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. Author manuscript; available in PMC: 2019 Jul 1.
Published in final edited form as: Drug Alcohol Depend. 2018 May 8;188:109–112. doi: 10.1016/j.drugalcdep.2018.03.051

Prevalence and correlates of adolescents’ e-cigarette use frequency and dependence

Erin A Vogel a, Danielle E Ramo a, Mark L Rubinstein b
PMCID: PMC5999577  NIHMSID: NIHMS966473  PMID: 29763848

Abstract

Background

Understanding predictors of e-cigarette use among adolescents in the context of wide availability and extreme popularity of these products is important for prevention and treatment. This study identifies correlates of e-cigarette use frequency and dependence among adolescent users.

Methods

Adolescent e-cigarette users (N=173) were recruited from the San Francisco Bay Area. Participants reported demographic and psychosocial characteristics, e-cigarette use behaviors, and cigarette use. Bivariate relationships between potential correlates were examined, and correlates significant at p < .10 were included in full models predicting frequency and dependence.

Results

In the full models, frequent use was associated with receiving one’s first e-cigarette from a family member rather than a friend (r = −.23, p < .001) or a store (r = −.13, p = .037), using nicotine in all e-cigarettes versus some e-cigarettes (r = −.17, p = .007) or unknown nicotine use (r = −.15, p = .014), using a customizable device versus a Juul (r = −.22, p < .001), vape pen (r = −.20, p = .002), or other/unknown device (r = −.16, p = .009), and friends’ e-cigarette use (r = .20, p = .002). Dependence was associated with younger age of first use (r = −.18, p = .012), friends’ use (r = .18, p = .01), and recent cigarette use (r = .17, p = .019).

Conclusions

When assessing problematic e-cigarette use among adolescents, it is important to consider social factors (e.g., friends’ and family members’ e-cigarette use), device type, and dual use with cigarettes.

Keywords: Electronic Cigarette, E-Cigarette, Electronic Nicotine Delivery Systems, Vaping, Adolescent, Nicotine, Dependence

1. Introduction

Electronic cigarette (e-cigarette) use has eclipsed cigarette smoking among adolescents in the United States (Johnston et al., 2018). E-cigarette use predicts subsequent smoking initiation (Best et al., 2017; Conner et al.; Hammond et al., 2017; Leventhal et al., 2015; Lozano et al., 2017; Primack et al., 2015; Wills et al., 2017), especially among those who would otherwise be at low risk for smoking (Best et al., 2017; Conner et al., 2017; Wills et al., 2017). E-cigarettes have many features (e.g., flavors, devices, nicotine levels) that differentiate them from cigarettes and that may be associated with problematic use. Moreover, correlates of frequency and dependence may differ from those of experimentation. Frequent e-cigarette use in adolescence is associated with smoking (Lee et al., 2017; Warner, 2016) and other health risk behaviors (i.e., poor nutrition, drug use; Dunbar et al., 2017). To our knowledge, no study has explored features of e-cigarette use and psychosocial factors that may be associated with e-cigarette use frequency, and adolescent e-cigarette dependence is rarely studied. This study aims to identify correlates of e-cigarette use frequency and dependence in a sample of adolescent e-cigarette users.

2. Methods

2.1 Participants and procedures

Participants were 173 adolescents (age 13–18) who reported having used an e-cigarette product at least once in the past 30 days and at least 10 times in their lives (Rubinstein et al., 2018) (see Table 1). Participants were recruited for a longitudinal study on teen vaping using online advertising and fliers posted around the San Francisco Bay Area. Potential participants initiated contact with study personnel via phone, email, text messaging, or social media. Eligibility screening included a review of procedures and requirements via phone. Eligible adolescents were scheduled for an initial in-person session. Informed consent and assent were obtained. The research design and procedures were approved by the University of California, San Francisco Institutional Review Board.

Table 1.

Demographic and e-cigarette use characteristics of participants (N = 173)

