EXAMINATION OF NUTRITION-RELATED CONVERSATIONS WITHIN INSTAGRAM DIABETES ONLINE COMMUNITIES: A MIXED METHODS CONTENT AND SOCIAL NETWORK ANALYSIS By Deanne K. Kelleher A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Human Nutrition – Doctor of Philosophy 2023 ABSTRACT Diabetes is a chronic disease requiring daily self-management impacting over 37 million individuals in the United States.1 Those living with diabetes consistently work to maintain clinically acceptable blood glucose levels to prevent and delay long-term complications such as cardiovascular disease, nephropathy, neuropathy, and retinopathy.2 The length of time one has diabetes is associated with the development of these long-term complications at earlier ages. Therefore, type 1 diabetes (T1D) peak age of diagnosis of 10-14 years of age coupled with rising incidence of type 2 diabetes (T2D) in childhood, warrants greater focus on glycemic control influences in all age groups, but especially in those who are diagnosed at younger ages.2–4 Food and beverage choices are integral to daily self-management and ultimately blood glucose management. These choices are influenced by a host of factors including social media. Viewing health and nutrition posts on Instagram has been shown to be associated with greater body dissatisfaction and concerns with the development of disordered eating and subsequently elevated blood glucose levels.3–5 This study used a mixed methods approach that was first accomplished by completing a nutrition content analysis on 300 Instagram posts from two T1D-related hashtags to fully understand the type of Instagram users that post nutrition-related content and analyze the content being shared publicly for alignment with current diabetes nutrition recommendations. Descriptive statistics showed that the posts were created primarily by females, and persons with diabetes on a variety of topics with nearly half of all posts promoting a “diet”. Unfortunately, several potentially harmful diets such as the very low carbohydrate ketogenic diet and low carbohydrate diet were a key focus of Instagram posts. Next, social network analysis (SNA), both quantitative and qualitative, conducted on the T1D data set identified patterns of engagement of posts; networks among those engaging with the posts; and the themes of post comments. Engagement statistics for each post were analyzed using general linear modeling. Results demonstrated that Instagram posts related to nutrition education/food substation, food/beverage, and diabetes management were those with the greatest engagement. The qualitative analysis of comments highlighted six main themes within the posts including negative food relationships and overall support for others within this community. Finally, differences between the content analyses of nutrition-related T1D and T2D Instagram posts from four distinct diabetes associated hashtags were assessed. The descriptive statistics revealed differences between the types of Instagram post creators for the two groups with the T1D content creators mostly being persons with diabetes whereas the T2D content creators were primarily personal businesses. Differences in diets promoted between groups were analyzed using general linear models that showed the major contributor to promoting diets was the main theme of the post along with health information providers. This dissertation is the first to analyze nutrition-related content on Instagram within the diabetes online community, and hence groundbreaking. This research adds to the literature by elucidating the importance of incorporating social media as a component of healthcare assessments in addition to diabetes self-management education and support. Further, this research highlights the differences between the T1D and T2D nutrition-related Instagram communities relative to motivations for creating content and overall messages to better assist healthcare providers in addressing disease type (T1D vs T2D) specific factors that may contribute to development of long-term diabetes related complications. Copyright by DEANNE K. KELLEHER 2023 I dedicate this work to Julia and Thomas. You are best part of my life and I love you to the moon and back again. I hope that you look back at this “adventure” as an example of chasing your dreams no matter what. Always put your best foot forward and be proud of yourselves. v ACKNOWLEDGEMENTS Dreams are worth being chased. Hard work and hustle pay off. Pebble by pebble is the only way to climb a mountain. I have never taken the easy path in life because the journey makes the adventure worth it. I worked full-time (or nearly full-time) throughout each of my previous degrees so why would I do something different with my doctorate. I would not have been able to accomplish this dream of earning my doctorate without so many people to support me. I embarked on this journey six years ago with the confidence that I can do anything I put my mind to. This confidence was instilled in me by my parents. They raised me to be a self- sufficient, determined woman and I thank them for their confidence in me. While I lost my dad months before my official start to my doctorate, he knew that I had been admitted and I know he was smiling from heaven at every step. I couldn’t have been prouder to have my mom present at my defense. Thank you very much mom for teaching me to believe in myself. I am grateful for the mentorship provided by Dr. Lorraine J. Weatherspoon. I could not have asked for a better advisor. I admire her willingness to run with new creative approaches to advance nutrition research and allowing me to focus on my research passions. From our weekly meetings, to her understanding that a non-traditional student has so many other life priorities, to her advice on how to write a manuscript she has been nothing short of supportive. Thank you to my committee members Dr. Bree Holtz, Dr. Amy Saxe-Custack, and Dr. Sarah Comstock. You have all guided me to be a better researcher. Your advice has been so integral throughout this entire process. I owe many thanks to Dr. Liz Gardner, Department of Food Science and Human Nutrition Director of Graduate Studies, who has the biggest heart for all her graduate students. Your care and belief really pushed me when I needed it the most. vi Thank you to all of my lab mates throughout the years, Dr. Julie Plasencia, Dr. Getrude Mphwanthe, Dr. Dawn Earnesty, Dr. Gayle Shipp, and Makafui Borbi. I appreciate your camaraderie and collaboration. I owe gratitude to my undergraduate research assistants: Anita Dharwadkar RDN, Riley Guerra, RDN, Samantha Sherman, Shikha Advani, Miranda Deal, and Alex Hawley. Thank you for your interest in this work and hours of assistance. Thank you to those that have offered financial assistance for my research and pursuit of this degree. The Academy of Nutrition and Dietetics Foundation, Jorg A.L. Augustin Fellowship Fund for Graduate Studies in Food Science and Human Nutrition, Dr. Jerry and Stella Cash Professional Development Endowment, Glenn R. Dean and Anita C. Dean Endowed Fellowship, College of Agriculture and Natural Resources, and Michigan State University College of Graduate Studies. But most importantly many thanks to my family for their never-ending support. My sisters, Debbi Jones and Dawn Kennedy, you have been there for me at each turn in this journey. I appreciate your daily smiles and support especially during the writing of this dissertation. My husband, Thomas, you are my person. You give me the strength to run with my dreams with your belief in me. We have always worked hard to provide examples of work ethic to our children, and I could never have reached this milestone without you. My children, Thomas and Julia, I embarked on this when you were a tween and teen and now you are young adults. You have endured many nights listening to me talk about my research or my classes. The patience while I juggled so many different hats is admirable. I earn this degree with you and because of your love. Thank you Thomas for being curious and willingness to learn new skills to provide feedback. Julia you are my biggest cheerleader. I do this for you and with you to show you that women embody strength can achieve what they set their minds to. vii TABLE OF CONTENTS LIST OF ABBREVIATIONS ........................................................................................................ ix CHAPTER 1: INTRODUCTION ...................................................................................................1 CHAPTER 2: LITERATURE REVIEW ........................................................................................6 CHAPTER 3: CONTENT ANALYSIS OF NUTRITION-RELATED TYPE 1 DIABETES INSTAGRAM POSTS ..........................................................................................30 CHAPTER 4: NUTRITION-RELATED TYPE 1 DIABETES ONLINE INSTAGRAM COMMUNITY SOCIAL NETWORK ANALYSIS OF ENGAGEMENT: A MIXED METHODS APPROACH ....................................................................55 CHAPTER 5: COMPARISON OF TYPE 1 AND TYPE 2 DIABETES INSTAGRAM NUTRITION-RELATED CONVERSATIONS .....................................................84 CHAPTER 6: SUMMARY, CONCLUSIONS, AND FUTURE RESEARCH ..........................104 REFERENCES ............................................................................................................................108 APPENDIX ..................................................................................................................................132 viii LIST OF ABBREVIATIONS AIC BED CVD Hemoglobin A1C Binge eating disorder Cardiovascular disease DEPS-R Disordered eating problem survey -revised DOC DSM Diabetes online community Diagnostic and Statistical Manual of Mental Disorders DSMES Diabetes self-management education and support eAG ESP HIP LCD LDL Estimated average glucose Eating disorder screen for primary care Health information provider Low carbohydrate diet Low-density lipoprotein MNT Medical nutrition therapy PB Personal business PWD Person with diabetes PWD.PB Person with diabetes and a personal business RDN SNA Registered Dietitian Nutritionist Social network analysis SCOFF Sick-control-one stone-fat-food T1D T2D TPB Type 1 diabetes: Type 2 diabetes Theory of planned behavior ix VLCD Very low carbohydrate diet VLCKD Very low carbohydrate ketogenic diet x Background CHAPTER 1: INTRODUCTION Diabetes is a chronic disease that can develop in childhood which impacts one’s daily life in balancing food and beverage intake, physical activity, administration of medication/insulin, weight management, and stress management. Furthermore, diabetes can lead to damaging long- term complications such as neuropathy, nephropathy, retinopathy, and cardiovascular disease (CVD) due to elevated blood glucose levels.2,5 According to the Centers for Disease Control and Prevention’s (CDC) 2021 National Diabetes Statistics Report, over 28 million individuals living in the United States are diagnosed with diabetes which cost $412 billion from both medical costs and diminished productivity in 2022.1,6 There are two primary types of diabetes, type 1 diabetes (T1D) which is typically diagnosed before the age of 35 years, and type 2 diabetes (T2D) which is typically diagnosed in individuals with a body mass index in the overweight or obesity classification as outlined by the CDC.7 The long-term complications of diabetes result from an elevation of blood glucose above the clinically appropriate levels.2 These complications are known to occur earlier for those diagnosed with diabetes at younger ages in both T1D and T2D.2 The prevalence of T1D, a condition that disproportionately impacts minorities, continues to rise annually with a reported 30% increase in diagnoses over the last two years.1 The incidence of T2D in those diagnosed under the age of twenty is estimated to have a 700% increase in 2060 if current patterns of incidence continue.4 Having T1D itself is a major risk factor for CVD before 30 years of age.8 The development of CVD in this population is primarily mediated by hyperglycemia; therefore, identifying factors that contribute to suboptimal glycemic status in young adults are critical.8–10 Young adulthood (18-29 years of age) is often a time of 1 deterioration of glycemic control.11 Many factors contribute to glycemic control including adherence to medications, diet, physical activity, and disordered eating.5,12 Disordered eating and body image (referred to as diet culture) are associated with the use of social media platforms which may increase risk of hyperglycemia in a person with diabetes (PWD).13,14 Examining these and associated factors is imperative with one potential influencer being social media. Social media is used by over 90% of young adults every day.15 However, analysis of the nutrition-related conversations and potential sharing of misinformation, particularly among those with T1D, have yet to be studied. Specific Aims The goal of this research study is to elucidate diabetes healthcare providers about the nutrition-related social media content being shared within the public diabetes online communities and understanding their potential influence on glycemic control and hence long- term complications. Findings will help diabetes healthcare providers to better understand the extent to which social media is a behavioral influence that is integral to assessment, and educational goals for diabetes self-management education and support. The central hypothesis is that nutrition-related social media posts do not depict foods and beverages in alignment with current nutrition recommendations with the sharing of non-evidence-based diets as a method to manage body weight and metabolic control. To test the central hypothesis, the following specific aims were formulated: Specific Aim #1: Conduct a content analysis on Instagram, a platform popular in young adults, to identify nutrition trends and associations by examining the use of type 1 diabetes (T1D) hashtags #type1diabetes and #diabadass. 2 Research Question #1: What is the general Instagram biography information for content creators using #type1diabetes and #diabadass to create nutrition-related posts (creator type, gender, number of followers, number of following, ratio followers/following) Research Question #2: To what extent do the nutrition images and captions in nutrition- related Instagram posts using #type1diabetes and #diabadass align with current American Diabetes Association T1D evidence-based nutrition guidelines? Research Question #3: What are the main nutrition content themes of nutrition-related Instagram posts using #type1diabetes and #diabadass? Research Question #4: To what extent do the nutrition-related Instagram posts using #type1diabetes and #diabadass promote a diet? Research Question #5: To what extent do the nutrition-related Instagram posts using #type1diabetes and #diabadass include a discussion of diet culture? Specific Aim #2: Examine the social network engagement created within Instagram for T1D nutrition-related information shared using #type1diabetes and #diabadass. Research Question #1: What are the patterns of engagement (likes/comments) of nutrition-related Instagram posts using #type1diabetes and #diabadass? Research Question #2: What is the communication network for those engaging by commenting on posts? Research Question #3: What are the themes created through the comments within the social network on nutrition-related Instagram posts using #type1diabetes and #diabadass? Specific Aim #3: Compare the nutrition associations between T1D and T2D using a relevant social media platform, Instagram, through content analysis for T1D hashtags #type1diabetes and #diabadass and T2D hashtags #diabeteslife and #diabetestype2. 3 Research Question #1: What are the differences in the general biography information for Instagram content creators for T1D #type1diabetes & #diabadass and T2D #diabeteslife & #diabetestype2? Research Question #2: What are the differences between the main nutrition themes for nutrition-related Instagram T1D #type1diabetes & #diabadass and T2D #diabeteslife & #diabetestype2? Research Question #3: What are the differences between nutrition-related Instagram T1D #type1diabetes & #diabadass and T2D #diabeteslife & #diabetestype2 posts that promote a diet? Research Question #4: What are the differences in posts discussing diet culture between T1D #type1diabetes & #diabadass and T2D #diabeteslife & #diabetestype2? Research Question #5: What are the patterns of engagement (likes and comments) for nutrition-related Instagram T1D posts #type1diabetes & #diabadass and T2D posts #diabeteslife & #diabetestype2? Working Definitions: Diet Culture: disordered eating, body image dissatisfaction, and weight Disordered Eating: manipulation of food either restrictive or excessive but not in the same frequency or severity as a clinically diagnosed eating disorder. Glycemic control: an A1C value of <7.0%. Health 2.0: health-related interactive internet that includes social media sites. Social Network Analysis: is the study of both structure and patterns of relationships among people and groups.16 Young Adults: developmental period from 18-29 years of age. 4 Significance This proposed research is significant to nutrition behavioral science research as no previous studies have evaluated the content of nutrition messages shared on social media for individuals with diabetes, both T1D and T2D. Furthermore, there is a little understanding regarding the extent to which messages shared on social media align with the current evidence- based nutrition recommendations. The use of social network analysis on public Instagram nutrition-related posts is novel and pioneering to identify potential sources of influence, and spread of nutrition information and/or misinformation within the Instagram diabetes online community. This research creates a body of literature to spur further investigation, particularly on behavioral intent. The diabetes healthcare community can also recognize and use this information as an important adjunct to assessment and standards of diabetes self-management education and support. 5 CHAPTER 2: LITERATURE REVIEW Diabetes Overview Pathophysiology Diabetes is a disease of the pancreas that impacts the production or utilization of the hormone insulin. Insulin transports glucose from the serum into the cell for energy and thus in diminished levels or functioning results in elevated serum glucose.7 There are four main classification categories of diabetes: type 1 diabetes (T1D), type 2 diabetes (T2D), diabetes due to other causes, and gestational diabetes mellitus.7 This dissertation will focus primarily on T1D with a comparison to T2D. It is important to understand that T1D and T2D have their own unique pathophysiology. Type 1 diabetes (T1D) is an autoimmune condition in which the beta cells of the pancreas are destroyed, ultimately leading to insulin deficiency.17 Persons with T1D are typically diagnosed at less than 35 years of age. Those diagnosed as adults tend to have a healthy weight body mass index (BMI) of less than 25 kg/m2.18 The presenting symptoms of T1D at diagnosis include polyuria, polydipsia, unintended weight loss, and ketoacidosis with a more rapid onset of symptoms.7 This form of diabetes accounts for approximately 5-10% of all diabetes cases.18 Type 2 diabetes (T2D) accounts for approximately 90% of all cases of diabetes and is characterized by the impaired responsiveness of cells to insulin, known as insulin resistance, in combination with a decreased production of insulin by the beta cells of the pancreas.1,19 The onset of T2D symptoms is more gradual which allows the health care system to screen for prediabetes and focus on prevention of disease progression.2 While typically thought of as an adult-onset disease, the annual incidence of T2D in children and adolescents is increasing while that for adults is decreasing.4 The treatment of prediabetes and T2D focuses on lifestyle interventions including dietary manipulation for weight management and physical 6 activity.2,20 Treatment of T2D to assist with persistent hyperglycemia may include addition of oral medications and injectables, followed by insulin therapy in conjunction with lifestyle interventions.21 Diabetes Management Diabetes is a chronic condition with no cure, that requires daily management, commonly referred to as diabetes self-management. 22–24 Diabetes self-management in T1D includes injection of endogenous insulin to cover the daily requirement (basal needs) and meal requirement (bolus), multiple blood glucose checks, physical activity, problem-solving, and calculating the grams of carbohydrates to be consumed.25 The insulin dosage and carbohydrates consumed are matched to keep blood glucose within an acceptable range, referred to as glycemic control. Glycemic control is measured by a test known as hemoglobin A1c (A1C), which measures the estimated average blood glucose (eAG) level over 3 months. 26 The American Diabetes Association 2023 Standards of Care establishes a goal A1C value for adults and most children and adolescents of less than 7.0%.12 The A1C value correlates to an eAG of 154 mg/dL.27 The target A1C level is based on finding a balance between elevated blood glucose and the prevention of too many dangerous episodes of hypoglycemia.5,12 Blood glucose levels are not simply the interplay of insulin and carbohydrate intake and can be impacted by factors such as hormonal changes, stress, illness, and physical activity.12,28 Glycemic control is closely monitored by the diabetes care team since elevations are associated with several complications such as nephropathy, neuropathy, retinopathy, and cardiovascular disease.29 The transition to self-management of diabetes from youth to the young adult period is a critical period for glycemic control as SEARCH4 Diabetes in Youth Study, a national multi-center study, demonstrates that there is a 2.5 times greater odds of deterioration of glycemic control.30 7 Daily self-management for prediabetes and T2D focuses on lifestyle interventions with an emphasis on diet and physical activity as the primary treatment.24 While it may seem simplistic to merely alter one’s dietary intake and move more, many factors play into this self-management beyond just education, such as access to fresh, high-nutrient foods and safe places to engage in physical activity. The complexities of managing social determinants of health are intertwined with the health inequities that are present in persons with T2D with higher incidence for those with lower socioeconomic status, education, and food access.31 Moreover, the prevalence of T2D is disproportionately higher among American Indians and Alaska Natives, non-Hispanic Blacks, Hispanics, and non-Hispanic Asians when compared to Caucasian/White.32 Medications are initiated at the time of diagnosis with T2D as an adjunct to lifestyle factors.21 The management of T2D of those diagnosed in the youth and young adult periods is even more critical as the risk of long-term complications is higher than those diagnosed at later ages.33–35 Long-term complications Long-term complications including CVD, nephropathy, neuropathy, and retinopathy, are serious issues in individuals with diabetes that impact overall quality of life. 1,2 Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in persons with diabetes.36 The development of CVD in this population is primarily mediated by the degree of hyperglycemia. 