The Use of Social Media in Public Health Social Marketing Aimed at Adolescents

Omolade Alawode

MSc in Health & Society King’s College London September 2013 Acknowledgements

Abstract

The focus of this dissertation research is an experiment that attempts to encourage students at a high school in Delaware to choose the healthy options in the school cafeteria. A thorough literature reveals the rising rate of childhood obesity in America and the potential for school-based interventions. There is also increasing interest in the use of social marketing as a facilitator of behaviour change in public health programming. Additionally, social media is a growing mode of communication amongst teenagers. However, few interventions have tested the use of social media in public health social marketing, which appears to be a promising method of reaching adolescents. As such, this research examines the use of Twitter as a method of delivering nutritional information to nudge high school students to purchase healthy cafeteria items. Prior to launching the online intervention, a SurveyMonkey questionnaire gathers behavioural insight from a sample of 27 students who are perceived to be the school’s influencers. The nutritional Twitter intervention occurred for seven weeks and the data is analysed using Hootsuite, Twitter Analytics, and Twitonomy. Overall, the findings suggest that no relationship exists between the sale of healthy school lunch items and the Twitter timeline. As such, it can be assumed that the nutritional nudges did not influence student health behaviour of the students. This dissertation recommends further research into the role of social media as a tool for promoting health and prompting behaviour change. Future studies that include online instruments should investigate strategies for garnering user engagement and developing a systematic method of evaluation.

List of Contents Acknowledgements 1

Abstract 2

Chapter 1: Introduction 5

Chapter 2: Literature Review 6 2.1: Childhood Obesity 6 2.2: Adolescent Nutrition 7 2.3: National School Lunch Program 8 2.4: Defining Social Marketing 8 2.5: The Impact of Social Marketing on Public Health 9 2.6: Twitter and Other Social Media Platforms 10 2.7: The Relationship between Public Health and Social Marketing 10 2.8: Employing Social Media in Public Health Social Marketing 11 2.9: Considerations for Online Evaluation 12 2.10: Knowledge Gap and Justification of the Case Study 13

Chapter 3: Methodology 14 3.1: Research Aims 14 3.2: Supporting Organisation – HealthCorps 14 3.3: Case Site 15 3.4: Sample 16 3.5: Theoretical Framework 17 3.6: Questionnaire Design 19 3.7: Intervention Implementation 20 3.8: Evaluation Protocol 21 3.9: Ethical Considerations 22 3.10: Limitations 23

Chapter 4: Presentation and Analysis of Survey and Twitter Results 25 4.1: Key Insights from Survey Data 25 4.2: Evaluation of Twitter Data 31

Chapter 5: Presentation and Analysis of Cafeteria Results 38 5.1: Comparison of Cafeteria Sales Before and After the Intervention 38 5.2: Impact of Twitter 39

Chapter 6: Discussion 40 6.1: Summary and Interpretation 40 6.2: Conclusion 44 6.3: Recommendations for Future Research 45

Appendices 47 Appendix A: Twitter Dictionary 47 Appendix B: High School Lunch Menus for April and May 2013 48 Appendix C: SurveyMonkey Questionnaire 52 Appendix D: In-school Promotional Posters 62 Appendix E: Research Approval and Information Sheet 65 Appendix F: Examples of survey insights applied to tweets 70

References 78

Chapter 1: Introduction During previous investigations at a high school in Wilmington, Delaware, USA, I evaluated school wellness using two metrics: the School Health Index produced by the Centers for Disease Control and Prevention and the Healthy Schools Inventory created by the Alliance for a Healthier Generation. These two assessments illuminated numerous gaps in health-related services. I also gathered opinions from students through informal interviews. The data revealed that students found fault in the cafeteria food sponsored by the National School Lunch Program (NSLP), believing it to be unappetising and unhealthy. In 2011 the school received a grant-funded salad bar and in 2012 the cafeteria was remodelled to spotlight healthier items like sandwiches and salads. The cafeteria staff also began experimenting with local produce and healthier entrees. Despite these positive changes, students maintained an aversion to the healthy options. This dissertation therefore set out to improve the nutrition behaviours of students at this high school through a social marketing campaign. I proposed that by promoting healthy school lunches through a youth-friendly conduit, namely social media, students would be more apt to buy them. This behaviour shift towards eating nutritious food promotes a healthy lifestyle and contributes to childhood obesity prevention. The overall composition of this experiment takes the form of six chapters, including this introductory chapter. Chapter 2 reviews relevant studies in the literature. The third chapter describes the exploratory sample, lays out the theoretical framework, and explains the methodology. In Chapter 4, I present and analyse all of the online results. Chapter 5 lays out the school-related data analysis. In the final chapter, I briefly summarise the findings, critique the strengths and weaknesses of the study by comparing it to existing accounts, and give recommendations for future research. .

Chapter 2: Literature Review

In this chapter, I establish the knowledge base that informed this research. I start by examining existing literature on childhood obesity and adolescent nutrition in America, which serve drives the sense of urgency in this experiment. I employ recent studies to demonstrate the need for a school-based nutrition intervention and introduce to the National School Lunch Program. Next, I reference experts in the field to define social marketing as a tool for facilitating behaviour change and describe it within a public health context. Following, I characterise social media and its relationship to public health. Then, I investigate current accounts of health promoting campaigns that feature social media. I go on to search the literature for evaluative tools for online interventions. At the end of the chapter, I identify the knowledge gap and justify the reasoning for this study.

2.1: Childhood Obesity It is becoming increasingly difficult to ignore the childhood obesity problem in America. According to the Centers for Disease Control and Prevention (CDC), the public health institute of the United States Government, over one-third of American children and adolescents are obese or overweight, a rate that has tripled for adolescents over the past thirty years. Obesity and overweight are characterized by excess body fat as the result of caloric imbalance, meaning the number of calories expended is less than the number of calorie consumed. While obesity is purely defined as excess body fat, overweight is distinguished as excess body weight by height. In other words, overweight can escalate into obesity. They can be caused by various genetic, environmental, and behavioural factors (CDC, 2013a). The focus of this research is to combat behavioural factors that cause obesity, specifically eating unhealthy cafeteria lunches. A 2010 CDC report reveals that 12% of Delaware’s high school students are obese. Though this figure seems low, this population contributes to the one-third of American children who are obese. The health risks associated with childhood obesity include prediabetes, hypertension, high cholesterol, sleep apnoea, and stressed bones and joints (2013a). When left untreated, these diseases can become life threatening. The CDC warns that, “children and adolescents who are obese are likely to be obese as adults,” and “more at risk adult health problems such as heart disease, type 2 diabetes, stroke, several types of cancer, and osteoarthritis” (CDC, 2013a p.1). In other words, the severity of their disease risk intensifies when an obese child or adolescent transitions into an obese adult. This potential progression adds a sense of urgency to this research.

Adopting healthy lifestyle habits is the CDC’s recommended preventative measure against obesity. In fact, it proposes that, “schools… provide opportunities for students to learn about and practice healthy eating” (CDC, 2013a p.1). This endorsement supports the student-focused scope of this research. It also emphasises the significance of making healthy lifestyle changes in a didactic setting as a form of disease prevention. Bandura (2004) affirms that early modification is crucial because “it is easier to prevent detrimental health habits than to try to change them after they become deeply entrenched as part of a lifestyle” (p.157-8). While it is important for physicians to treat the poor health outcomes of obese patients, it is equally valuable to prevent obesity before obese teenagers because obese adults.

2.2: Adolescent Nutrition A number of sources verify that American adolescents suffer from poor nutrition. Cusatis and Shannon (1996) report that, “the diets of adolescents in the United States often fail to meet current dietary recommendations, both in terms of specific nutrient intake and on the more basic level of food consumption” (p.27). The CDC (2013b) confirms that American youth overeat the necessary amount of whole grains and exceed the maximum daily allowance of sodium; which are two habits that can lead to childhood obesity. In the state of Delaware, a 2011 study sponsored by Nemours Health and Prevention Services (2011) also uncovered that only 40% of adolescents ages 12-17 consume five or more servings of fruits and vegetables per day. This evidence demonstrates a need for nutrition interventions aimed at adolescents, particularly in the state of Delaware. Outside of the obvious health reasons, adolescents must maintain nutritious diets for three reasons unique to this demographic. First, the bodies of pubescent teenagers change and grow rapidly. It is imperative that they adapt healthy eating habits in order to satisfy their developmental transformations, “as the high rates of physical growth in adolescence bring with them heightened nutritional needs“ (Cusatis and Shannon, 1996 p.27). Secondly, since the adolescents in the study are students, their nutritional status predicates academic performance. In fact, “poor nutritional status and hunger interfere with cognitive function and are associated with lower academic achievement” (Story et al., 2006 p.110). Thirdly, as illustrated in Section 2.1, a poor diet can lead to obesity. Although adolescents eat meals at home or purchase food outside of the home, school- aged children actually consume 19% to 50% of their daily food at school (Story et al., 2006). With such a large proportion of their calories originating in the cafeteria, there is a consensus amongst social scientists that school-based nutrition interventions could potentially impact the nutrition of

7 students (Bandura, 2004, Neumark-Sztainer et al., 1999, Cusatis and Shannon, 1996, Story et al., 2006, Story et al., 2002).

2.3: National School Lunch Program The high school at the focus of this research is enrolled in the National School Lunch Program (NSLP), a federal assistance scheme operated by the United States Department of Agriculture (USDA). Participating educational institutions “get cash subsidies and USDA foods… for each meal they serve. In return, they must serve lunches that meet Federal requirements, and they must offer free or reduced price lunches to eligible children” (USDA, 2012 p.1). To be eligible for free lunches, the USDA requires that students come from a family with an annual income at or below 130% of the poverty line, which equates to $29,965 or less for a family of four. For reduced lunches, students must come from a family that makes an income between 130% and 185% of the poverty line per annum. For a family of four, 185% of the poverty line is $42,643. Students from families that earn salaries above this threshold must pay full price for school meals (USDA, 2012 p.2). These government-sanctioned prerequisites denote the significance of nutritious school lunches for low-income students. In order to receive federal reimbursements for school meals, the USDA obligates school districts in the NSLP to reach federal requirements for nutrition based on recommendations made by the Institute of Medicine of the National Academies (Nutrition Standards in the National School Lunch and School Breakfast Programs, 2012). The nutrition guidelines, as decreed by the Healthy, Hunger-Free Kids Act of 2010, “increase availability of fruits, vegetables, and whole grains [and] set specific calorie limits to ensure age-appropriate meals” (USDA, 2012 p.1). With such rigorous standards, qualifying school meals adequately satisfy the nutritional needs of students in the NSLP. In their analysis of school food and its role in obesity prevention, Story et al. (2006) declare that “school meal programmes significantly improve school-age children’s diets. Children who eat school lunches... have higher mean intakes of micronutrients, both at mealtime and over twenty-four hours, than those who do not” (p. 113). This finding signifies the long- lasting impact of school nutrition on students.

