1

Viewers vs. Doers:

The Relationship Between Watching Food Television and BMI."

Authors: Lizzy Pope, Lara Latimer, and Brian Wansink1

This is the author’s version of a work that was accepted for publication in Appetite. Changes resulting from the

publishing process, such as peer review, editing, corrections, structural formatting, and other quality control

mechanisms may not be reflected in this document. Changes may have been made to this work since it was

submitted for publication

1Lizzy Pope, PhD RD is a post-doctoral Fellow at the Charles H. Dyson School of Applied

Economics and Management, Cornell University, Ithaca, NY 14853 USA. Email is

efpope@uvm.edu . Lara A. Latimer, PhD is a post-doctoral Fellow at the Charles H. Dyson

School of Applied Economics and Management, Cornell University, Ithaca, NY 14853 USA

email: lara.a.latimer@gmail.com. Brian Wansink, PhD is the John S. Dyson Professor of

Marketing at the Charles H. Dyson School of Applied Economics and Management, Cornell

University. 110 Warren Hall, Ithaca, NY 14853 email:fblsubmissions@cornell.edu.

Corresponding author is Brian Wansink. Phone: 1-607-229-3896. Fax: 1-607-254-6302.

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“

Viewers vs. Doers:

The Relationship Between Watching Food Television and BMI

Abstract

The objective of this study was to examine where nutritional gatekeepers obtain information

about new foods, and whether information source is associated with Body Mass Index (BMI), as

well as whether any association varied according to how often the participant cooked from

scratch. A national panel survey of 501 females aged 20-35 assessed how participants obtained

information on new recipes, and asked a series of questions about their cooking habits, their

weight and height. Linear regressions were run to determine associations between information

source, cooking from scratch, and BMI. Obtaining information from cooking shows was

positively correlated with BMI (p<0.05), as was obtaining information from social media

(p<0.05), whereas obtaining information from other print, online, or in-person sources was not

significantly associated with BMI. A significant interaction between watching cooking shows

and cooking from scratch indicated that cooking from scratch, as well as watching cooking

shows was associated with higher BMI (p<0.05). Obtaining information about new foods from

television cooking shows or social media – versus other sources – appears to have a unique

relationship with BMI. Furthermore, watching cooking shows may have a differential effect on

BMI for those who are merely TV “viewers,” versus those who are “doers.” Promoting healthy

foods on cooking shows may be one way to positively influence the weight status of “doers” as

well as “viewers.”

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Keywords: BMI Body Mass Index, Cooking, Food Information, Food Television, Young

Women

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Introduction

Since the late 1980’s, a decreasing percentage of American meals have been cooked from

home (Kant & Graubard, 2004; Smith, Shu Wen, & Popkin, 2013). While this could be

interpreted as a growing disinterest in cooking and food preparation, there has puzzlingly been a

simultaneous increase in the popularity of watching other people cook on television. The Food

Network was established in 1993, and by 2012 it averaged over 1.1 million nightly viewers,

making it a top-ten cable network (Networks, 2012). With its immense popularity, the Food

Network – and food television in general -- may be a unique source of recipe and food

information for nutritional gatekeepers who are thought to influence 72% of the food eaten by

members of their households (Brian Wansink, 2006).

There is a wide range of food-related programming offered by the Food Network and

other TV networks, ranging from instructional, to aspirational, to experiential. In a study on how

the Food Network establishes consumer fantasies, Ketchum suggested that the programming

found on the network falls into one of four categories, traditional domestic instructional cooking,

personality-driven domestic cooking, food travel programs, and avant-garde programming

(Ketchum, 2005). Programs on the Food Network representing the traditional domestic

instructional shows, such as “Thirty Minute Meals” with Rachel Ray, appeal to those who watch

the Food Network in hopes of learning new cooking skills or techniques. These shows tend to

provide instruction on how to make meals that will please others and have been found to appeal

to women and professionals who are more likely to view the Food Network in order to learn

specific cooking skills (Caraher, Lange, & Dixon, 2000; De Solier, 2005; Ketchum, 2005). The

other three categories appeal to those who watch the Food Network for more aspirational or

vicarious experiential reasons. Programming in these categories often makes food appear sexual,

