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. Author manuscript; available in PMC: 2016 Apr 21.
Published in final edited form as: Tob Control. 2015 Aug 10;25(E1):e52–e59. doi: 10.1136/tobaccocontrol-2015-052349

E-cigarette Use and Willingness to Smoke in a Sample of Adolescent Nonsmokers

Thomas A Wills 1, James D Sargent 2, Rebecca Knight 1, Ian Pagano 1, Frederick X Gibbons 3
PMCID: PMC4840020  NIHMSID: NIHMS776717  PMID: 26261237

Abstract

Objective

There is little evidence on the consequences of electronic cigarette (e-cigarette) use in adolescence. With a multiethnic sample of nonsmokers, we assessed the relation between e-cigarette use and social-cognitive factors that predict smoking combustible cigarettes (cigarettes).

Methods

School-based cross-sectional survey of 2,309 high school students (M age 14.7 years). Participants reported on e-cigarette use and cigarette use; on smoking-related cognitions (smoking expectancies, prototypes of smokers) and peer smoker affiliations; and on willingness to smoke cigarettes. Regression analyses conducted for non-cigarette smokers tested the association between e-cigarette use and willingness to smoke cigarettes, controlling for demographics, parenting, academic and social competence, and personality variables. Structural equation modeling (SEM) analyses tested whether the relation between e-cigarette use and willingness was mediated through any of the three smoking-related variables.

Results

Nonsmokers who had used e-cigarettes (18% of the total sample) showed more willingness to smoke cigarettes compared to those who had never used any tobacco product; the adjusted odds ratio was 2.35 (95% confidence interval 1.73 – 3.19). Additionally, willingness prospectively predicted smoking onset. SEM showed that the relation between e-cigarette use and willingness to smoke was partly mediated through more positive expectancies about smoking but there was also a direct path from e-cigarette use to willingness.

Conclusions

Among adolescent nonsmokers, e-cigarette use is associated with willingness to smoke, a predictor of future cigarette smoking. The results suggest that use of e-cigarettes by adolescents is not without attitudinal risk for cigarette smoking. These findings have implications for formulation of policy about access to e-cigarettes by adolescents.

Keywords: e-cigarettes, adolescents, willingness, cigarette smoking, mediation

BACKGROUND

Use of electronic smoking devices (hereafter, e-cigarettes) has been increasing rapidly among adolescents.[1–3] The prevalence of e-cigarette use among adolescents has been increasing 2–3 times or more every year in the US[1, 2] and in other countries including Finland, Korea, and Poland.[4–6] Recent US studies have shown a prevalence of ever-use among high school students ranging from 10% to 25% of the adolescent population[7, 8], with comparable rates observed among adolescents in European and Asian countries.[3]

The increasing prevalence of e-cigarette use has sparked a debate about policy implications. Some have argued that e-cigarettes offer a lower-risk approach that may result in cessation of smoking tobacco cigarettes (hereafter, cigarettes) so restrictions on e-cigarette use should be minimal.[9] Others have raised a concern that frequent e-cigarette use in public places and aggressive advertising for e-cigarettes in venues where cigarette advertising was previously banned may lead to a renormalization of cigarette smoking, which would argue for restrictions on e-cigarette use.[1,10] Whether e-cigarette use encourages or discourages smoking among adolescents is an important policy question; however, there is little evidence bearing on this question. Accordingly, the present research tested whether e-cigarette use is associated with adolescents’ interest in smoking cigarettes and, if so, how this relation is mediated.