8– 10 T1D is a major risk factor for the development of cardiovascular disease (CVD) before 30 years of age.8 The risk of developing CVD is 20-40 times greater in all persons with T1D as compared to those without diabetes. This increased risk is likely related to the fact that those with T1D often develop the disease at an earlier age than those with T2D.37,38 The age of diagnosis of T2D is associated with the earlier onset of CVD with those diagnosed in adolescence having the greatest risk.33 Gerstein and colleagues found that with every 1% 8 increase in A1C level, there is a 7% increase in risk of developing CVD.39 Even with an A1C value less than 6.9%, those with T1D remain at twice the risk compared to those without T1D.40 The risk of developing CVD is also higher in women with T1D.41 Two separate studies have confirmed that cardiovascular risk, including elevated total cholesterol and low-density lipoprotein (LDL), is higher in women even when A1C levels are similar.41,42 The potential mechanism for the gender difference is thought to be related to sex hormones along with differences in pharmacological approaches to treatment of hyperlipidemia.43 Management of cardiovascular disease includes dietary manipulation to lower blood pressure, serum total cholesterol, low-density lipoprotein (LDL) cholesterol, and triglycerides.36 Approximately 30-40% of persons with diabetes will develop diabetic nephropathy, the leading cause of end-stage kidney disease in the United States.44 This long-term complication, which also places one at higher risk of CVD, develops over time in those with T1D though in contrast, those with T2D may have signs of nephropathy at diagnosis.45 Middleton et al (2021) report that those persons with T2D diagnosed at a younger age have two times more accelerated progression of kidney disease to the end-stage than those with T1D.46 Further, evidence exists that those with T2D of a lower socioeconomic status have higher rates of nephropathy in T2D.47 A primary intervention in diabetic nephropathy is dietary management of blood pressure and limitation of protein intake to slow the progression of kidney disease all while maintaining euglycemia.45 These long-term complications make the transition to successful independence of diabetes self-management crucial during the young adult period to minimize disease risk and progression. However, research points to a deterioration in glycemic control during this critical 9 time. Identifying potential contributors to poor metabolic control in all persons with diabetes is crucial, particularly among young adults with T1D and T2D. 48,49 Dietary Management of Diabetes Current Recommendations The hallmark of dietary treatment in diabetes is the provision of medical nutrition therapy (MNT) by a registered dietitian nutritionist (RDN) to create an individualized plan to meet the specific needs for PWD.50 The MNT focuses on strategies that will improve A1C including weight control, blood pressure control, and serum lipid levels which are intended to delay and prevent the onset of long-term complications.24,50 There is strong evidence to indicate that MNT will decrease A1C levels up to 2.0%, which has been associated with minimizing long-term complications. The first year after diagnosis the recommended schedule of MNT is three to six MNT visits with at least one visit a year subsequently.51 Weight management strategies are tailored to the person with diabetes taking into account their risk factors, level of health literacy, and current medical management. Weight loss is recommended for those with a BMI greater than 25 kg/m2 for disease progression in T2D and overall management in T1D to aid in cardiovascular-related complications.50,52 According to the 2019 American Diabetes Association Consensus Report on nutrition therapy, dietary intake should be low in saturated fats by replacing with unsaturated fats, contain less than 2300 milligrams of sodium, emphasize fruits, non-starchy vegetables and low-fat dairy, incorporate whole grains, plant-based foods and foods high in fiber, minimize added sugars, and include less processed foods.50 There are a variety of eating patterns investigated to test the effectiveness in managing diabetes such as plant- based/vegetarian diets and reduced carbohydrate diets. These approaches have gained more 10 popularity over recent years, especially with individuals seeking information from social media platforms. Plant-Based/Vegetarian/Vegan Eating Pattern The choice to follow a specific diet pattern can be driven by recommendations received from a healthcare provider, information sought on one’s own, religious choices, and personal decisions. Plant-based, vegetarian, and vegan diets have been backed by evidence to support their incorporation into dietary intake to prevent T2D though no evidence supports vegetarian or plant-based diets to manage or treat T1D.53 Vegetarian diets are largely self-initiated versus being directly prescribed by a health care provider.54 Low-fat vegetarian diets have consistently demonstrated their benefit in the treatment of T2D as a method to reduce A1C.55–58 A review by Pawlak (2017) showed that those with T2D following plant-based, vegetarian diets improved not only A1C levels but also decreased CVD disease risk with lower serum lipids and blood pressure.59 The mechanism of action for lower cardiac risk of the vegetarian diet is thought to be related to higher fiber, lower saturated fat, and lower heme iron in the diet.59 A randomized controlled study of 150 individuals with T2D by Jenkins et al (2022) similarly found that low carbohydrate vegetarian diets decrease blood pressure, A1C levels, and weight.60 Of note with this study, participants were also following a calorie level of 60% of their estimated needs over the 12-week study which could account for the findings.60 An expert consensus statement from the American College of Lifestyle Medicine in 2022 states that following a whole, plant-based foods diet is effective in those with T2D to achieve remission for many adults.61 Low Carbohydrate and Very Low Carbohydrate Diets Low carbohydrate diets (LCD) and very low carbohydrate diets(VLCD), commonly known as the ketogenic diet (VLCKD), are extremely popular in the general public, especially 11 through social media, for weight loss and subsequently gained attention within the diabetes community.62 These diets decrease the overall carbohydrate intake and replace carbohydrates with protein and fats. The definitions vary as to what constitutes each diet though generally LCD is approximately 130 grams of carbohydrate or about 26% of total caloric intake while VLCD/VLCKD are approximately less than 20-50 grams of carbohydrate or about less than 10% of total caloric intake.63–66 While weight loss is a short-term finding, a 2022 Cochrane Review by Naude et al on LCD and cardiovascular risk concluded that, in the long term, there are no significant differences in weight change or cardiovascular risk between LCD and weight reduction diets in overweight and obese persons with or without T2D.67 A meta-analysis comparing LCD and low-fat diets on cardiovascular risk in healthy individuals demonstrated that although the LCD contributes to weight loss and increases the high-density lipoprotein (HDL), it also increases LDL.68 Hancock et al (2023) found similar changes among studies on diabetes and LCD and VLCD of a distinct dyslipidemia with decreases in triglycerides and increases in both HDL and LDL.65 The increase in LDL was higher in those with T1D than T2D which is concerning given the higher risk of cardiovascular disease in persons living with T1D.65 The increase in LDL puts persons with diabetes at a higher risk of cardiovascular disease with no long-term studies to demonstrate their safety.36 Persons with diabetes are too often drawn to LCD, VLCD, or VLCKD to lose weight and improve glycemic control. Multiple reviews of these dietary approaches in those with T2D suggest that A1C are the result of a change in weight, rather than the dietary method.64,69–71 Furthermore, changes in A1C and weight appear to be temporary as longitudinal studies have demonstrated that improvements in A1C and weight status do not persist over multiple years.64,72 Kirkpatrick et al (2019) noted concerns composition of the weight loss seen in LCD and VLCD 12 is initially is water weight and lean body mass.66 Research related to the use of LCD and VLCD rarely addresses the important concern of chronic kidney disease. Given increases in total protein intake seen in these diets alongside the presence of nephropathy in 30-40% of those with diabetes, long-term use of LCD and VLCD diets is concerning.72 Hu et al (2023) examined LCD on mortality in the Nurses Health Study which found that a plant-based LCD but not LCD was associated with an overall lower mortality in persons with T2D.73 Little research has been conducted on LCD and VLCD in adults living with T1D. Leow et al (2018) found in their small sample (n=11) that there were improvements in A1C but at the expense of increases in LDL and hypoglycemic episodes.74 Lennerz et al (2018) completed an online survey combined with a review of health records in 138 individuals with T1D and similarly found improvements in A1C and dyslipidemia.75 The decreases in overall carbohydrate intake lead to decreases in total insulin use in those with T1D though the higher protein, higher fat meals did lead to increased post-prandial glucose necessitating an increase in insulin dose so the benefits of the lower insulin are not consistent.76 Longer-term studies are needed with the use of LCD and VLCD to demonstrate their safety and efficacy, especially in T1D.24 Disordered Eating in Type 1 Diabetes Persons with T1D often have to think about what to eat, the carbohydrate content of their food, and blood glucose levels to balance dietary intake with their insulin plan multiple times a day. This heightened awareness of nutrition, and the inability to simply eat without thinking about how food might impact their diabetes, can take a toll on psychological well-being and lead to depression and disordered eating.77,78 Disordered eating is the manipulation of food either restrictive or excessive, but not in the same frequency or severity as a clinically diagnosed eating disorder.79 Eating disorders and disordered eating are not synonymous, but it should be noted 13 that disordered eating behaviors are a risk factor for developing eating disorders.80 Eating disorders are classified using the Diagnostic and Statistical Manual of Mental Disorders (DSM- V) and include anorexia nervosa, bulimia nervosa, binge eating disorder, and avoidant/restrictive food intake disorder.81 Eating disorders have a prevalence nationally of approximately 2.8% and according to a meta-analysis completed by Young et al (2013), the prevalence is 7.0% in those with T1D.82 A large T1D patient registry in Germany and Austria found that 1.6% of the 31,556 females 6 months – 23 years were classified as having a physician-diagnosed eating disorder.83 Prevalence was notably less than related literature which may be by the requirement for physician-diagnosed eating disorder using the DSM criteria and thus may have been underreported. People with T1D may become increasingly preoccupied with their dietary choices, a situation that can escalate into an obsession with food. This focus on food leads to the necessity of administering insulin to balance serum glucose levels after eating, which, paradoxically, can stimulate appetite and potentially result in weight gain.84 This weight gain may then spark concerns over body image, thus completing a cycle that returns to vigilant monitoring of food and beverage consumption. Persons with T1D who struggle with weight concerns may partake in a method of weight control where insulin is reduced or omitted after meal time known as diabulemia.85 This form of an eating disorder is not classified under the DSM-V and thus is grouped under disordered eating behavior. The diabetes care community recognizes the concern of diabulimia. Recent studies have examined the concept of disordered eating and have found the prevalence of overall disordered eating in young adults with T1D to be between 26-39.3%82,86 Doyle and colleagues found a similar prevalence of disordered eating in young adults, 18-28 years of age, with 30% of females and 20% of males in their population of 60 participants as measured using the 14 Disordered Eating Problem Survey – Revised (DEPS-R).13 A study completed by Colton and colleagues (2015) at The Hospital for Sick Children in Toronto followed 126 girls over 14 years and found that the mean onset of disordered eating was 22.6 years with a cumulative probability of onset of disordered eating by age 25 of 60%.87 The body of literature strengthens the need to be able to address the risk factors that are associated with disordered eating in young adults with T1D, especially in females, and understand the potential they have to contribute to long-term health outcomes. Disordered eating and clinically diagnosed eating disorders are both associated with higher than recommended A1C values in persons with T1D compared to those without.82,83 This finding has been corroborated by Nip et al (2019) in a study analyzing data from 2,156 youth and young adults diagnosed with T1D. Authors measured disordered eating using the DEPS-R, depression, quality of life, A1C, insulin sensitivity, and anthropometrics in this cohort and found that 21.2% had disordered eating, indicated by a DEPS-R score greater than or equal to 20, which is consistent with previous research.88 Those with disordered eating had a higher body mass index, depression, and lower insulin sensitivity.88 Wisting et al (2013) found similar results with 27.7% of females 11-19 years of age with disordered eating, and the risk of disordered eating increased with age and weight.89 Those with disordered eating in this sample of 770 also exhibited higher A1C values.89 This research helps establish that to address the concern with early cardiovascular disease in persons with T1D, identifying disordered eating is critical for diabetes care providers. The association between disordered eating and glycemic control becomes critical for healthcare providers who must identify potential causes of glycemic and metabolic aberrations. 15 Disordered Eating in Type 2 Diabetes Disordered eating concerns are not limited to those with T1D but are also noted in persons with T2D. Similar to T1D, lifestyle factors including manipulation of daily dietary intake to maintain glycemic control and minimize long-term complications, are complex. The most prevalent disordered eating pattern among persons with T2D, who tend to have higher BMI when compared to those with T1D, is the binge eating disorder (BED) as they tend to have a higher BMI than those with T1D. 88,90–98 Research is varied on the actual prevalence of BED with estimates from 2-50% depending on the study.88,93,94 The inconsistencies can be explained by the utilization of varying disordered eating screening tools across studies as well as by use of the various versions of the Diagnostic Statistical Manual of Mental Disorders (DSM) (versions vary between DSM IV and DSM V). The definition of BED changed from binge eating occurring at least 2 days a week for 6 months in the DSM-IV to binge eating at least 1 day a week for 3 months the DSM-V.99 This definition altered prevalence numbers. Nip et al. (2019) found that adolescents and young adults with T2D and disordered eating were predominately (51%) African American versus in T1D where 75% were non-Hispanic white. Those with T2D had higher disordered eating scores utilizing the DEPS-R than those with T1D (21.9 vs 12.7).88 Multiple studies agreed that BED was more common in those diagnosed with T2D at a younger age, with a shorter duration of T2D and higher BMI.91,92,97,98 The impact that BED has on glycemic control is mixed with most studies finding no association between BED and elevations in A1C after controlling for BMI, unlike what is seen in T1D and disordered eating.88,92,96 Early and frequent disordered eating screening in T2D populations, especially those diagnosed at younger ages, is equally as important to be completed as in T1D. While BED itself does not appear to be independently associated with increases in A1C, those with BED tend to have 16 higher body weights which is associated with less glycemic control placing persons with BED and T2D at elevated risk for associated long-term complications. Health 2.0 The acquisition of health and nutrition information is a dynamic process that has evolved over the last two decades with the advent of the internet.100 Users are now active participants in the conversation and actively contribute to the content on the internet, which was coined as Web 2.0 back in 2004.101 The available content of Web 2.0 includes blogs and social media sites such as Facebook, X (formerly known as Twitter), YouTube, TikTok, and Instagram.102 Sharing of health-related information on Web 2.0 is referred to as Health 2.0.103 The sharing of information by anyone allowed to post content creates concern that Health 2.0 can be an avenue for widespread dissemination of health misinformation.104 Those with chronic health conditions, such as T1D, often seek out peers for support within this space thus Health 2.0 has the potential to impact long-term health outcomes, especially for those who require daily self-management of their condition.105 Young adults are one subset of individuals who may use Health 2.0 for quick access to information and then share that information with others.106 Current literature is mixed on the direction in which social media influences nutrition behaviors in young adults; for some, it is positive by expanding food choices,107 for others it may be detrimental by encouraging increased consumption of nutrient-poor foods and beverages,107,108 or restrictive eating behaviors.109–112 Social media can also support for those diagnosed with chronic health conditions, like T1D, by offering connections to peers or healthcare providers between visits. 113–117 This support has been described as having a connection with a peer with diabetes or exposure to diabetes resources that provide a different perspective regarding diabetes care.113 There may be an added 17 benefit with improved metabolic control for some with T1D participating in social media groups. 118–120 However, the accuracy of information shared by users on social media, as well as the extent to which the information influences behavior are unknown.116,121 Therefore, it is critical for healthcare providers to understand the congruence of the content of diabetes-related social media posts with current standards of care and the extent to which social media influences self- management behaviors, especially dietary behavior and subsequent glycemic control, especially in young adults with diabetes. Current Social Media Usage Social media was a part of the daily routine of approximately 90% of young adults, ages 18-29 years in 2021.122 Similarly, use of social media is very common among adults with 81% of those 30-49 years of age, 73% of those 50-64 years of age, and 45% of those greater than 65 engaging in social media every day.122 The likely draw to social media is due to the collaborative nature of viewing and interacting with different posts by liking, commenting, and sharing.123 There are a variety of social media platforms available with Facebook (76%) and Instagram (75%) considered the most popular among young adults.124 Instagram is a photo- dominant social media platform with users spending 30-58 minutes daily on this site alone.125 Young adults account for a higher proportion of Instagram content creators with 71% of those 18-29 years of age using it daily as compared to 48% of those 30-49 years and 29% of those 50- 64 years.122 Users of Instagram often attempt to connect with others outside of their following network on a post topic by adding hashtags to the photo and the caption. Hashtags are a way to increase the social network of the user with those of similar interests. Social media can become addictive as users search for validation from others on posts. This may in turn lead to psychological impacts,126 including disordered eating.109,110,127 Social media is a part of the 18 daily routine of the majority of young adults who make it a “go-to” source of information, entertainment, or connection. Personal information is very often shared on social media.128 That information increasingly includes health information with users both seeking and sharing.129 Young adults with T1D are more likely to share health information on social media than those with inflammatory bowel disease as part of the supportive online diabetes community.130 This provides a unique opportunity for health care to look to social media to engage with patients to provide the skills necessary to assess messages for disinformation. Diabetes-Related Social Media One of the primary drivers of social media use in individuals with diabetes is creating a community of support known as the diabetes online community (DOC).114,117,131 The DOC includes those with T1D as well as T2D although the majority of the community are those with T1D.119 Instagram is one of the social media platforms that the DOC uses for support. McLarney et al (2022) found that Instagram is the platform of choice when one was not sure what they wanted to learn and follow.132 While support is the primary reason to be part of DOC, the DOC is also used to increase confidence in daily self-management, motivation, inspiration, and seeking health information.133 Much of the prior research has been related to intentional viewing of social media sites often in closed groups where individuals feel free to share information.119 Engagement with social media has differed based on group members.134 For example, when healthcare providers are included as opposed to patients with T1D who share more personal information in a closed patient group.134 Other researchers have found that social media increases engagement between patients and healthcare providers with patients trusting healthcare provider advice over that shared within the DOC.113,132,135 The benefit of engagement with others that live with diabetes is known as a key component of care and thus 30% of 19 surveyed diabetes care practitioners recommend participation in a DOC as a component of DMSES.136 Engagement in a DOC has been shown to improve glycemic control.119 Litchman et al. (2018) found that those who were engaged in the DOC had a 33.8% decrease in A1C.119 The DOC is not limited to just young adults; older adults utilize these communities for support as well.135,137 The sharing that takes place on social media platforms includes advice to peers even when it has not been requested.138 Previous research points to perception of the DOC sharing minimal general diabetes misinformation, with peers perceiving as correcting this misinformation when it is presented to the community.139,140 Whether this advice is related to nutrition and the advice is evidence-based has yet to be researched. Diabetes-related social media groups often include information on nutrition and diets with approximately 30% of posts focusing on that topic.141 As discussed previously this only highlights the need to make sure the information is accurate or at least for healthcare providers to include deciphering social media nutrition information within diabetes education.142 A review of a popular T1D non-profit organization's social media presence has promotion of very low carbohydrate, ketogenic diets (VLCKD) as a highlight on their nutrition information tab.143 Evidence to support this claim is absent from the literature. The results of one small observational study in 11 adults did demonstrate a lower A1C, but episodes of hypoglycemia and dyslipidemia may make it more harmful.74 The issue with social media is that one small study, such as that by Leow et al (2018) on the VLCKD, is shared and then laypersons will implement this on their own without fully understanding the risks and benefits. Social media while helpful for some, can hence also lead to dangerous dietary behaviors.74,144 20 Social Media Health Influences Social media has been shown to positively and negatively influence on overall health. One widely-known negative health influence of social media is to mental health. Shensa et al. (2017) described the addictive nature of social media with increasing depressive symptoms in young adults, which alone is damaging, but for individuals with T1D, this could be worse since higher amounts of depression are already seen in this population.145,146 Problematic social media use occurs when time spent on these platforms becomes all-consuming to a point that is similar to substance abuse addiction.147 A systematic review and meta-analysis by Shannon et al (2022) found that problematic social media use is associated with more stress, anxiety, and depression in adolescents and young adults.148 This is concerning for long-term health in those with T1D since those with depression have higher A1C levels.146 On the other hand, social media has demonstrated positive influences on some health behaviors. Social media is considered the main source of health