Faculty Bibliography

Background/UNASSIGNED:Social media advertising by fast food companies continues to increase globally, and exposure to food advertising contributes to poor diet and negative health outcomes (eg, cardiovascular disease). McDonald's-the largest fast food company in the world-operates in 101 countries, but little is known about their marketing techniques in various regions. The objective of this study was to compare the social media advertising practices of McDonald's-the largest fast food company in the world-in 15 high-income, upper-middle-income and lower-middle-income countries. Methods/UNASSIGNED:We randomly selected official McDonald's Instagram accounts for 15 high-income, upper-middle-income and lower-middle-income countries. We captured all the screenshots that McDonald's posted on those Instagram accounts from September to December 2019. We quantified the number of followers, 'likes', 'comments' and video views associated with each account in April 2020. We used content analysis to examine differences in the marketing techniques. Results/UNASSIGNED:The 15 accounts collectively maintained 10 million followers and generated 3.9 million 'likes', 164 816 comments and 38.2 million video views. We identified 849 posts. The three lower-middle-income countries had more posts (n=324; M, SD=108.0, 38.2 posts) than the five upper-middle-income countries (n=227; M, SD=45.4, 37.5 posts) and seven high-income countries (n=298; M, SD=42.6, 28.2 posts). Approximately 12% of the posts in high-income countries included child-targeted themes compared with 22% in lower-middle-income countries. Fourteen per cent of the posts in high-income countries included price promotions and free giveaways compared with 40% in lower-middle-income countries. Conclusions/UNASSIGNED:Social media advertising has enabled McDonald's to reach millions of consumers in lower-middle-income and upper-middle-income countries with disproportionately greater child-targeted ads and price promotions in lower-middle-income countries. Such reach is concerning because of the increased risk of diet-related illnesses, including cardiovascular disease, in these regions.

This study measured the total quantity and composition of waste generated in a large, New York City (NYC) hospital kitchen over a one-day period to assess the impact of potential waste diversion strategies in potential weight of waste diverted from landfill and reduction in greenhouse gas (GHG) emissions. During the one-day audit, the hospital kitchen generated 1515.15 kg (1.7 US tons) of solid waste daily or 0.23 kg of total waste per meal served. Extrapolating to all meals served in 2019, the hospital kitchen generates over 442,067 kg (487 US tons) of waste and emits approximately 294,466 kg of CO2e annually from waste disposal. Most of this waste (85%, 376,247 kg or 415 US tons annually) is currently sent to landfill. With feasible changes, including increased recycling and moderate composting, this hospital could reduce landfilled waste by 205,245 kg (226 US tons, or 55% reduction) and reduce GHG emissions by 189,025 kg CO2e (64% reduction). Given NYC's ambitious waste and GHG emission reduction targets outlined in its OneNYC strategic plan, studies analyzing composition, emissions, and waste diversion potential of large institutions can be valuable in achieving city sustainability goals.

Several U.S. jurisdictions have adopted policies requiring healthy beverage defaults on children's menus, but it is unknown whether such policies or restrictions leads to fewer calories ordered. We recruited 479 caregivers of children for an online choice experiment and instructed participants to order dinner for their youngest child (2-6 years) from two restaurant menus. Participants were randomly assigned to one type of menu: 1) standard beverages on children's menus (Control; n = 155); 2) healthy beverages on children's menus (water, milk, or 100% juice), with unhealthy beverages available as substitutions (Default; n = 162); or 3) healthy beverages on children's menus, with no unhealthy beverage substitutions (Restriction; n = 162). We used linear regression with bootstrapping to examine differences between conditions in calories ordered from beverages. Secondary outcomes included percent of participants ordering unhealthy beverages (full-calorie soda, diet soda, and/or sugar-sweetened fruit drinks) and calories from unhealthy beverages. Calories ordered from beverages did not differ across conditions at Chili's [Default: 97.6 (SD = 69.8); p = 0.82; Restriction: 102.7 (SD = 71.5); p = 0.99; Control: 99.4 (SD = 72.7)] or McDonald's [Default: 90.2 (SD = 89.1); p = 0.55; Restriction: 89.0 (SD = 81.0); p = 0.94; Control: 96.5 (SD = 95.2)]. There were no differences in the percent of orders or calories ordered from unhealthy beverages. Though Restriction participants ordered fewer calories from full-calorie soda [(3.0 (SD = 21.6)] relative to Control participants [13.4 (SD = 52.1); p = 0.04)] at Chili's, we observed no such difference between Default and Control participants, or across McDonald's conditions. Overall, there was no effect of healthy default beverages or restrictions in reducing total calories ordered from unhealthy beverages for children in our experiment.

OBJECTIVES:We aimed to determine the frequency with which kid influencers promote branded and unbranded food and drinks during their YouTube videos and assess the nutritional quality of food and drinks shown. METHODS:Researchers used Socialbakers data to identify the 5 most-watched kid influencers (ages 3 to 14 years) on YouTube in 2019. We searched for 50 of their most-watched videos and 50 of their videos that featured food and/or drinks on the thumbnail image of the video. We coded whether kid influencers consumed or played with food or toys, quantified the number of minutes food and/or drinks appeared, and recorded names of branded food and/or drinks. We assessed the nutritional quality of foods using the Nutrient Profile Model and identified the number of drinks with added sugar. RESULTS:= 7; 2.4%; eg, Yoplait yogurt). CONCLUSIONS:Kid influencers generate millions of impressions for unhealthy food and drink brands through product placement. The Federal Trade Commission should strengthen regulations regarding product placement on YouTube videos featuring young children.

