Skip to main content
NCBI home page
Nicotine & Tobacco Research logoLink to Nicotine & Tobacco Research
. 2020 Dec 17;23(6):1019–1029. doi: 10.1093/ntr/ntaa265

Young Adults’ Vaping, Readiness to Quit, and Recent Quit Attempts: The Role of Co-use With Cigarettes and Marijuana

Carla J Berg 1,2,✉, Xuejing Duan 3, Katelyn Romm 1,2, Kim Pulvers 4, Daisy Le 5, Yan Ma 2,3, Nandita Krishnan 1, Lorien C Abroms 1,2, Betelihem Getachew 6, Lisa Henriksen 7
PMCID: PMC8628650  PMID: 33331889

Abstract

Introduction

E-cigarette cessation intervention research is limited. Young adult e-cigarette use and cessation is particularly nuanced, given various user profiles (ie, polytobacco use, co-use with marijuana) warranting different intervention approaches.

Methods

The current study is an analysis of baseline survey data (collected September–December 2018) among 1133 young adult (aged 18–34) e-cigarette users in a 2-year longitudinal study. We examined (1) e-cigarette user profiles (ie, e-cigarette only; e-cigarette/other tobacco; e-cigarette/marijuana; e-cigarette/other tobacco/marijuana) and (2) correlates of readiness to quit e-cigarette use in the next 6 months and past-year e-cigarette quit attempts.

Results

In this sample (Mage = 23.91, 47.3% male, 35.5% sexual minority, 75.2% White, 13.7% Hispanic), e-cigarette user profiles were as follows: 16.8% e-cigarettes-only, 23.4% e-cigarette/other tobacco, 18.0% e-cigarette/marijuana, and 41.8% e-cigarette/other tobacco/marijuana. Multinomial logistic regression (referent: e-cigarette-only use) indicated that all polyuse groups were more likely to use high-nicotine e-liquids (containing ≥9 mg of nicotine). Other predictors included e-cigarettes/other tobacco users being older and male; e-cigarettes/marijuana users using closed systems; and e-cigarettes/other tobacco/marijuana users being sexual minority (p’s < .01). Readiness to quit e-cigarettes and past-year quit attempts were reported by 20.8% and 32.3%, respectively. Per multilevel regression, readiness to quit and quit attempts correlated with using fewer days, high-nicotine e-liquids, and closed systems, but not marijuana, as well as being heterosexual and Black (vs White); readiness to quit also correlated with being single; past-year quit attempts correlated with other tobacco use and being Hispanic.

Conclusions

Young adult e-cigarette users demonstrate distinct user profiles and cessation-related experiences that should be considered in developing cessation interventions.

Implications

The vast majority of young adult e-cigarette users use other tobacco products and marijuana. Unfortunately, few reported readiness to quit or attempting quit. Moreover, certain subgroups (eg, sexual/racial/ethnic minorities) are more likely to be ready or attempt to quit, but may not be successful. Vaping cessation interventions must attend to these nuances.

Introduction

E-cigarette use—or vaping—has become an increasingly prominent public health concern globally and domestically.1 Vaping disproportionally affects young adults,2 with recent increases in prevalence.3 Despite some literature indicating that e-cigarettes may deliver fewer harmful chemicals than traditional cigarettes and potentially support cessation efforts,4–6 e-cigarettes contain chemicals that may increase risks of addiction and disease.7–9 Given the particularly high rates of e-cigarette use among young adults,2 focused efforts on reducing e-cigarette use—and addressing critical factors conducive to cessation—are critical.

There is a dearth of cessation intervention research aimed at addressing vaping among young people. The 2020 Surgeon General’s Report on Smoking Cessation10 called for research to develop effective vaping cessation interventions. Vaping cessation intervention research is sparse, including a commentary on clinical practice to address vaping cessation,11 a Canada-based review of efforts to address vaping,12 2 case reports,13,14 the development of a dental practitioner-based cessation program,15 and research examining the dissemination of a vaping cessation intervention.16,17 Thus, a greater evidence base is needed to advance e-cigarette cessation interventions, particularly for young adults.

Social cognitive theory (SCT) is one framework that has been effectively applied to tobacco use cessation.18 SCT describes the influence of individual experiences, social factors, and environmental factors on individual health behaviors. Key components of SCT predicting behavior change include prior experiences with attempts to change behavior, behavior-related outcome expectations, self-efficacy to engage in or change behaviors, and reinforcements to achieve behavior change. SCT posits several factors to predict or promote cessation, including (1) behavioral characteristics such as patterns of e-cigarette use (eg, e-cigarette use profile), which may alter expectations regarding the utility of e-cigarettes (eg, taste vs reducing nicotine cravings); (2) past experiences with quitting, which may influence one’s self-efficacy regarding quitting; and (3) readiness to quit, which reflects individual’s motivation and behavioral intention.18

Regarding this first point, young adult e-cigarette use is associated with increased odds of other tobacco use,19,20 as well as other substance use, particularly marijuana.21 Recent Population Assessment of Tobacco and Health (PATH) data indicates that, among current young adult e-cigarette users, ~80% were also current cigarette smokers22 and 53% were current marijuana users,23 substantially higher than in the overall young adult sample (eg, 40% cigarettes, 39% marijuana).22,23 These findings underscore that e-cigarette use may be an indicator of overall high-risk substance use behaviors among young adults; given the high prevalence of e-cigarette use, identifying e-cigarette users may provide a strategic entry point for intervening on such substance use more broadly. However, the literature is less advanced regarding characteristics of young adults representing different e-cigarette use profiles, for example, those using (1) only e-cigarettes; (2) other tobacco but not marijuana; (3) marijuana but not other tobacco; and (4) e-cigarettes, other tobacco, and marijuana. Young adult vaping cessation interventions must be informed by knowledge of other substance use, as well as the extent to which different sociodemographics or use characteristics distinguish these user profiles.

Moreover, despite the importance of understanding cessation-related attitudes and experiences for intervening among young adult e-cigarette users, this area has been relatively understudied. One PATH analysis indicated that only 13% of young adult e-cigarette users tried to quit e-cigarettes and only 7% tried to reduce use.22 In other studies, only a fraction of adult e-cigarette users report intention to quit (3%,24 25%,25 33%,26 and 47%27). A longitudinal PATH analysis indicated that predictors of cessation included nondaily e-cigarette use and not using other combustible products or customizable devices.28

Informed by SCT,18 this study aimed to (1) identify sociodemographic and e-cigarette use characteristics associated with various e-cigarette user profiles (ie, e-cigarette only; e-cigarette/other tobacco; e-cigarette/marijuana; e-cigarette/other tobacco/marijuana) and (2) examine these factors in relation to readiness to quit e-cigarette use and past-year e-cigarette quit attempts. Based on aforementioned research,28 we hypothesize that nondaily e-cigarette use and not using other combustible cigarettes or customizable devices predict readiness to quit e-cigarettes and past-year e-cigarette attempts.

Methods

Study Design

The current study is an analysis of baseline survey data (collected in September–December 2018) among 3006 young adults (aged 18–34) participating in a 2-year, five-wave longitudinal cohort study, the Vape shop Advertising, Place characteristics and Effects Surveillance (VAPES) study. VAPES examines the vape retail environment and its impact on e-cigarette use, drawing participants from six metropolitan statistical areas (MSAs; Atlanta, Boston, Minneapolis, Oklahoma City, San Diego, and Seattle).29 These MSAs were selected for their variation in state policies regarding tobacco control (eg, strongest in California; weakest in Georgia)30,31 and differences in retail markets for recreational marijuana.32 This study was approved by the Emory University Institutional Review Board.