information for young adults with many sharing personal health information.149,150 One of the positive aspects is enhancing communication between the patient and the healthcare team in between visits. The types of messages delivered on social media matter to patients. Empowering messages that are utilized for health intervention instead of traditional health information sharing are known to be effective in creating positive behavior change.151 One of the most positive findings from research is the increase in self-efficacy for making these behavior changes, especially in those with chronic health conditions.152,153 Researchers at Boston Children’s Hospital surveyed 204 adolescents and young adults, 12-22+ years of age, about health information sharing on social media.150 Over 88% of participants used more than two social media sites every month. The platforms used were 21 similar to those included in previous reports with 83% of participants using Instagram, and more than 50% of participants using it multiple times every day. Health information was posted by 51.5% of participants with 43.8% hoping to connect with others diagnosed with similar conditions and 41% seeking health advice. While social media was used to connect with others, it was not necessarily used to obtain specific health information (25%).150 This study did not indicate the exact health information shared but 57.1% posted on wellness and prevention, which could incorporate nutrition.150 Another omission in this research is discussing the relative alignment of this information with evidence-based standards. A meta-analysis highlights gaps in the literature related to the content of health information available on social media because young adults go to social media sites seeking health information.154 Health 2.0 platforms offer support, especially to those with chronic diseases, though there are varying levels of engagement in social media. Levels of engagement were studied qualitatively by Fergie et al (2016) in a sample of 18-30 year olds with diabetes or a common mental health disorder.121 The authors described levels as non-engagers, those not engaging online regularly; tacit consumers, those consuming health-related information but not creating content; and prosumers, most frequently engaging with and posting online content.121 The prosumers tended to be diagnosed with T1D at an older age, lacked offline support, valued the support of the community and challenged healthcare provider advice.121 Prosumers are individuals who are populating the user information created on social media and, as a group, tended to challenge advice and may add nutrition misinformation. Social network analysis demonstrates that these same individuals often engage in network conversation.155 Young adults often look to Instagram prosumers, commonly seen as influencers, as credible with regards to purchasing and it remains unstudied about health care.156 This remains an area that necessitates 22 investigation to determine whether the health information being shared, particularly by prosumers, may be seen as credible and whether young adults with T1D are incorporating that advice into their daily self-management. Social Media Eating Behavior Influences Much like traditional advertisements, social media is known to alter dietary behaviors.157 Young adults report that social media can aid in expanding food choices but also increase the less healthy options.107,108,111 A study of 189 young adults demonstrates that on Instagram men tended to rate unhealthy foods as healthier, and images didn’t change their desire to consume that food. In contrast, women were more likely to want to eat the foods with the healthier images.158 Images of food can create preoccupation with food itself and with the desire to have a specific online identity it can lead to problematic eating patterns. The negative impacts of social media has on eating in young adults is related to the volume of social media together with the frequency of use throughout the day.109 There is an association between the frequency of social media use and disordered eating and body image concerns.109,111,112 Mohsenpour et al. (2023) postulate that the problematic social media use leads to body image issues which in turn creates the restriction of overall food and beverage intake.144 Social media often draws one to highlight only what they want to show to others and not necessarily what is reality. Food sharing is common on social media with some people becoming obsessed with having the “perfect diet”, known as orthorexia, where the individual isn’t concerned about weight but just eating the healthiest possible diet.159 Social media has been implicated as a risk factor for developing orthorexia with Instagram being the principal contributing platform.119 There are mixed findings regarding whether and on how viewing food posts on Instagram influences eating behavior. Research 23 suggests that engagement may be the mediating factor of actually intending to change behavior from the social media content viewed.160 Diet Culture and Social Media Diet culture will be operationalized for the purpose of this dissertation as disordered eating and body image concerns either individually or combined. This definition aligns with the body of research examining “fitspiration”. Fitspiration is a common term used to describe photo-based social media posts that promote exercise and healthy eating which are designed as an inspiration to those viewing them.161,162 The content creators will draw individuals outside of their established followers to their posts using the hashtags #fitspiration or #fitspo.162,163 The promotion of healthy habits would make one believe that these types of posts would have a positive impact on overall health. On the contrary, research on the associations of fitspiration on diet culture indicates that viewing this content may have negative impacts on body image and risk for disordered eating.111,164–168 The fitspiration images on different social media platforms include captions that accompany the images. A qualitative analysis of 1050 fitspiration images on Pinterest, a photo-based social media platform, found that the images were perpetuating the thin, fit ideal.169 Engelen et al. (2020) found in their study of 308 young adults that when compared to Facebook, Instagram content creators experienced more body dissatisfaction and increased negative affects viewing a fitspiration photo which seemed to cause more harm than just the text alone.170 This phenomenon of a seemingly positive trend, fitspiration, having a deleterious impact is similarly seen with recent research on the hashtag #healthyeating. Raiter et al. (2023) found that in a content analysis among 250 TikTok posts, a video-based social media platform, 59.6% actually had negative messages such as losing weight, body objectification, and dieting instead of positive as would be thought.171 24 Increased body dissatisfaction and the concept of one’s own body image has shown to influence eating behaviors including restriction and overeating.144 Sidani et al. (2016) cross- sectionally studied a nationally representative sample of young adults (ages 19-32 years) to examine the association of volume and frequency of social media use on eating concerns.109 The researchers measured eating concerns using two brief screening tools utilized in primary care for patients at risk for eating disorders, the SCOFF (Sick-Control-One-Fat-Food) and Eating Disorder Screen for Primary Care (ESP). They found that social media use, both how time spent daily and how frequently visited in a week, were strongly associated with eating concerns. These findings were consistent between genders and all ages in the study.109 Jiosta et al. (2021) similarly found the risk of eating disorders in participants in their study of 1331 young adults examining social media and body dissatisfaction with 72% of participants screening positive using the SCOFF.172 The hashtag #clean eating has also been positively associated with orthorexia and disordered eating.173 Body positivity posts are another trending hashtag among social media users. One study of 246 Instagram posts that used #bodypositivity was analyzed and found that while some are truly positive, there were contradictory messages for weight loss within most posts.174 Additionally, while body positivity was the focus and included some images of larger bodies, the majority of posts highlighted the thin ideal.174 Cohen et al. (2019) analyzed 20 Instagram posts from 32 different body positive accounts that had over 50,000 followers.175 Authors found that a diverse group of body shapes and sizes were represented with nearly two-thirds highlighting bodies that appear to meet the Center for Disease Control and Prevention BMI criteria for overweight or obesity.175 The differences highlighted in these two studies could be explained by the differences in methodology by analyzing random posts on a hashtag versus posts from those 25 dedicated to posting about body positivity. The body of research on body positivity does not include any direct causality studies on social media and disordered eating that highlighting a possible elevated risk of developing disordered eating among those at highest risk of disordered eating, such as those with diabetes, need to be monitored closely for progression of symptoms. Social Network Analysis Social Network Analysis Overview Social network analysis (SNA) is the study of both structure and patterns of relationships among people and groups.16 The network is made of up individuals, referred to as nodes or actors, and relationships that the individual has with others, referred to as edges or ties.16,176 Networks have different characteristics with two main types that are the focus of the analysis either being centered on one main node, ego-centric, or for the entire network on how nodes interact, socio-centric.16 The study of the network is used to identify smaller communities and examine the overall flow of information within the network which is done with visualization and quantitative analysis.16,177 Networks are quantitatively analyzed to examine the density, centrality, and modularity of the network. Density is used to examine the cohesion of the entire network measuring how many edges each node has for a maximum score of one. Nodes with higher density scores are more highly connected to each person within the network.16 The density will measure how fast information will spread within the network.114 Centrality measurements are computed to find the main nodes within the entire network and which ones are the information gatekeepers.16 Modularity examines the structure and the divisions within the network.178 The use of SNA has long been used in education, mathematics, and sociology but more recently used in health care.114,179 26 Social Network Analysis in Healthcare Healthcare research based on SNA has increased over the last nearly thirty years including its use to analyze health and social media.180 The majority of this research has focused on the analysis of X, formerly known as Twitter. Gruzd et al. (2013) examined the social network on X surrounding a hashtag associated with healthcare innovation, #hcsma.114 Researchers found loose connections within the community with the most engaged users being longer members of the community and part of multiple communities in the network.114 A SNA on health-related topics on X found that nodes mostly shared knowledge and not personal stories.181 These conversations were mostly by healthcare providers and rarely were short-term relationships without reciprocity.181 Tougas et al. (2018) examined X, Facebook, and Instagram as part of their SNA on children’s pain and found that this small clustered community on Instagram shared personal stories and was not a two-way conversation.182 The only nutrition- related Instagram SNA examined was the large community of healthy food with over two million posts and was able to identify four main communities.183 The four emerging communities were 1) active lifestyle, which included weight loss; 2) healthy food blogs, for sharing recipes and food ideas; 3) diets, including vegan and gluten-free; and 4) keto, sharing about low carbohydrate and very low carbohydrate diets. No previous work has studied social network communities regarding nutrition in chronic health. Theoretical Framework Incorporating new health information, such as what one finds on social media, is a complex process. The impetus to change health-related habits, like dietary intake, can be explained by behavior change theories. The Theory of Planned Behavior (TPB) was first described by Ajzen to predict why someone alters their behavior.184 This theory posits that a 27 change in behavior is precipitated by intention which is driven by attitude, subjective norms, and self-efficacy.184 This research study utilizes TPB as the basis for explaining potential dietary behavior changes in persons with diabetes who view and engage in nutrition-related social media posts. Engaging with social media community, by connecting with others via hashtags, is a method to establish subjective norms such as adopting a specific diet approach to manage diabetes. The social media posts and community create the change in attitude by highlighting the benefits of the specific diet through improvement in glycemic control. Lastly, self-efficacy is fostered by the supportive nature of the community. Gaps in the Literature There is a growing body of literature regarding the influence social media has on a variety of chronic health conditions, such as diabetes. Much of the prior research focuses on connections created within Health 2.0 including social media platforms, such as private groups on Facebook but to a lesser extent on open platforms such as connecting through hashtags on Instagram. While there have been demonstrated benefits of the supportive nature of social media for persons diagnosed with diabetes such as glycemic control improvements, there are also aspects of social media that can increase disordered eating behaviors. This may, in turn, lead to elevated A1C, placing a person with diabetes at a elevated risk for long-term complications like cardiovascular disease. Instagram is the social media platform that contributes most to disordered eating.185 Therefore, it is hypothesized that this platform will be most damaging to glycemic control. The content of nutrition- and diabetes-related Instagram posts is not yet known in the literature and is hence a missing piece of understanding the relationships between social media and dietary behaviors. Additionally, while the diabetes online community has been studied, there is no literature that has examined networks created within Instagram, specifically 28 those that focus on nutrition, and the messages shared by the main nodes within that community. Lastly, there have been no comparisons of T1D and T2D nutrition-related social media to examine any differences between the main content creators and messages shared. The current study aims to address gaps in the literature and provide guidance to the diabetes healthcare teams regarding the direction for diabetes self-management education and support. 29 CHAPTER 3: CONTENT ANALYSIS OF NUTRITION-RELATED TYPE 1 DIABETES INSTAGRAM POSTS Introduction Social media is used as a method to connect with others on a personal level and share and acquire a variety of information.186 Social media encompasses an array of applications including social networking, messaging, media sharing, blogging, and discussion forums, which are part of the daily routine for over 90% of all young adults living in the United State (18-29 years of age). 29,113,187 There are a variety of social media platforms including: Facebook, X (formerly known as Twitter), Snapchat, TikTok, Pinterest, YouTube, and Instagram. Instagram, a photo-based social networking platform, is extremely popular with those 18-24 years of age with over 75% reporting using this application.114 Engagement with Instagram by young adults is only expected to grow with users currently spending 30+ minutes daily on this platform alone. 115,116 While sharing of health information is certainly not the sole purpose of social media, it has become a “go to” source for many to learn more about their health versus relying solely on healthcare practitioners; this concept has been coined Health 2.0 188,189 In this respect, social media health messages from general or typical users have been perceived by individuals as effective and less disempowering than those from health experts.190,191 However, the information available on social media is generated by the individual creating the content and posting it to the social media platform and is not necessarily evidence-based or accurate. This is concerning since individuals may see a social media content creator as a credible source of information and incorporate their suggestions without first checking with a health care provider. Social media has been shown to be associated with negative physical and mental health impacts, especially in teens and young adults such as anxiety, depression, poor sleep quality, and low self-esteem. 145,192–195 The magnitude of these negative impacts is correlated with the total 30 time spent on social media. 194,196–198 Teens with social media use that is described as moderate to problematic, based on how it impacts their day, experience more headaches, neck and shoulder pain, nervousness, irritability, and sleep disturbances. 196 This may be further amplified due to the algorithms in place that drive the content the social media user will see based on previous posts they view, who has posted the information, and the extent to which they interact with the posts. 199 On the other hand, engaging in social media may also provide the user benefits. A notable benefit of social media is its ability to connect people to one another and provide a sense of belonging. 190,200,201 200 Social media also provides an opportunity for self-expression and can help shape someone’s identity .187 One such chronic health condition of concern to examine the influence social media has on overall health is type 1 diabetes (T1D). This autoimmune health condition that is caused by the destruction of the beta cells of the pancreas leads to insulin deficiency if blood glucose is not properly controlled and can progress to long-term health complications such as cardiovascular disease.17,24 Given the length of disease together with typical diagnosis before the age of 35 years, an examination of influence of social media on overall health of those with T1D is warranted.18 Persons living with chronic health issues that require daily self-management, such as T1D, have been known to use Health 2.0 to create a network of support with others.113,115,116,202 Social connection is viewed as one positive aspect of Health 2.0, particularly when individuals do not have easy access to others with their diagnosis. There is also benefit from exposure to information that individuals typically would not have access to without Health 2.0. Young adults with T1D often utilize social media for disease-specific information and interaction with peers with the majority freely sharing their diagnosis and associated information on social media platforms.130 Social support posts in diabetes online communities are known to 31 have the most engagement with users liking and commenting.203 This network has specifically been shown in persons with T1D to improve A1C values, a measure of three month glycemic control, by 0.9% in T1D. 118–120 Proper nutrition is a key to successful self-management of diabetes, critical for improving diabetes outcomes but proper nutrition may be impacted by social media. Current literature is however mixed on the direction in which social media influences specific nutrition behaviors. There are some positive benefits of nutrition-related posts such as expanding food choices and learning how to prepare foods.107,127 However, social media is also a well-recognized an avenue to market various food products, including nutrient-rich and nutrient-poor foods and beverages. Importantly, recommendations from social media content creators, persons who are creating and posting to social media platforms, can be deemed more persuasive than from a company itself due to the personal nature of the posts. 191 For example, young adults report that they are more likely to consume fast food if those food images are part of their social media feed.204 When a social media content creator is perceived as an influencer, their posting of themselves consuming nutrient-poor snacks is known to influence the viewer to consume nutrient-poor snacks; but the same does not hold true for consumption of nutrient-rich snacks.205 Additionally, the literature reveals that social media has led many young adults to restrictive eating practices and body image issues. Female social media users tend to compare themselves more to the content creator as a detriment to health and body image perception. 204 Such behaviors may compound the challenges faced by those with T1D. Although, the long-term impact social media can have on health remains to be elucidated, those living with T1D are at an elevated risk for long-term health complications such as nephropathy, neuropathy, retinopathy, and cardiovascular disease.29 Type 1 diabetes alone is a 32 major risk factor for developing cardiovascular disease before the age of 30.8–10 These complications are concerning let alone any added effect that may be associated with restrictive/altered eating practices. Instagram is critical to use in this study as it is a photo-based platform with the highest proportion of young adult users with previous research showing associations with body image dissatisfaction and disordered eating. The purpose of this study is to conduct a qualitative content analysis to characterize nutrition information with regards to alignment with current evidence-based recommendations shared on Instagram targeted to young adults with T1D utilizing popular hashtags, which categorize and track social media content. Methods Hashtag selection: We selected the hashtags for the study using a purposive sampling technique to include Instagram content available to the public in order to locate those hashtags with the highest proportion of nutrition related content.114 A review of Instagram’s popular T1D hashtags (>20,000 posts per hashtag) was completed in January 2020 by signing into the web version of Instagram using the search function by two trained undergraduate research assistants. A total of twenty hashtags met this criterion of having more than 20,000 posts and being related to diabetes. The sampling technique to scan the most popular hashtags was similar to those of other researchers when the specific studied hashtag was not previously identified.207 Every fifth public, English language post for up to twenty total posts on four different dates (January 4, 6, 8, 9, 2020) (n=1127) were analyzed for the following: main post topics (nutrition, daily diabetes self-management, physical activity, meme, support, other); Instagram content creator type (person with diabetes, health information provider, organization, other); estimated age of the Instagram content creator based on their shared account biography information (parent of child/adolescent, adolescent, young adult (18-29 years), adult); and type 1 or type 2 diabetes 33 focus. Different days of the week were selected to provide for variability of weekday and weekend posts. The two hashtags with the highest proportion of nutrition-related posts by young adult T1D Instagram content creators were determined to be #type1diabetes and #diabadass. These hashtags were subsequently used for in-depth analyses. The content analysis protocol was reviewed by the Michigan State University Institutional Review Board and deemed to be exempt. Coding procedures: This was a pioneer study; therefore, there were no a priori coding themes available. A codebook for data analysis was created specifically for this project using inductive content analysis after review of the hashtag sampling posts and the study research questions.208 Previous Instagram content analyses methodology were used to guide the creation of the variables for photo, caption, hashtags, demographics, and engagement related statistics.175,209–212 The principal investigator (DK) created the codebook study variables with operationalized definitions. Two trained undergraduate research assistants (RG and AD) completed reliability testing on a random sample of 20 nutrition-related diabetes Instagram posts until a coder reliability for nominal variables with a Cohen’s Kappa cut-off point of κ >0.85 (n=60) was reached.175 Coding discrepancies were discussed after each round of testing. A total sample size (n=300) was determined based on previous content analysis methodology and that time intensive manual coding was necessary.213–215 Each research assistant coded 150 Instagram publicly available nutrition-related posts in chronological order between May-July 2020 per assigned hashtag for use in the study by logging into the web version of Instagram. The coders did not engage with any of the posts and only collected the data as outlined in the codebook. Weekly meetings were held with the principal investigator and research assistants as an iterative process for codes that didn’t fit into the previous categories with subsequent updates to the codebook. 