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Associations between Use of Specific Social Media Sites and Electronic Cigarette Use among College Students

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Title: Associations between Use of Specific Social Media Sites and Electronic Cigarette Use among College Students
Language: English
Authors: Lin, Shuo-Yu (ORCID 0000-0003-4688-1424), Cheng, Xiaolu, Rossheim, Matthew E. (ORCID 0000-0003-4388-5251), Gress, Dustin, Cuellar, Alison Evans, Cheskin, Lawrence, Xue, Hong
Source: Journal of American College Health. 2023 71(7):2217-2224.
Availability: Taylor & Francis. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 8
Publication Date: 2023
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Social Media, Smoking, Correlation, College Freshmen, Incidence, Probability, Gender Differences, Racial Differences, Ethnicity, Mental Health
DOI: 10.1080/07448481.2021.1965149
ISSN: 0744-8481
1940-3208
Abstract: Objective: To examine dose-response associations between use of specific social media sites and the use of electronic cigarettes (e-cigarettes) and traditional cigarettes. Methods: This was a cross-sectional study of 298 first-year college students enrolled in the fall 2019 semester at a large state university. Heckman selection and Probit model were used to estimate associations between use of specific social media sites and e-cigarette/traditional cigarette use. Results: Each additional hour per day spent on Snapchat was associated with a 4.61% increase in the probability of lifetime e-cigarette use. In addition, among current e-cigarette users, more time spent on Snapchat was associated with more frequent e-cigarette use (marginal effects: 0.13, p = 0.001). Facebook, Twitter, Snapchat and Instagram were not associated with traditional cigarette smoking. Conclusion: Snapchat was the only major social media platform associated with both lifetime and current e-cigarette use.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1402448
Database: ERIC
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  Value: <anid>AN0173157562;acl01oct.23;2023Oct26.06:37;v2.2.500</anid> <title id="AN0173157562-1">Associations between use of specific social media sites and electronic cigarette use among college students </title> <p>To examine dose-response associations between use of specific social media sites and the use of electronic cigarettes (e-cigarettes) and traditional cigarettes. This was a cross-sectional study of 298 first-year college students enrolled in the fall 2019 semester at a large state university. Heckman selection and Probit model were used to estimate associations between use of specific social media sites and e-cigarette/traditional cigarette use. Each additional hour per day spent on Snapchat was associated with a 4.61% increase in the probability of lifetime e-cigarette use. In addition, among current e-cigarette users, more time spent on Snapchat was associated with more frequent e-cigarette use (marginal effects: 0.13, p = 0.001). Facebook, Twitter, Snapchat and Instagram were not associated with traditional cigarette smoking. Snapchat was the only major social media platform associated with both lifetime and current e-cigarette use.</p> <p>Keywords: College students; electronic cigarette; two-part model; social media</p> <hd id="AN0173157562-2">Introduction</hd> <p>Electronic cigarettes (e-cigarettes) are tobacco products that aerosolize a liquid usually containing propylene glycol, vegetable glycerin, nicotine and chemical flavorants for user inhalation.[<reflink idref="bib1" id="ref1">1</reflink>] E-cigarette emissions contain high levels of ultrafine particles and other toxins that are likely to increase the risk of cardiovascular and lung disease, conditions which account for the majority of smoking-related mortality.[<reflink idref="bib2" id="ref2">2</reflink>] Recent laboratory evidence from mice shows that, despite containing lower levels of tobacco specific nitrosamines (TSNAs, a potent chemical carcinogen), e-cigarette emissions causes lung cancer and bladder hyperplasia (hyperplasia is often an initial stage in the development of cancer).[<reflink idref="bib3" id="ref3">3</reflink>] Further, there have been thousands of hospital emergency department visits from child ingestion of e-cigarette liquid nicotine, as well as thousands of explosion injuries from e-cigarette device malfunctions.[<reflink idref="bib4" id="ref4">4</reflink>]</p> <p>Following a significant increase in e-cigarette use in 2018, the U.S. Food and Drug Administration Commissioner and the U.S. Surgeon General declared youth e-cigarette use an epidemic.[<reflink idref="bib5" id="ref5">5</reflink>],[<reflink idref="bib6" id="ref6">6</reflink>] Nationally-representative estimates support that the prevalence of current e-cigarette use among young adults (18 to 24 years old) increased 46% from 2017 to 2018.[<reflink idref="bib7" id="ref7">7</reflink>],[<reflink idref="bib8" id="ref8">8</reflink>] Moreover, estimates from 2019 support that more than one-in-four high school students and high school seniors are current e-cigarette users.[<reflink idref="bib9" id="ref9">9</reflink>],[<reflink idref="bib10" id="ref10">10</reflink>] As this cohort of high school students matriculate to college, there is expected to be an even greater burden of e-cigarette use among these emerging young adults. Because of the growing prevalence of e-cigarette use and the severity of its consequences, more information is needed regarding how to prevent young people from initiating e-cigarette use and how to promote cessation among current users.</p> <p>Peer effects appear to play an important role in the smoking[<reflink idref="bib11" id="ref11">11</reflink>],[<reflink idref="bib12" id="ref12">12</reflink>] and e-cigarette use behaviors of youth and young adults.[<reflink idref="bib13" id="ref13">13</reflink>],[<reflink idref="bib14" id="ref14">14</reflink>] One possible factor promoting e-cigarette use is social media sites, which are commonly used by college-age, young adults.[<reflink idref="bib15" id="ref15">15</reflink>] While Facebook remains the most widely used social media platform among adults in the U.S., Instagram and Snapchat use is highly prevalent among young adults age 18 to 24. A 2018 survey conducted by the Pew Research Center showed that 75% of young adults had used Instagram and 73% used Snapchat.[<reflink idref="bib16" id="ref16">16</reflink>] Further, more than three-fourths of Snapchat (77%) and Instagram (76%) users aged 18 to 29 reported using these apps daily.