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extreme, or fun (Caraher et al., 2000; Ketchum, 2005; Meister, 2001). By watching Guy Fiere

travel around the country trying food from various “Diners, Drive-Ins, and Dives,” viewers may

feel that they too can experience the comfort cuisine of a variety of communities. Viewers of

this type of programming are not necessarily looking for cooking instruction, but rather for

entertainment, and represent the majority of those who watch food TV (Caraher et al., 2000;

Ketchum, 2005).

It is possible, given today’s pervasive media portrayal of the thin ideal and dieting

culture, that individuals, particularly women, may engage in what we are coining “vicarious

gluttony.” That is, they may use food television as an outlet for actual behaviors that are less

acceptable in today’s society, where attention to the dangers of obesity and promotion of healthy

eating have become commonplace. To these viewers, cooking programs may offer pleasure

vicariously, as food programming on TV often promotes overconsumption and gratification,

which are generally frowned upon in today’s culture of “dieting” (Caraher et al., 2000; Ketchum,

2005; Meister, 2001). Other sources of food information such as magazine articles and blogs

could also be seen as sources of vicarious gluttony, but they don’t necessarily combine modeling

by admired “authority” figures and the ability to “transport” viewers to fantasy locations as

skillfully as TV food programs can. It is known that behavioral modeling can powerfully

influence behavior (Pliner & Mann, 2004), so if food TV provides unhealthy models, it could

have an unintended negative influence on viewers.

The health impact of getting food information from food television is not clear. If

watching cooking shows helps teach viewers skills to prepare healthful meals at home, then

watching food TV might promote health (Clifford, Anderson, Auld, & Champ, 2009).

Conversely, if the overconsumption and unhealthiness often portrayed help set cultural norms

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that reflect eating excess calories and fat, watching food TV might have a negative affect on

one’s weight and health. Consistent with this view, a recent study performed in the United

Kingdom, found that TV chefs’ recipes were high in fat, saturated fat, and sodium compared to

the World Health Organization’s nutritional guidelines (Howard, Adams, & White, 2012). The

TV recipes also contained more calories, protein, and fat than supermarket pre-prepared meals,

which traditionally are not that nutritious either (Howard et al., 2012). Therefore, if viewers are

watching food TV to learn cooking skills and gather recipes they may be at-risk for unhealthy

eating patterns. One way to explore the relationship between watching food TV and body mass

index (BMI), may be to examine the differential effects of watching food TV on “doers” – those

who watch food TV and actually cook, versus on “viewers” – those who watch TV but do not

actually cook. Previous studies have indicated that involvement in food preparation is associated

with better diet quality, and lower BMI (Kolodinsky & Goldstein, 2011; Thorpe, Kestin, Riddell,

Keast, & McNaughton, 2013). However, getting recipe information from TV chefs and cooking

from scratch could actually lead to higher BMI from the preparation of recipes high in calories

and fat.

The impact of food TV may be the greatest on those who are less experienced with

cooking and are either living on their own or with a young family (Day, Kyriazakis, & Rogers,

1998). Given the impact these nutritional gatekeepers have or will soon have on their family,

and given that 70% of nutritional gatekeepers are still believed to be women (Day et al., 1998),

the objectives of this study were to focus on women 20-35 to determine whether obtaining

information about new foods from cooking shows was differentially associated with their BMI,

compared to information from other sources. Secondly, the study examined whether any

relationship between watching cooking shows and BMI was different for those who often cooked

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from scratch versus those who did not. We hypothesize 1) obtaining food information from

cooking shows will be positively correlated with BMI whereas obtaining food information from

other sources will not be. 2) that young women who receive guidance from cooking shows will

weigh more than those who do not watch these shows, and 2) the more frequently a viewer of

cooking shows cooks from scratch, the more she will weigh relative to someone who cooks less

frequently.