34 Coding themes: The posts were coded for Instagram content creator information, main content, and pictured food alignment with the 2019 American Diabetes Association Nutrition Consensus Report.50 Instagram content creator information: Each identified nutrition-related post was further evaluated for Instagram content creator information by accessing the Instagram content creator biography via the web version of Instagram for ease in viewing multiple posts with dates. The biography was coded for type of user (person with diabetes, health information provider (unable to verify all credentials), personal business, and other); user gender (male, female, unknown, multiple gender users). Additionally, information was gathered on the number of followers and the number of Instagram content creators the account was following. The number of followers was used to calculate the engagement factor and the number of accounts following was to assess for probable prosumer status. Main content: Content analyzed on each post included the photo, caption, and hashtags. The post was coded for including a food photo, description of the food, classification of the main and secondary nutrition themes: food/beverage, food/beverage with physical activity, body image/emotion, diabetes management, nutrition education/food substitution, promotion of food or diabetes-related services, and other. Posts were also coded for promotion of a diet, the diet the post promoted (very low carbohydrate/ketogenic, low carbohydrate, vegetarian/plant- based/vegan, gluten-free, intermittent fasting, low sugar/sugar-free), discussion of diet culture (body image and/or disordered eating), and glycemic management information (picture of glucose monitoring equipment, discussion of blood glucose) shared. Alignment with nutrition standards: The combined food-related post data was further coded for alignment with the 2019 American Diabetes Association Nutrition Consensus report 35 (low sodium, high fiber, plant-based/vegetarian, non-starchy vegetables, low saturated fat (<10% of total calories), minimal added sugar, non-processed (based on NOVA classification216)) by the principal investigator, a Registered Dietitian Nutritionist with greater than 25 years of experience (DK) and a trained undergraduate research assistant (SM).50 The photos and captions were evaluated for products depicted using the USDA Food Data Central Database or internet information for a branded food product. Statistical Analysis: Data cleaning was completed to check for duplicated posts between hashtags (n=298). Data was analyzed descriptively using Statistical Package for Social Sciences (SPSS) version 27. Associations were measured using the general linear model generalized estimating equations (GEE) model with engagement factor and a diet promoted as dependent variables. Independent variables included Instagram content creator gender, content creator type, main theme, a diet promoted, specific diets promoted, discussion of diet culture and sharing of glycemic information. This statistical test was used due to nesting of data with multiple posts being created by the same Instagram content creator. The GEE model was built using the Instagram content creator as the subject variable. Separate models for each dependent variable and independent variable were paired with those statistically significant independent variables being included in the full GEE model. A p value of <0.05 was considered statistically significant in all models. Results A total of 296 non-duplicated nutrition-related Instagram posts with complete information from 195 unique Instagram content creators were analyzed from the hashtags #type1diabetes and #diabadass. A total of 280 (94%) posts included pictures of food and/or beverages included in the analysis for meeting the 2019 American Diabetes Association nutrition standards. The 36 majority (n=187; 95%) of Instagram content creators shared 1-2 nutrition-related posts during the three-month data collection period with a range of 1-11 posts. The Instagram content creators were predominantly female (n=153; 78.5%) persons with T1D (n=149; 76.4%) with a mean of 3770 followers (2-160,000). The type of users with the most Instagram followers were persons with diabetes and a personal business (n=12,438) and those accounts with multiple users (n=9,977). Table 3.1 outlines the characteristics of the unique Instagram content creators. Generalized estimating equations was performed to measure average number of followers between Instagram content creator types. Results demonstrated persons with diabetes had fewer average Instagram followers when compared to health information providers (Table 3.2). Table 3.1 – Instagram Content Creator Characteristics Instagram Content Creators (n=195) Gender Female Male Multiple Users Unknown Content Creator Type Person with Diabetes Person with Diabetes and Personal Business Personal Business Health Information Provider N 153 21 2 19 149 19 10 17 Table 3.2 – Content Creator Type Average Number of Followers Content Creator Type Average Number of Followers Person with Diabetes Person with Diabetes and Personal Business Personal Business Health Information Provider 1317.0 9085.8 6014.7 9442.9 % 78.5 10.8 1.0 9.7 76.4 9.7 5.1 8.7 37 Main Nutrition Themes The main nutrition theme identified was sharing of food/beverage images consumed for meals, snacks, and celebrations (n=211; 71%) with the second most common theme being nutrition education/food substitution (n=34; 12%). The average engagement factor for main nutrition theme was analyzed using the generalized estimating equations model. Results demonstrated significantly higher average engagement factors for nutrition education/food substitution, food/beverage, and diabetes management as compared to promotion as the reference group (Table 3.3). Table 3.3 – Instagram Post Main Nutrition Theme Engagement Factor Main Nutrition Theme OR (95% CI) Average Engagement Factor 8.8%** Nutrition Education/Food Substitution Food/Beverage Diabetes Management Body Image/Emotion Food/Beverage with Physical Activity Promotion General Linear Model – Generalized Estimating Equations, **p<0.001 6.6%** 6.4%** 5.4% 4.9% 2.5% 567 (28-11334) 61 (11-327) 47 (6-381) 18 (0.8-371) 11 (6-381) Reference Gender distribution for main nutrition themes illustrated that females were the exclusive Instagram content creators who posted about body image/emotions (n=5; 100%), and accounted for the majority of nutrition education/food substitution posts (n=31; 91%). As compared to the total males in the entire sample (11%), males had a higher proportion of posts about food and beverage with physical activity (n=4; 50%) and diabetes management (n=4; 19%). Multiple users only posted on food/beverages (n=6; 2%), while unknown users predominantly shared posts with the main theme of promotion of food products or services (n=11; 42%). Figure 3.1 illustrates the main nutrition theme by gender distribution. 38 Persons with diabetes contributed to the majority of posts that include food/beverage (n=169; 77%), food/beverage with physical activity (n=6; 75%), body image/emotion (n=5; 100%), diabetes management (n=17; 85%), and nutrition education/food substitution (n=19; 63%). Persons with diabetes and a personal business most often posted about food/beverages (n=17; 63%), nutrition education/food substitution (n=4; 15%), and promotion (n=3; 12%). More than half of the personal businesses posted about promotion of products and services (n=11 65%) or food/beverage (n=5 29%). Health information providers posted about food/beverages (n=14 52%) and nutrition education/substitution (n=6 22%) most frequently. The other category for Instagram content creators predominately posted about food/beverage (n=5; 83%). Figure 3.2 highlights the distribution of the main nutrition theme and Instagram content creator type. Figure 3.1 - Instagram Post Main Nutrition Theme Distribution by Gender 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Food/Beverage with Physical… Food/Beverage Body Im age/E m otion N utrition Education/Food … Diabetes M anage m ent Pro m otion Other Female Male Multiple User Unknown 39 Figure 3.2 - Instagram Post Main Nutrition Theme by Instagram Content Creator Type 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Food/Beverage with Physical… Food/Beverage Body Im age/E m otion Diabetes M anage m ent N utrition Education/Substituion Pro m otion Other PWD PWD with Business Personal Business Health Information Provider Other Diet promoted in Instagram posts Nearly one-half of all Instagram posts promoted a specific diet within the image, caption or hashtag (n=145 48.6%). Posts that promoted a diet were 11.6 times (p<0.05, OR 11.6; SE 0.93; 95% CI 1.9-72.2) more likely to be authored by a personal business than a person with diabetes and a personal business. Posts often promoted multiple diets, such as low carbohydrate and vegetarian, within the caption and/or hashtags, which accounted for the total from each category being greater than the total number of diets promoted. The most common diets promoted were low carbohydrate (n=72) and ketogenic diets(n=70) which were commonly both used to describe the post. Vegetarian/plant-based diets (including vegan) were the next most popular diet/eating pattern highlighted (n=39), followed by gluten-free (n=19), low sugar/sugar- free (n=13), other (n=15), and intermittent fasting (n=12). Gluten-free posts had the highest average likes (n=189) and comments (n=12) followed by vegetarian/plant-based (likes-n=104, comments-n=7). There were no significant differences between average number of likes between each type of diet. 40 The gender distribution for posts that promoted a diet was similar to the overall sample distribution (females 75%, males 10%, multiple users 0.71%, and unknown 14%). Persons with diabetes and persons with diabetes and a personal business accounted for 78% of all diet promotion posts, which is slightly lower than that of the total sample at 83%. Personal businesses accounted for 10% (n=14) of diet promoting posts; which was double of the total sample at 5%. Ketogenic diets were promoted by females (n=43; 61%), unknown gender (n=16; 23%), and males (n=11; 16%), while low carbohydrate diets were promoted by females (n=50 69%), unknown gender (n=15; 21%), males (n=6; 8%), and multiple genders (n=1; 1%). Females were the most likely to post about vegetarian/plant-based diets (n=31; 80%), compared to males (n=5; 13%) and those of unknown gender (n=3 8%). Intermittent fasting diets were promoted by those of unknown gender (n=6; 50%) and females (n=6; 50%). Unknown gender content creators accounted for 53% (n=10) of gluten-free diet promotion followed by females (n=8; 42%), and only one multiple gender content creators (5%). Low sugar/sugar-free diets were promoted by unknown gender content creators (n=8 ; 62%) and females (n=5; 39%). The other diets mentioned were shared mostly by females (n=13; 87%) and those of unknown gender (n=2; 13%). The types of Instagram content creators also differed on the types of diet promoted. Persons with diabetes (n=50; 71%) and those with personal businesses were significantly more likely to promote ketogenic diets (p<0.001, OR 29.2; SE 0.85; 95% CI 5.4-156.8) when compared to persons with diabetes and personal businesses (n=2; 3%), health information providers (n=3; 4%) or others (n=1; 1%). Persons with diabetes promoted low carbohydrate diets less often than the overall distribution of this Instagram content creator type (n=44; 61% vs 41 76%) than persons with diabetes and personal business (n=9; 13%), health information providers (n=5; 7%) or others (n=1; 1%). Vegetarian/plant-based diets were promoted only by persons with diabetes (n=32; 82%), persons with diabetes and personal business (n=5; 13%) and health information providers (n=2; 5%). Intermittent fasting was promoted by persons with diabetes (n=7; 58%) and those with personal businesses (n=5; 42%). Gluten free was promoted by persons with diabetes (n=7; 37%), personal businesses (n=6; 32%), persons with diabetes and personal businesses (n=4; 21%) and health information providers (n=2; 11%). Low sugar/sugar- free diets were most often promoted by those with personal businesses (n=7; 54%), persons with diabetes (n=3; 23%), persons with diabetes and personal businesses (n=2; 15%) and health information providers (n=1; 8%). The main nutrition theme for diets promoted is outlined in Table 3.4 The ketogenic diet accounted for very few diabetes management posts (n=1; 1%), but had a greater than normal distribution for posts that promoted foods and/or diabetes related products (n=12; 17%) which followed a similar pattern for low carbohydrate diets with no posts on diabetes management and 12 posts (17%) that promoted foods and/or diabetes related products . Posts that highlighted vegetarian/plant-based diets had fewer posts in diabetes management (n=1; 2.5%) and nutrition education/food substitution n=2; 5%) than the full data set. Posts that endorsed intermittent fasting (n=3; 25%), gluten-free diets (n=7; 37%), and low-sugar/sugar-free diets (n=8; 37%) together had a higher percentage of posts with the theme of promotion of foods and/or products and services. 42 Nutrition Standards The 275 posts with clear, visible food pictures and captions were further evaluated for meeting the nutrition standards outlined in the 2019 American Diabetes Association (ADA) Consensus Report: low sodium, fruits/vegetables, low-fat dairy, vegetarian/plant-based, low saturated fat, high soluble fiber, non-starchy vegetables, minimize added sugar, and non- processed food.50 Not all of the foods could be analyzed for each of the standards based on the picture and caption provided (e.g. unable to determine if low-fat dairy used). Only 6% of all posts (n=16) met all of the identified nutrition standards for the foods highlighted (e.g. if no dairy included in the picture, then not evaluated for the low-fat dairy standard). Associations were evaluated for meeting the ADA nutrition standards. Specifically, when a diet is promoted in the post it is 2.09 times more likely to meet the non-starchy vegetables standards than the non- processed foods (Table 3.4). Table 3.4 - Characteristics of Diet Promoted with 2019 ADA Nutrition Standards Nutrition Standard Diet Promoted Low Sodium Fruits and Vegetables Low-fat Dairy Vegetarian Low Saturated Fat High Soluble Fiber Non-Starchy Vegetables Minimize Added Sugar Odds Ratio (SE) 0.68 (-0.37) 1.6 (0.48) No diets promoted 1.46 (0.38) 0.81 (-0.21) 0.99 (-0.01) 2.09* (0.74) 1.41 (0.31) Reference 95% Confidence Interval 0.37-1.28 0.80-3.22 0.73-2.92 0.44-1.50 0.55-1.78 1.09-3.99 0.65-3.06 Non-Processed Food General Linear Model-Generalized Estimating Equations *p<0.05 43 Table 3.5 included the nutrition standards met for each of the specific diets promoted. The standard that was met the greatest in this study was the inclusion of fruits and vegetables (n=198; 72%). A specific diet was promoted in more than three quarters of the fruit and vegetable posts (n=110; 80%) with ketogenic diet (n=50; 46%) and low carbohydrate diet (n=47; 43%) representing the two most common diets in these posts. Non-starchy vegetables were included in 37% of posts (n=102) and accounted for 52% of the fruit and vegetable containing posts. The next most common standard met was low saturated fat (n=115; 42%) though important to note that this was seen in fewer than half of all posts. Vegetarian/plant-based diets (n=24; 43%) and low carbohydrate diets (n=20; 36%) were the two most common diets promoted (n=56; 49%) within posts that met the low saturated fat standard. Posts that featured low sodium foods (n=102; 37%) only promoted diets approximately one third of the time (n=37; 36%) including the low carbohydrate diet (n=19; 51%) and vegetarian/plant-based diet (n=16; 43%). Foods high in soluble fiber were featured in over one third of the posts (n=96; 35%) with vegetarian/plant-based diets (n=27; 57%) and low carbohydrate diets (n=19; 40%) accounting for the majority of all diets promoted for this standard (n=47; 49%). The vegetarian/plant-based standard was met in 32% of all posts (n=87) which promoted a diet in the hashtags more than half the time (n=46; 53%). Of the diets promoted in the posts meeting the vegetarian/plant-based standard, vegetarian/plant-based diets were included in the hashtags nearly two thirds of the time (n=30; 65%) with low carbohydrate diets accounting for nearly one quarter (n=11; 24%). Foods that met the NOVA criteria for non-processed foods were included in 31% (n=86) of posts with diets being promoted in over half of that standard (n=45; 52%). Ketogenic diets (n=20; 44%) and low carbohydrate diets (n=20; 44%) accounted each for nearly one half of all diets promoted in non-processed foods. The minimize added sugars standard was only applicable to posts 44 without whole foods and was present in 34% of posts (n=45/133). The minimize added sugar standard promoted diets nearly half of the time (n=22; 49%) with low carbohydrate diets (n=10; 45%) and ketogenic diets (n=8; 36%) being the most common diets. The low-fat dairy standard was met the least with only 5 posts (2%) meeting the standard with none of those posts promoting any diets. Table 3.5 – Specific Diet Promoted by Instagram Post Main Nutrition Theme and 2019 American Diabetes Association Nutrition Standard Met Specific Diet Promoted Ketogenic (n=70) Main Nutrition Theme Food/Beverage (n=50; 71%) Food/Beverage with Physical Activity (n=2; 3%) Body Image/Emotion (n=0) Diabetes Management (n=1; 1%) Nutrition Education/Food Substitution (n=5; 7%) Promotion (n=12; 17%) Nutrition Standard Low Sodium (n=14; 20% ) Fruits/Vegetables (n=50; 71%) Low-Fat Dairy (n=0) Vegetarian (n=9; 13%) Low Saturated Fat (n=17; 24%) High Soluble Fiber (n=14; 20%) Non-Starchy Vegetables (n=36; 51%) Minimize Added Sugar (n=8; 11%) Non-Processed (n=20; 29%) Low Sodium (n=19; 26%) Fruits/Vegetables (n=47; 65%) Low-Fat Dairy (n=0) Vegetarian (n=10; 14%) Low Saturated Fat (n=20; 28%) High Soluble Fiber (n=19; 26%) Non-Starchy Vegetables (n=31; 43%) Minimize Added Sugar (n=10; 14%) Non-Processed (n=20; 28%) Low Carbohydrate (n=72) Food/Beverage (n=48; 67%) Food/Beverage with Physical Activity (n=2; 3%) Body Image/Emotion (n=1; 1%) Diabetes Management (n=1; 1%)) Nutrition Education/Food Substitution (n=8; 11%) Promotion (n=12; 17%) 45 Table 3.5 (cont’d) Vegetarian/Plant-Based (n=39) Intermittent Fasting (n=12) Gluten-Free (n=19) Food/Beverage (n=33; 85%) Food/Beverage with Physical Activity (n=1; 2.5%) Body Image/Emotion (n=0) Diabetes Management (n=1; 2.5%) Nutrition Education/Food Substitution (n=2; 5%) Promotion (n=2; 5%) Food/Beverage (n=9; 75%) Food/Beverage with Physical Activity (n=0) Body Image/Emotion (n=0) Diabetes Management (n=0) Nutrition Education/Food Substitution (n=0) Promotion (n=3; 25%) Food/Beverage (n=10; 53%) Food/Beverage with Physical Activity (n=1; 5%) Body Image/Emotion (n=0) Diabetes Management (n=0) Nutrition Education/Food Substitution (n=1; 5%) Promotion (n=7; 37%) Low Sodium (n=16; 41%) Fruits/Vegetables (n=38; 97%) Low-Fat Dairy (n=0) Vegetarian (n=30; 77%) Low Saturated Fat (n=24; 62%) High Soluble Fiber (n=22; 56%) Non-Starchy Vegetables (n=23; 59%) Minimize Added Sugar (n=5; 13%) Non-Processed (n=16; 41%) Low Sodium (n=3; 25%) Fruits/Vegetables (n=9; 75%) Low-Fat Dairy (n=0) Vegetarian (n=0) Low Saturated Fat (n=3; 25%) High Soluble Fiber (n=2; 17%) Non-Starchy Vegetables (n=6; 50%) Minimize Added Sugar (n=1; 8%) Non-Processed (n=2; 17%) Low Sodium (n=6; 32%) Fruits/Vegetables (n=14; 74%) Low-Fat Dairy (n=0) Vegetarian (n=5; 26%) Low Saturated Fat (n=6; 32%) High Soluble Fiber (n=5; 26%) Non-Starchy Vegetables (n=6; 32%) Minimize Added Sugar (n=1; 5%) Non-Processed (n=2; 11%) 46 Table 3.5 (cont’d) Low Sugar/Sugar-Free (n=14) Other (n=15) Food/Beverage (n=4; 29%) Food/Beverage with Physical Activity (n=2; 14%) Body Image/Emotion (n=0) Diabetes Management (n=0) Nutrition Education/Food Substitution (n=0) Promotion (n=8; 57%) Food/Beverage (n=9; 60%) Food/Beverage with Physical Activity (n=0) Body Image/Emotion (n=2; 13%) Diabetes Management (n=0) Nutrition Education/Food Substitution (n=2; 13%) Promotion (n=2; 13%) Low Sodium (n=2; 14%) Fruits/Vegetables (n=6; 43%) Low-Fat Dairy (n=0) Vegetarian (n=2; 14%) Low Saturated Fat (n=3; 21%) High Soluble Fiber (n=3; 21%) Non-Starchy Vegetables (n=3; 21%) Minimize Added Sugar (n=1; 7%) Non-Processed (n=5; 36%) Low Sodium (n=4; 27%) Fruits/Vegetables (n=10; 67%) Low-Fat Dairy (n=0) Vegetarian (n=2; 13%) Low Saturated Fat (n=4; 27%) High Soluble Fiber (n=5; 33%) Non-Starchy Vegetables (n=2; 13%) Minimize Added Sugar (n=4; 27%) Non-Processed (n=2; 13%) n=number of posts; % = % of posts in the diet category Glycemic Management Information Glycemic management information was shared in 33% (n=97) of the posts. These posts had an average of 80 likes and 6.6 comments per post. Glycemic management information was most frequently shared in posts with the main nutrition theme of food/beverage (n=54; 56%) and diabetes management (n=17; 18%), from persons with diabetes (n=79; 81%), persons with diabetes and personal business (n=6; 6%), and health information providers (n=6; 6%) that were female (n=82; 85%) and male (n=8; 8%). Diets were promoted in 35% (n=34) of the posts that shared glycemic information with the low carbohydrate (n=17; 50%) and ketogenic (n=14; 41%) diets accounting for the majority of diets promoted. 47 Diet Culture Diet culture (weight, body image and disordered eating) was mentioned in 11% of posts (n=32). These posts had a greater number of average likes(n=132) and comments (n=11) than the entire data set though not reaching statistical significance via ANOVA (p=0.47 and p=0.25 respectively). The main nutrition theme for diet culture posts was food/beverage (n=20; 63%) and nutrition education/food substitution (n=4; 13%). The Instagram content creator was persons with diabetes (n=20; 63%) and persons with diabetes and personal business (n=8; 25%) that were nearly all female (n=31; 97%) with only one male content creator that discussed diet culture. Discussion This paper is the first to characterize nutrition information sharing on Instagram using two diabetes related hashtags (#type1diabetes and #diabadass) popular among young adults. Results demonstrated that females (79%) and persons with diabetes (74%) were most likely to post nutrition-related information. This finding is consistent with national averages which suggest that 71% of young adults use Instagram in the United States, but our study had a higher proportion of females at 78% versus the national average of 44%.217,218 More specifically females in this study sample were more likely to share nutrition-related information, which is consistent with findings from Tricas-Vidal et al that females follow nutrition-related content on Instagram more than males. 219 The use of Instagram to share and seek out health-related information is common among young adults with T1D as seen in our previous study.130 The preponderance of females sharing nutrition-related T1D information via Instagram made this an important target group to include social media use as part of their healthcare assessment and diabetes self-management education and support visits. 