Abstract
Background.
This longitudinal randomized controlled trial examined the effects of anti-vaping social media content on e-cigarette attitudes and intentions among U.S. young adults (ages 18–24; n=3,400).
Methods.
Targeted content was embedded directly into participants’ social media feeds, with varying levels of impressions.
Results.
Results indicate that increased exposure to anti-vaping messages significantly elevated perceived risk of harm (β=0.11, 95% CI: 0.03–0.20, p < .01) and social unacceptability of e-cigarette use (β=0.10, 95% CI: 0.03–0.18, p < .01), while decreasing intentions to vape (RRR = 0.59, 95% CI: 0.36–0.97, p < .05). Notably, these attitudinal shifts occurred even with relatively low ad exposure and over an extended intervention period, while controlling for the e-cigarette use status (never, former, or current) of participants.
Discussion.
These findings support the effectiveness of digital media campaigns in influencing health-related behaviors and attitudes among young adults. Specifically, embedding anti e-cigarette content directly in the feed of young adults is shown to be effective in shifting attitudes in a space where these young adults are already engaged. Limitations include limited ad impressions and minimal change in ad awareness recall, suggesting future research should explore longer interventions and broader nicotine product messaging.
Keywords: intervention, e-cigarette, social media, RCT, attitudes
1. INTRODUCTION
Electronic cigarette (e-cigarette) use continues to be prominent among youth and young adults, with recent estimates showing 14.8% of young adults engaging in regular e-cigarette usage.1 As the dangers of e-cigarette use continue to be studied, researchers are also attempting to understand what influences e-cigarette use behavior.
Mass media campaigns have been impactful in shifting smoking-related attitudes, intentions and behaviors.2 Nicotine behavior change campaigns have seen a change in recent years from traditional mass media to digital platforms like social media.3 These campaigns can be especially effective in their ability to reach young adults, as more than 80% of adults aged 18–29 years consistently use social media.4 Digital media interventions in the health behavior space can be a valuable tool while also presenting unique challenges due to its accessibility and potential for influence.5 As digital behavior change interventions related to tobacco and nicotine use continue to grow, it is increasingly important for researchers to evaluate the effectiveness and impact of these interventions.6
Digital media interventions can influence social norms among young people via anti-vaping social media content that showcases vaping as being less common and less socially acceptable among their peers.5 Online interventions in the form of ads or social media posts have presented some evidence for being able to change beliefs and attitudes towards e-cigarettes. A recent study evaluating the effectiveness of an anti-vaping social media intervention found a significant treatment effect on both anti-industry beliefs and lower vaping intentions among current e-cigarette users.5 Another randomized controlled study found anti-vaping ad exposure was significantly associated with stronger anti-industry attitudes, independence from e-cigarettes, and risk perceptions.7 These attitudes were also associated with greater intentions to quit e-cigarettes.7 Though social media interventions have potential in terms of preventing vaping among young adults,8 more research is needed to determine how these interventions impact attitudes and intentions of those who already use e-cigarettes currently.
This study aims to build upon previous research by examining how public health messaging about e-cigarettes, embedded directly in social media feeds, can affect the attitudes and intentions of young adults who both do and do not use e-cigarettes. According to the Theory of Planned Behavior, changing attitudes, beliefs, and intentions eventually lead to behavioral change9. Previous research has found that short-term exposure to anti-vaping social media content can increase beliefs that the e-cigarette industry is manipulating its consumers, and reduce future vaping intentions among young adults who currently vape.5 The current study examines this pathway towards behavioral change in a larger, and longer-term randomized controlled trial. By evaluating the impact of placing these ads directly into individual’s social media feeds, this study also aims to highlight the importance of tailoring interventions to specific user profiles. This strategy is both novel and fundamental in current efforts to reduce e-cigarette use among young people.