Methods

Recruitment was conducted from a subset of a national panel maintained by the MSR

group (Omaha, NE) in 2012 (B Wansink & Sudman, 2002). Because the focus of the study was

on how new cooks learned about recipes, the sample concentrated on women age 20-35 who

were living on their own, were non-vegetarian, and whose family had lived in the U.S. for at

least two generations. The generational requirement was relevant to another, larger study aim.

The 501 participants were chosen randomly from the larger national consumer panel, and

received a $4 e-coupon to participate. After providing informed consent, participants were asked

to complete an online survey identifying their three favorite places to learn about new foods

using an unranked checklist. Options for new food information sources were as follows: recipes

on the package, health websites, YouTube, magazines, newspapers, point of purchase recipes,

cooking shows, cooking blogs, dietitians or health experts, in-store samples, social media, family

and friends, dining out, and cooking classes. Participants were also asked to identify on a Likert

scale (1=strongly disagree;; 9=strongly agree) how much they agreed with the statement, “I often

cook meals from scratch.” Participants decided what “cooking from scratch” meant to them

individually, so the definition of what it meant to cook from scratch could have varied between

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participants. BMI was obtained from participants’ self-reported height and weight, which has

been found to be highly correlated with actual height and weight (Harvey-Berino et al., 2011).

The study protocol was approved by the Institutional Review Board for Human Subjects.

Before performing regression quantitative analysis for this cross-sectional study, tests of

distribution normality were conducted across key variables. There were no missing cases on any

variable, nor any concerning outliers. To determine if our information sources were highly

correlated we obtained Pearson Correlation coefficients. We performed a regression analysis

with each recipe information source as an independent variable and BMI as the dependent

variable, with age, education, and race/ethnicity as covariates. A second regression was

conducted to examine the extent to which each recipe information source and cooking from

scratch predicted BMI, as well as the interaction between each information source and cooking

from scratch on BMI, As with the first regression, we controlled for age, education, and

race/ethnicity in the regression with the interactions. The interaction regression was conducted

by mean centering the cooking from scratch variable to reduce colinearity between cooking from

scratch and the interaction terms of each recipe source and cooking from scratch. An interaction

term was computed by multiplying the centered cooking from scratch value by each source of

new recipe information for each participant. All variables were entered in one step. To arrive at

the final model, all interaction terms that were not significant were eliminated from the model.

To compute group means, those who responded with a six or greater to the item asking about

their frequency of cooking from scratch were designated as “cook from scratch” (n=286), those

who responded with a four or less on the same item were designated as “do not cook from

scratch” (n=152), and those who responded with five were excluded (n=63). SPSS (version

21.0, 2013, IBM) was used for all analyses.

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Results

Complete surveys were obtained from 501 females (99.8%), with an average age of 26.8

(S.D.=3.13, range=20-35 years) and a mean BMI of 25.96 (S.D.=6.17, range=16.34-57.58).

Forty-three percent of the sample was Caucasian, 27% was Black, 25% was Hispanic, and 5%

was other. Our information sources were not highly correlated (Table 1). Therefore, we were

not concerned with multicolinearity and did not group our information sources into categories.

Regression analyses indicated that the only sources of information about new foods

significantly related to BMI were watching cooking shows (b=1.60, t(481)=2.11, p=0.04) and

using social media (b=1.82, t(481)=2.22, p=0.03. (Table 2)

Linear regression analysis testing the interactions between cooking from scratch and each

of our recipe-information-source variables (Table 3) showed that the overall R2 for the model

was not significant (R2=0.05, F(21, 479)=1.46, p=0.09). The simple effect of cooking from

scratch was not significant when the value of watching cooking shows was equal to zero (b=-

0.11, t(479)=-0.76, p=0.45). The simple effect of watching cooking shows was also not

significant when the value of cooking from scratch was equal to zero (b=1.45, t(479)=1.91,

p=0.06). The simple effect of using social media was significant when the value of cooking

from scratch was equal to zero (b=1.87, t(479)=2.28, p<0.05. The only significant interaction

between cooking from scratch and food information source was when new food information was

sourced from watching cooking shows. The interaction of watching cooking shows and cooking

from scratch was significant (b=0.54, t(479)=2.24, p=0.03). The significant interaction indicates

that the relationship between cooking from scratch and BMI is different for those who do, versus

those who do not, watch cooking shows. The mean BMI for those who watch cooking shows