M (SD)/ %
Demographics
Age 16.6 (1.2)
Sex, % male 75.1
Race, % Non-Hispanic White 54.9
Mother’s Education
 Graduated college 54.3
 Did not graduate college 39.9
 Unknown 5.8
E-Cigarette Use History
Age of first use 14.8 (1.3)
Source of first e-cigarette
 Friend 68.5
 Family member 6.5
 Purchased 25.0
Nicotine content of first e-cigarette
 Contained nicotine 48.3
 Did not contain nicotine 39.5
 Nicotine content unknown 12.1
First flavor
 Tobacco 6.4
 Fruit 49.1
 Candy 14.5
 Menthol 8.7
 Other 9.2
 Unknown 12.1
Current E-Cigarette Use  
Frequency (days of use in a typical month) 15.4 (9.8)
Dependence (score) 3.5 (4.1)
Current nicotine use
 All e-cigarettes contain nicotine 41.6
 Some contain nicotine 38.7
 None contain nicotine 7.5
 Unknown nicotine content 12.1
Current flavor use
 Tobacco flavor 11.0
 Fruit flavor 61.3
 Candy flavor 16.8
 Menthol flavor 15.0
 Other flavor 12.7
First heard of e-cigarettes
 Family member 7.6
 Peer (friend or social media) 79.1
 Media (TV, magazine, or Internet site) 9.3
 Other 4.1
Type of e-cigarette used
 Customizable 32.4
 Juul 22.0
 Vape pen 34.1
 Other or unknown 11.6
% of friends who use 42.4 (27.7)
Past-month cigarette use (% yes) 26.6
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2.2 Measures

2.2.1 Frequency

Frequency of use was measured with the question: “In a typical month (i.e., 30 days), on how many days do you use e-cigarettes?”. Responses were approximately normally distributed.

2.2.2 Dependence

Dependence on e-cigarettes was measured using the Penn State Electronic Cigarette Dependence Index, which has been validated previously with adults (Foulds et al., 2015), and was approximately normally distributed.

2.2.3 Demographics

Demographics included age, sex, race (White/Caucasian, African American/Black, Asian, Native American, Pacific Islander, mixed race/biracial, or other; re-categorized into Non-Hispanic White or Non-White), and mother’s education (a validated measure of adolescents’ socioeconomic status; Ensminger et al., 2000).

2.2.4 E-cigarette use history

Measures included age of first use (“How old were you when you first used an e-cigarette?”), source of first e-cigarette (“Where did you get your first e-cigarette from?”: “friend”, “my brother/sister”, “I ordered it on the Internet”, “from another family member”, “I bought it in a store”, “other”; recoded into friend, family, purchased, or other), nicotine content of first e-cigarette (“Did your first e-cigarette contain nicotine?”: “yes”, “no”, “I don’t know”), first flavor (“What flavor was your very first e-cigarette?”: “tobacco”, “fruit”, “candy”, “menthol”, “other”, “I don’t remember”), and source of first e-cigarette knowledge (“Where did you first hear about e-cigarettes?”: “an e-cigarette website”, “social media [i.e., Facebook]”, “advertisement on the web”, “TV show”, “a friend”, “a family member”, “other website [i.e., Amazon]”, “magazine”, “other”; recoded into family, peer, or media).

2.2.5 Current e-cigarette and cigarette use

Measures included current nicotine use (“Do the e-cigarettes you use now contain nicotine?”: “all”, “some”, “none”, “I don’t know if they contain nicotine”), current flavor use (“What flavor e-cigarette do you usually use?”: “tobacco”, “fruit”, “candy”, “menthol”, “other”; select all that apply), device type (“What kind of e-cigarette do you use most often?”: “brand name [i.e., Blu or Njoy]”, “hookah pen/e-hookah”, “vape pen”, “I build/make my own”, “e-pipe”, “e-cigar”, “Juul”, “other”, “I don’t know”; recoded into customizable [e.g., modifications/“mods”], Juul, vape pen, or other/unknown), friends’ e-cigarette use (“On a scale of 0–100%, how many of your friends use e-cigarettes?”), and recent smoking (“Have you smoked a cigarette in the past 30 days?”: “yes”, “no”).

2.3 Analyses

Bivariate associations between potential correlates and outcome variables (frequency and dependence) were assessed using linear regression. Categorical correlates with more than two levels were dummy-coded. For bivariate screening, a more liberal p-value (p < .10) was used so as not to exclude potentially important correlates (e.g., Prochaska et al., 2014). Statistically significant correlates (p < .10) were entered into two multiple linear regression models; one predicted frequency, and another predicted dependence (all variables evaluated at p < .05).

Missing data were handled using listwise deletion. Among potential correlates, all cases were complete except for 1 missing response to nicotine content of first e-cigarette (0.6%) and 1 missing response to source of first hearing about e-cigarettes (0.6%). Four cases (2.3%) were missing data on the dependence measure and were excluded from dependence analyses. Thirty regression models were conducted using IBM SPSS 25.

3. Results

Participant characteristics are presented in Table 1.

3.1 Use frequency

Significant bivariate correlates of frequency of use (p< .10) were older age (r=.23, p=.003), SES (model R2=.04, p=.028), source of first e-cigarette (model R2=.10, p=.001), presence of nicotine in one’s first e-cigarette (model R2=.04, p=.039), current use of nicotine in e-cigarettes (model R2=.17, p<.001), device type (model R2 = .17, p<.001), having friends who use e-cigarettes (r=.30, p<.001), and recent cigarette smoking (r=.30, p<.001). The final adjusted model is presented in Table 2 (model R2=.42, p<.001).

