Health and Social Development, Faculty of (Okanagan)
Social Work, School of (Okanagan)
DSpace
UBCO
Belliveau, Michelle
Master of Social Work - MSW
University of British Columbia
Problem: Rates of vaping and smoking have increased across Canada since 2015. Several factors appear to influence these rates, yet there is limited research on the relationships among these factors. The purpose of the current study was to explore the associations between social media use and vaping and smoking behaviours, accounting for the mediational effects of mental illness symptoms (i.e., anxiety and depression). Method: An online survey was conducted with adults 19 years and older living in Canada. The study utilized four indicators of the latent construct, social media use (i.e., social media dependency, engagement, frequency, and the number of platforms), two measures of mental illness symptoms (i.e., anxiety and depression), and two measures of smoking/vaping (i.e., status and nicotine dependency). The data were analyzed using R. Our final analysis included both general and generalized structural equation models. Results: Vaping status: Social media use had a significant negative relationship with vaping status, but when mediated by symptoms of mental illness, the association was not significant. Nicotine dependency among vapers: The relationship between social media use and nicotine dependency was not significant, but the relationship mediated by mental illness symptoms was both positive and significant. Smoking status: As with vaping status, social media use had a significant, negative relationship with smoking status. However, the relationship when mediated by mental illness symptoms was positive and significant. Nicotine dependency among smokers: Again, social media use had a significant, negative effect on nicotine dependency, and the mediated relationship was positive and significant. Conclusions: Mental illness symptoms appear to mediate the association between social media use, smoking status and nicotine dependency among vapers and smokers. These findings suggest that it is essential to examine the contributing factors related to social media use associated with vaping and smoking. Future studies should seek to include additional types of mental illness symptoms across various at-risk groups to determine appropriate intervention and prevention approaches.
The association between vaping, smoking, mental illness, and social media use: A national survey of adults in Canada by Michelle Belliveau B.A., University of British Columbia, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SOCIAL WORK in THE COLLEGE OF GRADUATE STUDIES Social Work THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) December 2021 © Michelle Belliveau, 2021 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: The association between vaping, smoking, mental illness, and social media use: A national survey of adults in Canada submitted by Michelle Belliveau in partial fulfillment of the requirements of the degree of Master of Social Work . Dr. Dixon Sookraj, School of Social Work Co-Supervisor Dr. Sarah Dow-Fleisner, School of Social Work Co-Supervisor Dr. Laura Struik, School of Nursing Supervisory Committee Member Dr. Shelly Ben-David, School of Social Work Supervisory Committee Member Dr. Brian O’Connor, Irving K. Barber Faculty of Arts and Social Sciences University Examiner iii Abstract Problem: Rates of vaping and smoking have increased across Canada since 2015. Several factors appear to influence these rates, yet there is limited research on the relationships among these factors. The purpose of the current study was to explore the associations between social media use and vaping and smoking behaviours, accounting for the mediational effects of mental illness symptoms (i.e., anxiety and depression). Method: An online survey was conducted with adults 19 years and older living in Canada. The study utilized four indicators of the latent construct, social media use (i.e., social media dependency, engagement, frequency, and the number of platforms), two measures of mental illness symptoms (i.e., anxiety and depression), and two measures of smoking/vaping (i.e., status and nicotine dependency). The data were analyzed using R. Our final analysis included both general and generalized structural equation models. Results: Vaping status: Social media use had a significant negative relationship with vaping status, but when mediated by symptoms of mental illness, the association was not significant. Nicotine dependency among vapers: The relationship between social media use and nicotine dependency was not significant, but the relationship mediated by mental illness symptoms was both positive and significant. Smoking status: As with vaping status, social media use had a significant, negative relationship with smoking status. However, the relationship when mediated by mental illness symptoms was positive and significant. Nicotine dependency among smokers: Again, social media use had a significant, negative effect on nicotine dependency, and the mediated relationship was positive and significant. iv Conclusions: Mental illness symptoms appear to mediate the association between social media use, smoking status and nicotine dependency among vapers and smokers. These findings suggest that it is essential to examine the contributing factors related to social media use associated with vaping and smoking. Future studies should seek to include additional types of mental illness symptoms across various at-risk groups to determine appropriate intervention and prevention approaches. v Lay Summary Problem: The study explored factors contributing to vaping, smoking, and nicotine dependency among adults. Specifically, we looked at the roles of social media use and mental illness symptoms. Method: We conducted an online survey and used a variety of statistical tests to examine the relationship between social media use, symptoms of mental illness, and vaping and smoking. Results: The more adults use social media the less likely they were to vape and smoke, and be nicotine dependent. However, social media use was associated with greater mental illness symptoms (i.e., anxiety and depression) which were, in turn, related to a greater chance of smoking and nicotine dependency among vapers and smokers. Conclusions: These results suggest that increased social media use may impact vaping, smoking, and nicotine dependency, by first increasing symptoms of anxeity and depression. vi Preface Thesis received initial ethics approval through the Brief Behaviour Ethics Board of the University of British Columbia, Okanagan Campus (#H20-01453). Post-approval was also received for changes to increase the number of sub-reddits used in recruitment. The identification and design of the research program was developed by Michelle Belliveau, Dr. Dixon Sookraj, and Dr. Sarah Dow-Fleisner. Michelle Belliveau wrote the introduction, theory, literature review, method, results, and discussion section with the aid of Dr. Sarah Dow-Fleisner and Dr. Dixon Sookraj. Specially, Dr. Sarah Dow-Fleisner provided outlines for each section and writing guidance. Dr. Dixon Sookraj provided feedback, editing, and guidance for organizing the contents and structure to improve coherence. Thesis committee members, Dr. Shelly Ben-David and Dr. Laura Struik provided invaluable feedback that helped clarify several matters and improve comprehension of this thesis. Dr. Shelly Ben-David ensured the positive aspects of social media use were evaluated and inspired careful and thoughtful consideration to the role of mental illness symptoms. Dr. Laura Struik provided insight into the measurement of vaping and smoking, and theory, including the suggestion of the theoretical diagram. Michelle Belliveau conducted analyses of the research data under Dr. Sarah Dow-Fleisner’s supervision and Dr. Dixon Sookraj provided feedback and consultation. With the aid of Dr. Sarah Dow-Fleisner, Michelle Belliveau wrote and submitted two abstracts based on this thesis to the Society of Social Work Research conference. vii Table of Contents Abstract .......................................................................................................................................... iii Lay Summary ...................................................................................................................................v Preface ............................................................................................................................................ vi Table of Contents ........................................................................................................................... vii List of Tables .................................................................................................................................. xi List of Figures ............................................................................................................................... xiv Glossary ........................................................................................................................................ xvi Acknowledgements ..................................................................................................................... xviii CHAPTER 1: INTRODUCTION .....................................................................................................1 1.1 Background Information ....................................................................................................... 1 1.1.1 Vaping, Smoking, and Health Risks.......................................................................... 1 1.1.2 Similarities and Differences between Vaping and Smoking ........................................ 2 1.1.3 Psychological Factors Associated with Vaping and Smoking ..................................... 3 1.1.4 Psychological Factors Associated with Social Media Use........................................... 4 1.1.5 Social Media Use .................................................................................................... 5 1.1.6 Helpful and Harmful Effects of Social Media Use ..................................................... 5 1.1.7 Vaping and Smoking Representation on Social Media ............................................... 7 1.2 Study Rationale.................................................................................................................... 8 1.3 Theoretical Underpinnings .................................................................................................. 10 1.3.1 Full Mediated Relationships ................................................................................... 11 1.3.2 Vaping, Smoking, and Symptoms of Anxiety and Depression .................................. 12 CHAPTER TWO: LITERATURE REVIEW ................................................................................ 14 2.1 Search Strategy .................................................................................................................. 14 2.2 Search Results ................................................................................................................... 16 2.3 Overview ........................................................................................................................... 18 viii 2.3.1 Smoking, SMU, Anxiety Symptoms, and Depression Symptoms .............................. 18 2.3.2 Vaping on Social Media......................................................................................... 20 2.3.3 Smoking on Social Media ...................................................................................... 21 2.3.4 Anxiety Symptoms, Depression Symptoms, Vaping, and Smoking ........................... 22 2.3.5 Anxiety Symptoms, Depression Symptoms, and Social Media Use ........................... 22 2.3.6 Research Study Rationale ...................................................................................... 23 CHAPTER THREE: METHOD .................................................................................................... 27 3.1 Recruitment and Data Collection ......................................................................................... 27 3.2 Participants ........................................................................................................................ 28 3.3 Measurement ..................................................................................................................... 28 3.4 Analytic Approach ............................................................................................................. 32 CHAPTER FOUR: RESULTS ...................................................................................................... 43 4.1 Univariate Descriptive Statistics .......................................................................................... 43 4.2 Bivariate Descriptive Statistics ............................................................................................ 48 4.3 Logistic Regressions .......................................................................................................... 53 4.4 Linear Multiple Regressions ............................................................................................... 54 4.5 Results from Binary Mediation Analysis.............................................................................. 57 4.6 Results from Confirmatory Factor Analysis ......................................................................... 65 4.7 Results from Generalized Structural Equation Models .......................................................... 66 4.8 Results from Structural Equation Models ............................................................................. 70 4.9 Summary of Findings ......................................................................................................... 73 4.9.1 Vaping Findings .................................................................................................... 73 4.9.2 Smoking Findings ................................................................................................. 74 CHAPTER FIVE: DISCUSSION .................................................................................................. 76 5.1 Statistical Effects ............................................................................................................... 77 5.2 Discussion of Principal Findings ......................................................................................... 79 ix 5.2.1 Vaping, Social Media Use, and Symptoms of Anxiety and Depression...................... 79 5.2.2 Smoking, Social Media Use, and Symptoms of Anxiety and Depression ................... 83 5.2.3 Differences in Vaping and Smoking ....................................................................... 85 5.2.4 Methodological Considerations .............................................................................. 87 5.2.5 Social Media Dependency ...................................................................................... 88 5.2.6 Dependency Begets Dependency. ........................................................................... 90 5.2.7 Mental Illness Symptoms Mediating Nicotine Dependency ...................................... 91 5.3 COVID-19 Pandemic ......................................................................................................... 94 5.4 Practise Implications .......................................................................................................... 98 5.5 Limitations and Strengths ................................................................................................... 99 5.5.1 Sample Based Limitations and Strengths ................................................................. 99 5.5.2 Measurement Based Limitations and Strengths ...................................................... 101 5.5.3 Statistical Analysis Limitations and Strengths ....................................................... 102 5.6 Future Analyses ............................................................................................................... 103 5.7 Future Research ............................................................................................................... 104 5.8 Conclusions ..................................................................................................................... 105 References ............................................................................................................................................. 108 Appendices............................................................................................................................................ 161 Appendix A: Theoretical Diagram .......................................................................................... 161 Appendix B: Key Findings from Literature Review ................................................................. 162 Appendix C: Full list of sub reddits used ................................................................................. 164 Appendix D: Letter of Invitations ........................................................................................... 165 Appendix E: Questionnaire .................................................................................................... 166 Appendix F: Response Options and Coding ............................................................................. 184 Appendix G: Histograms and distributions for CFA ................................................................. 188 x Appendix H: Histograms and distributions for GSEM .............................................................. 189 Appendix I: Histograms and distributions for SEM .................................................................. 190 Appendix J: Summary of Results ............................................................................................ 192 Appendix K: Fit indexes from CFA ........................................................................................ 197 Appendix L: Confirmatory Factor Analysis Diagram ............................................................... 198 Appendix M: Residual Correlations for GSEMs ...................................................................... 199 Appendix N: Standardized Residuals for the SEM models ........................................................ 200 Appendix O. Residual Correlations for SEM models ................................................................ 201 Appendix P: Exploring Social Media Dependency ................................................................... 202 Appendix Q: Effect Sizes ....................................................................................................... 213 Appendix R: Directionality .................................................................................................... 218 Appendix S: Contrary Findings .............................................................................................. 224 xi List of Tables Table 1: Type of nicotine user by frequency of use. ……………………………………………………...44 Table 2: Sociodemographic characteristics of the whole sample and by nicotine use groups. ………….44 Table 3: Type of nicotine user by average nicotine dependency …………………………………………45 Table 4: Average social media use of the whole sample and by nicotine use groups.……………………45 Table 5: Anxiety and depression symptoms categories of the whole sample and by nicotine use groups……………………………………………………………………………………………………... 46 Table 6: Mild, moderate, and high categories of symptoms of anxiety and depression for the total sample, among vapers, and among smokers. ………………………………………………………………………47 Table 7: Average scale score of anxiety and depression symptoms of the whole sample and by nicotine use groups …………………………………………………………………………………………………47 Table 8: Posting and viewing of media contents on vaping and smoking, by the whole sample and by nicotine use groups.………………………………………………………………………………………..48 Table 9: Pearson correlation matrix for entire sample of age, SMUF and number of platforms. ………..49 Table 10: Pearson Correlation matrix for entire sample. …………………………………………………51 Table 11: Pearson correlation matrix for vaping sub-sample.…………………………………………….52 Table 12: Pearson Correlation matrix for smoking sub-sample. …………………………………………52 Table 13: Logistic regression for vaping status and smoking status predicted by anxiety symptoms, and depression symptoms. ……………………………………………………………………………………..53 Table 14: Logistic regressions for social media use as predictors of vaping, smoking, anxiety, and depression …………………………………………………………………………………………………54 Table 15: Linear multiple regression of anxiety symptoms and depression symptoms as predictors of nicotine dependency among vapers and smokers. ………………………………………………………...55 Table 16: Multiple regressions between nicotine dependency among vapers, social media use, anxiety symptoms, and depression symptoms, and nicotine dependency among smokers, social media use, anxiety symptoms, and depression symptoms. …………………………………………………………………….57 xii Table 17: KHB for relationship between vaping and social media use with anxiety symptoms as a mediator. …………………………………………………………………………………………………..59 Table 18: KHB for vaping and social media use with depression symptoms as a mediator. …………….61 Table 19: KHB for smoking and social media use with anxiety symptoms as a mediator. ………………63 Table 20: KHB for smoking and social media use with depression symptoms as a mediator.…………...64 Table 21: Goodness-of-fit indicators for Confirmatory Factor Analyses models with the indicators of SMU ……………………………………………………………………………………………………….66 Table 22: Fit indexes for vaping and smoking GSEM models. ………………………………………….68 Table 23: Parameters for vaping and smoking GSEM models …………………………………………..70 Table 24: Fit indexes for vaping and smoking SEM models. …………………………………………….71 Table 25: Parameters for vaping and smoking SEM models ……………………………………………..73 Table 26: Key Findings From Literature Review ……………………………………………………….162 Table 27: Response Options and Coding ………………………………………………………………..184 Table 28: Summary of Results …………………………………………………………………………..192 Table 29: Factor loadings for four factor CFA with covariance. ……………………………………….197 Table 30: Standardized residuals for CFA ………………………………………………………………197 Table 31: Residual Correlations for CFA ……………………………………………………………….197 Table 32: Residual correlations for the vaping GSEM model …………………………………………..199 Table 33: Residual correlations for the smoking GSEM model ………………………………………...199 Table 34: Standardized residuals for the SEM vaping model …………………………………………...200 Table 35: Standardized residuals for the smoking model ……………………………………………….200 Table 36: Residual correlations for the SEM vaping model …………………………………………….201 Table 37: Residual correlations for smoking model …………………………………………………….201 Table 38: Item correlation (reliability) of social media dependency ……………………………………204 Table 39: The items of social media dependency provided by the scale authors ……………………….205 Table 40: Andreassen et al. (2012) correlation matrix from their scale development paper ……………205 xiii Table 41: Correlation Matrix for whole sample and items of social media dependency………………..206 Table 42: Correlation Matrix for vaping sub-sample and items of social media dependency…………..206 Table 43: Correlation Matrix for smoking sub-sample and items of social media dependency ………...207 Table 44: Correlation Matrix for non-vaping/non-smoking sample and items of social media dependency……………………………………………………………………………………………………………207 Table 45: Logistic Regression for items from social media dependency and vapers, smokers, symptoms of anxiety and symptoms of depression ………………………………………………………………….209 Table 46: Goodness-of-fit indicators for models for the social media dependency compared………….209 Table 47: Factor loadings for CFA with current study no modification ………………………………...210 Table 48: Factor loadings for CFA with covariation added …………………………………………….210 Table 49: Standardized residuals for CFA from current study ………………………………………….210 Table 50: Residual Correlations for CFA from current study…………………………………………...211 Table 51: Chi Square Effect Sizes ………………………………………………………………………214 Table 52: Logistic Regression Effect Sizes ……………………………………………………………..215 Table 53: Multiple Regression Effect Sizes ……………………………………………………………..215 Table 54: Binary Mediation Analyses (KHB) Effect Sizes ……………………………………………..216 Table 55: GSEM Effect Sizes …………………………………………………………………………...216 Table 56: SEM Effect Sizes ……………………………………………………………………………..217 Table 57: Contrary Findings……………………………………………………………………………. 224 xiv List of Figures Figure 1: Conceptual model of the relationship between social media use (SMU) and vaping as mediated by symptoms of anxiety and depression .............................................................................................. 9 Figure 2: Conceptual model of the relationship between social media use (SMU) and smoking as mediated by symptoms of anxiety and depression. ............................................................................. 10 Figure 3: PRISMA chart outlining search process for literature review. .............................................. 17 Figure 4: Visual model depicting the total effect of SMUF on vaping status with anxiety symptoms. .. 59 Figure 5: Visual model depicting the direct effect of SMUF on vaping status...................................... 59 Figure 6: Visual model depicting of the total effect of SMUD on vaping ............................................ 60 Figure 7: Visual model depicting of the direct and indirect effects of SMUD on vaping status with anxiety symptoms as a mediating variable. ........................................................................................ 60 Figure 8: Visual model depicting of the total of SMUF on vaping status in depression symptom mediation model. ............................................................................................................................. 62 Figure 9: Visual model depicting of the direct effect of SMUF on vaping status in depression symptom mediation model. ............................................................................................................................. 62 Figure 10: Visual model depicting of the total of SMUD and depression symptoms on vaping status in depression symptom mediation model ............................................................................................... 62 Figure 11: Visual model depicting of the direct and indirect effects of SMUD on vaping status with depression symptoms as a mediating variable. ................................................................................... 63 Figure 12: Vaping Status GSEM model. ........................................................................................... 69 Figure 13: Smoking Status GSEM model. ........................................................................................ 69 Figure 14: Nicotine dependency among vapers SEM model............................................................... 72 Figure 15: Nicotine dependency among smokers SEM model ............................................................ 72 Figure 16: Theoretical Diagram ..................................................................................................... 161 Figure 17: Histograms and distributions for CFA ............................................................................ 188 Figure 18: Histograms and distributions for GSEM ......................................................................... 189 xv Figure 19: Histograms and distributions for SEM, vaping ................................................................ 190 Figure 20: Histograms and distributions for SEM, smoking ............................................................. 191 Figure 21: Confirmatory Factor Analysis Diagram .......................................................................... 198 Figure 22: Standardized loading factors and latent structure from Andreassen et al. (2012). ............... 212 Figure 23: Standardized loading factors and latent structure from the current study. .......................... 212 Figure 24: Nicotine Dependency among Vapers Model Employed in Thesis ..................................... 219 Figure 25: Reversed Nicotine Dependency among Vapers Model from Thesis .................................. 219 Figure 26: Nicotine Dependency among Smokers Model Employed in Thesis .................................. 220 Figure 27: Reversed Nicotine Dependency among Smokers Model from Thesis ............................... 220 Figure 28: Vaping Status Model Employed in Thesis ...................................................................... 221 Figure 29: Reversed Vaping Status Model from Thesis ................................................................... 222 Figure 30: Smoking Status Model Employed in Thesis.................................................................... 223 Figure 31: Reversed Smoking Status Model from Thesis ................................................................. 223 xvi Glossary Term Definition Nicotine Use Vaping The use of e-cigarette (i.e., comparable to the term smoking). Vaping Status Determining if someone vapes or not. Nicotine Dependency Among Vapers The amount of dependence on nicotine experienced by someone who vapes as determined by the Penn State Electronic Cigarette Dependence Index. E-cigarette Electronic cigarette that may or may not contain nicotine. E-liquid The solution used in e-cigarettes, which often but not always includes flavouring and/or nicotine. Vaper A person who breaths in e-liquid vapor (i.e., comparable to the term smoker). Smoker A person who breaths in tobacco smoke. Smoking Status Determining if someone smokes or not. Nicotine Dependency Among Smokers The amount of dependence on nicotine experienced by someone who smokes as determined by Fragerstrom Test for Nicotine Dependence. Cigarette Rolled tobacco. Social Media Social media use The uncapitalized term refers to engaging with a broad array of social media platforms. Social media dependency (SMUD) A negative marker of social media use measured by Bergen Social Media Addiction Scale. Social media engagement (SMUE) A positive mark of social media use measured by Social Media Engagement Questionnaire (SMEQ). Frequency of social media use (SMUF) The frequentness often social media is used, ranging from not at all to less than once a week to five or more times a day. Number of social media platforms used (# of platforms) The number of social media platforms used. Social Media Use (SMU) The capitalized term refers to a latent construct used to represent social media use in this study. The indicators of SMU are: social media dependency; social media engagement; frequency of social media use; and number of platforms used. Promotive markers of social media use Indicators of social media use that support mental health. Negative markers of social media use Indicators of social media use that are associated with symptoms of mental illness. Objective social media use Indicators of social media use that do not inherently support mental health, and are not inherently associated with symptoms of mental illness. xvii Mental Health Terms Mental Health One’s overall mental wellbeing, both adverse and benefical. Mental illness symptoms A latent construct used to represent mental health status in this study. The indicators used mental illness are anxiety symptoms and depression symptoms. Anxiety symptoms Symptoms of anxiety as determined from the screening tool GAD-7. Depression symptoms Symptoms of depression as determined from the screening tool PHQ-8. Health Terms Health risks The risk of serious illness or mortality. Risk behaviours Behaviours that put heath at risk (i.e., smoking). Health-related behaviours Behaviours that are associated with improvement or worsening og health. Statistical Analyses OR An effect size resulting from the coefficient of regression analysis. AOR An effect size resulting from the coefficient of regression analysis, controlling for various socio demographic variables. CI The defined upper and lower limits of the destruction around a coefficient from statistical analysis. KHB The Karlson-Holm-Breen method for conducing logistic mediation analysis. CFA Confirmatory factor analysis; means of assessing latent constructs. GSEM Structural equation modeling regressing a dichotomous outcome variable on latent constructs. SEM Structural equation modeling regressing a continuous outcome variable on latent constructs. Spurious associations/ relationships When two events appear to be related but actually aren’t, or are only related through a third, unknown, variable. Theories and Models Social media engagement theory Social media provides gratification, which increases use. Normative social behaviour theory Social norms influence behaviour. Social comparison theory People compare themselves to others to assess belonging. Affective regulation theory Nicotine use is a form of self-medication for mental illness symptoms. Personality theory of addiction A person experiencing dependency on one behaviour or substance may be likely to experience other dependencies. Biosocial-psychological model A method for comprehensively understanding the contributing factors associated with illness, mental illness, and substance use. Mood management theory People intentional or unintentionally select stimulus to reduce negative affect. Public health model of addiction An approach to guide policy and prevent substance use based on harm reduction philosophy. xviii Acknowledgements I would like to thank my supervisors, Dr. Dixon Sookraj, and Dr. Sarah Dow-Fleisner for their patience, guidance, support, and providing the opportunity to conduct research. With all the time and effort that they put into my education, they shaped my understanding of graduate school and professionalism. I would also to thank my committee members Dr. Laura Struik and Dr. Shelly Ben-David for their contributions and thoughtful feedback, helping me to grow, both as a student and a person. My appreciation extends to other members of the School of Social Work. Specifically, Hilla Shlomi, Amanda Hancock, and Dr. John Graham who provided guidance in navigating the process of doing a thesis, ongoing support, and encouragement. 1 CHAPTER 1: INTRODUCTION This chapter presents an introduction to the key variables examined in this study. First, we discuss background information related to key variables. Second, we provide the study rational and present our conceptual models. Finally, we explore the theoretical underpinnings of this study. 1.1 Background Information In this section, we provide the background information related to key variables in this study. First, we provide background information on our key outcome variables, including the health risks associated with vaping and smoking, and we discuss similarities and differences between vaping and smoking. Second, we provide the background information related to the key mediators used in this study, including psychological factors associated with vaping and smoking, and psychological factors associated with social media use. Finally, we present the background information related to the key predictor variables used in this study include, social media use, helpful and harmful social media use, and the representation of vaping and smoking vaping and smoking. 1.1.1 Vaping, Smoking, and Health Risks The use of e-cigarettes, also known as vaping has increased among Canadian adults over the past 5 years, whereas combustible cigarettes, also known as smoking, has seen a decrease in use (Health Canada, 2019; Statistics Canada, 2020a). Nationally, in 2019, 3.7 million people (12%) in Canada currently smoked, and 5.0 million (16%) of people in Canada over the age of 15 had ever vaped (Government of Canada, 2020). In 2017, 4.6 million (15%) people either vape or smoke, increasing from 3.9 million (13%) in 2015 (Health Canada, 2019). Notably, there are serious health risks associated with vaping and smoking. For example, lung injury and 2 neurological toxicity, including deficits in short-term memory and other health-related complications, are associated with vaping (Bowler et al., 2017; Piper et al., 2020; Ruszkiewicz et al., 2020; Scungio et al., 2018; Xie et al., 2019). In addition, smoking is a leading cause of death, reducing life expectancy by approximately nine years and increasing the risk of developing heart disease and lung cancer (Government of Canada, 2018a; Statistics Canada, 2015; Van Teijlingen et al., 2019). Given the potential harm related to vaping and smoking, it is essential to understand the factors associated with vaping and smoking behaviours. 