48 While the majority of content creators in this study were persons with diabetes, nearly 25% of posts were created by personal business or health information providers looking to add to their client base. This is concerning because those with for-profit businesses, including health information providers promoting their businesses, received the highest engagement of likes and comments related to their posts. This level of engagement may be partially explained by their higher average number of followers when compared to those with diabetes who did not have an associated business. The significance in the ratio of followers to following is related to the status of being an influencer with the highest ratio being the accounts of persons with diabetes that have a personal business. The influencer status can be a concern with the quality of the information being provided at the expense of gaining followers for their business. The most common nutrition theme for individual posts was food and beverages such as meals, snacks, and celebrations to gather interest and engagement with others. The online/Instagram sharing of a wide variety of foods is common with young adults especially foods new to them and home cooking, which is consistent with findings that young adult’s perceive food sharing as a positive influence on dietary choices.107 Conversely, food images and promotion of nutrient-poor foods could negatively influence overall intake by contributing to food cravings.111 This was evident in 13% (n=35) of posts with a photo of a food such as ice cream, French fries, cakes, and flavored cocktails. Interestingly, the themes with the highest engagement included discussion of physical activity with food and beverage and those with body image and emotions. A trend in social media known as fitspiration, which depicts healthy eating and exercise, has been associated with an increase in body dissatisfaction.1 The increased engagement with these posts in the current study sample should signal diabetes practitioners to consider this when screening for disordered 49 eating.168,206 Males in our study contributed more frequently to the food and physical activity posts while posting specifically about body image or emotions was exclusively from females with diabetes in our sample which supported research that the issues with body dissatisfaction exist with both males and females.168 Previous research demonstrated that photo-based social media, like Instagram, is associated with body dissatisfaction which could account for the increased interest in body image and emotion posts.163,170 The higher level of engagement with these themes can be alarming as research as demonstrated that young adults find posts with more engagement to have higher credibility.220 Other popular themes aligned with creating the community of those with T1D sharing diabetes management and nutrition education and ways to substitute foods as methods of diabetes self-management and creation of overall community and information sharing.119 Young adults preferred to incorporate health advice learned from Instagram content creators versus healthcare providers which is consistent with persons with diabetes and persons with diabetes and personal business accounting for 95% of all diabetes management posts which had higher engagement than just food and beverage related posts.150 Health information providers on Instagram accounted for a higher proportion of posts with the theme of nutrition education and provide food substitutions aiding followers in ideas to increase nutrient density of foods and beverages. Our study sample included higher engagement with health information providers which is promising as the posts were more closely aligned with the 2019 American Diabetes Association Nutrition Standards.50 The spreading of misinformation and disinformation on social media was common and in the realm of nutrition that may be through promotion of diets lacking strong evidence in T1D. The study demonstrated that Instagram content creators with personal businesses were more 50 commonly promoting diets than the general study sample. The diets being promoted included those that were not supported by current T1D practices including ketogenic and low carbohydrate diets as well as intermittent fasting.24 Personal businesses were not the only ones that are promoting diets lacking evidence in the treatment of T1D. Persons with diabetes accounted for 71.4% of all posts promoting the ketogenic diet and 58.3% of intermittent fasting. The implications for this should concern diabetes practitioners since those following these Instagram content creators may believe that since others are following these practices they were safe for them as well. Without long-term evidence regarding the safety of these diets, engagement in these posts, and subsequent dietary adjustments, may increase risk of long-term complications, like CVD, and hypoglycemia. The foods and beverages highlighted in posts, as a whole, did not follow the current nutrition recommendations for treatment of diabetes outside of the promotion of fruits and vegetables, including non-starchy vegetables.50 The sharing of foods that did not align with recommendations could potentially lead to increased consumption among young adults who are part of these detrimental social media feeds.107 Long-term complications of diabetes such as cardiovascular disease, neuropathy and retinopathy could be accelerated with foods higher in saturated fat and sodium and more than half of the posts promoted these types of foods.68 The foods higher in saturated fat aligned with the promotion of ketogenic and low carbohydrate diets. Those with personal businesses did not promote foods meeting the standards which draws concerns about influence of social media on food choices and how this could be swayed by users.157,204 On the other hand, health information providers, such as registered dietitian nutritionists, tended to promote foods that met standards especially vegetarian, high soluble fiber and non-processed foods which align to foods known to promote long-term health.50 51 Disordered eating behaviors in T1D have been shown to increase A1C levels placing these individuals at higher risk of long term complications.82 The use of social media had the additive effect by increasing disordered eating behaviors which makes it concerning that these themes exist within the T1D nutrition social media conversations with over 10% of study related posts.109 The study sample included only two hashtags so this may not even uncover the full breadth of diet culture. While the engagement of the posts did not reach statistical significance these posts tended to have higher amounts of likes and comments. Body image concerns have been a mediator of disordered eating behavior in persons living with T1D and these were the individuals that are largely posting on these topics.221 Low carbohydrate and ketogenic diets have been linked to weight loss and touted on social media so the promotion of these diets could be linked to potential disordered eating as these were the diets most often promoted in our sample. Limitations This study was completed on only two popular diabetes related hashtags popular with young adults. Other hashtags could include a different picture of nutrition information shared with others with different diets promoted. The sampling of the potential hashtags was completed in January which could have led to a seasonal emphasis on nutrition in posts and therefore if sampled throughout the year different hashtags may have emerged as those to study. The full study was completed during the beginning of the COVID lockdown and social media was an avenue to connect with others in a personal manner. This could have skewed the data to more information sharing as a way to compensate for not being able to interact with others in person. Additionally, this time period was during the spring/summer months and did not capture other seasonal food habits. Not all foods in posts could be evaluated for meeting the standards due to 52 limited information in the photos and captions and therefore not all could be included within the nutrition standards discussion. While the hashtags selected appear to be those with highest use of young adults, these are not exclusive to young adults and thus the posts are from those with a wide age range. Implications for Practice Social media is now considered as a source of health information and diabetes care providers should be aware of the breadth of information shared on these platforms. Since individuals tend to follow the advice shared on social media platforms it’s use needs to be considered within overall health histories.149 Diabetes self-management education and support should include discussion of social media use, discern misinformation shared on these platforms, and provide clear information to patients on how non-evidenced suggestions could contribute to diabetes-related complications. Diekman et al (2023) provide a recommended framework for addressing social media misinformation with patients to gain their trust while addressing evidence.222 This is crucial as persons with diabetes are part of sharing of diets such as ketogenic and intermittent fasting which some research has demonstrated having adverse impacts with hypoglycemia and dyslipidemia.74 Current literature does not support the use of intermittent fasting as a sustainable long-term weight loss strategy. Initiating the conversation with patients to inquire about social media platforms used and information gleaned from these sites is a critical component of meeting the patient where they are at and providing evidence-based education and support. This open conversation can lead to further education regarding adverse impacts, especially with some of these practices being promoted by persons with diabetes. Two aspects of particular concern that need to be further investigated are whether and how information shared is associated with profit versus appropriate patient care as well as the type of non-evidence-based 53 nutrition practices shared by patients with T1D that might be harmful. Policies to protect the public from misinformation, especially adolescents and young adults with diabetes but this age group overall, could act as a safeguard from marketing and promoting foods that contribute negatively to health. Conclusion This study was the first to describe the nutrition information being shared on publicly available Instagram hashtags related to type 1 diabetes. Those sharing information on two diabetes specific hashtags, #type1diabetes and #diabadass, were predominately females that have diabetes. While the nutrition information shared helps promote community within the group, not all those sharing this information had diabetes and some were primarily using this platform to promote their own businesses with those following the studied hashtags. The majority of foods that were being highlighted were not in alignment with most of the 2019 American Diabetes Association nutrition recommendations outside of fruits and non-starchy vegetables. The diets of concern that were being promoted within posts were those with non-evidence-based recommendations that may negatively impact glycemic and/or lipid parameters and hence be associated with diabetes related complications. In our current environment where social media is integral to our daily lives in one way or another, it is imperative that diabetes practitioners incorporate a conversation about the use of social media within diabetes-self management education as a tool to better assess and assist patients with achieving health goals. 54 CHAPTER 4: NUTRITION-RELATED TYPE 1 DIABETES ONLINE INSTAGRAM COMMUNITY SOCIAL NETWORK ANALYSIS OF ENGAGEMENT: A MIXED METHODS APPROACH Introduction With nearly 5 billion users worldwide, social media platforms, such as Facebook, YouTube, and Instagram, are part of everyday life.224 The use of these social media platforms intersects all generations; however, young adults (ages 18-29 years) have the highest percentage of use with 84% scrolling through these platforms multiple times a day.225 Younge adults gravitate towards social media platforms that are focused on photos and videos including Instagram and TikTok.225 Unfortunately, photo-based platforms, such as Instagram, have been associated with poor body image and body dissatisfaction, especially in teens and young adults with recent research recommending limited use of Instagram in those with pre-existing disordered eating behaviors.111,161,169,172 Disordered eating and poor body image are alarming in general, but can have far reaching impact on the lives of those living with type 1 diabetes, who have a higher prevalence of disordered eating than the general population.82,86,88 Type 1 diabetes (T1D) is an autoimmune chronic disease in which the beta cells of the pancreas are destroyed and stop the production of the hormone insulin thus requiring the administration of exogenous insulin as therapy7 The peak onset of diagnosis of T1D is 10-14 years of age with dietary intake considered to be a critical component of treatment.23 The daily self-management of T1D requires activities such as balancing insulin, monitoring dietary intake, maintaining blood glucose levels, and engaging in physical activity. This balance is to achieve optimal glycemic and blood lipid management in order to minimize long term complications such as nephropathy, neuropathy, retinopathy, and cardiovascular disease.29 The young adult period requires independence of medical care along with competing priorities that go along with 55 this stage of the life cycle and thus can also be a time of deteriorating glycemic and blood lipid control.226 The options for diabetes self-management engagement are complex with health- related information coming from a variety of sources including social media. Social media offers a network for those living with T1D to connect with others to gain support.227 Little is known about how engagement with social media posts influences nutrition, and potentially glycemic and lipid control and could have on long-term complications in the T1D community. Current research regarding social media’s influence on dietary behaviors is mixed with reported benefits related to sharing of food ideas, increasing variety and supporting more nutrient rich food and beverage options.107,158,228 However, conflicting studies have found that social media use among adolescents and young adults may lead to preferences for energy dense foods and preoccupation with healthy eating to the point of disordered eating behaviors.109,157,185,229 Studies have found that the nutrition-related information available on social media does not align with current nutrition recommendations, conflicts with advice from healthcare, and lacks reputability.230–234 To date, no studies have assessed whether and how engagement with social media impacts dietary behaviors of those with T1D. Little is known about whether engagement, liking and/or commenting, with social media posts influence eating choices in the general population let alone those with chronic health conditions like type 1 diabetes. Kilb et al (2023) found that creating social media healthy eating posts can have positive impact on the fruit and vegetable intake on not only the content creators but also the users in their social media network. However, the magnitude of these impacts is influenced by the level of engagement with higher engagement having greater impact.235 Research related to the influence of social media engagement on dietary behaviors of persons with diabetes is limited, especially with regards to posts contrary to current healthcare provider 56 recommendations. Social media users can scroll through their feed as passive consumers of information. Conversely, they can choose to engage with a post by liking it, commenting on it, or sharing it with others. This type of social media engagement takes effort from the social media consumer and deemed by researchers to be considered a form of active engagement.210,236 The majority of social media engagement research is with the marketing of products and services and not specifically about health behaviors.237 Cvijikj and Michahelles (2013) found that videos led to higher engagement than photos alone like on Instagram.210 Social media users on Instagram are more likely to like a post as it is a simple double click of the photo, while commenting takes more effort.238 The number of likes and comments of a post are all relative to the total number of Instagram followers for a specific account, and allows measurement of actual engagement of the post.239 Within the context of T1D Instagram content creators, Holtz and Kanthawala (2020) found that likes of a post indicated social support.240 This aligns with the online diabetes community showing support and sharing disease-related information and tips to others within the community.117,241 Persons with diabetes, in creating this community, act as patient influencers within Instagram as they are sharing their first-hand experiences and daily self-management.242 It is unclear whether more active forms of engagement, like commenting, with social media leads to higher dissemination of information and incorporation into one’s lifestyle. Because information shared on social media does not follow regulations for sharing only evidence-based material and is primarily created by non-credentialed individuals, there could be negative implications.243 A more expansive way to explore interactions on social media of a community is through social network analysis (SNA). SNA allows the identification of smaller communities within the larger network to examine the flow of information. The analysis of this type of network within 57 nutrition social media is crucial for practitioners as it can highlight areas of influence into daily self-management decisions that can alter long-term health and hence inform counseling efforts and strategies. The technique has been used within other non-health disciplines including sociology, education, and politics.177,179 There are a variety of approaches to SNA including socio-centric designs of random groups or egocentric designs following the flow of information from one individual throughout the network. 244 The use of SNA within social media allows a better understanding of individuals who are connected to others with the transmission of information. The decisional balance to change behavior based on ideas learned within the social network, as described by Ajzen in the Theory of Planned Behavior, is based on intention which is influenced by attitude, social norms and self-efficacy.184 The social network is integral in this behavior change process by creating the social norms for specific health practices that lead to intention to change. Since social media is created by anyone who is developing posts, the influential gatekeepers are key to being able to fully grasp the reach of advice, evidence-based or not, that is disseminated within the larger community. Beyond understanding the metrics and visualization of the social network created by those actively engaging with nutrition-related Instagram posts, is the content of the comments and what they reveal about the network. Tenderich et al (2019) completed a qualitative analysis of diabetes conversations in a variety of social media platforms and found that this medium filled a void of connection with others to share accomplishments as well as frustrations of daily management of diabetes. These researchers identified six major themes including: humor, pride in living with diabetes, diabetes technology, sharing of tips and pearls of wisdom for daily self- management, creation of a community, and venting.117 Similarly Chalmers et al (2020) found that adolescents with T1D used social media to share their stories about living with diabetes, 58 sought advice from others, and shared disease-related humor.245 Research based on X, formerly known as Twitter, analyzed main topics regarding T1D and found similar themes of daily self- management and diabetes technology, but also discussion of research and insulin prices.246 They demonstrated overlap within the social network of topics without main information gatekeepers identified.246 These main themes qualitatively identified within T1D-related social media are similar to other research highlighting social media as a form of support.246 This study aims to examine the engagement created within the Instagram T1D community specifically with regard to the sharing of nutrition related information using the hashtags of #type1diabetes and #diabadass, which have been shown to be specifically associated with T1D posts. Engagement demonstrates a stronger interest in the topic which potentially can be shared with others or incorporated into daily health practices. The study includes three main research questions: RQ#1: What are the patterns of engagement (likes/comments) and differences in post engagement for Instagram content creator types, main nutrition theme, diet promotion, glycemic information shared, and discussion of diet culture (body image, weight, and/or disordered eating)? RQ#2: Is there an identified network among those actively engaging in nutrition-related T1D Instagram use via comments noted on a post using hashtags #type1diabetes and #diabadass? RQ#3: What are the major themes from the comments within the social network for nutrition-related T1D Instagram posts when #type1diabetes and #diabadass are used? 59 Methods Engagement This secondary analysis on engagement was obtained from a subset of 296 unique Instagram posts with the hashtags #type1diabetes and #diabadass posted between May 2020 and July 2020. The study hashtags were determined by a purposive sampling of popular T1D hashtags (>20,000 posts per hashtag) on Instagram in January 2020. A total of twenty hashtags met this criterion. The purposive sampling posts were obtained by two undergraduate research assistants signing into the web version of Instagram and using the search function. Using a systematic approach every fifth public, English language post for up to twenty total posts on four different dates (January 4, 6, 8, and 9, 2020) (n=1127) were analyzed for the following: main post topic (nutrition, daily diabetes self-management, physical activity, meme, support, other), Instagram content creator type (person with diabetes, health care provider, organization, other), estimated age of Instagram content creator (parent of child/adolescent, adolescent, young adult (18-30 years), adult), type 1 or type 2 diabetes focus.207 The Michigan State University Institutional Review Board reviewed the content analysis protocol and deemed it exempt for human subjects research. Two trained undergraduate research assistants (RG and AD) completed reliability testing on a random sample of 20 nutrition-related diabetes Instagram posts until a-coder reliability for nominal variables of Cohen’s Kappa with a cut-off point of κ >0.85 (n=60). This procedure followed that of previous content analyses.214 Coding discrepancies were discussed after each round of testing between the research assistants and the principal investigator. The study sample of 150 Instagram posts from each study hashtag was determined based on previous research and necessitation to hand code the posts.213–215 Each research assistant coded 150 Instagram 60 publicly available nutrition related posts in chronological order between May-July 2020 per assigned hashtag by logging into the web version of Instagram. The coders did not engage with any of the posts and only collected the data from the code book. Weekly meetings were held with the principal investigator and research assistants to resolve issues with data coding. Measures of engagement gathered included number of likes and comments in addition to Instagram content creator information (type of content creator, gender, number of followers, number of following), diet promotion and discussion of diet culture. Social network engagement analytic metrics were used to measure the engagement of each individual post for how those viewing the post engaged with it by liking it or commenting on it. Engagement factor scores were calculated by adding the number of likes and comments the post received and dividing that by the Instagram content