2. METHODS
2.2. Data source/study sample
A longitudinal randomized control trial study was conducted to collect data on the effect of tobacco-related health communication messaging on e-cigarette usage among young adults. All participants were recruited through Meta platforms - Facebook and Instagram – via the use of the Virtual Lab Platform. The Virtual Lab Platform allows the recruitment of target audiences and the ability to deliver content directly into the social media feeds of those individuals who are targeted. To be eligible to participate in the trial, participants had to be young adults (ages 18 to 24), living in the United States of America. Responses were collected from 5,027 individuals at baseline, and the analytical sample was 3,400 individuals who responded at follow-up.
Participants were randomly assigned to one of four different treatment conditions, with each corresponding to a different number of impressions delivered to their social media feeds. Impressions were defined as the total number of instances in which the ad was displayed in a participant’s social media feed. The original design called for the following impressions per arm: Arm 1 (Control) – 0 impressions, Arm 2 – 4 impressions, Arm 3 – 8 impressions, Arm 4 – 16 impressions. This design was selected as it would allow for an investigation of a dose response with increasing ad exposure in arms 2–4 and a control condition to serve as a comparison point in this between subjects design. Our previous studies have shown that there is typically variation in actual impressions delivered due to a variety of factors including campaign reach.5 The actual average received impressions for each participant assigned to those treatment arms, were: Arm 1 (Control) – 0 impressions, Arm 2 – 3.19 impressions, Arm 3 – 9.28 impressions, Arm 4 – 13.28 impressions.
2.2. Intervention
The intervention consisted of 4 ads developed by the investigative team specifically for this study, and 4 ads drawn from previous Truth Initiative anti-vaping social media content (Figure 1). The Truth content was not currently being delivered on any social media platforms during the timeframe of this study. The posts all focused on the negative impacts of vaping on mental health and personal wellbeing, and on the personal benefits of abstaining from vaping and were inserted directly into the Facebook and Instagram feed of the participants included. The content consisted both of short, engaging videos, and graphic images depicting individuals of diverse backgrounds talking about the consequences of vaping and benefits of avoiding e-cigarettes. The posts used a variety of message strategies, including humor and testimonials. We developed new content specifically for this study, in addition to the Truth content, to account for the fact that many young adults in our sample would have previously been exposed to Truth social media. This prior awareness has been shown to affect outcomes of exposure to anti-vaping content in our previous research.5
Figure 1.
Advertisement Example
2.3. Ethical Considerations
The George Washing University Institutional Review Board reviewed and approved this protocol as not greater than minimal risk human participants research (IRB number NCR202837). All participants read an IRB-approved statement informing them about the purposes and nature of the research before providing consent by clicking on a button to proceed with the survey. Confidentiality was ensured by deidentifying all data and storing information via IRB-approved procedures.
2.4. Predictors
Current vape status was a categorical variable derived from ever e-cigarette use and past 30 day e-cigarette use questions. Each respondent was asked “Have you ever tried using an e-cigarette/vape (even one or two puffs)”. If they answered no, they were considered a “Never user”. If they answered yes, they were then asked, “During the past 30 days, on how many days did you use an e-cigarette/vape (even one or two puffs)?”. If they used an e-cigarette 0 days, they were an “Ever, but not current user”, and if they used an e-cigarette 1–30 days, they were considered a “Current user”. Impressions were calculated as the average number of impressions participants received given their treatment arm. To increase interpretability in the models, these values were increased by a factor of 10. Duration of intervention was calculated as the days between baseline and follow-up. This was added as a covariate due to significant variability in the duration of the intervention period caused by technical difficulties with the Meta platform. Due to the average length of intervention, these values were increased by a factor of 100 in our statistical models to increase interpretability.
2.5. Outcome variables
Five variables were outcomes of interest. Frequency of ad awareness was a continuous variable calculated from the average of four ad questions “Overall, about how many times do you think you’ve seen this ad”. Responses were on a 4-point scale and included, “Never”, “1–2 times”, “3–5 times”, “More than 5 times”.