Participants and Recruitment

Details regarding the young adult survey research methods and study sample are provided elsewhere29 and summarized here. Potential participants were recruited via social media. Eligibility criteria were as follows: (1) 18–34 years old; (2) living in one of the six MSAs, as indicated by residential zip code; and (3) English speaking. Purposive sampling was used to ensure sufficient proportions of the sample representing past 30-day e-cigarette and cigarette users (roughly 1/3 each), both sexes, and racial/ethnic minorities.

Ads posted on Facebook and Reddit targeted individuals by (1) age group and geography; (2) identifying interests of young adults (eg, sports/athletics, entertainment, technology, fashion), as well as tobacco-related interests (eg, Marlboro, Juul, Swisher Sweets); and (3) using ad images of young adults of diverse racial/ethnic backgrounds. Tag lines for ads included “Help researchers learn more about what young adults in your city think about tobacco products!”

Potential participants who clicked on an ad were directed to a webpage with a study description and consent form. Individuals who consented were screened for eligibility. This screener included questions regarding sex, race, ethnicity, and past 30-day use of e-cigarettes and cigarettes to facilitate reaching recruitment targets of subgroups in each MSA (ie, limiting participation among specific subgroups once their target enrollment was reached). Subgroup enrollment was capped by MSA (ie, Boston and Minneapolis survey enrollment eventually closed to women and nonusers of e-cigarettes and cigarettes; Atlanta enrollment closed to women and male nonusers; Seattle enrollment closed to women). Eligible individuals allowed to advance were then routed to complete the online baseline survey (administered via SurveyGizmo). Participants were notified that, 7 days after completing the baseline survey, they would be asked to confirm their participation by clicking a “confirm” button included in an email sent to them. The email reiterated study procedures and timeline. Once participants clicked “confirm,” they were officially enrolled into the study and emailed a $10 Amazon e-gift card.

The recruitment flowchart is included in Supplementary Figure. The duration of the recruitment period ranged from 87 to 104 days across MSAs (<90 days for Atlanta, Boston, and Minneapolis; >90 days for Seattle, San Diego, and Oklahoma City). Of the 10 433 Facebook and Reddit users who clicked on ads, 9847 consented, of which 2751 (27.9%) were not allowed to advance because they were either (1) ineligible (n = 1427) and/or (2) excluded to reach subgroup target enrollment (n = 1279). Of those allowed to advance to the survey, the proportion of completers versus partial completers was 48.8% (3460/7096) versus 51.2% (3635/7096). Of the 3460 who completed the baseline survey, 3006 (87%) confirmed participation at the 7-day follow-up. Those who did not fully complete the baseline survey differed from those who did complete the survey in that they were younger and more likely male, Black or other race, and past 30-day e-cigarette and cigarette users; those who did not (vs did) confirm participation were more likely Black or other race and past 30-day e-cigarette and cigarette users.29

The current analyses focus on baseline survey data of past 30-day e-cigarette users (n = 1133, 37.7% of the sample). (Data not presented in current results: Correlates [p’s < .05] of current e-cigarette use included being younger, male, sexual minority, White, non-Hispanic, and current users of cigarettes [50.4% of e-cigarette users vs 12.7% of nonusers], other tobacco [41.5% vs 10.2%], and marijuana [59.8% vs 26.7%]).29 Note that, although this study focuses on baseline data, future studies will leverage the longitudinal data.

Measures

Sociodemographics

Participants were asked to report their age, sex, sexual orientation, race, ethnicity, highest level of educational attainment, current employment status, relationship status, and whether they had children (under age 18) in their home.

E-cigarette, Tobacco, and Marijuana Use

Participants were asked to report whether they had used the following products in their lifetime: e-cigarettes, cigarettes, hookah/waterpipe, little cigars/cigarillos, large cigars, smokeless tobacco, or marijuana.33 For those indicating lifetime use, they were asked to indicate the frequency of use in the past 30 days.33 Participants were able to “refuse” marijuana-related assessments. Lifetime use and past 30-day use were dichotomized (any vs none). Based on self-reported past 30-day use, participants were categorized as nonusers of e-cigarettes, cigarettes, and marijuana or users of (1) e-cigarettes only; (2) e-cigarettes/other tobacco; (3) e-cigarettes/marijuana; or (4) e-cigarettes/other tobacco/marijuana.

Among lifetime e-cigarette users, we assessed age at first use and age at first regular use (with the option to indicate never regular use); among past 30-day users, we assessed number of days of use in the past 30 days, average puffs per day on days used, characteristics of the e-liquids they typically use (contain nicotine salt; nicotine levels, categorized as 0, 3, 6, ≥9 mg, and don’t know), and device type (ie, disposable or rechargeable; open or closed tank systems).33

E-cigarette Cessation Factors

Among past 30-day e-cigarette users, we assessed readiness to quit e-cigarettes, recent quit attempts, motivation to quit, and confidence in quitting. Readiness to quit was assessed by asking, “Are you seriously thinking about quitting the use of e-cigarettes? Yes, within the next 30 days; Yes, within the next 6 months; Yes, in more than 6 months; I am not thinking about quitting the use of e-cigarettes.”  33 This item was dichotomized as ready to quit in the next 6 months versus other responses; we also explored this item dichotomized as ready to quit in the next 30 days but chose the 6-month alternative due to distribution in this sample (ie, only 8% reported readiness in the next 30 days vs 20.8% in the next 6 months). Recent quit attempts were assessed by asking, “During the past 12 months, how many times did you stop using e-cigarettes for one day or longer because you were trying to quit using e-cigarettes for good?”  33 This item was dichotomized as any past-year quit attempt versus none. Importance of quitting was assessed by asking, “On a scale of 0 to 10, how important is it that you quit using e-cigarettes (or not use e-cigarettes if you don’t currently), with 0 being not at all important and 10 being extremely important?” Confidence in quitting was assessed by asking, “On a scale of 0 to 10, how confident are you that you could quit using e-cigarettes if you wanted to (or not use e-cigarettes if you do not currently), with 0 being not at all confident and 10 being extremely confident?”  34

Data Analysis

General descriptive statistics were used to characterize the sample. To examine Aim 1, bivariate analyses (chi-square tests, t-tests, and ANOVAs) examined participant sociodemographics, e-cigarette use characteristics, and cessation-related factors in relation to e-cigarette user profiles (ie, e-cigarette only; e-cigarette/other tobacco; e-cigarette/marijuana; e-cigarette/other tobacco/marijuana). We then conducted a multilevel multinomial logistic regression to characterize the polyuser profiles relative to e-cigarette only users. We included (1) sociodemographics, specifically age, sex, sexual orientation, race, ethnicity, and relationship status (excluding education, employment, and children in the home as these factors were highly associated with age and relationship status) and (2) e-cigarette use characteristics, specifically number of days used in the past 30 days, e-liquid nicotine levels, and device type (open vs closed; excluding puffs per day [correlated with number of days used], use of nicotine salt [correlated with nicotine levels and device type], and rechargeable vs disposable [highly correlated with open vs closed system]).

