INTRODUCTION

Indonesia, often labeled a “baby-smoking country,” continues to face a critical public health challenge. The prevalence of smoking among children aged 13 to 15 remains among the highest globally. Although many countries have successfully reduced the disability-adjusted life years attributable to smoking in children aged 5 to 14, Indonesian data show no meaningful decline from 1990 to 2019 [1]. The prevalence of smoking among children aged 10 to 18 has steadily risen, from 7.2% in 2013 to 8.8% in 2016 and 9.1% in 2018 [2]. The highest prevalence occurs among those aged 16 years to 18 years, with more than 1.2 million smokers in this age group out of a population of 11.9 million [2]. These figures present a stark picture, reinforcing the notion that Indonesia remains a conducive environment for youth smoking.

Through the National Medium-Term Development Plan, Indonesia aims to reduce the prevalence of smoking among children aged 10 to 18 to 8.7% by 2024. Cessation-support efforts have been introduced, including integrated smoking cessation clinics at community health centers (puskesmas) and the availability of pharmacotherapy. However, these services continue to have limited reach, and resource constraints hinder their effective implementation. Cigarettes are still sold individually at low prices, starting at Indonesian rupiah (IDR)1500 (US dollar [USD]0.11) per stick [3]. Retailers located near schools often allow children to buy cigarettes on credit [3]. Although tobacco excise taxes increased by 10% in 2023 and 2024, the complex excise structure has resulted in widely varying cigarette prices, presenting an additional challenge to reducing youth smoking to meet the 2024 target.

Children’s smoking behavior has substantial adverse effects. It encourages peers to adopt smoking [4]. Early initiation is associated with longer smoking duration and contributes to an epidemiological shift in high-cost diseases such as heart disease, lung cancer, and stroke occurring at younger ages. The rapid rise of electronic cigarette use has contributed to “dual use,” meaning concurrent use of conventional and electronic cigarettes, which increases the risk of non-communicable diseases throughout adolescence and early adulthood [5]. Economically, street children from low-income backgrounds who must contribute to household income are vulnerable to deeper poverty when daily earnings are spent on cigarettes [6].

A primary reason children struggle to quit smoking is the nicotine content in cigarettes, which is highly addictive. Nicotine contributes to both dependence and relapse, even after a period of abstinence [7,8]. Smoking relapse refers to resuming smoking following cessation, and relapse remains common among children. Various enablers and reinforcing factors can cause children with smoking experience to attempt cessation but subsequently return to smoking, underscoring the need for further research.

To the best of the authors’ knowledge, few studies have explored the diverse causes of smoking relapse among children in Indonesia. Prior research has examined relapse prevention interventions [9], treatment efficacy [10], cessation trajectories [11], and predictors of successful quitting [12]. However, a critical gap remains in understanding the causal mechanisms that contribute to relapse among pediatric smokers in low-income and middle-income countries such as Indonesia.

Therefore, the purpose of this study was to examine factors that reinforce smoking relapse in Indonesian children. This study had 3 specific objectives: first, to describe the profile of children with prior smoking experience who attempted to quit but experienced relapse; second, to analyze the impact of cigarette prices and affordability on relapse; third, to assess the influence of advertising-related exposures on relapse behavior among children in Indonesia.

METHODS

Data

This study used data from the Global Youth Tobacco Survey (GYTS). GYTS collects comprehensive information on tobacco-use prevalence, cessation attempts, secondhand smoke exposure, and tobacco-related media exposure among adolescents [1]. For this analysis, data from 4 rounds of GYTS conducted in Indonesia (2006, 2009, 2014, and 2019) were combined to create a single baseline dataset of 25 690 respondents (Figure 1).

The respondent selection procedure began by categorizing all participants from the combined GYTS datasets (2006, 2009, 2014, and 2019) into 2 groups: never-smokers and ever-smokers (Figure 1). The ever-smoker category was then divided into current and non-consistent smokers. Within the non-consistent smoker group, respondents were further classified based on whether they had consistently quit smoking, experienced a relapse, or had missing data due to non-response (including student absences or sensitivity questions). Initial missing data counts for smoking status were 210 in 2006, 171 in 2009, 182 in 2014, and 441 in 2019, resulting in an analytical sample of 24 686 respondents. Additional missing values were identified for several control variables: sex (8), years of education (7), age group (9), parental smoking (6), exposure to secondhand smoke (18), and anti-tobacco media exposure (5). After addressing these missing cases, the final sample size for complete-case analysis was 23 948 respondents. Missing data were addressed using survey weights [13].

