Advertising Disclaimer »

Article

Smoking Intention and Progression From E-Cigarette Use to Cigarette Smoking

Olusegun Owotomo, Haley Stritzel, Sean Esteban McCabe, Carol J. Boyd and Julie Maslowsky
Pediatrics December 2020, 146 (6) e2020002881; DOI: https://doi.org/10.1542/peds.2020-002881
Download PDF

Abstract

Video Abstract

OBJECTIVES: To investigate whether the prospective association between electronic cigarette (e-cigarette) use and cigarette smoking is dependent on smoking intention status.

METHODS: Waves 2 and 3 data of the Population Assessment of Tobacco and Health (PATH) Study, a US nationally representative prospective cohort study of tobacco use. Data were collected in 2014–2015 (wave 2) and 2015–2016 (wave 3) and analyzed in 2019.

RESULTS: At wave 2, 12.8% of adolescent never-smokers of conventional cigarettes had intention to smoke and 8.5% had ever used an e-cigarette. At wave 3, 3.2% had ever smoked a cigarette. Both smoking intention and ever using e-cigarettes at wave 2 were positively associated with cigarette smoking at wave 3 (adjusted odds ratio [aOR] = 3.03; 95% confidence interval [CI] = 1.97–4.68, P < .001; aOR = 4.62, 95% CI = 2.87–7.42, P < .001, respectively). The interaction between smoking intention and ever using e-cigarettes was significant (aOR = 0.34, 95% CI = 0.18–0.64, P < .01). Among adolescents who had expressed intention to smoke conventional cigarettes at wave 2, the odds of cigarette smoking at wave 3 did not significantly differ for e-cigarette users and never e-cigarette users (aOR = 1.57; 95% CI 0.94–2.63; P = .08). Among adolescents who had no intention to smoke at wave 2, e-cigarette users, compared with never e-cigarette users, had >4 times the odds of cigarette smoking (aOR = 4.62; 95% CI 2.87–7.42; P < .0001).

CONCLUSIONS: E-cigarette use is associated with increased odds of cigarette smoking among adolescents who had no previous smoking intention. E-cigarette use may create intention to smoke and/or nicotine use disorder that lead to onset of cigarette smoking.

  • Abbreviations:
    aOR —
    adjusted odds ratio
    CI —
    confidence interval
    e-cigarette —
    electronic cigarette
    GED —
    general equivalency diploma
    PATH —
    Population Assessment of Tobacco and Health
    PBC —
    perceived behavioral control
    TPB —
    theory of planned behavior

    • What’s Known on This Subject:

    Adolescents’ electronic cigarette (e-cigarette) use and intention to smoke cigarettes are strong predictors of future cigarette smoking. However, it is unknown whether the association of e-cigarette use with future cigarette smoking differs on the basis of adolescents’ intentions to smoke cigarettes.

    What This Study Adds:

    In prospective longitudinal data of 8661 adolescents, e-cigarette use was associated with higher risk of cigarette smoking among adolescents who had no previous smoking intention but not among those who had previously expressed smoking intention.

    Cigarette smoking remains a leading preventable cause of morbidity and mortality in the United States. Lifelong cigarette smoking is fueled by nicotine dependence (hereafter referred to as nicotine use disorder according to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria), which typically begins during adolescence.1,2 Although adolescent cigarette smoking has declined over the past several decades, electronic cigarette (e-cigarette) use presents a new risk for nicotine use disorder.1 Adolescent e-cigarette users are at heightened risk of developing symptoms of nicotine use disorder and progressing to conventional cigarette smoking.3–9 However, which e-cigarette users progress to cigarette smoking, and why, remains unclear. Some prevailing hypotheses include the potential addictiveness of nicotine-containing e-cigarettes, similar commercial and social sources for e-cigarettes and conventional cigarettes, and characteristic smoking techniques that mimic and possibly prime e-cigarette users for conventional cigarette smoking.10 Empirical evidence suggests 2 major potential pathways from e-cigarette use to cigarette smoking: an addiction pathway and a smoking intention pathway.11,12

    The addiction pathway model suggests that e-cigarettes are capable of delivering high concentrations of nicotine to the blood,13–15 which may lead to nicotine use disorder.16 Nicotine use disorder, in turn, potentially fuels continued e-cigarette use and future cigarette smoking.11 In the current study, we test the smoking intention pathway, which posits that e-cigarette use may lead to the development of smoking intention,12 namely, lack of firm commitment not to smoke cigarettes,17,18 which according to the theory of planned behavior (TPB),19–21 predicts cigarette smoking.22,23 TPB posits that attitudes, subjective norms, and perceived behavioral control (PBC) predict behavioral intention, which in turn predicts behavior.19–21 Although both e-cigarette use and smoking intention predict cigarette smoking, it remains unclear how they interact to influence cigarette smoking among adolescents. For example, does having previous smoking intention increase the risk of progressing from e-cigarette use to cigarette smoking? According to TPB, intention is more likely to lead to behavior in the presence of factors that facilitate the behavior’s performance,19–21,24 and e-cigarette use facilitates cigarette smoking.7,9 Accordingly, adolescent e-cigarette users who have previous intention to smoke cigarettes may be at a higher risk of progressing to cigarette smoking than those without smoking intention. However, emerging evidence questions whether smoking intention is a necessary antecedent of cigarette smoking among adolescent e-cigarette users.3,4,25