1.1.2 Similarities and Differences between Vaping and Smoking Vaping and smoking are similar in that they are both methods of nicotine inhalation (Kim et al., 2015). However, within the literature, there has been little agreement on which of the two methods produces more nicotine dependency. Some studies indicate vaping leads to more nicotine dependency than smoking (Soule et al., 2020) while other studies show the reverse (Liu et al., 2017; St. Helen et al., 2020), and still, other studies suggest vaping and smoking have similar association with nicotine dependency (Hartmann-Boyce et al., 2018; Vandrevala et al., 2017). One possible explanation for these contradicting findings is the different amounts of nicotine concentration found in e-cigarettes and cigarettes (Ashton et al., 1979; Romberg et al., 2019; Taghavi et al., 2012). Furthermore, cigarettes Several other factors influence the amount of nicotine delivered by an e-cigarette. For instance, the generation of the device (e.g., first-generation devices looked like cigarettes, and second-generation were vape pens), differences in available heat settings, and concentration of e-liquid all influence nicotine delivery from e-cigarettes (Farsalinos et al., 2014). With smoking, the concentration of nicotine in cigarettes remains relatively consistent (Farsalinos et al., 2014; Williams & Talbot, 2019). Although these studies provide conflicting findings regarding the relative effects of vaping and smoking on 3 nicotine dependency, the literature suggests that both vaping and smoking result in nicotine dependency (Soule et al., 2020). The literature indicates that differences exist between vaping and smoking. For example, vapers (i.e., individuals who use e-cigarettes to vape) vape more frequently and in more varied locations when compared to smokers (Soule, 2020). Consequently, the behavioural patterns of vaping may alter perceptions of use and dependency; that is, vapers may not comprehend how frequently they use their devices or the amount of nicotine they consume (Soule, 2020). This phenomenon may explain why smokers are significantly more likely to self-identify as addicted to nicotine than vapers (Liu et al., 2017). 1.1.3 Psychological Factors Associated with Vaping and Smoking When examining psychological factors associated with risk behaviours such as vaping and smoking, it is essential to understand the meanings of mental health and mental illness. Everyone has mental health, but not everyone has mental illness (Government of BC, 2019). Generally, mental health is a blanket term covering the full range of mental statuses, whether beneficial or adverse (Government of BC, 2019). Furthermore, mental illness is often a sign of adverse mental health (Government of BC, 2019). More specifically, mental health refers to an individual’s overall mental wellbeing, including but not limited to emotions, cognitions, feelings, problem-solving, resilience, and connections to others and the world (CMHA BC [Canadian Mental Health Association, British Columbia Division], 2015). In contrast, mental illness refers to the adverse impact of emotions, cognitions, feelings, and symptoms on feelings, mood, problem-solving, resilience, and our connections to both others and the world (CMHA BC, 2015). 4 Several psychological factors often referred to as mental illness are associated with vaping and smoking, which indicates that considering mental illness is essential when examining vaping and smoking behaviours (Fardouly et al., 2016; Grant et al., 2019; Jan et al., 2017; Napolitano et al., 2020; Thorstad & Wolff, 2019). Specifically, in 2019, 14% of the population experienced anxiety symptoms or mood disorders (including depression symptoms), increasing from 12% in 2013 (Statistics Canada, 2020b). In addition, experiencing anxiety symptoms and depression symptoms increases the risk of vaping and smoking (Chase et al., 2018; Mayorga et al., 2019; Pham et al., 2020; Ranjit et al., 2019; Secades-Villa et al., 2018; Soule et al., 2020; Watson et al., 2018). Additionally, it is worth noting that nicotine acts as a stimulant in the brain and targets the brain regions associated with muscular control, emotional regulation, anxiety, and fear (Maglia et al., 2018; Molas et al., 2017; Seth et al., 2002). Thus, nicotine may intensify pre-existing anxiety symptoms (Seth et al., 2002). Alternatively, the neurochemical process of withdrawal from nicotine is also associated with increased anxiety and depression symptoms (Reid & Ledgerwood, 2016; Soyster et al., 2016). Therefore, the relationships between nicotine use, anxiety symptoms, and depression symptoms may be multi-directional. 1.1.4 Psychological Factors Associated with Social Media Use While symptoms of anxiety and depression are associated with vaping and smoking, they are also associated with social media use. Both social media dependency and engagement are associated with an increased risk of anxiety and depression symptoms (Al Mamun & Griffiths, 2019; Reer et al., 2019; Smith et al., 2017). Specifically, individuals who have high social media engagement, controlling for social media dependency, are 2.2 times more likely to have anxiety symptoms and 1.9 times more likely to have depression symptoms than the general population (Shensa et al., 2018). Moreover, individuals with high social media engagement and dependency 5 levels are 4.5 times more likely to have anxiety symptoms and 3.1 times more likely to have depression symptoms than the general population (Shensa et al., 2018). 1.1.5 Social Media Use Social media use is a growing area of research as it has become common practice; 64% of Canadians have at least one social media account, and 50% of Canadians have accounts on more than one social media platform (Sherpa Marketing, 2018). More over, most Canadians (94%) have access to the internet in their homes (Statistics Canada, 2019). Excluding online video games and video/music streaming, almost half of Canadians spend more than 10 hours a week on the internet (Statistics Canada, 2019). Furthermore, between 2015 and 2020, the number of people worldwide accessing the five major social media platforms (Facebook, Pinterest, Twitter, Reddit, and Instagram) each month has increased by 243% (Curry, 2020; Dean, 2021; Gilbert, 2018; Iqbal, 2021a; Iqbal, 2021b; Iqbal, 2021c; Lin, 2020; Lynley, 2016; Mohsin, 2020; StatCounter, 2020; Statista Research Department, 2016; Tankovska, 2021a; Tankovska, 2021b; Tankovska, 2021c; Tankovska, 2020). 1.1.6 Helpful and Harmful Effects of Social Media Use Past studies show social media use has both beneficial and harmful effects. The beneficial effects include helping individuals form and maintain relationships, providing social gratification, and improving mental health by facilitating social contact (Kircaburun et al., 2018; Leas et al., 2020; Naslund et al., 2020). Social media is also a valuable tool for promoting healthy changes in behaviour, including quitting vaping and smoking (Maher et al., 2016; Scott & Mars, 2020; van der Tempel et al., 2016). Moreover, recent studies have suggested that social media use can predict the development of mental illness and aid in treatment (Budenz et al., 2020; De Choudhury et al., 2013; Gamaarachchige & Inkpen, 2019; Naslund et al., 2019). 6 However, social media use can also be harmful by increasing the risk of experiencing mental illness (Chancellor et al., 2016; Daniel et al., 2018; Shensa et al., 2018). In effect, examining social media use can aid in predicting and treating mental illness and also lead to increased mental illness. Therefore, social media use appears to have a multi-directional association with mental illness. Determining the positive and negative aspects of social media use remains challenging. Social media engagement is actively using and thinking about social media, which often includes posting, sharing, liking, and responding to messages (Reer et al., 2019; Smith et al., 2017). Social media engagement has beneficial effectsas it facilitates social connection and may help improve mental health (Lau et al., 2020; Ng et al., 2020). However, social media engagement may also pose risks, such as increase the risk of anxiety and depression symptoms (Shensa et al., 2018). Therefore, whether social media engagement has helpful or harmful effects remains murky. Other aspects of social media use are more straightforward to classify as harmful. One example is social media dependency, which occurs when social media use negatively impacts an individual’s emotional wellbeing and functioning (Kircaburun et al., 2018). Social media dependency may increase the risk of experiencing symptoms of anxiety and depression (Shensa et al., 2018). The number of social media platforms used and the frequency of social media use are more objective indicators of overall social media use, which may help differentiate between the helpful and harmful aspects of social media use. For example, the number of social media platforms used and frequency of use appear to act as indicators for both social media engagement and social media dependency (Shensa et al., 2018). For instance, the frequency of checking social media platforms positively correlates with social media dependency (Shensa et al., 2018). 7 Therefore, it is essential to examine multiple indicators of social media use, including positive, negative, and objective indicators, when examining the potential effects of social media use. The indicators of social media use described above (i.e., frequency of use, engagement, dependency, and number of platforms used) are commonly used in research. However, it is worth noting that there are additional indicators, which are beyond the scope of the present study. For example, duration of social media use, preoccupation with a physical and social appearance on social media, fear of missing out (FOMO), cyberbullying, and patterns of social media use, such as using social media right before bed, are all associated with an increased risk of experiencing symptoms of anxiety and depression (Balta et al., 2020; Bettman et al., 2020; Hamilton et al., 2020; Hawes et al., 2020; Tandon et al., 2021). 1.1.7 Vaping and Smoking Representation on Social Media The representation of vaping and smoking on social media appears to interact with vaping and smoking behaviours. Notably, exposure to vaping and smoking content on social media appears to be associated with an increased risk of vaping and smoking (Daniel et al., 2018; Depue et al., 2015; Emery et al., 2014; Sherpa Marketing, 2018; Yoo et al., 2016). Viewing positive vaping and smoking advertisements across various platforms normalizes vaping and smoking and promotes use (Elmore et al., 2017; Maloney et al., 2019). Both vapers and e-cigarette companies generate vaping content on social media (Elmore et al., 2017; Maloney et al., 2019). This type of content fosters the belief that vaping is healthier, safer, and more socially acceptable than smoking, and this belief is associated with an increased risk of vaping (Maloney et al., 2019; McCausland et al., 2019). Likewise, smoking contents on social media are generated by smokers and the tobacco industry, creating positive perceptions about smoking that may lead to higher rates of smoking (Elmore et al., 2017). 8 Vapers and smokers are equally likely to use various social media platforms such as Twitter, Facebook, Instagram, Snapchat, Vine, Pinterest, Tumblr, Google+, LinkedIn, and YouTube (Ilakkuvan et al., 2019). The only significant difference between vapers and smokers regarding social media platforms appears to be the platform of choice. Mainstream platforms (e.g., Snapchat) are more strongly associated with vaping, possibly due to advertisements and to younger adults using these platforms (Massey et al., 2021). In comparison, professional platforms (e.g., LinkedIn) are more strongly associated with smoking, possibly because individuals using professional platforms have higher education and income (Ilakkuvan et al., 2019). Engaging with social media appears to increase exposure to promotional vaping and smoking content, whether it is created by individuals or marketing companies, which then appears to increase the risk of both vaping and smoking. Thus, the current study seeks to further assess the association between engaging with social media and vaping and smoking. 1.2 Study Rationale Given the harms associated with vaping and smoking behaviours, it is essential to understand the various factors contributing to these behaviours. Past studies have examined individual indicators of social media use as potential contributing factors. However, a more comprehensive, integrated understanding of the associations between social media use, vaping, and smoking is required to assess this complex association. In addition, past studies have demonstrated positive association between symptoms of mental illness (i.e., anxiety and depression symptoms) and vaping and smoking. However, it remains vital to also understand the role of mental illness symptoms in mediating the relationships between social media use and vaping and smoking. Therefore, the current study aims to explore whether there exists direct relationships between vaping and smoking and social media use and, if so, to determine whether 9 anxiety and depression symptoms mediate these associations. We will develop a latent construct consisting of multiple indicators of social media use (SMU), including social media dependency, social media engagement, number of platforms used, frequency of social media use The SMU construct will then be used to examine the impact of overall social media use on vaping and smoking behaviours, along with the mediating roles of anxiety and depression symptoms. We developed conceptual models to summarize the nature and directions of the relationships to be tested in this study. Figure 1 below shows the conceptual model used for vaping, and Figure 2 shows the conceptual model used for smoking. Draw.io was used to create all figures in this study (Seibert Media Corporation, 2017). Figure 1: Conceptual model of the relationship between social media use (SMU) and vaping as mediated by symptoms of anxiety and depression 10 Figure 2: Conceptual model of the relationship between social media use (SMU) and smoking as mediated by symptoms of anxiety and depression. 1.3 Theoretical Underpinnings Four complimentary theories provide a foundation to understand the relationship between multiple indicators of social media use, vaping, smoking, and anxiety and depression symptoms. The first three theories primarily address the relationship between vaping, smoking, and social media use (social media engagement theory, normative social behaviour theory, and social comparison theory). Social media engagement theory states individuals seek media to provide gratification in some way, including information, entertainment, or social interaction (Dolan et al., 2016; Katz & Foulkes, 1962). Normative social behaviour theory illustrates how social norms influence behaviour by defining standards and expectations of group membership, which alters perceptions about how frequently behaviours occur (McDonald & Crandall, 2015; Rimal & Real, 2005). Social comparison theory asserts individuals engage in social comparisons, which influences how individuals view themselves in comparison to other people (Taylor & Lobel, 1989). The final theory, affective regulation theory, underpins the relationship between vaping, 11 smoking, and anxiety and depression symptoms by explaining vaping and smoking as a means to reduce negative affect symptoms by using nicotine as a form of self-medication (Morrell & Cohen, 2006; Rzetelny et al., 2008). Appendix A outlines the theoretical relationships under investigation in this study. 1.3.1 Full Mediated Relationships Social media engagement theory suggests social media use facilitates social relationships and provides gratification (Di Gangi & Wasko, 2016). Therefore, continued social media use maintains gratification, which increases social media use engagement and the risk of social media dependency while also increasing exposure to smoking and vaping content that may encourage vaping and smoking (Di Gangi & Wasko, 2016; Dolan et al., 2016; Marino et al., 2016; Men & Tsai, 2013; Katz & Foulkes, 1962). Additionally, the application of normative social behaviour theory suggests that social media use allows individuals to visually assess their degree of belonging to a social group based on feedback provided by peers informed by the individual’s ability to conform to the group norms (Chen et al., 2017; Gil de Zúñiga et al., 2012). Thereby, viewing vaping and smoking content on social media has the potential to normalize, and normalize vaping and smoking as a coping strategy for those with depression symptoms, which may influence an individual’s willingness to vape or smoke (Chaiton & Zhang, 2007; Henry et al., 2012). Moreover, social media also normalizes vaping through association with healthy foods and aspirational lifestyles, which minimizes perceptions of harm from vaping (Basáñez et al., 2018; Chu et al., 2017; Martinez et al., 2018). In addition, social comparison theory suggests the degree to an individual is perceived to belong to a desirable social group or idealized lifestyle are closely associated with social capital, the perception of gaining or losing social status may increase anxiety symptoms (Chen et al., 2017; Gil de Zúñiga et al., 2012; 12 Ferguson et al., 2014; Tiggemann & Policy, 2010). Specifically, among some groups on social media, such as women experiencing eating disorders, young adults, and emerging young adults may be influenced by the idealized lifestyle presented on social media that includes vaping and starting vaping (Ferguson et al., 2014; Hanna et al., 2017). Thus, some individuals may start vaping or smoking to help to decrease anxiety symptoms by enabling individuals to move closer to the idealized lifestyle (Tiggemann & Polivy, 2010; Wert & Salovey, 2004). Finally, there is also a link between engaging in social comparison on social media and depression symptoms, which in turn may lead to an increased risk of smoking (Cacioppo & Cacioppo, 2016; Neira & Barber, 2014; Primack et al., 2018). 1.3.2 Vaping, Smoking, and Symptoms of Anxiety and Depression According to affective regulation theory, vaping or smoking behaviour might start to reduce symptoms of anxiety and depression due to the impact nicotine has on the affective state (Besson & Forget, 2016). Individuals experiencing anxiety symptoms and depression symptoms, either at the clinical or subclinical level, may use nicotine to help manage their symptoms (Morrell & Cohen, 2006). This relationship occurs as nicotine reduces the attentional bias to the mood states related to anxiety and depression symptoms (Rzetelny et al., 2008). Moreover, nicotine use may also reduce symptoms of anxiety and depression by improving the underlying cognitive and mood symptoms and serving as a form of self-medication (Besson & Forget, 2016). However, it is essential to note, although prior theory and empirical evidence indicate an association between mental illness symptoms and risk behaviours such as vaping and smoking, the directionality of these associations remains complex. Altogether, these theories contribute to our understanding of how social media use may impact vaping and smoking and the potential mediating role of anxiety symptoms and depression 13 symptoms. Moreover, different patterns of social media use may increase the risk of developing social media dependency or problematic social media engagement. In turn, these patterns of social media use appear to impact anxiety and depression symptoms, which then influences vaping and smoking. Furthermore, the content viewed on social media also may influence vaping and smoking behaviours due to group norms. The associations between vaping, smoking, mental illness symptoms, and social media use outlined by empirical findings and theory guided the selection of search terms and informed the literature review. 14 CHAPTER TWO: LITERATURE REVIEW This chapter presents a systematic search and scoping review of the current literature related to vaping, smoking, social media use, anxiety symptoms, and depression symptoms. The literature review starts with examining smoking, social media use, and anxiety and depression symptoms, followed by vaping and social media use and smoking and social media use. Next, we examined anxiety symptoms, depression symptoms, vaping, and smoking, followed by anxiety symptoms, depression symptoms, and social media use. Finally, we present the significant gaps in the literature and discuss how the current study will address those gaps and add to the existing body of literature. 2.1 Search Strategy A scoping review was conducted to examine the associations between vaping, smoking, social media use, anxiety symptoms, and depression symptoms. The systematic search used Boolean phrasing and six databases, including PsychINFO, ERIC EBSCO, Education Source, Social Work Abstracts, LGBT Life with full text, and Medline EBSCO. The search included several key terms related to the central concepts of this study. The searches consisted of the following terms combinations: 1) “social media” AND “vap*” OR “e-cigarette*” AND “depress*” OR “anxi*”; 2) “social media” AND vap* OR “e-cigarette*”; “social media” AND smok* OR “cigarette*” NOT “e-cigarette*” NOT vap*; 3) “e-cigarette*” AND “depress*” AND “anxi*; “cigarette*” AND “depress*” AND “anxi* ”; and 4) “social media” AND “anxi*” AND “depress*.” Due to the small number of records for the searches that included all search terms, we conducted another search in Elsevier Science Direct, which provided 142 results. We then added the Elsevier Science Direct search results to the 11 articles initially found for all search terms 15 from the previous six databases for a total of 153 results for the fully mediated relationship. Given the limited number of articles that addressed this topic, we conduct additional searches. The first search used terms related to vaping and social media use, and the second using terms associated with vaping, anxiety symptoms, and depression symptoms. The third search included terms for smoking, anxiety symptoms, and depression symptoms, and the fourth search consisted of social media use, anxiety symptoms, and depression symptoms. We conducted these literature searches between May 29, 2020, and June 4, 2020. The limiters used to filter the records included: published between 2015-2020, participants over the age of 18 (in databases that allowed this limiter), text in English, and scholarly and peer-reviewed. We selected this five-year-long timeframe for publication dates for the limiters intentionally for the following three main reasons. First, 2014 was when e-cigarettes became a big industry, and second, 2014 was also when vaping was first formally recognized as a public health issue (CASSAA [Consumer Advocates for Smoke-Free Alternatives Associated], 2020). Finally, this approximate time period was also when big vaping companies started advertising on social media (CASSAA, 2020; Chayknowski, 2018). Articles were downloaded and loaded into Zotero, a free reference management program (Zotero, 2006), and screened. Exclusion criteria included articles focused on vaping cessation, smoking cessation, individuals under the age of 18, cannabis use, suicide, cyberbullying, online support groups, video games, and if the documents were government or policy records. Inclusion criteria included articles that examined the key variables outlined in the search terms and had adult samples. 16 2.2 Search Results With all the search terms, we identified 6142 articles. Duplicates were removed, screened, and assessed for eligibility. Figure 3 is a PRISMA diagram that outlines the search process (Moher et al., 2009). In total, 33 articles were then loaded into NVIVO 12 (NVIVO, 2018) and reviewed. Appendix B contains a table of the main findings from the records generated through this search process and used in the literature review organized by theme. 17 Figure 3: PRISMA chart outlining search process for literature review. 18 2.3 Overview Of the 33 articles included in this review, 21 were quantitative, and 12 were qualitative. The searches for this scoping review did not yield any studies examing the fully mediated relationships under investigation in this study. However, the searches yielded two studies that examined smoking, social media use, and depression symptoms, but none examined the relationships between vaping, social media use, anxiety symptoms, and depression symptoms. Thus, for vaping and social media use, we reviewed 12 articles, and for smoking and social media use, we examined four articles. For anxiety symptoms, depression symptoms, vaping, and smoking, we reviewed eight articles, and for anxiety symptoms, depression symptoms, and social media use, we reviewed seven articles. 2.3.1 Smoking, SMU, Anxiety Symptoms, and Depression Symptoms The searches for vaping, social media use, anxiety symptoms, and depression symptoms yielded no results, but some examined vaping and social media use; however, the searches for smoking, social media use, anxiety symptoms, and depression symptoms provided two articles. Ilakkuvan and colleagues (2019) examined the use of different social media platforms and various health-related behaviours, including smoking. They found individuals who used professional social media platforms (e.g., LinkedIn) were more likely to have smoked cigarettes ever and/or the past thirty days than individuals with high general social media use. Also, creative social media users (e.g., mainly using vine for creative reasons) were more likely to smoke and less likely to experience depression symptoms than individuals with high general social media use. However, Ilakkuvan and colleagues (2019) did not measure smoking frequency or nicotine dependence, which may alter this relationship given the theoretical assertions of 19 Affective Regulation Theory that some individuals use nicotine to reduce negative affect (Morrell & Cohen, 2006; Rzetelny et al., 2008). Furthermore, Ilakkuvan and colleagues (2019) examined both anxiety symptoms and depression symptoms using a two-item screening question for anxiety symptoms and an additional two-item screening question for depression symptoms. Other authors note using scales with a limited number of questions is associated with instability in responses and recommend using scales with multiple questions measuring the same construct to increase a study's overall reliability and validity (Diamantopoulos et al., 2012; Robinson, 2018). Also, Ilakkuvan and colleagues (2019) measured social media use by assessing the frequency of use for ten preselected social media platforms and then turning the scores into a latent class. Yet, several indicators comprise social media use. By only measuring the frequency of specific platforms, the nature of the relationship between the other indicators of social media use, smoking, and depression symptoms remains unexplored. Similarly, Brailovskaia and colleagues (2020) investigated the impact of reducing Facebook use on subjective wellbeing and smoking. They found Facebook use and depression symptoms had a significant association, yet the relationship between Facebook use, smoking status, and depression symptoms was statistically insignificant. Of note, the sub-sample of smokers was small (n = 33), which reduces statistical power and may hinder discovering statistically significant findings (Faber & Fonseca, 2014). Additionally, Brailovskaia and et al., (2020) measures for Facebook only included use and dependency, and other platforms such as Twitter and Instagram and other indicators of social media use such as engagement and frequency of social media use remained unexplored. Both of these studies examined the association between social media use and smoking with consideration of depression symptoms. While the association between smoking, social 20 media use, and depression symptoms was unclear from these two studies, they both suggested the association between social media use, depression symptoms, and smoking warrants further investigation using better measurements of depression symptoms, including anxiety symptoms and larger sample size. Also, neither study explored the indicators of social media use together, highlighting the need for more precise measurements of social media uses to inform recommendations. Additionally, given the growing prevalence of vaping, it remains essential to examine the relationship between vaping and social media to understand the mechanisms contributing to nicotine use, anxiety symptoms, and depression symptoms. 2.3.2 Vaping on Social Media Promotional vaping content is commonplace across different social media platforms (Dai & Ho, 2017; Kim et al., 2015; Laestadius et al., 2019; Scaioli et al., 2018). Common themes of these advertisements included freedom, vaping as different than smoking, and vaping as a socially acceptable behaviour (Willis et al., 2017). Promotional vaping content takes many forms; for example, vape trick videos on YouTube, meaning using vapes in new and innovative ways (Kong et al., 2019). In addition, different features of the advertisements increase vaping intentions, such as having the product promoted by a credible celebrity, peer use, or believing vaping prevalence is high (Majmundar et al., 2019; Phua et al., 2018). More specifically, searching for vaping information, viewing vaping advertisements, viewing vaping content generated by peers, and generating vaping content are all associated with an increased risk of vaping (Emory et al., 2019; Kreitzberg et al., 2018; Sawdey et al., 2017; Yang et al., 2019). These studies suggest vaping promotion on social media informs use by altering vaping intentions. However, across the literature, there were some common limitations. For example, few studies examined if the frequency of social media use, number of social media platforms 21 used, social media engagement, and social media dependency alter the impact of vaping content or are related to vaping status. Therefore, to address these limitations, we should carefully consider the different indicators of social media use. Given these findings, the current study measured the role of posting and viewing peers’ posts about vaping and general social media use on vaping behaviour. 2.3.3 Smoking on Social Media Cigarettes are promoted and distributed online (Williams & Derrick, 2018). Viewing smoking content supported by peers or containing a celebrity endorsement increased positive perceptions about smoking (Yoo, 2016). Several factors related to viewing positive smoking content on social media are associated with an increased risk of future smoking, such as expressing and receiving positive smoking content (Yoo et al., 2016; Zhu, 2017). However, there are a couple of limitations regarding the literature for smoking and social media use. Online, peers promote smoking content, but the impact of an individual promoting smoking and smoking status is unclear, which requires further investigation. In comparison to our understanding of vaping status and vaping social media content to our understanding of smoking status and smoking social media content, social media use remains unexplored, most likely because up until recently, the well-established regulations for promoting cigarettes that do not apply to the promotion of e-cigarettes (Canadian Cancer Society, 2021; Government of Canada, 2018b). Furthermore, very little research exists that examines the impact of the indicators of social media use (including frequency, number of platforms, dependency, and engagement) on smoking behaviour. 22 2.3.4 Anxiety Symptoms, Depression Symptoms, Vaping, and Smoking Both anxiety and depression symptoms are associated with vaping, smoking, and increased nicotine dependence (Bianco, 2019; Hefner et al., 2019; Pham et al., 2020; Spears et al., 2018; Wiernik et al., 2019; Zvolensky et al., 2019). Coping with social stress is significantly associated with an increased risk of smoking (Watson et al., 2018). Furthermore, using nicotine to aid in emotional regulation appears to moderate the association between anxiety symptoms, depression symptoms, and smoking (Leventhal & Zvolensky, 2015). Across the literature, measuring vaping, smoking, anxiety symptoms, and depression symptoms remain challenging. Smoking is consistently measured only by the number of cigarettes smoked. Yet, few studies consider nicotine dependence, which creates challenges in comparing vaping and smoking, without nicotine dependence as a standard variable. Furthermore, more than half of the studies reviewed measured mental illness in terms of having or not having a diagnosis rather than using scales to measure anxiety and depression symptoms. As a result, it is unclear if anxiety and depression symptoms impact vaping and smoking at sub-clinical levels. 2.3.5 Anxiety Symptoms, Depression Symptoms, and Social Media Use Multiple factors influence the complex relationship between anxiety symptoms, depression symptoms, and social media use, which most studies do not consider (Bhat et al., 2018; Frost & Rickwood, 2017; Lin et al., 2016; Primack et al., 2018). However, the investigation of social media use has recently started to include the different indicators (e.g., frequency, number of platforms, engagement, and dependency). For example, Shensa et al. (2018) took a more holistic view of the indicators of social media use by including dependency, engagement, frequency, and multiple platforms use when examining the association between social media use and anxiety and depression symptoms. They found all of the indicators of social 23 media use are associated with an increased likelihood of anxiety symptoms and depression symptoms. Additionally, variations exist for the association between social media use, anxiety symptoms, and depression symptoms. Specifically, higher levels of passive social media use (e.g., scrolling) are associated with depression symptoms, whereas; depression symptoms are not associated with active social media use (e.g., liking a post) (Escobar-Viera et al., 2018). Furthermore, higher levels of investment in social media use are also associated with an increased risk of depression symptoms (Lowe-Calverley et al., 2019). However, few studies have approached social media use holistically, and understanding the different indicators of social media use is a rapidly developing area of study. This study seeks to add to our understanding of social media use anxiety symptoms and depression symptoms by including measures for anxiety symptoms, depression symptoms and examining the multiple indicators of social media use holistically. 2.3.6 Research Study Rationale Prior research supports greater social media use is associated with higher rates of anxiety symptoms and depression symptoms. Also, anxiety symptoms and depression symptoms are associated with an increased risk of vaping and smoking. However, few studies have examined the role of social media use on vaping and smoking behaviour, considering the mediating role of anxiety and depression symptoms. Furthermore, many studies examining the impact of social media use on anxiety symptoms, and depression symptoms focus only on a few aspects of social media use rather than considering the different indicators of social media use. However, social media use is a multidimensional construct related to the frequency, type of platform used, engagement, and dependency of use. This study will add to the existing literature by examining the relationship between vaping and smoking and social media use by including a 24 multidimensional construct of social media use, including a mediators of anxiety symptoms and depression symptoms. In addition, this study will provide a more holistic understanding of the factors influencing the initiation and escalation of vaping and smoking behaviours. The following research questions and hypotheses guide this research. Research questions and hypotheses RQ1: Does social media use directly impact vaping and smoking? Sub-Question 1: Does social media use directly increase the likelihood of vaping and smoking? Sub-Question 2: Is social media use positively associated with increased nicotine dependency among vapers and smokers? Sub-Question 3: Does social media use directly impact the likelihood of experiencing anxiety symptoms and depression symptoms? H1: We hypothesize that social media use, consisting of four indicators (e.g., frequency, number of platforms, engagement, and dependency), will be positive associated with vaping and smoking status and nicotine dependency. We anticipate that increased frequency of use, more platform types used, higher levels of engagement and higher levels of dependency will be associated with a greater probability of vaping and smoking, as well as with higher level of nicotine dependency among vapers and smokers. RQ2: Does social media use indirectly impact vaping and smoking through anxiety symptoms and depression symptoms? Sub-Question 4: Does social media use impact vaping status when symptoms of anxiety and depression are considered? 