Outcome and Control Variables

The primary outcome was a 4-category nominal variable representing smoking trajectory, defined as follows: never-smokers were respondents who answered “no” to ever trying cigarette smoking; consistent quitters were ever smokers who reported a quit attempt in the past 12 months and were not current smokers (0 smoking days in the past 30 days); current smokers were those who reported smoking on 1 or more days in the past 30 days; and smoking relapse was identified among ever smokers who reported a past-year quit attempt but were currently smoking (one or more days in the past 30 days).

The health belief model guided the selection of control variables to ensure theoretical relevance [14]. The 4 GYTS waves in Indonesia were treated as distinct datasets due to inconsistent variable availability. Control variables included sex, educational grade, age group, parental smoking status, history of receiving free cigarettes, and exposure to anti-cigarette messages. Additional variables available only in the 2014 and 2019 waves included having smoking friends, purchasing single sticks of cigarettes, peer smoking, cigarette price category (unit: IDR/USD) (<15 000/0.91; 15 000–20 000/0.91–1.21; 21 000–25 000/1.27–1.51; 26 000–30 000/1.57–1.81; >31 000/1.87), stick purchase, and exposure to advertisements (magazines, social media, TV, sports, and posters). Exposure to cigarette advertising across media formats was available only in the 2019 dataset.

Descriptive Analysis

Descriptive statistics, including frequencies and percentages, were calculated to summarize the characteristics of the study population across the 4 smoking trajectory categories. The chi-square test was used to assess significant differences in the distribution of these characteristics among the 4 groups.

Multinomial Logistic Regression

This study used a multinomial logistic regression model to analyze smoking trajectories categorized as never-smokers, current smokers, consistent quitters, and smoking relapse. The model’s fit was supported by lower Akaike information criterion (AIC) and Bayesian information criterion (BIC) values compared with alternative models. Results are reported as relative risk ratios (RRRs) with 95% confidence intervals, standard errors (SEs), and p-values. Due to variations across GYTS waves, 4 models were estimated: Model 1 (combined 2006, 2009, 2014, and 2019); Model 2 (2006, 2009, and 2014); Model 3 (2014 and 2019); and Model 4 (2019 only). The research model, incorporating individual error terms and δ as the time error term, is expressed as follows:

Yit relapse= β0+β1 sexit+β2 educational gradeit+β3 age groupit+β4 parental smoking statusit+β5 history of receiving free cigaretteit+β6 exposure to anti cigarette messagesit+β7 close friends who smokeit+β8 single stick cigarette purchasingit+β9 peer smokeit+β10 cigarette price categoryit+β11 stick purchseit+β12 exposed to magazine adsit+β13 exposed to social media Adsit+β14 exposed to TV adsit+β15 exposed to ads in sportsit+β16 exposed to poster adsit+ɛ+δ

Propensity Score Matching

We conducted propensity score matching (PSM) to complement the regression analysis. PSM provides a less model-dependent approach by reducing bias from observed confounders [15]. A balancing test assessed matching quality. The treated group (children exposed to advertising) was matched with unexposed controls based on similar propensity scores. Kernel matching was used to calculate the average treatment effect on the treated (ATT). A counterfactual group of unexposed children was constructed using these matching strategies.

In this study, exposure to cigarette advertising is represented by the treatment variable T_i, where T_i =1 indicates that a child is exposed to cigarette advertisements, and T_i =0 otherwise. The set of child characteristics used to predict advertising exposure—such as sex, age group, education, and spending cost—is denoted by the covariate vector X_i. The propensity score is estimated using a logistic regression model of the form:

P(Ti=1|Xi)=F(θ^Xi),

where F(·) s the cumulative logistic distribution and θ̂ represents the estimated coefficients from the model. This formulation ensures consistency between the notation used in the equation and the variables described in the text. The resulting propensity scores are then used to match treated and untreated children using a kernel matching algorithm to construct an appropriate counterfactual group. This matching procedure allows us to estimate the ATT by comparing children exposed to cigarette advertisements with comparable children who were not exposed.

