedingly high exposure to food marketing during their usual time on mobile devices. During each hour that a child spends on the internet on their mobile device, they would see more than 17 food and beverage promotions, equating to 168 promotions per week and 8736 promotions per year. For each hour increase in usual time on the internet on mobile devices per week, children’s exposure to food promotions was found to increase by 6%. Our food marketing exposure estimates are likely to be highly conservative, given that they capture exposures only on mobile devices and not on desktop computers. There is some evidence to suggest that marketing on mobile and nonmobile devices is similar [
The rates of promotions for unhealthy products were far greater than promotions for healthier choices. Each week, children would be exposed to almost 100 promotions on their mobile devices for foods and beverages that would not be recommended to be marketed to children according to WHO nutrient profiling criteria. In addition, children would see around 17 promotions per week for fast food restaurant companies without a specific product promoted. These could not be appropriately classified using the WHO criteria as recommended to be permitted or otherwise, thus identifying a major limitation of these criteria for classifying food-related brands that should or should not be marketed to children. Although many fast-food outlets sell and promote
We found that the greatest proportion of food promotion exposures earned media impressions. Although these promotions ostensibly derive from children’s web-based social networks, the brand is often the initiator of earned media messages [
Children in our study engaged with food and beverage promotions by
Our finding of the high rates of earned media impressions for unhealthy foods and beverages has major implications for public policy responses to protect children from this marketing. To inform new regulations planned in the United Kingdom to protect children from unhealthy food marketing on television and on the web, the government undertook an impact assessment to evaluate the potential costs and benefits of marketing restrictions [
To date, most studies seeking to assess the nature and extent of food marketing to children on the internet have been limited to measuring either paid advertising on third-party websites [
This study has some limitations. We did not achieve our target sample of 150 children. The final sample was lower than our original anticipated sample because of the substantial time involved in subject recruitment, technical errors with video uploads, and difficulties in obtaining complete data from participants. In an earlier pilot study, we had achieved a response rate of approximately 50%. This was substantially reduced in the main study, as we introduced a minimum threshold for daily video upload time. Surprisingly, we did not find significant associations between the number of overall accounts or food accounts that children followed on the internet or the number of food apps they had on their device and their marketing exposures. The CIs around the IRRs for these variables were wide, and future studies may be more adequately powered to detect significant associations. The minimum threshold for daily video upload of 30% of the usual time spent on the web on mobile devices meant that we did not capture all time spent on the internet on mobile devices. However, a comparison of the rates of food marketing exposures across data sets manipulated to include between 30% and 80% of the usual time spent on the internet recorded found there to be excellent reliability across the data sets (data not shown). Finally, the recruitment of children through the market research company survey panel may affect the generalizability of the findings to a broader population. However, this approach allowed us to capture a national sample, with representation from metropolitan and regional areas. This panel recruits multiple web-based and offline sources to recruit a broad spectrum of participants.
Opportunities for protecting children from web-based food marketing span legislative or regulatory controls, industry codes of practice for responsible marketing, and interventions that operate on an individual level to block exposure to marketing content. Internationally, some governments have introduced or are introducing restrictions on unhealthy food marketing to children on the web. As mentioned previously, the UK government announced plans to introduce a ban on all web-based marketing of unhealthy foods and beverages by 2022, as part of its national obesity prevention strategy [
To date, food industry codes of practice for responsible marketing largely fail to cover the types of web-based platforms that children use or the types of marketing they see or engage with on these platforms. For example, the International Food & Beverage Alliance Global Policy on Marketing Communications to Children only applies to media primarily directed to children aged <12 years and only applies to company-owned websites [
Finally, ad blockers and antitracking apps are available to block paid advertising and web-based tracking, which enables targeted advertising, on desktop computers and mobile devices. This includes software to block advertising and sponsored posts on social media. Some paid versions of social media platforms, such as YouTube Premium, also offer ad-free content. Although this study and others have highlighted that paid advertising is only a minority of the marketing impressions that children see on the web, this software may still be useful in reducing up to one-fifth of the web-based food marketing that children are exposed to. However, it is likely that widespread uptake of ad blockers would lead brands to invest further in earned and owned media, thereby further increasing those types of media impressions.
Using real-time monitoring over a 3-day period, this study identified that Australian children are exposed to an outstanding volume of web-based food marketing on their mobile devices. This marketing is predominantly for unhealthy products and is shared through web-based communities. Children typically engage in web-based marketing multiple times each week. This exposure to, and interaction with, brand content that is seemingly endorsed by peers or web-based communities likely heightens the effects of marketing on children’s brand attitudes and consumption behaviors. Governments and the media industry can and have designed policies to protect children from this marketing. The rapid acceleration and the use of data analytics and technologies used to capture personal data for targeted marketing is outstripping current legislation and policies for appropriate marketing regulations and related ethical concerns. To ensure that such policies are effective, they need to extend beyond paid advertising to paid content in posts generated through web-based communities, influencers, and celebrities.
International Network for Food and Obesity/noncommunicable diseases Research, Monitoring and Action Support
incidence rate ratio
World Health Organization
This research was funded by the Australian Research Council Discovery Early Career Researcher Award (DE170100051). Piloting of the study was funded by an Australian National Preventive Health Agency grant (application ID1022310). The authors wish to thank Nina Balla, who contributed to the piloting of the data collection and analysis methods as part of her honors research project. The authors thank Amy Vassallo, who led the early testing of data collection methods and participant recruitment, and Grace Norton, who greatly contributed to the project through their management of data collection and data coding. The authors appreciate the time and effort given by the study participants in providing the video data.
None declared.