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and do not cook from scratch frequently was 25.63(S.D.=5.24), versus a mean BMI of 27.49

(S.D.=7.37) for those who watch cooking shows and cook frequently from scratch. Figure 1

illustrates that BMI does not differ greatly for those who do not watch cooking shows and who

do or do not cook from scratch, but that BMI is greater for those who watch cooking shows and

often cook from scratch. In terms of weight, those who watched cooking shows and cooked

frequently from scratch had a mean weight of 164.2 lb. (S.D.=49.61), and those who watched

cooking shows and did not cook frequently from scratch had a mean weight of 152.8 lb.

(S.D.=30.51). The average weight for those individuals who did not watch cooking shows was

152.9 lb. (S.D.=37.98). The weights and BMIs reported above are raw means generated from the

dichotomization of the cooking from scratch variable, a process explained in the Methods

section.

Discussion

The only sources of recipe information related to one’s BMI were cooking shows and

social media. Watching chefs prepare indulgent dishes on TV, watching a famous host enjoy

over-the-top foods with other people all over the country, or viewing others’ social media food

pictures and recipes might suggest a social norm for preparing these types of foods. Social

norms and the modeling of eating behavior have been shown to impact food intake in many

settings and contexts (Robinson, Blissett, & Higgs, 2013). Relevant to the current work,

previous research has shown that food-intake modeling can occur just by watching someone who

is not physically present eat, the exact situation that may occur when people watch cooking

shows on television (Robinson et al., 2013). Additionally, it is possible that social media sites

(e.g., Facebook, Twitter) may contribute to an individual’s perception of others’ food purchases,

or what they cook and eat. This could, in turn, affect their own food-related behavior. For

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example, based on our results, individuals who obtain food information from social media sites

may be viewing others who are eating or cooking less healthy meals, which could make it seem

like their own unhealthy habits are the norm, and may ultimately contribute to the continuation

of such habits.

The cooking “authority” figure who prepares the food may be another possible factor

influencing people’s behavior when they watch cooking shows. Hosts of TV cooking shows

could be seen as authorities on food, and since human behavior is influenced by authority

figures, these personalities may impact viewers’ food practices (Cialdini, 2009). Viewers who

are highly involved with cooking shows, signaled by the fact that they actually cook the recipes

they see on TV, may be more impacted by the recipes portrayed on the shows than viewers who

are less involved. In the current study, combining the influence of social norms with the

influence of an “authority” figure using the recipes may account for the fact that watching TV

cooking shows was associated with BMI, but getting information from friends was not.

Although we would not expect to see an “authority” effect from information obtained from social

media, it may have a differential affect than obtaining information from friends because people

may post their most indulgent “picture-perfect” recipes. Furthermore, social media surrounds us

24 hours/day, possibly leading it to be more influential on food information sources than our

actual in-person friends.

In contrast to the literature showing a positive benefit to BMI by cooking from scratch,

our results indicate that for those who watch cooking shows, cooking frequently from scratch is

associated with a higher BMI. Cooking shows were the only information source to exhibit this

unique interaction effect with cooking from scratch. Although some cooking shows present

healthy recipes, many others present more indulgent fare. Our results may be interpreted in

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conjunction with a recent study by Howard et al., showing that food and recipes prepared by TV

cooks were higher in fat and calories than World Health Organization recommendations

(Howard et al., 2012). If higher calorie recipes are indeed common on cooking shows, it is

possible that those who get food information from these shows and cook using this information

might have higher BMIs. Another recent study by Dohle et al., found that participants who

prepared their own milkshakes exhibited increased liking and consumption of those milkshakes

compared to participants who drank experimenter-prepared milkshakes (Dohle, Rall, & Siegrist,

2014). In the present study it may be the case that those who watched cooking shows and

actually cooked the recipes exhibited increased consumption behavior because they “made the

food themselves.” Our results may also be consistent with other “viewer vs. doer” situations,

such as watching sports on TV versus actually playing sports. The impact on health for those

who watch sports on TV and also play sports is likely very different from those who watch sports

on TV but do not actually play sports.