    In a regional study of adolescents in Southern California, e-cigarette use predicted future smoking among adolescents with no previous intention to smoke at baseline.3 This finding suggests that e-cigarettes are not simply a transitional product used by adolescents with intention to smoke on their journey to cigarette smoking, but that e-cigarette use can predispose nonintending tobacco-naive adolescents to cigarette smoking.3 In other studies, e-cigarette use predicted cigarette smoking among adolescents who were considered low risk for cigarette smoking on the basis of several factors including smoking intention.4,25 These findings challenge the importance of smoking intention as a predictor of cigarette smoking among adolescent e-cigarette users. In the current e-cigarette use epidemic, identifying whether smoking intention continues to be a reliable predictor of cigarette smoking is essential to guide adolescent smoking prevention research and intervention efforts.

    In the current study, we employ a theory-guided approach to investigate whether the prospective association between e-cigarette use and conventional cigarette smoking is dependent on baseline smoking intention status. Specifically, we use TPB to examine whether the likelihood of progressing from e-cigarette use to cigarette smoking differs on the basis of smoking intention in a national sample of adolescent never-smokers of conventional cigarettes.

    Methods

    Study Participants

    Deidentified data on adolescents aged 12 to 17 years were obtained from public-use files of the Population Assessment of Tobacco and Health (PATH) Study, a nationally representative household cohort study of tobacco use among the civilian, noninstitutionalized US population.26,27 Wave 1 data were collected in 2013–2014, wave 2 in 2014–2015, and wave 3 in 2015–2016. Waves 2 and 3 data were used in the current study because of the higher prevalence of e-cigarette use in wave 2 versus wave 1, reflecting the increased popularity of e-cigarette use in wave 2 years. Inclusion criteria for the current study were that participants were ages 12 to 17 at waves 2 and 3, reported at both waves 1 and 2 that they had never smoked conventional cigarettes, and provided valid data on cigarette smoking at wave 3 (n = 6779). Youth who were <12 years at wave 1 (“shadow youth”) but aged up to ≥12 years in wave 2 were also included (n = 1882) for a total final sample of 8661 youth. Youth aged ≥18 were not included because they have characteristics (eg, legal access to cigarettes) that differ from the target adolescent population. The current study was determined to be non–human subjects research by the sponsoring university’s institutional review board.

    Measures

    All variables were measured at wave 2 except ever smoking, which was measured at wave 3.

    Key Outcome and Predictors

    Ever smoking, the key outcome variable, was measured via the item, “In the past 12 months, have you smoked a cigarette, even one or two puffs?” Smoking intention was measured by using the item, “Do you think you will smoke a cigarette in the next year?” Responses were on a 4-point scale: 1 = “definitely yes,” 2 = “probably yes,” 3 = “probably not,” and 4 = “definitely not.” Responses were dichotomized with definitely not coded “no smoking intention” and other responses coded “smoking intention.”17,18,28,29 As specified in TPB, we operationalized smoking intention as a unique behavioral construct rather than as a component of a smoking susceptibility index as in previous studies.4,17,30–36 Ever using e-cigarettes was measured via participants’ yes or no response to the item, “Have you ever used an electronic nicotine product, even one or two times? (Electronic nicotine products, such as e-cigarettes, e-cigars, e-pipes, e-hookahs, personal vaporizers, vape pens and hookah pens).”

    Smoking-Related Covariates

    As in previous studies,10,37,38 we adjusted for TPB constructs that may confound the associations between smoking intention and e-cigarette use and cigarette smoking, including protobacco and antitobacco media exposures,39,40 attitudes and norms toward smoking,41,42 PBC,30,31,43 and 2 factors (ie, access to cigarettes and parental monitoring) that may facilitate or constrain cigarette smoking.44–46