Ethics Statement

Ethical approval was not required for this study as it performed a secondary analysis of publicly available, anonymized data from the GYTS. All data were collected by the World Health Organization and the Indonesian Ministry of Health in accordance with international ethical standards for research involving human subjects.

RESULTS

Descriptive Statistics

There was an upward trend in child smoking from 2006 to 2019, accompanied by an overall increase in smoking experimentation across survey years (Figure 2). The proportion of children who attempted to quit but relapsed remained above 49.9%. Smoking relapse was most frequently observed among males (28.7%), which was considerably higher than among females (1.9%), and males also exhibited the highest proportion of current smokers (4.2%) (Table 1). Family context also played a significant role, as children with smoking parents had higher relapse rates (15.1%) than those with non-smoking parents (11.7%), with this difference reaching statistical significance (p<0.001).

A history of receiving free cigarettes emerged as 1 of the strongest contributors to relapse (31.6%). Purchasing single-stick cigarettes was also associated with a very high relapse rate (75.9%), and having peers who smoked was similarly influential (40.0%). Cigarette price also contributed to relapse behavior, with the highest relapse proportion observed among children exposed to cigarette prices below IDR15 000/USD0.91 (25.4%). Exposure to cigarette advertising further elevated relapse risk, with the highest proportions found among children exposed to advertisements in sports venues (32.2%), posters (28.8%), and social media (26.6%).

Determinants of Children’s Smoking Trajectories

Male children were significantly more susceptible to relapse than female, with an RRR of 32.984 (SE=0.063) (Table 2). A similar pattern emerged with increasing age, as children older than 17 exhibited more than twice the risk of relapse (RRR, 2.080; SE, 0.008). Family environment and accessibility also had meaningful effects: having a smoking parent increased relapse risk by 72%, and receiving free cigarettes doubled relapse likelihood.

Children with friends who smoked faced a 4-fold higher risk of relapse (model 2). The strongest predictor remained the ability to purchase cigarettes individually, which increased the likelihood of relapse by more than 57.418 times (SE=0.259). This finding highlights how access to single-stick purchases—typically inexpensive and easily obtained—creates a substantial barrier to maintaining abstinence.

Cigarette prices showed a non-linear association with relapse. Children exposed to mid-priced cigarettes (IDR21 000–25 000/USD1.27–1.51) experienced a 1.437-fold increased relapse risk (SE=0.012). In contrast, exposure to the highest price tier (above IDR31 000/USD1.87) was associated with a sharply reduced relapse risk (RRR, 0.527; SE, 0.014). These estimates were derived from model 3, which was selected for interpretation based on lower AIC and BIC values compared with model 4.

Exposure to cigarette advertising on television was associated with a 1.44-fold increase in relapse risk (model 4). The impact of poster advertising was particularly strong, corresponding to a 36.67-fold increase in relapse, emphasizing the influence of outdoor media on youth behavior. Conversely, exposure to anti-smoking advertisements did not demonstrate a consistent or statistically significant protective effect across models.

Association Between Advertising Exposure and Relapse

Exposure to cigarette advertising through magazines, sporting events, and the receipt of free cigarettes was associated with a statistically significant increase in the likelihood of relapse. Specifically, exposure to magazine advertisements increased relapse probability by 7.5 percentage points, exposure to sporting-event advertising by 10.2 percentage points, and receiving free cigarettes by 23.1 percentage points (Table 3).

In contrast, exposure to advertising on television, social media, and posters did not yield statistically significant effects, as their estimated ATT values were small and accompanied by non-significant t-statistics. Among all exposure variables, receiving free cigarettes exhibited the strongest association with relapse, increasing the probability by 23.1 percentage points, consistent with its large effect size and high statistical significance.

DISCUSSION

Research on predictors of smoking cessation among in-school children has confirmed that males and those aged ≥16 are more likely to be in the contemplation rather than action phase [8], which helps explain their greater vulnerability. Consistently, relapse rates were substantially higher among boys (28.7%) than girls (1.9%). Male adolescents also face distinct cultural and social pressures that make quitting more difficult, including masculinity norms that associate smoking with maturity [16]. Older children (>17 years, 17.3%) experienced higher relapse rates, likely due to longer smoking histories and stronger nicotine dependence [17]. Evidence on socioeconomic, demographic, and geographic correlates of smoking among Indonesian children underscores the need for targeted prevention and cessation approaches that account for sex, age, socioeconomic status, and parental smoking [18,19]. These patterns align with global findings that children are especially prone to relapse because their prefrontal cortex remains underdeveloped and reward-seeking tendencies are heightened [20].

