EXPLORING THE RELATIONSHIP BETWEEN ONLINE MEDIA EXPOSURE AND

COOKING SELF-EFFICACY, SKILLS AND BEHAVIORS AMONG COLLEGE

STUDENTS

A THESIS

Presented to the Department of Family and Consumer Sciences

California State University, Long Beach

In Partial Fulfillment

of the Requirements for the Degree

Master of Science in Nutritional Science

Committee Members:

Rachel Blaine, D.Sc. (Chair)

Brooke Dekofsky, M.S.
Kim Jebo, M.S.

College Designee:

Wendy Reiboldt, Ph.D.

By Grace Elizabeth Aguirre

B.S., 2017, California State University, Sacramento

August 2019




ProQuest Number: 22583552




All rights reserved

INFORMATION TO ALL USERS
The quality of this reproduction is dependent upon the quality of the copy submitted.

In the unlikely event that the author did not send a complete manuscript
and there are missing pages, these will be noted. Also, if material had to be removed,
a note will indicate the deletion.






ProQuest 22583552

Published by ProQuest LLC (2019 ). Copyright of the Dissertation is held by the Author.


All rights reserved.
This work is protected against unauthorized copying under Title 17, United States Code
Microform Edition © ProQuest LLC.


ProQuest LLC.
789 East Eisenhower Parkway
P.O. Box 1346
Ann Arbor, MI 48106 - 1346
 ABSTRACT

The prevalence of cooking is on the decline, especially among young adults, despite the

many health benefits. Online media, primarily social networking sites, have become the primary

platform for promoting ideas and encouraging positive behavior change. Examples include the

promotion of healthy behaviors like cooking through posting and sharing recipes, food

demonstrations, articles all themed around cooking. The purpose of this study was to investigate

the relationship between cooking attitudes, self-efficacy and behaviors in relation to the exposure

of cooking-related online media among adults in college. Specifically, this study assessed

university students’ exposure to cooking-related online media as it correlated with students’

attitudes towards cooking and preparing homemade meals, the frequency of preparing

homemade meals, and their self-efficacy in cooking. Cooking related online media exposure,

cooking attitudes and cooking self-efficacy were all significantly correlated with cooking

behaviors. Cooking self-efficacy was a significant predictor of cooking behavior over and above

all other factors. No other Factors alone were significant predictors of cooking behaviors.

Significant influencers of cooking self-efficacy were learning how to cook from books, learning

to cook from a class, being age 40+, and being White, Native Hawaiian, Pacific Islander,

American Indian or Alaska Native.

ii
 AKNOWLEDGEMENTS

A huge thank you is dedicated my chair, Dr. Rachel Blaine for all the time and

mentorship you gave throughout the whole process. Thank you to my committee members,

Brooke Dekofsky & Kim Jebo for all your leadership and ideas. And a special thank you to Dr.

Erin Arruda for helping with data analysis as well as additional mentorship along the way. I

could not have had completed this project without everyone’s guidance and support!

iii
 TABLE OF CONTENTS

ABSTRACT................................................................................................................................... ii

ACKNOWLEDGEMENTS .......................................................................................................... iii

LIST OF TABLES ........................................................................................................................ v

LIST OF FIGURES ...................................................................................................................... vi

1. INTRODUCTION ............................................................................................................. 1

2. REVIEW OF LITERATURE ............................................................................................ 9

3. METHODOLOGY .......................................................................................................... 20

4. RESULTS ........................................................................................................................ 23

5. DISCUSSION AND CONCLUSION ............................................................................. 33

APPENDICES ............................................................................................................................. 40

A. CONSENT FORM ........................................................................................................... 41

B. SURVEY QUESTIONS .................................................................................................. 43

C. EXTENSIVE TABLE OF QUOTES FROM FREE RESPONSE QUESTION ….......... 48

D. STUDENT ACCESS LETTER OF APPROVAL BY CORSE INSTRUCTOR............. 53

REFERENCES ............................................................................................................................ 55

iv
 LIST OF TABLES

1. Sample Characteristics ……………………………………………………………………. 24

2. Correlations Between Cooking Behavior, Attitude About Cooking, Cooking Self-

Efficacy, Cooking-Related Media Exposure and Number of Social Networking
Sites (SNS) Used Among College Students …………………………………...…….… 25

3. Regression Analysis Coefficients: Factors Predicting Cooking Behavior ……………….. 26

4. Quotes From Optional Free Response Question About Participant Cooking

Background …………………………………………………………………...………. 28

5. Correlations of Demographics, Cooking Skill Learning Sources, and SNS with

Cooking Self-Efficacy ………………………………………………………………… 32

v
 LIST OF FIGURES

1. Social Cognitive Theory Model …………………………………………………………... 5

2. Scatter plot of the correlation between Cooking Behavior and Self-Efficacy …………… 26

3. Which SNS participants use ………………………………………….………..…………. 27

4. Where participants learned to cook percent …………………..………………..………… 28

5. How frequently participants cook "Healthy" meals …………………..…………..……… 31

vi
 CHAPTER 1

INTRODUCTION

In 2015–2016, the prevalence of obesity was 39.8% in adults and 18.5% in youth and

rates of the condition are on an increasing trajectory. The global prevalence of obesity has more

than doubled since 1980 (Centers for Disease Control and Prevention, 2017; Ng et al., 2014). It

has become common knowledge in the health industry that obesity is more than a body image

concern but is also A major risk factor for many diseases and conditions. The most common

conditions associated with obesity are Type 2 Diabetes, Stroke, Osteoarthritis, breathing

problems, some cancers (endometrial, breast, colon, kidney, gallbladder, and liver), mental

disorders, body pain, Heart disease and morbidity and mortality (Centers for Disease Control and

Prevention [CDC], 2015; Chandra et al., 2014; Eheman et al., 2012).

The general well-being of Americans is a legitimate motive for lowering obesity rates,

however, the greatest force driving obesity prevention efforts lies in the immense financial

impact it has. The condition has a major financial impact on the United States and around the

world. The annual cost to United States alone is $147 billion in direct healthcare costs and

secondary costs including lost productivity, sum to an annual $4 billion (McKnight, Demuth,

Wilson, Leider, & Knudson, 2018). The largest areas of spending include diabetes care at $237

billion and ischemic heart disease treatment at $88 billion, which are both greatly associated with

obesity as the primary risk factor (Dieleman et al., 2016; Finkelstein, Trogdon, Cohen, & Dietz,

2009).

There are many factors that contribute to the risk for obesity and comorbidities including

certain dietary choices and patterns. For example, increased consumption of whole, low

processed, nutrient-rich foods (including fruits and vegetables, whole grains, legumes, lean meat,

1
fish, low fat dairy, nuts and seeds) has been associated with a reduced risk for obesity in adult

and child populations. Additionally, an increased consumption of energy-dense, highly processed

and refined foods with added sugar and fat has been associated with an increased risk for obesity

and comorbidities (Livingstone & McNaughton, 2016; Monteiro, Moubarac, Cannon, Ng, &

Popkin, 2013; Verschuren, 2012). Environmental, social and emotional factors are also known to

play a major role in the rising obesity rates in addition to eating habits (National Heart, Lung,

and Blood Institute, 2012).

In the last few decades, it has been noted that cooking prevalence has been on the decline

whereas the consumption of convenience foods from restaurants, grocery and convenience stores

are increasing in popularity. Recent research has started to investigate all the potential benefits

that the act of cooking one’s own food can provide. Though the term “cooking” is not well

defined in the literature, studies are finding that those who prepare their own meals or eat home

prepared/cooked meals are more likely to have a healthy body weight, consume smaller portion

sizes, and have a larger variety in food choices. Home cooking is also associated with decreased

food cost, better social eating behaviors, increased family time and overall health and emotional

well-being (Brouwer, Hogervorst, Grootjen, Erp, & Zandstra 2017; Chen, Lee, Chang, &

Wahlqvist, 2012; Hartmann, Dohle, & Siegrist, 2013; Herbert et al., 2014; Jones, Walter, Soliah,

& Phifer, 2014; Mills, Brown, Wrieden, White, & Adams, 2017; Minkow, Gray, Reiboldt, &

Gonitzke, 2017; Wolfson & Bleich, 2014; Woodward-Lopez et al., 2014). Many cooking

interventions have shown to be an effective way to instill cooking skills and behavior among

families and individuals, especially among lower socioeconomic groups who are at greatest risk

for obesity and comorbidities (Reicks, Trofholz, Stang, & Laska, 2014).

2
 Online media outlets and social networking sites including blogs, video streaming

websites, social media and online idea sharing boards are frequently used as a platform to

promote the idea and frequency of cooking in the home. The use of online media to promote

health-related ideas and behaviors, such as cooking, is common practice and shown to be a

potentially effective way to reach a wide audience for little-to-no cost to the promoter (Korda &

Itani, 2011). Although this method of outreach is vastly used, due to limited studies in the area, it

is still unclear how much of an impact is being made by the use of online media for health

promotion and behavior change. Balatsoukas, Kennedy, Buchan, Powell, and Ainsworth (2015)

conducted a full-text review of 42 qualifying studies summarized that further investigation of the

effect of theory on the effectiveness of Social Networking Sites for health promotion is still

needed. Furthermore, very few studies describe cooking-related online media (CROM) content

exposure and the impact it has on attitude, self-efficacy and behaviors of cooking.

Research Purpose

The purpose of this study is to investigate the exposure of cooking-related online media

(CROM) in relation to cooking attitudes, cooking self-efficacy and cooking behaviors among

college students. Specifically, this study assesses university students’ exposure to CROM as it

correlates with students’ attitudes towards cooking and preparing homemade meals, the

frequency of preparing homemade meals, and their self-efficacy in cooking.

Research Questions

1. Are cooking attitudes positively associated with and cooking behaviors?

2. Are cooking attitudes positively associated with cooking self-efficacy?

3. Is cooking self-efficacy positively associated with cooking behaviors?

3
 4. Is exposure to CROM positively associated with cooking attitudes, self-efficacy and

cooking behaviors?

5. Which common online media websites are most being used among college students?

6. What is the primary influence on student’s cooking behaviors?

7. What is the primary way that students are learning to cook?

8. Is cooking perceived to be a healthy?

9. Are students aiming to cook healthy meals when they cook?

Theoretical Framework

The Transtheoretical Model (TTM) of Behavior Change is used as a guide for exploring

various influences over college students cooking behaviors in this study. The TTM has been used

for decades to describe and explain the stages readiness for behavior change and is noted as

generalizable across a broad range of problem behaviors and populations (Prochaska et al.,

1994). The TTM is often used to aid health behavioral interventions and studies with topics such

as diet, exercise and disease prevention (Johnson et al., 2008; Johnson et al., 2014; Jones et al.,

2003 ; Lenio, 2006). The 5 stages of change include precontemplation, contemplation,

preparation, action, and maintenance (Lenio, 2006). Using this model, online media is theorized

to have an impact on helping transition people from precontemplation of the problem behavior

(not cooking/choosing convenience foods) to contemplation and possibly action

(cooking/preparing one's own food). Consciousness raising, the process in which the individual

needs to increase his or her awareness about the negative consequences, the causes, and the cures

of the problem behavior, is a primary initial component in transitioning one from

precontemplation to contemplation (Prochaska, DiClemente, & Norcross, 1992; Patten, Vollman,

4
& Thurston, 2000). Awareness can be increased through feedback, education, confrontation,

interpretation, and media campaigns (Lenin, 2006; Prochaska et al.,1992).