2.4: Defining Social Marketing In this experiment, I intend to improve student nutrition by using social marketing techniques. The term social marketing, coined by the pioneering Kotler and Zaltman (1971), is “the design, implementation, and control of programmes calculated to influence the acceptability of social ideas and involving considerations of product planning, pricing, communication,

8 distribution, and marketing research” (p. 5). The scope of social marketing overlaps with health communication, health promotion, health education, and behavioural science. These correlations represent the multidimensional nature of social marketing campaigns. When employed in the public health arena, successful social marketing initiatives beget positive health behaviour change. This is accomplished by customising the intervention and accompanying messages to suit the client’s exact preferences (Evans, 2006, French, 2009, Korda and Itani, 2013, Kotler and Zaltman, 1971, Thackeray et al., 2007). This widely accepted hallmark of social marketing ensures that programming is meaningful and effective. As such, social marketing places a weighty emphasis on the product or message presented to the audience (French, 2009). For public health practitioners, the product is health information ranging from disease descriptions to pharmaceutical recommendations, nutrition facts, lifestyle advice, local resources, and any knowledge with the potential to educate and enhance. Health information can be exchanged in a variety of clinical and non-clinical settings, including but not limited to, at home, at school, at the general practitioner’s office, at the hospital, at the gym, on the pitch, at the grocer, in magazines and newspapers, in books, on television, and via the internet. The goal of health-focused social marketing is to propagate information that improves the health of the target population through the appropriate medium.

2.5: The Impact of Social Marketing on Public Health Despite the ubiquity of obesity-related health promotion programmes around the world, my exploration of the literature located relatively few that employ the system of social marketing. However, Frattori et al. (2009) demonstrate the effectiveness of social marketing in obesity prevention through their case study on the installation of 13 healthy vending machines at schools, universities, and workplaces in Modena, Italy. The project, entitled “Choose Health” (Scegli la salute), intended to increase the consumption of nutritious foods amongst local residents by selling fruit salads, fresh vegetables, yoghurt, and other healthy snacks through strategically placed vending machines. The Local Health Unit, which sponsored this project, created a marketing scheme that encouraged a healthy lifestyle rooted in making healthy choices. To properly test the strength of their influence, the vending machines sold both healthy and traditional items (i.e.: fatty foods, sugary beverages, etc.). After six months, programme evaluation indicated that 30% (25,000) of all food and beverage items sold were healthy. Such promising results substantiate the likelihood of increasing the sale of healthy foods that are sold in the vicinity of unhealthy foods, like in a school cafeteria. Drawing from the model set forth by

Fattori and the Modena Local Health Unit, a key to inducing health behaviour change in a nutrition-based social marketing campaign is to promote healthy foods by emphasising choice.

2.6: Twitter and Other Social Media Platforms Korda and Itani (2013) define social media as “internet-based social networking services such as Facebook and MySpace, Twitter, wikis for collaborative content development, blogs, and two-way mobile messaging platforms that connect people through cell phones and personal digital assistants” (p.1). According to a 2009 study sponsored by the Pew Research Center, 93% of American adolescents ages 12-17 regularly surf the internet and 73% of them routinely use social media (Lenhart et al., 2010). The Center also discovered that 1 in 4 American teenagers use Twitter (Madden et al., 2013). Therefore this research employs Twitter, a microblogging social media website on which users can send 140-character-long messages. These messages, or tweets, integrate text, URL links, photos, videos, and audio. For a full description of the Twitter jargon incorporated into this study, turn to Appendix A. The study intervention is delivered via Twitter in response to the recent increase in adolescent usage (Lenhart et al., 2010, Madden et al., 2013). With the expansion of mobile applications, now various social medias can be downloaded onto smart phones, tablets, and other handheld devices and transported anywhere. Internet surfing no longer necessitates being tethered to a computer. Today websites like Facebook, Twitter, and Instagram can be retrieved on the go. In fact, the Pew Research Center found that 25% of Twitter users access the internet on their mobile phones (Fox et al., 2009). This evidence suggests that health messages communicated across these channels can be accessed on the tube, on the bus, on the street, in a queue, and anywhere that one travels with a handheld device.

2.7: The Relationship between Public Health and Social Media Since most social media sites launched less than twenty-years-ago, its employment in public health is fairly recent. Nonetheless, social media engagement is rapidly growing amongst public health practitioners. For example, “60% of [American] state health departments now use at least one application” (Thackeray et al., 2011 as cited in Neiger et al., 2012b p.159). The low/no cost of usership and the power to widely broadcast messages instantaneously attract health promoters to social media (Neiger et al., 2012b, Korda and Itani, 2013). A number of researchers have labelled Twitter as a potentially useful tool for public health and health promotion (Neiger et al., 2012a, Korda and Itani, 2013, Neiger et al., 2012b, Chou et al., 2013, Young, 2010, Mackert et al., 2012, Guse et al., 2012).

To effectively deliver health messages on social media, Korda and Itani (2013) advise “using tailored messaging, repurposing and applying multiple complementary delivery modes to reinforce key themes, and encouraging users to engage with web-based applications” (p.8). As I describe in Section 2.4, tailored messaging is central to the social marketing model used in this research. To ensure the complementary delivery of health information, this experiment was conducted in cooperation with HealthCorps, a youth-oriented health promoting charity (see Section 3.1). By combining an online nutrition intervention with HealthCorps’s multifaceted approach to childhood obesity prevention, the integrated dissemination of parallel health messages increases the likelihood of noticeable results. Despite increased usage in the field, few studies assess the efficacy of harnessing social media in public health interventions. The limited findings on the subject focus on internet- or computer-based interventions. For example, Bandura (2004) determines that “interactive computer-assisted feedback provides a convenient means for informing, enabling, motivating, and guiding people in their efforts to make lifestyle changes” (p.150). Though this evidence does not directly relate to social media, it does provide insight into the capacity for computer-based programmes to facilitate behaviour change. Furthermore, Fleisher et al. (2002) and Fox et al. (2005) find that “patients may experience empowerment in decision making about their health through online learning” (as cited in Korda and Itani, 2013 p.5). In summary, these two findings illustrate the power of computer- and online-focused initiatives in promoting positive health behaviour modification. Nonetheless, there is more to learn about using social media in this context.

2.8: Employing Social Media in Public Health Social Marketing Little is published about employing social media in public health social marketing. In an in-depth review of the literature produced only two peer-reviewed studies. Interestingly, both involve Twitter. In the first, Young (2010) designed a Twitter-like micro-blogging website to allow teenage female subjects to communicate their pedometer use and ultimately amplify their physical activity. Though the results exhibit an increase in steps over the course of the study, one major drawback is the small sample size. With only four participants, the study would have been more persuasive if the author had tested a bigger sample. In the second study, Mackert et al. (2012) communicated tweets about pre-natal vitamin to female college students in an effort to reduce the risk of neural tube defects. The research team measured the participants’ beliefs about the benefits of vitamins by administering online surveys. Unlike the previous study, this experiment fails to generate positive behaviour change.

The results show no correlation between tweeted messages and positive beliefs about multivitamins. The author attributes this lack of change to using an unfamiliar Twitter handle that the subjects did not trust. In this research, I employ a HealthCorps Twitter account to which students are accustomed. Additionally, there is insufficient data about the use of social media in public health social marketing aimed specifically at teenagers. Besides the aforementioned Young (2010) paper, one investigation commissioned by the Centre for Health Promotion in South Australia explores the use of social media by youth-oriented health agencies, but it does not attempt to analyse the effect that their online presence has on their clients (Department of Health, 2012). What is not yet clear is the role of social media in public health social marketing. Without concrete evidence to the contrary, it still stands to reason that targeted social marketing via social media would appeal to adolescents because of their prominent engagement in this form of communication.

2.9: Considerations for Online Evaluation Although there is much uncertainty surrounding the use of social media in social marketing, there is even less research on evaluating Twitter-based health interventions (Neiger et al., 2012b, Chou et al., 2013). The existing accounts propose that the effect of an online study can be measured by A) the degree of participant self-efficacy (Korda and Itani, 2013, Bandura, 2004), B) appropriate online metrics and key performance indicators (KPIs) (Neiger et al., 2012a, Neiger et al., 2012b, Chou et al., 2013, Evers et al., 2003), and C) the reification of health beliefs into corresponding action (Frattori et al., 2009). However, previous studies have not dealt with how to employ these assessment methods individually or concurrently. Moreover, researchers have not treated the reliability of any evaluation method with much detail. The primary weakness in the field is the absence of a standard evaluation guideline (Evers et al., 2003). The literature to date includes one model for quantifying Twitter engagement developed by Neiger et al. (2012a). This method of analysis is limited because it makes no attempt to compute actual values and it has not been validated by any peer-reviewed research. As such, I can only refer to it loosely. Outside of the social marketing sphere, there is a large volume published about general recommendations for social media analysis. Social media consultancy firm e-consultancy identifies that, “retweets and other engagement metrics are good indicators of how a community is responding to activity on Twitter” (Whatmough, 2013 p.1). The agency also suggests that, “recording information on favourites is useful too. It helps build intelligence around content that is working and resonating with the community” (Whatmough, 2013 p.1). In

12 other words, while retweets and number of followers indicates community interaction, favourites depict positive community responses. As with Neiger et al. (2012a), Whatmough (2013) overlooks the inclusion of acceptable ranges of values for various metrics, which presents a major weakness in both papers. Disregarding the absence of a standard evaluative form, the available literature continuously points to Twitter metrics and KPIs as benchmark criteria for evaluation.

2.10: Knowledge Gap and Justification of the Case Study In this literature review, I establish the need for a youth-oriented obesity prevention intervention by highlighting the rising rates of obesity amongst America’s teenagers, emphasising the insufficiencies in American adolescent nutrition, and examining the importance of school lunch for students’ overall nutrition. In addition, I also confirm a knowledge gap pertaining to social media in public health social marketing. Few papers analyse social media as a mode of delivering public health programming or as a tool for producing positive behaviour change via social marketing. Moreover, even less is written about the use and reliability of evaluative benchmarks for measuring the influence of social media on health behaviours. Lastly, most studies in the literature only focus on adults rather than adolescents. The justification of this research lies in its relevance to human development. Promoting healthy habits and preventing obesity are especially germane for adolescents because they are on the cusp of adulthood. Neumark-Sztainer et al. (1999) stress that during this development stage, “adolescents are becoming more autonomous” and “behavioural patterns acquired during this period are likely to influence long-term behaviours” (p.929). An intervention that encourages the early adoption of positive health behaviours, like the one in this study, can impart lifelong and life-affirming implications on overall nutrition and risk of obesity. Social marketing can be employed to facilitate this healthy behaviour change. Social media, a newly pervasive part of society, is an undervalued resource amongst social marketers.

Chapter 3: Methodology Building on the literature, this research tested the efficacy of a nutrition social marketing intervention on Twitter aimed at adolescents. The methodology follows an empirical research design, in which I surveyed students’ lunchtime behaviours and social media usage, developed a customised Twitter feed to disseminate health information about school lunches, measured follower engagement with the online intervention, and compared the sales of healthy cafeteria items before and after the intervention. In this chapter, I commence by outlining the research aims, which express the intention behind the methodology. Then I describe the organisation that supported this work, the case site, and the sample population to give the reader a vivid understanding of the target population. Next, I lay out the theoretical framework, which provides the necessary academic foundation for this research. I go on to explain the development of the research tools, the evaluation protocol, and the timescale; all of which pertain to the fieldwork that I conducted. Finally, I end the chapter by dealing with ethical considerations and methodological limitations in order to balance the rationalisation of the methodology with careful reflection about sensitive issues and potential weaknesses.

3.1: Research Aims The aims of this dissertation are: • Aim 1: Gather insights into the lunchtime behaviours and social media usage of high school students. • Aim 2: Create a Twitter-based social marketing campaign that contributes to obesity prevention and improved nutrition amongst high school students by promoting the purchase of healthy items in the school cafeteria. • Aim 3: Critically evaluate the efficacy of the Twitter intervention by monitoring important metrics and key performance indicators (KPIs). • Aim 4: Increase the sale of healthy cafeteria foods.