To examine Aim 2, we conducted bivariate analyses to examine sociodemographics, e-cigarette use characteristics, and tobacco/marijuana use characteristics in relation to readiness to quit e-cigarettes in the next 6 months and past-year e-cigarette quit attempts. Finally, we conducted multilevel binary logistic regression models to examine correlates of readiness to quit e-cigarettes in the next 6 months and past-year e-cigarette quit attempts, including the same predictors in the models as indicated in Aim 1.

Multilevel models were used to take into account the hierarchical structure of the data:35–37 young adults (individual level) nested within MSA (MSA level). All descriptive and bivariate analyses were conducted using SPSS version 26 (IBM, www.ibm.com/products/spss-statistics); multilevel regression modeling was conducted using R version 4.0.0 (R Foundation, www.r-project.org/). Significance level was set at α = .05.

Results

Aim 1: E-cigarette Use Profiles

In this past 30-day e-cigarette user sample (Mage = 23.91, 47.3% male, 35.5% sexual minority, 75.2% White, 13.7% Hispanic; Table 1), 50.4% also used cigarettes, 17.7% hookah, 13.2% cigars, 25.3% little cigars/cigarillos, 7.6% smokeless tobacco, and 59.8% marijuana. Regarding use profiles, 16.8% were e-cigarette-only users, 23.4% e-cigarette/other tobacco dual users, 18.0% e-cigarette/marijuana dual users, and 41.8% e-cigarette/other tobacco/marijuana polyusers.

Table 1.

Participant Characteristics and Bivariate Analyses Examining Correlates of E-cigarette, Other Tobacco Product (Including Cigarettes), and Marijuana Use Profiles Among Past 30-d E-cigarette Users, N = 1133

Variables Total E-cigarettes only E-cigarettes and other tobacco dual use E-cigarettes and marijuana dual use E-cigarette, other tobacco, and marijuana polyuse
N = 1133 (100%) N = 190 (16.8%) N = 265 (23.4%) N = 204 (18.0%) N = 474 (41.8%)
N (%) or M (SD) N (%) or M (SD) N (%) or M (SD) N (%) or M (SD) N (%) or M (SD) p
Sociodemographics
 Age, M (SD) 23.91 (4.78) 23.55 (5.09) 25.43 (4.58) 22.55 (4.51) 23.79 (4.65) <.001
 Sex, N (%) .027
  Male 536 (47.3) 78 (41.1) 147 (55.5) 90 (44.1) 221 (46.6)
  Female 560 (49.4) 105 (55.3) 107 (40.4) 110 (53.9) 238 (50.2)
  Other 37 (3.3) 7 (3.7) 11 (4.2) 4 (2.0) 15 (3.2)
 Sexual minority, N (%) 402 (35.5) 63 (33.2) 70 (26.4) 74 (36.3) 195 (41.1) .001
 Race, N (%) .155
  White 852 (75.2) 145 (76.3) 198 (74.7) 164 (80.4) 345 (72.8)
  Black 43 (3.8) 4 (2.1) 12 (4.5) 3 (1.5) 24 (5.1)
  Asian 90 (7.9) 18 (9.5) 21 (7.9) 18 (8.8) 33 (7.0)
  Other 148 (13.1) 23 (12.1) 34 (12.8) 19 (9.3) 72 (15.2)
 Hispanic, N (%) 155 (13.7) 23 (12.1) 36 (13.6) 25 (12.3) 71 (15.0) .699
 Education ≥bachelor’s degree, N (%) 714 (63.0) 118 (62.1) 166 (62.6) 128 (62.7) 302 (63.7) .979
 Employment, N (%) <.001
  Student 294 (25.9) 54 (28.4) 50 (18.9) 65 (31.9) 125 (26.4)
  Unemployed 90 (7.9) 19 (10) 25 (9.4) 17 (8.3) 29 (6.1)
  Full-time 422 (37.2) 70 (36.8) 132 (49.8) 61 (29.9) 159 (33.5)
  Part-time 327 (28.9) 47 (24.7) 58 (21.9) 61 (29.9) 161 (34.0)
 Relationship status, N (%) <.001
  Single 694 (61.3) 115 (60.5) 127 (47.9) 145 (71.1) 307 (64.8)
  Married/living with partner 421 (37.2) 71 (37.4) 137 (51.7) 57 (27.9) 156 (32.9)
  Other 18 (1.6) 4 (2.1) 1 (0.4) 2 (1.0) 11 (2.3)
 Children in the home, N (%) 263 (23.2) 52 (27.4) 74 (27.9) 45 (22.1) 92 (19.4) .027
E-cigarette use characteristics
 Age at first use, M (SD) 19.98 (4.24) 20.26 (4.26) 20.64 (4.14) 19.71 (4.00) 19.62 (4.34) .010
 Age at first regular use, M (SD) 21.50 (4.33) 22.08 (4.25) 22.17 (4.19) 20.99 (4.25) 21.07 (4.42) .004
 Never regular user, N (%) 259 (22.9) 59 (31.1) 32 (12.1) 61 (29.9) 107 (22.6) <.001
 Number of days used, past 30 days, M (SD) 17.65 (11.94) 18.46 (12.90) 18.66 (11.17) 16.49 (12.80) 17.26 (11.54) .159
 Puffs per day, M (SD) 33.4 (34.99) 36.17 (36.26) 35.08 (35.06) 31.64 (36.98) 32.11 (33.52) .398
 Use level relative to year ago, N (%) <.001
  Less 174 (15.4) 23 (12.1) 36 (13.6) 32 (15.7) 83 (17.5)
  About the same 393 (34.7) 84 (44.2) 108 (40.8) 57 (27.9) 144 (30.4)
  More 349 (30.8) 37 (19.5) 80 (30.2) 56 (27.5) 176 (37.1)
  Did not use e-cigarettes a year ago 217 (19.2) 46 (24.2) 41 (15.5) 59 (28.9) 71 (15.0)
 Typically use nicotine salt, N (%) 307 (27.1) 37 (19.5) 69 (26.0) 54 (26.5) 147 (31.0) .057
  No 604 (53.3) 115 (60.5) 149 (56.2) 111 (54.4) 229 (48.3)
  Don’t know 222 (19.6) 38 (20.0) 47 (17.7) 39 (19.1) 98 (20.7)
 Nicotine level, N (%) <.001
  0 mg 109 (9.6) 22 (11.6) 13 (4.9) 36 (17.6) 38 (8.0)
  3 mg 282 (24.9) 64 (33.7) 82 (30.9) 34 (16.7) 102 (21.5)
  6 mg 270 (23.8) 41 (21.6) 70 (26.4) 39 (19.1) 120 (25.3)
  ≥9 mg 242 (21.4) 20 (10.5) 64 (24.2) 42 (20.6) 116 (24.5)
  Other 13 (1.1) 5 (2.6) 3 (1.1) 0 5 (1.1)
  Don’t know 217 (19.2) 38 (20.0) 33 (12.5) 53 (26.0) 93 (19.6)
 Rechargeable (vs disposable) device use, N (%) 1059 (93.5) 184 (96.8) 240 (90.6) 194 (95.1) 441 (93.0) .041
 Open (vs closed) system/tank use, N (%) 765 (67.5) 141 (74.2) 182 (68.7) 116 (56.9) 326 (68.8) .002
E-cigarette cessation-related factors
 Ready to quit e-cigarettes, next 6 mo, N (%) 236 (20.8) 37 (19.5) 74 (27.9) 39 (19.1) 86 (18.1) .013
 Past-year e-cigarette quit attempt, N (%) 366 (32.3) 50 (26.3) 107 (40.4) 55 (27.0) 154 (32.5) .003
 Importance quitting e-cigarettes, M (SD) 3.46 (3.33) 3.43 (3.37) 3.68 (3.25) 3.53 (3.42) 3.33 (3.33) .590
 Confidence quitting e-cigarettes, M (SD) 7.27 (3.12) 8.04 (2.66) 6.80 (3.35) 7.87 (2.74) 6.97 (3.22) <.001
Open in a new tab

p-values indicate omnibus tests (per ANOVA and chi-square test).

