Social Media Use and Subsequent E-Cigarette Susceptibility, Initiation, and Continued Use Among US Adolescents
ORIGINAL RESEARCH — Volume 20 — September 7, 2023
Juhan Lee, PhD1; Suchitra Krishnan-Sarin, PhD1; Grace Kong, PhD1 (View author affiliations)
Suggested citation for this article: Lee J, Krishnan-Sarin S, Kong G. Social Media Use and Subsequent E-Cigarette Susceptibility, Initiation, and Continued Use Among US Adolescents. Prev Chronic Dis 2023;20:220415. DOI: http://dx.doi.org/10.5888/pcd20.220415.
What is already known on this topic?
The prevalence of social media use among adolescents is high, and social media has extensive e-cigarette content.
What is added by this report?
Use of social media among adolescents is associated with being susceptible to and initiating e-cigarette use in subsequent years.
What are the implications for public health practice?
Preventing adolescent exposure to e-cigarette content on social media is important.
Social media has a large amount of e-cigarette content. Little is known about the associations between social media use and a wide range of e-cigarette use behaviors, including susceptibility, initiation, and continued use. We analyzed national data on US adolescents to assess these associations.
We used data on adolescents participating in the Population Assessment of Tobacco and Health (PATH) Study Wave 4 (2016–2018) and Wave 5 (2018–2019). We conducted 2 models: 1) a multinomial logistic regression on e-cigarette use susceptibility and use behaviors at Wave 5 by social media use at Wave 4 among adolescents who never used e-cigarettes at Wave 4 and 2) a binomial logistic regression on current e-cigarette use at Wave 5 by social media use at Wave 4 among adolescents who ever used e-cigarettes at Wave 4.
Among adolescents who never used e-cigarettes at Wave 4 (n = 7,872), daily social media use (vs never) was associated with a higher likelihood of being susceptible to e-cigarette use (adjusted odds ratio [aOR] =1.46; 95% CI, 1.20–1.78), past e-cigarette use (aOR = 3.55; 95% CI, 2.49–5.06), and current e-cigarette use (aOR = 3.45; 95% CI, 2.38–5.02) at Wave 5. Among adolescents who ever used e-cigarettes at Wave 4 (n = 794), we found no significant association between social media use at Wave 4 and continued e-cigarette use at Wave 5.
Our study found that social media use is associated with subsequent susceptibility to e-cigarette use and initiation but not with continued use of e-cigarettes among US adolescents. These findings suggest that understanding and addressing the association between social media and e-cigarette use is critical.
In 2022, 95% of adolescents aged 13 to 17 years used social media (1). Social media platforms have extensive e-cigarette–related content (2). This content may be user-generated, such as a person posting about e-cigarettes to their own social network, or the industry posting marketing content with themes that appeal to adolescents (eg, vape tricks) (3–5). In general, e-cigarettes are positively portrayed on social media as “glamourous,” “healthy,” and “safe” (6).
Previous longitudinal studies showed that social media use behaviors, such as exposure to and engagement with tobacco-related content on social media, are associated with e-cigarette initiation among adolescents (7,8). Nonetheless, understanding of whether social media use is associated with the full spectrum of e-cigarette use behaviors among adolescents, such as susceptibility to e-cigarette use and continued use of e-cigarettes, is limited. Understanding susceptibility to use is important because it is an established predictor of e-cigarette use initiation among adolescents (9). Examining continued use of e-cigarettes among adolescents who are already using these products is also important because progression to regular use can lead to nicotine addiction and exposure to other toxicants and chemicals (10,11). Thus, understanding the association between susceptibility to e-cigarette use and continued use of e-cigarettes and social media is critical to fully understanding a wide range of adolescent e-cigarette use behaviors.
We used a nationally representative sample of adolescents in the US to examine longitudinal associations between social media use and susceptibility to, initiation of, and continued use of e-cigarettes. We hypothesized that more frequent social media use would be associated with higher levels of susceptibility to e-cigarette use, initiation of, and continued use of e-cigarettes.
We used data on adolescents participating in Wave 4 (2016–2018) and Wave 5 (2018–2019) of the Population Assessment of Tobacco and Health (PATH) Study, a nationally representative longitudinal panel survey data set in the US (12). The PATH Study uses multistage stratified sampling; thus, responses in the adolescent data set represent the US population of adolescents aged 13 to 17 years. We used 2 analytic samples of adolescent respondents who completed surveys at both Wave 4 and Wave 5: 1) adolescents who never used e-cigarettes at Wave 4 (n = 7,872), to examine the likelihood of susceptibility to and initiation of e-cigarette use at Wave 5; and 2) adolescents who ever used e-cigarettes at Wave 4 (n = 794), to examine the likelihood of continued use of e-cigarettes at Wave 5. Because respondents aged 17 years at Wave 4 moved to an adult survey at Wave 5, we did not include them in our analysis.