3.2: Supporting Organisation – HealthCorps I conducted this research in cooperation with my former employer, an American charity called HealthCorps. Its mission is “to implement an innovative in-school model that inspires teens to make healthier choices for themselves and their families (HealthCorps, 2013 p.1).” Targeting high-need populations, HealthCorps Coordinators use peer mentoring to provide a progressive

14 curriculum in nutrition, fitness and mental strength through a variety of methods including classroom instruction, lunchroom demonstrations, after-school clubs, and community events. HealthCorps estimates that it has impacted approximately 142,900 students and 285,800 friends and family through its work (HealthCorps, 2013). HealthCorps also “serves as a unique research laboratory – exploring the complex, underlying causes of the obesity crisis and discovering and communicating solutions” (HealthCorps, 2013 p.1). Due to its investment in research, the organisation agreed to support my experiment. Moreover, this study contributes to the literature on childhood obesity and investigates a novel solution by championing school-based nutrition through online social media. I collaborated closely with Miss Dorsey, the Coordinator at the target high school in Delaware; the school at which I served as the Coordinator from 2010-2012. I make the effort to describe HealthCorps at length to emphasise that, unlike at most American high schools, nutrition education is tightly woven into the school environment. I specifically chose to conduct this research at the Delaware high school with the assumption that students would be more accepting of nutrition messages. In addition, my familiarity with HealthCorps administrators, Miss Dorsey, the school, the staff, the students, the cafeteria, and school district authorities proved to be instrumental for my research. The evidence suggests that health promoting social marketing strategies are not effective unless integrated within a larger scheme (Thackeray et al., 2012, Korda and Itani, 2013). Accordingly, the nutrition messages on the Twitter feed concur with Miss Dorsey’s classroom lessons and extracurricular activities.

3.3: Case Site Leaders in the field of social marketing agree that in order to communicate health information in a manner that triggers positive behaviour change, one must create a customer- oriented scheme (Thackeray et al., 2007, French, 2009, Reynolds, 2012, Kotler and Zaltman, 1971). Customers, or the target audience, would not adhere to a programme formulated without respect to their specific needs because the imprecision would render it inadequate. As such, it is imperative to create a clear profile of the customer. In this case study, the target audience consists of adolescent students who attend a public 9-12th grade school in Wilmington, Delaware, USA. Internal records (e-school) indicate that the school enrolled 851-854 students during the study timeframe and the attendance rate oscillated between 64.95% and 79.84%. Due to an unusual zoning pattern, students hail from suburban, rural, and urban settings. This creates rich geographic and cultural diversity in the student body. The school is a Title I funded institution

15 where 65% of students come from low-income families and over 60% of students qualify for the NSLP’s free and reduced lunch programme (DDOE, 2013 p.1). The relationship between low- income students and school nutrition has been investigated by Story et al. (2006), who assert that students from low-income families have a higher risk of obesity and a higher probability of eating more than one school meal per school day. These findings indicate that school-based nutrition interventions are particularly significant for students from economically deprived backgrounds. The school district nutritionist determines the monthly school lunch menus for all of the schools in the district. The high school lunch menus published during the study time period can be found in Appendix B. The focus of this study remained on school lunches subsidised by the NSLP. I did not analyse competitive foods and beverages (e.g.: vending machines, snack bar, booster sales) because these item do not fall under the jurisdiction of the USDA nutrition standards. Moreover, it would have proven infeasible to track items not listed on the monthly cafeteria menus. I mention them only to point out the popular unhealthy choices that are available to students.

3.4: Sample Before I launched the Twitter intervention, I surveyed a segment of the student body to gauge their lunchtime behaviours and social media usage. This exploratory sample afforded me a conduit for investigating a relatively novel area of research (Denscombe, 2010). The market research gathered from the survey allowed me to customise the Twitter timeline to students’ preferences. My sample consisted of the two class sections of Student Leaders, an elite group of students taught by Miss Dorsey. As excellent all-around students, they maintain a grade point average (GPA) above 2.50, uphold active school citizenship, frequently perform community service, and follow a code of ethics. To gain entry into the program, students must be recommended by teachers, administrators, or peers. The initial sample contained 45 students, but only 27 completed the survey. This cohort consisted of 16- to 18-year-old high school juniors and seniors across every racial group. Though 18 additional responses would have enriched data pool, I still received enough responses to carry on with the research. This sample was chosen because the Student Leaders are a group of the school’s perceived influencers. They command their own weekly advisories of about 20 freshman students. The Freshman Advisory Program, as it is called, connects Student Leaders, who are typically juniors and seniors, to students who are new to the school community. The Student Leaders teach weekly forty-minute lessons about navigating teenage life. In other words, Student Leaders potentially influence the behaviours of their underclassmen peers. Story et al. (2002)

16 contend that adolescent “[peers] help to create the norms concerning behavior, particularly whether the behavior is acceptable to the peer group” (p.45). Hence, the assumption was made that granting them input and primary access to the Twitter feed would make them early adopters who could wield their seeming social influence to increase followership. It is important to note that few studies explore the relationship between peer influence and food choice amongst adolescents. Furthermore, the small number of existing accounts find no correlation between the two (Story et al., 2002). Nonetheless, Story et al. (2002) contend that “adolescents spend a substantial amount of time with friends, and eating is an important form of socialization and recreation” (p.45). This observation suggests the potential for a connection between peer influence and eating habits, but concrete evidence has yet to surface.

3.5: Theoretical Framework Several authors argue that online interventions based in theory deliver enhanced rigour, legitimacy, and reproducibility (Evers et al., 2003, Korda and Itani, 2013, Chou et al., 2013). For example, in their review of online health behaviour change programmes, Evers et al. (2003) conclude that “theory driven programs can have advantages of building on the system that supports the theory and providing a more coherent and systematic guide to behaviour change” (p.69). The theoretical framework for this research integrates the system for developing a promotional social marketing strategy outlined by Thackeray et al. (2007), the Social Cognitive Theory and Self-Efficacy Model theorised by Bandura (2004), Nudge Theory created by Thaler and Sunstein (2009), and Choice Architecture as described by Thaler et al. (2010). Thackeray et al. (2007) consider three criteria for effectively promoting a social marketing campaign: “(a) communication purpose, (b) customer preference, and (c) tool or material cost” (p.336). Henceforth, I refer to this protocol as the Thackeray Model. In this case study, the communication consisted of messages about healthy items sold in the cafeteria. Addressing point (a), the purpose of circulating this information was to influence the students’ behaviours by encouraging them to purchase healthy food instead of more fatty or sugary options. Concerning point (b), I gathered responses from the questionnaire administered to the Student Leaders in order to determine customer preferences about cafeteria purchases and online media engagement. Lastly, the promotional tool was Twitter, a social media platform commonly used by adolescents. Although Twitter is free to use, I paid for a monthly subscription to Hootsuite, a social media manager that allowed me to preschedule tweets from a different time zone. Additionally, I purchased a plan on SurveyMonkey, the online survey service that hosted the

17 questionnaire. The Thackeray Model’s straightforward procedure for promoting a social marketing campaign adds clarity to this methodology. Within a health context, Bandura’s (2004) preeminent Social Cognitive Theory posits “a multifaceted causal structure in which self-efficacy beliefs operate together with goals, outcome expectations, and perceived environmental impediments and facilitators in the regulation of human motivation, behaviour, and well-being” (p.143). Bandura (2004) postulates that, A) “efficacy beliefs influence goals and aspirations”, B) “self-efficacy beliefs shape the outcomes people expect their efforts to produce,” and C) “self-efficacy beliefs also determine how obstacles and impediments are viewed” (p.145). These three causal structures form the Self-Efficacy Model. As applied to this research, students’ perceived levels of self-efficacy determined their willingness to purchase healthy cafeteria food, the assumed effect that nutritious food bears on their bodies, and their ability to overcome observed obstacles against buying healthy school lunches. I purposely integrated Social Cognitive Theory into the theoretical framework because it “offers both predictors and principles on how to inform, enable, guide, and motivate people to adapt habits that promote health and reduce those that impair it” (Bandura, 2004 p.146). The Self-Efficacy Model informed the positive and motivating tone of the tweets. Previous studies have demonstrated the significance of Social Cognitive Theory on research about adolescent nutrition. In their study on adolescent eating behaviours, which is guided by the theory, the results of Cusatis and Shannon (1996) “indicate the value of focusing on self-efficacy in nutrition strategies aimed at adolescents, especially with regard to the reduction of unhealthy eating behaviour” (p.32-3). This finding implies the importance of boosting self-efficacy amongst the student population to facilitate the adoption of healthy behaviours. The final concept within the theoretical framework is Nudge Theory, a prominent social marketing notion conceived by Thaler and Sunstein (2009) that operates within the structure of Choice Architecture. According to Thaler et al. (2010), a choice architect “has the responsibility for organizing the context in which people make decisions” (p.2). In this research, I evoke the role of choice architect by influencing students’ buying power with tweeted endorsements for healthy school lunch options. The authors note that in the case of school cafeteria lunch sales, “small and apparently insignificant details can have major impacts on people’s behaviour” (Thaler et al., 2010 p.3). The power of a seemingly trivial idea speaks to the weight of a nudge. Thaler and Sunstein (2009) define a nudge as, “any aspect of the choice architecture that alters people’s behaviours in a predictable way without forbidding any options or significantly changing their economic incentives” (p.6). In this experiment, each tweet represented a nudge. At 140- characters in length, these small suggestions possessed the power to influence students’ school

18 meal choices. I did not impose upon students’ selections by banning pizza, hotdogs, or any less healthful foods. In addition, I did not increase or decrease the price of school lunches. Instead, I strategically marketed the healthy foods that are already within the National School Lunch Program. French (2011) challenges Nudge Theory, arguing that nudges “should not be seen as the default solution to social change” (p.3). His primary critique is that nudges propagate a controlling paternalistic top-down approach that does not produce measurable change. Thaler and Sunstein (2009) retort that nudges and choice architecture fall under the umbrella of libertarian paternalism, which they define as “a relatively weak, soft, and nonintrusive type of paternalism because choices are not blocked, fenced off, or significantly burdened” (Thaler and Sunstein, 2009 p.6). In this sense, underscoring one choice does not restrict access to another choice. If that causal structure were accurate, then nudging would indeed constitute paternalism. Instead, nudge recipients maintain control over their volition, which enables them to choose any option that they please. To reiterate, I did not implicitly or explicitly force any student to follow me on Twitter, nor did I obligate them to buy healthy meals. Once again, those actions would signify paternalism, a practice that is not included in the theoretical framework of this study.

