Determinants of Smoking Relapse Among Indonesian Children: A National Cross-sectional Analysis Using Global Youth Tobacco Surveillance Data - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice J Prev Med Public Health . 2025 Dec 12;59(2):162–173. doi: 10.3961/jpmph.25.703 Search in PMC Search in PubMed View in NLM Catalog Add to search Determinants of Smoking Relapse Among Indonesian Children: A National Cross-sectional Analysis Using Global Youth Tobacco Surveillance Data Risky Kusuma Hartono Risky Kusuma Hartono 1 Department of Public Health, Universitas Indonesia Maju, Jakarta, Indonesia Find articles by Risky Kusuma Hartono 1, ✉ , Muhammad Abdul Rohman Muhammad Abdul Rohman 2 Department of Economics, Faculty of Economics and Business, Universitas Indonesia, Depok, Indonesia Find articles by Muhammad Abdul Rohman 2 , Renny Nurhasana Renny Nurhasana 3 Urban Studies Program, School of Strategic Global Studies, Universitas Indonesia, Central Jakarta, Indonesia Find articles by Renny Nurhasana 3 , Aryana Satrya Aryana Satrya 2 Department of Economics, Faculty of Economics and Business, Universitas Indonesia, Depok, Indonesia Find articles by Aryana Satrya 2 , Salsabila Nadya Salsabila Nadya 4 Center for Social Security Studies, School of Strategic and Global Studies Universitas Indonesia, Central Jakarta, Indonesia Find articles by Salsabila Nadya 4 , Ni Made Shellasih Ni Made Shellasih 4 Center for Social Security Studies, School of Strategic and Global Studies Universitas Indonesia, Central Jakarta, Indonesia Find articles by Ni Made Shellasih 4 , Fadhilah Rizky Ningtyas Fadhilah Rizky Ningtyas 4 Center for Social Security Studies, School of Strategic and Global Studies Universitas Indonesia, Central Jakarta, Indonesia Find articles by Fadhilah Rizky Ningtyas 4 , Astri Hanna Waruwu Astri Hanna Waruwu 4 Center for Social Security Studies, School of Strategic and Global Studies Universitas Indonesia, Central Jakarta, Indonesia Find articles by Astri Hanna Waruwu 4 Author information Article notes Copyright and License information 1 Department of Public Health, Universitas Indonesia Maju, Jakarta, Indonesia 2 Department of Economics, Faculty of Economics and Business, Universitas Indonesia, Depok, Indonesia 3 Urban Studies Program, School of Strategic Global Studies, Universitas Indonesia, Central Jakarta, Indonesia 4 Center for Social Security Studies, School of Strategic and Global Studies Universitas Indonesia, Central Jakarta, Indonesia ✉ Corresponding author: Risky Kusuma Hartono, Department of Public Health, Universitas Indonesia Maju, Jl. Harapan No. 50 Lenteng Agung, Jakarta 12610, Indonesia, E-mail: [email protected] ✉ Corresponding author. Received 2025 Sep 2; Revised 2025 Nov 11; Accepted 2025 Nov 29; Issue date 2026 Mar. Copyright © 2026 The Korean Society for Preventive Medicine This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/ ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. PMC Copyright notice PMCID: PMC13084231 PMID: 41531155 Abstract Objectives This study aimed to identify the determinants associated with 4 smoking trajectories (never smoking, current smoking, consistently quitting smoking, and smoking relapse) among Indonesian children using pooled national data. Methods This study analyzed children’s smoking trajectories using pooled data from the Indonesian Global Youth Tobacco Survey (2006, 2009, 2014, and 