[<reflink idref="bib17" id="ref17">17</reflink>] By providing unique environments whereby users can interact with others, social media platforms are an important tool for socializing. Such interactive environments, however, could increase young adults' exposure to tobacco-related content shared through their personal network as well as industry sponsored advertisements, thus contributing to the initiation and sustained use of e-cigarettes.[<reflink idref="bib15" id="ref18">15</reflink>],[[<reflink idref="bib18" id="ref19">18</reflink>], [<reflink idref="bib20" id="ref20">20</reflink>]]</p> <p>Research on the association between social media use and young adults' e-cigarette use is limited.[<reflink idref="bib18" id="ref21">18</reflink>],[<reflink idref="bib19" id="ref22">19</reflink>],[<reflink idref="bib21" id="ref23">21</reflink>],[<reflink idref="bib22" id="ref24">22</reflink>] The current evidence only provided simple associations, describing whether the social media use is associated with the unset of e-cigarette.[<reflink idref="bib18" id="ref25">18</reflink>],[<reflink idref="bib19" id="ref26">19</reflink>] To our knowledge, the current study is the first to examine dose-response associations between the amount of time spent on specific social media sites and the odds and frequency of e-cigarette and traditional cigarette use. A separate examination of each social media platform was warranted because each site has its own policies, norms, and differentiating user features.</p> <hd id="AN0173157562-3">Methods</hd> <p></p> <hd id="AN0173157562-4">Data</hd> <p>This cross-sectional study utilized baseline data from an ongoing longitudinal cohort study—the <emph>Mason: Health Starts Here</emph> project. This cohort study was designed to help understand and improve the health and well-being of university students. This project follows a broad sample of young adults (newly enrolled first-year college students) at a state university with a diverse student body, seeking to capture the diversity of their experiences in college and how various factors affect their health and well-being.</p> <hd id="AN0173157562-5">Analytic sample</hd> <p>Participants were 351 first-year college students enrolled in the fall and spring semesters of 2019–2020. Students with incomplete data were excluded from analyses. The final analytical sample sizes for e-cigarette use and conventional cigarette use were 298 and 297, respectively. The protocol was approved by the Institutional Review Board (IRB) of George Mason University.</p> <hd id="AN0173157562-6">Key outcome variables</hd> <p>E-cigarette and traditional cigarette use were measured using items adapted from the Monitoring the Future (MTF) study. The three main outcome variables in this study were: lifetime e-cigarette use, past 30 day e-cigarette use frequency, and lifetime traditional cigarette smoking. Lifetime e-cigarette use was an ordinal variable derived from the five-scale question: "Have you ever used electronic cigarettes (e-cigs or vapes)?" Responses were classified as "never", "occasional",[<reflink idref="bib1" id="ref27">1</reflink>] and "regular"[<reflink idref="bib2" id="ref28">2</reflink>] use of e-cigarettes. Past 30 day e-cigarette use frequency was an ordinal variable measuring the number of days students used e-cigarettes. Responses were categorized into four groups: 0, 1–9, 10–19, and 20–30 days. Lifetime traditional cigarette smoking was a binary variable derived from the question: "Have you ever smoked cigarettes?"</p> <hd id="AN0173157562-7">Key exposure variables</hd> <p>Daily social media use was measured by asking students how many hours they used each specific social media platform each day. The four most popular social media sites were included in this study: Facebook, Twitter, Instagram, and Snapchat.[<reflink idref="bib16" id="ref29">16</reflink>]</p> <hd id="AN0173157562-8">Covariates</hd> <p>Several demographic factors and mental health conditions were included as potential confounders. Demographic factors were comprised of self-identified gender, age, race/ethnicity, and parental education level. Mental health conditions—anger,[<reflink idref="bib3" id="ref30">3</reflink>] anxiety,[<reflink idref="bib4" id="ref31">4</reflink>] depression,[<reflink idref="bib5" id="ref32">5</reflink>] post-traumatic stress disorder (PTSD)[<reflink idref="bib6" id="ref33">6</reflink>]—were derived from level 2 cross-cutting symptom measures developed by the DSM-5 Task force.[<reflink idref="bib23" id="ref34">23</reflink>] Tendency toward sensation-seeking was a binary variable coded from the question that "I welcome new and exciting experiences and sensations, even if it is a little frightening.[<reflink idref="bib24" id="ref35">24</reflink>],[<reflink idref="bib25" id="ref36">25</reflink>]</p> <hd id="AN0173157562-9">Statistical analysis</hd> <p>Bivariate descriptive analyses were used first to examine associations between e-cigarette/cigarette use patterns and students' demographic characteristics, self-reported mental health conditions, and social media usage. F-tests and Chi-square tests were used to test differences in sample characteristics. Second, we applied the Heckman selection model to examine associations between social media use and e-cigarette lifetime use and current frequency of use. Heckman selection is a statistical technique to correct bias from using non-randomly selected or truncated samples. The selection model consists of two stages. In the first stage (selection equation), the probability of whether an outcome would be observed is estimated. In the second stage (outcome equation), given those who have an observable outcome, the association between exposures and the outcome is examined. The Heckman selection model was chosen for two reasons. First, it was used to account for sample selection—outcome is censored and can only be observed if students used e-cigarettes. Second, it takes into account the fact that factors influencing e-cigarette use may be different from factors influencing the frequency of use.[<reflink idref="bib26" id="ref37">26</reflink>] Variables included in both the selection and outcome equations were: age, gender, race/ethnicity (White, Black, and Asian), anger, anxiety, depression, post-traumatic stress disorder (PTSD) and the tendency toward sensation-seeking. Parental education level (mother's and father's was included in the selection equation but excluded in the outcome equation, using Wooldridge's recommended approach because it has not been found to be a significant determinant of e-cigarette use frequency.