Abstract
Background:
E-cigarette prevention education aims to mitigate adolescent e-cigarette use. Such education is increasingly delivered through virtual/video-based teaching platforms (e.g. Zoom, Google Classrooms). However, there is little evidence about the effectiveness of virtual e-cigarette education compared to in-person education on adolescents’ knowledge about e-cigarettes, perceived addictiveness and intent to try e-cigarettes, cigarettes, and marijuana.
Objective:
To evaluate the effectiveness of virtual e-cigarette education compared to in-person education on student knowledge and perceived addictiveness of e-cigarettes and intent to try e-cigarettes.
Design, Setting and Method:
We conducted a pre- and post-education evaluation among 10 middle and high school students in the Greater Birmingham area, Alabama, who were non-randomly assigned to receive either virtual (n = 745) or in-person e-cigarette education (n = 286) (mean age: 14.36 years). The study used a 25-minute educational presentation about the health effects of e-cigarettes, the risks of second- and third-hand smoke, the addictive nature of nicotine, and marketing strategies of e-cigarette companies. Participants completed a 10-minute self-administered survey immediately before and after the presentation.
Results and conclusion:
Except for certain e-cigarette knowledge-related items, our study shows that both virtual and in-person education had similar effects on improving knowledge about e-cigarettes, increasing perceived addictiveness and reducing intent to try e-cigarettes, cigarettes and marijuana among participants. Virtual education may be applied where in-person education is not feasible (e.g. in rural communities).
Keywords
Introduction
E-cigarettes have been the most commonly used tobacco product among US middle and high school students since 2014 (Office on Smoking and Health, 2016). In 2021, 11.3% (1.7 million) of high school students and 2.8% (320,000) of middle school students reported having used an e-cigarette in the past 30 days (Park-Lee et al., 2021). Prevention of adolescent e-cigarette use is important because of known negative health effects (Gotts et al., 2019; Kuntic et al., 2020; Osei et al., 2019; Riehm et al., 2019), the risk of nicotine addiction and an association with future use of cigarettes (Pierce et al., 2021). A study also found that adolescents and young adults who used e-cigarettes only or used e-cigarettes and cigarettes were more likely to self-report COVID-19-related symptoms, testing and a positive diagnosis (Gaiha et al., 2020a). Thus, school-based e-cigarette prevention education remains a priority because young people continue to use e-cigarettes despite known health harms (Gaiha et al., 2020b; Park-Lee et al., 2021).
Educators delivering e-cigarette education often need to use virtual/video-based platforms like Zoom, Google Classrooms and Skype. Virtual education motivates students to learn, although it has varied effects on learning outcomes (Borgonovi and Pokropek, 2021) and across participants (Livingstone, 2012). Generally, few evidence-based e-cigarette education programmes exist (Liu et al., 2020). Prior evaluation of e-cigarette prevention curricula has exclusively evaluated in-person e-cigarette education, including a study showing that e-cigarette education was associated with increased knowledge about e-cigarettes and increased perceived addictiveness and lower intent to try e-cigarettes (Gaiha et al., 2021).
The continued use of e-cigarettes by school-going adolescents amid the proliferation of virtual e-cigarette education warrants an assessment of how virtual e-cigarette education compares to in-person education. The Stanford Tobacco Prevention Toolkit created one such virtual e-cigarette education session; however, its effectiveness compared to an in-person session remains unknown. This study aims to evaluate the effects of the Toolkit’s virtual e-cigarette education compared to in-person education on student knowledge, perceived addictiveness of e-cigarettes and intent to try e-cigarettes.
Methods
Between November 2020 and May 2021, we conducted a pre- and post-education evaluation among middle and high school students in Birmingham, Alabama receiving either virtual or in-person e-cigarette education. Schools were assigned to virtual or in-person e-cigarette education based on school preference. Within the virtual group (n = 745), we included 46 students where neither presenter nor students were physically present in school; 645 students who were present in school, but the live presenter was not; and 54 students who were present in school or at home and watched a pre-recorded session (presenter was not live; Table 1). For the in-person group, the presenter and students were both physically present in the classroom (n = 286). We are broadly defining the virtual group based on the mode of delivery (i.e. whether the presenter was physically present in the same classroom as students).
Participant characteristics by study group, n (%). a
SD: standard deviation.
Assessed only at baseline (pre-education). Bold values indicate significance at p < .05.