3.6: Questionnaire Design In a comprehensive review of environmental and individual influences on teenager’s eating behaviours, Story et al. (2002) report that, “development of effective nutrition interventions rests on identifying factors most predictive of adolescent eating behaviours, and the relative strengths of the individual and environmental influences need to be elucidated” (p.49). In this sense, gathering insight into the lunchtime and online behaviours of students enhances the prospect of an effective intervention. With the theoretical framework as a guide, I developed an online survey hosted by SurveyMonkey, which can be found in Appendix C. Miss Dorsey administered it to the Student Leaders in a school computer lab. The survey takes the form of twenty-nine questions: twenty-seven closed and partly-closed multiple choice questions, one ranking question, and one open-answer question. I blocked the skip feature with the purpose of circumventing incomplete data sets. To reassure completion, I placed a progress bar at the top of each page. Bryman (2012) identifies ease of completion and enhanced comparability of answers as two chief advantages of closed questions, so I composed closed-ended questions where possible. However, he warns that “closed questions may be irritating to respondents when they are not able to find a category that they feel applies to them”(Bryman, 2012 p.252). To counter this

19 challenge, I offered open-ended questions and “other” boxes, especially in areas where I sought additional or more personal insight (Cohen et al., 2011). These responses elicited small qualitative data sets amongst the overall quantitative data. As noted by Bryman (2012), respondents are prone to boredom during long surveys, so I minimised the length as much as possible. I included one ranking question because it “it moves beyond multiple choice items in that it asks respondents to identify priorities... [and] enables a relative degree of preference” (Cohen et al., 2011 p.385). With Cohen et al. (2011) as a guide for sequencing, I carefully ordered the questions, arranged related questions on the same page, and used descriptive headings as literal signposts. Questions with more than five answer choices were allotted their own pages, lest students be deterred or overwhelmed. As another method for maintaining brevity, I organized long answer choices into multiple columns. It is important to note that some of this attention to detail is lost in the printable version of the survey found in Appendix C. To maintain an uncomplicated tone, I removed nutrition jargon like “calories”. In addition, I avoided the mention of obesity because as a powerful buzzword it could have possibly affected students’ responses. A pilot of the final survey was tested by students who were 1-2 years removed from high school so as to gauge wording and length. Writing the questionnaire on SurveyMonkey provided four main advantages. First, an online survey mirrored the online nature and global accessibility of the study. Secondly, it cost nothing to the students or to the school. Thirdly, the website permitted me to download quantitative data, charts, and graphs into Microsoft Excel. Lastly, I could integrate “logic” into the survey design; that is, I could determine the order of the questions based on students’ responses. Selecting certain answer choices automatically skipped to particular questions. For example, a student who answered “never” to the number of days per week that s/he purchased a school lunch did not answer any further questions about his/her lunchtime behaviours.

3.7: Intervention Implementation The final stage of Thackeray Model is to determine the appropriate promotional tool and cost. Since the school’s internet service restricts access to most non-academic websites, Twitter was the ideal vehicle for this project because it is the only social media website allowed through the firewall. Free usership cancelled costs for me and avoided a possible barrier to access for students. I used HootSuite to preschedule tweets in a different time zone and ensure that tweets posted when I was away from Twitter. The Twitter user name for this experiment was Ms Alawode and the handle was @HCatMcKean. Although I created the account during my tenure as the HealthCorps Coordinator,

20 students were reminded of the Twitter feed via in-school and online advertising. Miss Dorsey posted promotional flyers around the school and I tweeted them online. I ended the tweets with “RT pls” (shorthand for “retweet please”) or #TeamFollowBack (a promise to reciprocate followership) in order to increase the number of retweets and followers, two metrics that indicate online engagement. The posters can be found in Appendix D. I also inserted the Twitter handle at the end of the survey to invite Student Leaders to follow me. The results of the survey informed the content of each tweet. When composing tweets, I nudged students towards healthy lunch choices and made them feel capable of achieving a positive health outcome. I rejected “scare tactics” as a form of behaviour modification and instead implemented messages of self-management and self-empowerment (Bandura, 2004 p.148). This method of operation was chosen because it aligns with the Self-Efficacy Model and Social Cognitive Theory.

3.8: Evaluation Protocol Data management and analysis of the questionnaire were performed by SurveyMonkey and Microsoft Excel. As I stated in the Literature Review, a lack of understanding surrounds evaluating a public health social marketing study on social media, particularly Twitter. As such, I independently developed a set of metrics and key performance indicators (KPIs) loosely based on Twitter analysis guidelines described by Whatmough (2013) and Neiger et al. (2012a). Throughout the study, I monitored the following quantitative metrics: 1. Number of tweets: how many messages I wrote. 2. Followers: how many people subscribed to my posts. 3. Location: from where followers tweeted. 4. Time: when I wrote messages. 5. Retweets: how many users re-posted my tweets. 6. Mentions: how many users mention @HCatMcKean in their tweets. 7. Replies: how many users responded to my tweets. 8. Favourites: how many users bookmarked my tweets. These metrics gauged of how the Twitter feed operated and how other users interacted with it. Twitter data analysis was facilitated using Twitter Analytics, Hootsuite, and Twitonomy. As mentioned in the Literature Review, previous studies have not revealed measures by which to expect responses and engagement.

KPIs were calculated using the aforementioned analytic software. The difference between a metric and a KPI is that “a metric is any single variable that gets measured (e.g., number of posts, tweets, fans, etc.), whereas a KPI is a unique form of a metric identified by an organisation as central to its assessment of social media and related benefits” (Neiger et al., 2012b p.152). The KPIs that I tracked are: 1. Link clicks: how many people opened links that I posted. 2. Most retweeted tweets: posts that users retweeted with the greatest frequency. 3. Most favourited tweets: posts that users favourited with the greatest frequency. These KPIs give a clearer description of the impact of the Twitter feed by identifying which nudges the followers deemed as most important. Once again, existing accounts have not satisfactorily dealt with expected responses and engagement. Following the online data analysis, cafeteria sales were quantified in Microsoft Excel using daily cafeteria inventories that I received from the school district nutritionist. I specifically tracked salads, sandwiches (wraps and subs), fruit, vegetables, and healthy hot entrees. For comparison, I also monitored the sales of two popular less-than-nutritious items: hot entrees and pizza. Since this is a novel methodology, prior studies cannot predict likely results. However, if the tweeted nudges successfully influenced the students’ behaviour, then I expected to see a perceptible increase in the sale of healthy items.

3.9: Ethical Considerations I received low-risk ethical approval from King’s College London Research Ethics Subcommittee. Refer to Appendix E for the approval letter, information sheet, and letter of support from HealthCorps. Since all of the survey respondents were over 16-years of age, I did not require parental consent. Additionally, this study did not necessitate physical consent forms because, as the information sheet clearly stated, clicking submit implied consent. However, I recognised that this study involved young people, a population that is sometimes considered sensitive or vulnerable (Denscombe, 2010), but there are two points that their decrease risk. First, I obtained additional ethical approval from the school district (see Appendix E). This extra sanction demonstrates that district administrators trusted that my research posed no threat to students’ safety. Secondly, Twitter’s privacy policy permits me to gather data from the site. If a user sets his/her account to “public”, the content enters the public domain. In fact, the United States Library of Congress archives all public tweets, lists, retweets, favourites, tweet locations, and times. Twitter’s privacy statement declares that, “what you say on Twitter may be viewed all

22 around the world instantly” (Twitter, 2013). By signing up for Twitter with a public account, followers are consenting to the distribution of their Twitter data.

3.10: Limitations It is important to draw attention to the perceived limitations in this research and identify the approach taken to minimise their effects. This methodology contains three primary restraints to its generaliability: the sample, the online instrument, and the quantitative data sets.

3.10.1: Sample Limitations Due to the small sample size, the survey results fail to capture the full diversity of the student body’s behaviours. However, surveying the entire student body lies outside of the scope of this dissertation. Administering the questionnaire to two classes proved to be more practical. Moreover, the Student Leaders’ position as perceived influencers led me to believe that their behaviours influence those of their peers, which implies that similarities exist between the sample’s behaviours and the student body’s behaviours. However, the veracity of the Student Leaders’ peer influence is merely anecdotal. Sections 2.1-2.3 summarise overwhelming evidence supporting the importance of proper nutrition for adolescents, especially as a preventative measure against childhood obesity. Regardless, “a lack of a sense of urgency regarding future health may make nutrition a low concern among adolescents” (Neumark-Sztainer et al., 1999 p.929). Foresight deficient, teenagers do not give credence to future health risks. Consequently, the target audience might not have followed @HCatMcKean on Twitter because they are not invested in their personal health. For the same reason, they might not have purchased healthy school meals because they do not value preserving their well-being.

3.10.2: Online Instrument Limitations This intervention was purely Twitter-based, but I was not able to directly determine causality concerning cafeteria purchases. I could only infer that the tweets contributed to my followers’ overall healthy influences. Difficulties surrounding online engagement presented another set of issues. Students simply might have chosen not follow my Twitter feed. As Bandura (2004) points out, “interactive technologies... cannot do much if individuals cannot motivate themselves to take advantage of what they have to offer” (p.150). I attempted to overcome this obstacle by promoting the Twitter page offline with posters to enhance student awareness and increase followership. Furthermore,

I could have attracted followers who do not attend the target high school. Unfortunately, this is an unavoidable risk with online interventions involving public accounts. Bryman (2012) also emphasises the difficulty of identifying participants in online research simply based on their profiles. In this methodology, I did not need to ascertain personal details of my followers. All online methods carry with them one well-known limitation: they oppose public health’s attitude towards screens. The current belief is that increased screen-time – i.e.: time spent in front of a television, computer, mobile device, tablet, or game console – contributes to a sedentary lifestyle and ultimately increases the risk of obesity. It is also important for public health professionals to consider that gadgets with screens are becoming a normalised part of society. To maintain its relevance, public health must keep up with the evolution of technology. As I established in Section 2.7, new technologies like social media can be integrated into health promotion interventions in order to reach a greater audience.

3.10.3: Quantitative Data Limitations The final limitation that I explore is the reliance on quantitative data. A survey limits the overall range of understanding, as compared to an ethnography or interview. Creating an ethnography with sufficient richness lies outside of the scope of this research, so this method was rejected. I chose not to conduct interviews due to the location and time zone of the American school. Although online video chatting services like Skype are commonly used in contemporary research, the school computers are not fitted with webcams. Additionally, the qualitative data produced by interviews is time-consuming to acquire, transcribe, and code; meanwhile quantitative SurveyMonkey data was more quickly collected and analysed with a low risk of human error (Bryman, 2012). Nonetheless, quantitative data is not without its limitations. Denscombe (2010) notes that quantitative data directly reflects the quality of the questions and collection method. Inexact questions and poor collection could have inadvertently altered my results. Bryman (2012) also asserts that quantitative methodologies lack the spontaneity of a qualitative approach. Qualitative methods afford participants greater room to articulate bespoke replies, whereas a survey with closed responses is designed with pre-determined responses. The most comprehensive methodology might include mixed methods (Bryman, 2012, Denscombe, 2010).

Chapter 4: Presentation and Analysis of Survey and Twitter Results

In this chapter, the first of two chapters detailing the results of this research, I present and analyse the findings of the SurveyMonkey questionnaire and demonstrate how Student Leaders’ responses informed the nudges of the Twitter feed. Next, I evaluate Twitter metrics and KPIs to measure user engagement with the online intervention. In Chapter 5, I measure the efficacy of the nutritional tweets by comparing the rises and falls in the sales of healthy cafeteria foods.

4.1: Key Insights from Survey Data Twenty-seven Student Leaders, an elite segment of the high school student body, participated in a 29-question survey to gauge their lunchtime and social media behaviours in order to tailor the Twitter intervention to their specific preferences. Here are the key insights gathered from the survey:

4.1.1: Purchasing Power Of the 27 Student Leaders… • 17 (62.9%) students purchase lunch every day. • 5 (18.51%) students purchase lunch 1-4 days per week. • 5 (18.51%) students never purchase lunch. This question seeks to determine the how frequently students purchase lunch. This finding motivated me to focus on the responses of students who purchase lunch every day, since the Twitter intervention could have potentially impacted their behaviours the most (depending on their online status).

4.1.2: Motivation for Purchasing Lunch Of the 17 students who purchase lunch every day… • 8 (47.1%) students are enrolled in the Free and Reduced Lunch Program. • 8 (47.1%) students have enough money to purchase lunch every day. • 1 (5.9%) student wrote “im hungry and I want to eat.” This query illuminates the main reason why students purchase lunch every day. This set of responses demonstrates the diversity of the sample, exemplified by an equal number of students who pay for lunch with cash and students whose lunches are subsidised through the NSLP.