Table 2.

Correlates of e-cigarette use frequency and dependence (adjusted models)

Frequency (N=173) Dependence (N=169)
β r p β r p
Current age .04 .04 .513 – – –
Mother’s education (Ref: did not graduate college)
 Graduated college .12 .10 .091 – – –
 Unknown .02 .01 .822 – – –
Age of first use −.19 −.18 .013*
Source of first e-cig (Ref: family member)
 Friend −.46 −.23 <.001*** −.28 −.09 .190
 Purchased −.24 −.12 .052 −.19 −.07 .306
 Other −.11 −.09 .151 −.01 −.004 .957
First e-cigarette nicotine (Ref: contained nicotine)
 Did not contain nicotine .01 .01 .878 −.06 −.05 .486
 Unknown −.01 −.01 .863 .03 .02 .747
Current nicotine use (Ref: all contain nicotine)
 Some e-cigarettes contain nicotine −.21 −.17 .007** −.12 −.10 .157
 No e-cigarettes contain nicotine −.13 −.11 .075 −.13 −.10 .139
 Unknown −.21 −.16 .010* −.13 −.10 .144
First heard of e-cigarettes (Ref: family)
 Peer (friend or social media) – – – −.13 −.05 .474
 Media (Internet, TV, or magazine) – – – −.02 −.008 .913
 Other – – – .04 .02 .728
Device type used most often (Ref: customizable)
 Juul −.25 −.21 .001** .03 .02 .748
 Vape pen −.25 −.20 .001** −.14 −.11 .114
 Other or unknown −.20 −.17 .008** −.01 −.01 .869
Percentage of friends who use e-cigarettes .22 .20 .001** .19 .18 .013*
Past-month cigarette use .13 .11 .071 .19 .17 .014*
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Note: Correlations reported here are adjusted for all other variables in the model (i.e., semipartial correlations).

*

p < .05,

**

p < .01,

***

p < .001

3.2 Dependence

Significant bivariate correlates of dependence (p< .10) were younger age of first use (r= −.20, p=.013), source of first e-cigarette (model R2=.09, p=.002), presence of nicotine in one’s first e-cigarette (model R2=.03, p=.089), current use of nicotine in e-cigarettes (model R2=.10, p < .001), source of first hearing about e-cigarettes (model R2=.08, p=.004), device type (model R2=.07, p=.008), having many friends who use e-cigarettes (r=.25, p=.001), and recent cigarette smoking (r=.26, p=.001). The final adjusted model is presented in Table 2 (Model R2=.32, p<.001).

4. Discussion

We examined the prevalence and correlates of e-cigarette use frequency and dependence in a sample of adolescent e-cigarette users. Frequent use was associated with receiving one’s first e-cigarette from a family member, using nicotine in all e-cigarettes, using a customizable device, and having a higher percentage of friends who use e-cigarettes. Receiving one’s initial e-cigarette from a family member suggests possible household exposure to e-cigarette use, which has been associated with e-cigarette use in prior research (Barrington-Trimis et al., 2015; Joung et al., 2016). Permissive norms and rules in the household may enable adolescents to use e-cigarettes more frequently without fear of repercussions. Only 18% of the adolescents who received their first e-cigarette from a family member also reported recent smoking (compared to 28% in the full sample), suggesting that household rules and norms may differ for e-cigarettes versus combustible cigarettes.

Unlike prior research (Lee et al., 2017; Warner, 2016), we did not find a significant association between frequency of e-cigarette use and likelihood of smoking cigarettes. However, more frequent e-cigarette users were more likely to use nicotine in all of their e-cigarettes than were less frequent users. Thus, even adolescents who do not smoke may have substantial nicotine exposure from more frequent e-cigarette use.

Use of a customizable device was associated with frequent use. It is possible that participants’ e-cigarette use escalated in frequency before they began using customizable devices. However, customizable devices allow for greater nicotine delivery and may be more reinforcing (Farsalinos et al., 2014). Moreover, use of a customizable device was strongly correlated with nicotine use in this sample; χ2(9) = 31.05, p < .001. Although use of a customizable device was not associated with dependence on e-cigarettes, the average dependence score in this sample was low, and it is plausible that teens who use customizable devices frequently may be more likely to develop nicotine dependence over time.