creators’ total number of followers at the time of the post. The engagement factor considered the varying numbers of followers that would see the post. Data was analyzed descriptively using Statistical Package for Social Sciences version 27 (SPSS). Associations for engagement were measured using the generalized estimating equations model with engagement factor as the dependent variable, Instagram content creator as the subject variable, and content creator gender, content creator type, main post theme, diet promoted, diet culture discussion and glycemic information shared as independent variables. A p value of <0.05 was the level of significance. Social Network Analysis A socio-centric social network analysis (SNA) was undertaken utilizing the data set described above to extract the Instagram content creators and those commenting on each post. (n=167)16 The purpose of the SNA was to examine how random Instagram content creators (1) associated with the type 1 diabetes (T1D) community; (2) communicated with each other; and 61 (3) identified patterns and subgroups of this social network. The network structure created by Instagram content creators, nodes, and those linked to the Instagram content creator by one way and/or two-way conversations in the comments, edges. The network was analyzed through network visualization and network metrics with Gephi, an open source social network analysis software.247 The visualization was performed after uploading the data using the Fruchterman- Reingold layout and ForceAtlas 2 which is known to work well with large networks such as this data set.248 After review of the initial SNA visualization, an additional analysis was completed using the same procedures on the identified network. The network metrics provided a quantification of the interactions in the network, which included centrality measurements (betweenness, closeness, and eigenvector), and modularity. Betweenness centrality measured how often a node serves as a bridge to another network with higher scores that demonstrated that the individual was a gatekeeper of information249 Closeness centrality is the measurement of how close a node is to others within the entire network with lower scores indicating closer to center of network 249 Eigenvector centrality measures the extent that the node is connected to other influential nodes.249 Modularity is a measurement of conversations that are not as highly connected.178 Qualitative Analysis The posts that had comments, demonstrating a higher level of engagement compared to likes, were further analyzed qualitatively. The inclusion criteria for qualitative Instagram post comment data collection were nutrition-related images and/or captions discussing nutrition. The exclusion criteria were as follows: comments were non-nutrition related, comments only included hashtags from the original Instagram content creator, a change in original privacy 62 settings making the post no longer publicly available, and comments deleted from the original post. Figure 4.1 illustrates the process used to determine the posts to qualitatively analyze. Figure 4.1 - Flow Diagram for Qualitative Analysis of #type1diabetes and #diabadass At least one nutrition related comment (n=230) Duplicate posts (n=2) Coded Instagram Posts (n=300) Comment was from post creator (n=4) Posts with commenting data to analyze (n=167) Post made private at time of data extraction (n=59) The comment data (name and comments) from the identified Instagram posts were manually extracted from the web version of Instagram between February 2022 – August 2022 by a trained undergraduate research assistant (AH) and the principal investigator (DK) of publicly available posts. The data collectors did not interact with the posts. Comments used in this manuscript did not include the commenters’ names in order to maintain their privacy in this publicly available data. The comment data was qualitatively analyzed by a separate trained research assistant (AD) and the principal investigator (DK), both Registered Dietitians Nutritionists, utilizing inductive qualitative content analysis as described by Elo and Kynagas.250 Each researcher reviewed the data to identify the main codes which were grouped, categorized and main themes were determined. The two researchers came together to review the identified themes and create agreement. Trustworthiness was achieved by including an audit trail, triangulation of coders as well as reflexivity statements to identify preconceptions, beliefs, values, and assumptions brought into the coding process.251 63 Results Engagement The 300 posts were coded manually by two trained undergraduate research assistants with two duplicates and two posts that did not include full data for a total of 296 posts. Based on the first research question, the overall engagement factor of all posts (n=296) was 11.1% which examined the patterns of engagement and identified differences between user and posts characteristics. Instagram content creators with a low number of followers (n<100) were omitted as this could skew the engagement factor scores as these accounts were outliers in the data. The adjusted overall engagement factor of posts from Instagram content creators with more than 100 followers (n=272) was 6.5%. The engagement factor of different Instagram content creators was significantly different with persons with diabetes having the highest factor of post engagement at 7.4% while those with personal businesses had the lowest at 3.0% (see Table 4.1). There were no differences in mean engagement factors for gender, main nutrition theme, diet promotion or discussion of diet culture. Posts that included images or captions regarding glycemic control had significantly higher engagement scores at 7.6%. The top ten posts all came from persons with diabetes with half of the posts sharing glycemic information and half promoting a diet (see Table 4.2). The posts almost exclusively shared images of food for followers to engage with while one post included a before and after selfie of the Instagram content creator. 64 Table 4.1 - Instagram Post Engagement Characteristics Category Mean Engagement Factor (likes+comments)/number of followers)*100 Content Creator with greater than 100 followers n=272 Instagram Content Creator Type Person with Diabetes Person with Diabetes and Personal Business Personal Business Health Information Provider Instagram Content Creator Gender Female Male Multiple Users Unknown Main Nutrition Theme Food/Beverage Food with Physical Activity Body Image/Emotion Diabetes Management Nutrition Education/Substitution Promotion Diet Promoted Yes No Specific Diet Promoted Ketogenic Diet Low Carbohydrate Vegetarian/Plant Based Intermittent Fasting Gluten Free Low Sugar/Sugar Free Glycemic Control Information Yes No Discussion of Diet Culture Yes No General Linear Model – Generalized Estimating Equations, *p<0.05 7.4%* 4.7% 3.0% 3.8% 6.6% 7.4% 5.2% 5.5% 6.6%** 4.9% 5.4%** 6.3%** 8.9%** 2.5% 6.0% 7.0% 5.7% 5.5% 7.7% 5.9% 3.3% 3.5% 7.6% 6.0% 5.7% 6.6% 65 Table 4.2 - Top Ten Posts with Highest Engagement (more than 100 Instagram followers) Engagement Factor 1 32.7% Instagram content creator Number of Followers 101 2 28.8% 497 3 28.6% 569 4 27.4% 412 5 23.4% 273 6 20.9% 172 6 20.9% 110 Glycemic Information Shared Diet Promoted Yes No No No Yes No Yes No No No No Yes Yes Yes Instagram content creator Type Person with diabetes Person with diabetes Person with diabetes Person with diabetes Person with diabetes Person with diabetes Person with diabetes Post Topic Image of pasta and bread with discussion of blood glucose control Image of waffles with discussion on how can eat breakfast out and ask for sugar free syrup Image of child eating birthday cake with discussion of insulin dosing Image of milk shake with discussion of blood glucose Image of cheesecake Selfie of body in mirror with discussion of reaching body image goals Image of pasta and shrimp with discussion of having day off from low carbohydrate meals 66 Table 4.2 (cont’d) 19.8% 8 116 9 19.6% 433 10 19.5% 123 Social Network Analysis Yes Yes Person with diabetes No No Person with diabetes Person with diabetes Yes Yes Ingredients of low carbohydrate chili with discussion of total carbohydrates Image of vegan sausage tacos Image of sandwiches and macronutrient breakdown The second research question inquired about the existence of an identified network among content creators and those commenting on their posts. The first visual analysis performed examined the entire network of the posts that included at least one nutrition-related comment apart from the Instagram content creator and remained public to obtain name of commenters (n=167). The entire network visualization was presented in Figure 4.2 with the color of nodes corresponding to the Instagram content creator type. The size of the node indicated how many edges are associated with that node with the larger having more edges. The overall network had one main sub-network with isolated small nodes on the outside that did not connect to others within the identified main sub-network. The main sub-network had a few communities identified within but did not all communicate with each other. The resulting network included a total of 941 nodes (represented as dots) and 888 edges (represented as the lines). We performed an analysis focused on the main sub-network identified from the first visualization analysis (see Figure 4.3). This analysis included a total of 110 nodes and 122 edges 67 with only the nodes that were connected within the main sub-network. The visualization of this sub-network was presented in Figures 4.3 and 4.4 with the color of the nodes corresponded to the Instagram content creator type in Figure 4.3 and the color of the nodes corresponded to the identified communities in Figure 4.4. There were four main communities identified that communicate amongst each other. 68 Figure 4.2 - Instagram Communication of Entire Network for #type1diabetes and #diabadass (n=941 nodes) 69 Figure 4.3 - Instagram Communication in Main Sub-network for #type1diabetes and #diabadass by Content Creator type (n=110 nodes) 70 Figure 4.4 - Instagram communication main sub-network for #type1diabetes and #diabadass by identified community with colors corresponding to community 71 Social network analysis metrics were performed to determine the most influential members of the community for both the entire community and the identified main sub-network. The modularity of the network was 0.93 and the main sub-network score was 0.72 which indicated a well-defined community within each since they were both positive and nearing one.178 Tables 4.3 and 4.4 show the degree, betweenness centrality, closeness centrality and eigenvector centrality scores for the top ten users with user type of each. User #86 in the community metrics had the highest degree and eigenvector centrality and second in-betweenness but with metric analysis of the sub-network their influence did not hold. User #10 within the sub-network was highest for betweenness, degree and eigenvector which demonstrated that this individual was a main gatekeeper with high influence within the community. Each of the main influencers from each analysis were persons with diabetes and a personal business. Persons with diabetes and personal businesses accounted for a higher percentage of each network metric than the overall community, which indicates they were more influential within the networks. Closeness Centrality Eigenvector Table 4.3 - Top Ten Social Network Analysis Centrality Measures of Entire Community Degree #86 (PWD.PB) #37 (HIP) #64 (HIP) #120 (PWD.PB) #82 (HIP) #40 (PWD) #10 (PWD.PB) #128 (PWD) #90 (PWD) #132 (PWD) # is the node within the network; HIP=health information provider, PB=personal business, PWD=person with diabetes; PWD.PB=person with diabetes and personal business. #90 (PWD) #68 (PWD) #38 (PB) #65 (PWD) #41 (PWD) #130 (PWD) #119 (PWD) #85 (PWD) #78 (PWD.PB) #57 (PWD) Centrality #86 (PWD.PB) #37 (HIP) #63 (HIP) #10 (PWD) #120 (PWD.PB) #557 (unknown) #411(unknown) #40 (PWD) #128 (PWD) #143 (PWD) Betweenness Centrality #10 (PWD) #86 (PWD.PB) #49 (PB) #411 (unknown) #37 (HIP) #143 (PWD) #120 (PWD.PB) #103 (PWD) #40 (PWD) #138 (PWD) 72 Closeness Centrality Eigenvector Table 4.4 - Top Ten Social Network Analysis Centrality Measures of Main Sub-Network Degree #10 (PWD) #143 (PWD) #40 (PWD) #93 (PB) #91 (PWD) #79 (HIP) #69 (PWD) #138 (PWD) #104 (PWD.PB) #86 (PWD) # is the node within the network; HIP=health information provider, PB=personal business, PWD=person with diabetes; PWD.PB=person with diabetes and personal business. #97 (HIP) #107 #77 #52 (PWD) #7 (HIP) #6 (PWD) #76 (PWD) #59 (PWD) #27 (PWD) #78 (HIP) Centrality #10 (PWD) #143 (PWD) #55 (PWD.PB) #62 (PWD) #128 (PWD) #93 (PB) #54 (PWD) #24 (PWD) #104 (PWD) #19 (PWD) Betweenness Centrality #10(PWD) #49 (PWD) #411 (PB) #86 (PWD.PB) #143 (PWD) #104 (PWD) #40 (PWD) #37 (HIP) #55 (PWD.PB) #91 (PWD) Qualitative Analysis Table 4.5 displayed the characteristics of the Instagram content creators and specific posts for the sample that were analyzed qualitatively. The commenters were mostly female (80.2%), persons with diabetes (72.1%) with similar characteristics as the full sample from the content analysis (females 78% and persons with diabetes 76%). The commenting data analyzed included a higher proportion of posts promoting diets as compared to the full sample (53.9% vs 49%). % (n) 73.1% (122) 5.9% (10) 7.8% (13) 13.2% (22) Table 4.5 - Characteristics of Instagram Content Creators with Comments for Qualitative Analysis (n=167) Characteristic Instagram content creator Person with diabetes Person with diabetes and business Personal business Health information provider Instagram content creator gender Female Male Multiple user Unknown Main nutrition theme Food and beverage Food and beverage with physical activity Body Image/Emotion Diabetes Management 80.2% (134) 5.4% (9) 3.6% (6) 10.8% (18) 69.5% (116) 3.0% (5) 1.8% (3) 7.2% (12) 73 Table 4.5 (cont’d) Nutrition Education/Substitution Promotion Diet promoted Yes No Ketogenic/Low Carb Glycemic information shared Yes No Diet culture discussion Yes No 10.8% (18) 7.7% (13) 53.9% (90) 46.1% (77) 61.1% (55/90) 33.5% (56) 66.5% (111) 9.6% (16) 90.4% (151) Six main themes emerged from the thematic analysis of engagement with posts through comments which varied by the primary theme of the post. The major themes were: meal experience, support, diabetes self-management, food relationships, business, and displeasure with healthcare provider advice. Thematic discussion with subthemes and sample comments are provided below. Meal Experience Meal experience encompassed both taste expectations and visual appeal of the post image. Commenters used descriptive words to identify with the photo either through taste or how the food looked. Emojis helped illustrate the commenters’ appreciation for the food with the yummy face, heart eye face, surprised face, thumbs up, clapping hands, and red hearts all used frequently. “"Yummmmm ok I may need that for dinner cuz I’m starving haha" “All of that looks spectacular” “IM DROOOOLING BIG TIME” “The classic breakfast combo! Looks delicious! 😀” 74 Support Support was illustrated when it provided encouragement to others, praise for their choices and decisions; and camaraderie to the Instagram post creator. Blue heart emojis and the flexed arm muscle emojis, along with comments, appeared to signify overall support and strength. These emojis were used together by many of the commenters. The other emojis that indicated support were the hugging face and the hands raised. “Whoop you go 💙🙌” “You're doing a fab job 👍😊😊💙💙” “Love the pottery and plating. It's so fun to discover folks through hashtags in different parts of the world cooking similar things 😄” Diabetes self-management The captions associated with the Instagram images would often ask opinions of their followers on how to manage diabetes self-care such as carbohydrate counting or appropriate insulin adjustments for food and exercise. This was also shown in discussions about managing low blood glucose. The commenters readily provided suggestions and shared similar stories and similarly asked for advice from the Instagram content creator. “Pasta and pizza are tricky little suckers! I don't prebolus for them, take 1/2 right when I sit down to eat and the other 1/2 about 2 hours later. My pump has the extended bolus option so it makes a little easier” “Alcohol always makes me spike then completely plummet. So, I decide to just go high (unless it's extra sugary, then I bolus a bit) then I watch it closely to make sure I don't go too low. It's so weird that it does this. My graph looks similar to yours lol” 75 “I have to really check this out and get one! Thanks so much ! A different question for you I know you inject in your arm does that work fast and good for using before meal insulin bolus? I didn’t know if it works as fast as stomach?” “I am type 1 and can relate to fearing exercise because of fear of hypos. Before I used the omnipod I would avoid exercise, or eat about three 30g chocolate bars before doing a 90 mins intensive work out. Now I do light exercise after meals (otherwise I spike, even with 20g carbs) and ensure no insulin's in the system when exercising. My relationship with exercise is so much better - but I don't think I could have got there without the omnipod and the freestyle libre.” Relationship with Food Relationship with food might have been positive such as normalizing all foods being allowed or negative such as shaming of the content creator by the commenter. The negative food relationship also included discussion of weight, body image, avoiding foods, and labeling of foods as good and bad. The most common emoji used in these posts was a butterfly known to symbolize eating disorders and eating disorder recovery. Those positive comments normalized a variety of foods not to be off limits for those living with diabetes, supported views on incorporating carbohydrates into the diet, and connected food with culture and positive emotions. A negative food relationship theme of shaming emerged where the commenters disagreed with the content creator or provided comments that on face value were shaming about food choices and/or diabetes self-management. When these comments appeared the majority of fellow commenters provided positive support in a way to protect the content creator and validate their post. 76 “so many foods I love but don’t bother eating anymore because it’s not worth the blood sugar drama 😆” “Can relate so much with this feeling of being ashamed. At work a new colleague even said. „You are always eating“ and I was immediately ashamed.” “Oh so lucky unless I’m low I can only [have] a maki roll or sashimi” “Wow 19 units for McDonalds? I was going to ask what you had but saw it in the comments! It’s great reminder to anyone though that us diabetics can eat anything as long as we dose for it correctly 🔥” “Oh my goodness. This has been a family tradition for centuries for me!!! I want to make vegetarian ones with you though..” “I couldn’t agree with you more! Over the weekend I had pizza and Hershey’s, no shame 😋” “🙌 can you imagine a world without carbs? No thank you 😁” “That was my philosophy until I started getting diabetic complications ... sugar in what ever form is harmful to a non diabetic never mind a diabetic” “Isn't corn bad?” “You have enough there to feed an army❤” (original image is a small bowl of cereal) Business Businesses interacted with some Instagram content creators to give feedback on their account, request to collaborate with them, asked to follow their company back, and expressed appreciation for promoting their products. These interactions could be asserted to gaining new followers and promoted a variety of products. 77 “You are always classy! We would love to collab with you Jessica! DM us {company tagged in comment]” “😍🙌 ! P.S. We love your account, and think you would love ours! We are a start up, working to 'Better' 🥰 the future of nutrition (starting with a Bagel with no added sugar, and the carb content of two grapes!!) Check us out! 💕✨” (this same comment was on multiple posts) “You might like our drink Get Me High! too. we are launching in UK soon😍🧡” (in response to a post of a jar of items to treat low blood glucose) Displeasure with healthcare provider advice The last identified theme related to frustration and displeasure with advice and comments received from healthcare providers over time. These comments related to either an emotion sparked by an image or a story shared within the caption from the Instagram post creator. “I once had an Endo that said to me “an eating disorder is something teenage kids get”...... by that point it had taken me 15 years of living with diabetes to brave up and share...... it sent me spiraling backwards.” “It’s amazing how doctors are supposed to help us be well but many times they push us into very unhealthy places just by their verbalization and lack of compassion and understanding. Thank you for being so open.” “Yet another misconception around diabetes! You can eat lots of strawberries now to make up for all the ones you missed out on 😍” (response to caption about advice given as child at diagnosis to restrict strawberries) 78 Discussion and Implications for Practice Diabetes care practitioners have utilized private online diabetes communities, primarily on closed Facebook groups for peer support as a medium for education and sharing. The current study examines public accounts on a popular social media platform that includes those who may be linked to the diabetes community through a person with diabetes within their network or queried hashtags.115,119,131,252–254 This study provided the first social network analysis on Instagram nutrition-related social media associated with T1D identifying a global network centered around persons with diabetes as the content creators sharing nutrition related information (evidence based and non-evidence based), diabetes related self-management, and overall support within the community. The global nature of this network offers diabetes care practitioners new avenues to account for international trends in diabetes management during diabetes self-management education and support (DSMES). These closed communities included diabetes care providers and were found to improve glycemic control with young adults.255 Our work illustrated the dissemination of unproven diets used in T1D, such as the very low carbohydrate/ketogenic diet, within these international communities with half of the posts with active engagement promoting ketogenic diets. The long-term safety and efficacy of the very low carbohydrate/ketogenic diet in T1D had not been proven.74,75 The social network analysis of the main network and sub-network had high modularity demonstrating connectiveness. The sub-network included 52% of the nodes from the larger network that contained five clear communities. The interconnectedness interestingly crossed country borders creating a more robust group to learn from and support each other. The gatekeeper within the main network had many edges though no link to the sub-network and thus their information did not infiltrate the dynamic community. The sub-network communities (see 79 Figure 4.3) were comprised primarily of persons with diabetes but were infiltrated by personal businesses and at a lesser degree health information providers. The Instagram content creator with the highest degree of connection was a person with diabetes. While this content creator appeared influential within the larger network being directly connected to four of the five established communities, over 90% of the comments were about meal experience and not offering of support, advice, or discussion of diabetes self-management. This calls into question the true influence this user had within the sub-network outside of superficial comments and not meaningful discussion. This data set only covered two months of time and their engagement could have changed as it is unknown how long they were a member of the network. The social network studied included only those with active engagement through commenting. The engagement within the community was consistent across user characteristics with the majority of nodes and edges being persons with diabetes. These findings were similar to other research where Instagram was determined