Consequences of E-cigarette Use in Adolescence

A possible consequence of e-cigarette use is that it affects attitudes toward smoking cigarettes. One perspective on this issue would suggest that adolescents take up e-cigarette use because of curiosity and normative exploration typical of adolescence[11–13] and may receive relatively low doses of nicotine.[14] An alternative formulation notes that in addition to exposing the adolescent to nicotine, e-cigarettes mimic the sensory characteristics of smoking, and advertising campaigns for e-cigarettes are using the same strategies (e.g., independence, social attractiveness) that were traditionally successful in attracting adolescents to smoking but in a variety of new venues attractive to adolescents, such as the internet, social media, and prime-time television.[15–18] Epidemiologic studies in fact show that a substantial proportion of adolescents are dual users, engaging in both e-cigarette use and cigarette smoking[19,20] but it remains unclear whether e-cigarettes may serve as a cognitive/attitudinal gateway to cigarette smoking.[1,3]

At present, two published studies have considered how e-cigarette use is related to interest in smoking. Bunnell et al.[21] used school-based survey data from representative national samples of 6th–12th grade students, with data collected in 2011, 2012, and 2013. Two items on intention to smoke were administered with 4-point response scales; participants who said “definitely no” to both questions were coded as not having intention to smoke, otherwise they were coded as having intention. Analyses of data for nonsmokers indicated smoking intention was higher among ever e-cigarette users (43.9%) compared with youth who had never used e-cigarettes (21.5%). Coleman et al.[22] used data on persons 18–29 years of age from a nationally representative random-digit dial telephone survey conducted in 2012–2013. Respondents were classified as nonsmokers if they said no to questions about whether they had smoked at least 100 cigarettes lifetime and whether they smoked now. Intention to smoke, indexed by procedures similar to Bunnell[21], was present for 46% of those who had tried e-cigarettes compared with 14% for those who had not. Thus these studies, which had some multivariate controls, both indicated that e-cigarette use was related to more interest in smoking.

A primary issue is that these studies relied on measures of intention to smoke. Though measures of intentions can have value for predicting health-promoting behaviors such as immunization[23] they have less ability to predict risk behaviors such as smoking, because such behaviors in adolescence are not necessarily guided by reasoned decisions and are more influenced by social influences and reactions to situations.[24] This reactive pathway is typically assessed with measures of willingness, defined as an openness to opportunity in situations where substances happen to be available.[25] Studies of adolescents have indicated that willingness is often a better predictor of smoking compared with intention[26, 27] so previous studies focused on intention may provide an underestimate of the effect of e-cigarette use. Therefore we examined the effect of e-cigarette use on willingness to smoke among adolescent nonsmokers, using a measure that has been validated for predicting onset of smoking in adolescence.[25, 28] We included a range of psychosocial covariates that have not been assessed in previous studies and employed both continuous and dichotomous approaches for analyzing willingness because critics of e-cigarette research[29] have questioned how constructs such as intention have been defined.

METHODS

Participants and Procedure

The participants were 2,309 students (76% response rate) in four public and two private high schools (100% response rate) on Oahu, Hawaii. Data on e-cigarettes were first obtained in this study during 2013 and early 2014. The sample (48% 9th graders, 43% 10th graders, 9% 11th graders) was 53% female and mean age was 14.7 years (SD 0.7). Regarding race/ethnicity, 25% of the participants were of Asian-American background (Chinese, Japanese, or Korean), 19% were Caucasian, 27% were Filipino-American, 20% were Native Hawaiian or other Pacific Islander, and 9% were of Other race/ethnicity. Regarding family structure, 17% of participants were living with a single parent, 12% were in a stepparent family, 60% were with two biological parents, and 11% were in an extended family structure. The mean for father’s education on a 1–6 scale was 4.2 (SD 1.2), indicating some education beyond high school.

A self-report survey was administered to students in classrooms by trained research staff. Students were instructed that the data were totally confidential and they should not write their name on the survey. The research procedure was reviewed and approved by the Institutional Review Boards of the University of Hawaii and the Hawaii State Department of Education. A consent form was sent to parents, and students with parental consent read an assent form emphasizing that participation was voluntary and data were confidential. Research assistants gave general instructions to a class and then distributed a paper survey to assenting students. The assistants remained in the classroom while students worked on the survey to answer any individual questions about particular items.

Measures

The measures had generally been validated in other populations[30–32] but scale structure was verified with factor analysis and internal consistency analysis. A higher score reflects more of the attribute in the variable label.