Because of their possible impact as nutritional gatekeepers, we focused this initial study

on women ages 20-35. The association between watching food television and BMI in male or

older viewers may be very different than female viewers. We also did not assess actual food

intake or physical activity, both of which impact BMI. In addition, future research could

investigate if different types of shows and different types of social media – for instance,

instructional versus experiential – have different types of influence, as the current study did not

assess in detail the types of cooking shows or social media participants were using. It also may

be important to better define “cooking from scratch” in future research, as some women could

interpret pouring a bowl of cereal as cooking from scratch, while others would only consider

cooking a three-course meal as “cooking from scratch.” Given that the information source for

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new foods was not mutually exclusive (i.e., participants could have been in one or multiple

groups of information source), it is not possible to differentiate between the effects of

information sources. Lastly, as the study was powered for a larger project aim, we may not have

had enough power to detect significant associations between all information sources and BMI,

although with 501 participants we think this is unlikely. A strength of the current study is the

large sample of ethnically diverse women. Additionally, few studies have assessed where

participants obtain information about new foods and the impact of informational sources on

health indicators like BMI. Understanding where young women obtain information about new

foods may be important when attempting to influence or shape their food preferences. Finally,

food television and social media have become incredibly popular, yet little research has assessed

thier health impacts. This formative study opens the door to further exploration of these

entertainment and information mediums.

Conclusions

Results of the present preliminary study suggest that especially for “doers,” (i.e., those

who actively cook from scratch), obtaining information about new foods from sources other than

cooking shows may be advantageous. Because watching cooking shows was not associated with

higher BMI for non-doer viewers, it does not appear to be the exposure to the shows for

entertainment that relates to BMI, but rather the exposure plus the act of actually cooking.

Implications of this research for practitioners may be that when providing dietary counseling or

education one should not assume that just because a client cooks from scratch frequently, that

they are preparing healthy recipes. If a client identifies cooking shows or social media as a

frequent sources of recipes, practitioners may want to discuss the types of recipes normally

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prepared on cooking shows or displayed in social media, and ways these recipes could be easily

modified to be healthier.

Future research could examine more closely the type of cooking shows and social media

people of various cooking proclivities are using and their motives for using these sources.

Examining whether viewers see hosts of cooking shows as authority figures or social media as a

social-norm setter would be valuable. Furthermore, it may be that watching a “healthy” cooking

show or viewing “healthy” recipes from social media could nudge viewers toward preparing

healthy meals, which could be a powerful tool for improving public health.

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References:

Caraher, M., Lange, T., & Dixon, P. (2000). The influence of TV and celebrity chefs on public

attitudes and behavior among the English public. Journal for the Study of Food and

Society, 4(1), 27-46.

Cialdini, R. B. (2009). Influence: HarperCollins.

Clifford, D., Anderson, J., Auld, G., & Champ, J. (2009). Good Grubbin': Impact of a TV

Cooking Show for College Students Living Off Campus. Journal of Nutrition Education

and Behavior, 41(3), 194-200. doi: http://dx.doi.org/10.1016/j.jneb.2008.01.006

Day, J. E., Kyriazakis, I., & Rogers, P. J. (1998). Food choice and intake: towards a unifying

framework of learning and feeding motivation. Nutrition Research Reviews, 11(1), 25-44.

De Solier, I. (2005). TV dinners: culinary television, education and distinction. Continuum:

Journal of Media & Cultural Studies, 19(4), 465-481.

Dohle, S., Rall, S., & Siegrist, M. (2014). I Cooked It Myself: Preparing Food Increases Liking

and Consumption. Food Quality and Preference, 33, 14-16.