Three e-cigarette attitudinal constructs were measured. These constructs were derived from messaging themes previously identified through the Hornik and Woolf method10 as associated with intentions not to use e-cigarettes in youth and young adults11. Perceived risk of harm was the average of three questions, “Imagine you are someone who vapes. How likely are you to… 1) Feel bad physically because of vaping. 2) Feel bad mentally or emotionally because of vaping. 3) Develop brain changes that affect the way you think because of vaping”. Responses were on a 5-point scale from “Extremely unlikely” to “Extremely likely”. Social unacceptability was the average of three questions, “How strongly do you agree or disagree with the following statements? 1) Vaping is okay to do socially with friends. 2) Vaping is fine as long as you’re not addicted. 3) Vaping is not okay for people my age”. The first two were reverse coded. Responses were on a 5-point scale from “Strongly disagree” to “Strongly agree”. Anti-industry was a continuous variable based on the question “How strongly do you agree or disagree with the following statements? Vape companies make me angry.” Responses were on a 5-point scale from “Strongly disagree” to “Strong agree”.
Intention to use e-cigarettes was based on the question “Do you think you will use an e-cigarette/vape (even one or two puffs) in the next year”. Respondents were classified as “intend to vape” if they answered “Probably yes”, or “Definitely yes”, “may/may not vape” if they answered “Might or might not”, and were classified as “do not intend to vape” if they answered “Probably not” or “Definitely not”.
All outcome measures, except for “frequency of ad awareness” were assessed at baseline to ensure randomization on these traits was successful and that any differences at follow-up could be attributed to the intervention. The results of these analyses confirmed no significant differences on perceived risk of harm, social unacceptability, anti-industry attitudes, or intention to use e-cigarettes (all p-values > .05).
2.6. Covariates
Covariates included age (18–20; 21–24), race (Non-Hispanic, White; Non-Hispanic, Black; Hispanic; Non-Hispanic, another race), gender identity (Male; Female; Another identity), sexual orientation (Heterosexual; Another identity). Covariates were selected based on common sociodemographic factors associated with e-cigarette use11–14.
2.7. Statistical analysis
Unweighted frequencies and percents were calculated to describe sample characteristics. Linear regressions were administered to investigate the relationship between impressions, current vape status and four outcome variables: frequency of ad awareness, perceived risk of harm, social unacceptability, and anti-industry attitudes. Multinomial regression was used to model associations between current vape status, impressions and intention to vape. All models controlled for age, race, gender identity, and sexual orientation and were conducted using Stata 1912.
3. RESULTS
Sample characteristics are described in Table 1. The majority of the sample were never users (41.7%), followed by past 30-day users (37.5%), and ever but not current users (20.8%). Most respondents were 21–24 years old (63.2%) and Non-Hispanic, White (15.1%). Gender identity was mostly female (52.7%), and most respondents selected heterosexual for sexual orientation (62.8%). Individual awareness had an average score of 0.38 (SD: 0.56) on a scale from 0 to 3. Perceived risk of harm, social unacceptability and anti-industry attitudes had a mean score of 3.71 (SD: 1.07), 3.65 (SD: 0.98), and 2.63 (SD: 1.38), respectively, on a scale from 1–5. The average number of impressions for the sample was 0.65 impressions (SD: 0.60) and the average duration of intervention was 277 days (SD: 115).
Table 1.