25 Sub-Question 5: Does social media use impact smoking status when symptoms of anxiety and depression are considered? Sub-Question 6: Does social media use increase anxiety and depression symptoms among vapers? Sub-Question 7: Does social media use increase anxiety and depression symptoms among smokers? H2: We hypothesize that when mental illness is considered, social media use will be positively associated with vaping and smoking status and nicotine dependency. We anticipate that social media use will be positively associated with increased levels of mental illness symptoms (anxiety symptoms and depression symptoms), and increased levels of mental illness symptoms will be positively associated with vaping and smoking status and dependency. RQ3: Do the indicators of social media use impact anxiety/depression symptoms, vaping and smoking status, nicotine dependency among vapers and smoker?. Sub-Question 8: Is social media use a latent construct (SMU)? Sub-Question 9: Does SMU directly and indirectly impact vaping and smoking status? Sub-Question 10: Does SMU directly and indirectly impact nicotine dependency among vapers and smokers? H3: We hypothesize that the different indicators of social media use, specifically, frequency, engagement, and dependency, will be positively associated with anxiety symptoms, depression symptoms, vaping and smoking status and nicotine dependency. We anticipate that increased frequency of social media use, number of platforms, engagement and dependency will be associated with a greater probability of anxiety symptoms, depression symptoms, vaping and smoking status and nicotine dependency. It is expected that social media use will be positively 26 associated with vaping, smoking, anxiety symptoms, and depression symptoms, and vaping and smoking will be positively associated with social media use both when symptoms of anxiety and depression are considered and when they are not. 27 CHAPTER THREE: METHOD This chapter presents the methods used in the current study. The method section starts with an outline of recruitment and data collection and is followed by an outline of the process for determining the number of participants. Finally, we provide a description of measurement instruments used and the analyses plan. 3.1 Recruitment and Data Collection This study was an online survey open to all adults living in Canada. We administered the survey via the UBC-licensed Qualtrics (2005), an online survey program. We recruited participants via posts on a variety of Reddit forums. We selected Reddit forums based on the following criteria; 1) open to the general public (i.e., do not have to be a group member to post or see posts), 2) the forum focused on key topics related to this thesis or was related to a Canadian community (e.g., vaping, smoking, anxiety, depression, mental health), or a community form (e.g., a form for a province such as Alberta or city such as Saskatoon). See Appendix C for a complete list of subreddit posts and Appendix D for the letters of invitation. Participants were any English-speaking person living in Canada or Canadians over the age of 19 with access to the online survey. We posted the survey link to specific subreddit threads, and the survey took about fifteen minutes to complete. To ensure there were no participant duplications, the setting on Qualtrics to not allow the survey to be taken more than once from the same IP address was activated. However, as there was no incentive for participating, it was unlikely a participant took the survey more than once. We collected data in two waves. In the first wave, we distributed the survey link on 22 subreddit links from October 8, 2020, to November 9, 2020. In the second wave, we distributed the survey link on an additional 59 subreddits from November 12, 2020, to December 15, 2020 (See Appendix C for the 28 complete list of waves one and two subreddit posts). Of note, we selected measures before the COVID-19 pandemic, and the committee approved the proposal during the early days of the pandemic. Therefore, we collected data and analyzed it during the COVID-19 pandemic. 3.2 Participants A total of 933 individuals accessed the survey link, with ten individuals not consenting, 11 individuals not in Canada, and 17 individuals under the age of 19 years old. Among the 895 eligible participants, 176 individuals consented but did not complete the survey (completing less than 40% of the survey). Additionally, we did not include 12 individuals out of the 719 participants who completed the survey in the final sample because of missing data on key variables (e.g., vaping and smoking status or anxiety symptoms) and other sociodemographic items (e.g., age). Thus, the final sample included 707 participants with complete data. 3.3 Measurement The comprehensive online survey instrument included six validated scales and eleven single-item indicators. Appendix E contains a copy of the online survey. The study had two primary outcomes (vaping and smoking), four main predictors (social media use behaviours), and two mediating variables (symptoms of anxiety and depression). The survey included four measures related to the study outcomes of vaping and smoking status and nicotine dependency including two single-item screening questions to assess vaping and smoking status, and two validated measures to evaluate nicotine dependency for vaping and smoking. There were also four measures related to the various indicators of social media use (SMU), including the frequency of use, the number of platforms used, social media engagement, and social media dependency. Both social media engagement and social media dependency were validated measures, and the frequency of use and number of platforms were assessed using items from 29 Shensa et al. (2018). The survey also included two validated measures related to the mediating variables of symptoms of anxiety and depression. Additionally, six items were used to measure vaping and smoking content on social media. Finally, the survey included nine questions about sociodemographic characteristics. See Appendix F for a summary of response options and coding, including 1) list of variables, 2) how each variable was measured, 3) the response options/ranges of scales 4) variable/scale type, 5) how the response options were coded/interpretation of meaning from scores, and 6) Cronbach’s alpha for each scale for the current study’s sample. Vaping and Smoking Outcome. Vaping status was assessed through a single indicator question, and nicotine dependency among vapers was assessed by the Penn State Electronic Cigarette Dependence Index (Foulds et al., 2015). The scale has a summed scale score ranging from 0 to 20, with a higher score indicating more nicotine dependency. The scale has shown to be both reliable and valid (Dowd et al., 2019; Ellis et al., 2019), and among the current sample had a Cronbach’s alpha of 0.82. Smoking status was assessed using a single item, and nicotine dependency among smokers was assessed the Fagerstrom Nicotine Dependence Scale (Heatherton et al., 1991). The Fagerstrom scale has a summed scale score ranging from 0 to 10, with a higher score indicating more nicotine dependency and has been found both reliable and valid, and among the current sample, had a Cronbach’s alpha of 0.79 (Pomerleau et al., 1994). Of note, thirteen vapers had a vaping dependency score of less than one, and thirty-four of the smokers had a smoking dependency score of less than one. Thus, the number of participants for vaping and smoking varies when status and nicotine dependency were examined. Social Media Use. This study included four measures of social media use: 1) social media dependency, 2) social media engagement, 3) the frequency of use of social media, and 4) 30 the number of social media platforms used. Social media dependency (SMUD) was assessed using the Bergen Social Media Addiction Scale (BSMAS) which has a summed scale score ranging from 6 to 30 and a higher score indicating greater dependency (Andreassen et al., 2012; Andreassen et al., 2017). The scale has high item-total correlation, test-retest reliability, convergent validity, and discrimination validity, and among the current sample, it had a Cronbach’s alpha of 0.83 (Andreassen et al., 2012; Andreassen et al., 2017). Social media engagement (SMUE) was measured with the Social Media Engagement Questionnaire (SMEQ) (Przybylski, 2017). This scale has summated scores ranging from 0 to 35, with a higher score indicating higher social media engagement levels, and is reliable and valid (Al-Menayes, 2015). For the current sample, this scale had a Cronbach’s alpha of 0.80. Social media platforms and frequency of social media use (SMUF) were assessed using a single question for each platform (Shensa et al., 2018), “How often do you visit each of the following popular social media platforms?” The twelve social media platforms included: Twitter, Facebook, Instagram, Snapchat, Pinterest, Tumblr, Google+, LinkedIn, YouTube, Reddit, TikTok, and other. Participants were able to select how often they used each social media platform on a scale with items ranging from 0 (I don’t use this platform) to 6 (5 or more times a day). The frequencies of use for all the platforms were summed to create a total frequency of social media use score. Multiple social media platform use was assessed by counting how frequently each participant used several platforms. Social Media Content on Vaping and Smoking. Social media content for vaping and smoking was assessed separately using three questions drawn from Sawdey et al. (2017). These questions assessed viewing of vaping or smoking content, including posting content, peers posting content, and advertisements on social media. For each question, response options ranged 31 from 0 (never) to 5 (daily). Participants were asked about vaping and smoking content separately. Mental Illness Symptoms. The survey also included measures of symptoms for both anxiety and depression. Anxiety symptoms were assessed using the Generalized Anxiety Scale (GAD-7), which had seven items and total scores ranging from 0 to 21, with a higher score indicating more anxiety symptoms (Spitzer et al., 2006). The clinically significant cut-off values were set at a score of 10 or greater (Johnson et al., 2019). A score less than ten was coded as no anxiety symptoms, and a score ten or greater was coded as anxiety symptoms. This scale also has cut-off values for anxiety symptoms that provided a more detailed description of groupings, including none (0-4), mild (5-9), moderate (10-14), and high (15-21) (Spitzer et al., 2006). This scale is reliable and valid (Kroenke et al., 2009; Kroenke & Spitzer, 2010; Spitzer et al., 2006), and with the current sample, it had a Cronbach’s alpha of 0.92. Depression symptoms were assessed using the eight-item Patient Health Questionnaire (PHQ-8), with scores ranging from 0-24, with a higher score indicating more depression symptoms (Spitzer et al., 1999). The clinically significant cut-off value was set at a score of 10 or greater (Shin et al., 2019). A score less than ten was coded as no depression symptoms, and a score of 10 or greater was coded as depression symptoms. This scale also had cut off values for depression symptoms that provided a more detailed description of groupings, including none (0-4), mild (5-9), moderate (10-14), high (15-19), and severe (20-24) (Kroenke et al., 2009). This scale is both valid and reliable (Kroenke et al., 2009; Kroenke & Spitzer, 2010) and, in the current sample, had a Cronbach’s alpha of 0.88. Of note, we used the PHQ-8 instead of the PHQ-9 as the PHQ-8 omits an item related to suicidality. Given this study's online and anonymous nature, we felt it better not to ask about suicidality. 32 Socio-demographic characteristics. Socio-economic characteristics were assessed using nine single-item questions drawn from the Canadian Campus Wellbeing Survey (CCWS [Canadian Campus Wellbeing Survey], 2019). Specifically, this study included measures of age, self-identified ethnicity, Indigenous ancestry, country of birth, gender, LGBTQ2+ status, relationship status, education, and the province of residence. These items were selected as past research has shown a relationship with the outcomes (vaping and smoking) and mediating variables (symptoms of anxiety and depression), and also provide a means to determine the representativeness and context of the sample (Coulter et al., 2018; East et al., 2019; Gagné et al., 2019; Health Canada, 2019; Lee & Oh, 2019; Nguyen & Bornstein, 2020; Reid et al., 2019; Waa et al., 2020; Yimsaard et al., 2021). See Appendix F for categories and coding of socio-demographic characteristics. 3.4 Analytic Approach We used several descriptive, bivariate, and multivariate analyses to assess the data quality and answer the study research questions. First, using the scale authors’ recommendations, we generated scale scores for the previously validated measures. Next, we assessed them for skewness, and we recoded some variables into dichotomous variables if they had unacceptable skewness. Using an iterative and cumulative process, we conducted the statistical analyses in three main steps. First, univariate descriptive statistical summarized the sociodemographic characteristics for the sample. Second, bivariate statistical tests identified significant sociodemographic variables to be used as controls in subsequent analyses. Together, the univariate and the bivariate analyses established the initial associations between the outcomes and key predictors and informed the inclusion of covariates in multivariate analyses. Finally, 33 multivariate statistical analyses identified significant associations between the primary outcomes and key variables. Logistic and linear regression tested the association between key predictors and outcomes, informing the decision to examine these relationships using mediational modelling and a structural equation approach. In doing so, using structural equation modeling allowed us to test the association of the latent variable of SMU comprised of the different indicators of social media use with vaping and smoking status and dependency along with a latent variable of mental illness symptoms consisting of anxiety and depression symptoms mediating the association. Univariate Descriptive Statistics. Descriptive statistics were employed to examine the individual items and scales (e.g., means, standard deviations, and frequencies and percentages) and explore group-based differences. Cronbach’s alpha examined the reliability of the scale scores with the cut-off for acceptable values set at 0.70 (Nunnally, 1978). The Cronbach’s alpha scores indicated the scales used in this study were reliable (See Appendix F for alpha scores for each scale). The distributions of outcome and key variables were also assessed by examining skew and kurtosis using the R package psych (Revelle, 2019). Skew refers to how far the center of the distribution is away from zero with a cut-off value of greater than two for a sample larger than 300 participants (Kim, 2013). Kurtosis refers to the height of the distribution with a cut-off value of seven for a sample larger than 300 participants (Kim, 2013). Almost all the variables in this study had acceptable skew and kurtosis except for vaping and smoking content on social media. Therefore, we converted these questions from a Likert-type scale score to a dichotomous variable (yes or no). Bivariate Descriptive Statistics. We conducted a series of bivariate tests to assess the associations between sociodemographic characteristics and the outcome and key variables. 34 Levene’s test examined the homogeneity of sample variance. The result was non-significant, indicating it was acceptable to conduct independent t-tests rather than independent one-way tests using the car package (Fox & Weisberg, 2019; Gastwirth et al., 2009). First, t-tests determined if age was associated with vaping and smoking status and symptoms of anxiety and depression, based on clinically significant cut-off scores for the latter. Both the p-value (significance level set to 0.05) and confidence intervals (significance determined if the interval did not pass thought one) were assessed (Simon, 1986; Smithson, 2003). Second, we conducted chi-square tests to examine if categorical sociodemographic characteristics were associated with vaping and smoking status and anxiety and depression symptoms using the gmodels package (Warnes et al., 2018; Warens et al., 2017). We also used chi-squared tests to examine the impact of viewing vaping and smoking content on social media with vaping and smoking status by recoding viewing vaping and smoking content as “yes, it has ever been viewed,” and “no, it has never been viewed.” Third, Pearson correlation analyses examined the bivariate correlations among key variables for the whole sample, among vapers, and among smokers, using the stats package (R Core Team, 2020). The correlation ranges from -1 to +1, with 0 indicating no correlation, and +1 indicating a perfect positive correlation (Field et al., 2012), with |0.20| or less indicating a weak correlation, |0.30| to |0.49| indicating a moderate correlation, and |0.50| indicating a strong correlation (Statistical Solutions, 2021). Given the statistically significant results of the t-tests and chi-squared tests, age, gender, education, and LGBTQ2+ status were included as control variables in the subsequent multivariate data analyses. Additionally, differences between the Pearson correlation matrices for the vaping and smoking samples resulted in separate multivariable models for testing the associations between social media use and anxiety symptoms and depression symptoms with 35 vaping and smoking. Based on these analyses, separate models were also developed and tested for vaping and smoking. Logistic Regressions. Logistic regressions determined whether social media use and anxiety symptoms and depression symptoms were significantly associated with vaping and smoking status using the mlogit package (Yves, 2009). In all the logistic regressions, significant sociodemographic characteristics were included as controls. We tested the assumptions of logistic regression. First, multicollinearity was assessed and was within the acceptable range. We also examined the variance inflation factor (VIF) for each model to test for multicollinearity. A VIF score of 10 or less indicates that the predictor variables are independent of each other (Glen, 2015). However, it is worth noting that VIF scores are influenced by the number of predictors in the regression (Glen, 2015). For the social media use and vaping status model, the VIF was 9.06. For the social media use and smoking status model, the VIF was 9.01. For the social media use and clinically significant anxiety symptom model, the VIF was 8.86. For the social media use and clinically significant depression symptom model, the VIF was 8.80. Next, the assumption of linearity, meaning there was an underlying linear relationship between variables, was also assessed by re-running the logistic regression, including the log of key variables (e.g., vaping and smoking status, indicators of social media use, and anxiety and depression symptoms) with significant results meaning that the assumption of linearity had been violated (Field et al., 2012; Schreiber-Gregory, 2018). This linearity assumption was not violated; therefore, we deemed it acceptable to conduct logistic regressions. After determining the relationship between the indicators of social media use, anxiety symptoms, depression symptoms, and vaping/smoking status, we then sought to examine the relationship between the indicators of social media use, 36 anxiety symptoms, depression symptoms, and nicotine dependency among vapers and smokers to determine if the same factors were associated with staus and nicotine dependency. Linear Multiple Regressions. We then conducted multiple linear regressions to examine the association between symptoms of anxiety and depression symptoms, the indicators of social media use and nicotine dependency among vapers and smokers utilizing the car, and QuantPsyc packages (Fletcher, 2012; Fox & Weisberg, 2019). We first tested multiple regression with standardized coefficients to test if clinically significant cut-off scores for anxiety and depression predicted nicotine dependency among vapers and smokers controlling for the important sociodemographic characteristics (e.g., education, gender LGBTQ2+ status, and age). Next, we examined how well the indicators of social media use predicted nicotine dependency among vapers and smokers, controlling for the significant sociodemographic characteristics (e.g., education, gender, LGBTQ2+ status, and age). The standardized betas values allowed the coefficients to be expressed in terms of the association between a one-unit change in the predictor variable and a one-unit change in the outcome variable using the package lm.beta (Behrendt, 2014; Fletcher, 2012; Glen, 2016a). Finally, R2 values, which indicate the amount of variance nicotine dependency among vapers and smokers explained by the models, were also examined (Field et al., 2012). We also tested the underlying assumptions of linear regression. The Durbin-Watson (DW) test checked for autocorrelation among variables and independence of errors using the car package (Fox & Weisberg, 2019). Autocorrelation refers to the similarity of residuals from the regression, with a score of 2 indicating no autocorrelation, a score of less than 2 showing negative autocorrelation, and a score greater than 2, indicating a positive autocorrelation (Glen, 2016b). All the linear regressions in this study had a DW statistic of about 2, suggesting that 37 there was little to no autocorrelation for the values of each measure between participants. Independence of errors refers to having correlations among the error terms of the variables used in the regressions (Analyse-it, 2020). The non-significant independence of errors test means that there was no indication of violated assumptions in the current study (p > 0.30 - p > 0.91). The VIF of the social media use and nicotine dependency among vapers regression model was 9.19. The VIF of the social media use and clinically significant cut-off values for the anxiety symptoms regression model among vapers was 9.19. Likewise, the VIF of the social media use and clinically significant cut-off values for the depression symptoms regression model among vapers was 9.19. The VIF of the social media usea nd nicotine dependency among smokers regression model was 9.48. The VIF of the social media use/clinically significant anxiety symptoms regression model among smokers was 9.48. The VIF of the social media use and clinically significant depression symptoms regression model among smokers was 9.48. Thus, the assumptions were not violated for linear regression. Therefore, we deemed it acceptable to conduct the linear regressions. After examining the linear regressions associated with nicotine dependency among vapers and smokers, we examined the associations between key predictors and the categorical outcome related to vaping and smoking status as mediated by anxiety and depression symptoms. KHB Logistic Mediation Analysis. As the logistic and multiple regressions indicated significant associations between the indicators of social media use, anxiety symptoms, depression symptoms, and vaping and smoking, we decided to conduct logistic mediation analyses to examine the potential mediating effects of anxiety symptoms and depression symptoms. For this purpose, the Karlson-Holm-Breen method (KHB) for comparing coefficients within nested models was used (Karlson et al., 2012). This method allowed us to examine total, 38 direct, and indirect associations. A total effect refers to the association of all variables on the outcome (Breen et al., 2013). One example is the association of the frequency of social media use plus anxiety symptoms on vaping status. A direct effect refers to the association of the predictor variables on the outcome variable (Breen et al., 2013). For example, the association of the frequency of social media on vaping status without considering anxiety symptoms. An indirect effect refers to the association between the predictor variables on the outcome variable when the mediating variable is considered (Breen et al., 2013). For example, the association of the frequency of social media on vaping status, considering anxiety symptoms as a mediating variable. There are also some important features of interpreting these effects. An effect of 1 indicates no association between variables (Smith, 2018). Whereas an effect of less than 1 indicates a negative association and an effect greater than 1 indicates a positive association (Smith, 2018). Two separate models examined the mediating effects of anxiety symptoms and depression symptoms on vaping status and an additional two models for smoking status. Confidence intervals for the KHB models were constructed using the package asbio (Aho, 2020). R2 values assessed the amount of variance explained by these models. Following these analyses, we sought to examine the mediated association between a latent construct of social media use, SMU, (including frequency, number of platforms, engagement, and dependency) and vaping and smoking status and nicotine dependency, mediated by a latent construct of mental illness symptoms (including anxiety symptoms and depression symptoms). Thus, we decided to conduct structural equation models. However, before doing so, we needed to develop and validate a latent construct of social media use to include in the structural model. 39 Confirmatory Factor Analysis. In this study, our structural models contained two latent constructs, mental illness symptoms (anxiety and depression symptoms), and social media use. Given anxiety and depression symptoms often co-occur, and the high degree of multicollinearity in between these two variables in the bivariate and regression analyses requiring the use of separate models, a confirmatory factor analysis was not conducted for the latent construct of mental illness symptoms (Cummings et al., 2014; Ter Meulen et al., 2021). However, the latent construct of social media use (SMU) was a novel approach, and therefore, we conducted confirmatory factor analysis (CFA) using the package lavaan to develop the latent construct of SMU (Rosseel, 2012). The assumptions of CFA, including skewness, outliers, and multivariate normality, were assessed using histograms and the Royston Test contained within the MVN package (Korkmaz et al., 2014; Professional Statistical Consulting, 2018). The distributions for the number of platforms used, frequency of use, and social media engagement were acceptable as indicators of SMU. However, social media dependency was slightly skewed but still deemed acceptable (Professional Statistical Consulting, 2018). Also, although lavaan does not officially accept count variables (e.g., number of platforms), count variables can still be used in lavaan if the variable has a unimodal histogram and at least five response options (Jorgensen, 2018; Rosseel 2021). Given the distributions were acceptable, we conducted the CFA. See Appendix G for a copy of the distribution histograms. CFA uses multiple fit indexes to determine model fit. First, χ2 value and significance level (p-value) represent the critical value for the distribution and determine whether the null hypothesis should be rejected (Brown & Moore, 2012). A statistically significant χ2 indicates the null hypothesis should not be rejected as the underlying association between the variables is not adequately explained by the model (Brown & Moore, 2012). However, χ2 is sensitive to sample 40 size, as a large sample may fail to reject the null hypothesis even though the data fit the model well (Brown & Moore, 2012). Second, the standardized root mean square residual (SRMR) should be 0.08 or less (Brown & Moore, 2012; Schreiber et al., 2006). Third, the root mean square error approximation (RMSEA) should be 0.06 or less (Brown & Moore, 2012).. A non-significant RMSEA p-value indicates a good model fit (Brown & Moore, 2012; Professional Statistical Consulting, 2018; Schreiber et al., 2006). Fourth, the Tucker-Lewis Index (TIL) and the comparative fit index (CFI) should both be above 0.95 (Brown & Moore, 2012). We explored modification indexes to improve the model fit by identifying the potential change in the χ2 value if the specified model was altered (Brown & Moore, 2012). Modification indices identify changes in model fit due to new parameters (Professional Statistical Consulting, 2018). Modifications may include allowing the residual errors of variables to covary. Only modification indices with high values and sound theoretical, logical, and empirical support should be considered for alteration to improve model fit (Schreiber et al., 2006). After fitting the model, we examined factor loadings, parameter estimates, and error of variances to understand the structure of the underlying components in the CFA model (Brown & Moore, 2012). The usual cut-off value for factor loadings is 0.40; however, retaining smaller factor loadings may acceptable in the social sciences (Matsunaga, 2010). Parameter estimates should not be negative (Brown & Moore, 2012). Interpreting parameter estimates is similar to regression coefficients as a one-unit increase in the predictor relates to a one-unit increase in the latent construct (Professional Statistical Consulting, 2018). Finally, the standardized residuals and residual correlations were assessed to identify misfit within the model (Brown & Moore, 2012). Standardized residuals indicate the difference between the observed and expected values for each variable and are interpreted like z-scores with 41 values over 1.96, indicating a covariance not assessed by the model (Brown & Moore, 2012). Large residual correlations (above 0.10) suggests the model still does not explain correlations (Professional Statistical Consulting, 2018). Upon validating the latent construct of SMU, we then examined the fully mediated structural equation model, which included the outcomes (i.e., vaping status, smoking status, nicotine dependency among vapers, and nicotine dependency among smokers), all key predictors (i.e., the latent construct of SMU), and covariates (i.e., the latent construct of mental illness symptoms). In doing so, this provides us a more comprehensive analysis examining positive, negative, and objective measures of social media use. Generalized Structural Equation Modelling (GSEM). Given the results of the logistic regressions and the KHB mediation analysis, a GSEM model was constructed to examine the bivariate associations and the mediated associationsbetween SMU, mental illness symptoms (i.e., symptoms of anxiety and depression), and vaping and smoking status using the package lavaan (Rosseel, 2012). GSEM refers to a structural equation model that has a binary outcome (Huber, 2013). GSEM models use diagonally weighted least squares to indicate the goodness of fit indices and standard errors for the model (Rosseel, 2014). Model fit for GSEM was determined using the same fit indices and cut-offs as described above in the CFA section. We also examined the R2 values for the outcome variables. See Appendix H for copies of the vaping and smoking histograms depicting the distributions of variables used in this test. Structural Equation Modelling (SEM). Given the results of the multiple linear regressions and the GSEM model, we constructed an SEM model to examine the bivariate associations and the mediated associations between SMU, mental illness symptoms (i.e., symptoms of anxiety and depression) and nicotine dependency among vapers and smokers using the package lavaan (Rosseel, 2012). SEM is a structural equation model with a continuous 42 outcome (Huber, 2013). SEM models use maximum likelihood to indicate the goodness of fit indices and standard errors for the model (Rosseel, 2014). Model fit for SEM was determined using the same fit indices and cut-offs as described above in the CFA section. We also examined the R2 values for the outcome variables. See Appendix I for copies of the vaping and smoking histograms depicting the distributions of variables used in this test. Statistical corrections. Given the number of statistical tests conducted, we used two statistical corrections throughout this study, the Bonferroni correction and the Holm correction. Bonferroni correction limits the likelihood of failing to reject the null hypothesis (Type I Error) resulting from conducting multiple tests (Streiner & Norman, 2011). However, Bonferroni corrections are overly conservative and increase the risk of Type II Error (i.e., falsely rejecting a significant finding by limiting the power of a test) (Narum, 2006). Generally, there is no method to reduce the risk of Type II Error other than by conducting fewer tests, ensuring all the assumptions for each test have been met, and conducting power analyses. However, it is possible to reduce the risk of Type II Error from Bonferroni corrections by using Holm corrections (Osborne & Waters, 2002; Domb & Sabetian, 2021; Narum, 2006; Streiner & Norman, 2011). The Holm’s correction is closely related to Bonferroni corrections, but some authors feel the Holm’s correction is superior die to the different threshold levels and increased power (Aickin & Gensler, 1996; Haynes, 2013). Therefore, we used Holm corrections in addition to Bonferroni corrections. For effect sizes, see Appendix Q. All analyses were completed in R (R Core Team, 2020). 43 CHAPTER FOUR: RESULTS This chapter presents the results from the statistical analyses conducted to answer the research questions. First, following the order presented in the analytic approach, we present the descriptive statistics on the participants and summary statistics for each key outcome used in the analyses. Second, the results of the logistic and linear regression and then binary mediation analyses are presented. The third section contains the confirmatory factor analysis of SMU, followed by generalized structural equation models and structural equation models. The final section contains a summary of findings. See Appendix J for a summary of findings and analyses as they relate to the study research questions. 4.1 Univariate Descriptive Statistics The total sample consisted of 707 participants. The sub-sample of vapers included 207 participants, the sub-sample of smokers included 95 participants, and the sub-sample of non-vapers and non-smokers included 405 participants. See Table 1 for type of nicotine user by frequency of use. Dual use of vaping and smoking was not examined. In total, the average age of the overall sample was 30.80 years (SD = 9.15; Range 19 - 72), the average age for vapers was 30.31 years (SD = 9.46; Range 19-66) and the average age for smokers was 33.42 years (SD = 9.22; Range 20 - 66). Most participants were White (80.8%), non-Indigenous (94.8%), and born in Canada (91.2%). The sample had more men (55.3%) than women (40.5%), with 4.2% responding with “prefer not to say.” Additionally, 30.7% respondents identifying as LGBTQ2+. The majority were in relationships (63.6%), and a majority of participants also attended college or university (69.7%). Table 2 provides a summary of the sociodemographic characteristics of the sample and the distributions of key variables of interest. 44 Table 1: Type of nicotine user by frequency of use. Total Sample Vaping Sub-Sample Smoking Sub-Sample Frequency of Use % (N) Frequency of Use % (n) Frequency of Use % (n) Vaping Status Yes No 33.1% (220) 66.9% (487) 100% (207) 0% (0) 24.25% (23) 75.8% (72) Smoking Status Yes No 20.7% (129) 80.3% (568) 18.4% (38) 81.6% (169) 100% (95) 0% (0) Notes. To be in the vaping or smoking group, a nicotine dependency score of at least one is required. Dual use was not examined; therefore, some participants may be included in both the vaping and smoking groups. Table 2: Sociodemographic characteristics of the whole sample and by nicotine use groups. Total Sample % (N) Vaping Sub-Sample % (n) Smoking Sub-Sample % (n) Gender Men Women Prefer not to say/ Other 55.3% (391) 40.5% (286) 4.2% (30) 66.2% (137) 30.9% (64) 2.9% (6) 62.1% (59) 33.7% (32) 4.2% (4) Education Highschool Post-secondary Prefer not to say/ Other 29.7% (210) 69.7 (493) 0.6% (4) 36.2% (75) 62.8% (130) 0.9% (2) 33.7% (32) 67.4% (64) 1.1% (1) LGBTQ2+ Status Yes No Prefer not to say/ Other 30.7% (217) 67.8% (479) 1.6% (11) 29.5% (61) 70.0% (145) 0.5% (1) 31.6% (30) 66.3% (63) 2.1% (2) Race White Not White (i.e., Black, Indigenous) 80.8% (571) 19.2% (136) 80.7% (167) 19.3% (40) 80.0% (76) 20.0% (19) Relationship Yes No Prefer not to say/ Other 63.6% (450) 35.2% (249) 1.1% (8) 61.8% (128) 37.2% (77) 0.9% (2) 70.5% (67) 28.4% (27) 1.1% (1) Total 707 207 95 In total, 33.1% of participants in this sample vaped with an average nicotine dependency among vapers score of 9.67 (SD = 4.3), and 20.7% smoked with an average nicotine dependency among smokers score of 3.75 (SD = 2.09) (See Table 3 for type of nicotine user by average 45 dependency). The average social media use was consistent between the whole sample, vaping sub-sample, and smoking sub-sample. However the means and standard deviations of the vaping sub-sample and smoking sub-sample indicated group-based differences of social media use, which should be fully investigated with inferential statistics (See Table 4 for average social media use). Table 3: Type of nicotine user by average nicotine dependency Total Sample Vaping Sub-Sample Smoking Sub-Sample Nicotine Dependency Mean (SD) Nicotine Dependency Mean (SD) Nicotine Dependency Mean (SD) Vapers 2.83 (4.98) 9.67 (4.3) 2.11 (2.63) Smokers 0.5 (1.49) 0.32 (1.14) 3.75 (2.09) Notes. Scale score refers to the average score for each group from the scale. Whole sample (N = 707): Vaping sub-sample (n = 207): Smoking sub-sample (n = 95). Table 4: Average social media use of the whole sample and by nicotine use groups. Total Sample Mean (SD) Vaping Sub-Sample Mean (SD) Smoking Sub-Sample Mean (SD) Number of platforms (# of platforms) 5.25 (1.8) 5.28 (1.67) 4.79 (1.78) Frequency of social media use (SMUF) 19.85 (7.81) 20.37 (7.78) 17.81 (6.64) Social media dependency (SMUD) 13.23 (5.06) 11.84 (4.85) 12.34 (5.27) Social media engagement (SMUE) 15.92 (5.43) 15.67 (5.59) 14.93 (5.79) Notes. Whole sample (N = 707): Vaping sub-sample (n = 207): Smoking sub-sample (n = 95). The rates of anxiety and depression symptoms were consistent between the total sample, the vaping group and the smoking group (Table 5). Across all three groups (total sample, vaping sub-sample, and smoking sub-sample), there were high rates of anxiety symptoms and depression symptoms. Using clinically significant cut off scores, 40.5% of the total sample reported experiencing anxiety symptoms, and 43.1% reported experiencing depression symptoms. 