Demographics

The student was asked to indicate his/her gender and write in his/her age in years. The family structure item asked “What adults do you live with right now?” Nine response alternatives were provided and the student was told to check one or more as appropriate. For ethnicity, the student was first given 14 fixed ethnic options and was asked “What would you say you are” with the instruction to check one or more as appropriate. A following question told the student that if he/she had checked more than one ethnicity to indicate “If you had to choose only one, what would you say?” with a write-in response. A coding of responses to the latter item was used to index primary perceived ethnicity. Parental education items asked, “What is the highest level of education your father/mother has completed” with six fixed responses having anchor points Grade School and Post-College.

E-cigarette item

The item on e-cigarettes was introduced with the stem: “Which of the following is most true for you about smoking electronic cigarettes (E-cigarettes, Volcanos)? (Check One)” Responses were on a 7-point scale with anchor points Never Smoked an E-cigarette in My Life to Usually Smoke E-cigarettes Every Day.

Cigarette item

The item on cigarettes was introduced with the stem: “Which of the following is most true for you about smoking cigarettes? (Check One)” Responses were on a 7-point scale with anchor points Never Smoked Cigarettes in My Life to Usually Smoke Cigarettes Every Day.

Willingness items

The three items in the willingness measure were introduced with the stem: “Suppose you were with a group of friends and there were some cigarettes you could have if you wanted. How willing would you be to: Take one puff / Smoke a whole cigarette / Take some cigarettes to try later.” Responses were on 4-point scales with response points Not At All Willing (0); A Little Willing (1); Somewhat Willing (2); and Very Willing (3). A composite score for willingness to smoke was the sum of the three items (α = .91).

Psychosocial covariates

Variables included as covariates, because they might be correlated with e-cigarette use and with willingness[32–34], are summarized in Table 1. Several measures were derived from parenting theory[35] and assessed the quality of the parent-adolescent relationship. Two measures were derived from social-cognitive theory[36] and assessed perceptions of competence and efficacy in academic and social situations. Two measures were derived from theory on deviance-proneness[37] and sensation seeking[38] and assessed the tendency to rebel against constraints on behavior and to desire intense, novel stimulation and exciting activities.

Table 1.

Variables used as covariates and mediators in regression and structural modeling analyses

Variable (items) α Sample item
COVARIATES
Parental support (5)a .94 When I feel bad about something, my parent will listen.
Parental monitoring (5)a .75 My parent asks me what I do with my friends.
Parent-adolescent conflict (3)a .83 I have a lot of arguments with my parent.
Academic competence (5)a .79 I like school because I do well in class.
Social competence (5)a .81 I find it easy to make friends with other teens.
Sensation seeking (5)a .75 I like to do things that are a little frightening.
Rebelliousness (4)a .84 I like to break the rules.
MEDIATORS
Smoking expectancies (5)a .94 Smoking helps you feel more self-confident.
Prototypes of smokers (4)b .80 The type of person your age who smokes is popular.
Peer smoker affiliation (1)c .na Do any of your friends smoke cigarettes?
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Note:

a

response = 1–5 Likert scale (Not at all true - Very true).

b

response = 1–5 adjective scale (Not at all - Very)

c

Response = 1–5 count scale (None of my friends - 4 or more of my friends).

na = not applicable.

Hypothesized mediators

Three variables, also summarized in Table 1, represented plausible intermediate pathways in the relation between e-cigarette use and to willingness to smoke [for theoretical basis see 28,32,34]. A 5-item scale on positive expectancies about cigarette smoking assessed whether smoking was perceived as enhancing social confidence and providing relaxation and tension reduction. A 4-item scale on prototypes of smokers assessed the extent to which the typical same-age teen who smoked was perceived as popular, attractive, and cool. The item on peer smoking assessed the respondent’s affiliation with peers who smoked.