Average age at first e-cigarette use was 19.98 (SD = 4.24). Those classified as e-cigarette/other tobacco/marijuana polyusers were the youngest at first use; e-cigarette/other tobacco dual users were the oldest (p = .010). No prior regular e-cigarette use was reported among 22.9%, with e-cigarette/marijuana dual users being most likely to report no prior regular use (p < .001). Additionally, 30.8% reported using e-cigarettes more now than they did a year ago (more likely reported among e-cigarette/other tobacco/marijuana polyusers); 19.2% reported that they did not use e-cigarettes a year ago (more likely reported among e-cigarette/marijuana dual users, p < .001). Using high-nicotine e-liquids (with ≥9 mg nicotine) was reported by 21.4%, with e-cigarette-only users being more likely to report lower nicotine levels; 19.2% reported not knowing the nicotine level of their e-cigarettes (most commonly among e-cigarette/marijuana dual users; p < .001). Using rechargeable devices was reported by 93.5%, and using open systems/tanks was reported by 67.5% (most commonly among e-cigarette-only users and least commonly among e-cigarette/marijuana dual users, p = .002).

In multilevel multinomial logistic regression (Table 2; referent: e-cigarettes-only), predictors of e-cigarettes/other tobacco dual use included being older (p = .002) and male (p = .005) and using high-nicotine e-liquids (p = .001). E-cigarettes/marijuana dual use was correlated with using high-nicotine e-liquids (p = .008) and closed systems (p = .002). E-cigarettes/other tobacco/marijuana polyuse was associated with being sexual minority (p = .010) and using high-nicotine e-liquids (p < .001).

Table 2.

Multilevel Multinomial Logistic Regression Model Indicating Correlates of Use Profiles Among Past 30-day E-cigarette Users Relative to Users of E-cigarettes Only (Referent Group), N = 1133

Variable E-cigarette and other tobacco product dual users E-cigarette and marijuana dual users E-cigarette, other tobacco product, and marijuana polyusers
aOR CI p aOR CI p aOR CI p
Intercept 0.180 0.06, 0.56 .003 4.31 1.23, 15.18 .023 1.01 0.35, 2.91 .985
Sociodemographics
 Age 1.08 1.03, 1.13 .002 0.96 0.92, 1.01 .145 1.03 0.99, 1.07 .203
 Male (ref. = female)a 1.81 1.20, 2.73 .005 1.15 0.75, 1.77 .532 1.35 0.93, 1.95 .113
 Sexual minority (ref. = heterosexual) 0.87 0.56, 1.38 .560 1.28 0.81, 2.01 .290 1.67 1.13, 2.46 .010
 Race (ref. = White)
  Black 1.82 0.55, 6.03 .325 0.80 0.17, 3.75 .779 2.51 0.83, 7.59 .103
  Asian 1.11 0.55, 2.23 .765 0.79 0.38, 1.62 .518 0.85 0.45, 1.60 .606
  Other 1.05 0.57, 1.93 .888 0.71 0.36, 1.40 .320 1.3129 0.75, 2.22 .349
Hispanic (ref. = non-Hispanic) 1.05 0.57, 1.91 .881 0.92 0.48, 1.75 .801 1.27 0.74, 2.17 .380
Married/living with partner (ref. = single/other) 1.50 0.97, 2.32 .071 0.83 0.51, 1.34 .442 0.78 0.52, 1.17 .228
E-cigarette use characteristics
 Number of days used, past 30 days, M (SD) 1.00 0.98, 1.01 .648 0.99 0.97, 1.01 .248 0.99 0.98, 1.01 .393
 Nicotine level ≥ 9 mg (ref. = other responses) 2.74 1.55, 4.85 .001 2.24 1.23, 4.08 .008 2.93 1.73, 4.99 <.001
 Device type, open system (ref. = closed) 0.66 0.42, 1.02 .064 0.50 0.32, 0.78 .002 0.83 0.56, 1.24 .359
Open in a new tab

Null model ICCs for each outcome: e-cigarette and OTP dual users = 0; e-cigarette and marijuana dual users = .032; and e-cigarette, OTP, and MJ polyusers = .024. Full model ICCs for each outcome: e-cigarette and OTP dual users = 0; e-cigarette and marijuana dual users = .039; and e-cigarette, OTP, and MJ polyusers = .026. Null model R2s for each outcome: e-cigarette and OTP dual users = 0; e-cigarette and marijuana dual users = .032; e-cigarette, OTP, and MJ polyusers = .024. Full model R2s for each outcome: e-cigarette, OTP, and MJ polyusers = .124, e-cigarette and OTP dual users = .141, and e-cigarette and marijuana dual users = .118. aOR = adjusted odds ratio; CI = confidence interval; ICC = intraclass correlation coefficient; OTP = other tobacco products; MJ = marijuana.

aThose reporting “other” sex were excluded from these analyses.

Readiness to Quit and Quit Attempts

Readiness to quit e-cigarettes in the next 6 months was reported by 20.8% (Table 1), most likely reported among e-cigarette/other tobacco dual users (p = .013). Past-year e-cigarette quit attempts were reported by 32.3%, most likely reported among e-cigarette/other tobacco dual users (p = .003). Although no differences in importance of quitting were found, e-cigarette-only users indicated highest confidence in quitting, with e-cigarette/other tobacco dual users indicating the least confidence (p < .001).

Bivariate analyses (Table 3) indicated that readiness to quit correlated with being heterosexual (p < .001), Hispanic (p = .039), and single (p = .005), as well as using fewer days (p < .001), fewer times per day (p < .001), less relative to a year ago (p = .009), high-nicotine e-liquids (p = .013), and disposable and/or closed system devices (p’s < .001), but not marijuana (p = .015). Per multilevel regression analyses (Table 4), readiness to quit correlated with being heterosexual (p = .003), Black (vs White, p = .028), and single (p = .003), as well as using fewer days (p = .015), high-nicotine e-liquids (p < .001), and closed systems (p < .001), but not marijuana (p = .001).

Table 3.