Sample Characteristics (N=3,400)
| Total Sample (n=3,400) | ||
|---|---|---|
| n | % | |
| Treatment Arm | ||
| 1 (control) | 853 | 25.1% |
| 2 | 844 | 24.8% |
| 3 | 846 | 24.9% |
| 4 | 857 | 25.2% |
| Vape Status | ||
| Never user | 1,419 | 41.7% |
| Ever, not current user | 706 | 20.8% |
| Past 30-day user | 1,275 | 37.5% |
| Age Category (Years) | ||
| 18–20 | 1,253 | 36.9% |
| 21–24 | 2,147 | 63.1% |
| Race/Ethnicity (missing=71) | ||
| White, Non-Hispanic | 1,682 | 50.5% |
| Black, Non-Hispanic | 504 | 15.1% |
| Hispanic | 679 | 20.4% |
| Other, Non-Hispanic | 464 | 13.9% |
| Gender Identity | ||
| Male | 1,409 | 41.4% |
| Female | 1,792 | 52.7% |
| Another identity | 199 | 5.9% |
| Sexual Orientation (missing=273) | ||
| Heterosexual | 1,963 | 62.8% |
| Another identity | 1,164 | 37.2% |
| Mean | SD | |
| Individual awareness | 0.38 | 0.56 |
| Perceived risk of harm | 3.71 | 1.07 |
| Social unacceptability | 3.65 | 0.98 |
| Anti-industry attitudes | 2.63 | 1.38 |
| Impressions | 0.65 | 0.60 |
| Duration of intervention (x100 Days) | 2.77 | 1.15 |
Results from the linear and multinomial logistic regressions are presented in Tables 2 and 3. Critically, the level of impressions received significantly predicted perceived risk of harm, social unacceptability, and ad awareness. Specifically, increased impressions were associated with increased perceived risk of harm (β: 0.11; 95% CI: 0.03,0.20) and social unacceptability (β: 0.10; 95% CI: 0.03, 0.18). Additionally, impressions among current e-cigarette users were associated with higher frequency of ad awareness (β: 0.08; 95% CI:0.003, 0.15), compared to impressions among never users.
Table 2.
Associations Between Current Vape Status × Impressions and Individual Awareness, Perceived Risk of Harm, Social Unacceptability, Anti-Industry Attitudes, and Intention to Vape
| Frequency of Ad Awareness (n=3,072) β (95% CI) | Perceived Risk of Harm (n=3,072) β (95% CI) | Social Unacceptability (n=3,048) β (95% CI) | Anti-Industry Attitudes (n=3,046) β (95% CI) | |
|---|---|---|---|---|
| Impressions | 0.001 (−0.04, 0.05) | 0.11** (0.03, 0.20) | 0.10** (0.03, 0.18) | 0.09 (−0.03, 0.20) |
| Vape Status | ||||
| Never user | Reference | Reference | Reference | Reference |
| Ever, not current user | −0.10** (−0.18, −0.03) | −0.08 (−0.21, 0.06) | −0.33*** (−0.45, −0.20) | −0.08 (−0.27, 0.11) |
| Past 30-day user | 0.10** (0.04, 0.17) | −0.68*** (−0.80, −0.56) | −0.80*** (−0.91, −0.70) | −0.13 (−0.30, 0.03) |
| Vape Status × Impressions | ||||
| Never × Impressions | Reference | Reference | Reference | Reference |
| Ever, not current user × Impressions | 0.04 (−0.05, 0.13) | −0.09 (−0.25, 0.08) | 0.04 (−0.11, 0.19) | −0.07 (−0.29, 0.16) |
| Past 30 days user × Impressions | 0.08* (0.003, 0.15) | −0.01 (−0.15, 0.13) | −0.12 (−0.25, 0.001) | 0.04 (−0.15, 0.23) |
| Duration of Intervention (100 days) | 0.02 (−0.001, 0.03) | −0.03* (−0.07, −0.002) | −0.01 (−0.04, 0.02) | −0.004 (−0.05, 0.04) |
| Age Category (Years) | ||||
| 18–20 | Reference | Reference | Reference | Reference |
| 21–24 | 0.05** (0.01, 0.09) | −0.18*** (−0.25, −0.11) | −0.17*** (−0.24, −0.11) | −0.06 (−0.16, 0.04) |
| Race/Ethnicity | ||||
| White, Non-Hispanic | Reference | Reference | Reference | Reference |
| Black, Non-Hispanic | 0.13*** (0.08, 0.19) | −0.24*** (−0.35, −0.14) | −0.14** (−0.24, −0.05) | −0.20** (−0.35, −0.05) |
| Hispanic | 0.04 (−0.01, 0.09) | −0.07 (−0.16, 0.02) | −0.12** (−0.20, −0.04) | −0.12 (−0.25, 0.01) |
| Other, Non-Hispanic | −0.09** (−0.15, −0.03) | 0.05 (−0.06, 0.16) | −0.07 (−0.17, 0.03) | 0.04 (−0.11, 0.18) |
| Gender Identity | ||||
| Male | Reference | Reference | Reference | Reference |
| Female | −0.06** (−0.10, −0.02) | 0.17*** (0.09, 0.24) | 0.12 (0.05, 0.19) | 0.03 (−0.07, 0.14) |
| Another identity | −0.07 (−0.16, 0.02) | 0.13 (−0.03, 0.29) | −0.10*** (−0.24, 0.05) | 0.22 (−0.01, 0.44) |
| Sexual Orientation | ||||
| Heterosexual | Reference | Reference | Reference | Reference |
| Another identity | −0.09*** (−0.14, −0.05) | 0.07 (−0.01, 0.15) | −0.07 (−0.14, 0.01) | 0.10 (−0.01, 0.21) |
Note.
