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This study aimed to evaluate the effectiveness and engagement of an interactive simulation-based online smoking prevention program compared to a traditional offline program for Korean adolescents.
Methods
A total of 1,008 adolescents from elementary, middle, high, and alternative schools in Chungcheongnam-do Province, South Korea participated in this study from October 1 to 31, 2021. The offline program consisted of 2-hour lectures by teachers, whereas the online program included six interactive peer-oriented video modules. The evaluation compared and analyzed the survey results measuring changes in smoking-related knowledge and attitudes before and after each program, as well as the level of interest in and satisfaction with education.
Results
When comparing the pre- and post-intervention scores between the two groups, no statistically significant differences were found except for a decreased belief that smoking helps relieve stress (F=12.125, P=0.001). However, withingroup comparisons revealed that the online smoking prevention program led to positive changes in most smoking-related knowledge and attitude items, including reduced beliefs about smoking as a stress reliever (P<0.001) and lower misconceptions about the harm of e-cigarettes (P<0.001). In terms of engagement and satisfaction, the online program received significantly higher scores for interest (P<0.001), knowledge improvement (P<0.001), and program recommendations (P=0.021).
Conclusion
The online smoking prevention program was as effective as the traditional offline approach in enhancing smoking-related knowledge and attitudes. Moreover, its superior engagement and time efficiency highlight its strong potential as an effective alternative or complementary strategy to conventional school-based programs.
Relative to other nations, the prevalence of daily smoking among adolescents in Korea is somewhat subdued, registering at only 4.8% among male students and 2.4% among female students [1]. Nonetheless, there is an upward trajectory in the use of heated tobacco products and electronic cigarettes (e-cigarettes) [2]. This rising trend in the use of electronic nicotine delivery systems could potentially act as a stepping stone toward conventional tobacco smoking among adolescents [3,4]. Particularly during this crucial phase of physical and mental developmental, the initiation of tobacco smoking can lead to the early onset of nicotine addiction, premature activation of carcinogenic processes, and initial indications of pulmonary and cardiovascular diseases [5-7]. Given the medical and psychological consequences associated with early-age tobacco use, there is an urgent need for instituting preventive measures during adolescence to deter the onset of tobacco smoking [6-8].
School-based tobacco prevention programs have long been implemented to prevent smoking among adolescents [9,10]. Some programs have managed to reduce smoking initiation by approximately 30% and have demonstrated long-term effectiveness [11]. Consequently, school-based youth smoking prevention programs are strongly recommended, with initiatives typically aimed at enabling educators to maximize the impact of their instruction [11,12]. In essence, school-based educational programs, especially offline programs, are predominantly designed around educators to deliver information to students [13,14]. According to traditional social theory models, the likelihood of adolescent smoking increases through social interactions within peer groups [15,16]. Conversely, students who were heavily influenced by their peers were shown to follow smoking prevention advice promoted by their peer groups [16]. Therefore, beyond providing information, offering interactive educational programs involving peers is also a factor that should be considered in the design of educational programs.
With the advancement of information and communications technology, online smoking prevention programs, which offer the advantage of two-way interaction, are being leveraged as an educational means to inform youths about the dangers of smoking [17]. Online smoking prevention programs can be customized to deliver information that meets the unique circumstances and concerns of a specific individual, such as gender and smoking status, and can also enhance accessibility to smoking prevention education in schools where offline education is difficult to implement [18]. Moreover, online smoking prevention programs can incorporate animations and virtual scenarios to further engage adolescents, encourage them to immerse themselves in the content, and think more deeply about their smoking experiences [19]. However, online programs that provide information and are personalized to individuals are currently more widely used than those that consider peer interaction. Furthermore, evidence supporting the effectiveness of online programs compared to offline education programs is still insufficient.