46 Table 5: Anxiety and depression symptoms categories of the whole sample and by nicotine use groups Total Sample Vaping Sub-Sample Smoking Sub-Sample Frequency of Mental Illness % (N) Frequency of Mental Illness % (n) Frequency of Mental Illness % (n) Anxiety Symptoms Yes No 40.5% (286) 59.5% (421) 40.0% (83) 60.0% (124) 38.5% (37) 61.5% (58) Depression Symptoms Yes No 43.1% (305) 56.9% (402) 43.0% (89) 57.0% (118) 45.3% (43) 54.7% (52) Notes. Frequency of anxiety symptoms refers to the number of participants with scores over 10 that indicate clinically significant anxiety symptoms on the GAD-7 measure. Frequency of depression symptoms refers to the number of participants with scores over 10 that indicate clinically significant depression symptoms on the PHQ-8 measure. Whole sample (N = 707): Vaping sub-sample (n = 207): Smoking sub-sample (n = 95). Given the high rates of anxiety and depression symptoms, we examined the rates of no, mild, moderate, and high anxiety symptoms and no, mild, moderate, high, and severe depression symptoms for the total sample, the vaping sub-sample, and the smoking sub-sample (Table 6). The highest percentages of the total sample had no anxiety symptoms (31.7%), and mild depression symptoms (29.8%). The highest percentages of the vaping sub-sample had no anxiety symptoms (31.4%), and equal rates of no depression symptoms to mild depression symptoms (28.5%). However, unlike the total sample and vaping sub-sample, the highest percentages of the smoking sub-sample had mild anxiety symptoms (31.6%), and mild depression symptoms (30.5%). 47 Table 6: Mild, moderate, and high categories of symptoms of anxiety and depression for the total sample, among vapers, and among smokers. Total Sample Vaping Sub-Sample Smoking Sub-Sample Frequency % (N) Frequency % (N) Frequency % (N) Anxiety Symptoms None (0-4) 31.7% (224) 31.4% (65) 29.5% (28) Mild (5-9) 27.9% (197) 28.5% (59) 31.6% (30) Moderate (10-14) 19.0% (134) 17.9% (37) 16.8% (16) High (15-21) 21.5% (152) 22.2% (46) 22.1% (21) Depression Symptoms None (0-4) 27.0% (191) 28.5% (59) 24.2% (23) Mild (5-9) 29.8% (211) 28.5% (59) 30.5% (29) Moderate (10-14) 21.2% (150) 20.8% (43) 22.1% (21) High (15-19) 12.7% (90) 12.1% (25) 14.7% (14) Severe (20-24) 9.2% (65) 10.1% (21) 8.4% (8) Notes. Frequency of anxiety symptoms was assessed with the GAD-7. Frequency of depression symptoms was assessed with PHQ. Whole sample (N = 707): Vaping sub-sample (n = 207): Smoking sub-sample (n = 95). Using the scale score for the total sample, the average score for anxiety symptoms was 8.69 (SD = 6.22). The average score for depression symptoms was 9.23 (SD = 6.32). See Table 7 for scale scores of anxiety and depression symptoms by nicotine use groups. Table 7: Average scale score of anxiety and depression symptoms of the whole sample and by nicotine use groups Total Sample Vaping Sub-Sample Smoking Sub-Sample Scale Score Mean (SD) Scale Score Mean (SD) Scale Score Mean (SD) Anxiety Symptoms 8.69 (6.22) 8.62 (6.19) 9.20 (6.46) Depression Symptoms 9.23 (6.32) 9.33 (6.51) 9.55 (6.18) Notes. Scale score refers to the average score for each group from the scale. Whole sample (N = 707): Vaping sub-sample (n = 207): Smoking sub-sample (n = 95). Anxiety symptoms were assessed with the GAD-7 measure; depression symptoms were assessed with the PHQ-8 measure. Participants indicated they had posted, viewed peer posts, and/or viewed vaping/smoking content on social media. For posting vaping content, 17.9% of vapers and smokers reported ever posting whereas for posting smoking content, 7.3%% of vapers and 10.5% smokers reported ever posting. For peers posting vaping content, 19.3% of vapers reported peers ever posting, and in comparison, 25.3% of smokers reported peers ever posting. Whereas for peers posting smoking content, 24.3% of vapers reported peers ever posting, and in comparison, 34.7% of smokers reported peers ever posting. For viewing vaping advertisements, 24.6% of vapers reported ever 48 seeing vaping advertisements, whereas 34.7% of smokers reported viewing vaping advertisements. In comparison, 6.8% of vapers and 8.4% of smokers reported ever seeing smoking advertisements. Furthermore, the total sample reported posting smoking content more often than vapers or smokers (Table 8). Table 8: Posting and viewing of media contents on vaping and smoking, by the whole sample and by nicotine use groups. Total Sample % (N) Vaping Sub-Sample % (n) Smoking Sub-Sample % (n) Vaping Content: Self-Posting Yes No Prefer not to say 10.2% (72) 89.5% (633) 0.28% (2) 17.9% (37) 81.2% (168) 0.9% (2) 17.9% (17) 82.1% (78) 0% (0) Vaping Content: Peer Posting Yes No Prefer not to say 20.1% (142) 79.5% (562) 0.14% (1) 19.3% (40) 80.1% (166) 0.5% (1) 25.3% (24) 74.7% (71) 0% (0) Vaping Content: Viewing advertisements Yes No Prefer not to say 24.8% (175) 74.9% (530) 0.28% (2) 24.6% (51) 75.4% (156) 0% (0) 34.7% (33) 64.2% (61) 1.1% (1) Smoking Content: Self-Posting Yes No Prefer not to say 35.2% (37) 94.6% (669) 0.14% (1) 7.3% (16) 92.2% (191) 0% (0) 10.5% (10) 88.4% (84) 1.1% (1) Smoking Content: Peer Posting Yes No Prefer not to say 21.5% (152) 78.8% (555) 0% (0) 24.2% (50) 75.8% (157) 0% (0) 34.7% (33) 65.3% (62) 0% (0) Smoking Content: Viewing advertisements Yes No Prefer not to say 6.22% (44) 93.5% (661) 0.28% (2) 6.8% (14) 93.2% (193) 0% (0) 8.4% (8) 90.5% (86) 1.1% (1) Notes. Whole sample (N = 707): Vaping sub-sample (n = 207): Smoking sub-sample (n = 95). 4.2 Bivariate Descriptive Statistics We first examined the relationship between sociodemographic variables and the outcomes, then the association between predictors and outcomes, and finally the relationships between predictors and vaping and smoking content on social media. Thus, allowing us to 49 determine which sociodemographic variables needed to be controlled for in further analyses. First, we checked the assumptions for the tests were met. Levene’s test showed there were equal variances for all of the t-tests we conducted (p > 0.13). For the vaping sub-sample, there were no differences of vaping status based on age t(401.96) = 0.59, p = 0.55. Also, for the smoking sub-sample, there were no of smoking differences based on age t(216.45) = -1.72, p = 0.09. When considering anxiety symptoms, there appeared to be age-based differences t(620.64) = 1.94, p = 0.05, but upon examination of confidence intervals (95% CI [-0.1-2.72]) this association was determined to be non-significant as the CI passed through one. Also, there appeared to be age-based differences when examining depression symptoms t(664.4) = 2.79 p < 0.001, but upon examination of confidence intervals (95% CI [0.57-3.28]) this association was also determined to be non-significant as the CI passed through one. The association between age, the frequency of social media use (SMUF), and the number of platforms used were also explored and summarized a correlation matrix (Table 9). There was a significant weak negative correlation between age and SMUF (r = -0.20, p < 0.001). As age increased, SMUF decreased. There was also a significant yet weak negative correlation between age and number of platforms used (r = -0.33, p < 0.001). Given that the association between SMUF, the number of platforms used, and age were weak, age was only used as a control variable, and age-based categories were not explored as a key predictor in subsequent analyses. Table 9: Pearson correlation matrix for entire sample of age, SMUF and number of platforms. 1 2 3 1. Age - 2. # of platforms -0.20*** - 3. SMUF -0.33*** 0.77*** - Notes. * p < .05; ** p < .01; *** p < .001. N = 707. SMUF = Frequency of social media use. 50 Chi-squared tests showed sociodemographic characteristics were associated with vaping status, smoking status, anxiety symptoms, and depression symptoms. Although the total sample used in analyses was 707, when looking sociodemographic characteristics using chi-squared tests, the number of participants decreased to 666 as several participants did not fit into completely binary categories. For example, 4.2% (n = 30) of the participants in the total sample selected “prefer not to say” or “other” when asked about gender identity. Therefore, these participants were excluded from the binary categories for only the chi-squared tests although they were included in the rest of the analyses. There was significant between gender and vaping as men were more likely than women to vape (X2 (1, n = 666) = 9.98, p < 0.001). Also, individuals with a high school education were more likely than those with post-secondary education to vape (X2 (1, n = 666) = 4.74, p = 0.03). When considering anxiety symptoms and gender, there were significant differences as women were more likely to experience anxiety symptoms than men (X2 (1, n = 666) = 12.05, p < 0.001). Moreover, individuals who identified as LGBTQ2+ were more likely to experience anxiety symptoms than those who did not identify as LGBTQ2+ (X2 (1, n = 666) = 29.77, p < 0.001). When considering depression symptoms, there were significant differences as women were more likely to experience depression symptoms than men (X2 (1, n = 666) = 15.95, p < 0.001). Also, individuals who identified as LGBTQ2+ were more likely to experience depression symptoms than those who did not identify as LGBTQ2+ (X2 (1, n = 666) = 27.70, p < 0.001). Additionally, Pearson correlation matrix were constructed to examine the relationships between key predictors and outcomes for the whole study sample, the vaping sub-sample, and the smoking sub-sample (See Table 10-12). As expected, the social media use variables were positively correlated with each other (r = 0.27 - 0.77), with the strongest correlation between 51 SMUF and number of platforms. Additionally, as expected, anxiety symptoms and depression symptoms showed a strong, positive correlation with each other (r = 0.74). Finally, anxiety symptoms and depression symptoms showed only weak positive correlations with number of platforms and SMUF, and moderate positive correlations with social media dependency (SMUD) and social media engagement (SMUE). Unlike the whole sample and the vaping sub-sample, there are some differences in the relationship between the social media use variables. For instance, the SMUD was not correlated with number platforms. Also, nicotine dependency among vapers showed low to moderate positive correlations with SMUD (r = 0.20), anxiety symptoms (r = 0.33), and depression symptoms (r = 0.28). Similar to the whole sample analysis, social media use variables were positively correlated as were anxiety symptoms and depression symptoms. Additionally, there is a negative correlation between SMUD and nicotine dependency among smokers (r = -0.20), contrary to the positive correlation seen in the vaping sub-sample and whole sample analyses. Finally, unlike both the vaping and whole sanoke analyses, there was no relationship between nicotine dependency among smookers and anxiety symptoms or depression symptoms. Table 10: Pearson Correlation matrix for entire sample. 1 2 3 4 5 6 7 1. Nicotine Dependency - 2. # of platforms -0.04 - 3. SMUF 0.03 0.77*** - 4. SMUD -0.14*** 0.27*** 0.35*** - 5. SMUE -0.02 0.30*** 0.47*** 0.52*** - 6. Anxiety Symptoms 0.09* 0.05 0.11** 0.40*** 0.23*** - 7. Depression Symptoms 0.09** 0.04 0.12** 0.38*** 0.24*** 0.74*** - Notes. * p < .05; ** p < .01; *** p < .001. SMUF = Frequency of social media use. SMUD = social media dependency, SMUE = social media engagement. N = 707. 52 Table 11: Pearson correlation matrix for vaping sub-sample. 1 2 3 4 5 6 7 1. Nicotine Dependency - 2. # of platforms -0.05 - 3. SMUF 0.10 0.78*** - 4. SMUD 0.20** 0.31*** 0.38*** - 5. SMUE 0.12 0.32*** 0.45*** 0.51*** - 6. Anxiety Symptoms 0.33*** 0.09 0.17** 0.46*** 0.26*** - 7. Depression Symptoms 0.28** 0.09 0.18** 0.45*** 0.27*** 0.76*** - Notes. * p < .05; ** p < .01; *** p < .001. SMUF = Frequency of social media use. SMUD = social media dependency, SMUE = social media engagement. n = 207 (only vapers with a nicotine dependency score of at least one was included). Table 12: Pearson Correlation matrix for smoking sub-sample. 1 2 3 4 5 6 7 1.Nicotine Dependency - 2. # of platforms -0.17 - 3. SMUF -0.17 0.77*** - 4. SMUD -0.20* 0.11 0.35*** - 5. SMUE -0.12 0.20* 0.38*** 0.60*** - 6. Anxiety Symptoms 0.08 0.04 0.27** 0.42*** 0.39*** - 7. Depression Symptoms 0.09 -0.04 0.25* 0.39*** 0.34*** 0.75*** - Notes. * p < .05; ** p < .01; *** p < .001. p < .001. SMUF = Frequency of social media use. SMUD = social media dependency, SMUE = social media engagement. n = 95 (only smokers with a nicotine dependency score of at least one was included). Lastly, chi-squared tests were used to examine the association between vaping and smoking status and posting, viewing posts, and viewing advertisements for vaping and smoking on social media. The chi-squared tests showed posting vaping content on social media was associated with vaping as people who vaped were more likely to post vaping content than people who did not vape (X2 (1, n = 702) = 18.83, p < 0.001). Also, posting smoking content on social media was associated with smoking as people who smoked were more likely to post smoking 53 content than people who did not smoke (X2 (1, n = 702) = 12.68, p < 0.001). Moreover, individuals who smoked were more likely to report viewing peers’ posts about smoking content on social media than individuals who did not smoke (X2 (1, n = 702) = 14.26, p < 0.001). 4.3 Logistic Regressions Table 13 summarizes the first sets of logistic regressions examined the differences between vapers and non-vapers and smokers and non-smokers, based on their anxiety symptoms and depression symptoms scores. All models were tested for the assumption of linearity and did not violate this assumption. Neither anxiety symptoms nor depression symptoms were associated with vaping or smoking (p = 0.08). Also, depression symptoms did not increase the probability of vaping, and depression symptoms did not increase the probability of smoking (p = 0.08). Table 13: Logistic regression for vaping status and smoking status predicted by anxiety symptoms, and depression symptoms. Vapers VS Non-Vapers Smokers VS Non-Smokers AOR (95% CI) R2 AOR (95% CI) R2 Anxiety Symptoms 1.01 (0.98-1.04) 0.02 1.03(1.00-1.06). 0.01 Depression Symptoms 1.02 (0.99-1.04) 0.02 1.03 (1.00-1.06). 0.02 n = 207 n = 95 Notes. * p < .05; ** p < .01; *** p < .001. Controlling for education, gender, LGBTQ2+status, and age. AOR = Adjusted Odds Ratio. Adjusted R2 was used. Table 14 summarizes four logistic regressions examining three social media use variables and the dichotomous outcome measure for vaping, smoking, anxiety symptoms, and depression symptoms. All four models were tested for the assumption of linearity. None of the models violated this assumption. In the vaping model, there was a significant regression equation between vaping status and SMUD (X2 (7, N = 707) = 37.58, p < 0.001) with an R2 of 0.02, indicating 2% of the model variance for the probability of vaping was accounted for by SMUD. Specifically, for a one unit increase in SMUD, there was a 9% decreased in the probability of vaping. In the anxiety 54 symptoms model, there was a significant regression equation between experiencing anxiety symptoms and SMUD (X2 (7, N = 707) = 111.81, p < 0.001) with an R2 of 0.12, indicating 12% of the model variance for the probability of experiencing anxiety symptoms was accounted for by SMUD. Specifically, for a one unit increase in SMUD, there was a 16% increase in the probability of experiencing anxiety symptoms. In the depression symptoms model, there was a significant regression equation between experiencing depression symptoms and SMUD (X2 (7, N = 707) = 96.26, p < 0.001) with an R2 of 0.11, indicating that 11% of the model variance for the probability of experiencing depression symptoms was accounted for by SMUD. Specifically, for a one unit increase in SMUD, there was a 13% increase in the probability of experiencing depression symptoms. Table 14: Logistic regressions for social media use as predictors of vaping, smoking, anxiety, and depression Model 1 Vapers VS Non-Vapers Model 2 Smokers VS Non-Smokers Model 3 Anxious VS Not Anxious Model 4 Depressed VS Not Depressed AOR (95% CI) AOR (95% CI) AOR (95% CI) AOR (95% CI) SMUD 0.91(0.87-0.95) *** 1.00 (0.96-1.05) 1.16 (1.12-1.21) *** 1.13(1.08-1.18) *** SMUE 1.02(0.98-1.06) 0.99(0.94-1.03) 1.01(0.98-1.06) 0.99(0.98-1.06) SMUF 1.03 (1.00-1.05) 0.90(0.96-1.01) 0.99(0.96-1.01) 1.02(0.97-1.02) AIC = 814.01 R2 = 0.04 AIC = 673.85 R2 = 0.02 AIC = 802.53 R2=0.12 AIC = 833.06 R2 = 0.11 Notes. * p < .05; ** p < .01; *** p < .001. Controlling for education, gender, LGBTQ2+ status, and age. AOR = Adjusted Odds Ratio. Adjusted R2 was used. SMUF = Frequency of SMU. SMUD = social media dependency, SMUE = social media engagement. 4.4 Linear Multiple Regressions In total four linear multiple regressions were used to predict nicotine dependency among vapers and smokers and smoking dependency based on anxiety symptoms and depression symptoms, separately (See Table 15). The regression equation for the relationship between nicotine dependency among vapers and anxiety symptoms was significant (F (193) = 4.06, p < 55 0.001), with an R2 = 0.12, indicating 12% of the variance in nicotine dependency among vapers model was accounted for by anxiety symptoms. Nicotine dependency among vapers increased 0.38 for each one unit increase in anxiety symptoms. Similarly, the regression equation for nicotine dependency among vapers and depression symptoms was significant (F (193) = 4.03, p < 0.001), with an R2 = 0.08, meaning 8% of the variance in nicotine dependency among vapers model was accounted for by depression symptoms. Nicotine dependency among vapers increased 0.31 for each one unit increase in depression symptoms. Finally, the regression equations between nicotine dependency among smokers and anxiety symptoms were not significant (F (85) = 1.73, p = 0.23), nor was the regression equation between nicotine dependency among smokers and depression symptoms (F (85) = 1.61, p = 0.35). Table 15: Linear multiple regression of anxiety symptoms and depression symptoms as predictors of nicotine dependency among vapers and smokers. Nicotine Dependency among Vapers Nicotine Dependency among Smokers β (95% CI) R2 β (95% CI) R2 Anxiety Symptoms 0.38 (1.33-1.60) *** 0.12 0.12 (1.06 -1.23) 0.04 Depression Symptoms 0.31 (1.25-1.50) *** 0.08 0.09 (1.02 -1.19) 0.03 n = 207 n = 95 Notes. * p < .05; ** p < .01; *** p < .001. Controlling for education, gender, LGBTQ2+status, and age. Beta is standardized. Vaping dependency ranged from 1 to 20; smoking dependency ranged from 1 to 8; Anxiety symptoms ranged from 0 to 21, and depression symptoms ranged from 0 to 24. Adjusted R2 was used. Table 16 summarizes six multiple regressions predicting the continuous outcome measures for nicotine dependency among vapers and smokers using three social media variables and the mediating variables for anxiety symptoms and depression symptoms. Analyses were done separately for the vaping sub-sample and the smoking sub-sample. There was a significant regression equation between nicotine dependency among vapers and SMUD (F (191) = 1.43, p = 0.02), with an R2 = 0.02, indicating 2% of the variance in nicotine dependency among vapers model was accounted for by SMUD. In other words, nicotine dependency among vapers 56 increased 0.20 for each one unit increase in SMUD. Among vapers, there was a significant regression equation between anxiety symptoms and SMUD (F (191) = 9.23, p < 0.001), with an R2 = 0.23, indicating 23% of the variance in anxiety symptoms among vapers model was accounted for by SMUD. In other words, anxiety symptoms among vapers increased 0.20 for each one unit increase in SMUD. Also, among vapers, there was a significant regression equation between depression symptoms and SMUD (F (191) = 5.72, p < 0.001), with an R2 = 0.22, indicating 22% of the variance in depression symptoms among vapers model was accounted for by SMUD. In other words, depression symptoms among vapers increased 0.42 for each one unit increase in SMUD. Among smokers, there was a significant regression equation between anxiety symptoms and SMUD (F (83) = 5.85, p = 0.004), with an R2 = 0.27, indicating 27% of the variance in anxiety symptoms among smokers model was accounted for by SMUD. In other words, anxiety symptoms among smokers increased 0.34 for each one unit increase in SMUD. Also, among smokers, there was a significant regression equation between depression symptoms and SMUD (F (83) = 3.33, p = 0.01), with an R2 = 0.15, indicating 15% of the variance in depression symptoms among smokers model was accounted for by SMUD. In other words, depression symptoms among smokers increased 0.32 for each one unit increase in SMUD. 57 Table 16: Multiple regressions between nicotine dependency among vapers, social media use, anxiety symptoms, and depression symptoms, and nicotine dependency among smokers, social media use, anxiety symptoms, and depression symptoms. Vaping Sub-Sample Smoking Sub-Sample Nicotine Dependency Anxiety Symptoms Depression Symptoms Nicotine Dependency Anxiety Symptoms Depression Symptoms β (95% CI) β (95% CI) β (95% CI) β (95% CI) β (95% CI) β (95% CI) SMUF 0.03 (0.94-1.13) -0.04 (0.85-1.08) -0.09 (0.81-1.03) -0.11 (0.83-0.97) -0.06 (0.76-1.15) 0.19 (0.82-1.26) SMUD 0.20 (1.06-1.42) * 0.42 (1.26-1.83) *** 0.42 (1.16-1.86) *** -0.13 (0.79-0.98) 0.34 (1.05-1.87)** 0.32 (1.02-1.85)* SMUE -0.009 (0.87-1.13) 0.06 (0.89-1.25) 0.06 (0.89-1.26) -0.02 (0.89-1.08) 0.18 (0.93-1.54) 0.11 (0.09-1.45) n = 207 R2 = 0.02 n = 207 R2 = 0.23 n = 207 R2 = 0.22 n = 95 R2 = 0.04 n = 95 R2=0.27 n = 95 R2 = 0.15 Notes. * p < .05; ** p < .01; *** p < .001. p < .001. SMUF = Frequency of social media use. SMUD = social media dependency, SMUE = social media engagement. Controlling for education, gender, LGBTQ2+ status, and age. Beta is standardized. Adjusted R2 was used. 4.5 Results from Binary Mediation Analysis KHB analyses were then conducted to examine the mediating effects of anxiety symptoms and depression symptoms on the relationships between social media use variables and vaping status. Model 1 used SMUD, SMUE, and SMUF use to predict the probability of vaping, with anxiety symptoms as a mediator. Model 2 used SMUD, SMUE, and SMUF to predict the probability of vaping, with depression symptoms as a mediator. In Vaping Model 1 (KHB), three social media use variables and anxiety symptoms were used to assess the probability of vaping, along with the mediational effect of anxiety symptoms. In total, this model had an R2 of 0.09, indicating 9% of the model variance for probability of vaping was accounted for by SMUD, SMUE, and SMUF when anxiety symptoms were considered (See Table 17). There was a significant total effect between vaping status and SMUF and anxiety symptoms (AOR = 1.03, 95% CI [1.01-1.05]). Meaning for a one unit increase in 58 SMUF and anxiety symptoms, there was a 3% increase in the probability of vaping. Also, there was a significant direct effect between vaping status and SMUF (AOR = 1.02, 95% CI [1.00-1.06]); for every one unit increase in SMUF, there was a 2% increase in the probability of vaping. See Figure 4 for the visual depiction of vaping status and the total association with SMUF and anxiety symptoms, and see Figure 5 for the direct association between vaping status and SMUF. There was also a significant total effect between vaping status, SMUD and anxiety symptoms (AOR = 0.89, 95% CI [0.85-0.94]). Meaning for a one unit increase in SMUD and anxiety symptoms, there was a 11% decrease in the probability of vaping. In addition, there was a significant direct effect between vaping status and SMUD (AOR = 0.91, 95% CI [0.87-0.95]); for every a one unit increase in SMUD, there was a 9% decrease in the probability of vaping. Lastly, there was a significant indirect effect between vaping status and SMUD through anxiety symptoms (AOR = 0.89, 95% CI [0.89-0.91]); for every one unit increase in SMUD, anxiety symptoms increased and there was a 11% decrease in the probability of vaping. See Figure 7 for visual depiction of the the total association of vaping status, SMUD, and anxiety symptoms, and see Figure 7 for the direct and indirect vaping status, SMUD, and anxiety symptoms. 59 Table 17: KHB for relationship between vaping and social media use with anxiety symptoms as a mediator. Vaping Status Model 1: Anxiety Symptoms Frequency of Social Media Use (SMUF) AOR (95%CI) Social Media Dependency (SMUD) AOR (95%CI) Social Media Engagement (SMUE) AOR (95%CI) Total Effect 1.03 (1.01-1.06)* 0.89 (0.85-0.94) *** 1.02 (0.98-1.06) Direct Effect 1.02 (1.00-1.06) * 0.91 (0.87-0.95) *** 1.02 (0.98-1.06) Indirect Effect 1.0 (0.99-1.00) 0.89 (0.88-0.91) * 1.00 (1.00-1.01) N = 707 R2 = 0.09 Notes. * p < .05; ** p < .01; *** p < .001. Controlling for education, gender, LGBTQ2+ status, and age. Adjusted R2 was used. AOR = Adjusted Odds Ratio. Adjusted R2 was used. Figure 4: Visual model depicting the total effect of SMUF on vaping status with anxiety symptoms. Notes. Odds ratios and 95% CI were used. N = 707. Anxiety refers to anxiety symptoms. Figure 5: Visual model depicting the direct effect of SMUF on vaping status Notes. Odds ratios and 95% CI were used. N = 707. Anxiety refers to anxiety symptoms. 60 Figure 6: Visual model depicting of the total effect of SMUD on vaping Notes. Odds ratios and 95% CI were used. N = 707. Anxiety refers to anxiety symptoms. Figure 7: Visual model depicting of the direct and indirect effects of SMUD on vaping status with anxiety symptoms as a mediating variable. Notes. Odds ratios and 95% CI were used. N = 707. Anxiety refers to anxiety symptoms. In Vaping Model 2 (KHB), three social media use variables and depression symptoms were used to assess the probability of vaping, along with the mediational effect of depression symptoms. In total, this model had an R2 of 0.09, indicating 9% of the model variance for probability of vaping was accounted for by SMUD, SMUE, and SMUF when depression symptoms were considered (See Table 18). There was a total effect between vaping status and SMUF and depression symptoms (AOR = 1.03, 95% CI [1.01-1.06]). Meaning, for a one unit increase in SMUF and depression symptoms, there was a 3% increase in the probability of vaping. Also, there was a direct effect between vaping status and SMUF (AOR = 1.03, 95% CI [1.00-1.06]); for a one unit increase in SMUF, there was a 3% increase in the probability of vaping. See Figure 8 for visual depiction of the total association of vaping status, depression symptoms, and SMUF, and see Figure 9 for tthe direct association between vaping status and 61 SMUF. There was also a total effect between vaping status, SMUD and depression symptoms (AOR = 0.89, 95% CI [0.85-0.94]); that is, for a one unit increase in SMUD and depression symptoms, there was a 11% decrease in the probability of vaping. Also, there was a direct effect between vaping status and SMUD (AOR = 0.91, 95% CI [0.87-0.95]); for a one unit increase in SMUD, there was a 9% decrease in the probability of vaping. Lastly, there was an indirect effect between vaping status and SMUD through depression symptoms (AOR = 0.89, 95% CI [0.89-0.91]); for, a one unit increase in social media dependency, also increased depression symptoms, and produced a 11% decrease in the probability of vaping. See Figure 10 for visual depiction of total association between vaping status, SMUD, and vaping status, and see Figure 11 for the direct and indirect association between vaping status, SMUD, and depression symptoms. Table 18: KHB for vaping and social media use with depression symptoms as a mediator. Vaping Status Model 2: Depression Symptoms Frequency of Media Use (SMUF) AOR (95%CI) Social Media Dependency (SMUD) AOR (95%CI) Social Media Engagement (SMUE) AOR (95%CI) Total Effect 1.03 (1.01-1.06) * 0.89 (0.85-0.94) *** 1.02 (0.98-1.06) Direct Effect 1.03 (1.00-1.06) * 0.91 (0.87-0.95) *** 0.91 (0.87-0.95) Indirect Effect -1.03 (1.03-1.04) 0.89 (0.88-0.91) * 1.00 (1.00-1.01) N = 707 R2 = 0.09 Notes. * p < .05; ** p < .01; *** p < .001. Controlling for education, gender, LGBTQ2+ status, and age. Adjusted R2 was used. AOR = Adjusted Odds Ratio. Adjusted R2 was used. 62 Figure 8: Visual model depicting of the total of SMUF on vaping status in depression symptom mediation model. Notes. Odds ratios and 95% CI were used. N = 707. Anxiety refers to anxiety symptoms. Figure 9: Visual model depicting of the direct effect of SMUF on vaping status in depression symptom mediation model. Notes. Odds ratios and 95% CI were used. N = 707. Anxiety refers to anxiety symptoms. Figure 10: Visual model depicting of the total of SMUD and depression symptoms on vaping status in depression symptom mediation model Notes. Odds ratios and 95% CI were used. N = 707. Anxiety refers to anxiety symptoms. 63 Figure 11: Visual model depicting of the direct and indirect effects of SMUD on vaping status with depression symptoms as a mediating variable. Notes. Odds ratios and 95% CI were used. N = 707. Anxiety refers to anxiety symptoms. In the second set of KHB analyses, three social media use variables and anxiety symptoms were used to assess the probability of smoking as well as the mediational effect of anxiety symptoms. Smoking Model 1 (KHB) had an R2 of 0.04, indicating that 4% of the probability smoking was accounted for by SMUD, SMUE, and SMUF when anxiety symptoms were considered (See Table 19). There were no statistically significant associations after corrections were applied. Table 19: KHB for smoking and social media use with anxiety symptoms as a mediator. Smoking Status Model 1: Anxiety Symptoms Frequency of Media Use (SMUF) AOR (95%CI) Social Media Dependency (SMUD) AOR (95%CI) Social Media Engagement (SMUE) AOR (95%CI) Total Effect 1.03 (1.00-1.06) 1.00 (0.94-1.03) 0.98 (0.94-1.03) Direct Effect -0.98(0.95-1.01) 1.00 (0.96-1.05) 0.99 (0.94-1.03) Indirect Effect -1.00 (0.99-1.00) 0.89 (0.88-0.91) 1.00 (1.00-1.01) N = 707 R2 = 0.04 Notes. * p < .05; ** p < .01; *** p < .001. Controlling for education, gender, LGBTQ2+status, and age. Adjusted R2 was used. AOR = Adjusted Odds Ratio. Adjusted R2 was used. In Smoking Model 2 (KHB), three social media use variables and depression symptoms were used to assess the probability of smoking, and also examining the mediational effect of depression symptoms. In total, this model had an R2 of 0.04, indicating 4% of the probability 64 smoking was accounted for by SMUF, SMUD, and SMUE when depression symptoms were considered (See Table 20). There were no statistically significant associations after corrections were applied. Table 20: KHB for smoking and social media use with depression symptoms as a mediator. Smoking Status Model 2: Depression Symptoms Frequency of Media Use (SMUF) AOR (95%CI) Social Media Dependency (SMUD) AOR (95%CI) Social Media Engagement (SMUE) AOR (95%CI) Total Effect 1.03 (1.00-1.06) 1.00 (0,94-1.03) 0.98 (0.94-1.03) Direct Effect 0.98(0.95-1.01) 1.00 (0.96-1.05) 0.99 (0.94-1.03) Indirect Effect 1.00 (0.99-1.00) 0.89 (0.88-0.91) 1.00 (1.00-1.01) N = 707 R2 = 0.04 Notes. * p < .05; ** p < .01; *** p < .001. Controlling for education, gender, LGBTQ2+status, and age. Adjusted R2 was used. AOR = Adjusted Odds Ratio. Adjusted R2 was used. 65 4.6 Results from Confirmatory Factor Analysis Given the results of KHB analyses indicated that anxiety symptoms and depression symptoms have mediating roles with separate yet related variables, a general structural equation model was developed. This model reduced the problem of multicollinearity and provided a better estimate of the relationships under investigation in this study. To analyze the fully mediated models using social media use, mental illness symptoms (anxiety symptoms and depression symptoms), and vaping and smoking status, the latent construct for social media use called SMU was created, including SMUF, SMUD, SMUE, and number of platforms. The latent variable of SMU required validation as it was a novel approach. Therefore, we conducted confirmatory factor analysis (CFA) validate and test the latent construct. The initial model used the complete sample of 707 and showed poor fit, producing a significant chi-squared value, low CFI and TLI, and high RMSEA and SRMR (Table 21). As a result, the modification indexes were examined, suggesting the error residuals between SMUF and number of social media platforms could be allowed to covary. Allowing SMUF and number of platforms to covary makes sense conceptually, as the greater number of platforms with likely increase the frequency of use, and these variables showed a high degree of correlation in the Pearson correlation matrixes. Thus, upon allowing these two items to covary, the final model produced showed acceptable fit, CFI, TFI, and SRMR showing good fit, as well the RMSEA showing acceptable fit. 66 Table 21: Goodness-of-fit indicators for Confirmatory Factor Analyses models with the indicators of SMU Model χ2 df CFI TLI RMSEA (90% CI) SRMR Four factors 152.25*** 2 0.86 0.56 0.33 (0.28-3.71) 0.11 Four factors with covariance 5.41* 1 0.99 0.98 0.08 (0.03-1.15) 0.02 Notes. N =7 07. Factors include frequency of social media use, number of social media platforms, social media dependency, and social media engagement. Covariation was between frequency of social media use and number of social media platforms. The factor loadings for the CFA mostly fell within the acceptable range, although the factor loading for the number of platforms was low (0.38). However, given that the number of platforms was a count variable and a measure of objective social media use, it was retained in the model. The parameter estimates were deemed acceptable. The standardized residuals from the CFA indicated that the model was not assessing some of the variance for SMUD and SMUE given the standard residuals were over the 1.96 cut off value. The residual correlations for the CFA generated were acceptable. For factor loadings, parameter estimates, standardized residuals and residual correlations see Appendix K, and for diagram see Appendix L. 4.7 Results from Generalized Structural Equation Models After developing the SMU construct, we conducted two generalized structural equation models (GSEM). The first GSEM examined the bivariate associations and the mediated associations of the latent construct of SMU and a latent construct of mental illness symptoms that allowed for anxiety symptoms and depression symptoms to be examined together with vaping status. This approach contrasts with the KHB analysis, which required separate models for anxiety and depression symptoms. Vaping Model 1 (GSEM) used the latent construct of SMU (with covariation between number of platforms and SMUF) and mental illness symptoms (symptoms of anxiety and depression) to predict the probability of vaping. Vaping Model 2 (GSEM) added a covariation between SMUF and SMUE because the modification indexes 67 indicated that there was a high degree of shared variance between these two variables. Allowing them to covary not only improved model fit but also allowed for a more accurate depiction of the underlying relationships (mi = 24.99). Vaping Model 2 (GSEM) was selected as the final model because it had acceptable fit indexes (See Table 22). The second GSEM model used the latent construct of SMU (with covariation between number of platforms and SMUF) and mental illness symptoms (anxiety symptoms and depression symptoms) to predict the probablity of smoking. Smoking Model 1 (GSEM) used the latent construct of SMU (with covariation between number of platforms and SMUF) and mental illness symptoms (symptoms of anxiety and depression) to predict the probability of smoking, and had poor fit. Smoking Model 2 (GSEM) added a covariation between SMUF and SMUE because the modification indexes indicated there was a high degree of shared variance between these two variables (mi = 18.94). Although the model fit improved, it was still not acceptable. Therefore, we examined modification indexes again. Smoking Model 3 (GSEM) added a covariation between social media engagement and number of platforms, again due to the high degree of shared variance between these variables (mi = 13.85). Smoking Model 3 (GSEM) was selected as the final model because it had acceptable fit indexes (See Table 22). Overall, the final vaping model (GSEM) accounted for 2% of the variance in vaping status (see Figure 12). SMU was positively associated with mental illness symptoms (anxiety symptoms and depression symptoms), but mental illness symptoms were not associated with vaping status. As for the relationship between SMU and mental illness symptoms, for each unit increase in SMU, there was a 0.44 unit increase in mental illness symptoms. The bivariate relationship between SMU and vaping status was negative, and the probability of vaping decreased 0.15 for each one unit increase of SMU. 68 The final smoking model (GSEM) accounted for 3% of the variance in smoking status (Figure 13). SMU was positively associated with mental illness symptoms and mental illness symptoms were positively associated with smoking status. Meaning the probability of smoking increased 0.15 for each one unit increase of mental illness symptoms, and mental illness symptoms increased by 0.45 for each one unit increase in SMU. Also, SMU was negatively associated with smoking status; the probability of smoking decreased 0.18 for each one unit increase of SMU. Table 22: Fit indexes for vaping and smoking GSEM models. Model χ2 df CFI TLI RMSEA (90% CI) SRMR R2 Vaping Model 1 60.78*** 11 0.97 0.94 0.08 (0.06-0.10) *** 0.06 0.01 Vaping Model 2 33.97*** 10 0.99 0.97 0.06 (0.04-0.08) 0.04 0.02 Smoking Model 1 49.12*** 11 0.98 0.95 0.07 (0.05-0.09) 0.05 0.03 Smoking Model 2 28.97*** 10 0.99 0.97 0.05 (0.03-0.07) 0.04 0.03 Smoking Model 3 15.12 9 0.99 0.99 0.03 (0.0-0.06) 0.03 0.03 Notes. These models used DWLS (diagonally weighted least squares) for a robust indicator. N = 707. 