E-cigarette susceptibility, initiation, and continued use at Wave 5
For e-cigarette susceptibility at Wave 5, we used items assessing intention and willingness to use e-cigarettes (“Do you think you might try using e-cigarettes soon?” and “If one of your best friends were to offer you e-cigarettes, would you use it?”) (13). Both questions had the following response options: definitely not, probably not, probably yes, and definitely yes. When respondents reported “definitely not” to both questions, we categorized them as “nonsusceptible never-use” and others as “susceptible never-use” (9,13,14). For actual e-cigarette use behaviors at Wave 5, we used 2 variables: ever use and current (past 30 days) use of e-cigarettes. When respondents reported ever using e-cigarettes but not currently using e-cigarettes at Wave 5, we categorized them as past users. When respondents reported ever using e-cigarettes and using e-cigarettes in the past 30 days at Wave 5, we categorized them as current users.
We then created a 4-level outcome variable as follows: 0 = did not initiate and non-susceptible (nonsusceptible never-use); 1 = did not initiate but susceptible (susceptible never-use); 2 = initiated but did not currently use (past use); and 3 = initiated and currently used e-cigarettes (current use) at Wave 5 (Table 1).
Social media use at Wave 4
For social media use at Wave 4, respondents were asked if they had a social media account. The survey item was as follows: “Sometimes people use the internet to connect with other people online through social networks like Facebook, Google Plus, YouTube, LinkedIn, Twitter, Tumblr, Instagram, Pinterest, or Snapchat. This is often called social media. Do you have a social media account?” If the respondent reported having a social media account, the survey asked about the frequency of social media use: “About how often do you visit your social media account?” Response options were “never,” “less often [than every few weeks],” “every few weeks,” “1–2 days a week,” “3–5 days a week,” “about once a day,” and “more than once a day.” We categorized respondents who did not have a social media account and respondents who had a social media account but never visited social media as never-users and created a 3-level predictor variable coded as 0 = never; 1 = nondaily (ie, “less often [than every few weeks],” “every few weeks,” “1–2 days a week,” “3–5 days a week”); and 2 = daily social media use (ie, “about once a day” and “more than once a day”) (15,16).
The covariates at Wave 4 included age (12–14 or 15–16), sex (male or female), ethnicity (non-Hispanic or Hispanic), race (White, Black, or Other [American Indian or Alaska Native, Asian Indian, Asian, Native Hawaiian, and Pacific Islander]), parental education (less than high school graduate, GED [General Educational Development], high school graduate, some college (no degree) or associates degree, bachelor’s degree, or advanced degree; annual household income (<$10,000, $10,000–$24,999, $25,000–$49,999, $50,000–$99,999, or ≥$100,000), parental e-cigarette use (no or yes), peer e-cigarette use (no or yes), e-cigarette use susceptibility at Wave 4 (only in Model 1), and current use of other drugs (other tobacco products, alcohol, cannabis, and illicit drugs).
We conducted descriptive analyses to examine the bivariate associations between predictors at Wave 4 and outcomes at Wave 5. We further conducted 1) multinomial logistic regression analysis on e-cigarette use susceptibility and use behaviors at Wave 5 by social media use at Wave 4 among respondents who never used e-cigarettes at Wave 4 (Model 1) and 2) a binomial logistic regression model on e-cigarette use in the past 30 days at Wave 5 by social media use at Wave 4 among respondents who ever used e-cigarettes at Wave 4 (Model 2). Significance was considered at a 2-sided P value of <.05. The observational, secondary data analysis of publicly available, de-identified data was deemed exempt by the Yale University Institutional Review Board.
Among adolescents who had never used e-cigarettes at Wave 4 (n = 7,872), 16.4% reported nondaily use and 65.9% daily use of social media at Wave 4 (Table 2). At Wave 5, 62.9% still did not use e-cigarettes and reported not being susceptible to e-cigarette use, while 17.6% did not use e-cigarettes but reported being susceptible to e-cigarette use. Also at Wave 5, 10.8% of adolescents had initiated e-cigarette use but had not used e-cigarettes in the past 30 days, and 8.7% had initiated e-cigarette use and had used e-cigarettes in the past 30 days (Table 2).