to be a location to support for people with diabetes.203 For posts sharing glycemic information, higher engagement comprised 50% of this analysis whereas only 33% of all posts in the dataset included this information. These findings were congruent with previous findings that suggest diabetes online communities are considered at safe place to share frustrations and seek advice.119,131,254 Engagement was an important finding of this social analysis network main contributors as previous evidence of improved glycemic control existed from active participation in these networks.119 We found that 70% of posts with engagement were images of foods and beverages which included captions that asked for commenters for advice or to share their stories. The sub- network included 54% of posts promoting diets with 61% of those diets were the non-evidence based very low carbohydrate approach as discussed in Chapter Three of this dissertation. These 80 findings are alarming, as previously discussed, because the long-term safety of this diet approach had not been established in T1D especially with post images that featured high saturated fat foods which could contribute to cardiovascular disease.24 Given the support and incorporation of other diabetes self-care habits from these social networks it is plausible that dietary behaviors and advice could be incorporated especially if it was the community social norm to adopt types of diet advocated. We were able to not only identify the engagement in the community, but also evaluated the content in a manner not previously accomplished by incorporating with SNA on Instagram. The major themes of support and diabetes management were consistent with other researchers findings of the diabetes online communities.119 The emerging themes of food relationship were both welcoming with the reduction of the stigma of specific foods and normalizing a balanced diet but this is in contrast to negative relationships. The negative food comments and body image relationship on Instagram aligned with the work on #fitspiration having a deleterious consequence on body image.161,168 Fitspiration was a social media trend that included exercise and diet within a post.161 Persons with diabetes, particularly adolescents and young adults, have a higher prevalence of disordered eating than the general population.82 This finding indicates an urgent need to screen for social media use as a routine component of assessment with the standard of care for screening for disordered eating as outlined in the 2023 American Diabetes Association Standards of Diabetes Care.24 The last qualitative theme of displeasure with healthcare provider advice had not been documented in other research known to date. The 2023 Standards of Care stressed the personalization of DSMES emphasizing open communication with the patient.24 81 The study had limitations as it was exploratory in nature to examine the network and not a study to determine if social media had a direct impact on dietary behavior changes in young adults with T1D. Data were collected at the height of the pandemic quarantine when individuals were craving connection with others. During this time period, more people were cooking at home as eating out was not an option so there may have been a higher percentage of food photos because of this. The full commenting conversation was not obtained when the original data was obtained, so some of the posts were made private or removed in the interim. This could have led to important conversations not being evaluated. The use of only one social media platform and two hashtags could limit the full conversation occurring on diabetes social media. Tik Tok and Instagram Reels, short video based social media, popularity started at the time of data collection and use had only grown in the young adult population. The type of information and personal touch could change engagement and warrant further study. This novel study utilized a mixed methods approach by examining engagement with quantitative analysis and social network analysis, and qualitative content analysis of the active engagement. These findings supported the recommendation by the American Association of Diabetes Educators of participation in social networks as a component of DSMES to provide community support though with a caveat of caution for those with identified disordered eating tendencies and need for continuous screening of disordered eating. Diabetes practitioners should incorporate social media use and frequency as part of regular assessment given the potential of negative outcomes. This education could extend to open conversations to incorporate navigating nutrition-related social media and meeting the patient where they were at to dissect the non- evidence-based diets and messages being shared especially with short term advantages being touted by the users. This type of non-judgmental education and assessment is especially critical 82 for adolescents and young adults where social media use for most is daily and potentially the go- to source of health information. 83 CHAPTER 5: COMPARISON OF TYPE 1 AND TYPE 2 DIABETES INSTAGRAM NUTRITION-RELATED CONVERSATIONS Introduction Diabetes is a chronic disease affecting 537 million individuals worldwide with an expected 46% increase in incidence over the next twenty-two years.256 Type 2 diabetes (T2D) accounts for over 90% of those living with diabetes, type 1 diabetes (T1D) accounts for 5.10% and the remaining from mixed type.1 Although the disease pathophysiology for T1D and T2D are different, both are related to alterations in insulin production and/or utilization. In T1D, the body has little to no insulin produced from destruction of the pancreatic beta cells, whereas in T2D the body cells become resistant to insulin and have a subsequent decline in pancreatic production of insulin.257 The long-term metabolic complications impacting macro and microvasculature remain the same for both types of the disease being poorly controlled/managed. Diabetes complications include cardiovascular disease, chronic kidney disease, retinopathy, and neuropathy.29 Cardiovascular disease (CVD) is the leading cause of morbidity and mortality among those living with diabetes, with risk two to three times greater in T2D and ten times greater in T1D.8–10,258 Chronic kidney disease (CKD) occurs in more than 25% of those living with both types of diabetes.259 The initial stages of CKD may be present at T2D diagnosis and are known to develop within ten years of diagnosis of T1D.45 There is an increased risk of CVD among those diagnosed with CKD which leads to increased health care expenditures as well as alterations in quality of life.260–262 The development of these long-term metabolic complications associated with diabetes makes glycemic control imperative.12 One of the hallmarks of diabetes self-management for T1D and T2D patients is dietary intervention. Proper diet is integral to meeting glycemic targets to minimize complications. The nutrition goals for adults, as outlined by the American Diabetes Association are to “improve A1C 84 levels, cholesterol and blood pressure; achieve and maintain body weight goals, and to prevent or delay complications of diabetes.”50 Registered dietitians nutritionists (RDN) and certified diabetes care and education specialists (CDCES) work with patients to personalize their eating patterns maintaining cultural preferences and satisfaction while working with current evidence- based guidelines.50 Dietary approaches to manage overweight or obesity, which are often associated with T2D, are varied but focus on balancing energy intake and expenditure. The 2019 American Diabetes Association Consensus Report outlined the evidence regarding medical nutrition therapy which includes monitoring overall carbohydrate (CHO) intake to maintain glycemic control (A1C less than 7%). Low (60-130 g CHO per day) or very low carbohydrate approaches (20 g to 50 g CHO per day) are not universally recommended except for those with T2D who are not in glycemic control.12,50 Concerns with the low carbohydrate and very low carbohydrate approaches are centered around findings that improvements in glycemic and weight control do not appear to be sustained and that additional protein intake in these diets may negatively impact renal function in T2D patients who have chronic kidney disease.72,263 For T1D, there are concerns about the low carbohydrate and very low carbohydrate diets regarding the impacts of hypoglycemia and mixed data on lipid metabolism.74,75 The use of very low carbohydrate diet approaches can be an issue not only with metabolic aberrations but also with individuals who have a history of disordered eating.50 Disordered eating practices are more prevalent in those with diabetes, both T1D and T2D, placing them at a higher risk of concerns following a rigid meal plan such as the very low carbohydrate diet approach.13,92,264 Medical nutrition therapy should be individualized for each patient while addressing as needed current diet trends, including low carbohydrate and very low carbohydrate diet approaches. 85 In conjunction with dietary approaches to management of diabetes, an important consideration in our current technological era is patient engagement with social media. Unfortunately, social media engagement is often not considered by healthcare professionals. Social media is part of daily life for 81% of adults in the United States including platforms such as Facebook, YouTube, Instagram and X (formerly known as Twitter).225 The demographics of users differs between various platforms with approximately 71% of young adults (18-29 years of age) using the photo-based social media platform Instagram versus 48% of adults aged 30-49 years and 29% of adults 50-64 years of age. Approximately 59% of Instagram users engage with the platform a minimum of one time per day.225 These platforms have been increasingly used as a “go to” for health-related information including diets.154,265,266 Research focused on relationship between social media use and dietary behaviors has primarily been conducted with adolescents and young adults and has caused concern regarding marketing of food items that do not meet current nutrition guidelines and promotion of disordered eating behaviors.267 Persons living with diabetes utilize social media to create a supportive community of others experiencing similar issues with daily self-management.119,121,254,268 Disclosure of diabetes on social media is related to likes of social media posts and support from comments received from the Instagram community.240 This support through engagement may be problematic as there remain concerns with the use of social media not only spreading of misinformation but concerns with disordered eating and body image.14,161,169,269 Instagram is used by 71% of the young adults and hence an important avenue for social media sharing. Little is known about nutrition conversations that occur on social media, specifically Instagram, among those with diabetes of all ages. It is also not clear if type of diabetes is a differentiating factor in terms of information shared or sought. This study aims to 86 determine the T1D and T2D characteristics of nutrition related Instagram content creators, main themes, diet promotion, discussion of diet culture, and levels of engagement within four popular diabetes hashtags through an in-depth comparative content analysis. Methods Approach: Quantitative content analysis was used to evaluate nutrition-related information from four popular diabetes hashtags on Instagram: #type1diabetes and #diabadass for T1D, and #diabetestype2, and #diabeteslife for T2D. Instagram was the chosen social media platform as it is a photo-based platform popular among adults and young adults. The content analysis protocol was reviewed by the Michigan State University Institutional Review Board (see Appendix A). Sample: A systematic review of popular Instagram T1D and T2D hashtags (>20,000 posts per hashtag) used by persons with diabetes was completed in January 2020 for T1D and February 2021 for T2D. A total of twenty hashtags met the above criteria for T1D and eleven hashtags met the T2D criteria. A purposive sample of posts for each hashtag was completed by signing into the web version of Instagram versus the smart phone application and utilizing the search function. The sampling method used to identify the highest proportion of nutrition related posts was similar to that of other researchers which scanned every fifth, publicly available, English language post was manually coded for main post topic (nutrition, diabetes self- management, physical activity, meme, support, other), content creator type (person with diabetes, health information provider, organization, other), type of diabetes focus.207 Additionally, the T1D hashtag scan sample was coded for estimated age of content creator (parent of child/adolescent, adolescent, young adult (18-29 years of age), adult). The T1D hashtag scan sample (n=1127) included up to twenty posts on January 4, 6, 8, 9, 2020 to account for 87 variability in different days of the week. The T2D hashtag scan sample (n=1100) included up to one hundred posts on February 4, 8, 2021 to account for variability in days of the week. The hashtags with the highest proportion of nutrition-related posts for T1D were #type1diabetes and #diabadass and for T2D were #diabetestype2 and #diabeteslife. Coding procedures: This was a pioneer nutrition-related diabetes content analysis therefore no a priori codes were available. Codes were created through inductive analysis of the hashtag scan posts and research goals by the principal investigator (DK). Each study variable created was operationalized in the codebook used to train the undergraduate research assistants. Four trained undergraduate research assistants, two each for T1D (RG and AD) and two each for T2D (MD and SA), completed reliability testing on random samples of 20 nutrition related diabetes Instagram posts until a coder reliability for nominal variables of Cohen’s Kappa cut-off point of K >0.85 (n=60 for T1D and n=90 for T2D) was reached.175 Coding discrepancies with each group of research assistants were discussed with the principal investigator after each round of testing. The study sample was obtained by manually coding 150 nutrition-related Instagram posts for each T1D hashtag (n=300) using the web version of Instagram to search for the assigned hashtags in chronological order between May-July 2020. Each T2D research assistant manually coded 75 publicly available, nutrition-related Instagram posts using the web version of Instagram to search for each hashtag in reverse chronological order between July 2020-March 2021. The coders for both T1D and T2D did not engage with any of the posts and only collected the data from the codebook. Data coding issues were resolved and codebook updated through an iterative process via weekly meetings with the principal investigator and research assistants. 88 Coding themes: The posts were coded on three different levels: 1) Instagram content creator information; 2) main content; and 3) post engagement with further description below. Instagram content creator information: Each post was coded for Instagram content creator biography. The biography was coded for content creator type (person with diabetes, person with diabetes and personal business, personal business, and health information provider); content creator gender (female, male, multiple users, unknown), number of followers and number of accounts the content creator was following. Main content: Each post photo, caption, and hashtags were analyzed and coded for: photo, description of the food if included in photo, and main nutrition theme (food/beverage, food/beverage with physical activity, body image/emotion, diabetes management, nutrition education/food substitution, and promotion). Additionally posts (photo, caption and hashtags) were coded for promotion of a diet, the specific diet promoted, discussion of diet culture (body image and disordered eating), and glycemic management information shared (picture of glucose monitoring equipment, discussion of blood glucose). Post Engagement: Data was gathered on each post for the number of likes and comments from the date the information was obtained. This data was subsequently used to calculate the engagement factor by adding the post likes and comments and dividing by the number of Instagram followers at the time the post was collected. Statistical Analysis: Data cleaning was completed to check for duplicated posts between hashtags with complete analyzable data (n=447; T1D n=296; T2D n=151). Data was analyzed descriptively using Statistical Package for Social Sciences (SPSS) version 27. Independent t- tests were completed to compare Instagram content creator information. Chi square analysis was completed for categorical variables from the main content data between T1D and T2D groups. 89 Generalized estimating equation modeling was completed to determine the association of promoting and diet and magnitude of association for post engagement factor as dependent variables, Instagram content creator name as subject variable, independent variables were Instagram content creator characteristics, a diet promoted (for model testing engagement factor), promotion of specific diets, discussion of diet culture, and sharing glycemic information. A significance level of p<0.05 was used for all statistical analyses. Results A total of 275 unique Instagram content creators created nutrition-related posts for T1D (#type1diabetes and #diabadass) and T2D (#diabetestype2 and #diabeteslife). Characteristics of these users were shown in Table 5.1. The T1D posts were created primarily by females (78.5%) whereas the T2D posts were developed by those with unknown genders (66.3%). Instagram content creators for T1D posts were primarily persons with diabetes (76.4%) while for T2D Instagram post users with personal businesses (51.3%) contributed more than twice the number of posts as persons with diabetes. The Instagram content creators who developed T2D posts had nearly twice the number of followers when compared to Instagram content creators who created T1D posts which reached statistical significance with the independent samples t-test (p<0.001). The Instagram content creator types of persons with diabetes and a personal business, personal business, and health information providers had more followers in the T1D posts than the T2D posts. 90 Table 5.1- Comparison of Instagram Content Creators Characteristics (n=275) Biography Information Gender* Female Male Multiple users Unknown User Type* Person with diabetes Person with diabetes & personal business Personal business Health information provider Average Followers* Female Male Multiple users Unknown Person with diabetes Person with diabetes & personal business Personal business Health information provider ____________________________ *independent samples t-test p<0.05 T1D (n=195) n (%) 153 (78.5%) 21 (10.8%) 2 (1.0%) 19 (9.7%) 149 (76.4%) 19 (9.7%) 10 (5.1%) 17 (8.7%) 3805.5 3337.6 5026.6 9977 5574.2 1394.6 12438.1 6554.3 13671.8 T2D (n=80) n (%) 25 (31.3%) 1 (1.2%) 1 (1.2%) 53 (66.3%) 17 (21.3%) 14 (17.5%) 41 (51.3%) 8 (10%) 7181.7 6253.9 12800 18300 7248.8 2963.7 8142.7 7969.3 10711.3 The main nutrition theme of the posts (photo, caption, and hashtags) is shown in Table 5.2. For both types of diabetes, posts mainly highlighted a food and/or beverage as the main theme followed by nutrition education/food substitution for T1D, and diabetes management for T2DM. The distribution of main theme by gender is shown in Table 5.3 with the elimination of food/beverage with physical activity and promotion as neither of those main theme categories were included in T2D posts. Body image/emotion posts were created entirely by females in T1D posts whereas body image/emotion posts were split between all gender categories except males in the T2D posts. Table 5.4 highlights the distribution of Instagram content creator types 91 separated by main nutrition theme with the elimination of food/beverage with physical activity and promotion as those categories were not included in T2D. For T1D, the only theme that was not similar to the normal distribution was within health information providers as they account for more body image/emotion and nutrition education/food substitution posts. Persons with diabetes in the T2D cohort were less likely to post food/beverage posts, while those with a personal business did not post as much on body image/emotions or diabetes management. Table 5.2 – Comparison of Instagram Post Main Nutrition Theme (n=447) Theme Food/Beverage Food/Beverage with Physical Activity Body Image/Emotion Diabetes Management Nutrition Education/Food Substitution Promotion T1D (n=296) n (%) 211 (71.3%) 8 (2.7%) 5 (1.7%) 21 (7.1%) 34 (11.5%) 17 (5.7%) T2D (n=151) n (%) 114 (75.5%) 0 7 (4.6%) 21 (13.9%) 9 (6%) 0 Table 5.3 – Comparison of Instagram Post Main Nutrition Theme by Content Creator Gender Multiple Users T2D T1D T2D 2.4% 0.9% 0% 0% 0% 0% 11.1% 0% Unknown T1D T2D 13.2% 5.7% 72.8% 14.2% 0% 42.9% 0% 4.8% 42.9% 11.1% 5.9% 55.5% Female Male T1D T1D T2D 73.9% 14.0% 8.5% Food/Beverage Body Image/Emotion 100% Diabetes Management 75% Nutrition Education/Food Substitution 42.9% 0% 57.1% 19% 91.2% 27.3% 2.9% 92 Table 5.4 – Comparison of Instagram Post Main Nutrition Theme by Instagram Content Creator Type Person with diabetes T1D 77.9% 80% T2D 12.2% 14.3% Persons with diabetes and personal business T1D 8.5% 0% T2D 26.3% 42.8% Personal business Health information provider T1D 2.4% 0% T2D 63.2% 28.6% T1D 7.1% 20% T2D 23.7% 14.3% 81% 33.3% 9.5% 23.8% 4.8% 33.3% 4.8% 9.5% 63.3% 0% 11.8% 0% 0% 66.7% 26.5% 33.3% Food/Beverage Body Image/Emotion Diabetes Management Nutrition Education/Food Substitution Diets were promoted in about half of all posts for both T1D (49.0%) and T2D (52.3%). As compared to the overall gender distribution, diets were promoted more frequently by unknown gender content creators in T1D (13.8% vs 9.7%) and less frequently by females in T2D (16.5% vs 31.3%). The content creator types that promoted diets in T1D at higher rates than the general sample were personal businesses (9.7% vs 5.1%) and health information providers in T2D (36.1% vs 10%). The specific types of diets being promoted by Instagram content creator type was shown in Table 5.5. The low carbohydrate and ketogenic diets were the most promoted diets in both T1D and T2D. Females were less likely to promote low sugar/sugar-free diets (38.5%) in T1D and in T2D females were less likely to promote the ketogenic diet. Unknown gender content creators for T1D and T2D posts were more likely to promote the low sugar/sugar- free diets. 