Readiness to Quit E-cigarettes in the Next 6 mo and Past-Year E-cigarette Quit Attempts Among Past 30-day E-cigarette Users, N = 1133

Variables Total Readiness to quit Past-year quit attempt
Not ready Ready No Yes
N = 1133 (100%) N = 897 (79.2%) N = 236 (20.8%) N = 767 (67.7%) N = 366 (32.3%)
N (%) or M (SD) N (%) or M (SD) N (%) or M (SD) p N (%) or M (SD) N (%) or M (SD) p
Sociodemographics
 Age, M (SD) 23.91 (4.78) 23.90 (4.73) 23.95 (4.94) .873 24.01 (4.74) 23.69 (4.85) .289
 Sex, N (%) .230 .092
  Male 536 (47.3) 413 (46.0) 123 (52.1) 348 (45.4) 188 (51.4)
  Female 560 (49.4) 453 (50.5) 107 (45.3) 390 (50.8) 170 (46.4)
  Other 37 (3.3) 31 (3.5) 6 (2.5) 29 (3.8) 8 (2.2)
 Sexual minority, N (%) 402 (35.5) 342 (38.1) 60 (25.4) <.001 302 (39.4) 100 (27.3) <.001
 Race, N (%) .057 .001
  White 852 (75.2) 681 (75.9) 171 (72.5) 598 (78.0) 254 (69.4)
  Black 43 (3.8) 27 (3.0) 16 (6.8) 19 (2.5) 24 (6.6)
  Asian 90 (7.9) 70 (7.8) 20 (8.5) 53 (6.9) 37 (10.1)
  Other 148 (13.1) 119 (13.3) 29 (12.3) 97 (12.6) 51 (13.9)
  Hispanic, N (%) 155 (13.7) 113 (12.6) 42 (17.8) .039 88 (11.5) 67 (18.3) .002
 Education ≥bachelor’s degree, N (%) 714 (63.0) 561 (62.5) 153 (64.8) .517 488 (63.6) 226 (61.7) .541
 Employment, N (%) .332 .200
  Student 294 (25.9) 223 (24.9) 71 (30.1) 193 (25.2) 101 (27.6)
  Unemployed 90 (7.9) 74 (8.2) 16 (6.8) 58 (7.6) 32 (8.7)
  Full-time 422 (37.2) 334 (37.2) 88 (37.3) 302 (39.4) 120 (32.8)
  Part-time 327 (28.9) 266 (29.7) 61 (25.8) 214 (27.9) 113 (30.9)
 Relationship status, N (%) .005 .106
  Single 694 (61.3) 529 (59.0) 165 (69.9) 461 (60.1) 233 (63.7)
  Married/living with partner 421 (37.2) 351 (39.1) 70 (29.7) 290 (37.8) 131 (35.8)
  Other 18 (1.6) 17 (1.9) 1 (0.4) 16 (2.1) 2 (0.5)
 Children in the home, N (%) 263 (23.2) 207 (23.1) 56 (23.7) .833 171 (22.3) 92 (25.1) .289
E-cigarette use characteristics
 Age at first use, M (SD) 19.98 (4.24) 20.09 (4.29) 19.60 (4.03) .115 20.34 (4.29) 19.25 (4.04) <.001
 Age at first regular use, M (SD) 21.50 (4.33) 21.68 (4.39) 20.83 (4.05) .017 22.11 (4.36) 20.33 (4.04) <.001
 Never regular user, N (%) 259 (22.9) 208 (23.2) 51 (21.6) .607 192 (25.0) 67 (18.3) .012
 Number of days used, past 30 days, M (SD) 17.65 (11.94) 18.34 (12.00) 15.03 (11.34) <.001 18.80 (12.11) 15.24 (11.22) <.001
 Puffs per day, M (SD) 33.40 (34.99) 36.19 (36.17) 22.81 (20.68) <.001 37.46 (36.64) 24.88 (29.53) <.001
 Use level relative to year ago, N (%) .009 <.001
  Less 174 (15.4) 121 (13.5) 53 (22.5) 88 (11.5) 86 (23.5)
  About the same 393 (34.7) 317 (35.3) 76 (32.2) 263 (34.3) 130 (35.5)
  More 349 (30.8) 283 (31.5) 66 (28.0) 248 (32.3) 101 (27.6)
  Did not use e-cigarettes a year ago 217 (19.2) 176 (19.6) 41 (17.4) 168 (21.9) 49 (13.4)
 Typically use nicotine salt, N (%) 307 (27.1) 234 (26.1) 73 (30.9) .310 192 (25.0) 115 (31.4) .012
  No 604 (53.3) 483 (53.8) 121 (51.3) 409 (53.3) 195 (53.3)
  Don’t know 222 (19.6) 180 (20.1) 42 (17.8) 166 (21.6) 56 (15.3)
 Nicotine level, N (%) .013 <.001
  0 mg 109 (9.6) 94 (10.5) 15 (6.4) 78 (10.2) 31 (8.5)
  3 mg 282 (24.9) 232 (25.9) 50 (21.2) 203 (26.5) 79 (21.6)
  6 mg 270 (23.8) 219 (24.4) 51 (21.6) 180 (23.5) 90 (24.6)
  ≥9 mg 242 (21.4) 173 (19.3) 69 (29.2) 133 (17.3) 109 (29.8)
  Other 13 (1.1) 10 (1.1) 3 (1.3) 9 (1.2) 4 (1.1)
  Don’t know 217 (19.2) 169 (18.8) 48 (20.3) 164 (21.4) 53 (14.5)
 Rechargeable (vs disposable) device, N (%) 1059 (93.5) 857 (95.5) 202 (85.6) <.001 736 (96.0) 323 (88.3) <.001
 Open (vs closed) system/tank, N (%) 765 (67.5) 644 (71.8) 121 (51.3) <.001 546 (71.2) 219 (59.8) <.001
Past 30-day use, N (%)
 Cigarettes 571 (50.4) 450 (50.2) 121 (51.3) .763 366 (47.7) 205 (56.0) .009
 Other tobacco use, no cigarettes 470 (41.5) 372 (41.5) 98 (41.5) .988 297 (38.7) 173 (47.3) .006
 Any other tobacco use 739 (65.2) 579 (64.5) 160 (67.8) .351 478 (62.3) 261 (71.3) .003
 Marijuana use 678 (59.8) 553 (61.6) 125 (53.0) .015 469 (61.1) 209 (57.1) .194
Dual/polyuse profile, N (%) .013 .003
 No other tobacco/marijuana 190 (16.8) 153 (17.1) 37 (7.8) 140 (18.3) 50 (13.7)
 E-cigarette and other tobacco 265 (23.4) 191 (21.3) 74 (31.4) 158 (20.6) 107 (29.2)
 E-cigarette and marijuana 204 (18.0) 165 (18.4) 39 (16.5) 149 (19.4) 55 (15.0)
 E-cigarette, other tobacco, and marijuana 474 (41.8) 388 (43.3) 86 (36.4) 320 (41.7) 154 (42.1)
Open in a new tab

p-values indicate omnibus tests (per t-test and chi-square test).

Table 4.

Multilevel Regression Models Indicating Correlates of Readiness to Quit E-cigarettes in the Next 6 mo and Past-Year E-cigarette Quit Attempts Among Past 30-day E-cigarette Users, N = 1133

Variable Ready to quit, next 6 mo Past-year quit attempt
aOR CI p aOR CI p
Intercept 0.44 0.17, 1.11 .083 1.23 0.55, 2.75 .622
Sociodemographics
 Age 1.03 0.99, 1.06 .171 0.98 0.95, 1.01 .146
 Male (ref. = female)a 1.04 0.75, 1.43 .829 1.07 0.81, 1.41 .657
 Sexual minority (ref. = heterosexual) 0.57 0.40, 0.82 .003 0.63 0.46, 0.85 .003
 Race (ref. = White)
  Black 2.23 1.09, 4.57 .028 3.27 1.67, 6.41 <.001
  Asian 1.18 0.67, 2.07 .559 1.60 1.00, 2.56 .051
  Other 0.82 0.50, 1.33 .421 1.12 0.75, 1.68 .575
Hispanic (ref. = non-Hispanic) 1.54 0.99, 2.38 .053 1.74 1.19, 2.55 .004
Married/living with partner (ref. = single/other) 0.57 0.39, 0.82 .003 1.02 0.75, 1.40 .897
E-cigarette use characteristics
 Number of days used, past 30 days, M (SD) 0.98 0.97, 1.00 .015 0.98 0.97, 0.99 <.001
 Nicotine level ≥9 mg (ref. = other responses) 1.95 1.36, 2.80 <.001 2.21 1.61, 3.03 <.001
 Device type, open system (ref. = closed) 0.41 0.30, 0.57 <.001 0.64 0.48, 0.85 .002
Past 30-day use
 Other tobacco (ref. = no) 1.13 0.81, 1.59 .478 1.48 1.10, 1.99 .010
 Marijuana (ref. = no) 0.59 0.43, 0.82 .001 0.72 0.55, 0.96 .024
Open in a new tab