2019). Multinomial logistic regression was applied to examine 4 categories of smoking behavior, and propensity score matching (PSM) was used to address potential selection bias related to advertising exposure. Results Higher prices (>IDR31 000/USD1.87) (relative risk ratio [RRR], 0.527) were protective against relapse. This policy effect was systematically undermined by single-stick purchasing, which increased relapse risk by 57.41 times ( p <0.01). Peer smoking (RRR, 3.696) and exposure to advertisements in sports events (RRR, 1.477) were also significant risk factors for relapse. Free cigarette distribution demonstrated the strongest association, corresponding to a 23.1 percentage-point increase in relapse probability. Conclusions The findings indicate that children’s smoking relapse in Indonesia is shaped by distinct behavioral trajectories. Relapse was influenced by the widespread availability of single-stick cigarettes, pervasive marketing, peer dynamics, and relatively affordable prices. These results underscore the urgent need for a comprehensive strategy that includes substantially increasing excise taxes, eliminating single-stick sales, and fully banning tobacco advertising. Simultaneous implementation of these measures is crucial to prevent relapse and interrupt the progression toward lifelong nicotine addiction. Keywords: Smoking cessation; Recurrence; Adolescent; Health economics, Health behavior 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 ). Figure 1. Open in a new tab Flow of respondent selection from the Global Youth Tobacco Survey (GYTS) Datasets 2006–2019, in Indonesia. 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: Y i t r e l a p s e = β 0 + β 1 s e x i t + β 2 e d u c a t i o n a l g r a d e i t + β 3 a g e g r o u p i t + β 4 p a r e n t a l s m o k i n g s t a t u s i t + β 5 h i s t o r y o f r e c e i v i n g f r e e c i g a r e t t e i t + β 6 e x p o s u r e t o a n t i c i g a r e t t e m e s s a g e s i t + β 7 c l o s e f r i e n d s w h o s m o k e i t + β 8 s i n g l e s t i c k c i g a r e t t e p u r c h a s i n g i t + β 9 p e e r s m o k e i t + β 10 c i g a r e t t e p r i c e c a t e g o r y i t + β 11 s t i c k p u r c h s e i t + β 12 e x p o s e d t o m a g a z i n e a d s i t + β 13 e x p o s e d t o s o c i a l m e d i a A d s i t + β 14 e x p o s e d t o T V a d s i t + β 15 e x p o s e d t o a d s i n s p o r t s i t + β 16 e x p o s e d t o p o s t e r a d s i t + 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 ( T i = 1 | X i ) = F ( θ ^ X i ) , 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). Figure 2. Open in a new tab Description of child smokers aged 13–15 years in Indonesia based on Global Youth Tobacco Survey data, 2006–2019. Table 1. Smoking status among children in Indonesia Variables Never smoking Current smokers Smoking relapse Consistently quit smoking Total (n) Sex Female 12 299 (91.3) 39 (0.3) 258 (1.9) 869 (6.5) 13 465 Male 4829 (46.1) 445 (4.2) 3013 (28.7) 2196 (20.9) 10 483 Total 17 128 (71.5) 484 (2.0) 3271 (13.7) 3065 (12.8) 23 948 Pearson chi2(3)=6218.4740, Pr<0.001 Educational grade 7 5934 (78.3) 98 (1.3) 854 (11.3) 689 (9.1) 7575 8 4762 (72.9) 116 (1.8) 868 (13.3) 787 (12.0) 6533 9 3805 (64.8) 158 (2.7) 998 (17.0) 907 (15.5) 5868 10 852 (70.1) 36 (3.0) 149 (12.3) 179 (14.7) 1216 11 931 (68.0) 25 (1.8) 166 (12.1) 247 (18.0) 1369 12 844 (60.9) 51 (3.7) 236 (17.0) 