Data Analysis

Analyses were performed in SAS and Mplus.[39] Missing data rates were generally low for individual variables (1%–2%) but parental education was missing for 20% of the sample and multiple imputation (Proc MI in SAS) was used for regression analyses. Among persons who had never smoked a cigarette, t-test compared the mean level of willingness for persons who had used e-cigarettes and persons who had never used any tobacco product. We computed adjusted means in SAS including 16 covariates and examined how mean willingness varied as a function of level of e-cigarette use. The covariates included in the analyses were gender (dichotomous); ethnicity (four binary variables contrasting Caucasian, Native Hawaiian, Filipino, and other ethnicity against Asian Americans as the reference group); family structure (three binary variables, contrasting Single Parent, Blended Family, and Extended Family against Intact family as the reference group); and parental education, dichotomized to high school graduate or less and some college or more. Also included as covariates were continuous scores for parenting (parental support, monitoring, and conflict), social-cognitive variables (academic and social competence), and personality variables (rebelliousness and sensation seeking). Logistic and linear regression analyses including covariates determined the relation between e-cigarette use and willingness.

Structural equation modeling was then conducted in Mplus with the EM algorithm used to include missing data in the analysis and school included as a clustering factor. The model was specified with e-cigarette use and the covariates as exogenous. The three hypothesized mediators were specified as endogenous, with residual covariances of their error terms, and willingness to smoke was the criterion. Structural modeling analyses were performed with the criterion variable specified alternately as continuous and as dichotomous. The former model was estimated using maximum likelihood with robust estimates of standard errors, the latter model was estimated using the weighted least squares method.

RESULTS

Regarding prevalence, 31% of the participants had ever used e-cigarettes and 16% had ever smoked tobacco cigarettes. Cross-classification indicated 18% of the participants had used e-cigarettes but never smoked cigarettes.

E-cigarette Use and Willingness to Smoke

The analytic sample was restricted to participants who had used e-cigarettes but had never smoked cigarettes (n = 418) and participants who had never used either product (hereafter nonusers; n = 1,526). The mean score for willingness to smoke in this sample of nonsmokers was 0.28 (SD 0.84) and the distribution had skewness of 3.87; a log transform reduced skewness to 3.01. The mean score for willingness was 0.21 (SD 0.70) among nonusers and was 0.55 (SD 1.19) among e-cigarette users; the t (unequal variances) was 5.71 (p < .0001). Unadjusted means and adjusted means for willingness (controlling for the 16 covariates) are presented in Table 2 by frequency of e-cigarette use. Willingness to smoke was significantly higher for all levels of e-cigarette use compared with nonusers, but pairwise comparisons using Tukey-Kramer adjustment indicated means for different levels of e-cigarette use did not differ significantly from each other. Accordingly, a dichotomous index for e-cigarette use (never used vs. ever used) was employed in subsequent analyses.

Table 2.

Mean (SE in parentheses) for willingness to smoke in relation to level of e-cigarette use among adolescent nonsmokers

Frequency of use [n of cases]
Never 1–2 times 3–4 times Yearly/monthly Weekly/daily
Analysis [1526] [160] [169] [50] [39] F
Unadjusted 0.16 (0.02) 0.39a (0.05) 0.37a (0.05) 0.57a (0.09) 0.56a (0.10) 15.50****
Adjusted 0.17 (0.02) 0.33a (0.05) 0.33a (0.05) 0.47a (0.09) 0.47a (0.10) 7.32****
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Note: Adjusted means based on 16 covariates including demographics, parenting, competence, and personality. Higher levels of e-cigarette use are collapsed in cells for yearly/monthly use and weekly/daily use so as to increase power for pairwise comparisons. Cells with common subscript do not differ significantly (p > .05) in pairwise comparisons with Tukey-Kramer adjustment.

****

indicates p < .0001.

Logistic regression analysis, with e-cigarette use status (never vs. ever) as the predictor and dichotomized willingness to smoke (zero willingness vs. any willingness) as the criterion, indicated 26% of e-cigarette users showed willingness to smoke compared with 11% of nonusers. This difference was significant, with adjusted odds ratio = 2.35, 95% Confidence Interval (CI) = 1.73 – 3.19. Multiple regression with a continuous score for willingness (log transformed) as the criterion indicated the standardized regression coefficient for e-cigarette use was β = .18 (t = 6.54, p < .0001) in the unadjusted analysis and was β = .13 (t = 4.39, p < .0001) in the adjusted analysis (including 16 covariates). Thus the relation between e-cigarette use and willingness to smoke was significant for both dichotomous and continuous indices of willingness and was independent of a range of demographic and psychosocial covariates.