Association between social media and susceptibility to and initiation of e-cigarette use
Among adolescents who had never used e-cigarettes at Wave 4 (n = 7,872) (Model 1, Table 3), nondaily social media use (vs never) at Wave 4 was significantly associated with a higher likelihood of past e-cigarette use (adjusted odds ratio [aOR] = 2.26; 95% CI, 1.51–3.37) and current e-cigarette use at Wave 5 (aOR = 1.64; 95% CI, 1.04–2.60). Daily social media use (vs never) was significantly associated with a higher likelihood of being susceptible to e-cigarette use (aOR = 1.46; 95% CI, 1.20–1.78), past e-cigarette use (aOR = 3.55; 95% CI, 2.49–5.06), and current e-cigarette use (aOR = 3.45; 95% CI, 2.38–5.02) at Wave 5.
Associations between social media use and continued use of e-cigarettes
Among the 794 adolescents who ever used e-cigarettes at Wave 4 (Model 2, Table 4), 436 (52.9%, weighted) discontinued e-cigarette use at Wave 5, but 358 (47.1%, weighted) reported they still used e-cigarettes in the past 30 days at Wave 5. We found no significant association between continued e-cigarette use at Wave 5 and social media use at Wave 4 (all P values > .05).
We observed that social media use was associated with subsequent susceptibility to e-cigarette use and initiation but not with continued use of e-cigarettes among US adolescents aged 12 to 16 years. Susceptibility and e-cigarette initiation among adolescents may be driven by exposure to e-cigarette–related content on social media. Although we did not examine the content viewed by adolescents on social media, studies have documented pro–e-cigarette content and promotion on social media (3). Furthermore, previous studies identified reasons that adolescents experiment with e-cigarettes, including their use by peers and their various flavors (10,11), which are frequently reflected in social media e-cigarette content (17,18). We posit that exposure to such themes might be related to higher levels of susceptibility to and initiation of e-cigarette use among adolescents. Given the constantly changing social media environment and e-cigarette promotion on social media (4), future studies should monitor these associations as newer PATH data sets become available.
Unexpectedly, we did not find a significant association between continued use of e-cigarettes at Wave 5 and social media use at Wave 4. This finding might suggest that once an adolescent starts to use e-cigarettes, social media use may not contribute to the progression to continued e-cigarette use. This finding was somewhat unexpected because a previous study suggested that exposure among young adult college students to e-cigarette–related content on social media was associated with an increased level of e-cigarette use 1 year later (19). We speculate that once e-cigarette use is initiated, other factors, such as e-cigarette use dependence, might influence continued use more than social media. Future studies should examine factors associated with continued e-cigarette use among adolescents.
It is also important to consider other unmeasured psychological and environmental factors that may influence the relationship between social media use and e-cigarette use among adolescents. For example, lower levels of social media use might be associated with having parents who have a protective parenting style that may either restrict social media screen time or adolescent e-cigarette use (20). Furthermore, adolescents who do not use social media might be active in other extracurricular activities, which might protect against e-cigarette use (21). Future studies should examine the characteristics of adolescent survey respondents who do not use social media to understand protective factors that might contribute to preventing e-cigarette use.
Our study has several limitations. First, because our study was observational, we cannot assume a causal relationship between social media use and e-cigarette use behaviors. Second, we examined general social media use, so it was not possible to determine what kind of content was seen by respondents. Third, the PATH Study asked about general social media platforms; thus, we cannot determine which social media platforms PATH respondents visited. Because each social media platform has its own policies, unique characteristics (eg, video-based, text-based), and features (eg, retweets), future studies should examine the effects of these characteristics on e-cigarette use. Fourth, we did not test potential underlying mechanisms between social media use and e-cigarette use behaviors. However, previous studies observed that exposure to e-cigarette advertisements, risk perceptions of e-cigarettes, and e-cigarette expectancies mediated the association between social media use and e-cigarette use behaviors (16,19). Future studies should examine other potential mediators between social media use and e-cigarette use behaviors.
Our study highlights the need for social media–related strategies that prevent e-cigarette use among adolescents. These strategies could include developing and disseminating counter-messaging (ie, anti–e-cigarette campaigns) on social media. Incorporating social media components in e-cigarette use prevention strategies tailored for adolescents may increase effectiveness and eventually reduce e-cigarette–related health consequences in underage populations. Continued surveillance and regulations on e-cigarette–related content on social media at tobacco regulatory agencies could also help to curb e-cigarette use among adolescents.