Another theory used to frame the work of this study is the Social Cognitive Theory (SCT)

which was developed to better understand human behavior, motivation, and action (Bandura,

2004). The SCT is one of the most commonly used theories in nutrition research because of the

notions on the complexity of behavior and its determinants, potential mediators and mechanisms

(Harris, Yadrick, Anderson-Lewis, Brown, & Connell, 2017). In this theory it is understood that

personal factors, environmental factors, and human behavior reciprocally influence each other. A

structural path model is used to depict the key concepts as it displays how self-efficacy

influences health behavior directly, as well as indirectly through its influence on outcome

expectations (beliefs about the outcomes of behavior), and on goals, with “socio-structural

factors” (personal and environmental facilitators and barriers) influencing goals and in turn

behavior (Bandura, 2004).

FIGURE 1. Social Cognitive Theory Model.

Hypotheses

Ho1: There will be no significant association between the subjects’ cooking attitudes and

cooking behaviors.

5
 Ho2: There will be no significant association between the subjects’ cooking self-efficacy

and cooking behaviors.

Ho3: There will be no significant association between the subjects’ cooking attitudes and

cooking self-efficacy.

Ho4: There will be no significant association between the subjects’ CROM exposure and

cooking attitudes.

Ho5: There will be no significant association between the subjects’ CROM exposure and

cooking self-efficacy.

Ho6: There will be no significant association between the subjects’ CROM exposure and

cooking behavior.

Definitions and Terminology

Cooking: the process of preparing or transforming food ingredients by heating and/or

combining multiple simple raw ingredients together to create a transformed food item (Harris et

al., 2017; Minkow et al., 2017). This includes cooking "homemade" meals made from “scratch”

and “almost scratch,” while it does not include simply heating convenience foods. Examples of

cooking (all made from scratch or almost scratch): multi-step sandwiches, wraps, soups, stews,

casseroles, pasta dishes, baked goods, treats, cooked meat, layered bowls, dips, sauces, etc.).

Homemade/Home cooked: meals prepared or/cooked in the home from scratch or almost

scratch using simple ingredients.

Scratch: ingredients for the meal are in a raw or close-to-raw state (Woodward-Lopez et

al., 2014).

6
 Almost Scratch: some ingredients are not "raw" but still require mixing, cooking, and

preparation (Woodward-Lopez et al., 2014). For example, a stew made from canned beans,

frozen vegetables and raw beef chunks.

Convenience foods: any fully or partially prepared foods in which significant preparation

time, cooking skills or energy inputs have been handled in advance by food manufacturing

companies prior to entering the kitchen (Celnik, Gillespie, & Lean, 2012). These foods are also

called "packaged food," "fast food" and "precooked food".

Cooking attitude: the settled way of thinking or feeling about cooking (Minkow et al.,

2017).

Cooking self-efficacy: a feeling of self-assurance related to one’s cooking/food.

preparation ability, including such things as cooking from basic ingredients, following

a simple recipe, and preparing new foods and recipes (Harris et al, 2017).

Cooking frequency: number of homemade meals one made per month, week or day.

Online media: online social and information sharing networks such as blogs, Facebook,

Twitter, Instagram, Snapchat, LinkedIn, Pinterest and YouTube.

Cooking-related online media (CROM): online content that contains the subject of

cooking such as recipes, video cooking demonstrations, how-to articles or cooking tips/facts.

Limitations

This study was limited by the possible inaccurate representation of young adult college

students due to the use of convenience sampling at CSU, Long Beach (CSULB). Responses from

students in the specifically targeted courses at the specific university might differ from the rest of

the student population at universities across the nation. The study also depends on self-reporting,

7
which is known for varying accuracy and potential bias. Cooking behaviors, attitudes and self-

efficacy may also be influenced by confounding factors.

Assumptions

Several assumptions are being made in the conducting of this study, such as that the

survey collected an accurate and reliable measure of the variables (cooking behaviors, skill, self-

efficacy, attitudes and CROM exposure). It is also assumed the participants honestly and

accurately answered the questions to the best of their ability. This means that participants fully

understood what the terms and questions meant and were able to completely identify their

thoughts and behaviors for truthful responses to the questions. Also, because this survey was

accessible by anyone with a link, it is assumed that all responses were from college students

attending CSULB.

8
 CHAPTER 2

REVIEW OF LITERATURE

Research Purpose

The purpose of this study is to investigate the exposure of cooking-related online media

(CROM) in relation to cooking attitudes, cooking self-efficacy and cooking behaviors among

college students. Specifically, this study assesses university students’ exposure to CROM as it

correlates with students’ attitudes towards cooking and preparing homemade meals, the

frequency of preparing homemade meals, and their self-efficacy in cooking.

This section will review the existing literature on the relationship between diet and

disease, the prevalence and benefits of home cooked meals, cooking practices in relation to food

choices and diet quality, cooking attitudes and self-efficacy towards cooking behaviors,

successful cooking interventions, and online media for health promotion.

Diet and Disease

Heart disease has been the leading cause of death in the United States for the last 98 years

followed closely by cancer (Greenlund et al., 2014; CDC, 2017). Treatment and preventative

care for cardiovascular disease (CVD), also known as heart disease, has been studied in-depth

and with nutrition and weight management have been identified as the best preventative lifestyle

measures. The primary diet pattern that has been studied and associated with reduced risk for

CVD is the Mediterranean diet which is high in consumption of fruits, vegetables, legumes,

whole grain items, fish and unsaturated fatty acids (especially olive oil), a moderate consumption

of alcohol (mostly wine, preferably consumed with meals), and a low consumption of red meat,

dairy products and saturated fatty acids (Verschuren, 2012). In a meta-analysis about the

Mediterranean diet, adherence to the diet was significantly associated with a 10 % reduction in

9
cardiovascular incidence or mortality (95 % CI 0.87–0.93) and also with an 8 % reduction in all-

cause mortality (95 % CI 0.90–0.94; Sofi, Abbate, Gensini, & Casini, 2010).

Healthy eating patterns have also been heavily linked to reduced rates in obesity. A 2016

Australian study with 4,908 Australian individuals, using the Australian National Nutrition and

Physical Activity Survey and the Australian National Nutrition and Physical Activity Survey and

an automated, multi-pass, 24-h dietary recall, collected food and beverage consumption data and

compared dietary habits using food scores with obesity rates. The study found a higher quality

diet, using the Dietary Guideline Index (DGI) was associated with a lower odds ratio of being

overweight or obese in men and women (Livingstone & McNaughton, 2016).

Cancer is the second leading cause of death in the United States and, like heart disease, is

also strongly linked to obesity and diet patterns (Eheman et al., 2012). A recent review study,

including many clinical trials and meta-analyses, investigated the relationship between obesity

and cancer, specifically breast, colorectal, liver, pancreatic, ovarian and esophageal cancer.

Obesity was found to be significantly associated with breast cancer, and an increased prevalence

of triple negative breast cancer, the most aggressive subtype of breast cancer. Obesity-associated

inflammation was found to be the primary linking factor between the two cancers. Additionally,

elevated levels of specific inflammatory cytokines were found to be associated with increased

breast cancer tumor growth and poor patient outcomes (Kolb, Sutterwala, & Zhang, 2016). Kolb

et al. found similar results when investigating colorectal cancer and also linked colorectal cancer

to intestinal microbiota and dysfunction of the intestinal barrier which has also been associated

with obesity.

Liver cancer is also significantly associated with obesity through the development of non-

alcoholic fatty liver disease (NAFLD). In Western countries, NAFLD is the most common type

10
of chronic liver disease and typically leads to cirrhosis which is a major risk factor for

Hepatocellular carcinoma (HCC), accounting for 75–90% of all liver cancer. A growing amount

of evidence has also shown that HCC can develop in individuals with NAFLD without the

presence of cirrhosis, making liver cancer yet another concern for obese individuals. Though

NAFLD is associated with increased age, as many as 22% of U.S. adolescents and young adults

(15-39 years old) were found to have the condition (Doycheva et al., 2017). Excess caloric

intake, the primary component of obesity, can result in increased triglyceride production in the

liver that is greater than the ability of the liver to export, leading to accumulation of triglycerides

in the form of lipid droplets in parenchymal hepatocytes and hepatic steatosis (the beginning

stages of NAFLD; Kolb et al., 2016). Additionally, pancreatic, endometrial, ovarian and

esophageal cancer were all associated with obesity, though the mechanism for all of them is not

fully understood, it is clear that through inflammation and other metabolic disturbances obesity

plays a significant role in the second leading cause of death in the United States.

Many dietary factors have been shown to decrease the risk of cancer while others have

shown to increase cancer risk. Increased intake of whole and fresh foods such as whole grains,

nuts, legumes, seeds, and fresh fruits and vegetables has been significantly associated with

reduced cancer rates, while increased consumption of highly processed and refined foods that are

high in added fat, sugar and salt (most convenience foods) are associated with increased cancer

rates (Egeberg et al., 2010; Farvid et al., 2019; Lin, Li, Leung, Huang, & Wang, 2014; Mori et

al., 2017).

Prevalence and Benefits of Home Cooked Meals

In an attempt to reduce nutrition-related disease prevalence and improve overall health

outcomes, cooking behaviors are progressively being investigated to explain the change in

11
cooking prevalence and the role that it plays in human health. Cooking behaviors and cooking

skills have been on the decline, especially among young adults facing independence for the first

time (Mills et al., 2017). Qualitative studies have largely assisted in better understand barriers

that individuals face when deciding whether to cook and prepare their meals or choose

convenience foods. In a recent focus group study with college students, cooking behaviors were

investigated to identify barriers faced with cooking. The top identified barriers in this study were

summed together with a “non-familiarity with cooking due to lack of cooking models” a “limited

time,” which was broken down to three factors of a distaste for grocery shopping, preparation

and clean-up. Those who had no interest in cooking clearly had the pattern of eating out as a

solution to meal preparation and had no model of home-food preparation in their background

(Walter, Soliah, & Weems, 2010). In another recent study, barriers to cooking with raw

ingredients among people aged 18–58, living in Ireland were investigated through participant

interviewing. Although this study was conducted outside of the United States, challenged related

to cooking can be generalized since the same types of resources are typically used in the process.

The top five cooking barriers stated in this study included: (1) time pressures; (2) desire to save

money; (3) desire for effortless meals; (4) family food preferences; and (5) effect of kitchen

disasters (Lavelle et al., 2016). Considerably varied results were obtained in another similar

study which spanned all “scratch” cooking to just anything made at home. Perceptions of

cooking incorporated considerations of whether or how the food was heated, and the degree of

time, effort and love involved and if convenience foods were used. Regardless of neighborhood

income or food access, key barriers to cooking discovered in this study included affordability,

lack of time, and lack of enjoyment (Wolfson, Bleich, Smith, & Frattaroli, 2016).