In recent years, several online smoking prevention programs have been developed. However, compared to traditional offline educational methods, there is limited evidence regarding the impact of online interactive simulation education initiatives on adolescents’ perceptions, knowledge, and attitudes about smoking. This study investigated the comparative impact of an online smoking prevention program, designed to simulate real-life smoking scenarios that adolescents might encounter, on their understanding and attitudes toward smoking vis-a-vis offline smoking prevention programs. We theorized that this online program could (1) enhance adolescents’ knowledge of smoking, (2) shape their attitudes toward smoking, and (3) boost their interest in and satisfaction with smoking cessation education programs. The main objective of this study was to design and implement an online smoking prevention program capable of delivering concentrated information over a shorter time span than its offline counterparts. Additionally, we aimed to evaluate the effectiveness of this concise online smoking prevention program to support broader implementation of online smoking prevention programs in the future.
Methods
Study population and study design
This study evaluated and compared the effects of an online smoking prevention program with those of an offline smoking prevention program among 1,008 adolescents attending elementary schools (four schools), middle schools (seven schools), high schools (five schools), and alternative schools (out-of-school) in Chungcheongnam-do Province, South Korea, over 1 month from October 1 to October 31, 2021.
The control group (n=500) received an offline smoking prevention education, while each individual in the intervention group (n=508) received online smoking prevention education through a personal computer (PC) or mobile device. The assignment to the intervention and control groups was determined based on the preferences of the smoking cessation program teachers responsible for each participating school, considering the characteristics of the school’s classes. Realistically, due to the implementation of offline smoking cessation classes at the school level, it was challenging to allocate participants randomly on an individual basis.
The offline smoking prevention program for Korean adolescents was conducted through lecture-style classes led by school health teachers or homeroom teachers, focusing on smoking prevention and lasting 2 hours. Conversely, the platform for the delivery of the online smoking cessation program was a “Smoke-Free Friend,” which the students accessed through their PC or mobile device and watched six videos (Figure 1). Six content videos, each 5–7 minutes in length, were produced and categorized by school grade, with language and topics adapted to age-specific developmental levels. The online program incorporated interactive components through a “visual radio” format hosted by three peer presenters. Each session featured dramatic simulations of smoking temptation scenarios that adolescents may encounter in real life. The presenters conducted scripted dialogues, invited peers of the same age to share honest opinions about smoking, and consulted smoking cessation experts who addressed common misconceptions and provided coping strategies. These segments were designed to foster peer interactions by modeling realistic socially engaging conversations. To enhance active participation, the program included follow-up quizzes to reinforce key messages, and students who successfully completed mission tasks received virtual rewards (“prevention coins”) as part of a gamified learning structure.
Study outcome measurement
The control and intervention groups were administered an online survey before and after each smoking prevention program to assess attitudes toward and knowledge of smoking, as well as interest in and satisfaction with the program. When online surveys were not feasible, paper surveys were provided and entered separately. Each item was rated on a 1–4 point scale (1 point, not at all; 2 points, not; 3 points, yes; 4 points, very much so). The survey items listed below are based mainly on the core questionnaire of the annual national survey on adolescent health behaviors conducted in Korea and the Global Youth Tobacco Survey (GYTS) conducted by the World Health Organization.
Smoking-related knowledge and attitudes
(1) Curiosity (If a close friend encouraged me to try smoking, I would become curious and try it); (2) Smoking within 1 year (I think I will try smoking [or e-cigarettes] within the next year); (3) Friendship (If I smoke, it would be easier to fit in with my friends or peer groups); (4) Competence (even if someone starts smoking, they can easily quit if they have the determination); (5) Stress relief (smoking is harmful, but helps relieve mental stress); (6) Harmfulness of e-cigarettes (they are less harmful to adolescent health than regular cigarettes).
Interest and satisfaction with the program
(1) Interest (Through the program, I learned about the true nature of cigarettes in interesting ways); (2) Knowledge improvement (Through the program, I gained a lot of knowledge about cigarettes); (3) No smoking (Through the program, my commitment to quit smoking became stronger); (4) Recommendations (I would recommend the program to friends who smoke).