Participant recruitment
The study team contacted middle and high schools through pre-existing networks. Schools were included if school administration approved of the study and the school was located in the Greater Birmingham area. Students were included if they were physically present for in-person education or joined remotely for virtual education. Students were excluded from the evaluation if their parents submitted an opt-out form precluding them from completing pre-post surveys. However, no opt-out forms were received. All students present on the day of the e-cigarette education session did participate in the prevention education.
Intervention design
A 25-minute PowerPoint presentation on e-cigarettes was delivered by one of two tobacco prevention educators, with a 10-minute survey immediately before (pre) and after (post) in-person or virtual education. In-person education was delivered in school auditoriums and classrooms and virtual education was conducted using Google Meet, Microsoft Teams and VidGrid. Presentation content in both groups was identical and developed based on the Stanford Tobacco Prevention Toolkit’s Pod-based Systems 101 e-cigarette curriculum (Toolkit; https://med.stanford.edu/tobaccopreventiontoolkit). Education focused on the health effects of e-cigarettes, the risks of second- and third-hand smoke, the addictive nature of nicotine, and marketing strategies of e-cigarette companies. Surveys were administered by a tobacco prevention educator for in-person education and by a school representative for virtual education. Post-intervention surveys were only made available to students after the session was complete. The Institutional Review Board at the University of Alabama at Birmingham approved the study.
Measures
Students were asked to provide sociodemographic information, including age, grade and race/ethnicity. We included 3 survey items about ever-use of e-cigarettes, cigarettes, and marijuana, 15 survey items asking about students’ knowledge about e-cigarettes and 6 other items asking about perceived addictiveness and intent to try e-cigarettes, cigarettes, and marijuana (see Table 2 for list of all items), described below.
Comparison of study outcomes before and after the education session among participants receiving in-person or virtual e-cigarette education, n (%). a
AOR: adjusted odds ratio; CI: confidence interval.
Bold values indicate significance at p < .05.
Includes some participants who did not answer whether they were in high school or middle school.
Models report regression outcomes for in-person versus virtual group between pre- and post-survey (i.e. study group × time) adjusted for grade, race/ethnicity, ever e-cigarette use and school-level clustering.
For knowledge-related study outcomes, the answers reflecting correct knowledge are in parentheses and all percentages indicate the proportion identifying the correct answer (see detailed measures in Supplemental material). For knowledge-related questions, a higher percentage at post-survey compared to pre-survey indicates improved knowledge after receiving the education session.
A higher percentage at post-survey compared to pre-survey indicates that a higher proportion of participants found products to be addictive after receiving education.
A lower percentage at post-survey compared to pre-survey indicates decreased intent to try different products after receiving the education session.
Ever-use of e-cigarettes and other products
Participants were asked, ‘During your entire life, how many times have you EVER . . . (a) used e-cigarettes/vapes/JUUL/Puff bar, even 1 or two puffs, (b) smoked a cigarette, even 1 or 2 puffs, (c) used marijuana?’ Participants were asked to input a number with ‘0’ indicating no previous use of the product. Answers were then collapsed into ever-use (responses greater than or equal to 1) or never use (responses equal to 0).
Perceived addictiveness
Participants were asked, ‘How addictive do you think these products are?’ for (a) e-cigarettes/vapes/JUUL/puff bar, (b) cigarettes and (c) marijuana, with five answer-choices on a 5-point Likert-type scale: (1) not at all addictive, (2) slightly addictive, (3) moderately addictive, (4) quite addictive and (5) extremely addictive. Participants were noted as finding a product addictive if they provided any answer except (1) not at all addictive.
Intent to try
We asked participants, ‘How likely is it you will ever try the products below?’ for a list of products: (a) e-cigarettes/vapes/JUUL/puff bar, (b) cigarettes and (c) marijuana, with five answer-choices on a 5-point Likert-type scale: (1) I have already tried the product, (2) very unlikely, (3) somewhat unlikely, (4) somewhat likely and (5) very likely. Participants were noted as intending to try a product if they provided any answer except (2) very unlikely. We included responses for ‘I have already tried the product’ as part of intent to try, since prior use is associated with future use of tobacco (Pierce et al., 2021).
Data analysis
First, we described participant characteristics in the total sample and then among participants in the two study groups: (a) those receiving in-person education (herein referred to as ‘in-person group’) and (b) those receiving virtual education (herein referred to as ‘virtual group’).
