Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2022 Jul 1.
Published in final edited form as: Drug Alcohol Depend. 2021 Apr 24;224:108724. doi: 10.1016/j.drugalcdep.2021.108724

Correlates of E-Cigarette Use among Adults Initiating Smoking Cessation Treatment

Laili Kharazi Boozary 1, Summer G Frank-Pearce 2,4, Adam C Alexander 3,4, Joseph J C Waring 4, Sarah J Ehlke 4, Michael S Businelle 3,4, Amy M Cohn 4,5, Darla E Kendzor 3,4
PMCID: PMC8552999  NIHMSID: NIHMS1696850  PMID: 33940324

Abstract

Background:

Little is known about the correlates of e-cigarette (EC) use among adults seeking smoking cessation treatment, and it is unclear how EC use affects smoking treatment outcomes.

Methods:

Participants were 649 adult smokers enrolled in smoking cessation treatment. Participants completed a baseline (pre-quit) assessment with follow-up at 4-, 12-, and 26-weeks after a scheduled combustible cigarette (CC) cessation date. EC use was described before and after the CC cessation date, and the impact of baseline EC use on CC cessation at follow-up was evaluated.

Results:

At baseline, 66.6% of participants had ever-used ECs and 23.1% reported past 30-day EC use. Past 30-day EC users were younger, more socioeconomically disadvantaged, more CC dependent, and less likely to report Black race compared to non-users. At the 4-, 12-, and 26-week follow-ups, 6.4%, 7.4%, and 8.1% reported dual EC/CC use; and 2.7%, 3.4%, and 2.7% had switched to exclusive EC use. Past 30-day EC use at baseline was not associated with CC cessation at any follow-up. However, among baseline past 30-day EC users (n=150), using ECs ≥ once per week was associated with a lower likelihood of CC cessation at 26-week follow-up (adjusted OR 0.346, 95% CI: 0.120, 0.997).

Conclusion:

Findings indicated that dual users of CCs and ECs at baseline differed from CC-only users on sociodemographic and smoking characteristics. Baseline EC use did not impact smoking cessation overall. However, among past 30-day users, more frequent EC use at baseline adversely impacted longer-term cessation outcomes, perhaps due to greater baseline CC/nicotine dependence.

Keywords: Smoking Cessation, E-Cigarettes, Switching, Dual Use

1. Introduction

Smoking prevalence has declined to 13.7% among adults in the U.S., while adult use of e-cigarettes (ECs) has increased to 3.2% (Creamer et al., 2019). Those who smoke combustible cigarettes (CCs) disproportionately reflect socioeconomically disadvantaged individuals, certain racial minorities (e.g. American Indians/Alaska Natives), and adults 25-64 years old (Creamer et al., 2019). In contrast, adult EC use is more common among individuals of higher socioeconomic (Creamer et al., 2019; Wilson and Wang, 2017) and among younger adults (Creamer et al., 2019; Dai and Leventhal, 2019; Wilson and Wang, 2017). While Whites and Blacks use CCs at similar rates (Creamer et al., 2019), use of ECs is more common among Whites (Creamer et al., 2019; Wilson and Wang, 2017). Although EC use is growing within the general adult population and among former users of CCs (Creamer et al., 2019; Dai and Leventhal, 2019), little is known about EC use specifically among adults entering CC cessation treatment.

Switching from CCs to exclusive EC use may be a practical harm reduction strategy for some (Fairchild et al., 2018; Levy et al., 2017). ECs are associated with far fewer harmful exposures to carcinogens and other toxicants than CCs (Abrams et al., 2018; Czogala et al., 2014; Drummond and Upson, 2014; Goniewicz et al., 2014; Hajek et al., 2014; Nutt et al., 2014; Trtchounian et al., 2010; Wagener et al., 2017). Survey studies have shown that daily EC use is associated with previous CC cessation (Farsalinos and Barbouni, 2020; Farsalinos and Niaura, 2020). Likewise, evidence from randomized trials suggests that use of ECs is associated with similar or higher CC cessation rates relative to traditional nicotine replacement therapy (NRT) (Bullen et al., 2013; Hajek et al., 2019a; Hajek et al., 2019b; Hatsukami et al., 2019). Nevertheless, other data indicate that dual-use of CCs and ECs among adults making a quit attempt is associated with increased nicotine dependence (Martinez et al., 2020), which may interfere with the cessation of either or both products. In sum, it remains unclear how EC use might impact CC cessation among those initiating treatment.