Statistical analyses
Analysis of the effects involved comparing the scores before and after the smoking program for each group using t-tests and comparing the effects of the online and offline smoking programs using a two-way analysis of variance (ANOVA). All statistical analyses were performed using the IBM SPSS ver. 25.0 (IBM Corp.). All the participants provided informed consent. The Institutional Review Board (IRB) of Dankook University Hospital approved this study (IRB approval no., 2021-06-018-002). The trial was performed in accordance with Good Clinical Practice guidelines and the Declaration of Helsinki.
Results
Among the 508 adolescents in the intervention group, 479 completed the online smoking prevention program and the follow-up survey. Among them, there were 181 male students (37.79%) and 298 were female students (62.21%), including 189 elementary school students (39.45%), 69 middle school students (14.41%), 201 high school students (41.96%), and 11 out-of-school youths (4.18%). In the control group, out of a total of 500 individuals, 466 completed the smoking prevention program and the follow-up survey. Of them, there were 251 male students (53.86%) and 215 were female students (46.14%), including 323 elementary school students (69.31%), 116 middle school students (24.89%), and 27 high school students (5.79%) (Table 1).
Smoking-related knowledge and attitudes
When examining the changes in smoking-related knowledge and attitudes before and after program participation for each group, the intervention group that participated in the online smoking prevention program showed statistically significant differences in terms of curiosity (If a close friend encouraged me to try smoking, I would become curious and try it; P=0.004), friendships (If I smoke, it would be easier to fit in with my friends or peer groups; P=0.035), and stress relief (smoking is harmful, but helps relieve mental stress); P<0.001) and the harmfulness of e-cigarettes (e-cigarettes are less harmful to adolescent health than regular cigarettes; P<0.001). Conversely, the control group that participated in the offline smoking prevention program showed statistically significant differences only in stress relief (P=0.049) and the harmfulness of e-cigarettes (P=0.005) (Table 2).
The results of the analysis, stratified by gender and education grade, showed that in the intervention group, there were no statistically significant changes in the following four items for both male and female students: Curiosity (if a close friend encouraged me to try smoking, I would become curious and try it), smoking within one year (I think I will try smoking (or e-cigarettes) within the next year). Friendship (If I smoke, it would be easier to fit in with my friends or peer groups.), and competence (even if someone starts smoking, they can easily quit if they have the determination). The stress relief item (smoking is harmful but helps relieve mental stress), male students demonstrated a statistically significant change from 1.44 before education to 1.14 after education (P=0.000), and female students showed a change from 1.59 to 1.25 (P=0.000). Additionally, the item measures attitudes toward and knowledge of the harmfulness of e-cigarettes (e-cigarettes are less harmful to adolescent health than regular cigarettes), male students showed a statistically significant change from 1.31 before education to 1.17 after education (P=0.002), and female students showed a change from 1.43 to 1.20 (P=0.000). In the control group, there were no statistically significant changes in the five items for either male or female students. However, in the male student group, there was a statistically significant change only in the item measuring attitude toward and knowledge of the harmfulness of e-cigarettes (e-cigarettes are less harmful to adolescent health than regular cigarettes) (pre, 1.93; post, 1.73; P=0.022).
When analyzed by education level, in the intervention group, only the stress relief item (smoking is harmful but helps relieve mental stress) (pre, 1.21; post, 1.13; P=0.032) showed statistically significant changes for elementary students. Middle school students showed changes in two items: stress relief (smoking is harmful but helps relieve mental stress) (pre, 1.64; post, 1.13; P=0.000) and the harmfulness of e-cigarettes (e-cigarettes are less harmful to adolescent health than regular cigarettes) (pre, 1.36; post, 1.07; P=0.001), as did high school students (stress relief: pre, 1.78; post, 1.28; P=0.000; harmfulness of e-cigarettes: pre, 1.49; post, 1.19; P=0.000). In the control group, elementary school students showed no statistically significant changes in any of the six items. Middle school students showed changes in two items: stress relief (smoking is harmful but helps relieve mental stress) (pre, 1.72; post, 1.53; P=0.095) and the harmfulness of e-cigarettes (e-cigarettes are less harmful to adolescent health than regular cigarettes) (pre, 2.19; post, 1.75; P=0.001). High school students showed changes only in the stress relief item (smoking is harmful but helps relieve mental stress) (pre, 1.74; post, 1.23; P=0.011).