256 (18.5) 1387 Total 17 128 (71.5) 484 (2.0) 3271 (13.7) 3065 (12.8) 23 948 Pearson chi2(15)=451.5508, Pr<0.001 Age (y) <12 4028 (80.4) 64 (1.3) 520 (10.4) 395 (7.9) 5007 13–14 8810 (72.3) 199 (1.6) 1597 (13.1) 1582 (13.0) 12 188 15–16 3277 (64.1) 155 (3.0) 871 (17.0) 811 (15.9) 5114 ≥17 1013 (61.8) 66 (4.0) 283 (17.3) 277 (16.9) 1639 Total 17 128 (71.5) 484 (2.0) 3271 (13.7) 3065 (12.8) 23 948 Pearson chi2(9)=453.0178, Pr<0.001 Parental smoking status Not smoking 7421 (73.7) 195 (1.9) 1181 (11.7) 1271 (12.6) 10 068 Parents smoke 9707 (69.9) 289 (2.1) 2090 (15.1) 1794 (12.9) 13 880 Total 17 128 (71.5) 484 (2.0) 3271 (13.7) 3065 (12.8) 23 948 Pearson chi2(3)=59.9409, Pr<0.001 History of receiving free cigarettes No 16 296 (73.7) 376 (1.7) 2696 (12.2) 2758 (12.5) 22 126 Yes 832 (45.7) 108 (5.9) 575 (31.6) 307 (16.8) 1822 Total 17 128 (71.5) 484 (2.0) 3271 (13.7) 3065 (12.8) 23 948 Pearson chi2(3)=821.1495, Pr<0.001 Exposure to anti-cigarette messages No 3565 (71.1) 153 (3.1) 705 (14.1) 591 (11.8) 5014 Yes 13 563 (71.6) 331 (1.7) 2566 (13.6) 2474 (13.1) 18 934 Total 17 128 (71.5) 484 (2.0) 3271 (13.7) 3065 (12.8) 23 948 Pearson chi2(3)=39.2934, Pr<0.001 Single-stick cigarette purchasing No 4284 (82.9) 49 (0.9) 344 (6.7) 488 (9.4) 5165 Yes 43 (8.3) 59 (11.4) 393 (75.9) 23 (4.4) 518 Total 4327 (76.1) 108 (1.9) 737 (13.0) 511 (9.0) 5683 Pearson chi2(3)=787.9855, Pr<0.001 Peers who smoke No 4113 (80.6) 44 (0.9) 504 (9.9) 440 (8.6) 5101 Yes 214 (36.8) 64 (11.0) 233 (40.0) 71 (12.2) 582 Total 4327 (76.1) 108 (1.9) 737 (13.0) 511 (9.0) 5683 Pearson chi2(3)=787.9855, Pr<0.001 Cigarette price category (IDR/USD) <15 000/0.91 1097 (59.4) 72 (3.9) 469 (25.4) 209 (11.3) 1847 15 000–20 000/0.91–1.21 287 (58.9) 20 (4.1) 116 (23.8) 64 (13.1) 487 21 000–25 000/1.27–1.51 43 (59.7) 1 (1.4) 12 (16.7) 16 (22.2) 72 26 000–30 000/1.57–1.81 11 (73.3) 0 (0) 1 (6.7) 3 (20.0) 15 >31 000/1.87 8 (80.0) 0 (0) 2 (20.0) 0 (0) 10 Total 1446 (59.5) 93 (3.8) 600 (24.7) 292 (12.0) 2431 Pearson chi2(12)=17.3896, Pr=0.136 Single stick cigarette purchasing No 1410 (70.9) 43 (2.2) 264 (13.3) 273 (13.7) 1990 Yes 32 (7.3) 49 (11.2) 336 (77.1) 19 (4.4) 436 Total 1442 (59.4) 92 (3.8) 600 (24.7) 292 (12.0) 2426 Pearson chi2(3)=935.0561, Pr<0.001 Exposed to advertisement Magazine 1058 (54.8) 82 (4.3) 385 (20.0) 404 (20.9) 1929 No 1173 (45.6) 163 (6.3) 814 (31.6) 424 (16.5) 2574 Yes 2231 (49.5) 245 (5.4) 1199 (26.6) 828 (18.4) 4503 Pearson chi2(3)=96.2728, Pr<0.001 Social media No 323 (49.3) 50 (7.6) 176 (26.9) 106 (16.2) 655 Yes 1908 (49.6) 195 (5.1) 1023 (26.6) 722 (18.8) 3848 Total 2231 (49.5) 245 (5.4) 1199 (26.6) 828 (18.4) 4503 Pearson chi2(3)=8.8250, Pr=0.032 TV No 263 (47.8) 36 (6.5) 141 (25.6) 110 (20.0) 550 Yes 1968 (49.8) 209 (5.3) 1058 (26.8) 718 (18.2) 3953 Total 2231 (49.5) 245 (5.4) 1199 (26.6) 828 (18.4) 4503 Pearson chi2(3)=2.8985, Pr=0.408 Sports No 1053 (59.5) 62 (3.5) 319 (18.0) 336 (19.0) 1770 Yes 1178 (43.1) 183 (6.7) 880 (32.2) 492 (18.0) 2733 Total 2231 (49.5) 245 (5.4) 1199 (26.6) 828 (18.4) 4503 Pearson chi2(3)=160.0138, Pr<0.001 Poster No 1456 (49.5) 164 (5.6) 749 (25.5) 570 (19.4) 2939 Yes 775 (49.6) 81 (5.2) 450 (28.8) 258 (16.5) 1564 Total 2231 (49.5) 245 (5.4) 1199 (26.6) 828 (18.4) 4503 Pearson chi2(3)=9.1082, Pr=0.028 Open in a new tab Values are presented as number (%). Ads, advertisements; Pr, denotes the p -value associated with the Pearson chi-square test; IDR, Indonesian rupiah; USD, US dollar. 