The purpose of the current study was to characterize: 1) sociodemographic correlates of EC use among adults entering smoking cessation treatment, 2) rates of EC use prior to quitting CCs, 3) rates of EC switching and dual-use of CCs/ECs during a CC cessation attempt, and 4) the impact of baseline EC use on CC cessation at treatment follow-up. Findings will provide treatment-relevant information about EC use among CC users seeking treatment, and the impact of EC use on a CC cessation attempt.

2. Methods

2.1. Participants.

Participants were 649 adults enrolled in treatment through the Tobacco Treatment Research Program (TTRP; HPRC, 2020) between October 2016 and August 2019. The TTRP offers intensive tobacco cessation treatment to the public while 1) supporting recruitment and enrollment into clinical trials, and 2) contributing to a prospective data registry containing information about participants’ experiences as they quit tobacco. Individuals were eligible to participate if they were ≥18 years of age and interested in tobacco cessation treatment. Informed consent was obtained during an in-person enrollment visit.

2.2. Procedure

Participants were encouraged to quit CCs 1 week after a baseline visit (i.e., the quit date [QD]). The first of 6 counseling sessions was completed in-person 1 week prior to the QD, and weekly sessions continued through 4 weeks after the QD either in-person or by telephone. Participants were not advised regarding use of ECs as part of the standard counseling program. Twelve weeks of nicotine patches and gum/lozenges (NRT) were available to participants, and other medications were offered in some cases. Most participants received NRT (92%, n=597), while the remainder received varenicline (3.8%, n=25), no medication (16, 2.5%, n=16), bupropion (0.5%, n=3), or other medication combinations (1.2%, n=8). Medication type was not associated with CC cessation at any follow-up. Participants also had the opportunity to enroll in ongoing TTRP research studies where they may have received additional treatment components, though none involved ECs. Follow-up visits were scheduled for 4-, 12-, and 26-weeks post-QD. Participants were asked to complete questionnaire assessments, and self-reported smoking status was verified via carbon monoxide (CO) breath sample.

2.3. Measures

2.3.1. Baseline Participant Characteristics.

Race, ethnicity, sex, sexual orientation/gender, age, marital/partner status, years of education, insurance status, employment status, and annual household income were assessed at baseline. Participants also completed the Heaviness of Smoking Index (HSI; Heatherton et al., 1989). HSI scores may range from 0-6, with higher scores indicating greater CC dependence. Average CCs smoked per day and years of CC smoking were also assessed. Depression was assessed with the 10-item Center for Epidemiological Studies Depressions (CES-D) questionnaire (Andresen et al., 1994). CES-D scores may range from 0-30, with scores of ≥ 10 indicating depressive symptoms.

2.3.2. Treatment Adherence.

The Medication Adherence Questionnaire (MAQ; Morisky et al., 1986) is a 4-item measure that assessed adherence to cessation medication(s) over the past week. Possible scores on the MAQ ranged from 0-4, with scores ≥ 2 indicating moderate/high adherence. Past week medication adherence was assessed weekly from 1-4 weeks after the QD. Optimal treatment adherence was defined as ≥ 3 weeks (out of the first 4 weeks post-QD) with moderate/high medication adherence, in combination with a completed counseling session.

2.3.3. EC Use.

Ever-use (yes vs. no) and past-30 day (yes vs. no) use of ECs were assessed at baseline consistent with widely used definitions (e.g., see Amato et al., 2016; Boyle et al., 2020; USDHHS, 2016). Among past 30-day EC users, EC use frequency was assessed with the following categories: ≤ once per week, 1-2 times per week, 3-4 times per week, 5-6 times per week, and every day. Due to the small sample size, responses were dichotomized to reflect the following two categories: EC use ≥ once per week vs. EC use < once per week (58% vs. 42% of past 30-day users, respectively).