To compare the effects of online and offline smoking prevention programs, a two-way ANOVA was conducted. The results showed a significant interaction effect of program type and pre-and post-program on adolescents’ responses to the stress relief question (F=12.125, P=0.001). This indicates that the effect of the online smoking prevention program (21.50%) was greater than that of the offline program (6.30%) (Figure 2). No statistically significant differences were observed between the two program types for the remaining questions (Table 3).
When stratified by gender and education grade for analysis, excluding middle school students, the online smoking prevention program showed a significant educational effect on stress relief (smoking is harmful but helps relieve mental stress). compared with the conventional smoking prevention program. For middle school students, the effects of the offline smoking prevention program (pre, 1.72; post, 1.53; P=0.811) and online smoking prevention program (pre, 1.64; post, 1.13; P=0.227) were not significant. However, the results of the two-way ANOVA indicated an approximately significant interaction effect for students’ responses to the stress relief question based on the grades of education and pre- and post-education (F(1, 369)=3.635, P=0.057). In other words, changes in knowledge and attitudes toward smoking for stress relief in middle school students who received education through the online smoking prevention program (31%) appeared greater than those in the conventional smoking prevention program (11%).
Interest and satisfaction with program
The comparison of interest and satisfaction based on program type revealed that the following questions received significantly higher scores for the online smoking prevention program: interest (Through the program, I was able to learn about the true nature of cigarettes in an interesting way; P<0.001), and knowledge improvement (through the program, I gained a lot of knowledge about cigarettes; P<0.001), and recommendations (I would recommend the program to friends who smoke; P=0.021). However, the question about no smoking (through the program, my commitment to quitting smoking became stronger; P=0.004) had significantly higher scores for the offline smoking prevention program (Table 4).
Discussion
This study compared the effectiveness of an offline smoking prevention program with that of an online smoking prevention program that simulated real-life smoking scenarios among 1,008 teenagers. A survey of changes in smoking-related knowledge and attitudes before and after participation in the program revealed that the online smoking prevention program was not inferior in educational effectiveness compared to the offline program. Furthermore, the online smoking prevention program showed higher levels of interest and satisfaction among the participants. Notably, the online program conducted in this study took approximately one-third of the time compared to the offline method, confirming that efficient smoking prevention education is possible in a shorter time by switching from the conventional method to an online approach.
Smoking during adolescence can lead to long-term smoking-related health damage. Therefore, various smoking prevention programs, including traditional school-based programs, have been implemented [10,20,21]. Offline education has addressed numerous topics such as health damage from smoking and the risk of starting smoking, and has had a significant effect on smoking prevention [22]. However, because offline education is conducted in groups, its ability to be tailored to the recipients of the program is limited. Owing to these limitations, the effect of offline education may vary depending on factors such as environment and gender [23,24].
Online smoking prevention programs have been developed to overcome the one-way directionality, uniformity, and limited accessibility of offline methods [25,26]. Most online programs use Internet-based components to provide tailored feedback to participants and enhance participation by offering interactive activities based on participants’ characteristics, such as smoking status [26,27]. Moreover, most programs provide elements, such as video content and stories, to engage teenagers effectively [19]. These online programs showed statistically significant reductions in the intention to smoke [19,26,27]. One study also reported statistically significant and immediate improvements in knowledge of smoking and smoking exposure, in addition to the attitude of avoiding passive smoking [28].
As observed in a previous study, social interactions may positively affect the effectiveness of online smoking prevention programs [16]. That is, interaction with peer groups could be an essential factor to consider when developing online smoking prevention programs [29]. Even without incentives or other motivations, socially interactive online programs can show effects similar to those of traditional programs [30]. Therefore, there is a need to consider compact online programs based on interactions within peer groups for smoking prevention education. Online programs, as implemented in this study, can potentially deliver more practical education in a shorter time with greater accessibility than offline programs. In this study, we developed an online program that simulates situations with interactions in mind, and confirmed that such a program has a sufficient effect on smoking prevention.