    Exposure to antitobacco advertisements was measured by creating a composite variable from 6 items asking whether participants had seen or heard specific antismoking advertisements (eg, “Cigarettes are bullies. Don’t let tobacco control you.”). Dichotomized responses were dummy coded and summed to create a variable ranging from 0 to 6, with higher values indicating greater exposure to antitobacco advertisements. Similarly, exposure to protobacco marketing was measured by using 10 dichotomous (yes or no) items. Six items related to participants’ reports of whether they had noticed cigarettes or other tobacco products advertised in the past 30 days in print and electronic media. Four items asked about participants’ experiences with tobacco marketing in the past 6 months (eg, “Have you gotten a discount coupon for any tobacco product?”) Responses were dummy coded and summed to create an exposure index ranging from 0 to 10, with higher values indicating greater exposure to protobacco marketing. Perception of addictiveness of conventional cigarette smoking was measured by using the single item: “How likely is someone to become addicted to cigarettes?” (measured on a 5-point scale ranging from 1 = “very unlikely” to 5 = “very likely”). Perception of harm of conventional cigarette smoking was measured by averaging responses from 3 items, for example, “How much do you think people harm themselves when they smoke cigarettes?” (measured on a 4-point scale ranging from 1 = “no harm” to 4 = “a lot of harm”). Subjective norms were measured via 2 items capturing 2 dimensions of subjective norms: injunctive (perception of whether a certain group would approve of behavior) and descriptive (perception of other people’s typical behavior) norms.21,47–49 Parent or guardian disapproval of smoking conventional cigarettes (injunctive norm) was measured via the item, “If your parents or guardians found you using tobacco, how do you think they would react? Would they . . . ?” Responses were 1 = “be very upset,” 2 = “not be too upset,” and 3 = “have no reaction.” The variable was dichotomized: no reaction was considered “less disapproving,” whereas be very upset and not too upset were considered “more disapproving.” Peer smoking (descriptive norm) was measured via the item, “How many of your best friends smoke cigarettes?” Responses were on a 5-point scale ranging from 1 = “none” to 5 = “all.” PBC was measured in the PATH by using 1 item from the scale frequently used in previous studies30,31,35,36: “If one of your best friends were to offer you a cigarette, would you try it?” Responses were 1 = definitely yes, 2 = probably yes, 3 = probably not, and 4 = definitely not and were dichotomized with the response definitely not, indicating high PBC, and other responses suggesting low PBC. Access to tobacco products was measured by how easy participants think it is for people their age to buy tobacco products in a store, ranging from 1 = “very easy” to 4 = “very difficult.” Parental monitoring was measured by using parental yes or no responses on 2 items asking if the adolescent has a curfew or set time that they need to be home on school nights and weekend nights.50 Responses were summed, with higher values indicating higher parental monitoring. Participants reported other tobacco product use as ever using each of the following products: cigar, cigarillo, filtered cigar, pipe, hookah, smokeless tobacco, snus, dissolvable tobacco, bidi, and kretek. Responses were summed, with higher values representing more alternative tobacco products used. Because <1% of the sample used >1 alternative tobacco product, this variable was dichotomized to any use (1) versus none (0).

    Sociodemographic Variables

    Sociodemographic variables included race and ethnicity (white non-Hispanic, Black non-Hispanic; other non-Hispanic, and Hispanic); sex; age (categorized in the PATH public-use data file into 12–14 years and 15–17 years), and parental education (less than high school, general equivalency diploma (GED), high school degree or equivalent, some college, bachelor’s degree, or advanced degree). PATH investigators imputed missing data on race and ethnicity and parental education, as described in the user guide.51

    Statistical Analysis

    Multivariable logistic regression was used to analyze whether smoking intention moderated the association between e-cigarette use and ever smoking among adolescent never-smokers of conventional cigarettes. In model 1, smoking intention and ever using e-cigarettes at wave 2 predicted ever smoking at wave 3, with all covariates included except the interaction term (smoking intention X e-cigarette use). Model 2 added the interaction term to test whether the association between e-cigarette use and ever smoking is statistically different for adolescents with previous smoking intention versus those without.52,53 We generated odds ratios of ever smoking for each group on the basis of weighted marginal least square means coefficients using the LSMEANS statement in SAS (SAS Institute, Inc, Cary, NC).54 This procedure calculates population predicted probabilities and corresponding odds ratios at specified covariate levels.

    Missing data for each variable ranged from 0.01% (ever using other tobacco products) to 4.7% (e-cigarette use at wave 2). Data were assumed to be missing completely at random and were handled by using complete-case analysis (listwise deletion).55 Statistical analysis was conducted by using SAS version 9.4.56 As recommended, wave 3 all-waves replicate weights were applied and Fay’s variant of balanced repeated replication was used for accurate estimation of variances.26,51,57 Models were additionally weighted for attrition by wave 3. Statistical significance for all analyses was set at P < .05.

    As a sensitivity analysis to ensure that results were not due to missing data, 25 multiply imputed data sets were created in Stata 15 (Stata Corp, College Station, TX) by using chained equations.58 All independent variables were included in imputation models. The models described above were repeated by using the multiply imputed data but with no replicate or longitudinal weights. A second sensitivity analysis excluded all individuals who reported lifetime use of any tobacco products other than e-cigarettes at wave 2. Results of both sensitivity analyses were substantively the same as the primary analyses and are not reported here.