p<.001,
p<.01,
p<.05
Table 3.
Associations Between Current Vape Status × Impressions and Intention to Vape; reference is “Do not intend to vape” (n=3,031)
| May/may not vape RRR (95% CI) | Intend to vape RRR (95% CI) | |
|---|---|---|
| Impressions | 0.91 (0.64, 1.31) | 0.59* (0.36, 0.97) |
| Vape Status | ||
| Never user | Reference | Reference |
| Ever, not current user | 2.96*** (1.85, 4.74) | 2.61*** (1.54, 4.42) |
| Past 30-day user | 15.84*** (10.30, 24.34) | 36.85*** (23.42, 57.98) |
| Vape Status × Impressions | ||
| Never × Impressions | Reference | Reference |
| Ever, not current user × Impressions | 1.06 (0.61, 1.83) | 1.60 (0.81, 3.16) |
| Past 30 days user × Impressions | 0.85 (0.52, 1.38) | 1.69 (0.96, 2.99) |
| Duration of Intervention (100 days) | 0.95 (0.86, 1.05) | 0.96 (0.87, 1.06) |
| Age Category (Years) | ||
| 18–20 | Reference | Reference |
| 21–24 | 1.28 (0.99, 1.63) | 0.96 (0.76, 1.20) |
| Race/Ethnicity | ||
| White, Non-Hispanic | Reference | Reference |
| Black, Non-Hispanic | 0.83 (0.60, 1.16) | 0.40*** (0.29, 0.57) |
| Hispanic | 1.11 (0.82, 1.49) | 1.15 (0.87, 1.52) |
| Other, Non-Hispanic | 0.85 (0.59, 1.23) | 0.75 (0.52, 1.07) |
| Gender Identity | ||
| Male | Reference | Reference |
| Female | 1.08 (0.84, 1.38) | 0.98 (0.77, 1.24) |
| Another identity | 1.30 (0.76, 2.23) | 1.37 (0.84, 2.23) |
| Sexual Orientation | ||
| Heterosexual | Reference | Reference |
| Another identity | 0.85 (0.66, 1.09) | 1.21 (0.95, 1.53) |
Note.
p<.001,
p<.01,
p<.05
Effects were also found to vary based on the use status of the participants. Ever, but not current e-cigarette use was associated with lower frequency of ad awareness compared to never use (β: −0.10; 95% CI: −0.18, −0.03)), while current e-cigarette use was associated with higher frequency of ad awareness (β: 0.10; 95% CI: 0.04, 0.17). Current e-cigarette use was associated with lower perceived risk of harm (β: −0.68; 95% CI: −0.80, −0.56), and social unacceptability (β: −0.805; 95% CI: −0.91, −0.79). Ever, but not current e-cigarette use was also associated with higher risk of “may/may not vape” (RRR: 2.96, 95% CI: 1.85, 4.74) and “intend to vape” (RRR: 2.61, 95% CI: 1.54, 4.42) compared to never use. Current e-cigarette use was associated with much higher risk of “may/may not vape” (RRR: 15.84, 95% CI: 10.30, 24.34) and “intend to vape” (RRR: 36.85, 95% CI: 23.42, 57.98), compared to never use. Participants who reported being Non-Hispanic, Black had a lower risk of “intend to vape” (RRR: 0.40, 95% CI: 0.29, 0.57).