This study had several limitations. First, the intervention and control groups were not randomly selected. The study design implies that there could be differences between the intervention and control groups. We performed a sufficient analysis of the effects using a pre-post study design; however, a randomized controlled study is needed in the future. Second, the school sample was selected from a specific region. This implies that the results of the student survey may not be nationally representative, limiting the interpretation of the findings. Third, significant differences were observed in sex and age distribution between the control and intervention groups, which could have potentially influenced the results. However, we attempted to control for these differences through stratified analysis. Fourth, the questionnaire used in this study was not formally validated internally or externally. Although internal consistency (e.g., Cronbach’s alpha) was not calculated, the items related to smoking behavior were developed based on core questionnaires of the annual national survey on adolescent health behavior conducted in South Korea and the World Health Organization’s GYTS. Finally, although smoking prevention education targets non-smoking students, it is possible that smoking students were included in the study, as not all students honestly disclosed their smoking experiences.
The interaction simulation-based online smoking prevention program demonstrated a high level of effectiveness and interest, surpassing that of traditional offline programs. The participants in this study showed greater improvement in smoking-related knowledge and attitudes than those in the offline education program. The online program developed in this study may offer more benefits than the traditional offline programs. Future studies may require a randomized controlled study using this online program and consider nationwide implementation of the online education program. Furthermore, there is a need to expand the application of this model beyond the school environment through broader social networks. In addition, there is a need to explore the long-term impact and sustainability of online interventions beyond short-term outcomes. Expanding the application of this model through broader social networks could support its integration into public health strategies targeting the youth. These findings have practical implications not only for educators designing school curricula but also for policymakers seeking scalable, cost-effective prevention strategies and researchers aiming to develop sustainable digital health interventions.
In conclusions, this study compared and analyzed the effects of online smoking prevention programs for adolescents with those of traditional offline smoking prevention programs. Based on the results of this study, we observed that online smoking prevention programs were not inferior in their impact on knowledge and attitudes compared to offline programs. Moreover, they offer advantages in terms of interest and satisfaction. These findings suggest the possibility of introducing online programs as alternatives to existing offline programs, especially considering the time and cost benefits of online smoking prevention programs. These findings suggest that educators and public health professionals should consider adopting online programs as effective smoking prevention tools. From a policy perspective, integrating online programs may serve as a scalable and time-efficient alternative to traditional offline interventions. Further research is warranted to assess the sustainability and long-term effects of such programs beyond immediate educational outcomes.
Notes
Conflict of interest
No potential conflict of interest relevant to this article was reported.
Funding
This study was supported by a grant from the National Cancer Center, South Korea (grant number: 2210250-1).
Data availability
Data of this research are available from the corresponding author upon reasonable request.
Author contribution
Conceptualization: YC. Data curation: YC. Formal analysis: HRJ, EKK. Funding acquisition: all authors. Methodology: YC. Project administration: YC. Supervision: YC. Validation: HRJ, EKK. Visualization: HRJ. Writing–original draft: HRJ, EKK. Writingreview & editing: all authors. Final approval of the manuscript: all authors.
Figure. 1.
The flow of online smoking prevention program content topics.
Figure. 2.
Comparison of mean and standard deviation values pre- and post-participation in online and offline.
Table 1.
Demographic characteristics of intervention and control group
Characteristic
Intervention group (n=508)
Control group (n=500)
Pre
Post
Pre
Post
Sex
Male
193 (37.99)
181 (37.79)
255 (51.00)
251 (53.86)
Female
315 (62.01)
298 (62.21)
245 (49.00)
215 (46.14)
Education grade
Elementary school
198 (38.97)
189 (39.45)
332 (66.40)
323 (69.32)
Middle school
71 (13.98)
69 (14.41)
121 (24.20)
116 (24.89)
High school
213 (41.93)
201 (41.96)
47 (9.40)
27 (5.79)
Alternative school
26 (5.12)
20 (4.18)
0 (0.00)
0 (0.00)
Values are presented as number (%).
Table 2.
Comparison of knowledge of and attitude about smoking pre- and post-participation in smoking prevention program for each group
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