12
 While the prevalence of home cooking is decreasing, the intake of ultra-processed foods

is increasing. Ultra-processing is defined as a type of process that has become increasingly

dominant, at first in high-income countries, and now in middle-income countries, creates

attractive, hyper-palatable, cheap, ready to consume food products that are characteristically

energy-dense, fatty, sugary or salty and generally obesogenic (Monteiro et al., 2013; Soliah,

Walter, & Jones, 2012). This type of processing is different from the simple heating, mixing,

combining and preserving that is used in typical homemade meal preparation and typically

requires cheap chemical ingredients and large manufacturing facilities and equipment to make

the food items. "Ultra-processed" foods tend to be mass produced packaged goods, such as

sodas, packaged sweets, instant noodles, chicken nuggets, frozen meals, supplemental meal bars

and savory\snacks like chips and crackers. It appears that the convenient, energy dense and low

satiating nature of these foods is what contributes significantly to the over-consumption of

calories in adults and children, but many other factors such as genetics and environment might

also be at play.

There are many benefits of homemade meals including social, psychological and

physiological benefits. Homemade meals are positively associated with family mealtime which

has been found to promote a more healthful diet by decreasing the amount of added sugar, fat

and sodium in meals and increases the consumption of fruits, vegetables and whole grains.

(Crawford, Ball, Mishra, Salmon, & Timperio, 2007; Dwyer, Oh, Patrick, & Hennessy, 2015;

Minkow et al., 2017; Watts, Loth, Berge, Larson, & Neumark-Sztainer, 2017; Woodruff &

Kirby, 2013). Harrison et al., 2015 conducted a systematic review of the effects of frequent

family meals on psychosocial outcomes in children and adolescents by analyzing 14 peer-

reviewed articles which were screened for adequate study design and data analysis methods. This

13
review concluded that family meals are positively associated with increased self-esteem, school

success in children and positive family interactions including family communication, child

socialization, and the transmission of values and culture. Additionally, family meals inversely

associated with disordered eating, alcohol and substance use, violent behavior, and feelings of

depression or thoughts of suicide in adolescents. These findings are important additions to the

nutrition implications previously stated as health is composed of many elements that must work

together in conjunction with a healthy diet.

Cooking Interventions

Due to the substantial amount of evidence to support cooking as a positive health

behavior, a great deal of effort has gone into the promotion of increasing home-cooked meal.

Many community programs have implemented a variety of cooking interventions showing that

methods such as cooking classes and cooking demonstrations are an effective way to positively

influence cooking behaviors by educating participants and improving self-efficacy and attitudes

towards cooking. More significant outcomes in improved cooking behaviors and self-efficacy are

associated with longer and more intensive interventions, however, brief interventions to promote

cooking behaviors have still shown to influence changed (Garcia, Reardon, Mcdonald, &

Vargas-Garcia, 2016; Reicks et al., 2014).

In 2014 a longitudinal mixed methods design was used to evaluate the Australian

program Jamie’s Ministry of Food (JMoF). The program was a community-based cooking skills

program composed of ten weekly 90-minute classes, aimed at getting people of all ages and

backgrounds cooking simple, fresh, healthy food quickly and easily. The JMoF program focus

was on building positive attitudes and increasing knowledge, skills and self-efficacy related to

healthy eating, food and cooking. The quantitative component of this study used a quasi-

14
experimental design with a wait-list control group. Intervention participants were measured at

program commencement (T1), at program completion (T2) approximately 10 weeks after

commencement, and six months later (T3) approximately six months after program completion.

In the quantitative analysis, a total of 694 intervention participants completed T1 measurements,

383 at T2 and 259 at T3. In the wait-list control group, 237 participants completed the survey at

T1and 149 at T2. The results of the qualitative portion of the study showed that there was a

statistically significant increase in the numbers believing that they could prepare a meal from

basics that was low in price between T1 and T2 in the intervention group (p< 0.001) but not the

control. A significant increases between T1 and T3 in preparing low cost meal from scratch and

a small but statistically significant differences in the increases over time and between groups in

cooking enjoyment (p = 0.001), cooking satisfaction (p < 0.001) and cooking for others

(p = 0.004; Herbert et al., 2014).

Televised cooking shows have also been investigated to assess the impact they may have

on health behaviors and due to the very limited research has been done on this topic, the results

have greatly varied. There is still, however, a small amount of evidence that suggests that TV

cooking shows can influence viewers to try new foods, make healthier choices and develop

positive attitudes about cooking (Clifford, Anderson, Auld, & Champ, 2009; Neyens & Smits,

2017; Taşpinar & Temeloğlu, 2018). It appears, however, that in many cases, TV cooking shows

no significant impact on cooking behaviors and or dietary habits (Bodenlos & Wormuth, 2013;

De Backer & Hudders, 2016).

One randomized controlled trial looked into the impact of television cooking shows on

food preparation, choices and portion size in 111 Flemish children from the first and fifth grade.

Both the fifth and the first graders were evenly and randomly assigned to 1 of 3 cooking episode.

15
The experimental groups were assigned to 1 of 2 cooking shows. The first film was the

unmodified version of the educational cooking show ‘Dagelijkse Kost’ (Daily Meal) hosted by a

cordial cook who is well-known among the Flemish. The researchers selected an episode in

which the chef prepares pancakes and spreads them with a copious amount of brown sugar, a

traditional Flemish dish. The second experimental group watched a modified version of the same

episode which edited out the parts which showed the portion of brown sugar that was added to

the pancakes and makes it appear that the chef does not garnish the pancakes before eating them.

The control group was assigned to another show similar in length called “Greenland”, which is

about nature and gardening. Outcomes were measured by the amount of sugar added to pancake

plates prepared by children after the films had been watched. The results of this study concluded

that there is a minimal difference in the sugar added to the pancakes between the two

experimental groups, however, children who watched the cooking show (either version)

compared to those who did not, were influenced by observing food cue. Older children in this

study appeared to be more influential than the younger children, and it is unclear how much of

this difference in sugar use was attributed to varying levels of hunger in the children since this

factor was not controlled for (Neyens & Smits, 2017).

A randomized controlled trial was conducted in 80 college students ages 18-22 (72.5%

female) to test the impact of cooking shows on adult food choice and calorie consumption.

Students were randomized into two different groups, Group A and Group B. Group A watched a

10-min cooking show with Rachel Ray from The Food Network which showed a variety of

different foods: Prosciutto Wrapped Cod, “Pesto” Pasta, asparagus drizzled with balsamic, and a

fruit tart for dessert. Group B watched a 10-min clip from Planet Earth that was about elephant

and monkey behaviors. Before the video clips were played, participants filled out a pre-test

16
questionnaire about their desirability to eat food and their level of hunger. After the clips were

completed, participants were given three pre-weighed food options, which totaled 800 calories:

cheese curls, chocolate covered candies, and carrots to eat while filling out the post-test

questionnaire. Food was presented in a room set up like a kitchen and participants were told to

eat as much or little as they wanted. The results of this study found that students were not more

likely to have an overall increased caloric intake after watching a cooking program nor did it

affect calories consumed specifically from carrots and cheese curls. Though it did find that there

was a moderate increase in sweets in the form of chocolate covered candies after watching the

cooking program (Bodenlos & Wormuth, 2013).

Online Media and Health Promotion

The easiest way to reach people for health promotion appears to be through the use of

online media, especially social media due to the fact that most people have access to the internet

now and use online media daily. Online media has recently become a huge platform for

promoting healthy behaviors in Americans and throughout the world, yet research in this area is

still fairly ambiguous (Balatsoukas et al., 2015; Korda & Itani, 2011; U.S. Census Bureau, 2018).

One cross-sectional study shows the measured impact of an online campaign “More

Matters” promoting fruit and vegetable intake based on the 2005 Dietary Guidelines for

Americans. In addition to the internet, this campaign was promoted through advertisements

in supermarkets, on food packages, printed brochures and publications. Data were collected from

3021 adults via the United States’ National Cancer Institute’s 2007 Food Attitudes and

Behaviors Survey. Few participants were aware of the campaign (2%) The most recent results

are from 2012 and conclude that the number of unique website visitors increased by 110% from

2009; brand awareness increased to 26% from 11% in 2007; the proportion of mothers who

17
‘intend to serve more’ fruit and vegetables increased to 79% from 69% in 2007; mothers that are

more likely to purchase product with brand logo increased to 49% from 40% in 2007 (Erinosho

et al., 2012). It is very difficult to determine the actual effectiveness of this program due to the

number of assumptions and confounding factors present in this study which largely exemplifies

the nature of all studies of this nature.

One very recent review study examined 42 high-quality systematic reviews which

included a mix of cross-sectional and experimental study designs to investigate the impact of

social media on public health and medicine. After analyzing results and methodology of the 42

studies, reviewers concluded that due to the range of negatives and positives of social media use,

in addition to vast inconsistencies of measuring outcomes, there is not enough evidence and

more research is needed to clarify standards of measurement and the overall impact that social

media has on health-related behaviors (Giustini, Ali, Fraser, & Boulos, 2018).

The use of technology in general has been questioned as to whether is effective for skill

development among adult. A comprehensive exploration of technology's role in adult learning

Technology and Innovation introduces educators and students to the intersection of adult

learning and the growing technological revolution. This text addresses the powerful opportunities

that technology has to facilitate learning in adults through addressing their immediate needs in

the learning process (King, 2017; Nour, Chen, & Allman-Farinelli, 2016). The specific use of

video to facilitate learning and it’s benefits for enhance skills development and even specifically

applied to cooking skills has shown to be an effective tool (Surgenor et al., 2017). Surgenor et al.

conducted a study which compared the outcome between two groups who were given a recipe

and 60 minutes each to execute the recipe on their own. The experimental group was given a

video to accompany the recipe while the control was only given the recipe in text form, without

18
the video to accompany it. Results indicated that the combined use of audio-visual content

improved participants’ overall understanding of each stage within the cooking process across

conditions. Individuals that had video guidance demonstrated themes of improved

comprehension of the cooking process, real-time reassurance in the cooking process, assisting

the acquisition of new cooking skills and enhancing the enjoyment of the cooking process. This

study further demonstrated that the video technology reassured individuals about the cooking

process allowing them to take control of their learning, which served to promote self-efficacy

and enjoyment in food preparation.

19
 CHAPTER 3

METHODOLOGY

The purpose of this study is to investigate the exposure of cooking-related online media

(CROM) in relation to cooking attitudes, cooking self-efficacy and cooking behaviors among

college students. Specifically, this study assesses university students’ exposure to CROM as it

correlates with students’ attitudes towards cooking and preparing homemade meals, the

frequency of preparing homemade meals, and their self-efficacy in cooking.

Sampling

Participants were recruited using convenience sampling from students enrolled in general

education courses at CSULB from a variety of departments including Communications, English,

Psychology and Human Development. Courses were chosen for the diversity of students enrolled

in the courses generalizability of the results. Instructors were contacted using email addresses

obtained from the CSULB staff directory. The email sent to the instructors introduced the study

purpose and request permission to introduce the survey to their students in person and/or online.

Per instructor approval, in-class and online announcements were made to the students by both

the researcher and course instructors. Students were informed that, in addition to contributing to

research, they will also be eligible to win a $100 Amazon gift card for completing the survey but

will not receive any direct benefit in the course. Participants were informed that they would need

to assure that they fell within the inclusion criteria at the start of the survey and agree that they

voluntarily agree to participate, are at least 18 years of age and are currently a student enrolled at

CSULB. The targeted sample size of 136 was based on a similar study done by Harris et al.

(2017) who assumed a moderate effect size of .2, a probability of 0.05, 95% power, with 11

predictor variables.