Harvey-Berino, J., Krukowski, R. A., Buzzell, P., Ogden, D., Skelly, J., & West, D. S. (2011).

The accuracy of weight reported in a web-based obesity treatment program.

TELEMEDICINE and e-HEALTH, 17(9), 696-699.

Howard, S., Adams, J., & White, M. (2012). Nutritional content of supermarket ready meals and

recipes by television chefs in the United Kingdom: cross sectional study. BMJ: British

Medical Journal, 345.

Kant, A. K., & Graubard, B. I. (2004). Eating out in America, 1987–2000: trends and nutritional

correlates. Preventive Medicine, 38(2), 243-249. doi:

http://dx.doi.org/10.1016/j.ypmed.2003.10.004

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Ketchum, C. (2005). The Essence of Cooking Shows: How the Food Network Constructs

Consumer Fantasies. Journal of Communication Inquiry, 29(3), 217-234. doi:

10.1177/0196859905275972

Kolodinsky, J. M., & Goldstein, A. B. (2011). Time Use and Food Pattern Influences on Obesity.

Obesity, 19(12), 2327-2335. doi: 10.1038/oby.2011.130

Meister, M. (2001). Cultural feeding, good life science, and the TV Food Network. Mass

Communication & Society, 4(2), 165-182.

Networks, S. (2012). Food Network drew record viewership in 2012. Retrieved October 15,

2013, 2013, from http://www.scrippsnetworksinteractive.com/newsroom/company-

news/Food-Network-drew-record-viewership-in-2012/

Pliner, P., & Mann, N. (2004). Influence of social norms and palatability on amount consumed

and food choice. Appetite, 42(2), 227-237. doi:

http://dx.doi.org/10.1016/j.appet.2003.12.001

Robinson, E., Blissett, J., & Higgs, S. (2013). Social influences on eating: implications for

nutritional interventions. Nutrition Research Reviews, FirstView, 1-11. doi:

doi:10.1017/S0954422413000127

Smith, L. P., Shu Wen, N., & Popkin, B. M. (2013). Trends in US home food preparation and

consumption: analysis of national nutrition surveys and time use studies from 1965-1966

to 2007-2008. Nutrition Journal, 12(1), 1-10. doi: 10.1186/1475-2891-12-45

Thorpe, M. G., Kestin, M., Riddell, L. J., Keast, R. S., & McNaughton, S. A. (2013). Diet quality

in young adults and its association with food-related behaviours. Public Health Nutrition,

1-9.

17

Wansink, B. (2006). Nutritional Gatekeepers and the 72% Solution. Journal of the American

Dietetic Association, 106(9), 1324-1327.

Wansink, B., & Sudman, S. (2002). Consumer panels. Chicago: AMA.

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Table 1: Pearson Correlation Coefficient Matrix Between Information Source Variables

Recipes

Health sites

You tube

Magazines

News papers

POP Recipes

Cooking Shows

Cooking Blogs

Dietitians Samples Social Media

Family Dining Out

Classes

Recipes 1.00 -.096 -.006 -.069 .029 .125 -.110 -.226 -.046 .070 -.148 -.118 -.180 -.059 Healthsites -.096 1.00 -.016 -.080 -.069 -.029 -.146 .029 .106 -.110 -.136 -.173 -.115 -.061 Youtube -.006 -.016 1.00 -.106 .000 -.033 .004 -.053 -.040 -.007 -.113 -.119 -.080 -.044 Magazines -.069 -.080 -.106 1.00 -.066 .018 -.013 -.127 -.057 -.101 -.114 -.218 -.153 -.074 Newspapers .029 -.069 .000 -.066 1.00 -.023 -.002 -.095 .120 -.051 -.081 -.019 -.017 -.031 POP Recipes .125 -.029 -.033 .018 -.023 1.00 -.100 -.066 -.020 .026 -.075 -.046 -.031 -.022 Cooking Shows -.110 -.146 .004 -.013 -.002 -.100 1.00 -.116 -.069 -.090 -.187 -.207 -.061 .067 Cooking Blogs -.226 .029 -.053 -.127 -.095 -.066 -.116 1.00 .014 -.107 .072 -.041 -.155 -.029 Dietitians -.046 .106 -.040 -.057 .120 -.020 -.069 .014 1.00 .007 -.092 -.095 -.054 -.027 Samples .070 -.110 -.007 -.101 -.051 .026 -.090 -.107 .007 1.00 -.128 -.061 .089 .000 Socialmedia -.148 -.136 -.113 -.114 -.081 -.075 -.187 .072 -.092 -.128 1.00 .043 -.102 -.017 Family -.118 -.173 -.119 -.218 -.019 -.046 -.207 -.041 -.095 -.061 .043 1.00 -.067 -.067 Dining Out -.180 -.115 -.080 -.153 -.017 -.031 -.061 -.155 -.054 .089 -.102 -.067 1.00 -.034 Classes -.059 -.061 -.044 -.074 -.031 -.022 .067 -.029 -.027 .000 -0.17 -.067 -.034 1.00