The research reported in this publication was supported by grant number U54DA036151 and R01DA049878 from the National Institute on Drug Abuse and the US Food and Drug Administration Center for Tobacco Products. The authors have no conflicts of interest to disclose. No copyrighted surveys or tools were used in this research or article. Dr Lee conceptualized the study, carried out the initial analyses, drafted the initial manuscript, and revised the manuscript. Dr Krishnan-Sarin interpreted the findings, critically reviewed and revised the manuscript, and supervised the process. Dr Kong interpreted the findings, critically reviewed and revised the manuscript, and supervised the process. Information on the PATH Study is available at https://pathstudyinfo.nih.gov.
Corresponding Author: Juhan Lee, PhD, Department of Psychiatry, Yale School of Medicine, 34 Park St, S-206, New Haven, CT 06519 (email@example.com).
- Pew Research Center. Teens, social media and technology 2022. August 10, 2022. Accessed April 2, 2023. https://www.pewresearch.org/internet/2022/08/10/teens-social-media-and-technology-2022
- Donaldson SI, Dormanesh A, Perez C, Majmundar A, Allem J-P. Association between exposure to tobacco content on social media and tobacco use: a systematic review and meta-analysis. JAMA Pediatr 2022;176(9):878–85. PubMed doi:10.1001/jamapediatrics.2022.2223
- Kong G, Schott AS, Lee J, Dashtian H, Murthy D. Understanding e-cigarette content and promotion on YouTube through machine learning. Tob Control 2022;tobaccocontrol-2021-057243. PubMed doi:10.1136/tobaccocontrol-2021-057243
- Lee J, Suttiratana S, Sen I, Kong G. E-cigarette marketing on social media: a scoping review. Curr Addict Rep 2023;10(1):29–37.
- McCausland K, Maycock B, Leaver T, Jancey J. The messages presented in electronic cigarette–related social media promotions and discussion: scoping review. J Med Internet Res 2019;21(2):e11953. PubMed doi:10.2196/11953
- Collins L, Glasser AM, Abudayyeh H, Pearson JL, Villanti AC. E-cigarette marketing and communication: how e-cigarette companies market e-cigarettes and the public engages with e-cigarette information. Nicotine Tob Res 2019;21(1):14–24. PubMed doi:10.1093/ntr/ntx284
- Cavazos-Rehg P, Li X, Kasson E, Kaiser N, Borodovsky JT, Grucza R, et al. Exploring how social media exposure and interactions are associated with ENDS and tobacco use in adolescents from the PATH Study. Nicotine Tob Res 2021;23(3):487–94. PubMed doi:10.1093/ntr/ntaa113
- Pérez A, Spells CE, Bluestein MA, Harrell MB, Hébert ET. The longitudinal impact of seeing and posting tobacco-related social media on tobacco use behaviors among youth (aged 12–17): findings from the 2014–2016 Population Assessment of Tobacco and Health (PATH) Study. Tob Use Insights 2022;15:X221087554. PubMed doi:10.1177/1179173X221087554
- Bold KW, Kong G, Cavallo DA, Camenga DR, Krishnan-Sarin S. E-cigarette susceptibility as a predictor of youth initiation of e-cigarettes. Nicotine Tob Res 2017;20(1):140–4. PubMed
- Bold KW, Kong G, Cavallo DA, Camenga DR, Krishnan-Sarin S. Reasons for trying e-cigarettes and risk of continued use. Pediatrics 2016;138(3):e20160895. PubMed doi:10.1542/peds.2016-0895
- Kong G, Morean ME, Cavallo DA, Camenga DR, Krishnan-Sarin S. Reasons for electronic cigarette experimentation and discontinuation among adolescents and young adults. Nicotine Tob Res 2015;17(7):847–54. PubMed doi:10.1093/ntr/ntu257