Structural Equation Modeling of Mediation

Structural equation modeling analysis was performed in Mplus, with the model specified as described previously. The zero-order correlations among e-cigarette use, the covariates, the hypothesized mediators, and willingness to smoke are presented in Table 3. Almost all of the psychosocial covariates were significantly related to e-cigarette use. The hypothesized mediators all had significant zero-order correlations with willingness; the highest correlations were for expectancies and prototypes and the lowest was for peer smoker affiliations.

Table 3.

Correlations of study variables

Variable 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21.
1. E-cigarette use .--
2. Gender (male) .05 .--
3. Native Hawaiian .12 −.04 .--
4. Filipino .07 .00 −.30 .--
5. Caucasian −.03 .06 −.22 −.30 .--
6. Other ethnicity .03 .00 −.15 −.20 −.15 .--
7. Single parent .08 .00 .02 −.02 .01 .10 .--
8. Blended family .02 −.03 .02 −.08 .04 .04 −.14 .--
9. Extended family .07 .01 .04 .18 −.12 −.06 −.15 −.12 .--
10. Parent education −.10 −.01 −.20 −.08 .06 .01 −.09 −.05 −.08 .--
11. Parental support −.13 .06 .01 −.14 .05 .02 −.03 −.05 −.05 .03 .--
12. Par. monitoring −.11 −.02 .06 −.03 −.04 .03 −.03 −.04 −.03 −.03 .41 .--
13. Par.-adol. conflict .13 −.07 .01 .03 −.02 .02 .03 .06 .01 −.02 −.55 −.09 .--
14. Academic involve −.18 −.03 −.08 −.01 −.03 −.01 −.04 −.02 −.06 .10 .35 .32 −.23 .--
15. Social competence .05 .14 .06 −.10 .06 .01 −.02 −.03 −.03 .05 .29 .17 −.16 .44 .--
16. Sensation seeking .21 .10 .11 .06 .03 .02 .04 .07 .09 −.05 −.15 −.09 .22 −.12 .10 .--
17. Rebelliousness .27 .16 .11 −.04 .06 .02 .03 .05 .04 −.03 −.25 −.20 .35 −.29 .06 .49 .--
18. Peer smoking .20 .06 .07 −.04 .02 .01 .07 .04 .04 −.05 −.12 −.09 .18 −.16 .09 .25 .30 .--
19. Smok. expectancy .11 .02 −.01 .00 .01 −.03 .01 −.04 .04 .01 −.09 −.10 .12 −.10 −.03 .14 .20 .17 .--
20. Smoker prototype .04 −.05 −.01 .02 −.02 .04 .01 .00 .01 −.01 −.07 −.03 .13 −.11 −.05 .11 .13 .24 .20 .--
21. Willingness smoke .18 −.04 −.01 −.01 .02 −.01 .00 .00 .01 .04 −.14 −.13 .18 −.14 −.06 .16 .23 .15 .25 .17 .--
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Note: N for correlations = 1,944. Approximate significance levels are: |r| > .06, p < .01; |r| > .08, p < .001; r > |.10|, p < .0001.

Structural modeling analyses were performed with willingness as a continuous variable (log transformed) and as a dichotomous variable (zero willingness vs. any willingness). Initial models were estimated with all paths from the exogenous variables to the mediators and with paths from each of the hypothesized mediators to willingness. Nonsignificant exogenous paths were then trimmed from the model. Additional paths were included on the basis of modification indices > 30; these were direct effects to willingness from: parent-adolescent conflict, e-cigarette use, and parental monitoring. The final model with a continuous criterion had chi-square (51 df, N = 1,944) of 87.87 and Comparative Fit Index (CFI) = .95, parameters indicating reasonable fit of the model to the data. The final model with a dichotomous criterion had chi-square (computed 40 df, N = 1,944) of 69.42 and CFI = .99, these parameters indicating excellent fit. Structural coefficients were very similar in the two models, however the analysis with a dichotomous criterion had better fit and the model accounted for more of the variance in the criterion. This model is presented in Figure 1.