[<reflink idref="bib27" id="ref38">27</reflink>] The selection equation is described as:</p> <p>Graph</p> <p> <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mi>E</mi><mo>−</mo><mi mathvariant="italic">cigarette</mi><mi mathvariant="normal" /><mi mathvariant="italic">use</mi><mo>=</mo><msub><mrow><mi>α</mi></mrow><mrow><mn>0</mn></mrow></msub><mo>+</mo><mi mathvariant="normal" /><msub><mrow><mi>α</mi></mrow><mrow><mn>1</mn><mo>−</mo><mn>4</mn></mrow></msub><mi>S</mi><mi mathvariant="italic">ocial</mi><mi mathvariant="normal" /><mi mathvariant="italic">medi</mi><msub><mrow><mi>a</mi></mrow><mrow><mn>1</mn><mo>−</mo><mn>4</mn></mrow></msub><mo>+</mo><mi>X</mi><mi mathvariant="normal">Β</mi><mo>+</mo><msub><mrow><mi>μ</mi></mrow><mrow><mi>i</mi></mrow></msub></math> </ephtml> </p> <p>Graph</p> <p> <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mi>E</mi><mo>−</mo><mi mathvariant="italic">cigarette</mi><mi mathvariant="normal" /><mi mathvariant="italic">use</mi><mo>=</mo><mi mathvariant="normal" /><mo stretchy="true">{</mo><mrow><mtable><mtr><mtd><mrow><maligngroup /><mn>1</mn><mtext mathvariant="italic"> if </mtext><mi mathvariant="italic">students</mi><mi /><mi mathvariant="italic">used</mi><mi /><mi>E</mi><mo>−</mo><mi mathvariant="italic">cigarette</mi><mi /></mrow></mtd></mtr><mtr><mtd><mrow><maligngroup /><mn>0</mn><mi /><mi mathvariant="italic">otherwise</mi></mrow></mtd></mtr></mtable></mrow></math> </ephtml> </p> <p>Where</p> <p>Graph</p> <p> <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msub><mrow><mi>α</mi></mrow><mrow><mn>1</mn><mo>−</mo><mn>4</mn></mrow></msub></math> </ephtml> represent Facebook, Twitter, Instagram, and Snapchat. B is a matrix of control variables. The outcome equation is:</p> <p>Graph</p> <p> <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mi mathvariant="italic">Intensity</mi><mi mathvariant="normal" /><mo>=</mo><mi mathvariant="normal" /><mo stretchy="true">{</mo><mrow><mtable><mtr><mtd><mrow><maligngroup /><msub><mrow><mi>β</mi></mrow><mrow><mn>0</mn></mrow></msub><mo>+</mo><mi /><msub><mrow><mi>β</mi></mrow><mrow><mn>1</mn><mo>−</mo><mn>4</mn></mrow></msub><mi>S</mi><mi mathvariant="italic">ocial</mi><mi /><mi mathvariant="italic">medi</mi><msub><mrow><mi>a</mi></mrow><mrow><mn>1</mn><mo>−</mo><mn>4</mn></mrow></msub><mo>+</mo><mi>X</mi><mi>δ</mi><mo>+</mo><msub><mrow><mi>ε</mi></mrow><mrow><mi>i</mi></mrow></msub><mtext mathvariant="italic"> if E</mtext><mo>−</mo><mi mathvariant="italic">cigarette</mi><mi /><mi mathvariant="italic">use</mi><mtext mathvariant="italic"> is </mtext><mn>1</mn><mi /></mrow></mtd></mtr><mtr><mtd><mrow><maligngroup /><mi>U</mi><mi mathvariant="italic">nobserved</mi><mtext mathvariant="italic"> if E</mtext><mo>−</mo><mi mathvariant="italic">cigarette</mi><mi /><mi mathvariant="italic">use</mi><mtext mathvariant="italic"> is </mtext><mn>0</mn></mrow></mtd></mtr></mtable></mrow></math> </ephtml> </p> <p>We present the results of the two-stage selection model separately. First, we report the marginal effects of social media on youth likelihood of e-cigarette use from the selection equation. Second, given those who have used e-cigarettes, we report the conditional probability of e-cigarette use intensity (whether becoming occasional or regular e-cigarette user).</p> <p>For smoking initiation, we used the <emph>probit</emph> model to estimate the association between social media use and the uptake of conventional cigarettes, due to its binary feature. Control variables included in the <emph>probit</emph> model were identical to the Heckman selection model. <emph>Probit</emph> model was chosen to echo the fact that Heckman selection model was specified through two <emph>probit</emph> models. All statistical analyses were conducted using STATA 15.1 (StataCorp LP, College Station, TX).</p> <hd id="AN0173157562-10">Results</hd> <p>Table 1 presents the sample characteristics of e-cigarette and traditional cigarette use. Among first-year college students, 35.3% and 11.3% have tried e-cigarettes and traditional cigarettes, respectively. Among those who had used e-cigarettes in their lifetimes, 35.3% were occasional or regular users. 14.09% of e-cigarette users had used on 1 to 30 days in the previous month. On average, occasional or regular e-cigarette users spent 2.01 hours daily on Snapchat, which was significantly more time than non-users (0.94 hours/day) (<emph>p</emph> < 0.001). Similarly, there was a statistically significant trend whereby students who used e-cigarettes more frequently in the previous 30 days spent more time on Snapchat each day (<emph>p</emph> < 0.001). There were no significant differences between the frequency of e-cigarette or traditional cigarette use across mental health status, age, and racial/ethnic groups. Female students were less likely to have used traditional cigarettes in their lifetime than their male counterparts (7.25% vs. 19%, <emph>p</emph> < 0.01). Also of note, first-year college students who reported regular e-cigarette use in their lifetime and having used in the previous month had higher PTSD scores and had higher tendency of sensation seeking.</p> <p>Table 1. sample distribution of e-cigarette and traditional cigarette use.</p> <p> <ephtml> <table><thead><tr><td /><td>Vaping initiation and pattern</td><td>How many days vape in past 30 days</td><td>Conventional cigarette initiation</td></tr><tr><td /><td>Never</td><td>Occasional and regular</td><td>Never</td><td>1–30 days</td><td /><td>Yes</td><td>No</td><td /></tr></thead><tbody valign="top"><tr><td><italic>n</italic> (%)</td><td char=".">192 (64.7)</td><td char=".">106 (35.3)</td><td>p §</td><td char=".">256 (85.91)</td><td char=".">42 (14.09)</td><td>p §</td><td char=".">34 (11.3)</td><td char=".">263 (88.7)</td><td>p §</td></tr><tr><td><bold>Social media use (hour/day), mean (std)</bold></td><td /><td /><td /><td /><td /><td /><td /></tr><tr><td> Facebook</td><td char=".">0.22 (0.92)</td><td char=".">0.48 (3.43)</td><td char=".">0.54</td><td char=".">0.21 (0.84)</td><td char=".">0.14 (0.35)</td><td char=".">0.62</td><td char=".">0.21 (0.59)</td><td char=".">0.2 (0.81)</td><td char=".">0.96</td></tr><tr><td> Instagram</td><td char=".">1.11 (1.64)</td><td char=".">1.39 (1.66)</td><td char=".">0.16</td><td char=".">1.12 (1.54)</td><td char=".">1.78 (2.13)</td><td char=".">0.01</td><td char=".">1.12 (1.77)</td><td char=".">1.22 (1.64)</td><td char=".">0.73</td></tr><tr><td> Snapchat</td><td char=".">0.93 (1.59)</td><td char=".">2.01 (2.72)</td><td char="."><.0001</td><td char=".">1 (1.55)</td><td char=".">3.19 (3.69)</td><td char="."><.0001</td><td char=".">1.88 (3.26)</td><td char=".">1.24 (1.93)</td><td char=".">0.10</td></tr><tr><td> Twitter</td><td char=".">0.39 (0.97)</td><td char=".">0.75 (1.77)</td><td char=".">0.02</td><td char=".">0.4 (0.93)</td><td char=".">1.26 (2.54)</td><td char="."><.0001</td><td char=".">0.61 (1.94)</td><td char=".">0.51 (1.23)</td><td char=".">0.70</td></tr><tr><td><bold>Self-identified female, <italic>n</italic> (%)</bold></td><td char=".">127 (65.46)</td><td char=".">67 (37.54)</td><td char=".">0.61</td><td char=".">167 (86.08)</td><td char=".">27 (13.92)</td><td char=".">0.90</td><td char=".">14 (7.25)</td><td char=".">179 (92.75)</td><td char="."