- Hyland A, Ambrose BK, Conway KP, Borek N, Lambert E, Carusi C, et al. Design and methods of the Population Assessment of Tobacco and Health (PATH) study. Tob Control 2017;26(4):371–8. PubMed doi:10.1136/tobaccocontrol-2016-052934
- Trinidad DR, Pierce JP, Sargent JD, White MM, Strong DR, Portnoy DB, et al. Susceptibility to tobacco product use among youth in wave 1 of the population Assessment of Tobacco and Health (PATH) study. Prev Med 2017;101:8–14. PubMed doi:10.1016/j.ypmed.2017.05.010
- Pierce JP, Choi WS, Gilpin EA, Farkas AJ, Merritt RK. Validation of susceptibility as a predictor of which adolescents take up smoking in the United States. Health Psychol 1996;15(5):355–61. PubMed doi:10.1037/0278-6188.8.131.525
- Lee J, Tan ASL, Porter L, Young-Wolff KC, Carter-Harris L, Salloum RG. Association between social media use and vaping among Florida adolescents, 2019. Prev Chronic Dis 2021;18:E49. PubMed doi:10.5888/pcd18.200550
- Zheng X, Li W, Wong SW, Lin HC. Social media and e-cigarette use among US youth: longitudinal evidence on the role of online advertisement exposure and risk perception. Addict Behav 2021;119:106916. PubMed doi:10.1016/j.addbeh.2021.106916
- Sowles SJ, Krauss MJ, Connolly S, Cavazos-Rehg PA. A content analysis of vaping advertisements on Twitter, November 2014. Prev Chronic Dis 2016;13:E139. PubMed doi:10.5888/pcd13.160274
- Zhan Y, Liu R, Li Q, Leischow SJ, Zeng DD. Identifying topics for e-cigarette user-generated contents: a case study from multiple social media platforms. J Med Internet Res 2017;19(1):e24. PubMed doi:10.2196/jmir.5780
- Pokhrel P, Ing C, Kawamoto CT, Laestadius L, Buente W, Herzog TA. Social media’s influence on e-cigarette use onset and escalation among young adults: what beliefs mediate the effects? Addict Behav 2021;112:106617. PubMed doi:10.1016/j.addbeh.2020.106617
- Bloemen N, De Coninck D. Social media and fear of missing out in adolescents: the role of family characteristics. Soc Media Soc 2020;6(4).
- Burrow-Sánchez JJ, Ratcliff BR. Adolescent risk and protective factors for the use of electronic cigarettes. J Prev Heal Promot 2021;2(1):100–34.
|Wave 4 e-cigarette use||Wave 5 e-cigarette use|
|Nonsusceptible never-usea||Susceptible never-usea||Ever use but not current use||Current use|
|Never user (analytic sample 1)||Reference||1 = Never but susceptible||2 = Past use||3 = Current use|
|Ever user (analytic sample 2)||—||—||0 = Discontinued use (reference)||1 = Continued use|
|Predictors at Wave 4||Overall no. (weighted %)c||Outcome: E-cigarette use and susceptibility status at Wave 5, no. (weighted %)c||P valuef|
|Nonsusceptible/never e-cigarette use||Susceptible/never e-cigarette use||Past e-cigarette used||Current e-cigarette usee|
|Overall no. (%)||7,872 (100.0)||4,978 (62.9)||1,387 (17.6)||801 (10.8)||665 (8.7)||—|
|Social media use|
|Never||1,389 (17.7)||1,062 (21.7)||200 (14.8)||61 (6.7)||48 (6.6)||<.001|
|Nondaily||1,327 (16.4)||933 (18.4)||224 (15.5)||100 (12.3)||66 (9.8)|
|Daily||5,145 (65.9)||2,978 (59.9)||960 (69.6)||640 (81.0)||551 (83.7)|
|12–14||6,084 (77.6)||3,901 (79.1)||1,089 (79.0)||586 (72.4)||471 (69.6)||<.001|
|15 or 16||1,788 (22.4)||1,077 (20.9)||298 (21.0)||215 (27.6)||194 (30.4)|
|Female||3,828 (49.6)||2,343 (48.3)||713 (51.8)||413 (52.3)||344 (51.7)||.01|