While the analysis showed that higher cigarette pack prices (>IDR31 000/USD1.87) were associated with reduced relapse risk and increased odds of being a consistent quitter, this protective effect is nevertheless weakened by Indonesia’s retail environment [21]. Cigarette prices are heavily shaped by the excise tax system, meaning that achieving a meaningful rise in retail prices requires a substantial and sustained excise tax increase. Without this, the market continues to offer cigarettes at relatively affordable prices despite nominal tax increases [22,23].

The widespread availability of single-stick cigarettes creates a fundamental policy loophole [24]. For children attempting to quit, the financial and psychological barriers associated with purchasing a full pack are bypassed by the ability to buy a single cigarette at minimal cost, facilitating impulsive lapses that can quickly lead to full relapse [3,25]. This contextualizes the finding that relapse rates remained consistently high across all single-stick price categories, increasing relapse likelihood by more than 57 times (model 2).

Consistent with a large body of literature, peer smoking emerged as a powerful predictor [26,27]. In this study, 40% of children who relapsed reported having smoking friends. Children with smoking peers were almost 4 times more likely to relapse than their counterparts without smoking peers (model 2). This effect likely reflects multiple mechanisms, including social norms, peer pressure, and the social identity value attached to smoking in Indonesian youth culture [28]. Social network analyses have shown that students embedded in networks supportive of smoking tend to have more friends with similar views than those embedded in networks where smoking is discouraged, highlighting the role of homophily and network thresholds [29].

Similarly, parents’ smoking habits influence their children’s smoking behavior [30,31]. In this study, children with smoking parents had higher relapse rates (15.1%) than children of non-smoking parents (11.7%). Parental smoking increases the likelihood that children remain in the contemplation phase rather than transitioning to cessation maintenance [30]. Such familial modeling normalizes tobacco use, reduces perceived risk, and may increase physical access to cigarettes, creating a home environment that undermines cessation attempts [31].

The finding that receiving free cigarettes increases relapse likelihood 23.1 percentage points is particularly concerning. It demonstrates how direct promotional strategies eliminate financial barriers at moments of high vulnerability for children attempting to quit. In this context, longitudinal evidence indicates that direct-to-consumer tobacco marketing continues to reach youth, and that repeated exposure across multiple media channels is associated with an increased likelihood of smoking initiation [32]. Among advertising exposures in this study ads in sports (10.2 percentage points) and magazines (7.5 persentage points) advertisements had the strong associations with relapse, indicating that traditional media remain potent channels influencing adolescent smoking behavior. This highlights the substantial impact of outdoor advertising, which children encounter daily in their neighborhoods and near schools. Such advertising reinforces smoking as part of youth identity formation rather than a health threat [33]. Tobacco marketing leverages adolescents’ concerns about social belonging, autonomy, self-image, and adventure seeking, using imagery rather than factual information to portray smoking as appealing [34].

In stark contrast, exposure to social media advertising demonstrated a significant protective effect in the PSM analysis, reducing relapse probability by 15.3 percentage points. This finding suggests possible differences in how media platforms influence youth behavior. Social media may be more effectively utilized for anti-tobacco messaging, or the format and delivery of pro-tobacco content on these platforms may be less effective at triggering relapses than physical advertisements embedded in children’s daily environments.

A complex pattern emerged regarding exposure to anti-smoking messages and smoking status. Although exposure was associated with a lower risk of being a current smoker (RRR, 0.550), it did not provide protection against relapse (RRR, 1.027) and was associated with increased risk in certain models. This counterintuitive pattern likely reflects multiple processes rather than program failure.

First, reverse causality is highly plausible: children who are smoking or struggling to quit are more likely to encounter, attend to, and recall anti-smoking messages [35]. Smokers and recent quitters have an attentional bias toward smoking-related cues, including warnings, which can paradoxically heighten cravings rather than deter use [36]. Second, some messaging strategies may trigger psychological reactance, in which individuals who perceive restrictions on their freedom respond by engaging in the prohibited behavior [37]. Research shows that individuals with high reactance are less receptive to risk information and display reduced motivation to quit smoking [38].