><0.01</td></tr><tr><td><bold>Race/ethnicity, <italic>n</italic> (%)</bold></td><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td> Non-Hispanic White</td><td char=".">74 (60.66)</td><td char=".">48 (39.34)</td><td char=".">0.26</td><td char=".">101 (82.79)</td><td char=".">21 (17.21)</td><td char=".">0.19</td><td char=".">16 (13.11)</td><td char=".">106 (86.89)</td><td char=".">0.39</td></tr><tr><td> Others</td><td char=".">118 (67.05)</td><td char=".">58 (32.95)</td><td /><td char=".">155 (88.07)</td><td char=".">21 (11.93)</td><td /><td char=".">18 (10.28)</td><td char=".">157 (89.72)</td><td /></tr><tr><td><bold>Mental health, mean score (std)</bold></td><td /><td /><td /><td /><td /><td /><td /></tr><tr><td> Anger*</td><td char=".">53.34 (8.35)</td><td char=".">54.91 (8.52)</td><td char=".">0.12</td><td char=".">53.48 (8.17)</td><td char=".">56.44 (9.57)</td><td char=".">0.03</td><td char=".">56.41 (7.9)</td><td char=".">53.57 (8.47)</td><td char=".">0.06</td></tr><tr><td> Anxiety*</td><td char=".">57.29 (9.29)</td><td char=".">57.70 (8.62)</td><td char=".">0.71</td><td char=".">57.48 (9.24)</td><td char=".">57.16 (7.80)</td><td char=".">0.51</td><td char=".">57.8 (9.89)</td><td char=".">57.37 (8.96)</td><td char=".">0.80</td></tr><tr><td> Depression*</td><td char=".">52.19 (9.91)</td><td char=".">54.37 (9.41)</td><td char=".">0.06</td><td char=".">52.79 (9.8)</td><td char=".">54.06 (9.68)</td><td char=".">0.20</td><td char=".">55.33 (10.26)</td><td char=".">52.72 (9.66)</td><td char=".">0.14</td></tr><tr><td> PTSD*¶</td><td char=".">37.77 (14.8)</td><td char=".">39.29 (15.21)</td><td char=".">0.40</td><td char=".">37.42 (14.65)</td><td char=".">43.76 (15.67)</td><td char="."><0.01</td><td char=".">45.12 (13.92)</td><td char=".">37.49 (14.87)</td><td char="."><0.01</td></tr><tr><td> Sensation seeking, <italic>n</italic> (%)#</td><td char=".">145 (61.18)</td><td char=".">92 (38.82)</td><td char=".">0.03</td><td char=".">198 (83.54)</td><td char=".">39 (16.46)</td><td char=".">0.02</td><td char=".">32 (13.50)</td><td char=".">205 (86.50)</td><td char="."><0.05</td></tr></tbody></table> </ephtml> </p> <p>1 §: t-test was conducted for continuous variables, Chi-square test was used for categorical variables.</p> <ulist> <item>2 *: T-score, continuous (ref.).</item> <item>3 ¶: PTSD = Post Traumatic Stress Disorder.</item> <item>4 #: The question asked is "I welcome new and exciting experiences and sensations, even if ...little frightened".</item> <item>5 £: We excluded parent's education level in outcome regression because they were not a significant determinant of vaping behavior, as suggested by Wooldridge (Ref.).</item> </ulist> <p>There were three major findings in these data:</p> <p></p> <ulist> <item> <emph>More hours spent on Snapchat was associated with greater odds of lifetime e-cigarette use</emph> </item> </ulist> <p>Table 2 shows the association between social media and e-cigarette use. Social media use was associated with a higher likelihood of using e-cigarettes. In particular, each extra hour spent on Snapchat was associated with a 4.61% increase in the likelihood of lifetime e-cigarette use (95% CI = 1.74–7.47, <emph>p</emph> < 0.01). However, among lifetime e-cigarette users, social media use was not associated with whether they were occasional or regular users. Among students who had used an e-cigarette, each one unit increase in their PTSD measurement score was associated with a 0.94% increase in the likelihood of being a regular user of e-cigarettes (95% CI = 0.19–1.70, <emph>p</emph> = 0.014). Our data suggested that students who enjoyed sensation-seeking, compared to those who didn't, were 14.30% more likely to try using e-cigarettes (95% CI = 2.19–26.42, <emph>p</emph> = 0.02). However, among students who were lifetime e-cigarette users, tendency toward sensation-seeking was not associated with e-cigarette use pattern.</p> <p>Table 2. Associations between social media site use and lifetime e-cigarette use.</p> <p> <ephtml> <table><thead><tr><td /><td>Odds of lifetime e-cigarette use</td><td>Odds of regular vs. occasional e-cigarette use</td></tr><tr><td /><td>Marginal effect</td><td>95% CI</td><td>p</td><td>Marginal effect</td><td>95% CI</td><td>p</td></tr></thead><tbody valign="top"><tr><td><bold>Social media use (hour/day)</bold></td><td /><td /><td /><td /><td /></tr><tr><td> Facebook</td><td char=".">−1.19</td><td>(−9.82, 7.44)</td><td char=".">0.79</td><td char=".">−16.82</td><td>(−45.71, 12.06)</td><td char=".">0.25</td></tr><tr><td> Instagram</td><td char=".">0.4</td><td>(−3.67, 4.46)</td><td char=".">0.85</td><td char=".">0.01</td><td>(−6.73, 6.76)</td><td char=".">0.99</td></tr><tr><td> Snapchat</td><td char=".">4.61</td><td>(1.74, 7.47)</td><td char=".">< 0.01</td><td char=".">3.7</td><td>(−0.54, 7.94)</td><td char=".">0.09</td></tr><tr><td> Twitter</td><td char=".">2.93</td><td>(−1.87, 7.74)</td><td char=".">0.23</td><td char=".">−2.24</td><td>(−8.98, 4.5)</td><td char=".">0.52</td></tr><tr><td><bold>Self-identified female</bold></td><td char=".">−6.09</td><td>(−17.98, 5.81)</td><td char=".">0.32</td><td char=".">−3.09</td><td>(−21.83, 15.65)</td><td char=".">0.75</td></tr><tr><td><bold>Race/ethnicity</bold></td><td /><td /><td /><td /><td /><td /></tr><tr><td> White</td><td char=".">7.34</td><td>(−3.83, 18.51)</td><td char=".">0.43</td><td char=".">9.86</td><td>(−7.42, 27.13)</td><td char=".">0.26</td></tr><tr><td><bold>Mental health</bold></td><td /><td /><td /><td /><td /><td /></tr><tr><td> Anger*</td><td char=".">0.3</td><td>(−0.52, 1.12)</td><td char=".">0.47</td><td char=".">−0.07</td><td>(−1.38, 1.23)</td><td char=".">0.91</td></tr><tr><td> Anxiety*</td><td char=".">−0.58</td><td>(−1.53, 0.37)</td><td char=".">0.23</td><td char=".">−0.18</td><td>(−1.58, 1.21)</td><td char=".">0.80</td></tr><tr><td> Depression*</td><td char=".">0.97</td><td>(0.08, 1.87)</td><td char=".">0.03</td><td char=".">−1.29</td><td>(−2.85, 0.27)</td><td char=".">0.11</td></tr><tr><td> PTSD*¶</td><td char=".">−0.27</td><td>(−0.78, 0.24)</td><td char=".">0.30</td><td char=".">0.94</td><td>(0.19, 1.7)</td><td char=".">0.01</td></tr><tr><td> Sensation-seeking#</td><td char=".">14.3</td><td>(2.19, 26.42)</td><td char=".">0.02</td><td char=".">5.51</td><td>(−17.68, 28.71)</td><td char=".">0.64</td></tr></tbody></table> </ephtml> </p> <ulist> <item>6 Notes: Effects were estimated from probit model with sample selection.</item> <item>7 *: T-score, continuous (ref.).</item> <item>8 ¶: PTSD = Post Traumatic Stress Disorder.</item> <item>9 #: The question asked is "I welcome new and exciting experiences and sensations, even if ...little frightened".