BACKGROUND:Social media platforms have created a new advertising frontier, yet little is known about the extent to which this interactive form of advertising shapes adolescents' online relationships with unhealthy food brands. OBJECTIVE:We aimed to understand the extent to which adolescents' preferences for Instagram food ads are shaped by the presence of comments and varying numbers of "likes." We hypothesized that adolescents would show the highest preferences for ads with more "likes" and comments. We predicted that these differences would be greater among adolescents who were "heavy social media users" (ie, >3 hours daily) vs "light social media users" (ie, <3 hours daily). METHODS:We recruited Black and non-Latinx White adolescents (aged 13-17 years; N=832) from Dynata, a firm that maintains online participant panels. Participants completed an online survey in which they were randomized to view and rate Instagram food ads that either did or did not show comments. Within each condition, adolescents were randomized to view 4 images that had high (>10,000), medium (1000-10,000), or low (<100) numbers of "likes." Adolescents reported ad preferences and willingness to engage with the brand. RESULTS:Adolescents rated ads with medium or high numbers of "likes" higher than ads with few "likes" (P=.001 and P=.002, respectively). Heavy social media users (>3 hours/day) were 6.366 times more willing to comment on ads compared to light users (P<.001). CONCLUSIONS:Adolescents interact with brands in ways that mimic interactions with friends on social media, which is concerning when brands promote unhealthy products. Adolescents also preferred ads with many "likes," demonstrating the power of social norms in shaping behavior. As proposed in 2019, the Children's Online Privacy and Protection Act should expand online advertising restrictions to include adolescents aged 12 to 16 years.

INTRODUCTION/BACKGROUND:Previous research on sugar-sweetened beverage trends has focused on self-reported consumption from surveys. Few studies used objective store sales or explored differences by area-level demographics and store type. METHODS:The average volume of beverages sold per store per 3-digit zoning improvement plan code from 2006 to 2015 was calculated using national Nielsen Retail Scanner point-of-sale data from 24,240 stores. A multilevel regression model analyzed annual trends, with random intercepts for state and separate models for beverage type (regular soda, no/low-calorie soda, other sugary drinks, 100% fruit juice, bottled water). Differences by store type (convenience, supermarkets, drug stores, mass merchandisers) and area-level demographics (categorized as tertiles) were examined. Data were analyzed in 2019. RESULTS:The model-based estimates indicated that sales of regular soda (-11.8%), no/low-calorie soda (-19.8%), and 100% fruit juice (-31.9%) decreased over time, whereas sales of bottled water (+34.4%) increased and sales of other sugary drinks remained stable (+2.4%). Decreases in sugar-sweetened beverage sales were largely concentrated in supermarkets and larger in areas with high income and education levels and a high percentage of black and Hispanic people. There were also relatively larger increases in bottled water sales in states located in the South and Midwest. CONCLUSIONS:The finding that sales of sugar-sweetened beverages decreased over time, whereas sales of bottled water increased is encouraging because sugar-sweetened beverage consumption is linked to obesity and other chronic conditions. This study provides a novel, rigorous assessment of U.S. beverage sales trends and differences by community and store characteristics.

Sugary drinks and processed foods are associated with negative health outcomes in adults, including weight gain, and their consumption should be limited. However, they may be difficult to avoid if they are ubiquitously available in the retail environment. This study aimed to quantify the availability of such products for sale throughout New York City (NYC) at both food and non-food retailers. In 2018, ten one-mile retail-dense NYC street segments were selected for the sample. Data collectors canvassed each segment and visited all retailers, recording the type (food/non-food) and presence of processed food and beverages for sale. Descriptive statistics were analyzed for availability of products sold in retailers overall and by retailer type. In total, 491 retailers were identified (191 food, 300 non-food). Sugary drinks were available at 83% of food retailers and 19% of non-food retailers, while processed foods were available at 61% of food retailers and 16% of non-food retailers. Eighty-five percent of food retailers and 21% of non-food retailers sold sugary drinks and/or processed foods. This study supports and builds on results of previous research examining the availability of food and beverages in the retail environment. Sugary drinks and processed foods are ubiquitous at food and non-food retailers, providing pervasive cues to consume energy-dense, nutrient-poor products. Restrictions on where such products can be sold merit consideration.

No studies have documented the prevalence of the food industry's funding of academic programs, which is problematic because such funding can create conflicts of interest in research and clinical practice. We aimed to quantify the publicly available information on the food industry's donations to academic programs by documenting the amount of donations given over time, categorizing the types of academic programs that receive food industry donations, cataloguing the source of the donation information, and identifying any stated reasons for donations. Researchers cataloged online data from publicly available sources (e.g., official press releases, news articles, tax documents) on the food industry's donations to academic programs from 2000 to 2016. Companies included 26 food and beverage corporations from the 2016 Fortune 500 list in the United States. Researchers recorded the: (1) monetary value of the donations; (2) years the donations were distributed; (3) the name and type of recipient; (4) source of donation information; and (5) reasons for donations. Adjusting for inflation, we identified $366 million in food industry donations (N = 3274) to academic programs. Universities received 45.2% (n = 1480) of donations but accounted for 67.9% of total dollars given in the sample. Community colleges, schools (i.e., preschool, elementary, middle, and high schools), and academic nonprofits, institutes, foundations, and research hospitals collectively received 54.8% of the donations, but made up less than one-third of the monetary value of donations. Half of the donations (49.0%) did not include a stated reason for the donation. In our sample, donations grew from $3 million in 2000 to $24 million in 2016. Food companies in our sample donated millions of dollars to universities and other academic programs but disclosed little information on the purpose of the donations. Achieving transparency in donation practices may only be possible if federal policies begin to require disclosures or if companies voluntarily disclose information.

: Background: To determine how many adolescents follow food/beverage brands on Instagram and Twitter, and examine associations between brands' youth-targeted marketing practices and percentages of adolescent followers.