69 Figure 12: Vaping Status GSEM model. Notes. All solid lines represent statistically significant relationship that are a minimum of p = 0.05. N = 707. Dotted lines indicate non-significant relationships. Beta is standardized. Mental illness symptoms refers to experiencing anxiety symptoms and depression symptoms. Figure 13: Smoking Status GSEM model. Notes. All solid lines represent statistically significant relationship that are a minimum of p = 0.05. N = 707. Mental illness symptoms refers to experiencing anxiety symptoms and depression symptoms. The parameter estimates were deemed acceptable for both the vaping and smoking models (see Table 23). The residual correlations from both the vaping model (GSEM) and 70 smoking model (GSEM) were mostly acceptable with the exception of SMUD by SMUF, and number of platforms by SMUE in the vaping model (Appendix M). Table 23: Parameters for vaping and smoking GSEM models Latent Factor Indicator Standardized Factor Loading B (SE) CI Vaping Model SMU Frequency 0.34 - - SMU Number of platforms 0.30 0.21 (0.03) 0.15-0.26 SMU Social Media Dependency 0.96 1.84 (0.27) 1.34-2.37 SMU Social Media Engagement 0.58 1.20 (0.14) 0.92-1.49 Mental Illness Anxiety Symptoms 0.88 - - Mental Illness Depression Symptoms 0.85 0.98 (0.18) 0.75-1.21 Smoking Model SMU Frequency 0.35 - - SMU Number of platforms 0.25 0.17 (0.03) 0.11-0.22 SMU Social Media Dependency 0.99 1.85 (0.28) 1.31-2.39 SMU Social Media Engagement 0.54 1.10 (0.13) 0.84-1.35 Mental Illness Anxiety Symptoms 0.88 - - Mental Illness Depression Symptoms 0.85 0.98 (0.13) 0.75-1.21 Notes. N = 707. Dashes (-) indicate that a value was not provided. B represents parameter estimates. 4.8 Results from Structural Equation Models Given the significant associations found in the GSEMs, we constructed structural equation models to evaluate if nicotine dependency among vapers and smokers and vaping and smoking status shared similar paths. Two structural equation models (SEM) were tested examining the relationship between SMU and mental illness symptoms (anxiety symptoms and depression symptoms symptoms) among the vaping and smoking sub-samples, specifically looking at the association with nicotine dependency. The first SEM model used the latent construct of SMU (with covariation between number of platforms and SMUF) and mental illness symptoms to predict nicotine dependency among vapers. As the fit indexes were not acceptable, the modification indexes were examined. A covariation between SMUF and SMUE as the modification indexes indicated there was a high degree of shared variance between these two variables (mi = 9.32). After the covariation was added, Vaping Model 2 (SEM) had acceptable fit indexes and was selected as the final model. Smoking Model 1 (SEM) with covariation between 71 number of platforms and SMUF showed acceptable fit and therefore was selected as the final smoking model. Table 24 contains the fit indexes for the SEM models. Table 24: Fit indexes for vaping and smoking SEM models. Model χ2 df CFI TLI RMSEA (90% CI) SRMR R2 Vaping Model 1 6.15** 11 0.97 0.95 0.08 (0.40-0.12) * 0.06 0.12 Vaping Model 2 15.51 10 0.98 0.98 0.06 (0.00-0.10) 0.05 0.12 Smoking Model 1 11.31 11 0.99 0.99 0.02 (0.00-0.11) 0.05 0.12 Notes. Vaping Model 2 n = 207. Smoking Model 1 n = 95. Vaping Model 1 only allowed for frequency of social media use and number of social media platforms to covary. In Vaping Model 2, a covariance was added between SMUF and SMUE. Smoking Model 1 allowed for frequency of social media use and number of social media platforms to covary. Overall, the Vaping Model (SEM) accounted for 12.4% of the variance in nicotine dependency among vapers (Figure 14). SMU was positively associated with mental illness symptoms (anxiety symptoms and depression symptoms), and mental illness symptoms were positively associated with nicotine dependency among vapers. In other words, nicotine dependency among vapers increased 0.34 for each one unit increase of mental illness symptoms, and mental illness symptoms increased by 0.57 for each one unit increase in SMU. The bivariate relationship between social media use and nicotine dependency among vapers was non-significant. The Smoking Model (SEM) accounted for 12.4% of the variance in nicotine dependency among smokers (Figure 15). SMU was positively associated with mental illness symptoms and mental illness symptoms was positively associated higher scores of nicotine dependency among smokers. That is, nicotine dependency among smokers increased 0.34 for each one unit increase of mental illness symptoms, and mental illness symptoms increased by 0.61 for each one unit increase in SMU. Also, SMU was negatively associated with nicotine dependency among 72 smokers; for each one unit increase in SMU, nicotine dependency among smokers decreased by 0.43. Figure 14: Nicotine dependency among vapers SEM model. Notes. All solid lines represent statistically significant relationship that are a minimum of p = 0.05. n = 207. Dotted lines indicate non-significant relationships. Beta is standardized. Figure 15: Nicotine dependency among smokers SEM model Notes. All solid lines represent statistically significant correlations that are a minimum of p = 0.05. n = 95. Beta is standardized. The parameter estimates were deemed acceptable for both the vaping and smoking models (Table 25). The standardized residuals from the vaping and smoking models indicated that the model was not assessing some of the variance (Appendix N). The residual correlations from the vaping model had two areas of misfit, including, SMUE by number of platforms, and 73 number of platforms by nicotine dependency among vapers. Finally, the residual correlations from the smoking model were acceptable (Appendix O). Table 25: Parameters for vaping and smoking SEM models Latent Factor Indicator Standardized Factor Loading B (SE) CI Vaping Model SMU Frequency 0.44 - - SMU Number of platforms 0.35 0.18 (0.02) 0.13-0.22 SMU Social Media Dependency 0.90 1.29 (0.26) 0.78-1.81 SMU Social Media Engagement 0.57 0.95 (0.17) 0.62-1.28 Mental Illness Anxiety Symptoms 0.90 - - Mental Illness Depression Symptoms 0.85 0.99 (0.10) 0.81-1.18 Smoking Model SMU Frequency 0.48 - - SMU Number of platforms 0.18 0.10 (0.05) 0.00-0.20 SMU Social Media Dependency 0.80 1.32(0.32) 0.69-1.95 SMU Social Media Engagement 0.72 1.32 (0.33) 0.68-1.96 Mental Illness Anxiety Symptoms 0.89 - - Mental Illness Depression Symptoms 0.84 0.90 (0.13) 0.64-1.16 Notes. N = 707. Dashes (-) indicate that a value was not provided. B represents parameter estimates. 4.9 Summary of Findings 4.9.1 Vaping Findings Overall, the current study uncovered several factors underlying the relationships between social media use, symptoms of anxiety and depression, and vaping. At the bivariate level, social media dependency was associated with a decreased likelihood of vaping; yet, anxiety symptoms, depression symptoms, and social media dependency were associated with greater nicotine dependency among vapers. At a multivariate level, social media dependency remained associated with a decreased likelihood of vaping when mediated by symptoms of anxiety and depression. Unlike social media dependency, at the bivariate level, frequency of social media use was associated with an increased likelihood of vaping. At a multivariate level, frequency of social media use was associated with an increased likelihood of vaping when mediated by symptoms of anxiety and depression. Finally, social media engagement was not associated with vaping status or nicotine dependency among vapers in any of the statistical tests conducted. 74 Given the divergent impacts of the indicators of social media use on vaping, we then included a latent construct of SMU to estimate the overall impact of social media use on vaping. Using the latent construct revealed SMU was associated with a decreased likelihood of vaping but was not associated with greater nicotine dependency among vapers. SMU was also associated with increased likelihood of experiencing symptoms of anxiety and depression, but symptoms of anxiety and depression were not associated with vaping status. However, SMU had a positive association with nicotine dependency among vapers when mediated through symptoms of anxiety and depression. In other words, greater SMU was associated with higher amounts of reported anxiety and depression symptoms, which, in turn, was associated with higher scores of nicotine dependency among vapers. 4.9.2 Smoking Findings The current study also examined several factors underlying the associations between social media use, symptoms of anxiety and depression, and smoking. The analyses were conducted separately for smoking status and nicotine dependency among smokers. At the bivariate level, none of the key variables (i.e., anxiety symptoms, depression symptoms, social media dependency, social media engagement, or the frequency of social media use) were associated with smoking status or nicotine dependency among smokers. In the examination of smoking status using the latent construct of SMU in the structural equation model, SMU was associated with a decreased likelihood of smoking, and lower scores of nicotine dependency among smokers. SMU was also associated with an increased likelihood of experiencing symptoms of anxiety and depression, and symptoms of anxiety and depression, in turn, were associated with an increased likelihood of smoking and greater scores of nicotine dependency 75 among smokers. Again, a table of all findings is in Appendix J, and Appendix Q contains all effect sizes. 76 CHAPTER FIVE: DISCUSSION The overall purpose of this study was to gain a deeper understanding of the relationships between social media use and vaping and smoking, accounting for the mediating effects of anxiety and depression symptoms. As discussed in the literature review, previous studies have found associations between social media use and symptoms of anxiety and depression, and symptoms of anxiety and depression have been previously associated with both vaping and smoking. Yet very few studies have comprehensively examined the interconnectedness between these concepts. The current study employed structural equation modelling, among other tests, to estimate the associations between social media use and vaping and smoking while accounting for the mediating effects of anxiety and depression symptoms. We also employed the novel approach of creating a latent construct for social media use, Social Media Use (SMU), representing a range of social media use behaviours, including social media engagement, social media dependency, the frequency of social media use, and the number of platforms used. The SMU construct was used to determine if SMU was related to vaping and smoking status and nicotine dependency among vapers and smokers. Further, this study examined the role of anxiety and depression symptoms in mediating the association between SMU and vaping and smoking. In this chapter, we start by exploring the statistical effects that may have been present in the current study. Next, we compare the study findings to current literature, for several topics, including, the mediated association for vaping, social media use, and mental illness symptoms, then the mediated association for smoking. Next, we compare vaping and smoking, and look at the methodological implications of the latent construct of SMU. Then we examine social media dependency, the associations between types of dependency, and how mental illness symptoms mediate dependency. Second, we highlight commonalities and addressing novel findings related 77 to the effects of the COVID-19 pandemic. Third, we discuss practise implications, and overall limitations and strengths of the study. Finally, we present future directions and conclusions. 5.1 Statistical Effects This study hypothesized SMU and the indicators of SMU would be associated with increased vaping and smoking; however, some of the findings were contrary to our expectations. Given the number of analyses conducted, a summary table of the contradictory findings is provided in Appendix S. Some of these contradictory findings may be due to differences in vaping and smoking or possibly due to COVID-19 or even the novel approach of using a latent construct to measure social media use, all of which are explored later in this chapter. Another explanation may be due to novel findings associated with social media dependency, presented in Appendix P. Yet, another explanation for these findings could be statistical effects, which we will discuss in this section. First, a moderator effect may have changed the direction of the associations between the indicators of social media use and vaping status. A moderator effect refers to a third variable changing the nature of the association between an independent and a dependant variable (APA Dictionary of Psychology, 2020a). For example, in the current study, it remains possible that COVID-19 moderated the effect between the indicators of social media use, the latent construct of SMU, and vaping and smoking status. Second, suppressor effects may have been present in the current study. Suppressor effects refer to a third variable reducing the association between two other variables (APA Dictionary of Psychology, 2020b). Suppressor effects may help to explain why social media dependency was initially associated with increased nicotine dependency among vapers, but when included in the latent construct of SMU, it had no association. It is possible that the mood modification item on 78 the social media dependency scale may have had a degree of multilocularity with mental illness symptoms, given the modification indices suggested adding a covariation between these items in the structural equation models. The mood modification item of social media dependency was independently positively associated with anxiety and depression symptoms as outlined in Appendix P. Thus, it is possible that the unaccounted-for association between the mood modification item from the social media dependency measure altered the rest of the associations in the fully mediated models. Third, higher order interactions may have impacted results. Higher order interaction refers to the cumulative effect of at least three independent variables on the outcome variable (APA Dictionary of Psychology, 2020c). Higher order interactions may help explain the association between the initial finding of a positive association between social media dependency and nicotine dependency among vapers, but then, later on, SMU was not associated with nicotine dependency among vapers. It is possible that other variables in the latent construct of SMU reduced the association between social media dependency and nicotine dependency among vapers. Thus, including other indicators of social media use in the latent construct may have created an interaction that was not captured in the initial association between the indicators of social media use and nicotine dependency among vapers. Finally, social media dependency was not associated with nicotine dependency among smokers, but SMU was negatively associated with nicotine dependency among smokers and smoking status. One possible explanation for these findings may be a reinforcer effect where the associations between two or more independent variables strengthens their association with the dependent variable (APA Dictionary of Psychology, 2020d). Thus, social media dependency used as a single indicator was not associated with nicotine dependency among smokers, but 79 when other indicators of SMU were added to the latent construct, the association between SMU and nicotine dependency among smokers was strengthened to a statistically significant level. 5.2 Discussion of Principal Findings 5.2.1 Vaping, Social Media Use, and Symptoms of Anxiety and Depression Some of our findings related to the association between social media use and vaping status were contrary to our expectations; we expected each indicator of social media use to be positively associated with an increased likelihood of vaping. Recent literature suggests the frequency of social media use, social media platforms, engagement, and dependency are all, positively associated with an increased likelihood of vaping (Massey et al., 2021; Shensa et al., 2018). However, when these measures were used together as part of a latent construct of SMU, we found a negative association between SMU and vaping status in the structural equation model. Although we are unsure of why this relationship occurred, it may result from high levels of social media use across the entire sample during the COVID-19 pandemic. Specifically, it remains possible that increased amounts of social media use during the COVID-19 pandemic may have altered the association between SMU and the likelihood of vaping, which may partially explain these novel findings (Chan et al., 2020; González-Padilla & Tortolero-Blanco, 2020). Below, we provide a more detailed examination of the impact of the pandemic on this study. Another possible explanation for the discrepancies in these findings would be the inclusion of SMU as a comprehensive latent construct, which included multiple aspects of social media use. These aspects include elements of objective markers of use (e.g., frequency and number of platforms), promotive markers of use (e.g., engagement), and negative markers of use (e.g., dependency). It remains possible the objective, promotive, and negative markers of social 80 media use may each have a different association with vaping status. Given the implications of different markers of social media use, it may be informative to examine the associations between objective, promotive, and negative markers of social media use. For example, it may be beneficial to determine if negative markers decrease the positive effects of promotive markers of social media use and, in turn, alter associations with mental illness symptoms or vaping. A detailed analysis of these associations may provide a better understanding of how varying patterns of social media use impact health-related behaviours and clarify the benefits and risks associated with different indicators of social media use in the literature. The clarification of benefits and risks may also help create specific clinical guidelines around helpful and harmful social media use. When we examined the relationships between the individual indicators of social media use (i.e., frequency of use, dependency, and engagement) and the likelihood of vaping, there were novel findings compared to the literature. Social media dependency was associated with a lower likelihood of vaping, which appears to be a novel finding. However, the frequency of social media use was associated with a greater likelihood of vaping, and this relationship remained significant, even when symptoms of anxiety and depression were included in the model. These findings were consistent with the literature as previous studies have reported that the frequency of social media use is associated with an increased likelihood of vaping (Lee et al., 2021; Massey et al., 2020). These findings add to the current body of literature by increasing anxiety and depression symptoms along with the frequency social media use to assess the likelihood of vaping. Our findings for the associations between SMU and nicotine dependency among vapers mediated by symptoms of mental illness produced novel findings. SMU was positively 81 associated with a greater score of nicotine dependency among vapers only when symptoms of anxiety and depression were included to mediate the association. As no previous research has examined this fully mediated relationship, these findings are novel. This model appears to suggest that symptoms of anxiety and depression may have a vital role in understanding the association between SMU and nicotine dependency among vapers. The fully mediated structural equation model, including the latent construct of SMU, symptoms of mental illness, and nicotine dependency among vapers also adds to our understanding of the complexity of this relationship. We also examined the associations between the individual indicators of social media use (i.e., frequency of use, dependency, and engagement) and nicotine dependency among vapers. In the multiple regression analysis, we also found social media dependency was associated with higher rates of nicotine dependency among vapers, which indicates an association between these two types of dependency. Mental illness symptoms appear to mediate dependency between SMU and nicotine, which may be essential in future research and in the development of vaping prevention programs and screening for social media dependency to decrease the number of co-occurring dependencies and more effectively treat addiction (Ball, 2008; Urbanoski et al., 2015). This finding is also consistent with theory. According to the Personality Theory of Addiction, an individual experiencing addiction related to one behaviour or substance may be more likely to be susceptible to other addictions (Eysenck, 1997). This theory is also supported by neuroscience as addictive behaviours create neural pathways that increase susceptibility to other addictions later in life (He et al., 2017a; He et al., 2017b; Turel et al., 2018). Given the positive association between social media dependency and nicotine dependency among vapers, it appears this finding adds additional support to the Personality Theory of Addiction. 82 Previous studies have found using a greater number of platforms and more frequent use of social media were associated with an increased likelihood experiencing anxiety and depression symptoms (Primack et al., 2021; Vannucci et al., 2017). Furthermore, recent studies by Simpson et al. (2021) and Kittaneh et al. (2021) found symptoms of anxiety and depression are associated with increased nicotine dependency among vapers, which was consistent with the findings from the current study. These associations highlight the importance of considering multiple factors and mediating associations contributing to dependency on a substance. Exploring mediated associations related to dependency in research makes it possible to create more holistic care plans by allowing clinicians to address multiple indicators of wellbeing. One method for understanding the associations between social media use, anxiety and depression symptoms, and nicotine dependency among vapers is the Biosocial-Psychological Model (Engel 1977). This model states understanding and addressing challenges clients experience requires an examination of the physiological symptoms and social and psychological factors contributing to them. The main goal of this model is to approach the challenges experienced by an individual in a holistic and comprehensive manner to provide high-quality care and services (Borrell-Carrió et al., 2004; Inerney, 2018). This model represented a significant shift in the philosophy of providing care and conducting research as it stepped away from the traditional bio-medical model to reconnect mind, body, and environment, which allowed for researchers and clinicians to approach addiction in a more comprehensive manner (Binnie‐Dawson, 1982; Borrell-Carrió et al., 2004). Applied to only the concepts in the current study, addressing the biological-psychological aspects of the model means examining the impact of nicotine dependency has on the brain and body. Nicotine is an addictive chemical, but nicotine is also sometimes used as a form of self- 83 medication for anxiety and depression symptoms (Besson & Forget, 2016). Thus, this may help us understand the association between nicotine dependency among vapers and mental illness symptoms as both an addictive behaviour and a means of individuals trying to reduce negative affect. Next, addressing the social dimension of the model includes understanding the social influences and context of social media. Applying this model to the association between SMU, mental illness symptoms, and nicotine dependency among vapers may also be helpful with understanding and responding to both addiction and mental health concerns. For example, social media is often used to maintain social contact and facilitate relationships (Kircaburun et al., 2018; Leas et al., 2020; Naslund et al., 2020). However, some of the harmful aspects of social media use, such as dependency, increased the likelihood of anxiety and depression symptoms. These findings indicate that addressing the biosocial-psychological model’s social aspect though decreasing social media dependency may help to decrease anxiety and depression symptoms. Next, anxiety and depression symptoms are associated with vaping, and examining the psychological aspects of the model may provide an opportunity for decreasing vaping. Finally, including the biological impact of using nicotine as a form of self-medication for anxiety and depression symptoms and adding the additional consideration of co-occurring dependencies (nicotine dependency and social media dependency) allows for a comprehensive examination of this relationship and may aid clinicians in creating care plans or intervention programs. 5.2.2 Smoking, Social Media Use, and Symptoms of Anxiety and Depression Unlike the vaping models, findings for the fully mediated structural equation model including the latent construct of SMU, symptoms of anxiety and depression, and smoking status were mostly consistent with previous findings. Consistent with Ilakkuvan and colleagues (2019) 84 who examined two indicators of SMU (including frequency of use and social media platforms), we found SMU increased the likelihood of smoking when considering symptoms of anxiety and depression; however, Ilakkuvan et al., (2019) only included the moderating effect of depression symptoms. Our study used mediation instead of moderation and included anxiety symptoms and included a latent construct of SMU instead two indicators of social media use, adding to the body of literature. In practice, symptoms of anxiety and depression often co-occur (Cummings et al., 2014; Ter Meulen et al., 2021). Thus, understanding how these symptoms of anxiety and depression operate together in the model adds depth to the current understanding of the association between SMU and smoking. We also found a negative relationship between SMU and smoking status, which adds to the current body of literature by indicating some aspects of SMU may be associated with a decreased likelihood of smoking, which is a novel finding when compared to Turel et al., (2018), who found social media dependency neurological presented like nicotine dependency using MRI brain scans. The most likely explanation for these conflicting findings is that Turel et al., (2018), only examined a negative marker of social media use, and the current study employed a comprehensive measure of social media use. The findings for the fully mediated structural equation model including the latent construct of SMU, symptoms of anxiety and depression, and nicotine dependency among smokers produced novel findings compared to the literature, as no research examining this fully mediated relationship was available. SMU was negatively associated with nicotine dependency among smokers, whereas when symptoms of anxiety and depression mediated the relationship, SMU was positively associated with scores of nicotine dependency among smokers. There may be many possible explanations for the association between SMU and nicotine dependency among smokers. For example, COVID-19 appears to have led to higher levels of social media use, 85 which may have increased the likelihood of seeing social media campaigns to decrease smoking, increased quitting intentions, and increased attempts to quit smoking (Jackson et al., 2021; Kowitt et al., 2020; Luo et al., 2021). However, the current study did not inquire about quitting intentions or exposure to smoking cessation material. Another possible explanation is that individuals may be attempting to reduce smoking due to the COVID-19 pandemic. For example, reducing smoking as a means to maintain lung health. Or social media use may be increasing as an effect of the COVID-19 pandemic as people spend more time at home. Thus, the association between SMU and smoking may actually be explained as a spurious relationship due to the effects of the COVID-19 pandemic. It is essential to note that these relationships are correlational in nature, and the spurious relationship could result from other behavioural changes associated with COVID-19. Still, SMU was also positively associated with an increased likelihood of symptoms of anxiety and depression and symptoms of anxiety and depression were associated with increased nicotine dependency among smokers, which was consistent with previous findings (Shensa et al., 2018; Zvolensky et al., 2019). Furthermore, the examination of SMU and nicotine dependency among smokers mediated by symptoms of anxiety and depression adds to the current body of literature by providing a more comprehensive analysis of the complex relationships between SMU, nicotine dependency among smokers, and assessing the role of symptoms of anxiety and depression on this association. 5.2.3 Differences in Vaping and Smoking There were also two key differences between vapers and smokers in the mediated structural equation models for vaping status and smoking status and nicotine dependency among vapers and smokers. First, mental illness symptoms increased the likelihood of smoking but not 86 vaping. Mental illness symptoms have consistently been associated with vaping and smoking (Becker et al., 2021; Grant et al., 2019; McClave et al., 2009; Mykletun et al., 2008; Stanton et al., 2020). However, a recent study by Lanza et al., (2020) found that anxiety and depression symptoms are not associated with an increased the likelihood of vaping. These findings appear to suggest symptoms of anxiety and depression may have a more significant role in the likelihood of smoking than they do for vaping. Second, the association between SMU and nicotine dependency among vapers was non-significant whereas SMU and nicotine dependency among smokers were negatively associated. These differences from the structural equation models may indicate the way smokers engage with social media may be different from vapers. Other studies have found positive correlations between nicotine dependency and indicators of social media use. For example, Solem et al. (2021) found a positive association between problematic social media use and nicotine dependency among smokers. Also, Arshad et al. (2018) found a positive association between social media dependency and nicotine dependency. The most likely explanation for these novel findings is that previous studies mainly examined negative markers of social media use, rather than looking at a range of social media use behaviours. It is possible negative markers of social media use are positively associated with nicotine dependency, whereas promotive and objective markers are negatively associated with nicotine dependency, highlighting the need for a more comprehensive assessment of the markers of social media use and risk behaviours. These findings add to the current literature by indicating different factors contribute to nicotine dependency among vapers and smokers. Thus, the current study adds depth to our current superficial understanding of risk behaviours. 87 All together, these findings from the structural equation models suggest different paths exist between SMU and vaping and smoking status and nicotine dependency when mediated by symptoms of anxiety and depression. Specifically, vapers and smokers may have different associations with social media use as the latent construct of SMU had different associations with vaping and smoking in the structural models. Also, symptoms of anxiety and depression appear to have a different role in whether an individual vapes or smokes as symptoms of anxiety and depression increased the likelihood of smoking but not vaping. However, symptoms of anxiety and depression have a similar role in nicotine dependency among both vapers and smokers. It is worth noting that although we can theoretically compare the vaping and smoking models, statistical differences in the model coefficients cannot be compared due to the differing sample sizes and the different structures of the models. Thus, we are unable to determine any statistically significant differences between the four fully mediated structural equation models. 5.2.4 Methodological Considerations Many studies have examined social media use by individually assessing the various indicators of social media use (Massey et al., 2021; Primack et al., 2021; Vannucci et al., 2017). This study used the novel approach of creating a latent construct for social media use. By examining the various indicators of social media use together, it appears that social media use maybe even more complex and nuanced than previously thought. The findings from the current study suggest that when measuring social media use, the indicator (e.g., frequency of use or social media dependency) employed may be less important than the type of social media use marker the indicator measures. Specifically, three markers of use, objective, promotive, and negative, may comprise social media use. Separating social media use into three different markers could have significant impacts on examining the impact social media use has on other 88 health-related behaviours. For example, differentiating between promotive and negative markers of social media use may help to clarify how vaping and smoking prevention programs can best utilize social media use to help clients. Further, a better understanding of social media use markers may help inform efforts to decrease mental illness symptoms associated with social media use. Overall, these findings have a major implication. Neither using single indicators of social media use nor a single latent construct fully addresses the association between social media use and health-related behaviours. Going forward, the findings from this study appear to suggest the best approach includes using multiple indicators of social media use and then separating the indicators into three latent constructs to represent objective, promotive, and negative markers of social media use. Thus, creating a comprehensive and sensitive measure to assess the association between social media use and health behaviours. 5.2.5 Social Media Dependency Throughout data analysis and interpretation, the measure of social media dependency consistently showed contrary findings compared to the literature and theory; social media dependency was not consistently associated with vaping or smoking. A prior study indicated social media dependency mirrored addiction to nicotine among smokers, with both forms of dependency having an impact on work or school (Arshad et al., 2018). Another study suggested nicotine dependency among smokers was positively associated with problematic social media use (Solem et al., 2021). However, the findings from the smoking models (logistic regressions, multiple regressions, and structural equation models) in the current study did not align with Arshad et al. (2018). Social media dependency was not associated with the likelihood of smoking or associated with smoking when symptoms of anxiety and depression were considered, a 89 finding inconsistent with the literature (Baltaci, 2019; Hammett et al., 2019; Lin et al., 2016; Patton et al., 1998). Due to these unanticipated findings and given that research question three specified examining the impact of indicators of social media use on anxiety and depression symptoms, a closer examination of social media dependency was warranted (See Appendix P). The social media dependency scale consisted of six items (Andreassen et al., 2012). First, salience which referred to the amount of time spent using social media. Second, tolerance meaning using more social media than intended to maintain the same effect. Third, mood modification referring to social media use helping to reduce feeling negative affect. Fourth, relapse, meaning an inability to not use social media. Fifth, withdrawal, referring to symptoms of negative affect from not using social media, and finally, conflict meaning social media use having a negative effect on performance at work or school. The current study found higher levels of social media dependency than reported pre-pandemic (Andreassen et al., 2012; Błachnio et al., 2016). However, these findings were consistent with another study that was conducted during the pandemic, which also showed higher levels of social media dependency than previously reported (Lin et al., 2020). In the current study, five out of the six indicators of social media dependency were not associated with symptoms of anxiety or depression. However, one item (mood modification from social media use) was associated with an increased likelihood of experiencing symptoms of anxiety and depression, which was consistent with previous findings (Brailovskaia & Margraf, 2020; Griffiths & Kuss, 2017; Köse & Doğan, 2019; Sriwilai & Charoensukmongkol, 2016). And one item (conflict from social media use) was associated with an increased likelihood of 90 experiencing depression symptoms, which is also consistent with previous findings (Brailovskaia & Margraf, 2020; Frost & Rickwood, 2017). Mood Management Theory may explain these findings as this theory posits individuals unintentionally select different stimuli to decrease negative mood states and increase positive mood states (Zillmann, 1988a, Zillmann, 1988b). Thus, this association between mood modification and anxiety and depression symptoms may result from individuals attempting to manage increased negative mood states resulting from the COVID-19 pandemic by using social media to increase hedonic pleasure and thus increase positive affect, which is also consistent with empirical findings (Cauberghe et al., 2021; Dillman Carpentier, 2020). Therefore, this unanticipated finding may add to the literature by indicating in the event of a global pandemic, mental health professionals should consider encouraging clients to increase positive and healthy coping strategies and limit behaviours associated with social media dependency. 