</item> <p></p> <item> <emph>More time spent on Snapchat was associated with more frequent current e-cigarette use</emph> </item> </ulist> <p>Table 3 presents the associations between social media use and past 30-day e-cigarette use frequency. Our data suggested that each additional hour spent on Snapchat was associated with a 2.52% increase in the probability of having used an e-cigarette in the past month (95% CI = 0.64–4.39, <emph>p</emph> = 0.01). Moreover, for current e-cigarette users, more time spent on Snapchat was associated with more days of using e-cigarettes (marginal effects: 0.13, <emph>p</emph> = 0.001). Interestingly, we found that Instagram use, although not associated with whether a student would use e-cigarettes in the past month, was marginally negatively associated with the frequency of e-cigarette use among current e-cigarette users (marginal effects: −0.11, <emph>p</emph> < 0.1). Apart from social media use, two mental health conditions were associated with e-cigarette use. First, each one unit increase in PTSD score was associated with a 0.36% increase in past 30-day e-cigarette use. Second, sensation-seeking students had a 10.23% higher chance of having used e-cigarettes in the previous month, compared with those who were more conservative in their pursuit of excitement (95% CI = 3.21–17.26, <emph>p</emph> = 0.004). Sensation-seeking, however, was negatively associated with the frequency of past 30-day e-cigarette use (marginal effects: −1.17, <emph>p</emph> = 0.01).</p> <p>Table 3. Associations between social media site use and past 30-day e-cigarette use.</p> <p> <ephtml> <table><thead><tr><td /><td>Odds of current e-cigarette use (past 30 day)</td><td>Number of days using e-cigarettes in past 30 days</td></tr><tr><td /><td>Marginal effect</td><td>95% CI</td><td>p</td><td>Marginal effect</td><td>95% CI</td><td>p</td></tr></thead><tbody valign="top"><tr><td><bold>Social media use (hour/day)</bold></td><td /><td /><td /><td /><td /></tr><tr><td> Facebook</td><td char=".">−4.33</td><td>(−15.71, 7.06)</td><td char=".">0.46</td><td char=".">0.27</td><td>(−0.51, 1.06)</td><td char=".">0.49</td></tr><tr><td> Instagram</td><td char=".">−0.53</td><td>(−3.39, 2.33)</td><td char=".">0.72</td><td char=".">−0.11</td><td>(−0.25, 0.02)</td><td char=".">0.08</td></tr><tr><td> Snapchat</td><td char=".">2.52</td><td>(0.64, 4.39)</td><td char=".">0.01</td><td char=".">0.13</td><td>(0.05, 0.22)</td><td char=".">0.01</td></tr><tr><td> Twitter</td><td char=".">2.76</td><td>(−0.34, 5.86)</td><td char=".">0.08</td><td char=".">−0.12</td><td>(−0.25, 0.03)</td><td char=".">0.11</td></tr><tr><td><bold>Self-identified female</bold></td><td char=".">−2.36</td><td>(−10.24, 5.52)</td><td char=".">0.56</td><td char=".">−0.12</td><td>(−0.58, 0.34)</td><td char=".">0.61</td></tr><tr><td><bold>Race/ethnicity</bold></td><td /><td /><td /><td /><td /><td /></tr><tr><td> White</td><td char=".">4.83</td><td>(−2.47, 12.14)</td><td char=".">0.20</td><td char=".">0.56</td><td>(0.11, 1)</td><td char=".">0.02</td></tr><tr><td><bold>Mental health</bold></td><td /><td /><td /><td /><td /><td /></tr><tr><td> Anger*</td><td char=".">0.54</td><td>(−0.03, 1.11)</td><td char=".">0.06</td><td char=".">−0.01</td><td>(−0.05, 0.03)</td><td char=".">0.63</td></tr><tr><td> Anxiety*</td><td char=".">−0.52</td><td>(−1.15, 0.12)</td><td char=".">0.11</td><td char=".">0</td><td>(−0.04, 0.04)</td><td char=".">0.97</td></tr><tr><td> Depression*</td><td char=".">−0.24</td><td>(−0.91, 0.44)</td><td char=".">0.49</td><td char=".">0.04</td><td>(0, 0.09)</td><td char=".">0.07</td></tr><tr><td> PTSD*¶</td><td char=".">0.36</td><td>(0.02, 0.69)</td><td char=".">0.04</td><td char=".">0</td><td>(−0.03, 0.02)</td><td char=".">0.73</td></tr><tr><td> Sensation seeking#</td><td char=".">10.23</td><td>(3.21, 17.26)</td><td char=".">< 0.01</td><td char=".">−1.17</td><td>(−2.09, −0.24)</td><td char=".">0.01</td></tr></tbody></table> </ephtml> </p> <ulist> <item>10 Notes: Effects were estimated from Probit model with sample selection.</item> <item>11 *: T-score, continuous (ref.).</item> <item>12 ¶: PTSD = Post Traumatic Stress Disorder.</item> <item>13 #: The question asked is "I welcome new and exciting experiences and sensations, even if ...little frightened.".</item> <p></p> <item> <emph>Social media use was not significantly associated with smoking</emph> </item> </ulist> <p>Table 4 depicts the association between social media use and traditional cigarette use. Across platforms, social media use was not significantly associated with use of traditional cigarettes. Regarding emotional factors, similar to observations for e-cigarette use, PTSD (0.34%, <emph>p</emph> = 0.039) and sensation-seeking (10.12%, <emph>p</emph> = 0.001) were positively associated with traditional cigarette use.</p> <p>Table 4. Associations between social media site use and lifetime traditional cigarette smoking.</p> <p> <ephtml> <table><thead><tr><td /><td>Odds of lifetime traditional cigarette use</td></tr><tr><td /><td>Marginal effect</td><td>95% CI</td><td>p</td></tr></thead><tbody valign="top"><tr><td><bold>Social media use (hour/day)</bold></td><td /><td /></tr><tr><td> Facebook</td><td char=".">2.44</td><td>(−2.23, 7.11)</td><td char=".">0.31</td></tr><tr><td> Instagram</td><td char=".">−1.29</td><td>(−4.1, 1.51)</td><td char=".">0.37</td></tr><tr><td> Snapchat</td><td char=".">1.13</td><td>(−0.7, 2.96)</td><td char=".">0.23</td></tr><tr><td> Twitter</td><td char=".">0.53</td><td>(−2.02, 3.07)</td><td char=".">0.69</td></tr><tr><td><bold>Self-identified female</bold></td><td char=".">−2.22</td><td>(−10.62, 6.19)</td><td char=".">0.61</td></tr><tr><td><bold>Race/ethnicity</bold></td><td /><td /><td /></tr><tr><td> White</td><td char=".">3.4</td><td>(−3.74, 10.53)</td><td char=".">0.35</td></tr><tr><td><bold>Mental health</bold></td><td /><td /><td /></tr><tr><td> Anger*</td><td char=".">0.2</td><td>(−0.32, 0.73)</td><td char=".">0.45</td></tr><tr><td> Anxiety*</td><td char=".">−0.22</td><td>(−0.81, 0.37)</td><td char=".">0.47</td></tr><tr><td> Depression*</td><td char=".">0.04</td><td>(−0.54, 0.61)</td><td char=".">0.90</td></tr><tr><td> PTSD*¶</td><td char=".">0.34</td><td>(0.02, 0.65)</td><td char=".">0.04</td></tr><tr><td> Sensation-seeking#</td><td char=".">10.12</td><td>(4.15, 16.09)</td><td char=".">0.00</td></tr><tr><td><bold>Parent education</bold></td><td /><td /><td /></tr><tr><td> Father's</td><td char=".">2.05</td><td>(−0.34, 4.43)</td><td char=".">0.09</td></tr><tr><td> Mother's</td><td char=".">−2.87</td><td>(−5.26, −0.48)</td><td char=".">0.02</td></tr></tbody></table> </ephtml> </p> <ulist> <item>14 Notes: Effects were estimated from probit model.</item> <item>15 *: T-score, continuous (ref.).</item> <item>16 ¶: PTSD = Post Traumatic Stress Disorder.</item> <item>17 #: The question asked is "I welcome new and exciting experiences and sensations, even if ...little frightened.".