4.1.3: School Meal Selection

Of the 17 students who purchase lunch every day… • 12 (70.6%) students purchase the hot entrée. • 10 (58.8%) students purchase pizza. • 5 (29.4%) students purchase a sandwich. • 5 (29.4%) students purchase a la carte snacks • 3 (17.6%) students purchase a salad. • 1 (5.9%) student purchases a parfait. The purpose of this question is to reveal what students habitually purchase from the cafeteria. This result directly informed the Twitter feed by exposing which nudges might be most effective. Based on students’ preferences, tweets about healthy hot entrees, sandwiches, and salads would probably receive positive reception on Twitter and produce an increase in cafeteria sales. However, if only 1 lunch-eating student purchases parfaits regularly, I did not expect a sudden rise in sales. I did not focus my efforts on pizza because I aimed to maintain a positive tone in the tweets and possible tweets about pizza would only examine its deleterious health effects. Lastly, I did not give attention a la carte items because they exist outside of the scope of this research as discussed in Section 3.3.

4.1.4: Choice Motivator Of the 17 students who purchase lunch every day… • 13 (75.6%) students choose whatever looks the tastiest. • 3 (17.6%) students choose whatever will keep them full. • 1 (5.9%) student chooses whatever is served in the shortest line. • 0 students choose whatever is the healthiest. These answers uncover why students select particular cafeteria items. As anticipated in the Methodology limitations, none of the students sampled operate out of nutritional motivation. Instead, a majority make decisions about consumption based on taste. In order to make healthy food appealing to this demographic, I needed to make it look appetising. I accomplished this by repeatedly posting stylised Instagram pictures of school meals on the Twitter feed.

4.1.5: Fruit and Vegetable Selection Of the 17 students who purchase lunch every day… • 1 (5.9%) student never adds fruit to their lunch tray. • 16 (94.1%) students add fruit to their lunch trays. • 6 (35.3%) students never add vegetables to their lunch trays.

• 11 (64.7%) students add vegetables to their lunch trays. These responses indicate fruit and vegetable consumption. Due to the disproportionately greater number of fruit eaters, these answers influenced my decision to write more nudges towards vegetable consumption than fruit consumption. Unlike in the case of hot entrees and parfaits, there is not an overwhelming disparity between the two rates of selection. As such, promoting fruit was more realistic than promoting parfaits.

4.1.6: Twitter Usage • 17 out of 27 (63.0%) students have a Twitter account. • 9 out of 17 (52.9%) students who purchase lunch every day also use a Twitter account. This enquiry intends to simply exhibit Twitter usage, but the responses actually demonstrate no apparent correlation between buying lunch and using Twitter.

4.1.7: Consider Following @HCatMcKean • 10 out of 17 (58.9%) students who purchase lunch every day would take advice from a HealthCorps Twitter lunch feed run by Miss Alawode. Although the data shows no connection between lunch purchasing and Twitter usage, this result displays the potential for that relationship to exist.

4.1.8: Most Helpful Types of Tweets for Lunch Selection Q24: Which tweets would you find most helpful in selecting your lunch? Select all that apply: Response Response Answer Options Percent Count Tweets about what the cafeteria offers each day. 70.0% 7 Tweets about the nutrition of individual items 50.0% 5 offered in the cafeteria. Tweets about new healthy hot entrees. 50.0% 5 Tweets about how to build a healthy sandwich. 30.0% 3 Tweets about how to build a healthy salad. 20.0% 2 Tweets about what to add to a parfait. 50.0% 5 Twitter conversations with Miss Alawode. 30.0% 3 #FunFactFriday tweets 80.0% 8 Other 20.0% 2 Please specify: 2 answered question 10 skipped question 7 Table 1

The rationale behind this query is to elucidate the types of tweets that students would find most useful in choosing lunch. It is significant to note that the seven students who “skipped” this question said “no” to using Twitter, and therefore the logic built into the survey caused them to circumvent this question. Regardless, these insights directly shaped the substance of each tweet. I predominantly tweeted about the healthy items on cafeteria menu, food fun facts, nutrition facts, and information about healthy new hot entrees. This question also provided useful knowledge about tweets that students would not find helpful in selecting lunch, like direct conversations with me, and tweets about how to build a healthy salad. I chose not to tweet about parfaits in keeping with the results in Section 4.1.3.

4.1.9: Examples of Helpful Tweets Q28: Which of these example tweets would you like to see on Miss Alawode's Twitter feed? Please rank them from most interesting (1) to least interesting (9). Rating Response Answer Options Average Count It's #MixItUpMonday, the day of the week where everyone tries something new in the caf. Tweet me 3.88 17 your new food! Click the link to find out what's for lunch today: 3.06 17 [link for online cafeteria menu]. What's everybody having for lunch? Tweet a pic and let's see who has the #TopTray. [link to 3.76 17 instagram pic of school lunch]. Not sure what to get for lunch tomorrow? Tweet me 4.76 17 between 4pm - 6pm for realtime advice! Check out this video on building the most delicious & 5.41 17 nutritious salad: [link to YouTube video]. Here is this week's #FunFactFriday: [insert 4.53 17 educational yet entertaining fact]. RT @RCCSD: Reminder - today is a half day and there 5.82 17 will be no lunches served in the cafeterias. Ever wonder what's in your food? Wonder no more! Here are the nutrition facts for tomorrow's hot entree 6.24 17 [link to nutrition fact chart]. Yum! Look at what Justin Bieber and Katy Perry had for lunch last week [twitpic of celebrity lunches]. You 7.53 17 can recreate a similar meal in the caf by following these instructions: [infograph with instructions]. answered question 17 skipped question 0 Table 2

The intention of collecting these answers is to determine students’ preference and priority concerning types of tweets. Any option with a weighted average at or below the average (4.5) showed greater preference by students. The results reveal that students are most interested in viewing the cafeteria menu, trying new foods, being invited to report their lunch choices via photo with the hashtag #TopTray (refer to Appendix F), and learning fun facts. Interestingly, there

28 is an overlap in the topics of the top ranked tweets in Q28 and the highest scoring types of tweets in Q24. For example, the highest ranked option in Q28 and the response with the most ticks in Q24 both uncover that students want to know what the cafeteria serves each day.

This ranking also indicates which example tweets students would not find helpful in choosing a school lunch. It appears that they are not inspired by tweets about celebrity lunches, tweets published by the school district, or videos about assembling healthy lunches. In accordance with their preferences, I did not post any tweets with similarities to these examples.

4.1.10: Write a Sample Tweet In the final question of the survey, I request the following open-ended response: In the space provided, please write an example of a tweet that you would like to see on the HealthCorps twitter feed run by Miss Alawode. Do not worry about making it 140- characters-long. Please include links to other websites that you would like to see incorporated (i.e.: a related article URL, an Intagram photo, a Vine/Youtube/Vimeo video, an iTunes/Spotify/SoundCloud song, a Tumblr GIF, an infographic chart, a Pinterest board, etc). Please use the examples above as a reference. Out the 27 written examples from students, the most commonly used words in their suggested tweets are: 1. Lunch = 9 times (33.33%) 2. Healthy = 5 times (18.52%) 3. Food = 2 times (7.41%) 4. Twitter = 2 times (7.41%) In addition to the topic above, 8 (29.6%) sample tweets pertain to the notion of what’s for lunch, solidifying the cafeteria menu as the top piece of information that students wish to obtain from @HCatMcKean. Surprisingly, “I don’t know what the cafeteria offers every day” was not selected as a reason why students purchase lunch 1-4 days per week or never. In other words, uncertainty about the cafeteria menu does not necessarily explain why students do not purchase lunch every day. However, students are interested in gaining this information from my Twitter feed. Since the Thaler Model (2007) dictates that I must respond to customer preference, I tweeted about cafeteria options every day. Lastly, 5 (18.5%) sample tweets from students contained links, especially to pictures (3 out of 27 or 11.1%). This finding reinforces the importance of visual stimuli in students’ lunchtime behaviours; that is, students choose lunches that look the tastiest.

4.1.11: Summary of Insights about Sample Students Who Eat Lunch Every day • Access to school lunches for students who purchase lunch every day is facilitated by financial means: either existing means or via enrolment in the free and reduced lunch program. • The healthy items most commonly purchased by these students are salads and sandwiches. Considering their expressed preference, students might be apt to follow nudges towards these items and purchase more of them. • The most well-liked school meal amongst these students is the hot entrée. This finding implies that nudges for healthy entrees could possibly result in increased sales. • Due to their unpopularity amongst these students, tweets about parfaits probably will not amplify sales. • The visual appeal of cafeteria food is the strongest influence on student school meal choice. Students are also motivated to buy food that they believe will keep them the fullest. Accordingly, tweeted nudges must include appetising food pictures and emphasise ingredients that promote satiation. • These students consume fewer vegetables than fruit, so more tweets should be dedicated to endorsing vegetables. • About half of these students use Twitter, while the same is true for two-thirds of the total sample. • Nearly 3 in 5 of these students would conceivably follow @HCatMcKean on Twitter. • When selecting lunch, these students would find the following types of tweets most helpful: a list of cafeteria offerings, #FunFactFriday tweets, nutrition facts for individual items available in the cafeteria, and information about healthy new hot entrees. Effective nudges must align with these subjects. • When given a list of example tweets, these students gravitated towards the tweets about the cafeteria menu, trying new foods, #TopTray call for school lunch photos, and learning fun facts. Once again, successful nudges require incorporating these topics. • According to student contributions to tweet co-design, @HCatMcKean tweets must should include the words “lunch”, “healthy”, “food”, “Twitter”, news about “what’s for lunch”, links, and photos.

In Appendix F, I present specific tweets that satisfy students’ preferences. This information may be superfluous to the reader, but useful to a fellow social scientist that desires exhaustive examples of applying insight to practise. However, the level of detail detracts from the primary focus of this dissertation, hence its inclusion in an appendix.

4.2: Evaluation of Twitter Data As thoroughly detailed in Section 4.1, I based the composition of each tweet on questionnaire response supplied by students who purchase lunch every day and use Twitter. According to Twitter Analytics, I posted a total of 196 tweets over the course of 7 weeks from 15 April 2013 – 31 May 2013. This period of time represented the 4th quarter of the school year, which spanned 33 school days, 66 lunch periods, and 12 weekend days. I tweeted an average of 5-6 tweets per school day and 1 tweet per weekend day. As stated in the Methodology in Chapter 4, the evaluative protocol that I created for this experiment includes monitoring Twitter metrics and key performance indicators (KPI). I begin by analysing the following metrics: number of tweets, followers, time, retweets, mentions, replies, favourites, and location. I already discussed the number of tweets above, so I will not repeat that evidence. The KPIs that I monitored in this study are link clicks, most retweeted tweets, and most favourited tweets. I examine the most retweeted tweets in the retweets section. Similarly, I will assess the most favourited tweets in the favourites section.