The study has several limitations. The cross-sectional GYTS design does not allow for establishing temporality, limiting causal inference outside the PSM analysis. All data were self-reported and may be affected by recall and social desirability biases. In addition, the operational definition of relapse—based on any quit attempt in the past 12 months—may encompass individuals with varying abstinence durations and levels of motivation. Future research should employ longitudinal designs to follow smoking trajectories over time and incorporate qualitative approaches to better understand the experiences of children attempting cessation in Indonesia.

The evidence strongly indicates that the social environment, particularly peer influence—is a key driver of sustained smoking. Although higher cigarette pack prices show potential as a deterrent, their impact is negated by the ongoing availability of single-stick cigarettes, which facilitate relapse. Tobacco marketing also has a demonstrable effect in undermining quit attempts. Policy action must be decisive and comprehensive, including an immediate and complete ban on single-stick cigarette sales, full enforcement of bans on all forms of tobacco advertising and promotion, and strengthening school-based interventions that address peer networks and social norms.

Data Availability

Notes

Conflict of Interest

The authors have no conflicts of interest associated with the material presented in this paper.

Funding

This study is funded by Campaign for Tobacco-Free Kids (CTFK).

Acknowledgements

None.

Author Contributions

Conceptualization: Hartono RK. Data curation: Rohman MA. Formal analysis: Rohman MA, Hartono RK. Funding acquisition: Satrya A. Methodology: Hartono RK, Shellasih NM. Project administration: Ningtyas FR, Nadya S. Visualization: Shellasih NM. Writing – original draft: Ningtyas FR. Writing – review & editing: Waruwu AH.

Figure 1

Flow of respondent selection from the Global Youth Tobacco Survey (GYTS) Datasets 2006–2019, in Indonesia.

Figure 2

Description of child smokers aged 13–15 years in Indonesia based on Global Youth Tobacco Survey data, 2006–2019.