</item> </ulist> <hd id="AN0173157562-11">Discussion</hd> <p>The aim of this study was to explore associations between use of popular social media platforms and the use of e-cigarettes and traditional cigarettes. We found that, among the four major social media platforms included in this study (Facebook, Twitter, Instagram, and Snapchat), only Snapchat use was associated with lifetime use of e-cigarettes. An extra hour spent on Snapchat daily was associated with a 4.61% increase in the likelihood of lifetime e-cigarette use. Furthermore, among those who had used e-cigarettes in the previous 30 days, more time spent on Snapchat use was associated with more frequent e-cigarette use. Using Instagram, in contrast, was marginally associated with fewer days using e-cigarettes in the past month. Use of the four social media platforms examined in this study was not significantly associated with traditional cigarette smoking.</p> <p>In consistent with findings from previous research,[<reflink idref="bib19" id="ref39">19</reflink>] we further identified that more time spent one Snapchat was associated with greater likelihoods of lifetime e-cigarette use. Of relevance is that Twitter is less popular among younger adults—a 2018 survey from the Pew Research Center showed that only 44% of U.S. adults aged 18–24 reported ever having used Twitter, compared to 75% for Instagram and 73% for Snapchat.[<reflink idref="bib16" id="ref40">16</reflink>] Facebook, while still used at high frequency, has taken on a more focused purpose for young adults, who use it mainly to keep in touch with friends, rather than for posting/sharing content.[<reflink idref="bib28" id="ref41">28</reflink>]Table 1 shows that, on average, first-year college students spent fewer than 50 minutes on Facebook and Twitter, but more than 1 hour on Instagram and Snapchat on a daily basis. Unlike Facebook or Twitter, where each account is tied to the user's first and last name, Instagram and Snapchat accounts do not require identity or age verification, providing a more intimate environment where young adults may be more willing to share certain sensitive information.[<reflink idref="bib28" id="ref42">28</reflink>],[<reflink idref="bib29" id="ref43">29</reflink>] For example, research on alcohol drinking among first-year college students found that alcohol-related content displayed on Instagram and Snapchat may be more influential than that on Facebook.[<reflink idref="bib29" id="ref44">29</reflink>],[<reflink idref="bib30" id="ref45">30</reflink>]</p> <p>There is an important difference between Instagram and Snapchat that could explain why only Snapchat was associated with e-cigarette initiation: Snapchat posts can only be played once and disappear in seconds. This unique feature may result in students being more prone to posting sensitive material, without fear of lasting proof, thereby evading and potential negative social or legal consequences.[<reflink idref="bib28" id="ref46">28</reflink>],[<reflink idref="bib29" id="ref47">29</reflink>] For example, Boyle et al found that Snapchat was the preferred platform for sharing posts that contained information about negative consequences of alcohol drinking, including posting incriminating photos of alcohol misuse.[<reflink idref="bib29" id="ref48">29</reflink>]</p> <p>Snapchat use was associated with more frequent e-cigarette use during the previous 30 days, whereas, for first-year college students who had used e-cigarettes in the past 30 days, Instagram use was associated with fewer days using e-cigarettes. Previous research has shown that exposure to e-cigarette messages on Instagram and Snapchat was associated with more positive attitudes toward e-cigarette use, with self-expression and social learning being influential factors.[<reflink idref="bib31" id="ref49">31</reflink>] Differences in content posted by undergraduates could be one of the possible reasons. Research on college students' alcohol-drinking behavior suggested that Instagram's photo enhancement filters and esthetic beauty focus may make it a preferred destination for photos glamorizing drinking.[<reflink idref="bib29" id="ref50">29</reflink>],[<reflink idref="bib30" id="ref51">30</reflink>] On the contrary, Snapchat's higher level of privacy and the unique disappearing-posts feature may make it easier for students to share deviant behavior.[<reflink idref="bib29" id="ref52">29</reflink>],[<reflink idref="bib32" id="ref53">32</reflink>] In addition, Snapchat was viewed as a tool to enhance connections in existing relationships.[<reflink idref="bib32" id="ref54">32</reflink>] Qualitive studies targeting young adults showed that Snapchat was reserved for their closest interpersonal relationships—especially existing peers/friends, which makes such posts a part of the reinforcement of social learning.[<reflink idref="bib31" id="ref55">31</reflink>],[<reflink idref="bib32" id="ref56">32</reflink>] The extent of product promotion across social media platforms through so-called influencers may also play a role.[<reflink idref="bib33" id="ref57">33</reflink>],[<reflink idref="bib34" id="ref58">34</reflink>] Implemented after our data collection, Facebook and Instagram voluntarily banned sponsored posts from influencers in December 2019.[<reflink idref="bib35" id="ref59">35</reflink>] It is unclear how Facebook and Instagram enforce this policy and the subsequent short- and long-term impact. Future research is needed to further explore the differential associations between Instagram and Snapchat use observed in the current study.</p> <p>Although social media venues that are more young-adult-centered were associated with e-cigarette use, we did not observe significant associations between social media use and conventional cigarettes usage. Over the past several decades, tremendous effort has been dedicated to tobacco control,[<reflink idref="bib36" id="ref60">36</reflink>] leading youth to have a more accurate perception of the harm that can be caused by traditional cigarettes.[<reflink idref="bib37" id="ref61">37</reflink>] For non-cigarette tobacco products, however, the harm perception remains low.[<reflink idref="bib38" id="ref62">38</reflink>] Laestadius and others, using qualitative content analysis, observed that Instagram users characterized e-cigarettes primarily as novel devices rather than a potentially harmful product.[<reflink idref="bib39" id="ref63">39</reflink>] The low-harm perception of e-cigarettes is in part a reflection of its relatively recent emergence[<reflink idref="bib38" id="ref64">38</reflink>] but could also be due to social media posts sponsored by e-cigarette brands. Vanderwater et al found that posts by e-cigarette brands tended to promote not just a product, but a "lifestyle"— using words such as "laid back," "hard partying," "lounge," and "vapelife" to appeal to younger users.