4.2.1: Followers At the end of the intervention, @HCatMcKean boasted 49 followers. Forty-nine people only represent 5.6% of 854, the maximum range of students enrolled at the high school during school year 2012-2013. This lack to followership could be due to poor advertising, student unawareness, or lack of interest. Isolating the precise reason would require further exploratory sampling, which could not be accomplished within the time constraints of this study. According to Twitter Analytics, 49% (24) of followers are male and 51% (25) are female. This almost even gender split means that men and women followed the feed with almost equal frequency. This finding suggests that men and women were equally likely to follow me. Interestingly, zero followers unfollowed @HCatMcKean. This result indicates that the content of the feed adequately captured the attention of the audience. It is important to note that the questionnaire was directed toward Student Leaders, an exploratory sample of the McKean High School student body. The questionnaire revealed that students who purchase lunch every day are the most reasonable targets of the Twitter

31 intervention. It is critical to clarify that this target demographic and the Twitter followers overlap, but they are not identical populations. That is, some but not all of the followers attend the school, which I confirmed by recognising their profile pictures. This discrepancy comprises a methodological limitation in the use of Twitter, as described in Section 3.10.2. The inconsistency between students and followers yields difficulty in quantifying the impact of the nudges on behaviour change. In subsequent evaluation, it is safe to assume that measures of engagement adequately account for the @HCatMcKean followers, but not for a meaningful portion of the student body. I routinely gained new followers through the Twitter custom of #FF, or “Follow Friday,” in which users write mentions about people worth following. I sent #FF tweets to new followers and local Delaware organisations that support school children, and health and wellness. I ended each tweet with “RT pls” to encouraged the mentioned users to retweet the tweets to their followers, which broadened the reach of my tweets and consequently increased my followership. I frequently sent #FF tweets to users that I recognised as students, hoping that they would retweet the messages to their peers. As the number of followers climbed, so did other metrics of engagement like retweets, replies, mentions, and favourites. Additionally, I conceivably acquired followers through the in-school posters, but there is no way to measure a direct relationship between the flyers and followership.

4.2.2: Location

Table 3: Table and chart describing the location of @HCatMcKean followers, generated by TweepsMaps.

Based on an online service called TweepsMaps, @HCatMcKean followers hail from three countries: United States (87.5%), United Kingdom (9.4%), and Canada (3.1%). Having followers from English-speaking countries corroborates with an English-only timeline. More precisely, the greatest concentrations of followers are located in New York (15.6% or 8), Delaware (12.5% or 6), and London and Leeds (9.4% or 5). The supporting organisation in this research, HealthCorps, is headquartered in New York City and some HealthCorps-related Twitter accounts follow @HCatMcKean, so it stands to reason that a portion of followers tweet from NY. In addition, geotags from certain tweets might reveal my location in the UK, so Twitter’s “Who To Follow” feature could have led fellow UK tweeters to the @HCatMcKean page. It seems that the segment of followers that live in Delaware either attend the target high school or belong to a local health and wellbeing organisation. However, a large majority (87.5%) of followers tweet from outside of DE. This calculation substantiates that the intervention missed the target demographic of high school students. This finding further clarifies the discrepancy between the target audience and the follower population as discussed in Section 4.2.1.

4.2.3: Time

As I described in the in-depth tweet analysis in Appendix F, I choose one nutritionally rich cafeteria food to promote each day and I repeatedly wrote similar tweets about it in order to amplify the strength of each nudge. Another tactic that I employed to maximise influence was strategically scheduling tweets based on students’ schedules. I typically tweeted in the morning before school commenced, before A lunch period, before B lunch period, after school dismissal at 2:30 PM, after athletic practices ended at 4:00 PM, and every two hours afterward until around 10:00 PM Eastern Standard Time (EST). This timing technique ensured that followers viewed the tweets without being overwhelmed by overlapping content. Despite this intricate scheduling scheme, time is not a predictive factor in determining the most engaging tweets. Twitter Analytics measures engagement with favourites, retweets, and replies; that is, the higher these metrics, the “better” the tweet. The data indicates that the best tweets were sent at 12:05 PM EST (15 minutes before B lunch period), at 2:46 PM EST (after school dismissal), and at 8:00 PM EST (2 two-hour intervals after the 4:00 PM conclusion of sports practice). This temporal data lacks a unifying pattern, other than excluding morning time. This could insinuate the unpopularity of morning tweets, but more conclusive evidence is necessary to solidify this assumption.

4.2.4: Retweets Based on data from Twitter Analytics, the @HCatMcKean Twitter feed received 10 retweets of 7 (3.6% of 196) tweets, entirely during May 2013. The Twitonomy report reveals that none of the retweeted tweets were also favourited or replies. This discovery implies that information that is worth re-posting might not merit bookmarking or responding. However, additional evidence is required to verify this finding. Retweets (refer to Appendix A) expand the audience of a tweet. Based on the followership of the retweeting users, Twitonomy estimates that that the total reach of the 7 retweeted posts grew from 47 users (the number of @HCatMcKean followers) to a combined 19,517 users. A mathematical interpretation of this data shows that the spread of those tweets multiplied by a factor of 415.3 as a result of retweeting. The limitation of these calculations is that their weight lies in the assumption that all of followers of the retweeting users saw the @HCatMcKean tweets. Nevertheless, the number of Twitter users who viewed those 7 tweets is likely exponentially higher than @HCatMcKean’s 47 followers. The Twitonomy report also states that zero retweeted tweets include links, but 85.7% (6 out of 7) have hashtags. Four (57.1%) of them are #FF tweets, which all contain mentions. Two (28.6%) are #TopTray tweets, but none feature pictures. These findings endorse the significance

34 of hashtags in widening the broadcast of a tweet, which seems to be more effective than directly mentioning other Twitter users. It appears that @HCatMcKean followers value re-posting information about important followers over requests for pictures of school food. Based on Twitter Analytics data, the tweet in Figure 1 is the most engaging tweet on the @HCatMcKean timeline. It received 4 retweets, the most of any other tweet. It includes the #FF hashtag and mentions organisations that champion healthy school food. Only one of the mentioned organisations retweeted the tweet, per the “RT pls” request. One retweet came from an @HCatMcKean follower, which means that the other two retweets originated from their followers. That is to say, the other two retweeting user follow @HCatMcKean’s followers, but not @HCatMcKean directly. This deduction provides additional support for the power of retweets and hashtags as tools for swelling the reach of a tweet.

Figure 1: The “best” (most engaging) tweet, according to Twitter Analytics. If harnessed correctly, especially with hashtags, retweets possess the power to magnify the weight of a tweeted nudges. If all of the @HCatMcKean followers attended the target high school, and if those followers were followed by their classmates, then their retweets could have potentially reached a greater portion of the student body. This could have influenced the health behaviours of a greater number of students.

4.2.5: Replies The data from Twitter Analytics shows that @HCatMcKean received 1 (0.5%) reply during the seven-week timeframe of the study. This user interaction came as an expression of gratitude for a #FF mention. The user also favourited the tweet before replying. This finding adds minimally to the growing support for the use of hashtags to increase engagement. The scarcity of replies could be explained by the fact that I rarely initiated conversations with my followers. As stated in the Methodology limitations in Section 3.10.3, this research targeted a population in a different time zone, so coordinating real-time conversations would have been impractical.

4.2.6: Favourites According to Twitter Analytics, 12 (6.1%) tweets were favourtited 15 times, all during May 2013. As insinuated by previous data, none of the favourited tweets were retweeted or replies. This result suggests that tweets that what followers found worth bookmarking they did not feel merited reposting or responding. Unlike retweets or replies, favourites serve a rather different function, indicating support, ‘liking’ of, or affiliation with the tweet. In this sense a ‘favouriting’ response should be welcomed. With such a small data set, more proof is necessary to validate this conclusion. Based on the Twitonomy report, 25% (3 out of 12) of the favourited tweets include links, two of which connect to pictures. (Analysis). Three (25%) other favourited tweets contain mentions. This data set pertains to 3 #FF tweets that mention new followers and known McKean High School students. In fact, 8 (66.7%) favourited tweets feature hashtags, specifically 3 (25%) with #FF, 2 (16.7%) with #TopTray, 1 (8.3%) with #NutritionFacts. This finding implies that in order to encourage Twitter followers to ‘favourite’ a tweet, one should employ hashtags, mentions, and links. It is important to note that all of the favourited tweets that contain mentions or links also incorporate hashtags. This correlation further exemplifies the importance of hashtags in cultivating follower engagement. In the most frequently favourited tweet can be seen below in Figure 2, Twitter analytics reveals that two followers favourited this tweet, which includes two hashtags and nutrition information about a healthy hot entrée.

Figure 2: The most favourited tweet, with 2 bookmarks from @HCatMcKean followers.

4.2.7: Link Clicks Based on Twitter Analytics results, I posted links in 68 (34.5%) tweets, including links to the nutrition section of school district website, pictures of food, the #TopTray flyer (refer to Appendix F), nutrition facts, intervention advertisements, and health-related articles. According

36 to Hootsuite analytics, only 1 (1.5%) link was clicked 1 time from the United States. This finding may appear to suggest the unimportance of links in engaging followers, but that assumption is not entirely accurate. Twitter embeds images into tweets, which negates the need to physically click on an image URL in order to view it. Twitter Analytics reveals that 48 (70.6% of 68) tweets contain pictures, which means that 20 (29.4%) tweets include links that require clicking to view. Upon further re-evaluation, 1 link click represents 1 out of 20 (5.0%) tweets with links that require clicking. This small fraction actually points to the irrelevance of non-embedded links in engaging followers.

Chapter 5: Presentation and Analysis of Cafeteria Results

In this second chapter of results, I analyse the effect of the social marketing campaign, specifically the Twitter nudges and in-school promotional flyers, on changing health behaviours and increasing the sales of healthy items in the cafeteria. With cafeteria data collected by the school district nutritionist, I tracked the sales of salads, sandwiches (wraps and subs), fruit, vegetables, and healthy hot entrees. For comparison, I also monitored the sales of hot entrees and pizza.

5.1: Comparison of Cafeteria Sales Before and After the Intervention Baseline Last Week Baseline Last Week Cafeteria Daily Daily Daily Daily Item Averages Averages Difference Percentage Percentage Difference Hot Entrée 202.6 230 27.4 33.4% 50.4% 17.0% Healthy Hot Entrée 147 23.4 -123.6 24.2% 5.1% -19.1% Sandwich (Sub + Wraps) 128.2 65.4 -62.8 21.1% 14.3% -6.8% Salad 29 16.6 -12.4 4.8% 3.6% -1.1% Vegetables 149 36 -113 24.6% 7.9% -16.7% Fruit 379 253.4 -125.6 62.5% 55.5% -7.0% Pizza 169 62.2 -106.8 27.9% 13.6% -14.2% Table 4: Comparison of cafeteria data for the baseline and last week of the intervention. Table 4 presents a comparison of the baseline rate of cafeteria sales measured against the sales made in the final week of the Twitter intervention. The baseline data is derived from sales made during the first week in March 2013. The averages are based on calculations across the number of items sold each day during the two one-week periods. The percentages were calculated by dividing the average number of items sold by the average number of students present that during that week. The differences were computed by subtracting the last week sums from the first week sums. To determine the calculations for fruits and vegetables, I combined the sales of all fruit options and all vegetable options respectively. I did not include parfaits and bean salads in this evaluation because these are not items that lunch purchasing students selected from the cafeteria. As a reminder, the high school lunch menus published during this intervention can be found in Appendix B. According to the data, no increase in sale of healthy school lunch items was detected. In fact, the rates of sale for all of the healthy items (e.g.: healthy hot entrees sandwiches, salads, vegetables, and fruit) dropped after the intervention. However, the percentages of purchased

38 sandwiches, salads, and fruit decreased by a margin of less than 10%. By contrast, the rate of not- so-healthy hot entrees purchases increased by 17%. Interestingly, the amount of pizza decreased by 14.2%. This overwhelming decrease in healthy school food uptake may be explained by the dramatic change in the number of students at the end of the school year. Each year, the last day of classes for senior students occurs two weeks before the official last day of classes. In school year 2012-2013, this date landed on the first day of the last week of the intervention. In other words, there are 201 students who did not make cafeteria purchases that week, in addition to those students who regularly do not buy lunch. Hence, on reflection, it would have been more accurate to compare similar school weeks. Nevertheless, the results are disappointing in relation to expected outcomes following the intervention.