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Table 2: Associations of Sources of New Food Information With BMI Participants’ BMI b (S.E.) t p-value

Age 0.15 (0.09) 1.71 0.09

Years of Education

0.07 (0.25) 0.29 0.77

African American* 1.42 (0.72) 1.97

0.05

Hispanic* 1.41 (0.74)

1.92

0.06

Other* -1.28 (1.29)

-0.99

0.32

Recipes 0.45 (0.80)

0.56

0.58

Health Websites 0.11 (0.85) 0.13

0.89

Youtube 1.55 (1.31)

1.18

0.24

Magazines 0.21 (0.76)

0.28

0.78

Newspapers -0.58 (1.65)

-0.35

0.73

Point-of-Purchase Recipes

0.23 (2.28)

0.10

0.92

Watching Cooking Shows

1.60 (0.76)

2.11

0.04

Cooking Blogs -0.67 (0.84)

-0.80

0.43

Dietitians 1.47 (1.92)

0.78

0.44

In-Store samples -0.23 (1.18)

-0.24

0.81

Social Media (Pinterest, Facebook, Twitter, etc.)

1.82 (0.82)

2.22

0.03

Family and Friends

0.75 (0.76)

0.98 0.33

Dining Out 1.06 (0.86)

1.23

0.22

Cooking Classes -0.16 (1.78)

-0.09

0.93

*White is the reference category for each specified ethnicity.

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Table 3: Associations of Sources of New Food Information and Cooking From Scratch With BMI Participants’ BMI b (S.E.) t p-value

Age 0.15 (0.09)

1.67

0.10

Years of Education 0.12 (0.25)

0.48

0.63

African American* 1.50 (0.72)

2.07

0.04

Hispanic* 1.39 (0.73)

1.90 0.06

Other* -0.99 (1.30)

-0.77

0.44

Cook from scratch -0.11 (0.15)

-0.76

0.45

Recipes 0.51 (0.80)

0.64

0.52

Health Websites 0.11 (0.85)

0.13

0.90

Youtube 1.77 (1.31)

1.35

0.18

Magazines 0.21 (0.75)

0.28 0.78

Newspapers -0.62 (1.65)

-0.37 0.71

Point-of-Purchase Recipes

0.38 (2.28)

0.17

0.87

Watching Cooking Shows

1.45 (0.76)

1.91

0.06

Cooking Blogs -0.65 (0.84)

-0.77

0.44

Dietitians 1.30 (1.91)

0.68

0.50

In-Store samples -0.22 (1.18)

-0.18

0.86

Social Media (Pinterest, Facebook, Twitter, etc.)

1.87 (0.82)

2.28

0.02

Family and Friends 0.82 (0.76)

1.08 0.28

Dining Out 1.13 (0.86)

1.32 0.19

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Cooking Classes 0.10 (1.78)

0.06

0.96

Cooking from Scratch x Watching Cooking Shows Interaction

0.54 (0.24)

2.24

0.03

*White is the reference category for each specified ethnicity.