REFERENCES

• 1. World Health Organization. Global youth tobacco survey. 2019 [cited 2025 Aug 5]. Available from: https://www.who.int/teams/noncommunicable-diseases/surveillance/systems-tools/global-youth-tobacco-survey
• 2. Institute for Health Metrics and Evaluation (IHME). Indonesia basic health research; 2018 2018 [cited 2025 Aug 5]. Available from: https://ghdx.healthdata.org/record/indonesia-basic-health-research-2018
• 3. Hartono RK, Meirawan RF, Nurhasana R, Dartanto T, Satrya A. Retailer’s density and single stick cigarette’s accessibility among school-age children in Indonesia. Asian Pac J Cancer Prev 2023;24(2):675-682. https://doi.org/10.31557/APJCP.2023.24.2.675ArticlePubMedPMC
• 4. Azzahro AW, Putra A, Rohmah IN. Meta analysis: peer influence on smoking behavior in adolescents. J Health Promot Behav 2022;7(2):152-160. https://doi.org/10.26911/thejhpb.2021.07.02.07Article
• 5. Moeis FR, Hartono RK, Nurhasana R, Satrya A, Dartanto T. Relieving or aggravating the burden: non-communicable diseases of dual users of electronic and conventional cigarette in Indonesia. Tob Induc Dis 2024;22(5):https://doi.org/10.18332/tid/175755Article
• 6. Nurhasana R, Hartono RK, Satrya A, Ningtyas FR, Sihombing IM. Reducing the prevalence of smoking among street children to maintain the sustainability of a healthy city in the urban area of Jakarta, Indonesia. Plann Malays 2025;23(2):71-83. https://doi.org/10.21837/pm.v23i36.1703Article
• 7. Herd N, Borland R, Hyland A. Predictors of smoking relapse by duration of abstinence: findings from the International Tobacco Control (ITC) Four Country Survey. Addiction 2009;104(12):2088-2099. https://doi.org/10.1111/j.1360-0443.2009.02732.xArticlePubMedPMC
• 8. Dadras O. Predictor of smoking cessation among school-going adolescents in Indonesia: a secondary analysis based on the transtheoretical model of behavioral change. Front Psychiatry 2024;15: 1374731. https://doi.org/10.3389/fpsyt.2024.1374731ArticlePubMedPMC
• 9. Livingstone-Banks J, Norris E, Hartmann-Boyce J, West R, Jarvis M, Chubb E, et al. Relapse prevention interventions for smoking cessation. Cochrane Database Syst Rev 2019;2019;(10):CD003999. https://doi.org/10.1002/14651858.CD003999.pub6Article
• 10. García-Gómez L, Hernández-Pérez A, Noé-Díaz V, Riesco-Miranda JA, Jiménez-Ruiz C. Smoking cessation treatments: current psychological and pharmacological options. Rev Invest Clin 2019;71(1):7-16. https://doi.org/10.24875/RIC.18002629ArticlePubMed
• 11. Ahun MN, Lauzon B, Sylvestre MP, Bergeron-Caron C, Eltonsy S, O’Loughlin J. A systematic review of cigarette smoking trajectories in adolescents. Int J Drug Policy 2020;83: 102838. https://doi.org/10.1016/j.drugpo.2020.102838ArticlePubMed
• 12. Vallata A, O’Loughlin J, Cengelli S, Alla F. Predictors of cigarette smoking cessation in adolescents: a systematic review. J Adolesc Health 2021;68(4):649-657. https://doi.org/10.1016/j.jadohealth.2020.09.025ArticlePubMed
• 13. Little RJ, Rubin DB. Statistical analysis with missing data. 3rd ed. New York: John Wiley & Sons; 2019. p. 1-20
• 14. Kaufman AR, Dwyer LA, Land SR, Klein WM, Park ER. Smoking-related health beliefs and smoking behavior in the National Lung Screening Trial. Addict Behav 2018;84: 27-32. https://doi.org/10.1016/j.addbeh.2018.03.015ArticlePubMedPMC
• 15. Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivar Behav Res 2011;46(3):399-424. https://doi.org/10.1080/00273171.2011.568786ArticlePubMedPMC
• 16. Reskiaddin LO, Supriyati . The process of smoking cessation behavior change: a qualitative study of motives, social support and coping mechanisms. Indones J Health Promot Behav 2021;3(1):58-70. (Indonesian)https://doi.org/10.47034/ppk.v3i1.4142Article
• 17. National Center for Chronic Disease Prevention and Health Promotion (US) Office on Smoking and Health. Preventing tobacco use among youth and young adults: a report of the surgeon general. Atlanta: Centers for Disease Control and Prevention (US); 2012. 22876391
• 18. Ihyauddin Z, Putri DA, Tengkawan J, Ekawati FM, Sitaresmi MN. Tobacco use among school-age adolescents in Indonesia: findings from the 2015 Indonesia Global School-Based Student Health Survey. Korean J Fam Med 2023;44(6):327-334. https://doi.org/10.4082/kjfm.23.0010ArticlePubMedPMC
• 19. Pengpid S, Peltzer K. Behavioral risk factors of non-communicable diseases among a nationally representative sample of school-going adolescents in Indonesia. Int J Gen Med 2019;12: 387-394. https://doi.org/10.2147/IJGM.S226633ArticlePubMedPMC
• 20. Chambers RA, Taylor JR, Potenza MN. Developmental neurocircuitry of motivation in adolescence: a critical period of addiction vulnerability. Am J Psychiatry 2003;160(6):1041-1052. https://doi.org/10.1176/appi.ajp.160.6.1041ArticlePubMedPMC