20
 Survey Instrumentation

A survey was design for this study using questions acquired and adapted from previous

studies which measured similar variables. The survey was administered online survey through

Google Forms software. Participants were invited to take the survey by following the provided

web link which directed them straight to the survey. The consent form was the first page of the

survey where students were given the option to “Agree” or “Disagree” with the content on the

consent form. Students who select the “Agree” option were prompted to the first question of the

survey which was an eliminating question for those who are on a meal plan which was thought to

be a limiting factor the amount of cooking the student would be doing on a daily basis. Students

not on a daily meal plan were directed to the rest of the survey where there were asked a variety

of questions to assess their cooking attitudes, cooking self-efficacy, cooking skill/behaviors, and

CROM exposure through several likert-scale matrices. Several multi-select questions and an

optional free-response question, was also included at the end of the survey to collect responses

about social networking site use and where they learned to cook, if applicable. Several

demographic questions were asked at the end of the survey including participant’s age, gender,

ethnicity, residence type and kitchen status. The final survey used in this study has not been

tested for validity or reliability, however, many of the questions came from studies which used

previously validated question for measuring cooking behavior, attitudes about cooking and

cooking self-efficacy. A table with all survey questions and the studies they were acquired or

modified from is provided below. The final survey that will be used was also pilot tested via the

principal investigators (PI) personal social media account. Pilot test participants were invited to

provide feedback related to possible leading questions, biases, survey functionality or directions

clarity in the survey. A total of 60 participants volunteered to complete the survey. The only

21
issues related to the functionality of the survey were identified and were corrected early in pilot

testing. Over 20 participants reported specific positive feedback that the survey was clear,

functional, appeared unbiased and seemed to be an adequate measure of cooking attitudes, self-

efficacy, behaviors and cooking-related media exposure. See Appendix C. for a complete table of

survey questions.

Statistical Analysis

Data was analyzed using IBM SPSS Statistic Version 25. Descriptive statistics were used

to determine mean scores, standard deviations (SD) and frequencies. The variables “cooking

behavior,” “cooking attitude,” “cooking self-efficacy” and “media exposure,” were generated by

combining multiple related Likert scale questions. Related questions were testing for reliability

of measuring the same variable using Cronbach’s alpha. Each variable was calculated as an

average of all available question responses related to that variable. Final variable average score

calculations were adjusted to account for missing values from unanswered questions. Mean

scores for each variable were compared using stepwise multiple linear regression where

demographics were controlled for. F change test was used to determine significant differences in

regression outcomes based on adding demographics to the model. The significance level was set

at p ≤ 0.05.

22
 CHAPTER 4

RESULTS

The study was comprised of students (n=133) recruited from general education courses

on campus as well as by flyer in student common areas consisting of majority females (n=86,

64.7%) and majority Latino/Latinx (n=61, 46%). One subject did not identify their gender. Age

was reported in categories starting at “18-24” years” (81%) up to “40 or older.” All students

reported living in off-campus housing, likely due to the skip logic question which automatically

eliminated participants who have an on-campus meal plan. Almost all participants (98%)

reported access to at least a partial kitchen at their place of residence. Participants were asked 2

questions that have been verified as valid and reliable measures of food insecurity and in this

study revealed that 26% of participants appear to be food insecure which was defined by

answering “yes” to at least 1 of the 2 questions asked. See Table 1. for sample characteristics.

The mean cooking score of the sample was 3.33, standard deviation (SD)= 1.32. Scores were

assigned to frequency of “scratch cooking” responses as follows: 0 = Never; 1 = 1 time per

MONTH or less; 2 = 2-3 times per MONTH; 3 = 1-2 times per WEEK; 4 = 3-4 times per

WEEK; 5 = 5-6 times per WEEK; 6 = 1 time per DAY; 7 = More than 1 time per DAY. Based

on this coding, the mean score 3.33 lies in between the scale responses 1-2 times per week and 3-

4 times per week [1-4 times per week].

When testing for Pearson’s correlation, it was found that all six of the null-hypotheses

(Ho1-Ho6) in this study were rejected with statistical significance (p < .05) in the correlation

coefficient between each of the variables. The correlation outcome of each hypotheses were as

follows: Ho1: cooking attitudes and cooking behavior (r=0.42; p<.001); Ho2: cooking self-

efficacy and cooking behavior (r=0.667; p<.001); Ho3: cooking attitudes and cooking self-
TABLE 1. Sample Characteristics
N %
Total 133
Gender
Male 46 34.6
Female 86 64.7
Unidentified 1 0.8
Age
18-24 years 107 80.5
25-29 years 19 14.3
30-39 years 3 2.3
40+ years 4 3.0
Ethnicity
Latino/Latinx 61 45.9
Asian 30 22.6
White 22 16.5
Black 4 3.0
1
NH, PI, AI or AN 3 2.3
Multiethnic or Other 13 9.8
Housing
Off-campus house, apartment, or condo 131 98.5
On-campus dormitory 0 -
Sorority or fraternity housing 1 0.8
Prefer not to say 1 0.8
Access to kitchen at residence?
Yes2 123 92.5
Partial3 7 5.3
No 2 1.5
Food Insecurity4 35 26.3
N is the number of participants from the sample
% is the percent of total participants in the sample
1
Native Hawaiian, Pacific Islander, American Indian or Alaska Native
2
Full kitchen includes (a fridge, sink, stove, oven, counter space and pantry/dry food storage space)
3
Partial kitchen is described as “having some, but not all elements of a full kitchen”
4
“Yes” to at least 1 of 2 questions

efficacy and cooking behavior (r=0.667; p<.001); Ho3: cooking attitudes and cooking self-

efficacy (r=0.541; p<.001); Ho4: CROM exposure and cooking attitudes (r=0.326; p<.001);

Ho5: CROM exposure and cooking self-efficacy (r=0.243; p=.002); Ho6: CROM exposure and

cooking behavior (r=0.278; p=.001). A summary of Pearson’s correlations between cooking

behavior, attitudes, and self-efficacy, and media exposure can be found in Table 2.

24
TABLE 2. Correlations Between Cooking Behavior, Attitude About Cooking, Cooking Self-
Efficacy, Cooking-Related Media Exposure and Number of Social Networking Sites (SNS) Used
Among College Students (n=133)
1 2 3 4 5
1
1. Cooking Behavior Correlation - .42** .667** .278** -.07

Sig. (2-ailed) .000 .000 .001 .213

2. Cooking Attitude2 Correlation - .541** .326** -.04

Sig. (2-ailed) .000 .000 .323
3. Cooking Self-Efficacy3 Correlation - .243* .03
Sig. (2-ailed) .002 .368
4. Cooking-Related Media Exposure4 Correlation - .114
Sig. (2-ailed) .096
5. Number of SNS sused5 Correlation -
Sig. (2-ailed)
*Correlation is significant at p<.05 (2-tailed).
**Correlation is significant at p<.01 (2-tailed).
1
“Cooking Behavior” is an average score of 11 questions using an 8-point frequency scale.
2
“Cooking Attitude” is an average score of 11 questions using a 5-point Likert scale.
3
“Cooking Self-Efficacy is an average score of 5 questions using a 5-point Likert scale.
4
“Cooking-Related Media Exposure is an average score of 7 questions using a 5-point Likert scale.
5
“Number of SNS sites used is a measure of the number of boxes checked when asked which online media sites are
used.
In a stepwise multiple linear regression model, which controlled for age, gender, ethnicity

and food insecurity, participant cooking attitudes, CROM exposure and the number of social

networking sites used by participants were all non-significant predictors of cooking behaviors in

college students. Results from the regression analysis can be seen in Table 3. A collinearity test

showed no significant collinearity among variables in the model. An F change test showed no

significant change when adding demographic controls to the model, indicating that any

significant values exist among measured variables, regardless of addition of the controls to the

model. The only significant predictor of cooking behavior in this model was cooking self-

efficacy (p < .001). Additionally, there was a strong positive linear correlation observed in the

scatter plot in Figure 1.

25
TABLE 3. Regression Analysis Coefficients: Factors Predicting Cooking Behavior

B Std. E β
(Constant: Cooking Behavior ) 1
.221 .581
Cooking Attitude .089 .171 .041
Cooking Self-Efficacy3 .779 .097 .617**
Cooking Related Online Media .170 .094 .126
Exposure
Number of SNS sites used -.081 .052 -.101
Results from 1 large multiple linear regression model which controlled for Age, Gender and Ethnicity.
A collinearity test showed no significant collinearity among variables.
*significant at p<.05 (2-tailed)
**significant at p<.001 (2-tailed)
1
“Cooking Behavior” is an average score of 11 questions using an 8-point frequency scale.
2
“Cooking Attitude” is an average score of 11 questions using a 5-point Likert scale.
3
“Cooking Self-Efficacy is an average score of 5 questions using a 5-point Likert scale.
4
“Cooking-Related Media Exposure is an average score of 7 questions using a 5-point Likert scale.
5
“Number of SNS sites used is a measure of the number of boxes checked when asked which online
media sites are used.

FIGURE 2. Scatter plot of the correlation between Cooking Behavior and Self-Efficacy.

From the given list of the current top social networking sites available, participants

26
identified all the sites which they currently used. It was observed that the mean number of social

networking sites used among student was 3.2 and the most frequent number of social networking

sites used by each student was 4 (53%). The top 4 sites (used by more than 50% of students)

used were YouTube (98%), Instagram (80%), Snapchat (65%) and Facebook (54%). For a chart

depicting how many participants reported using each social networking sites see Figure 2.

n=119
100

90
n=107
80

70 n=87

n=72
% of Participants

60

50 n=57 n=55
40

30
n=26 n=24
20
n=12 n=11
10 n=6

Social Networking Site (SNS)

FIGURE 3. Which SNS participants use (N=133).

Participants were also asked to indicate where they learned to cook from by choosing a

list of pre-determined options. Of the options provided, including an “other” option to fill in the

blank, the top two sources for learning were from a parent or other family member (88%) and

from online sources (70%). A chart to depict how different sources of learning to cook compare

can be found in Figure 3.

27
 100 n=117

80 n=94
% of Participants

60

40 n=39
n=26
n=20
20 n=7 n=6 n=1
0

Source of learning to Cook

FIGURE 4. Where participants learned to cook percent (n=133).

Sources of learning to cook were illustrated by quotes extracted from the optional free

response portion of the survey where participants were asked to describe their background with

cooking and their sources of inspiration. See Table 4 for illustrative quotes to capture main

concepts of participants influences, motivators and discouragers (barriers). See Appendix D for a

more extensive table of response quotes.

TABLE 4. Quotes from Optional Free Response Question About Participant Cooking
Background
Influenced or Taught Illustrative Quotes
by…
Family (Mom, Dad, “I learned to cook when I was in elementary school. The biggest
Grandparent, sibling, influence for me was my mom. I remember helping her make
extended family) breakfast when I was younger, which eventually led to me
making breakfast for her by myself whenever I woke up earlier
than her.”

“My biggest influence in cooking would be my dad. We would

have family cooking days when I was little and we would all take
part in cooking a meal.”

28
TABLE 4. Continued
Influenced or Taught Illustrative Quotes
by…
Family (Mom, Dad, “I've always enjoyed being in the kitchen watching and learning a
Grandparent, sibling, variety of different foods to cook and make ever since I was a
extended family) young girl. My grandpa was a chef and so was my uncle so from
a young age I learned how to make and eat amazing food pretty
much all my life. Food is life.”
Friend, peer or significant “I learned how to cook when I moved out of my parents house
other around December of 2018 and moved in with my partner who
cooks most of my meals…”
Online sources “…Some times I get in the mood of trying to cook or bake
something new or simple I would’ve seen on Instagram. So I like
to some times explore around the kitchen and make something
with the ingredients available.”