Second, we tabulated participant responses by study group at pre- and post-education related to knowledge about e-cigarettes, and perceived addictiveness and intent to try e-cigarettes, cigarettes, and marijuana. In our multi-level data, observations may be similar among students attending the same school, and school as a shared attribute may affect outcomes being modelled for individuals nested within each school. We selected a mixed-effects model because there were potential unobserved variables that may be contributing to variance and therefore captures both fixed and random effects. Therefore, we used multi-level mixed-effects ordered logistic regression models to assess the effects of in-person education compared to virtual education (reference category) on knowledge, perceived addictiveness and intent to try products between pre- and post-survey. Although the effect of school is minimal in our dataset (the variance is small with intraclass correlation coefficient less than 0.2), our multi-level, mixed-effects regression accounts for variance in outcomes at both the individual level and the school level. Independent, individual-level variables included grade, race/ethnicity and any prior use of e-cigarettes.
Analyses were conducted using Stata version 15.1. Statistical significance was determined as p < .05.
Results
Our study included 286 students who received in-person education and 745 who received virtual education in 10 schools, including 2 middle and 7 high schools and 1 school with both middle and high school students. Participant characteristics are summarised in Table 1 (N = 1,031; mean age: 14.36 years, 48.3% White non-Hispanic, 21.5% other non-Hispanic, 26.2% unsure of their race/ethnicity, 4.0% did not answer). As shown in Table 1, 26.9% of in-person participants and 12.2% of virtual participants reported ever-use of e-cigarettes. In our total sample, 5.0% of students had ever tried cigarettes and 6.7% had ever tried marijuana. Table 1 also shows significant differences in participant characteristics at baseline between in-person and virtual groups, except for ever-use of cigarettes.
Overall changes in knowledge, perceived addictiveness and intent to try e-cigarettes
Table 2 shows a comparison of participant responses between in-person and virtual groups on knowledge, perceived addictiveness and intent to try e-cigarettes, cigarettes and marijuana from pre- to post-education. We observed an increase in the proportion of students who correctly answered knowledge-related questions between pre- and post-education in both in-person and virtual groups. Of the four questions on e-cigarette content and delivery, students’ knowledge improved in both in-person and virtual groups, with the largest increase from pre- to post-education observed regarding how e-cigarettes deliver nicotine (aerosol) (in-person group: 5.2% to 59.4%; virtual group: 11.5% to 56.5%). From among five questions on health harms of e-cigarettes, the greatest knowledge gain was observed in the question related to vaping, leading to a worse immune response in the lungs during the COVID-19 pandemic (in-person group: 83.6% to 91.6%; virtual group: 84.8% to 91.1%). Both groups showed an increase in knowledge in four out of six questions on nicotine and addiction; however, after education, slightly fewer students in both study groups correctly identified the definition of addiction.
After both in-person and virtual education, students found different products slightly more addictive: e-cigarettes (in-person group: 92.6% to 94.0% and virtual group: 92.6% to 94.2%), combustible cigarettes (in-person group: 92.3% to 92.6% and virtual group: 91.9% to 93.7%) and marijuana (in-person group: 83.6% to 86.4% and virtual group: 89.7% to 91.2%) (see Table 2). Table 2 also shows that after in-person education, students’ intent to try e-cigarettes reduced by 3.5% (from 38.1% to 34.6%) compared to 1.3% in the virtual group (from 23.4% to 22.1%). Students’ intent to try combustible cigarettes and marijuana in both in-person and virtual groups did not change by more than 1% between pre- and post-education.
Comparison between virtual and in-person education
As shown in Table 2, there was no significant difference between in-person and virtual education on most study outcomes, especially perceived addictiveness and intent to try products. In-person education was more effective than virtual education for only 4 out of 15 knowledge questions: (a) e-cigarettes/vapes/JUUL/puff bar are devices that deliver nicotine in the form of aerosol (adjusted odds ratios [aOR]= 2.48, 95% confidence interval [CI]: 1.30–4.72; p = .006); (b) the amount of nicotine in a JUUL pod is equivalent to 1–2 packs of cigarettes (aOR = 3.01, 95% CI: 1.65–5.49; p < .001); (c) we know the long-term effects of using e-cigarettes/vapes/JUUL/puff bar (aOR = 1.60, 95% CI: 1.06–2.42; p = .024); and (d) it is difficult to quit using tobacco products because such products contain nicotine, which is very addictive (aOR = 2.05, 95% CI: 1.10–3.82; p = .023).
Discussion
Our study shows that both virtual and in-person education had broadly similar effects on improving knowledge about e-cigarettes, increasing perceived addictiveness and reducing intent to try e-cigarettes, cigarettes and marijuana among participants in this sample. In addition, we found that in-person education was slightly more effective than virtual education on certain knowledge-related questions. It is unclear why this is the case. It is possible that students require additional nuanced information and clarification on some topics (e.g. addiction) that can be best provided by in-person teaching. Although school-level and student characteristics were included in our comparison of virtual versus in-person e-cigarette education, these factors were not significant in our sample.