5.2.6 Dependency Begets Dependency. Dependency on a substance such as nicotine remains complex (Bekiroglu et al., 2017; Castle, 2008; Young et al., 2015). Often, addiction behaviours appear to cluster as mental health and substance use often co-occurs and are influenced by biological, social, and environmental factors (Ball, 2008; Halpern et al., 2018; Østergaard et al., 2017; Urbanoski et al., 2015; Zilberman et al., 2018). The current study adds to the existing literature by indicating that nicotine dependency among vapers is associated with social media dependency. It remains essential in research and clinical practice to examine contributing factors related to dependence holistically and comprehensively. Given that social media dependency and engagement had different associations with mental illness symptoms and nicotine dependency among vapers, these findings may mean we need to carefully monitor and rethink how various groups use social 91 media. For example, it may be helpful to implement social media dependency screening measures in clinical practice to identify those at risk for both social media dependency and nicotine dependency and then offer early intervention. 5.2.7 Mental Illness Symptoms Mediating Nicotine Dependency The current study highlights the essential role of symptoms of anxiety and depression have in understanding the association between social media use and vaping and smoking behaviours. Mental illness symptoms appear to mediate dependency. The Public Health Model of Addiction is an Australian approach to guide and prevent substance dependency and is based on harm minimisation philosophy (Department of Health, 2004). This model has previously been employed to examine the association between symptoms of mental illness and nicotine dependency (Prochaska et al., 2017). The Public Health Model of Addiction is comprised examining the interaction between three key elements (Department of Health, 2004). First, the agent, meaning the characteristics and effects of a substance (Department of Health, 2004). Sometimes, nicotine is used as a form of self-medication for mental illness symptoms as the nicotine targets the nicotinic acetylcholine receptors in the brain, which help to improve mood and affect (Alexander & Ward, 2018; Morozova et al., 2015; Parikh et al., 2016; Phillip et al., 2010). Also, nicotine withdrawal may increase symptoms of mental illness (Ferris et al., 2015; Lechner et al., 2015). However, sustained discontinued use of nicotine does not result in more long-term mental illness symptoms (Mendelsohn et al., 2015). Furthermore, in some cases, long-term discontinued use of nicotine may result in improved mental health (Mendelsohn et al., 2015). Second, the host, meaning group or individual characteristics of the person using the substance (Department of Health, 2004). Nicotine dependency often co-occurs with symptoms of mental illness, potentially due to the self-medication benefits characteristic of 92 nicotine use, indicating experiencing some degree of mental illness symptoms may help to define the host grouping (Dani & Harris, 2005; Prochaska et al., 2017). Finally, the environment, meaning how the social or other environmental factors impact substance use behaviour (Department of Health, 2004). For smoking, the environment may include available mental health services and nicotine dependency education and policies (Prochaska et al., 2017). The environment also extends to include vaping and smoking advertisements on social media (Amin et al., 2020; Prochaska et al., 2017; Vandewater et al., 2018). Applied to the current study, the Public Health Model of Addiction helps to explain the mediating role of mental illness symptoms in nicotine dependency. Experiencing mental illness symptoms, was associated with nicotine dependency among both smokers and vapers, and the environmental factors of social media use appear to then contribute to both increased symptoms of mental illness and nicotine dependency. Employing the Public Health Model of Addiction is especially useful in determining preventative measures when considering mental illness symptoms mediating dependency. The Public Health Model of Addiction posits three main ways of responding to substance use (Department of Health, 2004). First, primary prevention which refers to addressing the broad factors to decrease the likelihood of substance use occurring, education, and use of media (Department of Health, 2004). The secondary prevention efforts identified by the Public Health Model of Addiction includes early identification and intervention if substance use occurs (Department of Health, 2004) Finally, tertiary prevention refers to addressing other factors that contribute to substance use for each individual and helping to reduce complications from substance use (e.g., offering therapy to help with troubled relationships) (Department of Health, 2004). 93 Smoking cessation programs utilizing social media are already providing primary prevention measures and promising interventions addressing the association between mental illness symptoms and nicotine dependency among smokers (Duke et al., 2014; Naslund et al., 2017; Pechmann et al., 2015; Struik & Baskerville, 2014; Thrul et al., 2019). However, vaping cessation programs utilizing social media have not had the same effect as the smoking cessation programs as social media appears to be dominated by promotional vaping content (Soweles et al., van der Tempel et al., 2016). Therefore, addressing primary prevention for vaping should include reducing the promotional vaping content to create space for similar interventions used by smoking, and thus, may help to limit the number of individuals experiencing nicotine dependency. Social media use may be critical in addressing secondary prevention measures for nicotine dependency as mediated by symptoms of mental illness. Recently, several studies have found it is possible to predict the future development of mental illness symptoms, and detection current mental illness symptoms by using social media (Chancellor, & De Choudhury, 2020; Guntuku et al., 2017; Kim et al., 2020; Saravia et al., 2016; Thorstad & Wolff, 2019; Wongkoblap et al., 2017). Thus, social media use may provide a viable means of directly addressing nicotine dependency by enabling early detection and treatment for individuals experiencing mental illness symptoms. In the context of the mental illness symptoms mediating nicotine dependency, tertiary prevention may include relapse prevention measures. For example, use of nicotine replacement therapy in combination with behavioural theory, which is effective treatment (Lee et al., 2021; Killen et al., 1990; Killen et al., 1984; Polak & Jacob, 2020). Another example is employing 94 smartphone apps and including mindfulness practice to prevent relapse (Chang et al., 2017; Weiss de Souza et al., 2020). This finding suggests that prioritizing a decrease in mental illness symptoms may help limit the likelihood of engaging in vaping and smoking and may help to reduce use among individuals who already use nicotine. These findings also highlight how we can innovate moving forward. For instance, interventions could focus on reducing the likelihood of vaping and smoking by addressing the contributing factors rather than mainly focusing on the health risk behaviour. It is possible that addressing contributing factors, including mental illness symptoms, or decreasing the amount of available vaping content on social media, may help to reduce the number of people vaping in ways that strengthening vaping regulations may not. 5.3 COVID-19 Pandemic Of note, data for this study were collected from October 8, 2020, to December 15, 2020, during the second wave of the COVID-19 pandemic in Canada. During that time, individuals became reliant on virtual spaces for school, work, medical care, church, volunteering, and socialization (Annaswamy et al., 2020; Bryson et al., 2020; Cheded & Skandalis, 2021; Dubey & Tripathi, 2020; Ettekal & Agans, 2020; Lachance, 2020; Ntem et al., 2020). Also, the effects of the pandemic were generally associated with increased mental health concerns related to anxiety symptoms, depression symptoms, and addiction, including vaping and smoking (Gao et al., 2020; World Health Organization, 2020). As a result, collecting data on social media use, mental illness symptoms, and dependency amidst the COVID-19 pandemic may have impacted our findings. As seen in our study sample, 33% reported vaping, and 21% smoked. These percentages are higher than the 15% of individuals who vaped and smoked, as reported by Health Canada 95 (2019). The higher prevalence of vaping and smoking in the sample may have resulted from the different patterns of nicotine use, which were reported throughout the COVID-19 pandemic (Kalkhoran et al., 2021). In some cases, smoking decreased, and in others, smoking increased (Kalkhoran et al., 2021). It is also possible that the higher prevalence of vaping and smoking may have resulted from the recruitment and sampling strategies used in this study or it may be a spurious association due to the effect of the COVID-19 pandemic. The COVID-19 pandemic may have increased interest in smoking due to the influence of a study, which suggested that individuals who smoked were hospitalized less often than individuals who did not, and that nicotine may have some therapeutic effects (Farsalinos et al., 2021; Kamiński et al., 2020). Some authors have hypothesized that since nicotine acts as a cholinergic agonist, it may reduce the inflammation resulting from the COVID-19 virus (Lagoumintzis et al., 2021). However, it is also worth noting other studies found that both vaping and smoking increased the likelihood of more severe symptoms of COVID-19 (Glantz, 2020; Majmundar et al., 2020). The conflicting information was available via social media and may have affected the rates of vaping and smoking. Accounting for the differences in prevalence was beyond the scope of the present study. The COVID-19 pandemic also impacted the prevalence of symptoms of anxiety and depression (Nikčević et al., 2021). In the current study sample, approximately 41% of participants reported experiencing moderate to severe anxiety symptoms, and approximately 43% of the sample reported experiencing moderate to severe depression symptoms. These rates of symptoms of anxiety and depression are higher than previously found within the Canadian population but similar to the current pandemic-related rates (CAMH 2020; Ni et al., 2020; Statistics Canada, 2020c). Social media use was a likely source of increased rates of anxiety and 96 depression symptoms during COVID-19. Specifically, social media use increased exposure to content that may have heightened fear of COVID-19, and viewing emotionally charged information about COVID-19 on social media is associated with increased anxiety and depression symptoms (Servidio et al., 2021; Wheaton et al., 2020). These findings suggest both living through the pandemic and viewing content about the pandemic on social media may have influenced how participants responded to the survey. The COVID-19 pandemic had additional effects on social media use. Physical distancing regulations associated with the pandemic changed how people used social media. For example, social media became an essential tool for rapid dissemination of information during the COVID-19 pandemic (Chan et al., 2020; González-Padilla & Tortolero-Blanco, 2020). Moreover, the COVID-19 pandemic also appear to have increased the frequency of social media use and social media dependency (Bendau et al., 2021; Brailovskaia & Margraf, 2021). These findings suggest that the COVID-19 pandemic may have impacted the current study by increasing both the overall social media use and the associated outcomes. The nature of social media engagement may also have been impacted by the effects of the COVID-19 pandemic. Previous authors determined that social media engagement was part of the latent construct of SMU and was associated with increased symptoms of anxiety and depression (Majmundar et al., 2020; Shensa et al., 2018). However, the current study found that social media engagement did not have a bivariate association or a mediated association with symptoms of anxiety and depression or vaping and smoking. Social Media Engagement Theory helps explain this finding in the context of a global pandemic. Social media engagement results from using social media for entertainment, information, and social interaction, which could alter the individual’s mental health and behaviours (Dolan et al., 2016; Katz & Foulkes, 1962). When 97 many people were isolated, social media engagement may have helped them remain connected, facilitating social interactions, and improved mental health (Lau et al., 2020; Ng et al., 2020; World Health Organization, 2020). Of note, in the current study, there was no evidence to suggest social media engagement improved or supported mental health. However, the association between social media engagement and symptoms of anxiety and depression simply might not have been strong enough to overcome or mitigate the impact of the COVID-19 pandemic. In other words, the effect of the COVID-19 pandemic may have attenuated the beneficial effects of social media engagement as engagement became entangled with social media dependency during a time when social media use was a primary method of maintaining social relationships. Finally, in this study, the frequency of use and number of platforms used were not associated with an increased likelihood of anxiety or depression symptoms, which is inconsistent with the literature (Hawes et al., 2020; Primack et al., 2017). However, this finding may also be related to the effects of the COVID-19 pandemic, which increased both the frequency of social media use and mental health concerns (Bendau et al., 2021). The frequency and number of platforms appear to be more objective measures of social media use. In other words, the frequency and number of platforms used do not appear to be inherent negative markers of social media use unlike social media dependency. At the same time, the frequency and number of platforms used do not appear to be inherent promotive markers of social media use, in contrast to social media engagement. Under this rationale, it appears the deviation of these findings from the literature may be due, at least in part, to the COVID-19 pandemic. Specifically, although previous findings indicated social media use increased levels of anxiety and depression symptoms, the pandemic may have mitigated the more negative aspects of these objective marker as social media was used as a means to reduce social isolation. Thus, the promotive 98 aspects of the objective markers of social media use could have outweighed the negative aspects (Lau et al., 2020; Ng et al., 2020). 5.4 Practise Implications This study has several important practice implications. The findings demonstrate associations between vaping and smoking behaviours with SMU and symptoms of anxiety and depression. They suggest that practitioners providing mental health or addiction interventions should be aware of these relationships. In particular, SMU should be viewed as a multidimensional construct with both positive and negative associations with vaping, smoking, and mental health. Practitioners should be asking questions about the various aspects of social media use when conducting assessments and determining the impact of other clinical interventions for mental health concerns and vaping and smoking behaviors. For example, this may include differentiation between social media engagement and social media dependency. Also, psychoeducational programs may find it beneficial to educate young adults on the signs, symptoms, and risks of social media dependency and the benefits of social media engagement. Additionally, in the context of the COVID-19 pandemic, this study suggests that social media use has increased. Thus, clinicians may need to reconsider how problematic levels of social media use are determined. Clinicians may wish to consider helping clients to find effective and healthy coping mechanisms, avoiding the risks associated with social media use. Further to the point, vaping and smoking prevention programs, cessation programs, and programs to reduce nicotine dependency should take symptoms of anxiety and depression into account to increase their efficacy. Overall, this study fills important gaps in the literature related to the mediated relationship between SMU and vaping and smoking and provides us with additional directions 99 for future studies. Moreover, this study highlighted the need to assess the impact of different types of social media use and separate promotive, negative, and objective social media use in practice interventions. It also identified the importance of mental health status as a factor to consider in smoking cessation programs. 5.5 Limitations and Strengths This study had several important limitations that must be considered. Limitations were related to the sample (e.g., recruitment and representativeness), measurement of the outcomes and key predictors, and the statistical analyses. We will also present relative strengths related to identified limitations. 5.5.1 Sample Based Limitations and Strengths Selection bias. This study may have a selection bias, given that the sample was collected via Reddit, a social media platform. There is some evidence that self-selection bias, which refers to the process of participants selecting to take part in a study, remains a concern in Reddit-based samples (Shatz, 2017). Also, using Reddit for data collection potentially leads to samples with higher levels of social media use compared to non-Reddit users, which may have influenced the amounts of social media use reported. However, given the effects of the pandemic and higher social media use, a Reddit based sample may more accurately reflect the population during this time period than previously reported. Additionally, since the survey was conducted online, these findings are not generalizable to those without internet access. However, the strengths of using Reddit outweigh these limitations. Reddit has been shown to produce high-quality data that can potentially replicate better than findings from typical convenience samples (Jamnik & Lane, 2017). 100 The current study also had a large, relatively representative sample (N = 707), which increased statistical accuracy of the tests conducted, decreased statistical errors, and allowed for use of advanced statistical analysis, controlling for multiple factors (Anderson & Gerbing,1984; Fritz & MacKinnon, 2007; Marsh et al., 1988; Stockwell & Peterson, 2002). Furthermore, the current study sample had a relatively equal number of men (55.3%) and women, which is uncommon in online surveys; online surveys tend to have more women than men participating (Holdcroft, 2007; Smith, 2008). In using Reddit, we were able to recruit a relatively representative sample in terms of gender identity, trans experience, and sexual orientation. Approximately 31% of the sample identified as LGBTQ2+, which is considerably higher than Canadian population (Statistics Canada 2021). The study has additional limitations, which could affect the generalizability of the findings. For instance, the intended study population was Canadians or people living in Canada who were over the age of 19, but it was not possible to verify that the sample excluded all individuals living outside of Canada and youth. Moreover, the sample was primarily White (approximately 80%), which suggests that the findings may require further validation with other ethno-racial groups. However, the sample does reflect the Canadian population, as approximately 72.9% of Canadians are White (Statistics Canada, 2017). As for education, 61.7% of the sample attended post-secondary, which is comparable to the 55.3% of the Canadian population (Statistics Canada, 2017). Finally, the current study had 63.6% of the participants in a relationship, which is similar to the Canadian population (57.6%) (Statistics Canada, 2017). Even though this sample was relatively representative of Canadians overall, these findings may vary among specific groups (e.g., ethno-racial or cultural groups), and future studies may wish to explore these potential group based differences. 101 Thus, with some limitations, the sample was representative of the population, increasing the overall generalizability of the results. Even with these cautions, this study is widely generalizable as the sample appears to be more representative of a community sample than traditional convenience samples from online surveys. The results from this study are generalizable across sex, gender identities, and sexual orientations. Also, these results are generalizable on the grounds of education and age. 5.5.2 Measurement Based Limitations and Strengths Vaping and Smoking Measurement. Although we measured vaping and smoking status, and nicotine dependency among vapers and smokers, we may not have captured the full range of vaping and smoking behaviours. Future studies focused on vaping or smoking could benefit from including more comprehensive questions related to use behaviours, such as location, nicotine concentration, type of device use, and dual use of vaping and smoking. Also, this study did not assess differences between occasional and regular smokers. Differences, such as motivation and dependency, should be explored more fully (Brown et al., 2011; Hoch et al., 2004; Wells et al., 1998). Measurement of Social Media Use. Due to this study’s limited scope, several indicators of the construct of SMU remain unexplored. First, the measurements of SMU did not include duration of social media use or patterns of use (e.g., using social media at bedtime), which previous studies have found to be associated with anxiety and depression symptoms (Bhat et al., 2018; Kara et al., 2021; Primack et al., 2017). Second, this study did not assess whether participants were using social media actively or passively. Other studies have demonstrated that active SMU (e.g., staying connected) is associated with decreased symptoms of anxiety and depression while passive SMU (e.g., 102 scrolling) increases symptoms of anxiety and depression (Thorisdottir et al., 2019). These additional aspects of SMU could be considered in future studies. Symptoms of Anxiety and Depression Measurement. This study used two validated screening tools for anxiety symptoms and depression symptoms, examining both the continuous scale score and clinically significant cut-off values. However, it is important to note these screening tools are not diagnostic tests (Spitzer et al., 2006; Spitzer et al., 1999), and we examined symptoms for only two highly prevalent and often co-occurring mental health concerns. It is possible the study participants were also experiencing other forms of mental health or addiction-related concerns. Therefore, these findings should be taken with caution when applied to clinical groups or samples. Future studies could benefit from including additional mental health issues and relying on actual clinical diagnoses. 5.5.3 Statistical Analysis Limitations and Strengths Finally, although this study utilized structural equation modelling, and these models are often viewed in a temporal way, no temporal order for the variables was established. The study was cross-sectional, with all variables collected at the same point in time. Thus, we did not determine if increased symptoms of anxiety and depression led to greater social media use or vice versa. As a result, findings from this study should not be inferred as causal. The literature suggests bidirectional relationships exist between SMU and symptoms of anxiety and depression (Guntuku et al., 2017; Guntuku et al., 2019; Shensa et al., 2018). Thus, future studies should continue to examine the complex nature to these relationships. Even with these general limitations, it is essential to note that the statistical tests employed provided compelling insights into the co-occurring relationships between SMU, symptoms of anxiety and depression, and vaping and smoking. High-level statistics like 103 structural equation modelling are powerful tools for examining the relationships underlying key concepts and providing critical insights for future studies (MacCallum & Austin, 2000; Thompson, 1997). Thus, using these statistics provided a new perspective on these relationships. These advanced statistics could be used in future comprehensive studies examining relationships between social media use and smoking and vaping. 5.6 Future Analyses While this study addressed significant gaps in the literature related to the original research questions, it also illuminated new possible avenues of inquiry. Some areas of future research emerged from the dataset. First, differences between vapers and smokers based on their social media platform use could be explored.. This line of inquiry may provide deeper insights into the nature of the relationships between vaping and smoking behaviours and social media use and also validate existing knowledge. Previous studies have found that Facebook, Twitter, and Instagram use are associated with an increased likelihood of vaping (Chu et al., 2021; Kong et al., 2021; McCausland et al., 2020; Sidani et al., 2020). Others found that Instagram, Twitter, and YouTube use are associated with an increased likelihood of smoking (Ferguson et al., 2020; Johnson & Mays, 2020; Park 2020). Therefore, it may be beneficial to examine the associations of vaping andsmoking with the various social media platforms, incorporating other aspects of social media use, such as dependency or engagement. Second, the current study did not explicitly examine the role of LGBTQ2+ status, as this was not part of the initial research questions. However, our sample included approximately 31% of individuals who identified as LGBTQ2+; it provides opportunities for additional explorations specific to LGBTQ+ status. Previous studies found that individuals who identify as LGBTQ2+ use social media differently, experience more vaping and smoking-targeted advertisements on 104 social media, have higher rates of symptoms of anxiety and depression, and are more likely to vape or smoke than the general population (Emory et al., 2019; Han et al., 2019; Kcomt et al., 2020; Lucero 2017; Nama et al., 2017; Steele et al., 2017). Therefore, the sample data may be used to examine SMU, anxiety, depression, vaping, and smoking among the LGBTQ2+ population. 5.7 Future Research Beyond the scope of the data from the current study, future research should assess the benefits of social media use in promoting mental health and wellness. The current study examined the impact of SMU on symptoms of mental illness. Yet, given what we know about social media engagement, it may be useful to examine how positive markers of social media use improve mental health and decrease health risk behaviors, such as vaping and smoking. Future studies may also benefit from examining SMU motivations associated with passive and active use behaviours. Given some of the challenges and unexpected findings related to social media dependency in this study, future research may consider developing new and innovative methods to assess dependency more objectively. For example, an app could be created to measure social media dependency objectively. The prevalence of social media dependency was higher in the sample than in earlier studies, and this difference may be due to COVID-19. Therefore, future research may wish to examine if the presentation of social media dependency differs based on other factors, such as age. For example, does the meaning of social media dependency change with age between younger samples, who often are among the highest social media users, and older adults, who may use social media less but experienced different social contexts which then had to be expanded to include social media. 105 Finally, we recognize that these data were collected during an unprecedented time, and the findings may not be generalizable to non-pandemic times. As a result, future research could replicate this study post- COVID-19 to determine whether the findings are generalizable under normal conditions. Future replications could also examine the generalizability of the findings to more diverse populations in relation to ethno-racial characteristics, age, mental health concerns, forms of risk behaviours, and residency outside of Canada. 5.8 Conclusions This study was conducted with a relatively large and representative sample of people over 19 years old who live in Canada. This study also employed a variety of statistical tests and theory to assess various aspects of the associations between social media use, vaping and smoking status and nicotine dependency, and symptoms of mental illness. Overall, the results indicated six key areas of interest. First, using a single type of social media use such as frequency or engagement appears to not fully capture the association between social media use and health-related behaviours. However, using a latent construct with several commonly used media use indicators also does not appropriately capture the relationship between social media use and health-related behaviours either. Thus, in the future, categorizing social media use indicators as promotive, objective, and negative markers of social media use and assessing their impact health-related behaviours may provide a more comprehensive understanding of this complex relationship. This approach would further our understanding to the beneficial, harmful, and neutral aspects of social media use. Second, vaping and smoking are often considered to be extremely similar behaviours. However, different factors appear to influence the likelihood of vaping and smoking, which may 106 indicate it is problematic to treat these two methods of nicotine inhalation as if they are the same. Thus, going forward, a more in-depth assessment of the differences between vaping and smoking may yield results that could better inform prevention strategies. Third, mental illness symptoms appear to mediate dependency. Mental illness symptoms seem to have a more influential role in the likelihood of smoking than vaping. However, mental illness symptoms appeared to mediate the association between SMU and nicotine dependency among both vapers and smokers. These findings suggest that addressing symptoms of mental illness may help to reduce nicotine dependency. Future research may wish to consider adding additional symptoms of mental illness to create a more comprehensive assessment of the association between mental illness symptoms and nicotine dependency. Fourth, dependency begets dependency; that is, dependency remains complex, but this study added to the current literature which suggests that dependencies often co-occur along with mental illness symptoms. Social media dependency was associated with both nicotine dependency among vapers and mental illness symptoms. In clinical practice, these findings indicate it may be helpful to identify individuals experiencing both behavioural and chemical dependencies with co-occurring mental illness symptoms. In comprehensively examining both types of dependency, clinicians may be able to provide early intervention and treatment. Fifth, this study highlights some of the various ways COVID-19 impacted the Canadian population, even though this study did not specifically examine COVID-19. Specifically, in the current study, we saw similar rates of anxiety and depression symptoms reported in other studies. We also found higher rates of social media dependency than previously reported. Further to the point, these findings indicated an association between social media dependency and anxiety and 107 depression symptoms through the item mood modification from the social media dependency scale, consistent with Mood Management Theory. Finally, although this study provided a comprehensive examination of the mediated association between social media use, vaping and smoking, and symptoms of anxiety and depression, there are three significant remaining gaps in the literature that should be addressed. First, future studies should examine the differences between vaping and smoking. A better understanding of these differences may clarify the different contributing factors to vaping and smoking and may provide insights into how to improve vaping prevention and cessation methods. 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The circles represent latent constructs, and the rhombus explain the theoretical associations Appendix B: Key Findings from Literature Review Table 26: Key Findings From Literature Review Study Design Key Findings SMOKING, SMU, AND DEPRESSION SYMPTOMS Brailovskaia et al., 2020 Quantitative: experimental randomized control study Facebook dependency and engagement are associated with depression and appears to be associated with smoking. However, sample size precluded significant findings related to smoking. Ilakkuvan et al., 2019 Quantitative: secondary data analyses of longitudinal data The social media platform a person uses is associated with both smoking and depression. VAPING AND SMU Dai & Ho, 2017 Quantitative: content analysis of Twitter Vaping industry generated vaping promotional content outnumber private citizen generated content and is associated with more positive perceptions among specific groups. Emory et al., 2017 Quantitative: cross-sectional survey Being exposed to more vaping content increases the at risk of starting vaping. Kim et al., 2015 Qualitative: content analysis of Twitter Vaping content and promotion are commonly posted on social media. Kong et al., 2019 Qualitative: content analysis of YouTube Vape tricks are prevalent and popular and show vaping as a form of entertainment. Kreitzberg et al., 2018 Quantitative: cross-sectional survey Increased exposure to vaping content is associated with increased beliefs of vaping being prevalent and peer use. Laestadius et al., 2019 Qualitative: content analysis of Instagram Vaping content aims to promote the benefits of vaping while minimizing perceptions of harm. Majmundar et al., 2019 Quantitative: cross-sectional survey Increased exposure to promotional vaping content on social media decreases perceptions of harm and increases negative perceptions of regulation. Phua et al., 2018 Quantitative: experimental Vaping content that has a celebrity and role models vaping on Instagram increases positive attitudes about vaping. Sawdey et al., 2017 Quantitative: cross-sectional survey Viewing both advertisements and peers’ posts about vaping on social media creates an increased risk of vaping. Scaioli et al., 2018 Qualitative: content analysis of YouTube Health benefits, smoking cessation, flavours, are the most common themes of vaping promotional content on YouTube Willis et al., 2017 Qualitative: content analysis of YouTube The lack of regulation of vaping advertisements allows for rampant vaping promotional content. Yang et al., 2019 Quantitative: longitudinal survey Seeking out information about vaping and being exposure to vaping content is significantly associated with vaping at a six month follow up point. SMOKING AND SMU Williams & Derrick, 2018 Qualitative: content analysis of online retailers Cigarettes are promoted and distributed online. Yoo 2016 Quantitative: experimental Viewing celebrities and other role models smoking increases positive beliefs about smoking. Yoo et al., 2016 Quantitative: cross-sectional survey Peers have an important role in promoting smoking content on social media. 