“…I also like to cook italian food and Mediterranean so I learned

those through youtube. I enjoy cooking by myself.”
TV “Food Network – Chopped”

“When I was younger, in elementary school, I really enjoyed

watching cooking shows because I thought putting several
ingredients to make a final product was a fun idea. I would
always watch all the different shows like Barefoot Contessa,
Giada at Home, 30-Minute Meals, Iron-Chef America, and etc.
That was where my enjoyment of cooking started…”
School “I learned to cook in college…”

“The biggest influence of learning how to cook was in high

school in my cooking class.”
Reading Recipes “…Following recipes help me a lot as well and I like adding lots
of flavor so I do experiment a lot…”
Motivated by…
Culture “I learned when I was young to cook Central American food…”

“Coming from an Asian and Latino background, I learned how to

cook from my parents. They taught me how to blend spices and
marinate meats and veggies along with kicking it up a notch by
creating an infusion of Latino and Asian flavors.”

29
TABLE 4. Continued
Influenced or Taught Illustrative Quotes
by…
“I’m from Mexican culture and I know how tasty our food is, I
want to learn old recipes that have been passed down generation
after generation.”
Self-sufficiency/Necessity “I learned to cook because I had to take care of my sick brother
when my parents would be at work.”

“I started cooking a little at home but when I moved into my first

apartment I had to cook all my meals myself…”
Health “One of my biggest influences for me to learn how to cook would
be my diet. I am currently on the ketogenic diet (low-carb diet)
and would prefer that my meals are prepared at home since they
taste better. I have been on the diet since January of 2019 and
noticed that I cook a lot more than before I was on the diet. I find
myself constantly trying out new recipes that I find.”

“…after taking a nutrition class I have wanted to learn more and

cook more for my health.”

“…I would say that other things that have influenced me to cook
would be that I want to be a healthier person and I also do take
some joy from doing it.”
Discouraged by… (Barriers)
Cost “I do not cook often, as it is expensive to buy ingredients in
bulk.”
Time/Effort “…I try to make my own food as often as possible but sometimes
it is hard because of school and work.”

“I always enjoyed cooking. More recently I don’t have the time to

cook for myself so I buy myself fast food.”
When participants were asked how much they agreed that cooking impacts their health,
14% said that they “slightly agree” and as many as 72% said that they “totally agree.” Only 3%
disagreed. On average, participants reported cooking healthy meals 1-2 times per week. About
18% of participants reported cooking at least 1 healthy meal per day. To explore the relationship
between “cooking frequency” and “healthy cooking frequency” an additional correlation model

30
was run which showed an extremely strong positive statistically significant correlational (r=.845,
p<.000). To see how frequently respondents report cooking “healthy” meals, see Figure 4.

30
26.3%

25

20
% of participants

16.5%
15%
15
12.8%

10 8.3% 8.3%
7.5%
5.3%
5

0
Never 1 time per 2-3 times 1-2 times 3-4 times 5-6 times 1 time per More than 1
MONTH or per MONTH per WEEK per WEEK per WEEK DAY time per DAY
less
Frequency of Cooking From "Scratch"

FIGURE 5. How frequently participants cook "healthy" meals (N=133).

An additional correlational analysis to investigate other factors associated with self-

efficacy was also conducted. It was found that being age 40 and up (r=.210, p=.015), white

(r=.249, p=.004), Native Hawaiian, Pacific Islander, American Indian or Alaska Native

Multiethnic/Other (r=.171, p=.048), learning to cook from books (r=.366, p<.000) and learning

form school or class (r=.245, p=.005) were all significantly positively correlated with cooking

self-efficacy. Being Asian was significantly negatively associated with cooking self-efficacy (r=-

.247, p=.004). A model to depict correlational coefficients and significant p values can be viewed

in Table 5.

31
TABLE 5. Correlations of Demographics, Cooking Skill Learning Sources, and SNS With
Cooking Self-Efficacy
Correlation
Influential Factors Coefficient p value
Gender (n=132)
Female -.089 .310
Male .089 .310
Age (n=133)
Age 18-24 -.150 .085
Age 25-29 .060 .494
Age 30-39 .075 .393
*
AGE 40 and up .210 .015
Ethnicity (n=133)
Black or African American .101 .249
Latinx -.073 .404
**
White .249 .004
Asian -.247** .004
1 *
NH, PI, AI or AN .171 .048
Multiethnic/Other .010 .906
Cooking Learning Source
Parent, Grandparent, Extended Family or Sibling .095 .279
Friend, Roommate or significant other .016 .859
Online .135 .122
**
Books .366 <.000
School or Class .245** .005
TV Show/Network .151 .085
Self-Taught .122 .165
*Correlation is significant at p<.05 (2-tailed).
**Correlation is significant at p<.001 (2-tailed).
1 Native Hawaiian, Pacific Islander, American Indian or Alaska Native

32
 CHAPTER 5

DISCUSSION AND CONCLUSION

Discussion

The purpose of this study was to investigate the exposure of cooking-related online media

(CROM) in relation to cooking attitudes, cooking self-efficacy and cooking behaviors among

college students. Specifically, this study assessed university students’ exposure to CROM as it

correlated with students’ attitudes towards cooking and preparing homemade meals, the

frequency of preparing homemade meals, and their self-efficacy in cooking.

This study showed a significant positive correlation between cooking attitudes, cooking

self-efficacy and cooking behavior, indicating that as cooking self-efficacy and positive cooking

attitudes increased, cooking behavior also increased. This finding aligns with principles of the

social cognitive theory as well as previous studies investigating the interaction of these factors

(Herbert et al., 2014; Garcia et al., 2016; Minkow et al., 2017). Cooking-related online media

exposure was also significantly positively associated with cooking attitudes, cooking self-

efficacy and cooking behavior, indicating that as CROM increased, cooking attitude, self-

efficacy and cooking behaviors also increased. This coincides with previous findings about the

impact that videos, a major component CROM, have on cooking self-efficacy and skill

development (Lewis & Phillipov, 2018; Surgenor et al., 2017).

In a regression model to analyze cooking attitude, self-efficacy and CROM exposure as

predictors of cooking behavior, self-efficacy was the only statistically significant predictor

(p<.000), over and above all other factors including demographics. Since self-efficacy and

behavioral factors are known for having a bidirectional relationship, this finding is not
particularly novel; however, it does generate the question, “what influences cooking self-

efficacy?” (Bandura, 2009).

An additional correlational model to explore these potential influential factors of cooking

self-efficacy demonstrated that older participants (40+ years) appeared to have higher self-

efficacy compared to all other age groups. This could be due to increased experience in cooking

related to time and exposure to successes in cooking trials. There may also be an increase in

acquired support and less barriers such as financial and children in the home. Certain ethnicities

were positively associated with cooking self-efficacy including “White,” “Native Hawaiian,

Pacific Islander, American Indian, Alaska Native” and “Multiethnic/Other” Indicating that self-

efficacy is higher in these ethnicities. Being Asian was significantly negatively associated with

cooking self-efficacy indicating that self-efficacy was lower in those who identified as Asian.

Cultural differences such as those related to family dynamics, social standards, character

standards and complexity of cuisines could play a role in the building of cooking self-efficacy.

For example, cultures that support children and other family members exploring cooking skills

may increase self-efficacy. Additionally, cultures that have more restrictive environments my

result in reduced self-efficacy.

The source by which participants learned to cook was also explored as a potential

influencer of cooking self-efficacy. Learning to cook from books had the strongest correlation to

self-efficacy, secondarily to learning form school or class which was the only other significantly

correlated learning source. In a study which explored perceived learning differences among print

vs. electronic readings in college courses, respondents said they usually study/learn more when

printed readings are supplied (Ji, Michaels, & Waterman, 2014). This could be validation that

there is an increase in self-efficacy when one learns from a book, over a digital version and

34
therefore, it may still be highly beneficial to promote cookbooks as a way for individuals to learn

how to cook. The correlation could also be explained by the level of motivation one has when

reaching for a book to learn how to cook and how that manifests into self-efficacy and positive

behavior outcome expectations (Bandura, 2004; Lenio, 2006). The finding that learning to cook

from school or class coincides with current evidence that in-person, hands on training has a

major impact on self-efficacy, as observed in many cooking intervention programs and studies

(Garcia et al., 2016; Reicks et al., 2014).

Though cooking behavior and self-efficacy were not directly related to exposure to

CROM and the use of social networking sites in this study, 71% of participants still reported

learning how to cook from online sources and shared that they were motivated by exposure to

online content in open-ended responses. One participant said, “the rise of Instagram definitely

contributed to my interest in healthy cooking.” Another participant said, “As I got older, I started

using recipes found on social media.” Of all the social networking sites participants report using,

YouTube had the highest usage at 89% of all participants and followed closely by Instagram at

80%. The primary similarity between these two social networking sites lies in visual content

sharing. Since the visual imagery plays such a major role in attention capturing, inspiration and

influence, especially in conjunction with the power of peer and cultural influence, these sites

appear to have a major influential advantage over and in addition to all the other motivating and

influential factors of cooking behavior (Highfield & Leaver, 2016).

Perhaps those who are specifically looking for CROM are finding it by “knowing what

they are looking for” and therefore seeing and being influenced by it more readily. It has been

found that simply knowing what you are looking for drastically increases how quickly and

efficiently information is presented to an individual (Brumby, Cox, Chung, & Fernandes, 2014;

35
Surgenor et al., 2017). But this brings up the important question, what might be influencing an

individual to “look” for CROM, or resources for learning how to cook in general?

It appears that the desire to be healthy has been a motivator for individuals to learn to

cook. When participants where asked if they believed cooking impacts their health, the response

was overwhelming. Only 3% of participants did not agree that cooking had an impact on their

health. Several participants also felt that the primary reason they sought to add cooking into their

lives was due to the desire to achieve some sort of health-focused goal. Two key quotes from the

qualitative portion of the survey include:

One of my biggest influences for me to learn how to cook would be my diet. I am

currently on the ketogenic diet (low-carb diet) and would prefer that my meals are

prepared at home since they taste better. I…noticed that I cook a lot more than before I

was on the diet. I find myself constantly trying out new recipes that I find,

and “…after taking a nutrition class I have wanted to learn more and cook more for my health.”

When participants where asked how frequently they cooked healthy meals the average number of

times was 1-4 times per week which compares to the mean number of times reported for cooking

in general. It can be implied from this and strong correlation value (r=.845) that those who

reported cooking from scratch also reported cooking what they perceive to be healthy meals.

Implications

Continuing to promote healthy eating may be one of the best routes for encouraging those

who do not routinely cook to increase the cooking frequency in order to gain many of the

benefits associated with the behavior. Repeatedly posting healthy eating material and CROM

may have an impact on creating awareness in those who are not looking for means to build

36
cooking skills and may possibly motivate individuals to cross over from the “precontemplation

phase” to the “contemplation phase” and maybe even in the “action phase” (Lenin, 2006,

Prochaska et al.,1992). Since learning to cook from books and classes appeared to have the

largest impact on self-efficacy, which was most influential on cooking behavior, encouraging

individuals to seek healthy behaviors through these means may result in better outcomes. Despite

the lack of evidence that exposure to CROM was significantly associated with cooking behavior,

the use of top social networking sites such as YouTube and Instagram may still be beneficial for

reaching many individuals with healthy messages as well as encouraging the use of self-efficacy

building methods such and books and classes for learning cooking skills.