    Results

    Demographic and smoking-related characteristics of the sample are listed in Tables 1 and 2. Bivariate associations between e-cigarette use and ever smoking by smoking intention group are shown in Table 3. Among adolescents who had no smoking intention at wave 2, 9.71% of e-cigarette users, compared with 1.51% of never e-cigarette users, progressed to cigarette smoking at wave 3 (543% higher rate of progression). Among adolescents with smoking intention at wave 2, 17.36% of e-cigarette users, compared with 10.04% of never e-cigarette users, progressed to cigarette smoking at wave 3 (73% higher rate of progression). Although a larger proportion of those who intended to smoke cigarettes progressed to cigarette smoking compared with those without an intention, the relative rate of progression to cigarette smoking for e-cigarette users versus never e-cigarette users was larger among those who did not intend to smoke cigarettes than those who intended to smoke (543% vs 73%).

    View this table:
    TABLE 1

    Sample Descriptive Characteristics, N = 8661

    View this table:
    TABLE 2

    Smoking-Related Characteristics of Sample at Wave 2, N = 8661

    View this table:
    TABLE 3

    Bivariate Association of E-Cigarette Use at Wave 2 and Smoking Initiation at Wave 3, by Smoking Intention at Wave 2, n = 8242

    Regression results are contained in Table 4. These analyses adjust the above bivariate associations for a number of potential confounders prescribed by TPB. Supplemental Table 5 contains coefficients for all covariates included in Table 4. In model 1, both smoking intention (adjusted odds ratio [aOR] = 2.14; 95% CI = 1.42–3.21; P < .001) and ever using e-cigarettes (aOR = 2.58; 95% confidence interval [CI] = 1.73–3.85; P < .0001) at wave 2 were positively associated with ever smoking at wave 3. In model 2, the interaction of smoking intention and ever using e-cigarettes was significant (aOR = 0.34; 95% CI = 0.18–0.64; P < .01), suggesting the association between e-cigarette use and ever smoking was dependent on previous smoking intention status. The aORs of ever smoking by e-cigarette use and smoking intention at wave 2 derived from the least square means estimates of predicted probabilities and corresponding odds ratios are displayed in Fig 1. Among adolescents who intended to smoke conventional cigarettes at wave 2, e-cigarette use was not significantly associated with ever smoking at wave 3 (aOR = 1.57; 95% CI 0.94–2.63; P = .08). Among those without intention to smoke at wave 2, e-cigarette users had 4 times higher odds of smoking at wave 3 than never e-cigarette users (aOR = 4.62; 95% CI 2.87–7.42; P < .0001).

    View this table:
    TABLE 4

    Adjusted Multivariable Logistic Regression Revealing Interaction Between Smoking Intention and Ever Using E-Cigarettes at Wave 2 in Prediction of Ever Cigarette Smoking, n = 7644

    FIGURE 1
    FIGURE 1

    aORs for smoking initiation by e-cigarette user category and smoking intention.

    Discussion

    We used a theory-guided approach to investigate whether smoking intention moderates the association between e-cigarette use and cigarette smoking among adolescent never-smokers of conventional cigarettes. This study builds on previous work establishing smoking intention and e-cigarette use as predictors of cigarette smoking among adolescents. We found that progression from e-cigarette use to cigarette smoking in a recent national cohort of adolescents was dependent on their baseline smoking intention status. Among adolescents who had expressed intention to smoke conventional cigarettes at baseline, e-cigarette use did not predict cigarette smoking at follow-up. However, among adolescents without previous intention to smoke conventional cigarettes, e-cigarette use predicted cigarette smoking.

    Although adolescents who have no intention to smoke are generally less likely to progress to cigarette smoking than those who intend to smoke,18 our findings indicate that lack of smoking intention may not be sufficient to protect against cigarette smoking among today’s adolescent e-cigarette users. Indeed, we found that among adolescent never-smokers who had no previous smoking intention, e-cigarette users were 4 times more likely than never e-cigarette users to have smoked cigarettes 1 year later. Thus, e-cigarette use may potentially override the protective association between lack of smoking intention and cigarette smoking, such that adolescents who have no intention to smoke conventional cigarettes still may progress to cigarette smoking if they use e-cigarettes.

    Our results partly support the smoking intention pathway from e-cigarette use to conventional cigarette smoking. E-cigarette use was associated with higher odds of cigarette smoking among those who did not previously express smoking intention, corroborating findings from previous studies that adolescent e-cigarette users who progress to cigarette smoking are not simply those with previous predisposition to cigarette smoking.3,25 However, e-cigarette use was not associated with cigarette smoking among adolescents who had expressed smoking intention at baseline. These findings suggest that e-cigarette use facilitates cigarette smoking primarily among tobacco-naive adolescents with no previous smoking intentions. Because smoking intention is an established antecedent to cigarette smoking,22,23 it is plausible that adolescent e-cigarette users begin without having intention to smoke conventional cigarettes, but develop nicotine use disorder (given emerging evidence on the addictiveness of nicotine in newer e-cigarette products11,13), which creates smoking intention and subsequent conventional cigarette smoking. E-cigarette use may also provide an introduction to smoking-related behaviors, peers who use tobacco products, and the culture of tobacco product use that subsequently lead to cigarette smoking in adolescents without previous smoking intention.