163 Zhu 2017 Quantitative: longitudinal survey Exposure to positive content on social media increases the risk of smoking. SYMPTOMS OF ANXIETY, DEPRESSION, VAPING, AND SMOKING Bianco, 2019 Quantitative secondary analysis from 2016 National Health Interview Survey Vaping is associated with both anxiety and depression. Hefner et al., 2019 Quantitative: cross-sectional survey Vaping is more common among individuals with anxiety and depression than the general population. Leventhal & Zvolensky, 2015 Meta-review Depression and anxiety are both strongly associated with smoking. Pham et al., 2020 Quantitative secondary analysis from 2015 and 2016 Canadian community health survey Anxiety and depression are associated with vaping, smoking, and dual use of vaping and smoking. Spears et al 2018 Quantitative: cross-sectional survey Individuals with anxiety are more likely believe vaping is associated with better health outcomes. Watson et al., 2018 Quantitative: cross-sectional survey Smoking is associated with anxiety. Wiernik et al., 2019 Quantitative secondary data analysis of longitudinal and cross-sectional survey Dual use of smoking and vaping is associated with both anxiety and depression. Nicotine concentration is associated with depression. Zvolensky et al., 2019 Quantitative: cross-sectional survey Anxiety and depression are associated with smoking and nicotine dependence. SYMPTOMS OF ANXIETY, DEPRESSION AND SMU Bhat et al., 2018 Quantitative: cross-sectional survey SMU in bed disrupts sleep and is associated with an increased risk of depression. Escobar-Viera et al., 2018 Quantitative: cross-sectional survey SMU and engagement are associated with increased depressive symptoms. Frost & Rickwood, 2017 Systematic review Facebook addiction is consistently associated with both anxiety and depression. Lin et al., 2016 Quantitative: cross-sectional survey SMU and depression are significantly related. Lowe-Calverley et al., 2019 Quantitative: cross-sectional survey Increased investment in Instagram is associated with increased anxiety and depression. Primack et al., 2018 Quantitative: cross-sectional survey Negative experiences on social media increase the risk of developing depression. Shensa et al., 2018 Quantitative: cross-sectional survey Social media dependency, engagement, frequency, multiple platforms, duration are associated with both anxiety and depression. Appendix B: Key Findings from Literature Review 164 Appendix C: Full list of sub reddits used Wave One Sub-Reddits r/mentalhealth, r/anxiety, and r/vaping, r/electronic_cigarettes, r/vaping, r/vape, r/Canadian_ecigarette, r/vapeitforward, r/stopvaping, r/quitvaping, r/Vape_Chat, r/smokingcessation, r/stopsmoking, r/cigarette, r/Cigarettes, r/Tobacco, r/socialskills, r/social anxiety, r/depression, r/health, r/mentalhealth, r/anxiety, r/trans, r/Canada, r/ britishcolumbia, r/alberta, r/Manitoba, r/newbrunswickcanada, r/newfoundland, r/NovaScotia, r/ontairo, r/PEI, r/Quebec, r/saskatchewan , r/yukon, r/NWT, r/Nunavut, r/samplesize Wave Two Sub-Reddits r/AskReddit, r/NoStupidQuestions, r/Drugs, r/Juul, r/hookah, r/PipeTobacco, r/vapePorn, r/Vape_Porn, r/Vape tricks, r/vapeheads, r/Vape_Selfies, r/VapersInRecovery, r/vapegoneWild, r/Vaping101, r/VapeWild, r/Drunkvapes, r/Vaperettes, r/ForeverAlone, r/depression_memes, r/DepressionRecovery, r/AnxietyDepression, r/Edmonton, r/Red Deer, r/Lethbridge, r/Fort McMurry, r/Medicine Hat, r/Airdire, r/Spruce Grove, r/Lloydminster, r/Vancouver, r/Greatervan, r/Kelowna, r/Abbotsford, r/Whiterock, r/Nanaimo,r/Kamloops, r/Surrey, r/Chilliwack, r/Prince George, r/Vernon, r/Penticton, r/PowellRiver, r/Kootenays, r/Banff, r/Canmore, r/Camrose, r/Leduc, r/Winnipeg, r/Winkler, r/Bathurst, r/CampbelltonNB, r/Dieppe, r/Edmundston, r/Fredericton, r/Miramichi, r/Moncton, r/Saint John, r/Appleton, r/Yellowknife, r/Halifax, r/Cape Breton, r/Truro, r/Amherst, r/New Glasgow, r/YarmouthNovaScotia, r/Ottawa, r/Hamilton, r/Kitchener, r/LondonOntario, r/Oshawa, r/WindsorOntario, r/Barrie, r/Guelph, r/KingstonOntario, r/Brampton, r/Belleville, r/Summerside, r/Stratford, r/Montreal, r/Quebec City, r/Sherbrooke, r/Trois Rivieres, r/Saskatoon, r/Regina, r/Moose Jaw, r/Swift current, r/North Battleford, r/EstevanSK, r/Warman, r/Whitehorse 165 Appendix D: Letter of Invitations r/mentalhealth, r/anxiety, and r/vaping Hello, I am messaging you to ask for permission to post a research survey on your sub-reddit. This study examines the impact social media has on vaping/smoking by impacting mental health factors such as depression and anxiety. The survey uses scales that have been found reliable and valid through previous studies. This study has undergone an ethical review at the University of British Columbia, and has been granted ethical approval. Thank you for your consideration. Post one for most message boards Survey: Canadians, living in Canada, social media use, and smoking/vaping. Hello! We are looking for Canadians or people living in Canada over the age of 19 to take part in our survey. We are interested in understanding social media use, anxious and depressive symptoms, and vaping/smoking. So, we will be asking a few questions about these topics. Please be assured that your responses will be kept completely anonymous. The study should take you about ten minutes to complete. Post two for r/samplesize [Academic] Social media use, anxious and depressive symptoms, and smoking (Canadians or people living in Canada over the age of 19) Hello! We are looking for Canadians or people living in Canada over the age of 19 to take part in our survey. We are interested in understanding social media use, anxious and depressive symptoms, and vaping/smoking. So, we will be asking a few questions about these topics. Please be assured that your responses will be kept completely confidential. The study should take you under ten minutes to complete. 166 Appendix E: Questionnaire Survey Flow Block: Informed consent (1 Question) Standard: Canadian (1 Question) Standard: Vaping (11 Questions) Standard: Cigarette Addiction (7 Questions) Standard: Social Media Platforms and frequency (1 Question) Standard: Social media addiction (6 Questions) Standard: Social media engagement (1 Question) Standard: Vaping/Smoking Content (6 Questions) Standard: Anxious Symptoms (1 Question) Standard: Depressive (1 Question) Standard: SES From CCWS (10 Questions) Q1 Study Title: The impact of social media use on vaping and smoking, mediated by anxiety and depression Principal Investigators: Dr. Sarah Dow-Fleisner, Assistant Professor, School of Social Work, University of British Columbia, Okanagan Campus Phone: 250-807-9685 Dr. Dixon Sookraj, Associate Professor, School of Social Work, University of British Columbia, Okanagan Campus Phone: 250-807-8703. Student Investigator: Michelle Belliveau, Masters Student, School of Social Work, University of British Columbia, Okanagan Campus vapingandsmokingresearch@gmail.com Purpose of the Study: The purpose of this study is to examine the relationship between vaping, smoking, social media use, depression, and anxiety. You are being invited to take part in this study because you are a person who lives in Canada and are 19 years or older. Study Procedures: If you choose to take part in this study, you will be invited to complete a ten minutes survey. The survey includes questions about if you vape or smoke, how you used and engage in social media, and a few questions about mental health. You may decline to answer any questions in the survey. Potential Risks and Benefits: There are no risks to participating in this research that we know of. We also don’t expect you to benefit personally from participating. However, in the future, others may benefit from what we learn. Your doctors and other health care providers are not involved in the study. Your future care is not influenced in any way by your decision to participate. If thinking about anxiety and depression brings up negative feelings, there are resources available to you. See https://www.healthlinkbc.ca/ for a list of resources. You can call 811 for free non-emergency health information, including mental health information. The BC mental Health Support line (310-6789) is also free and available 24 hours a day. You will not be compensated for participating in this survey. Confidentiality: We will keep all identifying information about you strictly confidential. We will use number codes on all data collected. The survey is being administered using Qualtrics, which is a Canadian survey platform. All data is secured and stored in Canada. The information you share will not be revealed to Qualtrics. Data will be kept for a minimum of five years after publication within a UBC facility. All copies of files will be kept in electronic format and will be password protected and stored on a secure UBC server. Only research staff will have access to the data collected in this study. Findings from the study may be shared through conference presentations, articles for publication, and other media outlets or used for teaching purposes. This study is being conducted for thesis use, and as such, the results will be publicly available on the internet via cIRcle. No information that would identify you will be shared. Findings will not be reported back directly to participants; however, a summary of the final research findings will be available to you if you request it from vapingandsmokingresearch@gmail.com Contact for information about the study” If you have any questions or desire further information with respect to this study, you may contact Drs. Sarah Dow-Fleisner or Dixon Sookraj at the numbers above. If you have any concerns or complaints about your rights as a research participant and/or your experiences while participating in this study, contact the Research Participant Complaint Line in the UBC Office of Research Ethics toll free at 1-877-822-8598 or the UBC Okanagan Research Services Office at 250-807-8832. It is also possible to contact the Research Complaint Line by email (RSIL@ors.ubc.ca ). Please reference the study number H20-01453 calling so the Complaint Line staff can better assist you. Consent: Your participation is voluntary. You may skip questions, refuse to participate, or withdraw at any time. 167 Choosing “Yes” below indicates that you have reviewed this consent form and you: Are 19 years old or older· Are currently living in Canada· Consent to participate in this study o Yes, I do want to take part in this survey. (1) o No, I do not want to take part in this survey. (2) o Yes, I do want to take part in this survey. (1) o No, I do not want to take part in this survey. (2) Skip To: End of Survey If Q1 = 2 End of Block: Informed consent Start of Block: Canadian Q46 Are you Canadian or currently living in Canada? o Yes. (1) o No. (2) Skip To: End of Survey If Q46 = 2 End of Block: Canadian Start of Block: Vaping Vape1 Thank you for agreeing to take this survey. In this section, you will be asked about using an e-cigarette and smoking cigarettes. Do you use an e-cigarette (eg vape)? o No. (0) o No, but I have tried vaping. (1) o Yes, but rarely. (2) o Yes, regularly. (3) Skip To: End of Block If Vape1 = 0 Skip To: End of Block If Vape1 = 1 168 VD1 How many times per day do you usually use your electronic cigarette? (Assume one "time" consists of around 15 puffs or lasts around 10 minutes) o 0-4 times a day. (0) o 5-9 times a day. (1) o 10-14 times a day. (2) o 15-19 times a day. (3) o 20-29 times a day. (4) o 30+ times a day. (5) VD2 On days that you can use your electronic cigarette freely, how soon after you wake up do you first use your electronic cigarette? o 0-5 minutes. (5) o 6-15 minutes. (4) o 16-30 minutes. (3) o 31-60 minutes. (2) o 61-120 minutes. (1) o 121+ minutes. (0) VD3 Do you sometimes awaken at night to use your electronic cigarette? o Yes. (1) o No. (0) Display This Question: If VD3 = 1 Vaping 169 VD4 How many nights per week do you typically awaken to use your electronic cigarette? o 0-1 night. (0) o 2-3 nights. (1) o 4+ nights. (2) VD5 Do you use an electronic cigarette now because it is really hard to quit (electronic cigarettes)? o Yes. (1) o No. (0) VD6 Do you ever have strong cravings to use an electronic cigarette? o Yes. (1) o No. (0) VD7 Over the past week, how strong have the urges to use an electronic cigarette been? o None/Slight. (0) o Moderate/Strong. (1) o Very Strong/Extremely Strong. (2) VD8 Is it hard to keep from using an electronic cigarette in places where you are not supposed to? o Yes. (1) o No. (0) VD9 Did you feel more irritable because you couldn't use an electronic cigarette when you haven't used an electronic cigarette for a while or when you tried to stop using? o Yes. (1) o No. (0) VD10 Did you feel nervous, restless, or anxious because you couldn't use an electronic cigarette when you haven't used an electronic cigarette for a while or when you tried to stop using? o Yes. (1) o No. (0) End of Block: Vaping Vaping 170 Start of Block: Cigarette Addiction SMOKE Do you smoke cigarettes? o No. (0) o No, but I have tried smoking. (1) o Yes, but rarely. (2) o Yes, regularly. (3) Skip To: End of Block If SMOKE = 0 Skip To: End of Block If SMOKE = 1 SD1 How soon after you wake up to you smoke your first cigarette? o Within 5 minutes. (3) o 6 to 30 minutes (2) o 31-60 minutes. (1) o After 60 minutes, (0) SD2 Do you find it difficult to refrain from smoking in places where it is forbidden? (e.g., in church, at the library, in the cinema)? o Yes. (1) o No. (0) SD3 Which cigarette would you hate most to give up? o The first one in the morning. (1) o Any other. (0) 171 SD4 How many cigarettes per day do you smoke? o 10 or less. (0) o 11-20. (1) o 21-30. (2) o 31 or more. (3) SD5 Do you smoke more frequently during the first hours after waking than during the rest of the day? o Yes. (1) o No. (0) SD6 Do you smoke when you are so ill that you are in bed most of the day? o Yes. (1) o No. (0) End of Block: Cigarette Addiction Cigarette Addiction 172 P1 You are almost half way through this survey. Thank you for your participation. The next few questions are going to ask you about your social media use. How often do you use each of the following platforms? I don't use this platform (0) Less than once a week (1) 1-2 days a week (2) 3-6 days a week (3) Once a day (4) 2-4 times a day (5) 5 or more times a day (6) Twitter (1) o o o o o o o Facebook (2) o o o o o o o Instagram (3) o o o o o o o SnapChat (4) o o o o o o o Pinterest (5) o o o o o o o Tumblr (6) o o o o o o o Google+ (7) o o o o o o o LinkedIn (12) o o o o o o o YouTube (8) o o o o o o o Reddit (9) o o o o o o o TikTok (10) o o o o o o o Other (11) o o o o o o o End of Block: Social Media Platforms and frequency Start of Block: Social Media Platforms and Frequency 173 SMUD1 The following questions are going to ask you about your social media use DURING THE LAST YEAR. In the last year, have you spent a lot of time thinking about social media or planned use of social media? o Very rarely. (1) o Rarely. (2) o Sometimes. (3) o Often. (4) o Very often. (5) SMUD2 Felt an urge to use social media more and more? o Very rarely. (1) o Rarely. (2) o Sometimes. (3) o Often. (4) o Very often. (5) SMUD3 Used social media in order to forget about personal problems? o Very rarely. (1) o Rarely. (2) o Sometimes. (3) o Often. (4) o Very often. (5) SMUD4 Tried to cut down on the use of social media without success? o Very rarely. (1) o Rarely. (2) o Sometimes. (3) o Often. (4) o Very often. (5) Start of Block: Social Media Addiction 174 SMUD5 Become restless or troubled if you have been prohibited from using social media? o Very rarely. (1) o Rarely. (2) o Sometimes. (3) o Often. (4) o Very often. (5) SMUD6 Used social media so much that it has had a negative impact on your job/studies? o Very rarely. (1) o Rarely. (2) o Sometimes. (3) o Often. (4) o Very often. (5) End of Block: Social Media Addiction Social Media Addiction 175 SMUE How often DURING THE LAST WEEK have you . . . Never (1) One or two days (2) Three or four days (3) Five or six days (4) Seven days (5) How many times have you used social media 15 minutes before going to bed? (1) o o o o o How many times have you used social media during breakfast in the morning? (2) o o o o o How many times have you used social media while eating dinner at night? (3) o o o o o How many time have you used social media within 15 minutes of waking up in the morning? (4) o o o o o How many times have you used social media while eating lunch? (5) o o o o o End of Block: Social media engagement Start of Block: Vaping/Smoking Content VC1 You're over half way through this survey! The next few questions will be about how often you see vaping and smoking content on social media. The following questions will ask you about E-CIGARETTE content on social media in the last six months. Start of Block: Social Media Engagement 176 How often have you posted or mentioned E-CIGARETTES on social media? o Never. (1) o About once a month. (2) o Every few weeks. (3) o Weekly. (4) o Daily. (5) o I prefer not to answer. (0) VC2 Your friends posted or mentioned E-CIGARETTES on social media? o Never. (1) o About once a month. (2) o Every few weeks. (3) o Weekly. (4) o Daily. (5) o I prefer not to answer. (0) VC3 Seen advertisements for E-CIGARETTES on social media? o Never. (1) o About once a month. (2) o Every few weeks. (3) o Weekly. (4) o Daily. (5) o I prefer not to answer. (0) Vaping/Smoking Content 177 SC1 The following questions will ask you about CIGARETTES content on social media in the last six months. You posted or mentioned CIGARETTES on social media? o Never. (1) o About once a month. (2) o Every few weeks. (3) o Weekly. (4) o Daily. (5) o I prefer not to answer. (0) SC2 Your friends posted or mentioned CIGARETTES on social media? o Never. (1) o Sometimes. (2) o Every few weeks. (3) o Weekly. (4) o Daily. (5) o I prefer not to answer. (0) SC3 Seen advertisements for CIGARETTES on social media o Never. (1) o About once a month. (2) o Every few weeks. (3) o Weekly. (4) o Daily. (5) o I prefer not to answer. (0) End of Block: Vaping/Smoking Content Start of Block: Anxious Symptoms Vaping/Smoking Content 178 ANXI You are almost done! Good job! The next few questions are going to ask you about how you feel. Over the last 2 weeks, how often have you been bothered by the following challenges? Not at all sure (0) Several days (1) Over half the days (2) Nearly every day (3) Feeling nervous, anxious, or on edge. (1) o o o o Not being able to stop or control worrying. (2) o o o o Worrying too much about different things. (3) o o o o Trouble relaxing. (4) o o o o Being so restless that it's hard to sit still. (5) o o o o Becoming easily annoyed or irritable. (6) o o o o Feeling afraid as if something awful might happen. (7) o o o o End of Block: Anxious Symptoms Start of Block: Depressive Anxious Symptoms 179 DEP Over the last 2 weeks, how often have you been bothered by the following challenges? Not at all (0) Several days (1) Over half the day (2) Nearly every day (3) Little interest or pleasure in doing things. (1) o o o o Feeling down, depressed, or hopeless. (2) o o o o Trouble falling or staying asleep or sleeping too much. (3) o o o o Feeling tired or having little energy. (4) o o o o Poor appetite or overeating. (5) o o o o Feeling bad about yourself or that you are a failure or have let yourself or your family down. (6) o o o o Trouble concentrating on things such as reading the newspaper or watching television. (7) o o o o Moving or speaking so slowly that other people could have noticed. Or the opposite -being so fidgety or restless that you have been moving around a lot more than usual. (8) o o o o End of Block: Depressive Start of Block: SES From CCWS Age In these last few questions, you will be asked questions about you. What is your age in years? (e.g. 25). _______________________________________________________________ Depressive 180 Ethnicity: Please indicate how you self-identify. ▢ Indigenous peoples of Canada. (1) ▢ Indigenous (outside of Canada). (2) ▢ Arab. (3) ▢ Black. (4) ▢ Chinese (including Mainland China, Hong Kong, Macau, and Taiwan). (5) ▢ Filipino. (6) ▢ Japanese. (7) ▢ Korean. (8) ▢ Latin, Central, or South American (e.g. Brazilian, Chilean, Colombian, Mexican). (9) ▢ South Asian (e.g. Indian, Pakistani, Sri Lankan, ect.). (10) ▢ Southeast Asian (e.g. Cambodian, Indonesian, Laotian, Vietnamese, ect.). (11) ▢ West Asian (e.g. Afghan, Iranian, Syrian, ect.). (12) ▢ White. (13) ▢ Other. (14) ________________________________________________ ▢ I prefer not to answer. (0) Display This Question: If Ethnicity = 1 IndigGroup Which Canadian Indigenous group do you identify with the most? o First Nations. (1) SES From CCWS SES From CCWS 181 o Métis. (2) o Inuit. (3) o I prefer not to answer. (0) BornInCanada Where you born in Canada? o Yes. (1) o No. (0) o I prefer not to answer. (0) GenderIdentity What is your gender identity? o Female. (1) o Non-Binary. (2) o Two-Spirit. (3) o Male. (4) o I prefer not to answer. (0) TransExperience Are you someone with trans experience (meaning that your gender identity does not align with your sex assigned at birth)? Select one. o Yes. (1) o No. (0) o I prefer not to answer. (0) 182 SexualOrientation Do you identify as: o Heterosexual/Straight. (1) o Gay/Lesbian. (2) o Bisexual/Pansexual. (3) o Asexual. (4) o Queer. (5) o Questioning/Unsure. (6) o None of the above. (7) ________________________________________________ o I prefer not to answer. (0) RelationshipStatus How would you characterize your current relationship status? o Single. (1) o In a relationship. (2) o Married, in a domestic partnership. (3) o Divorced or separated. (4) o Widowed. (5) o Other. (6) ________________________________________________ o I prefer not to answer. (0) Education What is the highest level of formal education you have obtained? o High school or less (e.g. GED, high school diploma, primary or elementary completion, ect.). (1) o Completed a college program (e.g. Apprenticeship, certificate, or college diploma, ect.). (2) o Completed a university degree (e.g. Bachelor). (3) o Completed a graduate or other professional degree (e.g. Master, PhD). (4) o I prefer not to answer. (0) SES From CCWS 183 Province Which province or territory do you live in? o Alberta. (1) o British Columbia. (2) o Manitoba. (3) o New Brunswick. (4) o Newfoundland and Labrador. (5) o Northwest Territories. (6) o Nova Scotia. (7) o Nunavut. (8) o Ontario. (9) o Prince Edward Island. (10) o Quebec. (11) o Saskatchewan. (12) o Yukon. (13) o Canadian living outside of Canada. (14) o I prefer not to answer. (0) End of Block: SES From CCWS Thank you so much for participating in this survey! I really appreciate the time and effort that you put into this project. If you are experiencing some personal challenges right now, please know that you are not alone. If you need to talk to someone, here are some resources that can provide assistance Canada: Canadian Crisis Hotline: 1 (888) 353-2273 Healthlink BC: https://www.healthlinkbc.ca/ United States National Suicide Prevention Lifeline: 1-800-273-TALK (8255) International Big White Wall anonymous peer support network: www.bigwhitewall.ca If any questions have come up for you during this survey, please email the principal investigators at sarah.dow-fleisner@ubc.ca or dixon.sookraj@ubc.ca. The results for this study will be posted on the VapingAndSmoking Reddit account at the conclusion of this project. Thanks again SES From CCWS 184 Appendix F: Response Options and Coding Table 27: Response Options and Coding Question Response Options/Scale Ranges Variable/Scale Type Coding/Meaning Cronbach’s Alpha Outcomes Vaping Status Do you use an e-cigarette (e.g. vape)? 0) No; 1) No, but I have tried vaping; 2) Yes, but rarely; 3) Yes 0 =No; 1= No; 2= Yes, 3= Yes Vaping Dependency Penn State Electronic Cigarette Dependence Index 11 questions and range of scores from 0-13+ Summative Scale 0 is indictive of no response, 0-3 is indicative of no dependence; 4-8 low dependence; 9-12 medium dependence, and 13+ high dependence. 0.82 Smoking Status Do you smoke cigarettes? 0) No; 1) No, but I have tried smoking; 2) Yes, but rarely; 3) Yes 0 =No; 1= No; 2= Yes; 3= Yes Smoking Dependency Fagerstrom Nicotine Dependence Scale This scale has 8 questions and a range of 0 to 8+. Summative Scale 0 is indictive of no response, 1-2 means low dependence; 3-4 low to moderate dependence, 5-7 moderate dependence, 8+ high dependence 0.79 Key Predictors Social Media Dependency Bergen Social Media Addiction Scale (BSMAS), This scale was scored one to five on six items that measure different elements of addiction, including salience, tolerance, mood modification, relapse, withdrawal, and conflict Summative Scale Higher scores indicate more social media dependency 0.83 Social Media Engagement Social Media Engagement Questionnaire (SMEQ) This is a five-item scale with a response range of 0 to 7 for a final score of 0 to 35 Summative Scale Higher scores indicate more social media engagement 0.80 Frequency of Social Media Use How often do you visit each of the following popular social media platforms? 0= I don’t use this platform, 1= Less than once a week; 2= 1-2 days a week 3 = 3-6 days a week 4 = About once a day 5 = 2-4 times a day 6 = 5 or more times a day Indicator scale Higher scores indicate more social media use Multiple Social Media Platform Use Counting the number of social media platforms used 0= I do not use this platform 1= all other responses Count score Count reflects how many social media platforms used. 185 Social Media Vaping/Smoking Content Vaping Content 1) ) In the past 6 months, how often did you post or mention e-cigarettes on social media? 2) In the past 6 months, how often did a friend post or mention e-cigarettes on social media? 3) In the past 6months, how often did you see advertisements about e-cigarettes on social media 1=Never; 2= once a month, 3= every few weeks. 4=weekly 5=daily, 0= I prefer not to say Responses were scored 0-5, Responses were coded as 0= No answer 1=No 2=Yes 3=Yes 4=Yes 5=Yes 0.48 Smoking Content 1) In the past 6 months, how often did you post or mention cigarettes on social media? 2) In the past 6 months, how often did a friend post or mention cigarettes on social media? 3) In the past 6months, how often did you see advertisements about cigarettes on social media? 1=Never; 2= once a month, 3= every few weeks. 4=weekly 5=daily, 0= I prefer not to say Responses were scored 0-5, Responses were coded as 0= No answer 1=No 2=Yes 3=Yes 4=Yes 5=Yes 0.54 Mediators Anxiety Symptoms Generalized Anxiety Scale 7 questions and a range of 0-21 Summative scale A score 0-4 is normal, 5-9 is mild, 9-14 is moderate symptoms, and 15 or over is classified as severe symptoms 0.92 Depression Symptoms Patient Health Questionnaire 8 questions and a range of 0-24 Summative scale 0-4 is normal, 5-9 is mild depression, 10-19 moderate depression, 20-24 severe depression 0.88 Appendix F: Response Options and Coding 186 Sociodemographic Age What is your age in years? (e.g. 25 Open ended Self-identified ethnicity Please indicate how you self-identify. 1 = Indigenous Peoples of Canada, 2= Indigenous Peoples outside of Canada, 3=Arab, 4=Black, 5= Chinses including Mainland China, Hong Kong, Macau, and Taiwan, 6=Filipino, 7= Japanese, 8= Korean, 9= Latin, central, or South American, 10 = South Asian, 11= Southeast Asian, 12= West Asian, 13=White, 14= Other, and 15= I prefer not to answer Indigenous group (asked if a participant identified as indigenous) Which Canadian Indigenous group do you identify with the most? 1= First Nations 2= Métis, 3=Inuit, 4= prefer not to answer Born in Canada Were you born in Canada? 0= not born in Canada, prefer not to answer, 1= born in Canada Gender What is your gender identity? 0= prefer not to answer, 1= female, 2 = non-binary, 3 = two-spirted, 4 = male LGBTQ2+ Status Do you identify as: Are you someone with trans experience (meaning that your gender identity does not align with your sex assigned at birth)? Select one 0= prefer not to answer, 1= heterosexual/straight, 2=gay/lesbian, 3= bisexual/pansexual, 4=asexual, 5=queer, 6=questioning/unsure, 7=none of the above 0= no trans experience, prefer not to answer, 1= no trans experience LGBTQ2+ = gay/lesbian, asexual, queer, questioning, or trans experience Non-LGBTQ2+ = heterosexual/straight with no trans experience. Appendix F: Response Options and Coding 187 Relationship Status How would you characterize your current relationship status? 0= prefer not to answer, 1= single, 2= in a relationship, 3= married, in a domestic partnership, 4= divorced or separated, 5= widowed, 6=other Highest Level of Education Obtained What is the highest level of formal education you have obtained? 0= prefer not to answer, 1= high school or less, 2= completed a college program, 3= completed a university degree, 4 = completed a graduate or other professional degree Province of Residence Which province or territory do you live in? 0= prefer not to answer, 1= Alberta, 2=British Columbia, 3=Manitoba, 4= New Brunswick, 5= Newfoundland and Labrador, 6= Northwest territories, 7= Nova Scotia, 8= Nunavut, 9=Ontario, 10= Prince Edwards Island, 11= Quebec, 12= Saskatchewan, 13= Yukon, 14= Canadian living outside of Canada Appendix F: Response Options and Coding 188 Appendix G: Histograms and distributions for CFA Figure 17: Histograms and distributions for CFA 189 Appendix H: Histograms and distributions for GSEM Figure 18: Histograms and distributions for GSEM 190 Appendix I: Histograms and distributions for SEM Nicotine Dependency among Vapers Histogram Figure 19: Histograms and distributions for SEM, vaping 191 Nicotine Dependency among Smokers Histogram Figure 20: Histograms and distributions for SEM, smoking 192 Appendix J: Summary of Results Given the number of variables considered in this study and the number of tests conducted, a summary table was constructed. The summary table includes the research question, the test used, and the findings. It is organized by research question followed by sub-questions outlined and then the implicit questions raised throughout the literature review. Table 28: Summary of Results Question Supported/ Not Supported Analysis (Table) Findings RQ1: Does social media use directly impact vaping and smoking? Sub-Question 1a: Does social media use directly increase the likelihood of vaping? Not supported. Logistic regression (14) Social media dependency was associated with a decreased likelihood of vaping. Sub-Question 1b: Does social media use directly increase the likelihood of smoking? Not supported. Logistic regression (14) Smoking had no statistically significant findings. Sub-Question 2a: Is social media use positively associated with increased nicotine dependency among vapers? Supported. Multiple regression (16) Social media dependency was associated with higher scores of nicotine dependency among vapers. Sub-Question 2b: Is social media use positively associated with increased nicotine dependency among smokers? Not supported. Multiple regression (16) Smoking had no statistically significant findings. Sub-Question 3a: Does social media use use directly increase the likelihood of experiencing anxiety symptoms? Supported. Logistic regression (14) Social media dependency was associated with an increased likelihood of anxiety symptoms. Sub-Question 3b: Does social media use use directly increase the likelihood of experiencing depression symptoms? Supported. Logistic regression (14) Social media dependency was associated with an increased likelihood of depression symptoms. Sub-Question 3c: Does anxiety symptoms/depression symptoms directly increase the likelihood of vaping? Not supported. Logistic regression (13) Vaping had no significant relationships. Sub-Question 3d :Are anxiety and depression symptoms directly associated with increased nicotine dependency among vapers? Supported. Multiple regression (15) Anxiety symptoms were associated with higher scores of nicotine dependency among vapers. Depression symptoms was associated with increased higher scores of nicotine dependency among vapers. Sub-Question 3e: Does anxiety annnd depression symptoms directly increase the likelihood of smoking? Not supported. Logistic regression (13) Smoking had no significant relationships. 193 Sub-Question 3f: Are anxiety and depression symptoms directly associated with increased nicotine dependency among smokers? Not supported. Multiple regression (15) Smoking had no significant relationships. RQ2: Does social media use indirectly impact vaping and smoking through anxiety symptoms and depression symptoms? Sub-Question 4a: Do the indicators of social media use indirectly increase the likelihood of vaping when mediated by anxiety symptoms? Partially supported. KHB (17) Frequency of social media use was directly associated with an increased the likelihood of vaping when anxiety symptoms was considered. Social media dependency was directly associated with a decreased likelihood of vaping. Social media dependency was indirectly associated with an decreased likelihood of vaping. Sub-Question 4b: Do the indicators of social media use indirectly increase the likelihood of vaping when mediated by depression symptoms? Partially supported. KHB (18) Frequency of social media use was directly associated with an increased likelihood of vaping when depression symptoms was considered. Social media dependency was directly associated with a decreased likelihood of smoking. Social media dependency was indirectly associated with a decreased likelihood of smoking. Sub-Question 5a: Do the indicators of social media use indirectly increase the likelihood of smoking when mediated by anxiety symptoms? Not supported. KHB (19) Smoking had no significant relationships. Sub-Question 5b: Do the indicators of social media use indirectly increase the likelihood of smoking when mediated by depression symptoms? Not supported. KHB (20) Smoking had no significant relationships. Sub-Question 6a: Does social media use increase anxiety symptoms among vapers? Supported. Multiple regression (16) Among vapers, social media dependency was associated with an increase of anxiety symptoms. Sub-Question 6b: Does social media use increase depression symptoms among vapers? Supported. Multiple regression (16) Among vapers, social media dependency was associated with increased depression symptoms. Sub-Question 7a: Does social media use increase anxiety symptoms among smokers? Supported. Multiple regression (16) Among smokers, social media dependency was associated with increased of anxiety symptoms. Sub-Question 7b: Does social media use increase depression symptoms among smokers? Supported. Multiple regression (16) Among smokers, social media dependency was associated with increased depression symptoms. Appendix J: Summary of Results 194 RQ3: Do the indicators of SMU including social media dependency, engagement, content, platform, and frequency impact anxiety symptoms, depression symptoms, vaping, and smoking? (Fully mediated relationships) Sub-Question 8: Is SMU a latent construct? Supported. CFA (21) SMU appears to be a latent construct captured by the variables in this study (although number of platforms has low factor loadings). Sub-Question 9a: Does the latent construct of SMU impact vaping status through mental illness symptoms, and does it have a direct relationship? Partially supported. GSEM (Figure 12) SMU was not indirectly associated with vaping status through mental illness symptoms. SMU was directly associated with a decreased the likelihood of vaping. Sub-Question 9b: Does the latent construct of SMU impact smoking status through mental illness symptoms,, and does it have a direct relationship? Partially supported. GSEM (Figure 13) SMU was indirectly associated with an increased the likelihood of smoking when mediated by mental illness symptoms. SMU was directly associated with a decreased the likelihood of smoking. Sub-Question 10a: Does the latent construct of SMU impact vaping dependency through mental illness symptoms, and does it have a direct relationship? Partially supported. SEM (Figure 14) SMU was indirectly positively associated with higher scores of nicotine dependency among vapers through mental illness symptoms. There was no direct relationship between nicotine dependency among vapers and SMU. Sub-Question 10b: Does the latent construct of SMU impact smoking dependency through mental illness symptoms, and does it have a direct relationship? Partially supported. SEM (Figure 15) SMU was indirectly positively associated with higher scores of nicotine dependency among smokers through mental illness symptoms. SMU was directly associated with decreased higher scores of nicotine dependency among smokers. Assessing Vaping/Smoking Content on Social Media Is posting vaping content positively associated with vaping status? Supported. Chi Square Posting vaping content was positively associated with vaping. Are peers posting vaping content positively associated with vaping status? Not supported. Chi Square Peers posting vaping content was not associated with vaping. Is viewing vaping advertisements on social media positively associated with vaping status? Not supported. Chi Square Viewing advertisements for vaping on social media was not associated with vaping. Is posting smoking content positively associated with smoking status? Supported. Chi Square Posting smoking content was associated with smoking. Are peers posting smoking content positively associated with smoking status? Supported. Chi Square Viewing peer smoking content was associated with smoking. Is viewing smoking advertisements on social media positively associated with smoking status? Not supported. Chi Square Viewing advertisements for smoking on social media was not associated with smoking. Appendix J: Summary of Results 195 Assessing Social Media Dependency Scale Was the social media dependency scale reliable in the current study? Supported. Cronbach Alpha Yes, the scale was reliable among the whole sample, and the vaping/smoking subsamples reliability (0.80-0.88). Does the current study have higher average social media dependency than previously reported? Partially supported. Means and Standard Deviation (Appendix P) The means for the first for items were higher in the current sample than previously found but similar values on the last two items (withdrawal and conflict) than reported by Andreassen et al. (2012). Does the current study have a different correlation between items of the social media dependency scale than previously reported? Supported. Pearson Correlation (Appendix P) There were a number of differences between the correlational matrix created by Andreassen et al. (2012) and the current study’s findings. Are the items from the social media dependency scale associated with increased likelihood of vaping? Not supported. Logistic regression (Appendix P) Vaping had no significant relationships. Are the items from the social media dependency scale associated with increased likelihood of smoking? Not supported. Logistic regression (Appendix P) Smoking had no significant relationships. Are the items from the social media dependency scale associated with increased likelihood of anxiety symptoms? Supported. Logistic regression (Appendix P) Mood modification was associated with an increased likelihood of experiencing anxiety symptoms. Are the items from the social media dependency scale associated with increased likelihood of depression symptoms? Supported. Logistic regression (Appendix P) Mood modification was associated with an increased likelihood of experiencing depression symptoms. Conflict was associated with an increased likelihood of experiencing depression symptoms. Is the factor structure of social media dependency the same as previously reported? Not supported. CFA (Appendix P) For acceptable fit indexes, the current study had to add a covariation between salience and tolerance that was not included by Andreassen et al. (2012). Appendix J: Summary of Results 196 Socio-Demographic Characteristics Are socio-demographic characteristics associated with an increased risk of vaping? Supported. Chi-Square Gender was associated with vaping with women vaping less than men. Having a high school education and no post-secondary was associated with vaping. Are socio-demographic characteristics associated with an increased risk of smoking? Not supported. Chi-Square No socio-demographic characteristics were associated with smoking. Are socio-demographic characteristics associated with an increased risk of experiencing anxiety symptoms? Supported. Chi-Square Anxiety symptoms were associated with gender with women more likely to experience anxiety symptoms than men. Anxiety symptoms were associated with LGBTQ2+ status with individuals identifying as LGBTQ2+ more likely to experience anxiety symptoms than individuals who do not identify as LGBTQ2+. Are socio-demographic characteristics associated with an increased risk of experiencing depression symptoms? Supported. Chi-Square Depression symptoms were associated with gender with women more likely to experience depression symptoms than men. Depression symptoms were associated with LGBTQ2+ status with individuals identifying as LGBTQ2+ more likely to experience depression symptoms than individuals who do not identify as LGBTQ2+. *Research questions were the main goal of these analysis. Sub-questions were outlined in the aims section. The remainder of the questions were noted throughout the literature review. Mental illness symptoms refers to anxiety symptoms and depression symptoms. Appendix J: Summary of Results 197 Appendix K: Fit indexes from CFA Table 29: Factor loadings for four factor CFA with covariance. Indicator Standardized Factor Loading B (SE) CI Frequency (SMUF) 0.56 - - Number of Platforms (# of platforms) 0.38 0.16 (0.11) 0.13-0.18 Social Media Dependency (SMUD) 0.61 0.70 (0.01) 0.58-0.83 Social Media Engagement (SMUE) 0.85 1.06 (0.05) 0.86-1.27 Notes. N = 707. Dashes (-) indicate that a value was not provided. B is parameter estimates. Table 30: Standardized residuals for CFA 1 2 3 4 1. Frequency (SMUF) - 2. Social Media Dependency (SMUD) 2.31 - 3. Social Media Engagement (SMUE) -2.31 0.00 - 4. Number of Platforms (# of platforms) 0.00 2.31 -2.31 - Notes. N = 707. Values are given in the format of Z-scores. Table 31: Residual Correlations for CFA 1 2 3 4 1. Frequency (SMUF) - 2. Social Media Dependency (SMUD) .01 - 3. Social Media Engagement (SMUE) .00 .00 - 4. Number of Platforms (# of platforms) .00 .05 .02 - Notes. N = 707. 