Limitations

This study was limited by the possible inaccurate representation of young adult college

students due to the use of convenience sampling at CSU, Long Beach (CSULB). Responses from

students in the specifically targeted courses at the specific university might differ from the rest of

the student population at universities across the nation. The study also depended on self-

reporting, which is known for varying accuracy and potential bias. Cooking behaviors, attitudes

and self-efficacy may also not have been accurately measured due to specific wording of

questions and components being measured to determine specific variables. Limitations also lie in

limited statistical testing. Few tests were used to analyze the data, resulting in limited

interpretation. The sample size, though yielding a power over 80%, was still rather small and

may not have accurately represented the large body of students.

Conclusion

Positive correlations between cooking attitudes, cooking self-efficacy and cooking

behaviors were consistent with current evidence. There was a significant positive relationship

37
between CROM and cooking attitudes, self-efficacy and behavior, however, was CROM not

identified as a significant predictor when controlling for demographic variables. Self-efficacy in

study was the primary predictor of cooking behavior, while self-efficacy was most strongly

related to learning to cook from books and from a school or class setting. The use of social

networking sites is still highly prevalent, however, the use of this flatform for promoting cooking

still needs further exploration to determine the effectiveness of the method.

Recommendations

Further testing of this data set may unveil more details about the sample and the variables

measured in this study. A similar study with a larger sample size as well as in a different

population may yield different, possibly more significant, results. A randomized controlled trial

looking at exposure to CROM and cooking attitudes, self-efficacy and cooking behavior may be

able to more accurately gauge the impact that CROM truly has. Future cooking intervention

studies should closely monitor the methods and tools used for instilling self-efficacy in cooks to

determine which methods in these interventions are potentially most influential in increasing

cooking behavior and helping develop long term habits of cooking meals from scratch.

Considering the findings in this study, future interventions may benefit from utilizing

cookbooks which participants can use in their own home. The use of video technology for in-

person or remote interventions may help participants feel more comfortable and build self-

efficacy as they learn new skills and where to find video resources at home. College and high

school courses should also focus on establishing this self-efficacy in students by implementing or

continuing the use of cookbooks as well as directing students to video sources that they can

utilize outside of class. As far as potential cooks who are not or have not taken any food

preparation/cooking classes in school or at some sort of center such as a church, medical facility

38
or non-profit community organization, the use of social networking site and other online media

may be a useful tool for reaching and encouraging these individuals to take some sort of class.

39
APPENDICES

40
 APPENDIX A

CONSENT FORM

41
 Consent form

You are invited to participate in a web-based online survey on cooking attitudes and behaviors.
This is a research project being conducted by Grace Aguirre, a student at CSULB. It should take
approximately 10-15 minutes to complete. You must be a student at CSULB and at least 18 years
old to participate in the study. Your participation in this survey is voluntary. You may refuse to
take part in the research or exit the survey at any time without penalty. You are free to decline to
answer any particular question you do not wish to answer for any reason. You will receive no
direct benefits from participating in this research study aside from the potential prize-winning.
Potential risks involved include loss of confidentiality by email address, psychological risks,
discomfort answering questions, and coercion into completing the survey. Your survey answers
will be sent to a link where data will be stored in a password protected electronic format. No
personal information will be stored and all responses will remain anonymous. Email addresses
will be used for prize results only and then deleted after prize distribution. If you have questions
or concerns about the study or the procedures, you may contact the researcher at
grace.aguirre@student.csulb.edu. You may also contact the Office of Research and Sponsored
Programs at ORSPCompliance@csulb.edu, or calling (562) 985-8147, if you have questions
about your rights as a research participant.

By clicking on the “Agree” button you agree that: you have read the above information, you
voluntarily agree to participate, are at least 18 years of age and are currently a student enrolled at
CSULB.

o Yes
o No

42
 APPENDIX B

SURVEY QUESTIONS

43
 Questions used in the survey

Eliminating question (Skip-logic: Yes -> End survey)

Do you have an on-campus meal plan that you use to receive 1 or more meals per day?
Yes
No

Demographics

What is your gender?

Female
Male
Prefer not to say
Other_____

What is your age? Adapted from (Harris et al., 2017)

18-24
25-29
30-34
35-39
40-49
50 or older
Prefer not to say

What type of residence do you live in? Adapted from (Minkow et al., 2017)
Off-campus house or housing
On-campus dormitory
Sorority or Fraternity housing
Prefer not to say
Other____

What race/ethnicity do you identify with? Choose all that apply. (Adapted from US census
Bureau, 2019)
Black or African American
Latino, Latinx, Hispanic, Spanish
White
Asian
Native Hawaiian or Pacific Islander
Other
Prefer not to say

Do you have a kitchen at your residence? Adapted from (Minkow et al., 2017)
Yes (Full kitchen containing: a fridge, sink, stove, oven, counter space and pantry/dry food
storage space)
No (No kitchen elements)

44
Partial (only some elements of a full kitchen)
Prefer not to say
Other____

Food Insecurity

Within the past 12 months, I worried whether my food would run out before I got money to
buy more. (O’Keefe, 2015)
Yes
No
Prefer not to say

Within the past 12 months, the food I bought just didn’t last and I didn’t have money to get
more. (O’Keefe, 2015)
Yes
No
Prefer not to say

Cooking Attitudes (Presented in a matrix)

Please carefully read the statements in the table below, and then mark the number that best
matches how much you agree with the statement. (1) Totally Disagree, (2) Slightly Disagree,
(3) Neutral, (4) Slightly Agree, (5) Totally Agree
a) I like the idea of cooking. *
b) I want to cook my meals. *
c) I think cooking is important.
d)Cooking brings me joy.
e) Cooking saves money.
f) I feel comfortable in the kitchen.
g) Cooking takes too much work.
h) Cooking takes too much time.
i)Cooking is too expensive. *
j) Cooking is inconvenient.
k) I do not want to cook. *
Questions d), g), h), j), are taken/adapted from (Minkow et al., 2017)
Questions c), d), e), f) are taken/adapted from (Harris et al., 2017)

Cooking Self-Efficacy (Presented in a matrix)

Please mark the number that best matches how confident you feel that you could…
[1] Not Confident, [2] Mildly Confident, [3] Moderately Confident, [4] Fairly Confident, [5]
Extremely Confident
a) Cook or prepare a tasty meal that you would want to eat by combining raw, fresh or other
simple ingredients together.
b) Follow a simple recipe (with 5 or fewer steps).
c) Follow a complex or detailed recipe (with more than 5 steps). *

45
d) Cook or prepare a tasty meal for yourself from raw or simple ingredients WITHOUT using
a recipe.
e) Use flavor enhancing ingredients like spices, herbs, vinegar, juices and extracts to make
flavorful food.

Questions a), d) e) are taken/adapted from (Minkow et al., 2017)

Questions b) are taken/adapted from (Harris et al., 2017)

Cooking Behavior (Presented in a matrix)

Mark the circle that best describes how frequently you do the following. (Never, 1 time per
month or less, 2-3 times per month, 1-2 times per week, 3-4 times per week, 5-6 times per
week, 1 time per day, More than one time per day)
a) Eat dine-in, take out or fast food.
b) Eat purchased premade meals (Frozen meals, packaged meals, canned or jarred meals, meal
supplements, etc.).
c) Cook or prepare your own meals by combining individual/single ingredients.
d) Use a knife or other equipment to chop or slice your own ingredients. *
e) Use the stove, hot plate or other hot cooking surfaces to prepare meals from scratch or
almost scratch. *
f) Use the oven (including toaster oven) to prepare meals from scratch or almost scratch. *
g) Prepare meals with 3-5 ingredients. *
h) Prepare meals with more than 5 ingredients. *
i)Use recipes to prepare meals.
j) Prepare your own meals without using a recipe.
k) Prepare family recipes or meals that your family made when you were growing up. *

Questions a), b), c), i), j) are taken/adapted from (Harris et al., 2017)
Questions c) are taken/adapted from (Minkow et al., 2017)

Media Exposure (Presented in a matrix)

Mark the circle that best describes how frequently you do the following. **
1 Never, 2 Rarely, 3 Some of the time, 4 Most of the time, 5 All of the time
a) Visit electronic or social media networks such as blogs, Facebook, Twitter, Instagram,
Snapchat, LinkedIn, Pinterest, YouTube, etc.
b) See cooking related posts such as recipes, cooking demonstrations, how-tos or cooking
tips/facts
c)Stop to read or watch cooking related media posts from social media websites or apps.
d)Respond to cooking related media posts. Ex. "like," "love," "comment" or "share"
e) Save or download content from cooking-related media posts.
f) Enjoy when you see cooking related media
g) Follow or subscribe to cooking related pages or channels

When I use online media, I use the following (check all that apply) **

46
Facebook
Snapchat
Instagram
Twitter
Tumblr
Buzzfeed
YouTube
Pinterest
Reddit
LinkedIn
Other_____

I learned to cook from... (Check all that apply) (Minkow et al., 2017)
□School/Class
□Parent
□Grandparent or Extended Family
□Friend or Roommate
□Online source(s)
□Books
□I have not learned to cook
□Other_____

(Optional Question) In a few sentences, describe your background with cooking. If applicable,
including when you learned and the biggest influence for you to learn to cook or begin
cooking. *
Free response_____________________________________________________

* Original question written by PI for this study

**All portions of section composed of original questions written by PI for this study

47
 APPENDIX C

EXTENSIVE TABLE OF QUOTES FROM FREE RESPONSE QUESTION

48
TABLE 6. Quotes from Optional Free Response Question About Participant Cooking
Background
Influenced or Taught Extensive List of Quotes
by…
Family (Mom, Dad, “I cooked at home with my mom when I was younger”
Grandparent, sibling,
extended family) “I learned to cook from my mom. She taught me the basics such
as rise and other recipes that are mainly cooked in mexican
household.”

“I learned to cook when I was in elementary school. The biggest

influence for me was my mom. I remember helping her make
breakfast when I was younger, which eventually led to me
making breakfast for her by myself whenever I woke up earlier
than her.”

“Not much of a cooker. If I was to cook, it’d be a family meal

that my mom had taught me from the past…”

“My biggest influence in cooking would be my dad. We would

have family cooking days when I was little and we would all take
part in cooking a meal.”

“I learned at a young age probably 8-9 from my dad.”

“I learned to cook from my mother and grandmother. I can make

basic foods like pasta, salads, stir fry, and other simple foods that
do not require much work…”

“I've always enjoyed being in the kitchen watching and learning a

variety of different foods to cook and make ever since I was a
young girl. My grandpa was a chef and so was my uncle so from
a young age I learned how to make and eat amazing food pretty
much all my life. Food is life.”

“…I learned what I know from mainly my sister and somewhat

from my mom and dad.”
Friend, peer or significant “I learned how to cook when I moved out of my parents house
other around December of 2018 and moved in with my partner who
cooks most of my meals…”
Online sources “…Some times I get in the mood of trying to cook or bake
something new or simple I would’ve seen on Instagram. So I like
to some times explore around the kitchen and make something
with the ingredients available.”

49
 “My resolution this year is to try to learn how to cook. I watch
videos on Youtube to get some inspiration of what to try.”