The relationship between friends’ e-cigarette use and frequency of e-cigarette use is consistent with the extant literature. Adolescents whose friends use e-cigarettes may perceive greater social approval and greater prevalence of e-cigarette use, both of which increase adolescents’ likelihood of use (Barrington-Trimis et al., 2015; Barrington-Trimis et al., 2016; Gorukanti et al., 2017). Relatedly, using e-cigarettes socially (i.e., while spending time with friends) may lead to more frequent use.

Dependence was associated with younger age of first use, recent cigarette use, and having a higher percentage of friends who use e-cigarettes. Smoking at an early age is known to be associated with greater likelihood of regular smoking and future nicotine dependence (Azagba et al., 2017; Dierker et al., 2008; Guo et al., 2010; Van De Ven et al., 2010; Walker and Loprinzi, 2014). Early substance use is often predictive of substance use disorders and other forms of externalizing psychopathology later in life (Iacono et al., 2008). Our findings suggest that early e-cigarette use may carry similar risks.

Recent cigarette use was also associated with dependence on e-cigarettes. Nicotine addiction may be a common underlying factor. Indeed, nicotine use in e-cigarettes was associated with cigarette smoking such that dual users of e-cigarettes and combustible cigarettes were more likely to use nicotine in their e-cigarettes than those who only used e-cigarettes; χ2(3) = 19.62, p < .001. Although e-cigarette use alone carries a risk of nicotine dependence, dual users may be a particularly high-risk group.

Friends’ use was correlated with both frequency and dependence. Perceived social approval, perceiving e-cigarette use as normative, and using e-cigarettes while spending time with friends may lead to more frequent use and dependence. Acceptance of e-cigarette use among adolescents is likely an important target for intervention, as social norms regarding novel products, such as customizable devices, are likely to be less entrenched and more malleable than those regarding older tobacco products.

Taken together, these correlates suggest a common risk profile for frequent use and dependence in a subset of adolescent e-cigarette users, particularly those who are exposed to nicotine and have family members and friends who are accepting of e-cigarette use. Along with our recent findings of toxicants in the bodies of adolescent e-cigarette users (Rubinstein et al., 2018), these findings suggest that more effort is needed to counter the promotion of these products to teens.

4.1 Limitations and future directions

This study has a few notable limitations. First, the cross-sectional design does not allow for identification of predictors of future e-cigarette use. These participants are enrolled in a longitudinal study that will allow us to investigate future use as well as potential associations between frequent use and the development of nicotine dependence over time. Second, there are currently no validated measures of e-cigarette dependence in adolescents. We chose a measure that is relatively comprehensive and has good psychometric properties among adults; however, further research on measuring e-cigarette dependence among adolescents is needed. Third, generalizability of the data may be limited due to the relatively small, non-representative sample in this pilot study. Many of the analyses conducted may have been underpowered. For instance, few participants reported first hearing about e-cigarettes from the media (1.3%). A larger, representative sample is needed to draw firmer conclusions. Moreover, the sample was drawn from an urban location in the United States, and e-cigarette use characteristics may differ across regions and countries (Noland et al., 2017; Pesko and Robarts, 2017). Future research could include adolescents from various social and regulatory environments.

4.2 Conclusions

This study was among the first to examine the prevalence and correlates of both frequency and dependence in a sample of adolescent e-cigarette users. Overall, results suggest that among adolescent e-cigarette users, those who have greater exposure to nicotine and greater acceptance of e-cigarette use in their social circles are at higher risk for more problematic e-cigarette use. In addition to screening for risk factors such as cigarette smoking and age of first use, clinicians should assess use of customizable e-cigarette devices and friends’ use. E-cigarette use risk profiles may differ from those of combustible cigarettes, and attention should be paid to specific features of e-cigarette use such as device type.

Highlights.

  • Prevalence and correlates of e-cigarette use frequency and dependence were studied.

  • Frequency correlates were first source, nicotine use, “mod” use, and friends’ use.

  • Dependence correlates were age of first use, friends’ use, and smoking.

Acknowledgments

Role of Funding Source

This work was supported by National Institute on Drug Abuse grant T32DA007250, National Institutes of Health grants R21DA040718, P50 CA180890, P30 DA012393, S10 RR026437, and California Tobacco Related Diseases Research Program grant 24XT-0007. The funding sources did not contribute to the collection, analysis, or interpretation of data, the writing of the report, or the decision to submit the article for publication.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Contributors

Dr. Vogel performed the data analysis, wrote the first draft of the manuscript, and revised the manuscript. Dr. Ramo and Dr. Rubinstein designed and oversaw the parent study, consulted on the analytic approach, and provided critical revisions to the manuscript. All authors have reviewed and approved the final version of the manuscript for submission.

Conflict of Interest

No conflict declared.

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