198 Appendix L: Confirmatory Factor Analysis Diagram Figure 21: Confirmatory Factor Analysis Diagram 199 Appendix M: Residual Correlations for GSEMs Table 32: Residual correlations for the vaping GSEM model 1 2 3 4 5 6 7 1. Frequency - 2. Social Media Dependency .22 - 3. Social Media Engagement .00 -.05 - 4. Number of platforms .00 -.02 .13 - 5. Anxiety Symptoms -.02 .03 .02 -.07 - 6. Depression Symptoms -.01 .02 .04 -.08 .00 - 7. Vaping .07 -.07 .04 .05 -.01 .01 - Notes. N = 707. Table 33: Residual correlations for the smoking GSEM model 1 2 3 4 5 6 7 1. Frequency - 2. Social Media Dependency .01 - 3. Social Media Engagement .00 .00 - 4. Number of Platforms .00 -.02 .00 - 5. Anxiety Symptoms -.02 .03 .01 .00 - 6. Depression Symptoms -.01 .00 .03 -.06 0.00 - 7. Smoking -.05 .06 -.02 -.08 -.00 0.00 - Notes. N = 707. 200 Appendix N: Standardized Residuals for the SEM models Table 34: Standardized residuals for the SEM vaping model 1 2 3 4 5 6 7 1. Frequency - 2. Social Media Dependency -1.30 - 3. Social Media Engagement 2.35 -1.70 - 4. Number of Platforms 2.35 -0.57 2.35 - 5. Anxiety Symptoms -1.16 0.49 -1.06 -1.85 - 6. Depression Symptoms -0.70 1.41 -0.24 -1.65 0.00 - 7. Nicotine Dependency 0.64 -0.59 0.09 -1.96 0.85 -0.85 - Notes. N = 207. Values are given in the format of Z-scores. Table 35: Standardized residuals for the smoking model 1 2 3 4 5 6 7 1. Frequency - 2. Social Media Dependency -1.24 - 3. Social Media Engagement 0.73 1.35 - 4. Number of Platforms 0.00 -0.81 1.20 - 5. Anxiety Symptoms 0.09 -0.45 -.18 -0.77 - 6. Depression Symptoms -0.02 -0.71 -0.90 -1.58 0.00 - 7. Nicotine Dependency -0.83 -0.73 0.69 -1.39 -0.28 0.28 - Notes. N = 95. Values are given in the format of Z-scores. 201 Appendix O. Residual Correlations for SEM models Table 36: Residual correlations for the SEM vaping model 1 2 3 4 5 6 7 1. Frequency - 2. Social Media Dependency -.02 - 3. Social Media Engagement .07 -.01 - 4. Number of Platforms .01 -.01 .12 - 5. Anxiety Symptoms -.05 .01 -.03 -.09 - 6. Depression Symptoms -.04 .02 -.01 -.08 .00 - 7. Nicotine Dependency .00 .01 .00 -.12 .01 -.02 - Notes. N = 207 Table 37: Residual correlations for smoking model 1 2 3 4 5 6 7 1. Frequency - 2. Social Media Dependency -.04 - 3. Social Media Engagement .03 .02 - 4. Number of Platforms .00 -.03 .07 - 5. Anxiety Symptoms .01 -.01 -.01 -.06 - 6. Depression Symptoms -.00 -.02 -.04 -.13 .00 - 7. Nicotine Dependency -.07 -.03 .04 -.14 -.00 .01 - Notes. N = 95 202 Appendix P: Exploring Social Media Dependency The scale for social media dependency is comprised of six unique elements that all define social media dependency (Andreassen et al., 2012). Salience refers to actively thinking about or planning using social media. Tolerance means that individuals feel the need to use social media more often. Mood modification refers to social media use improving mood. Relapse means continued use of social media when trying to not use it or use it less. Withdrawal refers to experiencing negative moods when social media is not used or is reduced. Conflict means using social media has a negative impact in other areas of an individual’s life (Andreassen et al., 2012). A series of statistical tests were conducted for a closer examination of social media dependency using the information provided by Andreassen et al. (2012), and grouping from the current study (whole sample, vapers, smokers, non-vapers/non-smokers). First, an examination of univariate statistics, including reliability (alpha) was assessed for the whole scale and then each individual scale item. Also, the mean and standard deviation were examined to isolated items that differed between the samples and determine if ceiling effects (when the scores on an item cluster around the upper end of the scale producing skewed results) or floor effects (when the scores on an item cluster around the lower end of the scale producing skewed results) were present. (Garin, 2014; Glen, 2017). Given the means and the standard deviations from Andreassen et al. (2012) differed from the means and standard deviations from the current sample, further exploration of the items was warranted. Next, Pearson correlation matrices were used to assess bivariate differences between the items of the scale. First, Andreassen et al. (2012) correlation matrix was included. Then, the correlation matrices were constructed for the current study, including for the whole sample, vaping sample, smoking sample, and non-vaper/non-smoker sample. Differences between the 203 correlation matrix from Andreassen et al. (2012) and the correlation matrices from the current study were noted. Due to differences in the item-correlation matrices between vaping sample and smoking sample, logistic regressions were performed to explore the association between items of the scale with vaping and smoking status. Also, due to novel findings in the results section, logistic regressions were also conducted to examine the association between scale items and individuals experiencing anxiety symptoms to those without, and individuals experiencing depression symptoms to those without. The purpose of the logistic regressions was to determine the impact of the different items on the key variables of interest in the current study. The assumption of linearity was assessed for each model, and it was not violated. The assumption of multicollinearity was assessed and found to be rather high for each model (VIF (vapers) = 9.70; VIF (smokers) = 10.39; VIF (anxiety) = 9.39; VIF (depression) = 9.52). Finally, a CFA was conducted and compared to the CFA conducted by Andreassen et al. (2012), using the same fit criteria as the previous CFA in this study to examine the latent construct between the samples. Factor loadings were examined, followed by modification indexes, standardized residuals, and residual correlations. A figure was created from Andreassen et al. (2012) standardized loading factors, and a second figure was created from the sample of the current study using the standardized loading factors to allow for visual depiction of the differences between the scales. P.1 Reliability The whole sample reliability was higher among the whole sample than previously reported, apart from the items of relapse, withdrawal, and conflict. In the vaping and smoking sub-samples, the alpha value was considerably higher than previously reported. Among the non- 204 vaper non-smoker sample, the alpha was still higher than previously reported, but lower than the vaping and smoking sub-samples (Table 38) Table 38: Item correlation (reliability) of social media dependency Andreassen et al. (2012) Alpha Whole Sample Alpha Vapers Alpha Smokers Alpha Non-Vaper Non-Smoker Alpha Item 1: Salience 0.61 0.74 0.83 0.86 0.78 Item 2: Tolerance 0.73 0.82 0.80 0.84 0.75 Item 3: Mood Modification 0.60 0.81 0.80 0.85 0.75 Item 4: Relapse 0.68 0.71 0.82 0.86 0.78 Item 5: Withdrawal 0.69 0.70 0.83 0.86 0.77 Item 6: Conflict 0.66 0.64 0.84 0.87 0.80 Notes. Andreassen et al. (2012) total scale reliability = 0.83. Whole sample scale reliability = 0.83. Vapers whole scale reliability = 0.85. Smokers whole scale reliability = 0.88. Non-vaper, non-smoker scale reliability = 0.80. Andreassen et al. (2012) N = 407. Current study: whole sample N = 707, vapers n = 207, smokers n = 95. Non-vaper, non-smoker, n = 291. P.2 Comparing Means and Standard Deviations The means and standard deviations for the whole sample were higher in the current study than found by Andreassen et al., (2012), except for the items of withdrawal and conflict (Table 39). Vapers and smokers had similar means and standard deviations, but non-vapers, non-smokers had higher means and standard deviations than all other groups. Of note, this scale was scored from 1 to 5 and did not appear to be exhibiting ceiling or floor effects. 205 Table 39: The items of social media dependency provided by the scale authors Andreassen et al. (2012) M (SD) Whole sample M (SD) Among Vapers M (SD) Among Smokers M (SD) Among non-vapers, non-smokers M (SD) Item 1: Salience 1.99 (1.02) 2.75 (1.24) 2.79 (1.24) 2.89 (1.24) 2.93(1.25) Item 2: Tolerance 2.10(1.01) 2.51 (1.20) 2.53 (1.20) 2.67(1.20) 2.69(1.22) Item 3: Mood Modification 1.48 (0.80) 2.45 (1.33) 2.45 (1.33) 1.35(1.35) 2.63(1.35) Item 4: Relapse 1.54 (0.95) 2.13(1.14) 2.14(1.12) 2.28(1.18) 2.28(1.19) Item 5: Withdrawal 1.65 (0.85) 1.64(0.91) 1.63 (0.90) 1.73(0.95) 1.74(0.96) Item 6: Conflict 1.78 (0.97) 1.74 (1.02) 1.74 (1.01) 1.82(1.06) 1.86(1.07) Notes. Andreassen et al. (2012) M (SD) N = 405. Current study: whole sample (N = 707); vapers (n = 207); smokers (n = 95); non-vaper/non-smoker (n = 391). P.3 Correlation Matrix Comparison Table 40 outlines Andreassen et al. (2012) correlation matrix. Table 41 contains the correlation matrix for the whole sample. The largest difference was for the correlation between salience and mood modification (Andreassen et al., (2012), r = 0.34; whole sample r = 0.50). The smallest difference was between relapse and salience (Andreassen et al., (2012), r = 0.37; whole sample r = 0.39). Table 40: Andreassen et al. (2012) correlation matrix from their scale development paper 2 3 4 5 6 1. Item 1: Salience .51 .34 .37 .47 .37 2. Item 2: Tolerance .48 .50 .54 .56 3. Item 3: Mood modification .43 .40 .38 4. Item 4: Relapse .46 .51 5. Item 5: Withdrawal .45 6. Item 6 Conflict Notes: N = 405. 206 Table 41: Correlation Matrix for whole sample and items of social media dependency 2 3 4 5 6 1. Item 1: Salience .62 .50 .39 0.39 .31 2. Item 2: Tolerance .60 .47 .47 .39 3. Item 3: Mood modification .47 .52 .40 4. Item 4: Relapse .43 .40 5. Item 5: Withdrawal .41 6. Item 6 Conflict Notes: N = 707. Table 42 contains the correlation matrix for the vaping sub-sample. The largest difference was for the correlation between tolerance and mood modification (Andreassen et al., (2012), r = 0.48; vaping sub-sample r = 0.66). The smallest difference was between withdrawal and conflict (Andreassen et al., (2012), r = 0.45; vaping sub-sample r = 0.44). Table 42: Correlation Matrix for vaping sub-sample and items of social media dependency 2 3 4 5 6 1. Item 1: Salience .65 .51 .43 .35 .33 2. Item 2: Tolerance .66 .54 .48 .45 3. Item 3: Mood modification .52 .53 .42 4. Item 4: Relapse .44 .41 5. Item 5: Withdrawal .44 6. Item 6 Conflict Notes. n = 207. All vapers had a vaping dependency score of at least 1. Table 43 contains the correlation matrix for the smoking sub-sample. The largest difference was for the correlation between mood modification and withdrawal (Andreassen et al., (2012), r = 0.40; smoking sub-sample r = 0.68). The smallest difference was between salience and withdrawal (Andreassen et al., (2012), r = 0.47; smoking sub-sample r = 0.47). 207 Table 43: Correlation Matrix for smoking sub-sample and items of social media dependency 2 3 4 5 6 1. Item 1: Salience .61 .55 .57 .47 .45 2. Item 2: Tolerance .65 .64 .59 .54 3. Item 3: Mood modification .57 .68 .47 4. Item 4: Relapse .49 .50 5. Item 5: Withdrawal .50 6. Item 6 Conflict Notes. n = 95. All smokers had a smoking dependency score of at least 1. Table 44 contains the correlation matrix for the non-vaper/non-smoker sub-sample. The largest difference was for the correlation between tolerance and conflict (Andreassen et al., (2012), r = 0.56; non-vaper/non-smoker sub-sample r = 0.33). The smallest difference was between mood modification and conflict (Andreassen et al., (2012), r = 0.38; non-vaper/non-smoker sub-sample r = 0.39). Table 44: Correlation Matrix for non-vaping/non-smoking sample and items of social media dependency 2 3 4 5 6 1. Item 1: Salience .58 .45 .31 .38 .23 2. Item 2: Tolerance .55 .39 .45 .33 3. Item 3: Mood modification .47 .39 4. Item 4: Relapse .39 .36 5. Item 5: Withdrawal .38 6. Item 6 Conflict Notes. n = 391. The non-vaping/non-smoking sub-sample had a vaping and smoking dependency score of zero. 208 P.4 Logistic Regressions Table 45 summarizes four logistic regressions that were conducted to examine each item of social media dependency to determine if they increased or decreased the probability of vaping, smoking, experiencing anxiety symptoms, or experiencing depression symptoms. For the vaping model, none of the indicators of social media dependency predicted an increased probability of vaping; however, tolerance (p = 0.07) and relapse (p = 0.10) were almost significant. For the smoking model, none of the indicators of social media dependency predicted an increased probability of smoking; however, salience and withdrawal were significant before corrections were applied ([before corrections: salience p = 0.04; tolerance p = 0.03]; [after corrections: salience p = 0.15; tolerance p = 0.14]). For the anxiety symptom model, mood modification predicted an increased probability of experiencing anxiety symptoms (OR = 1.67, 95% CI [1.42-1.98], p < 0.001). Relapse also predicted an increased probability of experiencing anxiety; however, it was no longer significant after corrections were applied (Before correction; p = 0.05: After correction p = 0.18). For the depression symptom model, mood modification predicted an increased probability of experiencing depression symptoms (OR = 1.70, 95% CI [1.44-2.02], p < 0.001). Conflict also predicted an increased probability of experiencing depression symptoms (OR = 1.26, 95% CI [1.05-1.51)], p = 0.05). 209 Table 45: Logistic Regression for items from social media dependency and vapers, smokers, symptoms of anxiety and symptoms of depression Vapers VS Non-Vapers Smokers VS Non-Smokers Anxious VS Not Anxious Depressed VS Not Depressed OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) Salience -0.95 (0.80-1.13) -0.81(0.66-0.98) . 1.07 (0.90-1.127) 1.00 (0.86-1.19) Tolerance -0.83 (0.68-1.02) . -0.99(0.66-0.98) -0.86 (0.71-1.05) 0.97 (0.89-1.18) Mood modification 1.01 (0.85-1.20) -1.00 (0.82-1.21) 1.67 (1.42-1.98) *** 1.70 (1.44-2.02) *** Relapse -0.86 (0.72-1.03) . -0.89 (0.72-1.10) 1.19 (1.00-1.42) 0.98 (0.83-1.17) Withdrawal -0.84 (0.66-1.07) -1.32(1.02-1.70) . 1.12 (0.90-1.39) 1.05 (0.85-1.31) Conflict -1.06 (0.87-1.30) -0.97 (0.77-1.20) 1.18 (0.98-1.41) . 1.26 (1.05-1.51) * N =707 AIC = 863.58 R2 = 0.03 N =707 AIC = 703.88 R2 = 0.02 N =707 AIC = 854.38 R2 = 0.12 N=707 AIC =878.45 R2 = 0.11 Notes: 0 ‘***’, 0.001 ‘**’ , 0.01, ‘*’0.05, ‘.’ 0.1‘ No controls were used. OR refers to odds ratio. P.5 Confirmatory Factor Analysis A confirmatory factor analysis (CFA) was conducted to examine the loadings of social media dependency to compare with Andreassen et al. (2012). The final model used the complete sample of 707 and had an acceptable fit once a covariation was added between salience and tolerance (mi = 48.70), which was not included by Andreassen et al. (2012) (Table 46). Table 46: Goodness-of-fit indicators for models for the social media dependency compared Model χ2/df CFI TLI RMSEA (90% CI) SRMR Andreassen et al. (2012) 1.84 0.99 - 0.05 (0.00-0.08) - Current Study 6.78* 0.96 0.95 0.09 (0.07-0.11) 0.04 Current study with add covariation 2.17 0.99 0.99 0.04 (0.01-0.07) 0.02 Notes. Andreassen et al N = 402. Current study, N = 707. Dashes (-) indicate that a value was not provided. The factor loadings for the CFA fell within the acceptable range. The parameter estimates were deemed acceptable. The error variance was also deemed acceptable (Table 47). The factor 210 loadings from the current study were similar to Andreassen et al. (2012) with the exceptions of mood medication and conflict (Table 48). The standardized residuals from the CFA indicated the model was not assessing some of the variance for tolerance by mood, tolerance by withdrawal, and mood and relapse (Table 49). The residual correlations for the CFA indicated were acceptable. (Table 50). To highlight differences in standardized loading factors and structure, figures were created. Figure 22 shows the latent construct constructed by Andreassen et al. (2012), and Figure 23 shows the latent construct for this sample. Table 47: Factor loadings for CFA with current study no modification Indicator Standardized Factor Loading (SE Andreassen et al. (2012) Standardized Factor Loading Current study B (SE) (Current study) CI (Current study) Item 1: Salience 0.61 (.37) 0.68 (-) (-) Item 2: Tolerance 0.80 (0.64) 0.80 1.13 (0.04) 1.01-1.26 Item 3: Mood modification 0.59 (0.35) 0.76 1.19 (0.07) 1.05-1.34 Item 4: Relapse 0.67 (0.44) 0.62 0.84 (0.06) 0.72-0.95 Item 5: Withdrawal 0.69 (0.47) 0.64 0.69 (0.05) 0.60-0.78 Item 6: Conflict 0.69 (0.47) 0.53 0.63 (0.05) 0.53-0.74 Notes. Andreassen et al. (2012) N = 407. Current study, N = 707. Dashes (-) indicate that a value was not provided. B is parameter estimates Table 48: Factor loadings for CFA with covariation added Indicator Standardized Factor Loading (SE Andreassen et al. (2012) Standardized Factor Loading Current study B (SE) (Current study) CI (Current study) Item 1: Salience 0.61 (.37) 0.61 (-) (-) Item 2: Tolerance 0.80 (0.64) 0.75 1.18 (0.07) 1.05-1.32 Item 3: Mood modification 0.59 (0.35) 0.78 1.37 (0.08) 1.18-1.55 Item 4: Relapse 0.67 (0.44) 0.64 0.91 (0.07) 0.82-1.11 Item 5: Withdrawal 0.69 (0.47) 0.67 0.80 (0.06) 0.68-0.92 Item 6: Conflict 0.69 (0.47) 0.55 0.75 (0.06) 0.63-0.88 Notes. Andreassen et al. (2012) N = 407. Current study, N = 707. Dashes (-) indicate that a value was not provided. B is parameter estimates. Table 49: Standardized residuals for CFA from current study 1 2 3 4 5 Item 1: Salience - Item 2: Tolerance 0.00 - Item 3: Mood modification 1.77 2.72 - Item 4: Relapse 0.02 -0.06 -2.67 - Item 5: Withdrawal -0.95 -1.96 -0.04 0.63 - Item 6: Conflict -1.46 -1.52 -1.90 2.31 2.07 Notes. N = 707. Values are given in the format of Z-scores. 211 Table 50: Residual Correlations for CFA from current study 1 2 3 4 5 Item 1: Salience - Item 2: Tolerance .00 - Item 3: Mood modification .02 .02 - Item 4: Relapse .00 -.00 -0.03 - Item 5: Withdrawal -.02 -.02 0.00 0.01 - Item 6: Conflict -.03 -.02 -0.03 0.05 0.04 Notes. N = 707. 212 . Figure 22: Standardized loading factors and latent structure from Andreassen et al. (2012). Notes. Andreassen et al. (2012). N = 407 Figure 23: Standardized loading factors and latent structure from the current study. Notes. N = 707. 213 Appendix Q: Effect Sizes Effect size. Effect sizes were used to illustrate differences between groups (McLeod, 2019). Traditionally, significance testing simply indicates if differences between groups are statistically significance, whereas effect sizes indicates the magnitude of the difference between groups (McLeod, 2019). Or, put more simply, significance testing indicates group differences, and effect size indicates how much difference there is between groups. Cohen’s W and Cramer’s V were used to examine the effect size of chi squared tests. Cohen’s W is equal to Phi and is interpreted from negative one to positive one with zero meaning no association (Ben-Shachar et al., 2021; Glen, 2016c). Cramer’s V is interpreted as a small effect from 0.1 to 0.3 whereas a medium effect from 0.4 to 0.5, and a large effect size is above 0.5 (Statistical Significance Tests, 2018). Cohen’s D and probability were used to examine the effect size for the logistic regressions and the binary mediation analyses. The current study employed Cohen’s D for effect size as it is suitable for larger samples of at least 50 participants (Glen, 2016d). When Cohen’s D has a value of 1, there is 1 standard deviation of difference between groups (Glen, 2016d). Generally speaking, a small effect is equal to 0.2 whereas a medium effect is equal to 0.5, and a large effect size is equal to 0.8 (Glen, 2016d). Cohen’s f and partial eta squared were used to assess the effect size for the multiple regressions as these tests require continuous independent variables and dependent variables (Selya et al., 2012; Statistical Consulting, 2021). Cohen’s f is interpreted using cut-off values (Salkind, 2010). A Cohen’s f value of 0.1 is deemed to be a small effect whereas a medium effect is 0.25, and a large effect is above 0.4 (Salkind, 2010). Partial eta squared is interpreted in a similar manner as R2, that is, it indicates the degree of variance accounted for by the regression equation (Brown, 2008). For the GSEM and the SEM models, odds ratios were assessed by taking the exponent of the unstandardized regression weights, and 214 Cohen’s D was then assessed by converting the odds ratios. Cohen’s D was used to assess effect sizes for the structural equation models, which was suitable as the distribution did not violate the assumption of normality (Gomer et al., 2019). Effect sizes were assessed using effectsize and asbio packages (Ben-Shachar et al., 2021; Aho, 2020). Table 51: Chi Square Effect Sizes Finding Cohen’s W W (95% CI) Cramer’s V V (95% CI) Gender and Vaping Status Men more likely to vape than women. 0.12 (0.05-0.20) 0.12 (0.05-0.20) Gender and Anxiety Symptoms Women more likely to experience anxiety symptoms than men. 0.13 (0.06-0.21) 0.13 (0.06-0.21) Gender and Depression Symptoms Women more likely to experience depression symptoms than men. 0.15 (0.08-0.23) 0.15 (0.08-0.23) Education and Vaping Status Individuals with a high school education are more likely to vape than those with post-secondary education. 0.08 (0.00-0.16) 0.08 (0.00-0.16) LGBTQ2+ and Anxiety Symptoms Individuals who identify as LGBTQ2+ are more likely to experience anxiety symptoms than those who do not identify as LGBTQ2+. 0.21 (0.14-0.29) 0.21 (0.14-0.29) LGBTQ2+ and Depression Symptoms Individuals who identify as LGBTQ2+ are more likely to experience anxiety symptoms than those who do not identify as LGBTQ2+. 0.20 (0.13-0.28) 0.20 (0.13-0.28) Posting vaping content and Vaping Status Posting vaping content is associated with an increased probability of vaping. 0.17 (0.10-0.25) 0.17 (0.10-0.25) Posting smoking content and Smoking Status. Posting smoking content is associated with an increased probability of smoking. 0.14 (0.07-0.22) 0.14 (0.07-0.22) Peers posting smoking content and Smoking Peers posting smoking content is associated with an increased probability of smoking. 0.15 (0.07-0.22) 0.15 (0.07-0.22) Notes. Cohen’s W is interpreted from negative one to positive one with zero meaning no association. Cramer’s V is interpreted as a small effect from 0.1 to 0.3 whereas a medium effect from 0.4-0.5, and a large effect size is above 0.5. 215 Table 52: Logistic Regression Effect Sizes Finding Cohen’s D Probability Social Media Dependency and Vaping Status Social media dependency is associated with a decreased probability of vaping. 0.50 48% Social Media Dependency and Anxiety Symptoms Social media dependency is associated with an increased probability of anxiety symptoms. 0.64 54% Social Media Dependency and Depression Symptoms Social media dependency is associated with an increased probability of depression symptoms. 0.62 53% Notes. A Cohen’s D = 0.20 is a small effect size; Cohen’s D = 0.50 is a medium effect size, and above 0.8 is a large effect size. Table 53: Multiple Regression Effect Sizes Finding Cohen’s f f (90%CI) Partial Eta Squared r2par (90%CI) Vaping and Anxiety Symptoms Increased anxiety symptoms are associated with increased scores of nicotine dependency among vapers. 0.37 (0.25-0.56) 0.12 (0.06-0.20) Vaping and Depression Symptoms Increased depression symptoms are associated with increased scores of nicotine dependency among vapers. 0.32 (0.19-0.43) 0.09(0.04-0.16) Vaping and Social Media Dependency Increased social media dependency are associated with increased scores of nicotine dependency among vapers. 0.18 (0.06-0.30) 0.03 (0.00-0.08) Anxiety Symptoms and Social Media Dependency among Vapers Increased social media dependency is associated with increased anxiety symptoms among vapers. 0.50 (0.37-0.62) 0.20 (0.12-0.28) Depression Symptoms and Social Media Dependency among Vapers Increased social media dependency is associated with increased depression symptoms among vapers. 0.48 (0.35-0.61) 0.19 (0.11-0.27) Anxiety Symptoms and Social Media Dependency Among Smokers Increased social media dependency is associated with increased anxiety symptoms among smokers. 0.51 (0.32-0.70) 0.21(0.09-0.33) Depression Symptoms and Social Media Dependency among Smokers Increased social media dependency is associated with increased depression symptoms among smokers. 0.40 (0.21-0.59) 0.14 (0.04-0.26) Notes. Cohen’s f of 0.1 = small effect; 0.25 = medium effect; above 0.4 = large effect. Partial eta squared represents the degree of variance accounted for by the regression. 216 Table 54: Binary Mediation Analyses (KHB) Effect Sizes Finding Cohen’s D Probability Frequency of SMU, Vaping Status, and Symptoms of Anxiety FSMU Total Effect, Vaping and Anxiety Model Together, frequency of social media use and anxiety symptoms increase the probability of vaping. 0.58 0.51 FSMU direct effect Vaping and Anxiety Model The frequency of social media use increases the probability of vaping. 0.56 0.50 Social Media Dependency, Vaping Status, and Symptoms of Anxiety SMUD Total Effect Vaping and Anxiety Model Together, social media dependency and anxiety symptoms decrease the probability of vaping. 0.49 0.47 SMUD direct effect Vaping and Anxiety Model Social media dependency decreases the probability of vaping. 0.50 0.48 SMUD indirect effect Vaping and Anxiety Model Social media dependency mediated through anxiety decrease the probability of vaping. 0.49 0.47 Frequency of SMU, Vaping Status, and Symptoms of Depression FSMU Total Effect, Vaping and Depression Model Together, frequency of social media use and depression symptoms increase the probability of vaping. 0.58 0.51 FSMU direct effect Vaping and Depression Model The frequency of social media use increases the probability of vaping. 0.58 0.51 Social Media Dependency, Vaping Status, and Symptoms of Depression SMUD Total Effect Vaping and Depression Model Together, social media dependency and depression symptoms decrease the probability of vaping. 0.49 0.47 SMUD direct effect Vaping and Depression Model Social media dependency decreases the probability of vaping. 0.50 0.48 SMUD indirect effect Vaping and Depression Model Social media dependency mediated through depression symptoms decrease the probability of vaping. 0.49 0.47 Notes. A Cohen’s D = 0.20 is a small effect size; Cohen’s D = 0.50 is a medium effect size, and above 0.8 is a large effect size. Table 55: GSEM Effect Sizes Finding OR (95% CI) Cohen’s D Vaping Status Model SMU is associated with a decreased likelihood of vaping. 0.97 (0.96-0.99) 0.53 SMU is associated with an increased likelihood of mental illness symptoms. 2.48 (2.23-2.74) 1.37 Smoking Status Model SMU is associated with a decreased likelihood of smoking 0.97 (0.96-0.99) 0.54 SMU is associated with an increased likelihood of mental illness symptoms. 2.48 (2.23-2.76) 1.37 Mental illness symptoms are associated with an increased likelihood of smoking. 1.01 (1.01-1.02) 0.56 Notes. Mental illness symptoms refers to anxiety and depression symptoms. A Cohen’s D = 0.20 is a small effect size; Cohen’s d = 0.50 is a medium effect size, and above 0.8 is a large effect size. 217 Table 56: SEM Effect Sizes Finding OR (95% CI) Cohen’s D Vaping Dependency Model SMU is associated with increased scores of mental illness symptoms. 2.55 (2.18-2.93) 1.41 Increased mental illness symptoms are associated with increased scores of nicotine dependency among vapers. 1.30 (1.16-1.45) 0.72 Smoking Dependency Model SMU is associated with a decreased scores of nicotine dependency among smokers. 0.76 (0.51-0.99) 0.42 SMU is associated with an increased scores of mental illness symptoms. 3.02 (2.41-3.62) 1.66 Mental illness symptoms are associated with increased scores of nicotine dependency among smokers. 1.14 (1.02-1.25) 0.63 Notes. Mental illness refers to anxiety and depression symptoms. A Cohen’s D = 0.20 is a small effect size; Cohen’s D = 0.50 is a medium effect size, and above 0.8 is a large effect size. 218 Appendix R: Directionality Structural Equation Models (SEM) Vaping In the model used in the thesis, SMU (IV) was not associated with nicotine dependency among vapers (DV), however, SMU was associated with increased mental illness symptoms (M), which was in turn associated with nicotine dependency among vapers (Figure 24). When the model was reversed, nicotine dependency among vapers (IV) was not associated with SMU (DV) or mental illness symptoms (M). However, mental illness symptoms were associated with increased SMU (Figure 25). These models suggest the association between SMU and mental illness symptoms may be bi-directional, however, the association between nicotine dependency among vapers and mental illness symptoms is unidirectional. 219 Figure 24: Nicotine Dependency among Vapers Model Employed in Thesis Figure 25: Reversed Nicotine Dependency among Vapers Model from Thesis Smoking In the model used in the thesis SMU (IV) was negatively associated with nicotine dependency among smokers (DV). Also, SMU was positively associated with nicotine dependency among smokers through mental illness symptoms (M) (Figure 26). When the model was reversed, nicotine dependency among smokers (DV) was not associated with SMU (DV). Mental illness symptoms were negatively associated with nicotine dependency among smokers; 220 however, mental illness symptoms were positively associated with SMU (Figure 27). These findings suggest that nicotine dependency among smokers has unidirectional associations with mental illness symptoms and SMU, and the association between SMU and mental illness symptoms may be bi-directional. Figure 26: Nicotine Dependency among Smokers Model Employed in Thesis Figure 27: Reversed Nicotine Dependency among Smokers Model from Thesis 221 General Structural Equation Models (GSEM) Vaping In the model used in the thesis, SMU (IV) was associated with a decreased probability of vaping (DV). SMU was associated with an increased probability of experiencing mental illness symptoms (M), but mental illness symptoms were not associated with vaping status (Figure 28). When the model was reversed, vaping status (IV) was still negatively associated with SMU (DV), and not associated with mental illness symptoms (M). However, mental illness symptoms were associated with an increased probability of SMU (Figure 29). These findings may suggest a bi-directional association between vaping status and SMU as well as between mental illness symptoms and SMU. However, these findings may also be a result of vaping status being a dichotomous variable, thus vaping (yes = 1) may be consistently negatively associated with SMU, and not associated with symptoms of mental illness. Figure 28: Vaping Status Model Employed in Thesis 222 Figure 29: Reversed Vaping Status Model from Thesis Smoking In the model used in the thesis, SMU (IV) was associated with a decreased probability of smoking (DV). SMU was associated with an increased probability of experiencing mental illness symptoms (M), which in turn was associated with an increased probability of smoking (Figure 30). When the model was reversed, smoking status (IV) was associated with a decreased probability of SMU (DV). Smoking Status was associated with an increased probability of experiencing mental illness symptoms (M), which in turn was associated with an increased probability of SMU (Figure 31). These findings may suggest a bi-directional association between smoking status, SMU, and symptoms of mental illness as well as between mental illness symptoms and SMU. However, these findings may also be a result of smoking status being a dichotomous variable, thus vaping (yes = 1) may be consistently negatively associated with SMU, and positively associated with symptoms of mental illness. 223 Figure 30: Smoking Status Model Employed in Thesis Figure 31: Reversed Smoking Status Model from Thesis 224 Appendix S: Contrary Findings Table 57: Contrary Findings Vaping Status Nicotine Dependency among Vapers Smoking Status Nicotine Dependency among Smokers Hypothesis Social media dependency and SMU would be associated with an increased probability of vaping. Social media dependency and SMU would be positively associated with increased nicotine dependency among vapers. Social media dependency and SMU would be associated with an increased probability of smoking. Social media dependency and SMU would be positively associated with increased nicotine dependency among smokers. Correlational Analysis - Supported. r = 0.20, p < 0.01 - Not supported. r = -0.20, p < 0.05 Logistic Regression Not supported. AOR = 0.91 (0.87-0.95), p < 0.001. - Not supported. AOR = 1.00 (0.96-1.01), p > 0.05 - Multiple Regression - Supported. β = 0.20 p < 0.05 - Not Supported. β = -0.13 p > 0.05 Binary Mediational Analysis Direct Effect Indirect through anxiety or depression. Not supported. AOR = 0.91 (0.87-0.95), p < 0.001. AOR = 0.89 (0.88-0.91), p < 0.05 - Not supported. AOR = 1.00 (0.96-1.01), p > 0.05. AOR = 0.89 (0.88-0.91), p > 0.05. - Generalized Structural Equation Model (GSEM) Not supported. β = -0.15 p < 0.001 - Not supported. β = -0.18 p < 0.001. - Structural Equation Model (SEM) - Not supported. β = 0.02 p > 0.05 - Not Supported β = -0.43 p < 0.05 Interpretation Social media dependency and SMU were associated with a decreased probability of vaping. Social media dependency was associated with increased nicotine dependency among vapers, but SMU was not associated with nicotine dependency among vapers. Social media dependency was not associated with smoking status. SMU was associated with a decreased probability of smoking. Social media dependency was not associated with nicotine dependency among smokers, but SMU was negatively associated with nicotine dependency among smokers. Possible Effect Moderator Effect Suppressor Effect/ Interaction Reinforcer Effect Reinforcer Effect Notes. For the entire sample correlational matrix, social media dependency was negatively associated with any nicotine dependency. The GSEM and SEM included social media dependency in the latent construct of SMU.
Thesis/Dissertation
eng
Social Work
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Sookraj, Dixon
Dow-Fleisner, Sarah
The association between vaping, smoking, mental illness, and social media use : a national survey of adults in Canada
Text