    These findings are instructive for future adolescent smoking prevention efforts. With the proliferation of e-cigarettes among adolescents, absence of smoking intention may no longer be sufficient to prevent cigarette smoking. Abstinence from e-cigarette use is also necessary to reduce likelihood of conventional cigarette smoking. It is essential that health care providers, parents, and education campaigns emphasize the dangers associated with e-cigarette use, including the risk of progressing to cigarette smoking that remains even without an intention to smoke conventional cigarettes. Tailored interventions that emphasize abstinence from e-cigarette use may be effective in preventing cigarette smoking among adolescents.

    The primary limitation was the relatively long (1 year) interval between study waves. Previous studies suggest intention-behavior associations are stronger closer in time, for example, 3 or 6 months.59 Thus, our study findings should be validated with shorter wave intervals. Our study is also subject to typical limitations of self-reported data. In addition, in the current study, we examine ever using e-cigarettes and ever smoking of conventional cigarettes. Future research examining regular use of each product will be fruitful in further understanding the role of intentions in progression from e-cigarette to cigarette use. Such analyses were not possible here because of data limitations and low endorsement of regular smoking at wave 3. Nonetheless, the large, nationally representative sample and prospective study design are major strengths. Finally, the study data were collected in 2014–2016 before the recent surge in adolescent e-cigarette use and the influx of newer and potentially more addictive e-cigarette products.13 Given that adolescent e-cigarette use is currently even higher than at the time of our study, it is likely that more adolescents are at increased risk of nicotine use disorder and subsequent smoking initiation.

    Conclusions

    E-cigarette use was associated with higher odds of cigarette smoking only among adolescents who had no previous intention to smoke conventional cigarettes, suggesting adolescent e-cigarette users can progress to cigarette smoking even when they have no previous intentions to do so. Pediatricians should continue to screen for and counsel adolescents against e-cigarette use to prevent onset of cigarette smoking.60 Indeed, abstinence from e-cigarette use should be framed as an adolescent smoking prevention strategy.

    Footnotes

      • Accepted September 16, 2020.
    • Address correspondence to Julie Maslowsky, PhD, Department of Kinesiology and Health Education, College of Education, The University of Texas at Austin, 2109 San Jacinto Blvd, D3700, Austin, TX 78712. E-mail: maslowsky{at}austin.utexas.edu

    • FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

    • FUNDING: Supported by grants from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (K01HD091416 and P2CHD042849), National Cancer Institute (R01CA203809), National Institute on Drug Abuse (R01DA44157), and from the William T. Grant Foundation Scholars Program. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders. Funded by the National Institutes of Health (NIH).

    • POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

    References

    1. ↵
      1. US Department of Health and Human Services
      . E-Cigarette Use Among Youth and Young Adults. A Report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health; 2016
    2. ↵
      1. Chung T,
      2. Martin CS,
      3. Maisto SA,
      4. Cornelius JR,
      5. Clark DB
      . Greater prevalence of proposed DSM-5 nicotine use disorder compared to DSM-IV nicotine dependence in treated adolescents and young adults. Addiction. 2012;107(4):810–818
      OpenUrlCrossRefPubMed
    3. ↵
      1. Barrington-Trimis JL,
      2. Urman R,
      3. Berhane K, et al
      . E-cigarettes and future cigarette use. Pediatrics. 2016;138(1):e20160379
      OpenUrlAbstract/FREE Full Text
    4. ↵
      1. Berry KM,
      2. Fetterman JL,
      3. Benjamin EJ, et al
      . Association of electronic cigarette use with subsequent initiation of tobacco cigarettes in US youths. JAMA Netw Open. 2019;2(2):e187794
      OpenUrl
      1. Bold KW,
      2. Kong G,
      3. Camenga DR, et al
      . Trajectories of e-cigarette and conventional cigarette use among youth. Pediatrics. 2018;141(1):e20171832
      OpenUrlAbstract/FREE Full Text
      1. Miech R,
      2. Patrick ME,
      3. O’Malley PM,
      4. Johnston LD
      . E-cigarette use as a predictor of cigarette smoking: results from a 1-year follow-up of a national sample of 12th grade students. Tob Control. 2017;26(e2):e106–e111
      OpenUrlAbstract/FREE Full Text
    7. ↵
      1. Soneji S,
      2. Barrington-Trimis JL,
      3. Wills TA, et al
      . Association between initial use of e-cigarettes and subsequent cigarette smoking among adolescents and young adults: a systematic review and meta-analysis. JAMA Pediatr. 2017;171(8):788–797
      OpenUrl
      1. Unger JB,
      2. Soto DW,
      3. Leventhal A
      . E-cigarette use and subsequent cigarette and marijuana use among Hispanic young adults. Drug Alcohol Depend. 2016;163(suppl C):261–264
      OpenUrlPubMed
    9. ↵
      1. National Academies of Sciences, Engineering, and Medicine
      . Public Health Consequences of E-Cigarettes. Washington, DC: The National Academies Press; 2018
    10. ↵
      1. Schneider S,
      2. Diehl K
      . Vaping as a catalyst for smoking? An initial model on the initiation of electronic cigarette use and the transition to tobacco smoking among adolescents. Nicotine Tob Res. 2016;18(5):647–653
      OpenUrlCrossRefPubMed
    11. ↵
      1. Goldenson NI,
      2. Leventhal AM,
      3. Stone MD,
      4. McConnell RS,
      5. Barrington-Trimis JL
      . Associations of electronic cigarette nicotine concentration with subsequent cigarette smoking and vaping levels in adolescents. JAMA Pediatr. 2017;171(12):1192–1199
      OpenUrl
    12. ↵
      1. Primack BA,
      2. Soneji S,
      3. Stoolmiller M,
      4. Fine MJ,
      5. Sargent JD
      . Progression to traditional cigarette smoking after electronic cigarette use among us adolescents and young adults. JAMA Pediatr. 2015;169(11):1018–1023
      OpenUrl
    13. ↵
      1. Jackler RK,
      2. Ramamurthi D
      . Nicotine arms race: JUUL and the high-nicotine product market. Tob Control. 2019;28(6):623–628
      OpenUrlAbstract/FREE Full Text
      1. Vansickel AR,
      2. Weaver MF,
      3. Eissenberg T
      . Clinical laboratory assessment of the abuse liability of an electronic cigarette. Addiction. 2012;107(8):1493–1500
      OpenUrlCrossRefPubMed
    15. ↵
      1. Vansickel AR,
      2. Eissenberg T
      . Electronic cigarettes: effective nicotine delivery after acute administration. Nicotine Tob Res. 2013;15(1):267–270
      OpenUrlCrossRefPubMed
    16. ↵
      1. Kandel ER,
      2. Kandel DB
      . Shattuck Lecture. A molecular basis for nicotine as a gateway drug. N Engl J Med. 2014;371(10):932–943
      OpenUrlCrossRefPubMed
    17. ↵
      1. Choi WS,
      2. Gilpin EA,
      3. Farkas AJ,
      4. Pierce JP
      . Determining the probability of future smoking among adolescents. Addiction. 2001;96(2):313–323
      OpenUrlCrossRefPubMed
    18. ↵
      1. Wakefield M,
      2. Kloska DD,
      3. O’Malley PM, et al
      . The role of smoking intentions in predicting future smoking among youth: findings from Monitoring the Future data. Addiction. 2004;99(7):914–922
      OpenUrlCrossRefPubMed
    19. ↵
      1. Ajzen I
      . The theory of planned behavior. Organ Behav Hum Decis Process. 1991;50(2):179–211
      OpenUrlCrossRef
      1. Ajzen I,
      2. Albarracin D
      . Predicting and Changing Behavior: A Reasoned Action Approach. In: Ajzen I, Albarracin D, Hornik R, eds. Prediction and Change of Health Behavior: Applying the Reasoned Action Approach. Mahwah, NJ: Lawrence Erlbaum Associates; 2007:3–18
    21. ↵
      1. Ajzen I
      . Martin Fishbein’s legacy: the reasoned action approach. Ann Am Acad Pol Soc Sci. 2012;640(1):11–27
      OpenUrlCrossRef
    22. ↵
      1. DeVries HD,