4. DISCUSSION
Youth and young adult e-cigarette use remains a significant public health concern, making the development and assessment of methods to combat this problem important to explore.1 The results of this study directly demonstrate that increasing levels of anti-e-cigarette messages embedded into social media feeds were associated with an increase in the perceived risk of harm of e-cigarettes, an increase in the social unacceptability of e-cigarette usage, and a decrease in intentions to vape in the future. This study further demonstrates a dosage effect with increased impressions associated with larger shifts in e-cigarette related attitudes.
Notably, these changes occurred following a relatively small exposure and were delivered over a relatively long intervention period. This suggests that even when these ads are fairly diluted, they are still effective at creating changes in the attitudes and intentions of youth and young adults. These ads were focused specifically on the risk inherent in e-cigarette use and the social unacceptability of using these products, and thus it is not surprising that we saw shifts in those corresponding attitudinal measures. Additionally, the anti-industry attitude which was measured as a comparison point did not change – suggesting that the attitudinal shifts we observed were specific to the messages inserted into the participants’ feeds, and not a result of a more global shift in attitudes.
4.1. Limitations and Future Research
While this study provides compelling evidence supporting the viability of online social media interventions to combat youth and young adult e-cigarette usage, there are limitations inherent in the design that leave room for future exploration. One limitation is the lack of a significant reported change in frequency of ad awareness as a function of the number of impressions provided. This suggests that participants didn’t consciously recall seeing the ad more often as a direct result of the number of times these ads were presented. Fortunately, we see that the targeted attitudes did change as a function of ad impressions which does confirm the effectiveness of presenting the ads in the feeds of participants despite this lack of reported recall.
Additionally, while we saw changes in the attitudes measured, we did not detect a significant corresponding shift in intentions to vape based on the number of impressions received. This is likely due to a significant chunk of the tested population being never users of e-cigarettes and thus already having an extremely low intention to use e-cigarettes in the future. It is also possible that the length of the study was not sufficient to detect changes in behavior or intentions, and thus future studies should consider an extended intervention. Further, the length of the intervention window was longer than originally anticipated due to the noted logistical issues with the Meta platform, and this resulted in the impressions being diluted over a longer time period – potentially lowering their efficacy. The fact that we still see a significant effect of impressions on the targeted attitudes despite this limitation speaks to the effectiveness of this type of intervention.
Future research in the current clinical trial will examine the full longitudinal information available for all participants following an intent-to-treat approach across all three waves of data collection. We will also examine pathways of influence from impressions to the intended outcomes, and effects of the anti-vaping intervention on measures of depression (a theme of the social media content). With final follow-up data, we will examine potential impacts of the intervention over time on vaping behavior among never, former, and current vapers, as well as long-term effects of determinants of e-cigarette use. Additional future research is necessary to more fully explore the ability of social media advertisements to affect other nicotine products, such as oral nicotine pouches, and the possibility of different responses to these ads for poly-use of multiple nicotine products simultaneously. Finally, while care was used in selecting covariates for this study it’s possible that other variables related to nicotine use and social media may be relevant, and thus future research would benefit from exploring topics such as mental health and peer networks.
5. CONCLUSION
The results of this study provide additional evidence for the viability and practicality of an online social media intervention to impact attitudes around e-cigarette utilization. By inserting the ads directly into the activity feed of the users, this approach allows for the easy dissemination of relevant advertisements in an environment with which youth and young adults are already engaged. According to the Theory of Planned Behavior, we believe that shifting these attitudes is the first step in ultimately changing behaviors and reducing the utilization of e-cigarettes for these individuals.
HIGHLIGHTS.
Anti-vaping social media content can reduce intentions to use e-cigarettes.
Embedding content directly into social media feeds effectively changes attitudes.
Health-related behaviors and attitudes can be improved using this approach.
ACKNOWLEDGEMENTS:
The authors would like to thank the survey participants for contributing their data to this research.
Financial Disclosure
Funding for this study was provided by the National Cancer Institute (grant number R01CA253013).
Footnotes
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DECLARATION OF COMPETING INTERESTS: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Declaration of Generative AI in Scientific Writing
The authors did not use AI or AI-assisted technologies in the writing process.