Null model ICCs for each outcome: readiness to quit = .015; quit attempt = 0. Full model ICCs for each outcome: readiness to quit = .008; quit attempt = 0. Null model R2s for each outcome: readiness to quit = .015; quit attempt = 0. Full model R2s for each outcome: readiness to quit = .156; quit attempt = .128. aOR = adjusted odds ratio; CI = confidence interval; ICC = intraclass correlation coefficient.

aThose reporting “other” sex were excluded from these analyses.

In bivariate analyses (Table 3), past-year quit attempts correlated with being sexual, racial, and ethnic minorities (p’s < .001), being younger at first (and first regular) use (p’s < .001), and using fewer days (p < .001), fewer times per day (p < .001), less now than a year ago (p < .001), high-nicotine e-liquids (p < .001), disposable and closed system devices (p’s < .001), and other tobacco (p = .003). Multilevel regression analyses (Table 4) indicated that attempting to quit in the past year correlated with being heterosexual (p = .003), Black (p = .001), and Hispanic (p = .004), as well as using fewer days (p < .001), high-nicotine e-liquids (p < .001), closed systems (p = .002), and other tobacco (p = .010), but not marijuana (p = .024).

We also examined readiness to quit and quit attempts looking at the three user categories (rather than other tobacco or marijuana use as dichotomous variables); interestingly, the only significant finding was that e-cvaigarette/other tobacco dual use only predicted greater likelihood of past-year quit attempt (odds ratio = 1.65, confidence interval: 1.06, 2.57, p = .025); other findings did not change. Finally, we examined readiness to quit among those who attempted to quit in the past year; multilevel regression indicated that correlates included being single (p = .001), using fewer days (p = .014), and closed systems (p < .001), but not marijuana (p = .037), largely similar to the initial findings regarding readiness to quit.

Discussion

There is a critical gap in the evidence base needed to advance e-cigarette cessation interventions. Young adult e-cigarette use and cessation is particularly nuanced, given diverse user profiles (ie, polytobacco use, co-use with marijuana) as well as highly variable e-cigarette cessation intentions and past experiences, which might warrant different intervention approaches or considerations, as SCT might suggest.18

Among past 30-day e-cigarette users, roughly half also used cigarettes, and ~60% used marijuana, whereas use of other tobacco products was lower (range of ~8% smokeless tobacco to ~25% little cigars/cigarillos). Note that this subsample of e-cigarette users had higher rates of other tobacco and marijuana use than the subsample of those not currently using e-cigarettes (~4 times higher for tobacco; ~2.3 times higher for marijuana), aligning with previous findings.22,23 Relative to e-cigarette-only users (16.8% of the sample), all polyuse groups (relative to e-cigarette only users) were more likely to use high-nicotine e-liquids, potentially marking overall greater addiction among dual and polyusers.

E-cigarettes, other tobacco, and marijuana polyusers accounted for 41.8% of the sample. They were the youngest at first use and more likely sexual minorities; they were also the group most likely to report using more relative to a year ago. These findings might suggest that sexual minorities, who may experience particularly high levels of stress from various sources (per minority stress theory38), may be at risk for this polyuse profile, which is also prone to younger initiation and greater escalation.

E-cigarette/other tobacco dual users comprised 23.4% of the sample. They were more likely to be male and were the oldest on average (in both bivariate and multivariable analyses) and the oldest on average at first use; this might also reflect having established a history of tobacco use and initiating use of e-cigarettes within this context. Indeed, some of the most commonly reported motives for using e-cigarettes among young adults implicate other tobacco use—such as beliefs that e-cigarettes might be less harmful to themselves and people around them, can be used in places where smoking is not allowed, can help quitting smoking or cutting down, or can be used an alternative to quitting tobacco altogether.22,39,40

E-cigarette/marijuana dual users accounted for 18.0% of the sample. They were the most likely to report never regularly using e-cigarettes, not using e-cigarettes a year ago, not knowing the nicotine level of their e-cigarettes, and using closed systems; these factors might reflect less experience with e-cigarettes, less overall use (ie, reported the least number of days of use and puffs per day on average, albeit nonsignificant), and perhaps experimentation or to get a “buzz.”  41

Readiness to quit e-cigarettes in the next 6 months was reported by 20.8% (range from 18.1% among e-cigarette/other tobacco/marijuana users to 27.9% among e-cigarette/other tobacco users). Only 8% reported being ready to quit in the next 30 days, similar to the low rates of readiness documented in prior research.24–27 Past-year quit attempts were reported by 32.3% (also with a wide range across user profiles: 26.3% in e-cigarette-only users to 40.4% in e-cigarette/other tobacco dual users), higher than prior estimates (13% tried to quit, 7% tried to reduce22). The reasons for relatively low intentions to quit and few quit attempts are unclear, but may be due to the controversy and lack of clarity among consumers regarding the dangers of vaping,42–44 as well as e-cigarette use profiles. Users in this study used an average of 17.65 days of the past 30, slightly higher than found in PATH data (~10 days),22 which also indicate that a third of e-cigarette users report being “not at all addicted” to e-cigarettes (despite one-third indicating at least one symptom of addiction).22 Prior research suggests that nondaily young adult smokers are less likely to identify as “smokers,”  45 which predicts low readiness to quit smoking.45 Moreover, nondaily users perceive the concept of “quitting” very differently.46

Per multilevel regression, readiness to quit and past-year quit attempts were not only associated with fewer days of use (marker of lower addiction) but also associated with using high-nicotine e-liquids and closed systems (potential markers of higher addiction47,48). A longitudinal analysis of PATH data indicated that predictors of cessation included being a nondaily e-cigarette user and using closed systems28 but also with using higher nicotine levels.47,48 This PATH analysis also found that not using other combustible products predicted cessation28; however, our study indicated that other tobacco use predicted past year quit attempt. Taken together, those using other tobacco products—despite their efforts to quit—are not any more likely to successfully quit, underscoring a particular intervention target in this subgroup of e-cigarette users. Perhaps relatedly, regression findings also suggested that readiness to quit e-cigarettes and past-year quit attempts were associated with not using marijuana, also consistent with prior research.49

Regression findings also indicated that readiness to quit and past-year quit attempts were associated with being heterosexual and Black (vs White) and that past-year quit attempt was associated with being Hispanic and other tobacco use. Findings from PATH data indicated that no association between these factors and subsequent cessation,28 potentially indicating a particular need to assist these minority groups to achieve successful cessation.