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. Table 2. Relative risk ratios from multinomial logistic regression predicting smoking trajectory status Variables Base outcome: never smoking Current smokers Smoking relapse Consistently quit smoking Model 1 (2006, 2009, 2014, 2019; n=23 948) Sex (male) 28.4 (28.1, 28.6) 33.0 (32.9, 33.1) 7.5 (7.5, 7.5) Educational grade 1.2 (1.2, 1.2) 1.2 (1.2, 1.2) 1.3 (1.3, 1.3) Age (y) 13–14 1.4 (1.4, 1.4) 1.4 (1.4, 1.4) 1.5 (1.5, 1.5) 15–16 2.7 (2.7, 2.8) 1.9 (1.9, 1.9) 1.6 (1.6, 1.6) ≥17 3.4 (3.3, 3.4) 2.1 (2.1, 2.1) 1.3 (1.3, 1.3) Parents smoking 1.5 (1.5, 1.6) 1.7 (1.7, 1.7) 1.3 (1.3, 1.3) Get free cigarettes 3.2 (3.2, 3.2) 2.4 (2.4, 2.4) 1.4 (1.4, 1.4) Exposed to anti-smoking 0.6 (0.5, 0.6) 1.0 (1.0, 1.0) 1.1 (1.1, 1.1) Constant 0.0 (0.0, 0.0) 0.0 (0.0, 0.0) 0.0 (0.0, 0.0) AIC/BIC 45 900 000 45 900 000 Model 2 (2006, 2009, and 2014; n=5683) Sex (male) 25.8 (25.2, 26.4) 16.4 (16.3, 16.6) 6.8 (6.8, 6.8) Educational grade 1.2 (1.2, 1.3) 1.1 (1.1, 1.1) 1.7 (1.6, 1.7) Age (y) 13–14 1.4 (1.4, 1.4) 1.3 (1.3, 1.3) 1.0 (1.0, 1.0) 15–16 2.8 (2.7, 2.8) 1.8 (1.8, 1.8) 0.8 (0.8, 0.8) ≥17 0.8 (0.8, 0.9) 1.2 (1.1, 1.2) 2.1 (2.0, 2.1) Parents smoking 2.1 (2.1, 2.1) 2.0 (1.9, 2.0) 1.5 (1.5, 1.5) Get free cigarettes 1.9 (1.9, 1.9) 1.4 (1.4, 1.4) 1.2 (1.2, 1.2) Exposed to anti-smoking 0.5 (0.5, 0.5) 1.0 (1.0, 1.0) 1.0 (0.9, 1.0) Having peer smokers 11.4 (11.3, 11.6) 4.0 (4.0, 4.0) 1.7 (1.7, 1.8) Single-stick cigarette purchasing 49.6 (49.0, 50.2) 57.4 (56.9, 57.9) 2.6 (2.6, 2.7) Constant 0.0 (0.0, 0.0) 0.0 (0.0, 0.0) 0.0 (0.0, 0.0) AIC/BIC 45 900 000 45 900 000 Model 3 (2014 and 2019; n=2431) Sex (male) 37.8 (36.7, 38.9) 17.2 (17.0, 17.3) 5.6 (5.5, 5.6) Educational grade 1.3 (1.3, 1.3) 1.2 (1.2, 1.2) 1.9 (1.9, 1.9) Age (y) 13–14 2.0 (1.9, 2.0) 1.6 (1.6, 1.6) 1.0 (1.0, 1.0) 15–16 4.4 (4.3, 4.5) 2.7 (2.6, 2.7) 0.9 (0.9, 0.9) ≥17 0.5 (0.5, 0.5) 1.8 (1.7, 1.9) 0.2 (0.2, 0.2) Parents smoking 1.8 (1.8, 1.9) 1.7 (1.7, 1.7) 0.9 (0.9, 0.9) Get free cigarettes 1.9 (1.9, 1.9) 1.5 (1.5, 1.5) 1.2 (1.1, 1.2) Exposed to anti-smoking 0.6 (0.6, 0.6) 1.1 (1.1, 1.1) 0.9 (0.9, 0.9) Having peer smokers 9.2 (9.1, 9.3) 3.7 (3.7, 3.7) 1.3 (1.3, 1.3) Cigarette price category (IDR/USD) 15 000–20 000/0.91–1.21 1.0 (1.0, 1.1) 1.0 (1.0, 1.0) 1.2 (1.2, 1.2) 21 000–25 000/1.27–1.51 1.6 (1.5, 1.6) 1.4 (1.4, 1.5) 2.7 (2.6, 2.7) 26 000–30 000/1.57–1.81 0.0 (0.0, 0.0) 0.1 (0.1, 0.1) 1.5 (1.5, 1.6) >31 000/1.87 0.0 (0.0, 0.0) 0.5 (0.5, 0.6) 0.0 (0.0, 0.0) Constant 0.0 (0.0, 0.0) 0.0 (0.0, 0.0) 0.0 (0.0, 0.0) AIC/BIC 6 635 995 6 636 238 Model 4 (2019; n=4515) Sex (male) 20.9 (20.6, 21.2) 31.6 (31.3, 31.8) 7.3 (7.3, 7.4) Educational grade 1.0 (1.0, 1.0) 1.1 (1.1, 1.1) 1.1 (1.1, 1.1) Age (y) 13–14 1.4 (1.4, 1.5) 1.6 (1.5, 1.6) 1.4 (1.4, 1.4) 15–16 1.8 (1.8, 1.8) 1.4 (1.4, 1.5) 1.5 (1.4, 1.5) ≥17 3.2 (3.2, 3.3) 2.0 (1.9, 2.0) 1.3 (1.3, 1.3) Parents smoking 1.0 (1.0, 1.0) 1.2 (1.2, 1.2) 1.2 (1.2, 1.2) Get free cigarettes 1.7 (1.7, 1.8) 1.9 (1.9, 1.9) 0.7 (0.7, 0.7) Exposed to anti-smoking 0.6 (0.6, 0.6) 0.9 (0.9, 0.9) 0.9 (0.8, 0.9) Cigarette price category (IDR/USD) 15 000–20 000/0.91–1.21 1.7 (1.6, 1.7) 1.3 (1.3, 1.3) 1.2 (1.2, 1.2) 21 000–25 000/1.27–1.51 2.0 (2.0, 2.0) 1.8 (1.8, 1.8) 1.4 (1.4, 1.4) 26 000–30 000/1.57–1.81 2.2 (2.1, 2.2) 1.9 (1.9, 1.9) 1.7 (1.6, 1.7) >31 000/1.87 1.3 (1.2, 1.3) 0.5 (0.5, 0.5) 1.8 (1.8, 1.8) Exposure