Figure 1.

Figure 1

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Structural model for relation between e-cigarette use and willingness to smoke cigarettes. Straight single-headed arrows are regression (path) effects, curved double-headed arrows indicate covariances. Values are standardized coefficients. ** indicates coefficient significant at p < .01; *** p < .001; **** p < .0001. Values in circles at top of figure are squared multiple correlation ns, the variance accounted for in a given construct by all constructs to the left of it in the model. Residual correlations among endogenous variables are in box in figure. For correlations among exogenous variables, included in the model but excluded from the figure, see Table 3. Parental support was included in the model but had no significant unique effects. Demographics (gender, ethnicity, family structure, and parental education) were included in the model but are excluded from the figure for graphical simplicity.

Overall the variables in the model accounted for 25% of the variance in willingness to smoke cigarettes. E-cigarette use had positive paths to peer smoker affiliations and smoking expectancies and a direct effect to willingness, but the path from e-cigarette use to prototypes of smokers was nonsignificant. Paths from expectancies and prototypes to willingness were both significant but the path from peer affiliations to willingness was not. (This path was significant in the initial model but became nonsignificant when the direct effect from e-cigarette use was included.) There was a significant indirect effect from e-cigarette use through expectancies to willingness, Critical Ratio (CR) = 2.12 (p < .05) and a significant direct effect from e-cigarette use to willingness, CR = 4.84 (p < .0001). The direct effect from e-cigarette use to willingness was the most salient feature in the model, representing 91% of the total effect whereas the indirect effect through expectancies represented 9% of the total effect.

The validity of willingness for predicting smoking onset was evaluated using longitudinal regression analysis based on initial nonsmokers in data previously obtained with this sample during 7th and 8th grades (one-year follow-ups). For a continuous predictor, the adjusted model (including the same covariates) showed the odds ratio for W1 willingness predicting W2 smoking (ever vs. never) was 1.45 (CI 1.13 – 1.86). For a dichotomized predictor, the odds ratio for W1 willingness predicting W2 smoking was 2.85 (CI 1.49 – 5.44). Thus the willingness measure had validity for predicting smoking onset in this sample.

Other results in the structural model were paths from parent-adolescent conflict to peer smoker affiliations and favorable prototypes of smokers, plus a direct effect to more willingness to smoke. Sensation seeking and rebelliousness had positive paths to peer smoker affiliations, expectancies about smoking, and prototypes of teen smokers. Regarding protective factors, participants with higher academic involvement had fewer peer smoker affiliations and more unfavorable prototypes of teen smokers, and parental monitoring had an inverse direct effect to willingness to smoke. Single-parent family was related to more peer smoker affiliations (p < .05) and male gender related to less favorable prototypes of smokers (p < .01). Social competence had a positive path to peer smoker affiliations, a suppression effect because the zero-order correlation with peer affiliations was nonsignificant (Table 3) but there was a significant effect in the multivariate model. Most of these effects have been observed before [31–34], including the suppression effect for social competence.[35] These findings together exemplify the multifactorial nature of adolescent substance use, which has predictive effects from parent, peer, and personality variables.[32–33, 40–42]

DISCUSSION

This research was conducted to obtain data bearing on the policy question of whether e-cigarette use among adolescents is related to their interest in smoking cigarettes. The data were from a diverse sample of high school students and analyses were conducted with control for a range of demographic and psychosocial covariates. Results indicated that e-cigarette use was positively related to willingness to smoke cigarettes. Also, the relation between e-cigarettes and willingness was partly mediated through expectancies about smoking, though a direct effect from e-cigarette use to willingness was the most salient aspect of the model. The findings were robust across different definitions of willingness and willingness was shown to be prospectively related to onset of smoking behavior in this sample.