Findings from this study have implications for research and practice. First, research is needed to disentangle how e-cigarette users see their use in the context of their other substance use behaviors, which may also help understand the reasons for low readiness to quit and few e-cigarette quit attempts. Vaping cessation interventions must also address dual and poly tobacco/marijuana use, and interventions promoting cessation of multiple substances at once should be examined. Finally, given that certain subgroups (eg, sexual/racial/ethnic minorities) reported greater readiness and/or attempts to quit but other research showing no increased odds of cessation,28 research examining the underlying reasons for unsuccessful cessation is needed in order to support such subgroups in their quit attempts.

Limitations

This study has some limitations, including limited generalizability to other young adults in the included MSAs or across the United States. As noted above, rates of tobacco and marijuana use should not be interpreted as use prevalence rates, nor should other participant characteristics, given the purposive sampling design used in this study. For example, our sample had a high prevalence of sexual minority (31.6% in the total sample of 3006, 35.6% of past 30-day e-cigarette users), likely due to the high proportion of e-cigarette, cigarette, and marijuana users in our sample and the higher likelihood of sexual minorities to report use of these products.23,50 In addition, the cross-sectional data used in this study also had limited theoretically based variables to fully test SCT as a framework for understanding and/or predicting e-cigarette cessation. These data also do not include comprehensive assessment of nicotine dependence, and along these lines, many participants reported being unaware of the nicotine levels in the e-cigarettes they used, thus limiting our ability to assess overall nicotine consumption. Finally, self-reported data have the potential for bias, the cross-sectional nature of these analyses precludes determining the directionality of associations. However, this analysis will establish a foundation for future analyses, which will leverage the longitudinal data.

Conclusion

The vast majority of young adult e-cigarette users also use other tobacco products and marijuana, and few e-cigarette users report readiness to quit or attempting quit. Moreover, despite certain subgroups (eg, sexual/racial/ethnic minorities) being more likely to report readiness and/or attempting to quit, they may not be successful. These nuances are critical considerations for developing effective vaping cessation interventions.

Supplementary Material

A Contributorship Form detailing each author’s specific involvement with this content, as well as any supplementary data, are available online at https://academic.oup.com/ntr.

ntaa265_suppl_Supplementary_Figure
Click here for additional data file. (230.3KB, docx)
ntaa265_suppl_Supplementary_Taxonomy_Form
Click here for additional data file. (188.1KB, pdf)

Funding

This publication was supported by the US National Cancer Institute (R01CA215155-01A1; PI: Berg). Dr. Berg is also supported by other US National Cancer Institute funding (R01CA179422-01; PI: Berg; R01CA239178-01A1; MPIs: Berg, Levine), the US National Institutes of Health/Fogarty International Center (1R01TW010664-01; MPIs: Berg, Kegler), and the US National Institute of Environmental Health Sciences/Fogarty International Center (D43ES030927-01; MPIs: Berg, Marsit, Sturua). Dr. Ma is supported by funding from the US National Institute of Minority Health and Health Disparities (R01MD013901; PI: Ma). Dr. Pulvers is supported by funding from the US National Institutes of Health (SC3GM122628; PI: Pulvers), the California Tobacco Related Disparities Research Program (27IP-0041; PI: Pulvers; 28IP-0022S; PI: Oren). Dr. Henriksen is supported by other NCI funding (5R01CA067850-17; PI: Henriksen; 1R01CA217165; PI: Henriksen; 1P01CA0225597; MPI: Henriksen, Luke, Ribisl).

Acknowledgments

This study was approved by the Emory University Institutional Review Board (IRB00097895).

Declaration of Interests

None declared.

Data Availability

Data not publicly available (available upon request).