      2. Backbier E,
      3. Kok G,
      4. Dijkstra M
      . The impact of social influences in the context of attitude, self-efficacy, intention, and previous behavior as predictors of smoking onset. J Appl Soc Psychol. 1995;25(3):237–257
      OpenUrlCrossRef
    23. ↵
      1. Pierce JP,
      2. Choi WS,
      3. Gilpin EA,
      4. Farkas AJ,
      5. Merritt RK
      . Validation of susceptibility as a predictor of which adolescents take up smoking in the United States. Health Psychol. 1996;15(5):355–361
      OpenUrlCrossRefPubMed
    24. ↵
      1. Sheeran P
      . Intention-behavior relations: a conceptual and empirical review. Eur Rev Soc Psychol. 2002;12(1):1–36
      OpenUrlCrossRef
    25. ↵
      1. Wills TA,
      2. Sargent JD,
      3. Gibbons FX,
      4. Pagano I,
      5. Schweitzer R
      . E-cigarette use is differentially related to smoking onset among lower risk adolescents. Tob Control. 2016;26(5):534–539
      OpenUrl
    26. ↵
      1. Hyland A,
      2. Ambrose BK,
      3. Conway KP, et al
      . Design and methods of the Population Assessment of Tobacco and Health (PATH) Study. Tob Control. 2017;26(4):371–378
      OpenUrlAbstract/FREE Full Text
    27. ↵
      1. United States Department of Health and Human Services
      2. National Institutes of Health
      3. National Institute on Drug Abuse
      4. United States Department of Health and Human Services
      5. Food and Drug Administration
      6. Center for Tobacco Products
      . Population Assessment of Tobacco and Health (PATH) Study. [United States] Public-Use Files. Ann Arbor, MI: Inter-university Consortium for Political and Social Research; 2019
    28. ↵
      1. Barrington-Trimis JL,
      2. Berhane K,
      3. Unger JB, et al
      . The e-cigarette social environment, e-cigarette use, and susceptibility to cigarette smoking. J Adolesc Health. 2016;59(1):75–80
      OpenUrl
    29. ↵
      1. Bunnell RE,
      2. Agaku IT,
      3. Arrazola RA, et al
      . Intentions to smoke cigarettes among never-smoking US middle and high school electronic cigarette users: National Youth Tobacco Survey, 2011-2013. Nicotine Tob Res. 2015;17(2):228–235
      OpenUrlCrossRefPubMed
    30. ↵
      1. Van de Ven MOM,
      2. Van den Eijnden RJJM,
      3. Engels RCME
      . Smoking-specific cognitions and smoking behaviour among adolescents with asthma. Psychol Health. 2006;21(6):699–716
      OpenUrlCrossRef
    31. ↵
      1. van den Eijnden RJJM,
      2. Spijkerman R,
      3. Engels RCME
      . Relative contribution of smoker prototypes in predicting smoking among adolescents: a comparison with factors from the theory of planned behavior. Eur Addict Res. 2006;12(3):113–120
      OpenUrlCrossRefPubMed
      1. Pierce JP,
      2. Sargent JD,
      3. Portnoy DB, et al
      . Association between receptivity to tobacco advertising and progression to tobacco use in youth and young adults in the path study. JAMA Pediatr. 2018;172(5):444–451
      OpenUrl
      1. Soneji S,
      2. Pierce JP,
      3. Choi K, et al
      . Engagement with online tobacco marketing and associations with tobacco product use among U.S. Youth. J Adolesc Health. 2017;61(1):61–69
      OpenUrlPubMed
      1. Strong DR,
      2. Hartman SJ,
      3. Nodora J, et al
      . Predictive validity of the expanded susceptibility to smoke index. Nicotine Tob Res. 2015;17(7):862–869
      OpenUrlCrossRefPubMed
    35. ↵
      1. De Vries H,
      2. Dijkstra M,
      3. Kuhlman P
      . Self-efficacy: the third factor besides attitude and subjective norm as a predictor of behavioural intentions. Health Educ Res. 1988;3(3):273–282
      OpenUrlCrossRef
    36. ↵
      1. Engels RC,
      2. Knibbe RA,
      3. de Vries H,
      4. Drop MJ
      . Antecedents of smoking cessation among adolescents: who is motivated to change? Prev Med. 1998;27(3):348–357
      OpenUrlCrossRefPubMed
    37. ↵
      1. Chapman S,
      2. Bareham D,
      3. Maziak W
      . The gateway effect of e-cigarettes: reflections on main criticisms. Nicotine Tob Res. 2019;21(5):695–698
      OpenUrlCrossRef
    38. ↵
      1. Etter JF
      . Gateway effects and electronic cigarettes. Addiction. 2018;113(10):1776–1783
      OpenUrl
    39. ↵
      1. Dube SR,
      2. Arrazola RA,
      3. Lee J,
      4. Engstrom M,
      5. Malarcher A
      . Pro-tobacco influences and susceptibility to smoking cigarettes among middle and high school students–United States, 2011. J Adolesc Health. 2013;52(suppl 5):S45–S51
      OpenUrlCrossRefPubMed
    40. ↵
      1. Farrelly MC,
      2. Davis KC,
      3. Haviland ML,
      4. Messeri P,
      5. Healton CG
      . Evidence of a dose-response relationship between “truth” antismoking ads and youth smoking prevalence. Am J Public Health. 2005;95(3):425–431
      OpenUrlCrossRefPubMed
    41. ↵
      1. Owotomo O,
      2. Maslowsky J,
      3. Loukas A
      . Perceptions of the harm and addictiveness of conventional cigarette smoking among adolescent e-cigarette users. J Adolesc Health. 2018;62(1):87–93