The direct path from e-cigarette use to willingness to smoke, independent of other variables in the model, had an effect size comparable to paths observed for established predictors of adolescent smoking such as parental monitoring and parent-adolescent conflict.[34,35] Clearly it is an important part of the process. A behavioral interpretation suggests that because many e-cigarettes are designed to mimic cigarette smoking, simply learning the physical process of inhaling and exhaling vapor and experiencing pleasurable effects from flavors could be an aspect of e-cigarette use that influences attitudes toward smoking. A physiological interpretation suggests that among regular users, frequent exposure to nicotine in adolescence, when the brain is particularly sensitive to this substance, could promote the transition to cigarettes because they are more efficient at delivering nicotine.[1–2,14,43] Further research is needed to understand the role of sensory and physiological factors for affecting interest in smoking, particularly because the e-cigarette industry is currently making efforts to enhance the nicotine dose acquired through e-cigarettes.[44]

The finding that e-cigarette use by adolescent nonsmokers was related to attitudes that predict their cigarette smoking in the future has significant implications for the policy debate about the risk/benefit ratio of e-cigarettes, the key question being whether use of e-cigarettes increases risk for transition to combustible products.[1,10,45–46] The present findings converge with results from other studies with various designs indicating that e-cigarette use is associated with disposition to smoke.[21,22] Moreover, the fact that e-cigarette only users are intermediate in risk status between nonusers and dual users[20] is consistent with the hypothesis that e-cigarettes may operate to recruit lower-risk adolescents to smoking. This accumulating body of evidence from different types of studies suggests a possible behavioral risk consequence of e-cigarette use in adolescence, which should be weighed together with any benefit that might occur for adult smokers who use e-cigarettes.[1–2,44–45]

Some aspects of the present study could be noted as possible limitations and should be considered for interpretation of the results. The study was conducted in one geographic area and although findings with Hawaii adolescents are consistent with studies conducted elsewhere[20,30], studying e-cigarette use in different settings is likely to provide a better understanding of etiological factors. The design was cross-sectional so temporal relationships are not decisively established, and studies are needed to determine longitudinal relations of variables and possible reciprocal relationships. Finally, e-cigarettes come in many varieties and the product lines and flavors may vary considerably across manufacturers.[1,2,8] Detailed studies of e-cigarette products are needed to keep up with a field that is likely to be continually evolving. In summary, our study contrasted a formulation suggesting that e-cigarette use carries little risk for adolescents with a model positing effects on cognitive-attitudinal factors that predispose adolescents to smoke cigarettes. On the balance, our results are most consistent with the latter model. These empirical findings provide evidence supporting policy recommendations to make e-cigarettes less accessible to adolescents through age restrictions, taxation, and clean-air policies that apply the same regulations to e-cigarettes as now apply for tobacco.

What this paper adds.

E-cigarette use is increasingly prevalent among adolescents in many countries but there is little evidence on the consequences of e-cigarette use in adolescence, for example whether use affects risk for transition to combustible products.

This study found that among adolescent nonsmokers, those who had used e-cigarettes showed more positive expectancies about smoking cigarettes and more willingness to smoke them, an attitude that prospectively predicted smoking in this sample. These results have implications for formulation of policy about access to e-cigarettes by adolescents.

Acknowledgments

We thank the Superintendent of the Hawaii Department of Education and the Principals of the schools for their support, the participating parents and students for their cooperation, and Zalydmar Cortez, Russel Fisher, Melissa Jasper, and Mercedes Harwood-Tappé for their able assistance with data collection.

Funding

This research was supported by grants R01 CA153154 and P30 CA071789-14S4 from the National Cancer Institute. The study sponsors had no role in study design; collection, analysis, and interpretation of the data; writing of the report; and decision to submit the manuscript for publication. The content is solely the responsibility of the authors and does not necessarily reflect the views of the National Institutes of Health.

Footnotes

Contributors

TW, FG, and JS designed the parent study. TW and RK were responsible for the conduct of the research. RK was responsible for the design of the e-cigarette study and preparation of the data for analysis. TW, IP, and JS conducted the statistical analyses. TW wrote the first draft of the paper. All authors provided input on manuscript drafts and made substantial contributions to the reporting and interpretation of the results.

Competing interests

None.

Provenance and peer review

Not commissioned, externally peer reviewed.

Ethics approval

Approved by the Institutional Review Boards for University of Hawaii and Hawaii Department of Education.

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