“I sometimes watch easy simple recipes on YouTube for common

take out food (i.e. chinese food, italian food, burgers,etc.) and
then make them at home where i can control how much fat and
sugar is in the meal.”

“When I was younger I loved to watch cooking videos on

YouTube. I used to think I was great at cooking but I was actually
terrible…”

“…I also like to cook italian food and Mediterranean so I learned

those through youtube. I enjoy cooking by myself.”

“The rise of Instagram definitely contributed to my interest in

healthy cooking.”

“…As I got older, I started using recipes found on social media”

“My mom is the biggest influencers and then second is social

media like Tasty Buzzfeed videos. I don’t cook often or at all. “
TV “Food Network – Chopped”

“When I was younger, in elementary school, I really enjoyed

watching cooking shows because I thought putting several
ingredients to make a final product was a fun idea. I would
always watch all the different shows like Barefoot Contessa,
Giada at Home, 30-Minute Meals, Iron-Chef America, and etc.
That was where my enjoyment of cooking started…”

“…Gordon Ramsay…”
School “I learned to cook in college…”

“The biggest influence of learning how to cook was in high

school in my cooking class.”

“I took a culinary arts class in high school and worked in a

restaurant”

Reading Recipes “…Following recipes help me a lot as well and I like adding lots
of flavor so I do experiment a lot…”
Motivated by… Quotes
Culture “I learned when I was young to cook Central American food…”

50
 “Growing up in a Mexican house hold, as a girl I was expected to
help around the kitchen especially when it came to help cook, so I
would say that like at around 7 years old I began to learn how to
cook simple things..”

“Coming from an Asian and Latino background, I learned how to

cook from my parents. They taught me how to blend spices and
marinate meats and veggies along with kicking it up a notch by
creating an infusion of Latino and Asian flavors.”

“I’m from Mexican culture and I know how tasty our food is, I
want to learn old recipes that have been passed down generation
after generation.”
Self-sufficiency/Necessity “I learned to cook because I had to take care of my sick brother
when my parents would be at work.”

“…my biggest influence is probably my parents lack of cooking

skills and a desire to have tastier food.”

“…I learned to cook when I was about 12 because my mom was

always working and sometimes I had to make foods for myself.”

“…I used to be scared of cooking, but now that I had to stand on

my own I’m fairly confident.”

“Usually cook easy stuff, like stir fry all the ingredients.
Occasionally, cook some more complex meals. I need to cook
because no one else would feed me if I don't.”

“I started cooking a little at home but when I moved into my first

apartment I had to cook all my meals myself…”

“Learned to cook from a young age at about 10. Parents weren’t

home a lot so me and my sisters had to learn.”
Health “…to live a healthy lifestyle while eating tasty foods!”

“One of my biggest influences for me to learn how to cook would

be my diet. I am currently on the ketogenic diet (low-carb diet)
and would prefer that my meals are prepared at home since they
taste better. I have been on the diet since January of 2019 and
noticed that I cook a lot more than before I was on the diet. I find
myself constantly trying out new recipes that I find.”

“…after taking a nutrition class I have wanted to learn more and

cook more for my health.”

51
 “…I would say that other things that have influenced me to cook
would be that I want to be a healthier person and I also do take
some joy from doing it.”

“…I love cooking, but it can sometimes be burdensome. I love to

make things from scratch because I think it's better for my health”

“i have been cooking since i was little with my mom and my aunt
so i tend to cook pretty often for myself and family. i also am an
athlete so i focus a lot on healthy foods and getting all of my
colors in and protein because to me it is very important. “
Discouraged by…
Cost “I do not cook often, as it is expensive to buy ingredients in
bulk.”
Time/Effort “I rarely cook due to time and laziness, but know how to prepare
a simple pasta, ramen, sandwiches, or hard boiled eggs. All easy
recipes.”

“…I try to make my own food as often as possible but sometimes

it is hard because of school and work.”

“I always enjoyed cooking. More recently I don’t have the time to

cook for myself so I buy myself fast food.”

52
 APPENDIX D

STUDENT ACCESS LETTER OF APPROVAL BY COURSE INSTRUCTOR

53
 CALIFORNIA STATE UN IVERS!TY. LONG BEACH
FAMILY AND CONSUMER. SCIENCES
NUTRITION AND DIETETICS. HOSPITALITY MANAGEMENT. FOOD SCIENCE
FASHION MERCHANDISING AND DESIGN. CHILD DEVELOPMENT AND FAMILY STUDIES
FAMILY L1rr EDUCATION. CoNIUMER AFFAIRS. AND GERONTOLOGY

Grace Aguirre
Graduate Student
CSU, Long Beach

Dear Grace Aguirre:

1 approve you. Grace Aguirre, to collect data for your thesis from students in the
class at CSU, Long Beach. Students may voluntarily participate in your study
titled "Exploring the Relationship Between Online Media Use and Cooking Self-Efficacy, Skills
and Behaviors Among College Students".

You will be allowed to announce your study topic and survey details in class and/or have your
survey sent digitally through instructor email or Beachboard post. You will be allowed to leave a
flyer with the survey information with the students to access your survey.

Additionally, 1 understand that I will only be receiving the final draft of the research study and
not have access to any additional data or analysis. I also understand that there is no direct benefit
to the instructor or student for participating in this study.

Sincerely.

Name:

Title:

1250 BELLFLOWER. BOULEVARD· MS 0501. FCS·00I · LONG BEACH. CALIFORNIA 90840·0501 · 562/985·4484 · FAX 562/985·4414
www.csulb.edu/depts/fcs

54
REFERENCES

55
 REFERENCES

Balatsoukas, P., Kennedy, C. M., Buchan, I., Powell, J., & Ainsworth, J. (2015). The role of
social network technologies in online health promotion: A narrative review of theoretical
and empirical factors influencing intervention effectiveness. Journal of Medical Internet
Research, 17(6). doi:10.2196/jmir.3662

Bandura, A. (2004). Health promotion by social cognitive means. Health Education & Behavior,
31(2), 143-164.

Bandura, A. (2009). Cultivate self-efficacy for personal and Organizational Effectiveness. In E.

A. Locke (Ed)., Handbook of principles of organizational behavior (170-200). West
Sussex, UK: John Wiley & Sons.

Brouwer, A., Hogervorst, M., Grootjen, M., Erp, J. V., & Zandstra, E. (2017).
Neurophysiological responses during cooking food associated with different emotions.
Food Quality and Preference, 62, 307-316.

Brumby, D., Cox, A., Chung, J., & Fernandes, B. (2014). How does knowing what you are
looking for change visual search behavior? Proceedings of the SIGCHI Conference on
Human Factors in Computing Systems, 3895-3898.

Bodenlos, J., & Wormuth, B. (2013). Watching a food-related television show and caloric intake.
A laboratory study. Appetite, 61(1), 8-12.

Centers for Disease Control and Prevention. (2015). The health effects of overweight and obesity.
Retrieved from https://www.cdc.gov/healthyweight/effects/index.html

Centers for Disease Control and Prevention. (2017, March 17). Leading cause of death.
Retrieved from https://www.cdc.gov/nchs/fastats/leading-causes-of-death.htm

Celnik, D., Gillespie, L., & Lean, M. (2012). Time-scarcity, ready-meals, ill-health and the
obesity epidemic. Trends in Food Science & Technology, 27(1), 4-11.

Chandra, A., Neeland, I. J., Berry, J. D., Ayers, C. R., Rohatgi, A., Das, S. R., & Turer, A. T.
(2014). The relationship of body mass and fat distribution with incident hypertension.
Journal of the American College of Cardiology, 64(10), 997-1002.

Chen, R., Lee, M., Chang, Y., & Wahlqvist, M. (2012). Cooking frequency may enhance
survival in Taiwanese elderly. Public Health Nutrition, 15(7), 1142-1149.

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.

56
Crawford, D., Ball, K., Mishra, G., Salmon, J., & Timperio, A. (2007). Which food-related
behaviors are associated with healthier intakes of fruits and vegetables among women?
Public Health Nutrition, 10(3),256-65.

De Backer, CJS. & Hudders, L. (2016). Look who's cooking. Investigating the relationship
between watching educational and edutainment TV cooking shows, eating habits and
everyday cooking practices among men and women in Belgium. Appetite, 96, 494-501.

Dieleman, J. L., Baral, R., Birger, M., Bui, A. L., Bulchis, A., Chapin, A., & Murray, C. J. L.
(2016). US Spending on personal health care and public health, 1996–2013. JAMA,
316(24), 2627–2646. http://doi.org/10.1001/jama.2016.16885

Doycheva, I., Watt, K. D., Rifai, G., Mrad, R. A., Lopez, R., Zein, N. N., Alkhouri, N. (2017).
Increasing burden of chronic liver disease among adolescents and young adults in the
USA: A Silent Epidemic. Digestive Diseases and Sciences, 62(5), 1373-1380.
doi:10.1007/s10620-017-4492-3

Dwyer, L., Oh, A., Patrick, H., & Hennessy, E. (2015). Promoting family meals: A review of
existing interventions and opportunities for future research. Adolescent Health,
Medicine and Therapeutics, 6, 115-31. doi:10.2147/AHMT.S37316

Egeberg, R., Olsen, A., Loft, S., Christensen, J., Johnsen, N. F., Overvad, K., & Tjønneland, A.
(2010). Intake of whole grain products and risk of colorectal cancers in the Diet, Cancer
and Health cohort study. British Journal of Cancer, 103(5), 730-734.

Eheman, C., Henley, S. J., Ballard-Barbash, R., Jacobs, E. J., Schymura, M. J., Noone, A., &
Edwards, B. K. (2012). Annual Report to the Nation on the status of cancer, 1975-2008,
featuring cancers associated with excess weight and lack of sufficient physical activity.
Cancer, 118(9), 2338-2366. doi:10.1002/cncr.27514

Erinosho, T., Moser, R., Oh, A., Nebeling, L., & Yaroch, A. (2012). Awareness of the Fruits and
Veggies - More Matters campaign, knowledge of the fruit and vegetable
recommendation, and fruit and vegetable intake of adults in the 2007 Food Attitudes and
Behaviors (FAB) Survey. Appetite, 59(1), 155-160.

Farvid, M., Chen, W., Rosner, B., Tamimi, R., Willett, W., & Eliassen, A. (2019). Fruit and
vegetable consumption and breast cancer incidence: Repeated measures over 30 years of
follow-up. International Journal of Cancer, 144(7), 1496-1510.

Finkelstein, E., Trogdon, J., Cohen, J., & Dietz, W. (2009). Annual medical spending attributable
to obesity: Payer-and service-specific estimates. Health Affairs (Project Hope), 28(5),
W822-W831.

Garcia, A. L., Reardon, R., Mcdonald, M., & Vargas-Garcia, E. J. (2016). Community
interventions to improve cooking skills and their effects on confidence and eating
behaviour. Current Nutrition Reports, 5(4), 315-322. doi:10.1007/s13668-016-0185-3

57
Giustini, D., Ali, S. M., Fraser, M., & Kamel Boulos, M. N. (2018). Effective uses of social
media in public health and medicine: A systematic review of systematic reviews. Online
Journal of Public Health Informatics, 10(2), e215. doi:10.5210/ojphi.v10i2.8270

Greenlund, K. J., Giles, W. H., Keenan, N. L., Malarcher, A. M., Zheng, Z. J., Casper, M. L., &
Croft, J. B. (2014, November 07). Heart Disease and Stroke Mortality in the Twentieth
Century. Retrieved from http://www.oxfordscholarship.com/view/10.1093/acprof:oso/9
780195150698.001.0001/acprof-9780195150698-chapter-18

Harris, N., Yadrick, K., Anderson-Lewis, C., Brown, D., & Connell, C. (2017). An exploration
of college students' cooking behavior and factors that influence that behavior using social
cognitive theory. Canadian Family Physician Medecin de Famille Canadien, 61(2), e96-
106.