|Male||4,044 (50.4)||2,635 (51.7)||674 (48.2)||388 (47.7)||321 (48.3)|
|No||5,498 (76.3)||3,442 (75.6)||926 (74.1)||587 (79.9)||518 (82.8)||<.001|
|Yes||2,374 (23.7)||1,536 (24.4)||461 (25.9)||214 (20.1)||147 (17.2)|
|White only||5,316 (69.6)||3,227 (66.2)||949 (70.8)||600 (77.2)||512 (81.8)||<.001|
|Black only||1,291 (15.3)||951 (18.1)||196 (13.6)||81 (9.2)||55 (6.7)|
|Otherg||1,265 (15.1)||800 (15.8)||242 (15.6)||120 (13.6)||98 (11.5)|
|Less than high school||1,124 (11.9)||737 (12.2)||210 (12.7)||97 (10.0)||73 (9.8)||.11|
|GED||326 (3.7)||207 (3.6)||50 (3.4)||32 (3.7)||35 (5.4)|
|High school graduate||1,348 (16.2)||886 (16.9)||225 (15.1)||126 (14.7)||106 (15.5)|
|Some college (no degree) or associates degree||2,441 (30.7)||1,512 (30.2)||420 (29.8)||260 (31.5)||235 (34.8)|
|Bachelor’s degree||1,555 (22.3)||980 (22.3)||277 (22.1)||156 (22.3)||133 (21.6)|
|Advanced degree||1,013 (15.3)||615 (14.8)||193 (16.9)||126 (17.9)||78 (12.8)|
|Annual household income, $|
|<10,000||663 (7.5)||458 (8.3)||113 (7.4)||48 (5.0)||41 (5.7)||<.001|
|10,000–24,999||1,161 (13.4)||754 (13.8)||205 (13.4)||111 (12.1)||80 (11.0)|
|25,000–49,999||1,743 (21.4)||1,128 (21.9)||281 (19.7)||165 (19.0)||162 (24.2)|
|50,000–99,999||1,872 (26.1)||1,162 (25.9)||312 (24.3)||206 (27.3)||182 (29.4)|
|≥100,000||2,048 (31.6)||1,214 (30.1)||403 (35.2)||248 (36.7)||177 (29.7)|
|Susceptibility to e-cigarette use at Wave 4|
|No||5,926 (83.1)||4,076 (92.9)||942 (73.7)||503 (66.5)||379 (57.8)||<.001|
|Yes||1,231 (16.9)||331 (7.1)||352 (26.3)||272 (33.5)||269 (42.2)|
|Parental e-cigarette use|
|No||7,565 (96.7)||4,819 (97.5)||1,330 (96.4)||757 (95.3)||622 (94.3)||<.001|
|Yes||252 (3.3)||124 (2.5)||48 (3.6)||40 (4.7)||39 (5.7)|
|Peer e-cigarette use|
|None||6,904 (88.3)||4,616 (93.4)||1,181 (86.5)||606 (76.0)||465 (70.7)||<.001|
|Any||920 (11.7)||336 (6.6)||196 (13.5)||188 (24.0)||196 (29.3)|
|Other tobacco use (eg, cigarettes, cigars, smokeless tobacco, hookah)|
|None||7,729 (99.3)||4,907 (99.7)||1,365 (99.6)||775 (98.4)||642 (97.1)||<.001|
|Any||54 (0.7)||13 (0.3)||8 (0.4)||15 (1.6)||18 (2.9)|
|Current alcohol use|
|No||7,565 (96.1)||4,871 (97.8)||1,326 (95.7)||736 (92.4)||592 (88.7)||<.001|
|Yes||304 (3.9)||106 (2.2)||60 (4.3)||64 (7.6)||73 (11.3)|
|Current cannabis use|
|No||7,789 (99.0)||4,950 (99.5)||1,374 (99.2)||777 (97.3)||648 (97.0)||<.001|
|Yes||83 (1.0)||28 (0.5)||13 (0.8)||24 (2.7)||17 (3.0)|
|Current any drug use (eg, misuse of prescribed drugs, illicit drugs)|
|No||7,642 (97.2)||4,862 (97.9)||1,343 (96.8)||764 (95.8)||633 (95.0)||<.001|
|Yes||228 (2.8)||115 (2.1)||43 (3.2)||37 (4.2)||32 (5.0)|
|Wave 4 social media use||Wave 5 e-cigarette susceptibility and use behaviors, adjusted OR (95% CI) [P value]c|
|Never used e-cigarettes, but susceptible to e-cigarette use (n = 1,387)||Past e-cigarette use (n = 801)d||Current e-cigarette use (n = 665)e|
|Never||1 [Reference]||1 [Reference]||1 [Reference]|
|Nondaily||1.17 (0.88–1.57) [.28]||2.26 (1.51–3.37) [<.001]||1.64 (1.04–2.60) [.03]|
|Daily||1.46 (1.20–1.78) [<.001]||3.55 (2.49–5.06) [<.001]||3.45 (2.38–5.02) [<.001]|
|Wave 4 social media use||Continued use of e-cigarettes at Wave 5, adjusted OR (95% CI) [P value] (n = 358)b,c|
|Nondaily||1.74 (0.81–3.74) [.15]|
|Daily||1.80 (0.97–3.34) [.06]|
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