5.2: Impact of Twitter on Cafeteria Sales Judging by the data in Section 5.1, it is unlikely that the Twitter nudges impacted the lunchtime behaviours of the target high school students. A probable explanation of this lack of influence is the low number of students following the Twitter feed, alongside the relatively low levels of online engagement. If more students had noticed the promotional flyers and followed the @HCatMcKean account, then perhaps the nudges could have produced a measurable impact on their healthy cafeteria-related habits. It is worth mentioning that although the overall rate for fruit fell, more students purchased apples, apple slices, applesauce, bananas, and fresh fruit during the intervention period. An increase was also detected in the number of carrot stick portions, parfaits, and Tuscan bean salads sold by the cafeteria. However, direct causality cannot be drawn between the intervention and students’ lunchtime behaviours.

Chapter 6: Discussion In this final chapter of this dissertation, I open by presenting a summary of the findings from the survey, Twitter intervention, and cafeteria sales by addressing each research aim. Next, I compare and contrast the findings with the literature reviewed in Chapter 2 and draw conclusions from my research. By integrating my results with existing research and theory, I determine the implications of my work and its place in relation to the literature. Finally, I end the dissertation by giving recommendations for future investigations.

6.1: Summary and Interpretation In this section, I give an overview of the findings and demonstrate how they satisfy the aims. Then I gauge my work against the existing literature for the purpose of measuring the strengths and weaknesses of my research, determining the implications, and drawing meaningful conclusions.

6.1.1: Aim 1: Gather insights into the lunchtime behaviours and social media usage of high school students. I will start by discussing student lunchtime behaviours, specifically how students make cafeteria selections. Even though this experiment focused on school-based nutrition, Neumark- Sztainer et al. (1999) indicate that adolescents rank nutrition as a low priority. The study results corroborate the literature, given that zero students listed healthfulness as a motivation for choosing a school lunch. In fact, Cusatis and Shannon (1996) suggest that specific influences on adolescent eating habits are difficult to identify. The authors conclude that various aspects of adolescent eating patterns are influenced by different fundamental factors. The findings also support this point. Students named visual appeal and satiation amongst the top reasons for picking a particular lunch item, which are two seemingly separate influences. Since the survey did not explore the underlying factors of student choice, it is unclear if these influences pertain to common or divergent underlying factors. This uncertainty points to the need for further research into the direct causal pathway between influences on teenagers and their behaviours surrounding food and nutrition. Now I will elaborate on why students purchase school lunches. Story et al. (2006) establish a relationship between subsidised school meals and low-income families. The results are consistent with the literature. The survey illustrates that nearly half of the sample who purchased school lunch every day reported enrolment in the free and reduced lunch program as their top reason for buying lunch each day. Based on this connection, one can infer that the free and

40 reduced lunch program grants students financial access to food that they might not otherwise be able to eat. This vital claim adds to our understanding of the need for school-based nutrition programmes for schools that serve economically deprived students. However, with such a small sample size, it is necessary to construe this correlation carefully. Additionally, it is important to keep in mind that the other half of the cohort purchased lunch every day due to sufficient funds. This point does not contradict the previous implication; it simply highlights a more obvious explanation for why students purchase lunch every day. One shortcoming in the literature is that it does not sufficiently explore the patterns surrounding what students purchase in school cafeterias. While there are anecdotal accounts from the likes of Jamie Oliver, no scholarly sources describe student preferences about specific cafeteria items that adhere to NSLP requirements (e.g.: hot entrees, salad, sandwiches, fruit, vegetables, etc.) in a meaningful way. Even though it would be difficult to generalise information about the vast variety of cafeteria foods, it would be useful as a guide for interpreting nutrition interventions at schools enrolled in the NSLP. Without some sort of gauge, I cannot draw any deep conclusions about what students select for lunch. Now I will move on to discussing Twitter usage amongst students. Researchers at the Pew Research Center (Lenhart et al., 2010, Madden et al., 2013) agree that Twitter use is a growing trend amongst teenagers, with 24% of them on the website regularly. It is encouraging to compare this statistic to the survey that discovered that 52.9% of students who purchase lunch every day also use Twitter. In the entire cohort, 63% of sampled students have a Twitter account. This accordance helps us understand the increasing utility of Twitter as medium for communication amongst and with adolescents. However, with a small sample size, caution must be employed with drawing general conclusions. Lenhart et al. (2010) identify an association between social media use and teenagers from low-income households. As depicted in Section 3.3, 65% of the high school students hail from low-income homes. This accordance must be interpreted with discretion. Income segmentation in the NLSP is straightforward in part to registration prerequisites for the free and reduced meal program. Conversely, no such economic requirements exist for social media and it is therefore infeasible to distinguish the earnings of social media user without explicitly asking. Given that I did not survey the sample about their parents’ revenue, it is unclear which portion (if any) belong the school’s low-income population. The survey instrument proved to be a major strength in this study. Overall, the SurveyMonkey tool successfully gauged the lunch-related motivators and social media usership of the sample. The data generated was easily translated into tweeted nudges. This combination of

41 findings lays the groundwork for further research into novel tools that assist in behaviour change research. Of course, the generalisability of this type of survey on populations outside of the sample demographic awaits further investigation.

6.1.2: Aim 2: Create a Twitter-based social marketing campaign that contributes to obesity prevention and improved nutrition amongst high school students by promoting the purchase of healthy items in the school cafeteria. There are no findings that address Aim 2, other than the existence of the @HCatMcKean Twitter feed.

6.1.3: Aim 3: Critically evaluate the efficacy of the Twitter intervention by monitoring important metrics and key performance indicators (KPIs). The online evaluation results indicate that the Twitter instrument was unsuccessful at attracting the target audience, thus decreasing the efficacy of the overall intervention. However the metrics and KPIs hold certain implications for online research in general. I will commence by discussing the evaluation of the Twitter timeline in relation to the target audience. The literature celebrates social media for its ability to disseminate health messages to a broad audience (Korda and Itani, 2013, Neiger et al., 2012b). My findings unintentionally agree with the literature, given that the tweets (conceivably) reached over 19,517 users. However, my intention was to target students at the high school in Delaware, not the general public. No studies to date deal with how to engage a specific large audience, in this case the segment of students who eat lunch every day. The primary weakness with Twitter as a research instrument is that it imprecisely targets the desired audience, regardless of size. This key nuance is missing from current studies. As the follower and location data reveal, the target audience was not entirely synchronous with the follower group. This discrepancy proved to be the primary downfall of the experiment, as I was unable to send messages to the appropriate users. Having a small followership of only 49 users also proved to be problematic, but the missed target is of greater consequence. Until the day that users can narrow their focus and create contained Twitter groups or fan pages similar to the ones on Facebook, this problem will persist in other empirical research. Despite the disappointing outcome of the intervention, the results of the metric and KPI computation contain implications for the use of Twitter as a social marketing tool in a more general sense. Analysing the trends in Twitter interactions from the diverse follower base illuminates potential response to other online campaigns concerning nutritional awareness across

42 a broad Twitter demographic. Looking at the data as a whole, it appears that the metric with the greatest implication for user engagement is the retweet. Whatmough (2013) identifies the number of retweets as a useful indicator of community response to timeline activity. I would clarify that the most retweeted tweets more accurately speaks to user response. This KPI does not merely quantify what users like, but it also demonstrates how much the messaging resonates with them. Since the most retweeted @HCatMcKean tweet is a #FF tweet, it appears that the followers most enjoyed talking about themselves. This interpretation aligns with the studies of Madden et al. (2013) and Lenhart et al. (2010) who discover that social media users, particularly adolescents, chiefly post status updates about themselves and personal content. It can therefore be assumed that tweets that allow other users to talk and share about themselves will elicit retweets and broaden reach. However, this assumption could possibly pertain exclusively to the followers in this study, so caution must be employed when extrapolating it to the general public. To add to the evidence in favour of retweets as a significant measure of online engagement, Mackert et al. (2012) purports that re-posting messages though retweets can cause the message to amplify quickly and potentially go viral. The data is in agreement with this assertion. As I alluded to above, the 7 retweeted tweets were disseminated to 19,517 users, which far exceeded the normal range of my 49 followers. The drawback with this finding is the infeasibility of quantifying the possible audience versus the actual audience. The scarce literature on social media usage in health promoting contexts does not strive to bring meaning to the content of online health messaging. The results seem to allude to the importance of hashtags in garnering user response. However, without the literature as a guide, I cannot draw positive or negative conclusions.

6.1.4: Aim 4: Increase the sale of healthy cafeteria foods. I will commence by discussing the causal relationship between the tweeted nudges and cafeteria purchases. The current literature on the connection between Twitter and behaviour change is scarce, and the little that is available is contradictory. One the one hand, Young (2010) employed a Twitter-like micro-blogging tool to successfully encourage teenage girls to increase their physical activity. The results do not demonstrate a similar positive outcome, since the @HCatMcKean nutritional Twitter intervention did not cause students to purchase more healthy items in the cafeteria. One the other hand, Mackert et al. (2012) found that Twitter did not change the health beliefs surrounding prenatal vitamins of female college students. The findings in this study corroborate this side of the literature. I am more apt to trust the validity of the Mackert et al. (2012) experiment because the research team tested a significantly larger sample

43 and employed more rigorous methods. Therefore it is possible that Twitter is not a viable vehicle of social marketing behaviour change to specific target audiences, but much additional research is needed to draw more conclusive inferences. Now I will elaborate on the evaluation in respect to nudging and the target audience. Another key weakness in this experiment is that the inconsistency between the target audience and the Twitter followers disrupted the flow of information from me to the students. Bandura (2004) asserts that online tools are useless to people who do not motivate themselves to take advantage of them. This case study exemplifies his claim. It is difficult to properly critique the power of the nudges and Nudge Theory itself if the nudges were barely “felt” by the students in the target audiences. For example, of those Student Leaders that reported to have Twitter accounts, few took the opportunity to follow @HCatMcKean. Additionally, the 1 reported reply illustrates the relatively minimal interactions between the followers and me. This lack of online impressions is to some extent demonstrated by the intervention’s failure to positively change school lunch behaviour across the school. In fact, healthy eating in school lunches actually decreased. In spite of an unanticipated factor that likely affected this result (i.e.: the decreased student population in the final week of school), the intervention clearly did not demonstrate positive impact. This suggests that Twitter and related messaging to students (e.g.: the posters) are not an adequately engaging or challenging approach to influencing the behaviours of this student demographic. Lastly, the results do not confirm a tangible connection between eating school lunches and using social media. This finding is not present in the literature and will continue to go unfounded until further research can firmly verify or deny a link.

6.2: Conclusions With mounting concern over the rise of childhood obesity in America, this dissertation research sought to test a novel solution to this health crisis. The evidence shows that eating healthily is a behaviour that contributes to obesity prevention. For this reason I tested a school- based Twitter nutrition intervention that adhered to the principles of social marketing and attempted to facilitate positive behaviour change. My goal in this experiment was to promote the healthy cafeteria foods in a manner that addressed students’ motivations for their lunchtime behaviours and encourage them to make healthier purchases. Upon dissecting and discussing the results, it is apparent that the experiment was unsuccessful. Both the online and cafeteria evaluations demonstrate that students were not satisfactorily engaged with the online intervention and therefore did not purchase more healthy items.