Ads Magazine 1.5 (1.5, 1.5) 1.5 (1.5, 1.5) 0.9 (0.9, 0.9) Social media 0.6 (0.6, 0.6) 0.9 (0.9, 0.9) 1.1 (1.1, 1.1) TV 0.7 (0.7, 0.7) 0.9 (0.9, 0.9) 0.9 (0.9, 0.9) Sports 1.8 (1.8, 1.9) 1.4 (1.4, 1.5) 1.2 (1.2, 1.2) Poster 1.0 (1.0, 1.1) 1.5 (1.5, 1.5) 1.0 (1.0, 1.1) Single stick cigarette purchasing 36.5 (36.1, 36.9) 36.7 (36.4, 37.0) 2.4 (2.3, 2.4) Constant 0.0 (0.0, 0.0) 0.0 (0.0, 0.0) 0.0 (0.0, 0.0) AIC/BIC 11 400 000 11 400 000 Open in a new tab Values are presented as relative risk ratio (95% confidence interval). IDR, Indonesian rupiah; USD, US dollar; Ads, advertisements; AIC, Akaike information criterion; BIC, Bayesian information criterion. 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 ). Table 3. ATT of Ads exposure on smoking relapse 1 Ads exposure ATT SE T-statistic Observations Magazine 0.075 0.007 10.450 9464 Social media −0.006 0.011 −0.540 9478 TV 0.005 0.011 0.420 9488 Sports 0.102 0.007 14.520 9483 Poster −0.007 0.007 −0.960 9427 Received free cigarettes 0.231 0.016 14.680 15 210 Open in a new tab ATT, average treatment effect on the treated; Ads, advertisements; SE, standard error. 1 Analysis used kernel matching; Covariates included sex, age group, education year, and price. 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. Footnotes 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. Data Availability The datasets analyzed in this study are publicly available from the World Health Organization (WHO) NCD Microdata Repository at https://extranet.who.int/ncdsmicrodata/index.php/catalog/GYTS . 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. doi: 10.31557/APJCP.2023.24.2.675. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 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. doi: 10.26911/thejhpb.2021.07.02.07. [ DOI ] [ Google Scholar ] 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) doi: 10.18332/tid/175755. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 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. doi: 10.21837/pm.v23i36.1703. [ DOI ] [ Google Scholar ] 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. doi: 10.1111/j.1360-0443.2009.02732.x. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 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. doi: 10.3389/fpsyt.2024.1374731. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 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. doi: 10.1002/14651858.CD003999.pub6. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 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. doi: 10.24875/RIC.18002629. [ DOI ] [ PubMed ] [ Google Scholar ] 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. doi: 10.1016/j.drugpo.2020.102838. [ DOI ] [ PubMed ] [ Google Scholar ] 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. doi: 10.1016/j.jadohealth.2020.09.025. [ DOI ] [ PubMed ] [ Google Scholar ] 13. Little RJ, Rubin DB. Statistical analysis with missing data. 3rd ed. New York: John Wiley & Sons; 2019. pp. 1–20. [ Google Scholar ] 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. doi: 10.1016/j.addbeh.2018.03.015. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 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. doi: 10.1080/00273171.2011.568786. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 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. doi: 10.47034/ppk.v3i1.4142. (Indonesian) [ DOI ] [ Google Scholar ] 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. [ PubMed ] [ Google Scholar ] 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. doi: 10.4082/kjfm.23.0010. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 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. doi: 10.2147/IJGM.S226633. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 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. doi: 10.1176/appi.ajp.160.6.1041. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 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. doi: 10.1002/wmh3.516. [ DOI ] [ Google Scholar ] 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. doi: 10.1057/jphp.2009.12. [ DOI ] [ PubMed ] [ Google Scholar ] 23. Zheng R, Marquez PV, Ahsan A, Hu X, Wang Y. Cigarette affordability in Indonesia: 2002–2017. Washington, DC: World Bank Group; 2018. [ DOI ] [ Google Scholar ] 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. doi: 10.1136/bmjopen-2024-087786. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 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. doi: 10.18332/tid/115798. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 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. doi: 10.1057/s41599-023-02124-9. [ DOI ] [ Google Scholar ] 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. doi: 10.1186/s12889-022-14727-z. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 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. doi: 10.1186/s12889-020-10090-z. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 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. doi: 10.1038/s41598-023-28161-7. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 30. van Zundert RM, Engels RC. Parental factors in association with adolescent smoking relapse. Eur Addict Res. 2009;15(4):209–215. doi: 10.1159/000231884. [ DOI ] [ PubMed ] [ Google Scholar ] 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. doi: 10.1159/000511145. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 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. doi: 10.1093/ntr/ntz096. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 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. doi: 10.1093/ntr/nty152. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 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. doi: 10.1136/tobaccocontrol-2017-054227. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 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. doi: 10.1080/10410236.2015.1099501. [ DOI ] [ PubMed ] [ Google Scholar ] 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. doi: 10.3389/fnbeh.2019.00154. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 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. pp. 213–232. [ DOI ] [ Google Scholar ] 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. doi: 10.1007/s11031-009-9137-1. [ DOI ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Data Availability Statement The datasets analyzed in this study are publicly available from the World Health Organization (WHO) NCD Microdata Repository at https://extranet.who.int/ncdsmicrodata/index.php/catalog/GYTS . Articles from Journal of Preventive Medicine and Public Health are provided here courtesy of Korean Society for Preventive Medicine ACTIONS View on publisher site PDF (630.7 KB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top