Our findings are consistent with recent data showing that live instruction, irrespective of whether it is delivered in-person or virtually, is a key factor in engaging students to learn effectively (Aguilar et al., 2021). To improve knowledge of other health issues, interactive content such as film has been found to be as effective as in-person education (Clement et al., 2012). Future studies may evaluate a combination of virtual e-cigarette education and complementary visual aids (namely, short videos, including news media and social media content such as TikTok) and quizzes.
Our findings suggest that virtual education can potentially be delivered to support needs of schools that may not have the ability to teach in person or want to supplement existing education, with the aim of reaching rural and underserved adolescent communities. Virtual and in-person e-cigarette education each pose advantages and disadvantages. Students may be more willing to discuss sensitive topics and personal experience when they are participating in a session virtually compared to in-person sessions where they may fear being judged by the tobacco educator or their teacher.
Virtual e-cigarette education may offer logistical benefits in terms of planning and managing staffing, training and resources, although availability and equitable access to the Internet are critical. Some advantages of in-person e-cigarette education compared to virtual education are that it is a familiar mode of receiving information and that interactivity and greater engagement may be expected during questions and answers. These features may make it easier to measure engagement, understanding and the extent to which young people’s opinions diverge from the session facilitator concerning the importance of e-cigarette prevention. In this study, we found no significant differences between virtual and in-person e-cigarette education groups on a majority of outcomes, which may be because the sessions were intentionally designed to be applicable in both settings.
Limitations
Our study findings are not generalisable as schools were not randomly selected. Data on students’ gender were also not collected. As some education was conducted before and other education during the COVID-19 pandemic, there may be differences in students’ cognition and fatigue, which may have impacted our results. There were some differences in how virtual education was delivered. For example, some students watched the presentation on a projector screen in the presence of a teacher in the classroom, while others watched without a teacher at home or off-site. All virtual education included a live presentation, except in one case when a pre-recorded presentation was played. Owing to low numbers of participants in some sub-conditions of the virtual group, especially where neither presenter nor students were physically present in school and where students watched a pre-recorded session, we could not conduct a more nuanced statistical analysis of how the in-person group performed compared to these specific conditions. Furthermore, as we did not collect qualitative data from students, we do not know whether these factors played a role in making the education more or less effective. Survey data were collected immediately after the education was provided, and we do not have information about whether these findings are sustained over time.
Conclusion
Virtual and in-person education had largely similar effects on improving school-going adolescents’ knowledge about e-cigarettes contents and delivery, related health harms and nicotine addiction. There was also no significant difference between in-person or virtual education on perceived addictiveness and intent to try e-cigarettes, cigarettes and marijuana. We recommend flexible and equitable application of virtual e-cigarette education where in-person education is not feasible.
Supplemental Material
sj-docx-1-hej-10.1177_00178969221119287 – Supplemental material for Does virtual versus in-person e-cigarette education have a differential impact?
Supplemental material, sj-docx-1-hej-10.1177_00178969221119287 for Does virtual versus in-person e-cigarette education have a differential impact? by Shivani Mathur Gaiha, Amelia Warnock, Shelby Kile, Kennon Brake, Clementino Vong do Rosario, Gabriela R Oates, Bonnie Halpern-Felsher and Susan Chu Walley in Health Education Journal
Footnotes
Declaration of conflicting interests
The author(s) declared the following potential conflicts of interest with respect to the research, authorship and/or publication of this article: Bonnie Halpern-Felsher is the founder and executive director of the Stanford Tobacco Prevention Toolkit, a free, online resource that was used as the foundation of both virtual and in-person educational sessions in this study. She is also a paid scientific expert in some litigation cases against e-cigarette companies. None of the other authors have any conflicting interests.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: this research was supported by California’s Tobacco-related Disease Research Program to conduct an evaluation of the Tobacco Prevention Toolkit (27IR-0043 [B.H.-F.]), the National Cancer Institute of the US National Institutes of Health (NIH/NCI; 1R01CA263121 [B.H.-F.]), the Alabama Department of Public Health Youth Tobacco Prevention Program Grant [S.W.] and NIH/NCI (K99CA267477 [S.M.G.]) and the Stanford Maternal and Child Health Research Institute (S.M.G.). The funding sponsors were not involved in the collection, analysis or interpretation of data, writing of the manuscript, or the decision to submit the manuscript for publication.
Supplemental material
Supplemental material for this article is available online.
References
Supplementary Material
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