References

  • 1. Kennedy RD, Awopegba A, De León E, Cohen JE. Global approaches to regulating electronic cigarettes. Tob Control. 2017;26(4):440–445. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. National Academies of Sciences Engineering and Medicine. Public Health Consequences of E-Cigarettes National Academies of Sciences Engineering and Medicine. http://nationalacademies.org/hmd/reports/2018/public-health-consequences-of-e-cigarettes.aspx. Published 2018. Accessed August 1, 2020.
  • 3. Centers for Disease Control and Prevention. National Health Interview Survey: 2018 Data Release. https://www.cdc.gov/nchs/nhis/nhis_2018_data_release.htm Centers for Disease Control and Prevention. Published 2018. Accessed August 1, 2020.
  • 4. Kalkhoran S, Glantz SA. E-cigarettes and smoking cessation in real-world and clinical settings: a systematic review and meta-analysis. Lancet Respir Med. 2016;4(2):116–128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Malas M, van der Tempel J, Schwartz R, et al. Electronic cigarettes for smoking cessation: a systematic review. Nicotine Tob Res. 2016;18(10):1926–1936. [DOI] [PubMed] [Google Scholar]
  • 6. Hartmann-Boyce J, McRobbie H, Bullen C, Begh R, Stead L, Hajek P. Electronic cigarettes for smoking cessation. Cochrane Database Syst Rev. 2016;( 9):CD010216. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. The National Academies of Sciences Engineering and Medicine. New Report One of the Most Comprehensive Studies on Health Effects of E-Cigarettes; Finds that Using E-Cigarettes May Lead Youth to Start Smoking, Adults to Stop Smoking. Washington, DC: The National Academies of Sciences Engineering and Medicine; 2018. [Google Scholar]
  • 8. Romberg AR, Miller Lo EJ, Cuccia AF, et al. Patterns of nicotine concentrations in electronic cigarettes sold in the United States, 2013–2018. Drug Alcohol Depend. 2019;203:1–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Centers for Disease Control. Outbreak of Lung Injury Associated with the Use of E-Cigarette, or Vaping, Products. Centers for Disease Control. https://www.cdc.gov/tobacco/basic_information/e-cigarettes/severe-lung-disease.html. Published 2019. Accessed August 1, 2020. [Google Scholar]
  • 10. Centers for Disease Control and Prevention. Smoking Cessation: A Report of the Surgeon General. Atlanta, GA: Centers for Disease Control and Prevention; 2020. https://www.cdc.gov/tobacco/data_statistics/sgr/2020-smoking-cessation/index.html. [Google Scholar]
  • 11. Hadland SE, Chadi N. Through the haze: what clinicians can do to address youth vaping. J Adolesc Health. 2020;66(1):10–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. O’Connor S, Pelletier H, Bayoumy D, Schwartz RI.. Interventions to Prevent Harms From Vaping. Special Report. Toronto, ON: Ontario Tobacco Research Unit; 2019. [Google Scholar]
  • 13. Silver B, Ripley-Moffitt C, Greyber J, Goldstein AO. Successful use of nicotine replacement therapy to quit e-cigarettes: lack of treatment protocol highlights need for guidelines. Clin Case Rep. 2016;4(4):409–411. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Sahr M, Kelsh SE, Blower N. Pharmacist assisted vape taper and behavioral support for cessation of electronic nicotine delivery system use. Clin Case Rep. 2020;8(1):100–103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Mungia R, Case K, Valerio M, et al. Development of an E-Cigarettes Education and Cessation Program: a South Texas Oral Health Network Study. Health Promot Pract. 2021;22(1):18–20. [DOI] [PubMed] [Google Scholar]
  • 16. Graham AL, Jacobs MA, Amato MS. Engagement and 3-month outcomes from a digital E-Cigarette Cessation Program in a cohort of 27 000 teens and young adults. Nicotine Tob Res. 2020;22(5):859–860. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Graham AL, Jacobs MA, Amato MS, Cha S, Bottcher MM, Papandonatos GD. Effectiveness of a quit vaping text message program in promoting abstinence among young adult E-cigarette users: protocol for a randomized controlled trial. JMIR Res Protoc. 2020;9(5):e18327. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Bandura A. Health promotion by social cognitive means. Health Educ Behav. 2004;31(2):143–164. [DOI] [PubMed] [Google Scholar]
  • 19. Primack BA, Soneji S, Stoolmiller M, Fine MJ, Sargent JD. Progression to Traditional cigarette smoking after electronic cigarette use among US adolescents and young adults. JAMA Pediatr. 2015;169(11):1018–1023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Unger JB, Soto DW, Leventhal A. E-cigarette use and subsequent cigarette and marijuana use among Hispanic young adults. Drug Alcohol Depend. 2016;163:261–264. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Cohn A, Villanti A, Richardson A, et al. The association between alcohol, marijuana use, and new and emerging tobacco products in a young adult population. Addict Behav. 2015;48:79–88. [DOI] [PubMed] [Google Scholar]
  • 22. Buu A, Hu YH, Wong SW, Lin HC. Comparing American college and noncollege young adults on e-cigarette use patterns including polysubstance use and reasons for using e-cigarettes. J Am Coll Health. 2020;68(6):610–616. [DOI] [PubMed] [Google Scholar]
  • 23. Bluestein M, Kelder S, Perry CL, Pérez A. Exploring associations between the use of alcohol and marijuana with e-cigarette use in a U.S.A. nationally representative sample of young adults. Int J Health Sci. 2019;13(1):30–39. [PMC free article] [PubMed] [Google Scholar]
  • 24. Skerry A, Lusher J, Banbury S. Electronic cigarette users lack intention to quit vaping. MOJ Addict Med Ther. 2018;5(5):204–207. [Google Scholar]
  • 25. Jankowski M, Lawson JA, Shpakou A, et al. Smoking cessation and vaping cessation attempts among cigarette smokers and E-cigarette users in Central and Eastern Europe. Int J Environ Res Public Health. 2019;17(1):28. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26. Etter JF. Are long-term vapers interested in vaping cessation support? Addiction. 2019;114(8):1473–1477. [DOI] [PubMed] [Google Scholar]
  • 27. Harrell PT, Simmons VN, Piñeiro B, et al. E-cigarettes and expectancies: why do some users keep smoking? Addiction. 2015;110(11):1833–1843. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Coleman B, Rostron B, Johnson SE, et al. Transitions in electronic cigarette use among adults in the Population Assessment of Tobacco and Health (PATH) Study, Waves 1 and 2 (2013–2015). Tob Control. 2019;28(1):50–59. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29. Berg CJ, Duan X, Getachew B, et al. Young adult e-cigarette use and retail exposure in 6 US metropolitan areas. Tob Regul Sci. In press. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Public Health Law Center. U.S. E-Cigarette Regulations – 50 State Review (2019). Public Health Law Center. https://publichealthlawcenter.org/resources/us-e-cigarette-regulations-50-state-review. Published 2019. Accessed August 1, 2020. [Google Scholar]
  • 31. American Lung Association. State of Tobacco Control, 2018. Chicago, IL: American Lung Association; 2018. [Google Scholar]
  • 32. Public Health Law Center. Commercial Tobacco and Marijuana . https://www.publichealthlawcenter.org/topics/commercial-tobacco-control/commercial-tobacco-and-marijuana. Published 2020. Accessed January 11, 2021.
  • 33. National Institutes of Health. The Population Assessment of Tobacco and Health. https://pathstudyinfo.nih.gov/UI/FAQsResMobile.aspx. Published 2016. Accessed October 1, 2016.
  • 34. Biener L, Abrams DB. The contemplation ladder: validation of a measure of readiness to consider smoking cessation. Health Psychol. 1991;10(5):360–365. [DOI] [PubMed] [Google Scholar]
  • 35. Aveyard P, Markham WA, Cheng KK. A methodological and substantive review of the evidence that schools cause pupils to smoke. Soc Sci Med. 2004;58(11):2253–2265. [DOI] [PubMed] [Google Scholar]
  • 36. Aveyard P, Markham WA, Lancashire E, et al. The influence of school culture on smoking among pupils. Soc Sci Med. 2004;58(9):1767–1780. [DOI] [PubMed] [Google Scholar]
  • 37. Bovaird JA, Shaw LH. Multilevel structural equation modeling. In: Laursen B, Little TD, Card NA, eds. Handbook of Developmental Research Methods. New York, NY: Guildford Press; 2012:501–518. [Google Scholar]
  • 38. Meyer IH. Prejudice as stress: conceptual and measurement problems. Am J Public Health. 2003;93(2):262–265. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Berg CJ. Preferred flavors and reasons for e-cigarette use and discontinued use among never, current, and former smokers. Int J Public Health. 2016;61(2):225–236. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40. Berg CJ, Haardörfer R, Schauer GL, et al. Reasons for polytobacco use among young adults: scale development and validation. Tob Prev Cessat. 2016;26(2):69. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41. Masters MN, Haardörfer R, Windle M, Berg C. Psychosocial and cessation-related differences between tobacco-marijuana co-users and single product users in a college student population. Addict Behav. 2018;77:21–27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Bareham D, Ahmadi K, Elie M, Jones AW. E-cigarettes: controversies within the controversy. Lancet Respir Med. 2016;4(11):868–869. [DOI] [PubMed] [Google Scholar]
  • 43. Bareham D, Ahmadi K, Elie M, Jones AW, McKee M. E-cigarettes: further flavours of controversy within the controversy. Lancet Respir Med. 2018;6(1):16–17. [DOI] [PubMed] [Google Scholar]
  • 44. Getachew B, Payne JB, Vu M, et al. Perceptions of alternative tobacco products, anti-tobacco media, and tobacco regulation among young adults: a qualitative study. Am J Health Behav. 2018;42(4):118–130. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45. Berg CJ, Lust KA, Sanem JR, et al. Smoker self-identification versus recent smoking among college students. Am J Prev Med. 2009;36(4):333–336. [DOI] [PubMed] [Google Scholar]
  • 46. Berg CJ, Schauer GL, Buchanan TS, et al. Perceptions of addiction, attempts to quit, and successful quitting in nondaily and daily smokers. Psychol Addict Behav. 2013;27(4):1059–1067. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47. Hammond D, Wackowski OA, Reid JL, O’Connor RJ. Use of JUUL e-cigarettes among youth in the United States. Nicotine Tob Res. 2020;22(5):827–832. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48. Boykan R, Goniewicz ML, Messina CR. Evidence of nicotine dependence in adolescents who use juul and similar pod devices. Int J Environ Res Public Health. 2019;16(12):2135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49. Schauer GL, King BA, McAfee TA. Prevalence, correlates, and trends in tobacco use and cessation among current, former, and never adult marijuana users with a history of tobacco use, 2005–2014. Addict Behav. 2017;73:165–171. [DOI] [PubMed] [Google Scholar]
  • 50. Schuler MS, Stein BD, Collins RL. Differences in substance use disparities across age groups in a national cross-sectional survey of lesbian, gay, and bisexual adults. LGBT Health. 2019;6(2):68–76. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

ntaa265_suppl_Supplementary_Figure
Click here for additional data file. (230.3KB, docx)
ntaa265_suppl_Supplementary_Taxonomy_Form
Click here for additional data file. (188.1KB, pdf)

Data Availability Statement

Data not publicly available (available upon request).

Articles from Nicotine & Tobacco Research are provided here courtesy of Oxford University Press

RESOURCES