• 21. Fauzi R, Pongpanich S. The effect of price on cigarette consumption among youth in Indonesia: implication for tobacco tax policy. World Med Health Policy 2022;14(4):665-678. https://doi.org/10.1002/wmh3.516Article
• 22. Barber S, Ahsan A. The tobacco excise system in Indonesia: hindering effective tobacco control for health. J Public Health Policy 2009;30(2):208-225. https://doi.org/10.1057/jphp.2009.12ArticlePubMed
• 23. Zheng R, Marquez PV, Ahsan A, Hu X, Wang Y. Cigarette affordability in Indonesia: 2002–2017. Washington, DC: World Bank Group; 2018. https://doi.org/10.13140/RG.2.2.33601.86883
• 24. Melinda G, Kamilah FZ, Rizki Amelia S, Nida S, Kusuma D, Adrison V. Loose cigarette purchase and adolescent smoking in Indonesia: a mixed-methods study. BMJ Open 2025;15(1):e087786. https://doi.org/10.1136/bmjopen-2024-087786ArticlePubMedPMC
• 25. Dewi DM, Sebayang SK, Lailiyah S. Density of cigarette retailers near schools and sales to minors in Banyuwangi, Indonesia: a GIS mapping. Tob Induc Dis 2020;18: 06. https://doi.org/10.18332/tid/115798ArticlePubMedPMC
• 26. Murray JM, Sánchez-Franco SC, Sarmiento OL, Kimbrough EO, Tate C, Montgomery SC, et al. Selection homophily and peer influence for adolescents’ smoking and vaping norms and outcomes in high and middle-income settings. Humanit Soc Sci Commun 2023;10(1):615. https://doi.org/10.1057/s41599-023-02124-9Article
• 27. Littlecott HJ, Moore GF, Evans RE, Melendez-Torres GJ, McCann M, Reed H, et al. Perceptions of friendship, peers and influence on adolescent smoking according to tobacco control context: a systematic review and meta-ethnography of qualitative research. BMC Public Health 2023;23(1):424. https://doi.org/10.1186/s12889-022-14727-zArticlePubMedPMC
• 28. Fithria F, Adlim M, Jannah SR, Tahlil T. Indonesian adolescents’ perspectives on smoking habits: a qualitative study. BMC Public Health 2021;21(1):82. https://doi.org/10.1186/s12889-020-10090-zArticlePubMedPMC
• 29. Montes F, Blanco M, Useche AF, Sanchez-Franco S, Caro C, Tong L, et al. Exploring the mechanistic pathways of how social network influences social norms in adolescent smoking prevention interventions. Sci Rep 2023;13(1):3017. https://doi.org/10.1038/s41598-023-28161-7ArticlePubMedPMC
• 30. van Zundert RM, Engels RC. Parental factors in association with adolescent smoking relapse. Eur Addict Res 2009;15(4):209-215. https://doi.org/10.1159/000231884ArticlePubMed
• 31. Scheffers-van Schayck T, Mujcic A, Otten R, Engels R, Kleinjan M. The effectiveness of smoking cessation interventions tailored to smoking parents of children aged 0–18 years: a meta-analysis. Eur Addict Res 2021;27(4):278-293. https://doi.org/10.1159/000511145ArticlePubMedPMC
• 32. Choi K, Rose SW, Zhou Y, Rahman B, Hair E. Exposure to multimedia tobacco marketing and product use among youth: a longitudinal analysis. Nicotine Tob Res 2020;22(6):1036-1040. https://doi.org/10.1093/ntr/ntz096ArticlePubMedPMC
• 33. Beleva Y, Pike JR, Miller S, Xie B, Ames SL, Stacy AW. Share of advertising voice at the point-of-sale and its influence on at-risk students’ use of alternative tobacco products. Nicotine Tob Res 2019;21(7):903-910. https://doi.org/10.1093/ntr/nty152ArticlePubMedPMC
• 34. Crosbie E, Eckford R, Bialous S. Containing diffusion: the tobacco industry’s multipronged trade strategy to block tobacco standardised packaging. Tob Control 2019;28(2):195-205. https://doi.org/10.1136/tobaccocontrol-2017-054227ArticlePubMedPMC
• 35. Namkoong K, Nah S, Record RA, Van Stee SK. Communication, reasoning, and planned behaviors: unveiling the effect of interactive communication in an anti-smoking social media campaign. Health Commun 2017;32(1):41-50. https://doi.org/10.1080/10410236.2015.1099501ArticlePubMed
• 36. Masiero M, Lucchiari C, Maisonneuve P, Pravettoni G, Veronesi G, Mazzocco K. The attentional bias in current and former smokers. Front Behav Neurosci 2019;13: 154. https://doi.org/10.3389/fnbeh.2019.00154ArticlePubMedPMC
• 37. Burgoon M, Alvaro E, Grandpre J, Voulodakis M. Revisiting the theory of psychological reactance: communicating threats to attitudinal freedom. In: Dillard JP, Pfau M, editors. The persuasion handbook: developments in theory and practice. 2nd ed. Thousand Oaks: SAGE Publications, Inc; 2002. p. 213-232 https://doi.org/10.4135/9781412976046.n12Article
• 38. Pavey L, Sparks P. Reactance, autonomy and paths to persuasion: examining perceptions of threats to freedom and informational value. Motiv Emot 2009;33(3):277-290. https://doi.org/10.1007/s11031-009-9137-1Article

Figure & Data

References

Citations

Citations to this article as recorded by