Nonetheless, the study has implications for social media as a means of spreading health information to broad populations. The results support Twitter-related trends noticed in other studies. However, the literature is insufficient at the intersection of adolescent nutrition, social marketing, public health, and social media. Despite the unachieved behaviour change, this study still contributes to a poorly researched area of the literature and offers guidance to future studies.

6.3: Recommendations for Future Research The future direction of public health social marketing campaigns of this kind is limitless, considering the multiplicity of health issues, target populations, and ever-growing social media platforms. In this final section of the dissertation I propose the subsequent future investigations as an offshoot of this experiment. After a more successful trial of this experiment, the self-efficacy of the students should be tested either through another quantitative survey or qualitative interviewing. Including a qualitative procedure to the quantitative methodology would add variance to the data sets and potentially reveal insights that are missed by exclusively quantitative methodologies. Regardless of the method, this current experiment does not analyse students’ feelings of self-efficacy in relation to purchasing healthy school lunches. Given that the experiment unsuccessfully impacted health behaviours, such a test would be unnecessary in this dissertation, but useful to forthcoming experiments. Future trials should also incorporate a larger sample size to assess the behaviours of more adolescents and increase the applicability of the findings to the general population. It would be particularly significant to examine other nutrition interventions amongst developing adolescents, especially those from low-income backgrounds, to gain a greater understanding of the underlying factors that influence their eating behaviours. Prospective nutrition programmes should also give more thought to strategies for countering the popularity of less healthy food choices, especially amongst teenagers who lack a sense urgency surrounding their future health outcomes. With upcoming online interventions, more effort should be made to directly engage with online followers. If Twitter is chosen as an intervention instrument, social marketers should capitalise on multi-directional communications when engaging with users; keeping in mind that unilateral messaging did not produce the desired outcome in this study.

The most impactful future endeavour would be the creation of a standardised social media evaluation model. This would bring order and precision to this growing research area in social science.

Word Count = 15, 429

Appendices

Appendix A

Source: Scanfeld et al. (2010)

Appendix B

Appendix C

Appendix D

Appendix E

Appendix F

Nudges Towards Salads and Sandwiches

Figure 3: Tweet about salad.

Figure 4: Tweet about sub sandwich.

I tweeted about salads in 44 (22.4%) tweets and sandwiches (including subs and wraps) in 3 (1.5%) tweets. An explanation for the discrepancy is the fact that the cafeteria serves more types of salads (i.e.: entrée-size salads, side salads, pasta salads, bean salads) than sandwiches.

Nudges Towards Healthy Hot Entrees

Figure 5: Tweet about a healthy hot entree.

After carefully reviewing the nutritional breakdown of the daily menu, I would select one particularly salubrious item each day. I nudged my followers to choose that item during the next lunch period by writing two or three tweets about the same item. By concentrating on one food, the nudges were focused and repeated for maximum impact. In fact, I tweet about hot entrée options 31 (15.8%) times. By choosing the healthy entrees, my followers carried out a behaviour that protects against obesity. Additionally, by encouraging my followers to choose the healthy items, instead of disparaging the unhealthy items, I maintained a positive empowering online atmosphere.

Nudges with Pictures

Figure 6: Tweet with an embedded link of a food photo.

Despite students’ partiality for tweets with pictures and their high reported usage of the photo-sharing app Instagram, I did not connect a photo-sharing app to the @HCatMcKean account. I made this choice in order to isolate the effect of Twitter on my followers’ behaviours. Since Instagram is a separate social media channel, I did not want its effect to complicate my results. Moreover, I did not require Instagram for photo sharing. Because I pre-scheduled my

71 tweets through Hootsuite, I also pre-scheduled picture posts with Ow.ly, Hootsuite’s photo hosting server.

The tweet in Figure 6 depicts Greek pasta salad, one of the new healthy entrees debuted by the cafeteria during school year 2012-2013. As the SurveyMonkey insight revealed, a majority of students choose school lunches that look the tastiest. By posting a photo of the Greek pasta salad, I intended to increase the visual appeal of what might be an unfamiliar food. As further explained in subsequent sections, I also tweeted pictures of the #TopTray flyer, nutrition facts, and Toilet Tiding advertisement. In total, I tweeted 48 (24.5%) messages with images, but only 13 (6.6%) tweets with photos of food.

Figure 7: Tweet with an embedded link for the #TopTray photo.

Though I did not create an @HCatMcKean Instagram account, I did post Instagram photos of school lunches through Ow.ly. I captured four Instagram images of healthy school lunches on my personal account during my tenure as the HealthCorps Coordinator. To accompany the #TopTray hashtag, I chose one of the filtered pictures of a tray with a colourful pasta salad, a bowl of bright melons, and a wheat roll. I superimposed the hashtag “#TopTray” in blue block lettering on top of the filtered image. This final design is embedded into the tweet in Figure 7. Sometimes the Ow.ly URL could not fit within the 140-character limit on Twitter, so I could only post #TopTray. In total, I used the hashtag 34 (17.3%) times.

The #TopTray hashtag acted as memorable catchphrase that encourage followers to assemble a healthy lunch tray when they visited the cafeteria. Top Chef, a reality cooking competition that airs on Bravo TV in the United States, inspired the name. In the show, contestants must masterfully assemble winning dishes using given ingredients. In the same manner, I employed the hashtag to encourage my followers to carefully create winning trays in the cafeteria using given options. In this instance, a “winning” tray included the cafeteria’s healthy items.

It is common knowledge that tweeting with hashtags expands a Twitter user’s reach. Using hashtags allows users to contribute to an overall conversation about a hashtagged subject. In this case, any Twitter user who employs the hashtag #TopTray is discussing school lunches at the high school in Delaware. To increase online traffic, I often tweeted hashtags for popular photo sharing apps #TwitPic, #Instagram, #IG (abbreviation for Instagram), and #PicStitch. Unfortunately this tactic did not raise user engagement. No other Twitter users tweeted the hashtag #TopTray and I received zero pictures of cafeteria lunches.

Nudges About Satiating Food

Figure 8: Tweet about foods that keep you full.

As reported in the questionnaire, potential satiation is the students’ second most selected explanation for how they choose their lunch each day. In a word, students choose the school lunch that will keep them full. I tweeted the words “full” and “fuller” 13 (6.6%) times. If the cafeteria served fibrous foods, I would highlight its hunger-reducing effect in order to nudge my followers towards purchasing them.

Nudges About Fruits and Vegetables

Figure 9: Tweet about fruit.

Figure 10: Tweet about vegetable.

I wrote about fruit in 58 (28.6%) tweets and vegetables (excluding salads) in 45 (27.6%) tweets. To keep the content dynamic, I posted a variety of messages about fruit and vegetable ingredients in individual cafeteria items, vegetables found in the salad bar, and proportioned fruit and vegetable cups which students can add to their trays. Fruits and vegetables are a vital source of vitamins and minerals, so adding them to the diet improves overall health and prevents obesity.

In the last week of intervention, the district nutritionist provided me with a special opportunity to promote local strawberries and asparagus that were newly added to the cafeteria menu. To support this healthy change, each day I tweeted fun facts, nutrition facts, pictures, information about eating local, and tips for incorporating these foods into existing school meals. By employing nudges with information and visual aids, two conduits of influence as reported by students, I intended to effectively nudge my followers towards purchasing these healthy items.

Nudges Including the Cafeteria Menu

Figure 11: Tweet featuring a link to the cafeteria menu.

When selecting lunch, students reported that the most helpful type of tweet explains what the cafeteria offers each day. Specifically, students wished to learn “what’s for lunch.” By tweeting actual language written by students in the questionnaire, I intended to increase their sense of self-efficacy. I essentially tweeted about the cafeteria’s choices every day, as thus is the nature of this nutrition intervention. As stated in Section 3.3, the school district nutritionist determines the monthly menu for all 28 schools in the district. I posted the link to the website or a downloadable PDF of the menu 9 (4.6%) times.

#FunFactFriday, #WeekendFunFact, and #FunFact Tweets and Nudges Featuring #DidYouKnow

Figure 12: Tweet incorporating #WeekendFunFact hashtag.

In spite of their preference, I did not tweet the hashtag #FunFactFriday nor post fun facts on Fridays. Instead, I posted them on Saturdays and Sundays to accommodate a lack of content for non-school days. To fill the gap, I posted fun facts with the hashtag #WeekendFunFact. This decision maintained the dialogue about healthy eating on the weekends and maximised the potential effect of this intervention. While this type of tweet satisfies students’ preferences and promotes healthy food, the hashtag #WeekendFunFact does not appeal to a broad audience. A search on Twitter for #WeekendFunFact does not yield as many results as just #FunFact. This means that more Twitter users are talking about the latter; hence why I decided to substitute

“#WeekendFunFact” with “Weekend #FunFact”. In doing so, I made my tweets easier to find and more appealing to a broader audience.

Figure 13: Tweet with #DidYouKnow hashtag.

The #DidYouKnow hashtag allowed me to post fun facts regardless of the day of the week. Adding pictures and relating the content back to the cafeteria made the information more engaging. This type of tweet is a nudge because it encourages healthy behaviour (i.e.: purchasing nutritious food in the cafeteria), while the other fact-relaying tweets merely state interesting information. In total, I employed the hashtags #WeekendFunFact, and #FunFact in 6 (3.0%) tweets and #DidYouKnow in 9 (4.6%) tweets.

#NutritionFacts

Figure 14: Tweet with #NutritionFacts hashtag and school lunch nutrition label.

The survey results indicated that possessing information about the nutrition of items offered in the cafeteria would help students select a school lunch. The school district website also features a month-by-month nutritional analysis of every item served in the cafeteria. I accessed the website to capture screenshots of the nutritional tables. When I tweeted them through Hootsuite, they appeared as pictures (as opposed to downloadable documents like the monthly menu). I marked these tweets with the hashtag #NutritionFacts, which I used 17 (8.7%) times. I often suggested that my followers use the daily nutrition facts to plan out their next lunch. By introducing this concept on Twitter, I hoped to strengthen the followers’ sense of self-efficacy and ability to choose lower calorie options.

In addition, I tweeted about the macro- and micro-nutritional content of individual components of particular dishes, like the calories, fat, protein, carbohydrates, fibre, vitamins, and the effect on the body. However, the 140-character limit denied from me using the #NutritionFacts hashtag with these most of tweets. Nonetheless, I posted about the nutritional makeup of specific foods in 76 (38.8%) tweets. In total, I fulfilled students’ interest in cafeteria nutrition 93 (47.4%) times. This finding demonstrates that nutrition drove the Twitter intervention.

Nudges Featuring #MixItUpMonday, #NewFoodChallenge, #TrySomethingNew Tweets

Figure 15: Tweet with #TrySomethingNew hashtag.

The data from Q28 in the survey demonstrates that students who use Twitter rank tweets with the hashtag #MixItUpMonday as the second most interesting sample tweet. The example states that #MixItMonday is the day when everyone tries something new. Unfortunately, there were not always new healthy items to try on Mondays. Instead, I replaced that hashtag with #NewFoodChallenge and #TrySomethingNew, which could be incorporated into a tweet scheduled for any day of the week. The purpose of these hashtags was to positively nudge students towards purchasing healthy cafeteria food that they might not usually eat. In summation, I tweeted the hashtag #NewFoodChallenge and #TrySomethingNew 23 (11.7%) times.

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