      OpenUrl
    42. ↵
      1. Gritz ER,
      2. Prokhorov AV,
      3. Hudmon KS, et al
      . Predictors of susceptibility to smoking and ever smoking: a longitudinal study in a triethnic sample of adolescents. Nicotine Tob Res. 2003;5(4):493–506
      OpenUrlCrossRefPubMed
    43. ↵
      1. Carvajal SC,
      2. Hanson C,
      3. Downing RA,
      4. Coyle KK,
      5. Pederson LL
      . Theory-based determinants of youth smoking: a multiple influence approach. J Appl Soc Psychol. 2004;34(1):59–84
      OpenUrlCrossRef
    44. ↵
      1. Bohnert KM,
      2. Ríos-Bedoya CF,
      3. Breslau N
      . Parental monitoring at age 11 and smoking initiation up to age 17 among Blacks and Whites: a prospective investigation. Nicotine Tob Res. 2009;11(12):1474–1478
      OpenUrlCrossRefPubMed
      1. Spivak AL,
      2. Monnat SM
      . Prohibiting juvenile access to tobacco: violation rates, cigarette sales, and youth smoking. Int J Drug Policy. 2015;26(9):851–859
      OpenUrlCrossRefPubMed
    46. ↵
      1. White MM,
      2. Gilpin EA,
      3. Emery SL,
      4. Pierce JP
      . Facilitating adolescent smoking: who provides the cigarettes? Am J Health Promot. 2005;19(5):355–360
      OpenUrlCrossRefPubMed
    47. ↵
      1. Fishbein M,
      2. Ajzen I
      . Predicting and Changing Behavior: The Reasoned Action Approach. New York, NY: Psychology Press; 2010
      1. Rimal RN,
      2. Real K
      . Understanding the influence of perceived norms on behaviors. Commun Theory. 2003;13(2):184–203
      OpenUrlCrossRef
    49. ↵
      1. Rivis A,
      2. Sheeran P
      . Descriptive norms as an additional predictor in the theory of planned behaviour: a meta-analysis. Curr Psychol. 2003;22(3):218–233
      OpenUrlCrossRef
    50. ↵
      1. Dever BV,
      2. Schulenberg JE,
      3. Dworkin JB,
      4. O’Malley PM,
      5. Kloska DD,
      6. Bachman JG
      . Predicting risk-taking with and without substance use: the effects of parental monitoring, school bonding, and sports participation. Prev Sci. 2012;13(6):605–615
      OpenUrlPubMed
    51. ↵
      1. United States Department of Health and Human Services
      2. National Institutes of Health
      3. National Institute on Drug Abuse
      4. United States Department of Health and Human Services
      5. Food and Drug Administration
      6. Center for Tobacco Products
      . PATH Study Public-Use Files User Guide. Ann Arbor, MI: Inter-university Consortium for Political and Social Research; 2018
    52. ↵
      1. Dawson JF
      . Moderation in management research: what, why, when, and how. J Bus Psychol. 2014;29(1):1–19
      OpenUrlCrossRef
    53. ↵
      1. Robinson CD,
      2. Tomek SE,
      3. Schumacker RE
      . Tests of moderation effects: difference in simple slopes versus the interaction term. Multiple Linear Regression Viewpoints. 2013;39(1):16–24
      OpenUrl
    54. ↵
      SAS Institute, Inc. SAS/STAT 9.22 User’s Guide. Cary, NC: SAS Institute, Inc; 2010. Available at: https://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/viewer.htm#statug_introcom_a0000003027.htm#statug.introcom.lsmbylevel. Accessed March 5, 2020
    55. ↵
      1. Pigott TD
      . A review of methods for missing data: educational research and evaluation. Educ Res Eval. 2001;7(4):35–383
      OpenUrl
    56. ↵
      . SAS Software [computer program]. Version 9.4. Cary, NC: SAS Institute, Inc; 2018
    57. ↵
      1. Zinn S
      . Variance Estimation with Balanced Repeated Replication: An Application to the Fifth and Ninth Grader Cohort Samples of the National Educational Panel Study. In: Blossfeld HP, von Maurice J, Bayer M, Skopek J, eds. Methodological Issues of Longitudinal Surveys: The Example of the National Educational Panel Study. Wiesbaden, Germany: Springer Fachmedien Wiesbaden; 2016:63–84
    58. ↵
      . Stata Statistical Software [computer program]. Release 15. College Station, TX: StataCorp LLC; 2017
    59. ↵
      1. Topa G,
      2. Moriano JA
      . Theory of planned behavior and smoking: meta-analysis and SEM model. Subst Abuse Rehabil. 2010;1:23–33
      OpenUrlPubMed
    60. ↵
      1. Gaiha SM,
      2. Halpern-Felsher B
      . Public health considerations for adolescent initiation of electronic cigarettes. Pediatrics. 2020;145(suppl 2):S175–S180
      OpenUrlAbstract/FREE Full Text
    Back to top

    In this issue

    Pediatrics
    Vol. 146, Issue 6
    1 Dec 2020
    View this article with LENS
    Email Article
    Request Permissions
    Article Alerts
    Citation Tools
    Smoking Intention and Progression From E-Cigarette Use to Cigarette Smoking
    Olusegun Owotomo, Haley Stritzel, Sean Esteban McCabe, Carol J. Boyd, Julie Maslowsky
    Pediatrics Dec 2020, 146 (6) e2020002881; DOI: 10.1542/peds.2020-002881
    del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
    Print
    Download PDF
    Insight Alerts

    Jump to section