[<reflink idref="bib40" id="ref65">40</reflink>] The laid back and hard partying labels were found to be particularly appealing to college students.[<reflink idref="bib41" id="ref66">41</reflink>] Cigarette users, unlike e-cigarette users, were more isolated on social media. Compared with nonsmokers, smokers had less connected and more dispersed Facebook friendship networks.[<reflink idref="bib42" id="ref67">42</reflink>] We also examined associations between social media and dual use of cigarettes and e-cigarettes. The effects were not statistically significant and are not reported here.</p> <p>Experiencing PTSD and showing a tendency toward sensation-seeking was associated with the use of both e-cigarette and traditional cigarettes. Anger, depression, and anxiety, however, were not related to e-cigarette nor cigarette use. The association between mental health status and e-cigarette/cigarette use remains mixed through the literature. Although some have suggested that e-cigarette or cigarette use is related to depression scores and stress scores,[<reflink idref="bib43" id="ref68">43</reflink>],[<reflink idref="bib44" id="ref69">44</reflink>] others have shown no differences for these emotional factors (depression, anxiety, etc).[<reflink idref="bib45" id="ref70">45</reflink>] In alignment with existing evidence,[<reflink idref="bib46" id="ref71">46</reflink>] we found PTSD and sensation-seeking are shared risk factors for e-cigarette and cigarette use among first-year college students. One systematic review concluded that PTSD symptoms were associated with expectations that smoking would reduce the negative affect and therefore fostered an increased smoking rate and dependence.[<reflink idref="bib47" id="ref72">47</reflink>] A meta-analysis further found that individuals with PTSD were about 22% more likely to be current smokers than those without PTSD.[<reflink idref="bib48" id="ref73">48</reflink>] Similar findings for e-cigarette use were also documented.[<reflink idref="bib49" id="ref74">49</reflink>] In the current study, sensation-seeking was shown to be associated with lifetime e-cigarette and cigarette use, but not to patterns of use. Prior research has indicated that sensation-seeking is a significant factor associated with future e-cigarette use,[[<reflink idref="bib50" id="ref75">50</reflink>], [<reflink idref="bib52" id="ref76">52</reflink>]] but not a determinant of current e-cigarette use.[<reflink idref="bib52" id="ref77">52</reflink>] Future research is needed to better understand the relationship among social media use, mental health issues and e-cigarette initiation.</p> <hd id="AN0173157562-12">Strengths and limitations</hd> <p>Although addressing potential selection bias by incorporating selection-sensitive models and comparing the effects of e-cigarettes with cigarettes strengthen this study, our findings are not without limitations. First, responses of the study subjects are subject to recall and social desirability biases in which cigarette/e-cigarette use rates and the use of social media could be under-reported. Nevertheless, our result would be more robust if responses are truly biased toward the null. Second, while tobacco advertising is banned on several social media platforms including Facebook and Instagram, we were not able to observe social media posts of so-called influencers who may have promoted nicotine use differentially across platforms. Third, only college students of one large, public university were surveyed in this study. As a result, additional research is needed to assess the external validity of the current study findings. Fourth, although using the same model specification for both e-cigarettes and cigarettes would make for a clearer comparison, the data only allowed for construction of a binary outcome variable for traditional cigarettes use. To address this limitation, we performed a sensitivity analysis in which a binary variable named lifetime e-cigarette usage was created using the question "Have you ever used electronic cigarettes". We estimated the impact of social media on lifetime e-cigarette initiation using the same model specification as estimating conventional cigarettes. The conclusion remained unchanged in which only the Snapchat use was associated with lifetime e-cigarette initiation (4.26%, <emph>p</emph> = 0.004).</p> <hd id="AN0173157562-13">Conclusion</hd> <p>We found that Snapchat had the distinction of being the only major social media platform associated with lifetime e-cigarette use and frequency of past 30-day e-cigarette use among first-year college students. Experiencing PTSD and the tendency toward sensation-seeking were also associated with lifetime e-cigarette use. Additional research is urgently needed to examine the role social media use might play in promoting engagement in health risk behaviors, including e-cigarette use, among emerging adults.</p> <hd id="AN0173157562-14">Conflict of interest disclosure</hd> <p>The authors have no conflicts of interest to report. 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Addict Behav. 2017; 73 : 151 – 157. doi: 10.1016/j.addbeh.2017.05.007.</bibtext> </blist> </ref> <ref id="AN0173157562-16"> <title> Footnotes </title> <blist> <bibtext> Thos e who answered "vape once or twice" or "once in a while but not regularly."</bibtext> </blist> <blist> <bibtext> Those who answered "vape regularly in the past" or "regularly now."</bibtext> </blist> <blist> <bibtext> Anger level was a T-Score converted continuous variable derived from the Level 2—Anger—Adult (PROMIS Emotional distress—Anger—5 questions Short form).</bibtext> </blist> <blist> <bibtext> Anxiety level was a T-Score converted continuous variable derived from the PROMIS—Anxiety—8 questions Short form.</bibtext> </blist> <blist> <bibtext> Depression level was a T-Score converted continuous variable derived from the PROMIS—Depression—8 questions Short form.</bibtext> </blist> <blist> <bibtext> PTSD symptom was measured by the a 20 items PTSD checklist for DSM-5.