Harrison, M., Norris, M., Obeid, N., Fu, M., Weinstangel, H., & Sampson, M. (2015).
Systematic review of the effects of family meal frequency on psychosocial outcomes in
youth. Canadian Family Physician, 61(2), E96-E106.

Hartmann, Dohle, & Siegrist. (2013). Importance of cooking skills for balanced food choices.
Appetite, 65, 125-131.

Herbert, J., Flego, A., Gibbs, L., Waters, E., Swinburn, B., Reynolds, J., & Moodie, M. (2014).
Wider impacts of a 10-week community cooking skills program-Jamie's Ministry of
Food, Australia. BMC Public Health, 14(1), 1161.

Highfield, T., & Leaver, T. (2016). Instagrammatics and digital methods: Studying visual social
media, from selfies and GIFs to memes and emoji. Communication Research and
Practice, 2(1), 47-62.

Ji, S., Michaels, S., & Waterman, D., (2014). Print vs. electronic readings in college courses:
Cost-efficiency and perceived learning. The Internet and Higher Education, 21, 17-24.

Johnson, S., Paiva, A., Cummins, C., Johnson, J., Dyment, S., Wright, J., Sherman, K. (2008).
Transtheoretical Model-based multiple behavior intervention for weight management:
Effectiveness on a population basis. Preventive Medicine, 46(3), 238-246.

Johnson, S., Paiva, A., Mauriello, L., Prochaska, J., Redding, C., Velicer, W., & Kazak, A. E.
(2014). Coaction in multiple behavior change interventions: Consistency across multiple
studies on weight management and obesity prevention. Health Psychology, 33(5), 475-
480.

Jones, S.A., Walter, J., Soliah, L., & Phifer, J. (2014). Perceived motivators to home food
preparation: Focus group findings. Journal of the Academy of Nutrition and Dietetics,
114, 10, 1552-1556. doi.org/10.1016/j.jand.2014.05.003

58
Jones, H., Edwards, L., Vallis, T. M., Ruggiero, L., Rossi, S. R., Rossi, J. S.,
& Zinman, B. (2003). Changes in diabetes self-care behaviors make a difference in
glycemic control: The diabetes stages of change (DiSC) study. Diabetes Care, 26(3),
732-7.

King, K. P. (2017). Technology and innovation in adult learning. Indianapolis, IN: John Wiley &
Sons.

Korda, H., & Itani, Z. (2011). Harnessing social media for health promotion and behavior
change. Health Promotion Practice, 14(1), 15-23. doi:10.1177/1524839911405850

Kolb, R., Sutterwala, F. S., & Zhang, W. (2016). Obesity and cancer: Inflammation bridges the
two. Current Opinion in Pharmacology, 29, 77-89. doi:10.1016/j.coph.2016.07.005

Lavelle, F., Mcgowan, L., Spence, M., Caraher, M., Raats, M. M., Hollywood, L., & Dean, M.
(2016). Barriers and facilitators to cooking from ‘scratch’ using basic or raw ingredients:
A qualitative interview study. Appetite, 107, 383-391. doi:10.1016/j.appet.2016.08.115

Lewis, T., & Phillipov, M. (2018). Food/media: Eating, cooking, and provisioning in a digital
world. Communication Research and Practice, 4(3), 207-211.

Lenio, J. A. (2006). Analysis of the Transtheoretical Model of behavior change. Journal of

Student Research, 5, 73-87.

Lin, S., Li, Y., Leung, K., Huang, C., & Wang, X. (2014). Salt processed food and gastric cancer
in a Chinese population. Asian Pacific Journal of Cancer Prevention: APJCP, 15(13),
5293-8.

Livingstone, K. M., & McNaughton, S. A. (2016). Diet quality is associated with obesity and
hypertension in Australian adults: a cross-sectional study. BMC Public Health, 16, 1037.
http://doi.org.csulb.idm.oclc.org/10.1186/s12889-016-3714

McKnight, T., Demuth, J. R., Wilson, N., Leider, J. P., & Knudson, A. (2018). Assessing
effectiveness and cost-benefit of the trinity hospital twin city fit for life program for
weight loss and diabetes prevention in a rural midwestern town. Preventing Chronic
Disease, 15, E98. http://doi.org/10.5888/pcd15.170479

Mills, S., Brown, H., Wrieden, W., White, M., & Adams, J. (2017). Frequency of eating home
cooked meals and potential benefits for diet and health: cross-sectional analysis of a
population-based cohort study. The international journal of behavioral nutrition and
physical activity, 14(1), 109. doi:10.1186/s12966-017-0567-y

Minkow, S., Gray, V., Reiboldt, W., & Gonitzke, D. (2017). Cooking attitudes, behaviors, and
self-Efficacy in relation to fruit and vegetable intake among young adults. Journal of the
Academy of Nutrition and Dietetics, 117(9), A21.

59
Monteiro, C. A., Moubarac, J., Cannon, G., Ng, S. W., & Popkin, B. (2013). Ultra-processed
products are becoming dominant in the global food system. Obesity Reviews, 14, 21-28.
doi:10.1111/obr.12107

Mori, N., Shimazu, T., Sasazuki, S., Nozue, M., Mutoh, M., Sawada, N., . . . Tsugane, S. (2017).
Cruciferous vegetable intake is inversely associated with lung cancer risk among current
nonsmoking men in the Japan Public Health Center (JPHC) study. The Journal of
Nutrition, 147(5), 841-849.

National Heart, Lung, and Blood Institute. (2012). What causes overweight and obesity?
Retrieved from https://www.nhlbi.nih.gov/health-topics/overweight-and-obesity

Neyens, E. & Smits, T. (2017). Seeing is doing. The implicit effect of TV cooking shows on
children's use of ingredients. Appetite, 116, 559-567.

Ng, M., Fleming, T., Robinson, M., Thomson, B., Graetz, N., & Gakidou, E. (2014). Global,
regional and national prevalence of overweight and obesity in children and adults 1980-
2013: A systematic analysis. Lancet (London, England), 384(9945), 766–781.
http://doi.org/10.1016/S0140-6736(14)60460-8

Nour, M., Chen, J., & Allman-Farinelli, M. (2016). Efficacy and external validity of electronic
and mobile phone-based interventions promoting vegetable intake in young adults:
Systematic review and meta-analysis. Journal of medical Internet research, 18(4), e58.
doi:10.2196/jmir.5082

O’Keefe, L. (2015). Identifying food insecurity: two-question screening tool has 97% sensitivity.
AAP News, 20151023-1.

Patten, S., Vollman, A., & Thurston, W. (2000). The utility of the transtheoretical model of
behavior change for HIV risk reduction in injection drug users. Journal of the Association
of Nurses in AIDS Care, 11(1), 57-66.

Prochaska, J., DiClemente, C., & Norcross, J. (1992). In search of how people change:
Applications to addictive behaviors. American Psychologist, 47(9), 1002-1114.

Prochaska, J., Velicer, W., Rossi, J., Goldstein, M., Marcus, B., & Rakowski, W. (1994). Stages
of change and decisional balance for 12 problem behaviors. Health Psychology, 13(1),
39-46.

Reicks, M., Trofholz, A. C., Stang, J. S., & Laska, M. N. (2014). Impact of cooking and home
food preparation interventions among adults: Outcomes and implications for future
programs. Journal of Nutrition Education and Behavior, 46(4), 259-276.
doi:10.1016/j.jneb.2014.02.001

60
Rekhy, R. & Mcconchie, R. (2014). Promoting consumption of fruit and vegetables for better
health. Have campaigns delivered on the goals? Appetite, 79(C), 113-123.

Soliah, L., Walter, J., & Jones, S. (2012). Benefits and barriers to healthful eating: What are
the consequences of decreased food preparation ability? American Journal of Lifestyle
Medicine, 6(2), 152-158.

Sofi, F., Abbate, R., Gensini, G., & Casini, A. (2010). Accruing evidence on benefits of
adherence to the Mediterranean diet on health: An updated systematic review and meta-
analysis. The American Journal of Clinical Nutrition, 92(5), 1189-1196.

Surgenor, D., Hollywood, L., Furey, S., Lavelle, F., Mcgowan, L., Spence, M.,… Dean, M.
(2017). The impact of video technology on learning: A cooking skills experiment.
Appetite, 114, 306-312.

Taşpinar, O., & Temeloğlu, E. (2018). Influence of Tv cooking shows on the behavioral
intention of participating in gastronomic tourism. Journal of Tourism and Gastronomy
Studies, 6(2), 23-39. doi:10.21325/jotags.2018.200

U.S. Census Bureau. (2018, August 20). Computer and internet use in the United States: 2016.
Retrieved from https://www.census.gov/library/publications/2018/acs/acs-39.html

U.S. Census Bureau. (2019, March 01). Census Bureau statement on 2020 census race and
ethnicity questions. Retrieved from https://www.census.gov/newsroom/press-
releases/2018/2020-race-questions.html

Verschuren, W. M. (2012). Diet and Cardiovascular Disease. Current Cardiology Reports, 14(6),
701-708. doi:10.1007/s11886-012-0318-2. Retrieved from https://link-springer-
com.csulb.idm.oclc.org/article/10.1007/s11886-012-0318-2

Walter, J., Soliah, L., & Weems, S. (2010). Motivators and barriers to home food preparation.
Journal of the American Dietetic Association, 110(9). doi:10.1016/j.jada.2010.06.174

Watts, A. W., Loth, K., Berge, J. M., Larson, N., & Neumark-Sztainer, D. (2017). No time for
family meals? Parenting practices associated with adolescent fruit and vegetable intake
when family meals are not an option. Journal of the Academy of Nutrition and Dietetics,
117(5), 707-714. doi:10.1016/j.jand.2016.10.026

Wolfson, J. A., & Bleich, S. N. (2014). Is cooking at home associated with better diet quality or
weight-loss intention? Public Health Nutrition, 18(08), 1397-1406.
doi:10.1017/s1368980014001943

Wolfson, J. A., Bleich, S. N., Smith, K. C., & Frattaroli, S. (2016). What does cooking mean to
you?: Perceptions of cooking and factors related to cooking behavior. Appetite, 97, 146-
154. doi:10.1016/j.appet.2015.11.030

61
Woodruff, S. J., & Kirby, A. R. (2013). The associations among family meal frequency, food
preparation frequency, self-efficacy for cooking, and food preparation techniques in
children and adolescents. Journal of Nutrition Education and Behavior, 45(4), 296-303.

Woodward-Lopez, G., Kao, J., Kiesel, K., Lewis Miller, M., Boyle, M., Drago-Ferguson, S.,
Crawford, P. (2014). Is scratch-cooking a cost-effective way to prepare healthy school
meals with US Department of Agriculture foods? Journal of the Academy of Nutrition
and Dietetics, 114(9), 1349-58. doi: 10.1016/j.jand.2014.05.002

62