REFERENCES
- 1.Cornelius ME, Loretan CG, Jamal A, et al. Tobacco Product Use Among Adults – United States, 2021. MMWR Morb Mortal Wkly Rep. 2023;72(18):475–483. doi: 10.15585/mmwr.mm7218a1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Vallone D, Cantrell J, Bennett M, et al. Evidence of the Impact of the truth FinishIt Campaign. Nicotine Tob Res. 2018;20(5):543–551. doi: 10.1093/ntr/ntx119 [DOI] [PubMed] [Google Scholar]
- 3.Noar SM, Rohde JA, Prentice-Dunn H, Kresovich A, Hall MG, Brewer NT. Evaluating the actual and perceived effectiveness of E-cigarette prevention advertisements among adolescents. Addict Behav. 2020;109:106473. doi: 10.1016/j.addbeh.2020.106473 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Auxier Brooke, Anderson Monica. Social Media Use in 2021. Pew Research Center. April 7, 2021. Accessed November 5, 2025. https://www.pewresearch.org/internet/2021/04/07/social-media-use-in-2021/ [Google Scholar]
- 5.Evans WD, Bingenheimer J, Cantrell J, et al. Effects of a Social Media Intervention on Vaping Intentions: Randomized Dose-Response Experiment. J Med Internet Res. 2024;26:e50741. doi: 10.2196/50741 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Ichimiya M, Gerard R, Mills S, Brodsky A, Cantrell J, Evans WD. The Measurement of Dose and Response for Smoking Behavior Change Interventions in the Digital Age: Systematic Review. J Med Internet Res. 2022;24(8):e38470. doi: 10.2196/38470 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.D’Esterre AP, Tulsiani S, Hair EC, et al. Pathway from Exposure to an E-Cigarette Prevention Social Media Campaign to Increased Quitting Intentions: A Randomized Trial Among Young Adult E-Cigarette Users. Int J Environ Res Public Health. 2025;22(2):307. doi: 10.3390/ijerph22020307 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Evans WD, Abroms LC, Broniatowski D, et al. Digital Media for Behavior Change: Review of an Emerging Field of Study. Int J Environ Res Public Health. 2022;19(15):9129. doi: 10.3390/ijerph19159129 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process. 1991;50(2):179–211. doi: 10.1016/0749-5978(91)90020-T [DOI] [Google Scholar]
- 10.Hornik R, Woolf KD. Using Cross-Sectional Surveys to Plan Message Strategies. Soc Mark Q. 1999;5(2):34–41. doi: 10.1080/15245004.1999.9961044 [DOI] [Google Scholar]
- 11.Dai H, Ramos AK, Faseru B, Hill JL, Sussman SY. Racial Disparities of E-Cigarette Use Among US Youths: 2014‒2019. Am J Public Health. 2021;111(11):2050–2058. doi: 10.2105/AJPH.2021.306448 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Vu THT, Hart JL, Groom A, et al. Age differences in electronic nicotine delivery systems (ENDS) usage motivations and behaviors, perceived health benefit, and intention to quit. Addict Behav. 2019;98:106054. doi: 10.1016/j.addbeh.2019.106054 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.DeVito EE, Stern E, Szpilzinger M, Petersen N, Pang RD, Weinberger AH. Sex and Gender in Relation to use of E-cigarette Product Characteristics in United States Datasets: A Systematic Review. Nicotine Tob Res. 2025;27(11):1874–1890. doi: 10.1093/ntr/ntaf092 [DOI] [PubMed] [Google Scholar]
- 14.Kierstead EC, Kucherlapaty P, Liu MS, Do EK, Brazinsky C, Hair EC. Examining the relationship between mental health and e-cigarette initiation among sexual and gender minority youth and young adults in the United States compared to their cisgender, heterosexual peers, 2021–2023. Prev Med. 2025;198:108331. doi: 10.1016/j.ypmed.2025.108331 [DOI] [PubMed] [Google Scholar]
- 15.StataCorp. Stata Statistical Software: Release 19. Published online 2025.