</bibtext> </blist> </ref> <aug> <p>By Shuo-Yu Lin; Xiaolu Cheng; Matthew E. Rossheim; Dustin Gress; Alison Evans Cuellar; Lawrence Cheskin and Hong Xue</p> <p>Reported by Author; Author; Author; Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib10" firstref="ref10"></nolink> <nolink nlid="nl2" bibid="bib11" firstref="ref11"></nolink> <nolink nlid="nl3" bibid="bib12" firstref="ref12"></nolink> <nolink nlid="nl4" bibid="bib13" firstref="ref13"></nolink> <nolink nlid="nl5" bibid="bib14" firstref="ref14"></nolink> <nolink nlid="nl6" bibid="bib15" firstref="ref15"></nolink> <nolink nlid="nl7" bibid="bib16" firstref="ref16"></nolink> <nolink nlid="nl8" bibid="bib17" firstref="ref17"></nolink> <nolink nlid="nl9" bibid="bib18" firstref="ref19"></nolink> <nolink nlid="nl10" bibid="bib20" firstref="ref20"></nolink> <nolink nlid="nl11" bibid="bib19" firstref="ref22"></nolink> <nolink nlid="nl12" bibid="bib21" firstref="ref23"></nolink> <nolink nlid="nl13" bibid="bib22" firstref="ref24"></nolink> <nolink nlid="nl14" bibid="bib23" firstref="ref34"></nolink> <nolink nlid="nl15" bibid="bib24" firstref="ref35"></nolink> <nolink nlid="nl16" bibid="bib25" firstref="ref36"></nolink> <nolink nlid="nl17" bibid="bib26" firstref="ref37"></nolink> <nolink nlid="nl18" bibid="bib27" firstref="ref38"></nolink> <nolink nlid="nl19" bibid="bib28" firstref="ref41"></nolink> <nolink nlid="nl20" bibid="bib29" firstref="ref43"></nolink> <nolink nlid="nl21" bibid="bib30" firstref="ref45"></nolink> <nolink nlid="nl22" bibid="bib31" firstref="ref49"></nolink> <nolink nlid="nl23" bibid="bib32" firstref="ref53"></nolink> <nolink nlid="nl24" bibid="bib33" firstref="ref57"></nolink> <nolink nlid="nl25" bibid="bib34" firstref="ref58"></nolink> <nolink nlid="nl26" bibid="bib35" firstref="ref59"></nolink> <nolink nlid="nl27" bibid="bib36" firstref="ref60"></nolink> <nolink nlid="nl28" bibid="bib37" firstref="ref61"></nolink> <nolink nlid="nl29" bibid="bib38" firstref="ref62"></nolink> <nolink nlid="nl30" bibid="bib39" firstref="ref63"></nolink> <nolink nlid="nl31" bibid="bib40" firstref="ref65"></nolink> <nolink nlid="nl32" bibid="bib41" firstref="ref66"></nolink> <nolink nlid="nl33" bibid="bib42" firstref="ref67"></nolink> <nolink nlid="nl34" bibid="bib43" firstref="ref68"></nolink> <nolink nlid="nl35" bibid="bib44" firstref="ref69"></nolink> <nolink nlid="nl36" bibid="bib45" firstref="ref70"></nolink> <nolink nlid="nl37" bibid="bib46" firstref="ref71"></nolink> <nolink nlid="nl38" bibid="bib47" firstref="ref72"></nolink> <nolink nlid="nl39" bibid="bib48" firstref="ref73"></nolink> <nolink nlid="nl40" bibid="bib49" firstref="ref74"></nolink> <nolink nlid="nl41" bibid="bib50" firstref="ref75"></nolink> <nolink nlid="nl42" bibid="bib52" firstref="ref76"></nolink>
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  Group: Ti
  Data: Associations between Use of Specific Social Media Sites and Electronic Cigarette Use among College Students
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  Data: English
– Name: Author
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  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Lin%2C+Shuo-Yu%22">Lin, Shuo-Yu</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-4688-1424">0000-0003-4688-1424</externalLink>)<br /><searchLink fieldCode="AR" term="%22Cheng%2C+Xiaolu%22">Cheng, Xiaolu</searchLink><br /><searchLink fieldCode="AR" term="%22Rossheim%2C+Matthew+E%2E%22">Rossheim, Matthew E.</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-4388-5251">0000-0003-4388-5251</externalLink>)<br /><searchLink fieldCode="AR" term="%22Gress%2C+Dustin%22">Gress, Dustin</searchLink><br /><searchLink fieldCode="AR" term="%22Cuellar%2C+Alison+Evans%22">Cuellar, Alison Evans</searchLink><br /><searchLink fieldCode="AR" term="%22Cheskin%2C+Lawrence%22">Cheskin, Lawrence</searchLink><br /><searchLink fieldCode="AR" term="%22Xue%2C+Hong%22">Xue, Hong</searchLink>
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  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Journal+of+American+College+Health%22"><i>Journal of American College Health</i></searchLink>. 2023 71(7):2217-2224.
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  Data: Taylor & Francis. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
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  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 8
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2023
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  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Social+Media%22">Social Media</searchLink><br /><searchLink fieldCode="DE" term="%22Smoking%22">Smoking</searchLink><br /><searchLink fieldCode="DE" term="%22Correlation%22">Correlation</searchLink><br /><searchLink fieldCode="DE" term="%22College+Freshmen%22">College Freshmen</searchLink><br /><searchLink fieldCode="DE" term="%22Incidence%22">Incidence</searchLink><br /><searchLink fieldCode="DE" term="%22Probability%22">Probability</searchLink><br /><searchLink fieldCode="DE" term="%22Gender+Differences%22">Gender Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Racial+Differences%22">Racial Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Ethnicity%22">Ethnicity</searchLink><br /><searchLink fieldCode="DE" term="%22Mental+Health%22">Mental Health</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1080/07448481.2021.1965149
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0744-8481<br />1940-3208
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Objective: To examine dose-response associations between use of specific social media sites and the use of electronic cigarettes (e-cigarettes) and traditional cigarettes. Methods: This was a cross-sectional study of 298 first-year college students enrolled in the fall 2019 semester at a large state university. Heckman selection and Probit model were used to estimate associations between use of specific social media sites and e-cigarette/traditional cigarette use. Results: Each additional hour per day spent on Snapchat was associated with a 4.61% increase in the probability of lifetime e-cigarette use. In addition, among current e-cigarette users, more time spent on Snapchat was associated with more frequent e-cigarette use (marginal effects: 0.13, p = 0.001). Facebook, Twitter, Snapchat and Instagram were not associated with traditional cigarette smoking. Conclusion: Snapchat was the only major social media platform associated with both lifetime and current e-cigarette use.
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  Data: 2023
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  Data: EJ1402448
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      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 8
        StartPage: 2217
    Subjects:
      – SubjectFull: Social Media
        Type: general
      – SubjectFull: Smoking
        Type: general
      – SubjectFull: Correlation
        Type: general
      – SubjectFull: College Freshmen
        Type: general
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      – SubjectFull: Mental Health
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      – TitleFull: Associations between Use of Specific Social Media Sites and Electronic Cigarette Use among College Students
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            – Type: volume
              Value: 71
            – Type: issue
              Value: 7
          Titles:
            – TitleFull: Journal of American College Health
              Type: main
ResultId 1