93 Table 5.5 – Comparison of Diets Promoted by Instagram Content Creator Type - % (n) Person with Diabetes Person with Diabetes and Personal Business T1D 2.9% (2) 12.5% (9) 12.8% (5) 0 T2D 40% (16) 11.5% (3) 16.7% (3) 0 T2D 10% (4) 15.4% (4) 5.6% (1) 0 75% (3) 0 15.4% (2) 0 0 11.8% (2) Personal Business Health Information Provider T1D 20% (14) 18.1% (13) 0 41.7% (5) 53.9% (7) 6.7% (1) T2D 37.5% (15) 15.4% (4) 55.6% (10) 0 25% (1) 47.1% (8) T1D 5.7% (4) 6.9% (5) 5.1% (2) 0 7.7% (1) 20% (3) T2D 12.5% (5) 57.5% (15) 22.2% (4) 100% (5) 0 41.2% (7) Ketogenic Low Carbohydrate Vegetarian/ Plant Based Intermittent Fasting Low Sugar/ Sugar Free Other T1D 71.4% (50) 62.5% (45) 82.1% (32) 58.3% (7) 23.1% (3) 73.3% (1) Glycemic information was shared twice as much in posts for T1D (32.6%) when compared to posts for T2D (14.6%). Persons with diabetes were most likely to share glycemic information in T1D posts (82.5%); however, in T2D posts persons with diabetes and a personal business accounted for nearly one-third of posts (31.8%). Diabetes management was the theme most prevalent to share glycemic information in both T1D and T2D. Females shared the most glycemic information in both T1D and T2D. Diet culture (discussion of disordered eating and/or body image) was discussed or shared more often in T2D (24.5%) than in T1D (10.7%). Persons with diabetes and a personal business shared about diet culture more frequently in both T1D (25%) and T2D (37.8%) posts. Diet culture was almost exclusively a female conversation in T1D (96.9%) while unknown gender content creators accounted for two thirds of all posts in T2D. Post engagement factors are outlined in Table 5.6. Post engagement was significantly different by gender for the average number of likes with greater engagement for T1D posts by 94 females compared to males. Within T2D posts the differences in engagement were between females and unknown gender user types. Persons with diabetes had the highest engagement for both the T1D and T2D posts. Average engagement for specific diets was greater for T2D than T1D posts except for the low sugar/sugar-free diets. Differences in post engagement for glycemic information shared and diet culture did not reach significance in T1D posts and T2D posts. Table 5.6 – Comparison of Type 1 and Type 2 Diabetes Instagram Post Engagement T2D Engagement 170.6/9.0 Average Likes/Comments per Post Average Likes/Comments by Gender T1D 80.6/6.2 • Female • Male • Multiple users • Unknown Average Likes/Comments by User Type • Person with diabetes • Person with diabetes & business • Personal business • Health information provider Average Likes/Comments by Promotion of Diet • Yes • No Average Likes/Comments by Diet Promoted • Ketogenic • Low Carbohydrate • Vegetarian/Plant Based • Intermittent Fasting • Low Sugar/Sugar-Free • Other 77/6.3 129/6.7 87/6.4 61/4.8 56/4.6 169/12.0 74/6.6 184/13.1 63/5.0 97/7.4 50.5/4.3 45.0/4.0 103.6/7.1 64.6/6.5 41.8/2 55.9/7.3 Average Likes/Comments by Glycemic Information • Yes • No Average Likes/Comments by Diet Culture • Yes • No 80/6.6 80/6.0 131.8/10.5 74.34/5.7 135.3/11.2 113.5/13 263.5/12.9 168.6/13 89.1/6.1 167.9/13.4 179.2/5.8 217.4/15.0 200.7/12.1 137.6/5.6 130.5/12 163.1/9.5 339.3/12.7 162.4/23.6 40.5/1.3 360.7/15.6 152.7/10.5 173.7/8.7 162.6/13.1 173.3/7.6 95 The engagement factor ratio was calculated by adding likes and comments together and dividing by the total number of Instagram followers at the time of the post to provide a more accurate picture of overall engagement by examining the potential numbers that would be exposed to the post from the content creator's network. Posts from content creators with greater than 100 followers were included in the analysis as is industry standard to omit outliers. Higher engagement ratios were seen in the T1D group with a diet being promoted and; glycemic information shared. Glycemic information remained statistically significant with other factors added to the GEE model with a 4.2 greater odds of higher engagement for those posts as compared to no glycemic information shared. There were three main themes with greater engagement: nutrition education/food substitution, diabetes management and body image/emotion. The Instagram content creators that had diabetes were 15.4 times more likely to have posts with higher engagement in the T1D cohort. Table 5.7 – Instagram Post Characteristics Contributing to Overall Engagement Ratio 95% Confidence Interval Engagement Ratio OR (SE) T1D T2D Diet Promoted 6.0% 3.4% 3.3 (0.56)** 1.1-10.0 Glycemic Information Diet Culture 7.6% 5.9% 5.5 (0.68)* 5.7% 3.2% 0.3 (0.54) 1.5-20.9 0.1-0.97 1.1-15.2 4.2 (0.67)* Glycemic Information*Diet Culture*Diet Promoted Nutrition Education Food Substitution Diabetes Management Body Image/Emotion 8.8% 2.3% 19.3 (1.4)* 1.4-269.0 6.3% 5.6% 9.0 (1.1)* 1.0-80.4 5.4% 2.3% 13.3 (1.1)* 1.5-117.2 96 Table 5.7 (cont’d) Nutrition Education Food Substitution*Diet Promoted Gluten-Free Diet 6.6% 3.0% 17.5 (1.3)* 1.4-219.9 3.4% 1.7% 14.39 (0.73)* 3.4-60.6 Diabetes (Yes) 6.9% 4.9% 15.4 (0.72)* 3.7-64.2 General Linear Model-Generalized Estimating Equations - *p<0.05, **p<0.001 Discussion The primary goal of this research was to understand the differences in the nutrition conversation on Instagram between T1D hashtags #type1diabetes and #diabadass and T2D hashtags #diabeteslife and #diabetestype2. The striking difference between the T1D and T2D cohorts was the type of Instagram content creator generating and posting content. The nutrition- related conversation created on T1D was dominated by female persons with diabetes whereas the T2D nutrition-related conversation was predominantly created and posts by unknown gender content creators with personal businesses. This suggested that the potential motivation of the community created in the hashtags with the T1D community was support for each other but in contrast in the T2D community based on these specific hashtags the focus was more about selling products and services. A portion of the unknown gender content creators in the T2D community were selling dietary supplements and non-evidence based online booklets to reverse diabetes. These dietary supplements and online booklets were not evidence-based and might prey on individuals who used Instagram to search for health information and advice. There was no guarantee that an individual would incorporate the information, but at the minimum, it could create doubt about evidence-based information. As these accounts that promoted the online booklets were all taking the social media consumer to the same information, this could have represented the use of social media bots, artificial intelligence programs to imitate users270, 97 which was not seen in the T1D cohort. Previous research on social media health messaging on X (formerly known as Twitter), had similar findings of social bots being a significant contributor in the conversation for spreading health misinformation.243 While these accounts were not the entire conversation, they did play a significant role in shaping the conversation within the T2D hashtags All of the T2D Instagram content creators who promoted themselves as health information providers, used accounts to attract individuals to their businesses. These were the accounts that had more average followers than other Instagram content creator types in the T2D cohort. In contrast, conversations in the T1D hashtags posted by persons with diabetes created an environment of support for each other to share experiences and advice.115,117,142 In T1D posts however, those Instagram content creators with higher average followings were those with businesses and health information providers where as 72% were posting to gain clients for their private practices. The health information providers may not have all been credentialed individuals and therefore, those Instagram users seeking advice may have accessed misleading/incorrect social media information.104 Subsequently, if appropriate treatment was delayed and/or glycemic and lipid control negatively impacted, there could be long-term complication implications. Of specific study interest was the examination of how diets were promoted between the two cohorts as well as the discussion of diet culture. The proportion of posts that promoted any diet was equal among both types of diabetes. The images and posts for specific diets used multiple hashtags; therefore, more specific diets were promoted than the total number of posts promoting any diet. The specific diets promoted were primarily related to weight loss and limiting carbohydrates for glycemic control Research supports that social media images could 98 impact overall diet, and therefore sharing of anecdotal and misinformation could be damaging to health and ultimately to long-term diabetes control and related complications.107,183,228 Vegetarian/plant-based diets were the only diets in full alignment with current diabetes nutrition recommendations and may lead to overall weight control and insulin sensitivity in T2D.50,61,271 There were differences in the users who promoted vegetarian/plant-based diets with persons with diabetes accounting for nearly 94% of all those in the T1D posts, whereas personal businesses accounted for over 50% of the vegetarian/plant-based diets in the T2D posts. Vegetarian/plant- based diets were popular on social media, especially on X (formerly known as Twitter) with analyses highlighting them as a trending diet topic with some of the highest post engagement.177,272 The ketogenic diet (very low carbohydrate) and low carbohydrate diet were the most popular diets shared within both T1D posts and T2D posts with over half using one or both hashtags to increased interest and viewing. The low carbohydrate diet accounted for more than half of posts for the T1D cohort but only 32% for the T2D cohort. The images associated with these diets demonstrated a lack of consistency in terms of what constitutes ketogenic and low carbohydrate diets as many included foods rich in carbohydrates such as grain foods and fruits. Current diabetes standards of care for medical nutrition therapy supported lower overall carbohydrate intake for glycemic target improvements, but there is not a standard definition for the total grams or percentage of total carbohydrates, which made this decision of total grams of carbohydrate up to the individual to interpret.24 The research was mixed on the long-term use of low carbohydrate diets in T2D for overall glycemic and cardiometabolic benefits with a 2022 Cochrane Review that concluded that in a two year follow up after initiation of the ketogenic diet the weight and metabolic changes did not persist.67 The low carbohydrate diet was the most 99 common diet mentioned in the T1D sample with little evidence of long-term safety for cardiovascular health.65 The ketogenic (very low-carbohydrate) diet with higher levels of fat has gained popularity with both T1D and T2D, despite a lack of consistent evidence for long-term sustainability, benefits, and risks for hypoglycemia, high low-density lipoprotein levels, and renal function.63,72 The ketogenic diet was promoted most often in T1D by persons with diabetes and in T2D persons with diabetes and a personal business. Since persons with diabetes were seen as most influential in the T1D social media conversation, this elicited concern about the dissemination of misinformation which could be damaging to overall health if not addressed.220 There were few studies in T1D that support this diet with the American Diabetes Association explicitly warning against these diets for specific populations, including those at risk for disordered eating.24 Disordered eating and body image discussion and posts were significantly greater among the T2D posts than those from the T1D posts. This may be explained by the higher prevalence of overweight and obesity in T2D than T1D posts nevertheless weight control is still a high priority in management of both.24 These findings were consistent with photo based platforms being associated with body dissatisfaction.273–275 The cohorts were significantly different with respect to those who are posting images with personal businesses being the greatest in T2D posts. The concepts of body image and disordered eating are linked as the drive for the socially acceptable image to portray on photo based social media alters food intake patterns.111 Persons living with either T1D or T2D diabetes, are already at higher risk of developing disordered eating, which has been shown to be associated with poorer glycemic control and increased cardiovascular risk.13,92 100 The main theme of sharing food/beverages predominated both in T1D and T2D posts which was similar to that found by other nutrition-related social media content analyses.203,276 The photo centered posting on Instagram led to the sharing of meal ideas and recipes among users in both T1D and T2D posts. The other main theme identified differed between the type of diabetes with nutrition education/food substitution being next most common within T1D and diabetes management in T2D posts. The secondary theme in T1D posts aligned with the community of sharing to improve daily self-management and self-identified health quality of life.119 The Instagram content creators that posted related to body image/emotion came from persons with diabetes and health information providers in T1D, whereas over two thirds of posts within the body image/emotion theme in T2D were posted by personal businesses and persons with diabetes with a personal business. Photo-based social media platforms have been known to contribute to body dissatisfaction and body image issues which makes this alarming.167,273,274 While these posts did not account for a large number within the data set, the trend of the users who posted in T2D was alarming as the intentions posting about body image/emotion may not have been supportive in nature but to increase traffic to their own business. Lastly, the engagement with the different posts within the two cohorts was particularly worthy of investigation as the act of engaging with a social media post leads to a greater intention to incorporate the sentiment of the post within one’s daily habits. The posts with the highest overall engagement in our study sample came from persons with diabetes in both T1D and T2D posts. This finding was supported by findings by Holtz and Kanthawala (2020) who found that likes on Instagram aided in gaining social support overall or in a specific group.240 Our study found higher engagement in body image/emotion and diabetes management among T1D posts with food and beverage than for T2D, compared to Gabarron et al (2020) who 101 reported lower engagement in food-related information and diabetes technology posts created by non-profit diabetes association.203 In T1D posts, this theme for active engagement followed along with the idea of creating community on social media. For T2D posts, this theme was highest in users with personal businesses, selling products and services. This trend was not too surprising as T2D accounts for 90% of all cases of diabetes and therefore businesses may feel this is an ideal method to increase their clientele with the prevalence of T2D continuing to rise.1 Engagement with nutrition education posts was lowest in both T1D and T2D posts, which is consistent with the Garbarron data so while helpful it did not spark an individual to feel compelled enough to interact with that information and thus may not impact overall food and health decisions.203 The engagement on social media has potential to impact behavior change yet research pointed to both positives, such as increases in health quality of life and glycemic control, but also negatives with disordered eating, and poorer diet quality. 109,112,119,185,277,278 Overall, this study was one of the first to examine the differences in the nutrition conversations on Instagram with popular diabetes related hashtags linked to T1D and T2D. The direct link between diet promotion on social media and disordered eating behaviors was not evaluated in this study. However, based on the literature establishing disordered eating risk with low carbohydrate diets, it is still concerning these are promoted in the T1D community. The tone of conversation and primary drivers remained different aligning with a community of support within the T1D posts while social bots were used to push misleading information in the T2D posts. While this study did not measure any behavior change that resulted from viewing these posts, research steered to engagement being a key driver of behavior change.279 There were limitations to this study due to the samples not being homogenous to the type of diabetes due to some hashtags being generic. Instagram content creators used both T1D and T2D hashtags to 102 gain interest across all types of diabetes. The study sample sizes were not from the same time frame. The T1D sample was taken at the beginning of the pandemic with people being home whereas the T2D sample was taken one year after with more people integrated back into in- person activities. Additionally, this study only used one social media platform with new platforms released gaining popularity such as TikTok. Different diet trends may be stronger at different times thus the diets promoted could change. Therefore, a follow-up study which addresses this discrepancy might provide additional information that expands the findings from this study. Implications for Research and Practice Social media is a significant source of health information for adults which transcends age groups. This makes understanding the contributors to daily health decisions an important component of diet-related and overall health assessments and education. It is imperative that nutrition and diabetes care practitioners screen for use and frequency of social media and account for this in the development of personalized nutrition care plans. Education should include being able to discern misinformation from Instagram content creators who are viewed by users as influential. In addition, the spread of misinformation on social media should be addressed at a policy level limiting predatory accounts for the overall safety of health. 103 CHAPTER 6: SUMMARY, CONCLUSIONS, AND FUTURE RESEARCH The research completed for this dissertation provided an innovative approach to examine and identify whether and how social media influences T1D and T2D self-management behaviors, especially regarding food choices. Both quantitative and qualitative approaches to content and social network analyses were used, which had not been previously undertaken in nutrition research. The findings of these studies made it imperative for diabetes healthcare providers to be aware of the conversations that are taking place on social media platforms and actively assess patient social media use. These efforts would certainly lead to improved patient assessments and education plans. Although recommendations to become a member of diabetes online communities are currently a support component of Diabetes Self-Management Education and Support (DSMES), the current study illuminates misinformation that is present in many of these public diabetes online communities. The misinformation and the “quick fixes” shared by uncredentialed content creators on social media could lead to poor metabolic control and in addition to acute aberrations, progression to long-term complications of diabetes. The first study characterized the overall nutrition-related conversation in the T1D Instagram public online community created through two popular hashtags, #type1diabetes and #diabadass. These results demonstrated willingness of persons with diabetes to self-disclose their chronic illness and perceptions about nutrition and related strategies through a social media “support group” with one another to share nutrition-related concerns on their health journey. These conversations were predominantly by female persons with diabetes, but messages were also shared by personal businesses and health information providers. When positive food information was shared within the posts, it was primarily with fruit and vegetable 104 recommendations. However, only a small percentage of posts adequately met the standards for nutrition as recommended by the American Diabetes Association. While there were a variety of main nutrition themes in the posts within the data set, those gaining the most engagement with likes and comments were the ones dealing with nutrition education, body image/emotion and diabetes management. The engagement with diabetes management was within the realm of support, but the body image/emotion posts were a point of concern as disordered eating is known to lead to poor glycemic control. Most concerning was the sharing of non-evidenced based very low carbohydrate/ketogenic diets, which have not been shown to be beneficial in the long-term and warrants more investigation. The second study found a smaller cluster of social networks within the larger public Instagram diabetes community. Further, the community within social media does not have country borders and created a varied network with five smaller communities within it. These communities were loosely linked to each other through gatekeepers. Those Instagram content creators with the greatest ties were personal businesses, which could mean that dissemination of information was primarily geared towards benefitting the personal business and not necessarily the person with diabetes. Triangulating the qualitative analysis of the comments further verified the supportive nature of the community, but also made evident the infiltration of diet culture with negative food relationships. These negative food relationships could be associated with disordered eating and thus contribute to long-term complications. Instagram content creators asked for advice and the community felt comfortable to offer that advice and share their own stories and struggles. While the network was generally supportive, some comments shamed the Instagram content creators for their decisions regarding food or diabetes management. This type of behavior might have 105 diluted and negated the quest for support through the network by likely instilling negative perceptions in those who were seeking positive reinforcement or advice. The social network analysis demonstrated non-evidence-based diets, including the ketogenic diet, were shared within the smaller communities which served as an example of these diets potentially being the social norm within that Instagram diabetes online community. The creation of a social norm is a known antecedent of intention to change behavior as outlined in Ajzen’s Theory of Planned Behavior.184 Furthermore, not only could these become social norms, the discussion of improved glycemic control within diet related posts may work to change the overall attitude of the Instagram user to further strengthen the intent to change dietary behaviors in favor of non-evidence-based approaches. These behavioral influences could lead to poor metabolic control and hence damaging long-term consequences in persons with diabetes. The final study expounded on the inherent differences in social media diabetes online communities. Since T2D accounts for the majority of cases of diabetes, it was not surprising to see that personal businesses used social media as a method to market non-evidence-based products in what they perceived to be a viable number of potential clients. As T2D is associated with higher body weight, the promotion of diets and greater posts on body image/emotion were not unexpected, but very concerning with regard to the development or perpetuation of disordered eating practices. This research does have limitations with the use of only four hashtags during a short period of time. Therefore, there may be other nutrition-related information and/or fad type diets being shared outside of hashtags used. The T1D data set was collected only in the summer months which could have reflected seasonality. The observational nature limited extent of content examination and did not actually measure behavior change or attitude toward the 106 information provided. The engagement statistics only gathered likes and comments but not sharing or total views since sharing and views are visible only to the content creator. It was hence difficult to gain a deeper understanding of the extent of the post reach and information shared. Social media platforms are an ever-evolving phenomenon. Instagram is hence only one social media platform studied and introduction of TikTok and Instagram Reels (short video platforms) have transformed the social media community and have become preferred platforms, making it important to further study this avenue of research. This dissertation provided a breadth of understanding on content in order to identify the salient issues to be included in the design of a longitudinal study on the influence social media may have on diet quality, glycemic control, and associated metabolic aberrations such as dyslipidemia, as well as disordered eating. A survey study of diabetes healthcare providers to examine the extent that social media is a component of care assessment and DSMES in addition to perceptions on information available on social media. The comparison of behavioral change and intent of photo-based and video-based platforms could be performed to assess the magnitude each has on diet. A further qualitative study could be performed to understand how exactly persons with diabetes react and intend to incorporate evidence-based and non-evidence-based nutrition advice. In addition, research could be conducted on the necessity for policy to protect those most vulnerable to social media influences, especially those with chronic conditions like diabetes. 107 REFERENCES 1. National Diabetes Statistics Report | Diabetes | CDC. Published June 29, 2022. Accessed September 26, 2023. https://www.cdc.gov/diabetes/data/statistics-report/index.html 2. ElSayed NA, Aleppo G, Aroda VR, et al. 3. 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