2.3.4. CC Abstinence, Switching, and Dual-Use at Follow-Up.

Self-reported CC abstinence at follow-up was assessed with the question, “Have you smoked, even a puff, during the last 7 days?” (yes vs. no) and abstinence was confirmed via expired CO level of <6 parts per million (ppm; Benowitz et al., 2019). Those who reported not smoking in the past 7 days and who had an expired CO ≤6 ppm were considered CC abstinent. Dual-use at follow-up was defined as self-reported CC use (or expired CO >6 ppm) and EC use within the past 7 days. Switching at follow-up was defined as CC abstinence with self-reported EC use during the past 7 days. Dual-abstainers were individuals who met the criteria for CC abstinence, who reported that they had not used ECs over the past 7 days.

2.3.5. Analytic Plan.

Descriptive statistics were generated to describe participant characteristics at baseline, and rates of switching and dual-use at follow-up. Chi-square analyses and t-tests were conducted to compare the baseline characteristics of EC users (ever and past 30-day users) and non-users. Logistic regression analyses were conducted to evaluate the impact of baseline EC use (all participants) and EC use frequency (past 30-day users) on CC cessation at follow-up, adjusting for covariates known to be associated with CC cessation, including: years of age (Kviz et al., 1995), sex (male vs. female; Smith et al., 2016; Wetter, 1999), race/ethnicity (non-Hispanic White vs. Hispanic/Non-White; Piper et al., 2010), years of education (Hiscock et al., 2012; Piper et al., 2010), HSI score (Rojewski et al., 2018; Zawertailo et al., 2018), treatment adherence (<3 vs. 3 or more weeks with optimal adherence; Raupach et al., 2014), (CESD score <10 vs. 10 or greater; Weinberger et al., 2017), and enrollment in other TTRP research studies (yes vs. no). An intent-to-treat approach was taken, such that participants who did not provide complete smoking status data at follow-up (e.g., self-reported abstinence with corroborating CO breath sample) were considered non-abstinent.

3. Results

3.1. Participant Characteristics.

Most participants were White (60.6%, n=393) or Black (27.7%, n=180), and female (58.1%, n=377), with an annual household income of <$21,000 (61.6%, n=400). See Table 1 for additional details. Assessment completion rates were 100% at baseline (N=649), 62.4% (n=405) at 4-weeks, 58.4% (n=379) at 12-weeks, and 51.5% (n=334) at 26-weeks. Baseline EC ever-use and past 30-day use were not significantly associated with follow-up visit completion at any time point. Note that 68.1% (n=442) of participants were enrolled in other TTRP research studies, and 12.8% (n=83) demonstrated ≥ 3 weeks of optimal treatment adherence.

Table 1.

Participant characteristics overall and by EC use status (N=649).

Overall EC Use, Past 30 days EC Use, Ever
n (%) or
mean ± SD		 No
(n=499)		 Yes
(n=150)		 P No
(n=217)		 Yes
(n=432)		 P
Baseline Demographic variables
Age, years (range 18-83 years) 51.8 ± 12.2 52.6 ± 12.1 49.2 ± 12.5 0.003 55.8 ± 10.6 49.9 ± 12.3 <0.0001
Education, years (range 0-20 years) A 12.5 ± 2.2 12.5 ± 2.2) 12.3 ± 2.0 0.23 12.5 ± 2.2 12.5 ± 2.2 0.93
Sex, % female 377 (58.1%) 280 (56.1%) 97 (64.7%) 0.06 101 (46.5%) 276 (63.9%) <0.0001
Sexual Orientation/Gender, % Straight/Gender Conforming A,B 576 (88.8%) 447 (89.6%) 129 (86.0%) 0.31 190 (87.6%) 386 (89.4%) 0.44
CESD, % score ≥10 311 (47.9%) 397 (79.6%) 121 (80.7%) 0.83 93 (42.9%) 218 (50.5%) 0.08
Ethnicity, % Hispanic A 28 (4.3%) 21 (4.2%) 7 (4.7%) 0.80 8 (3.7%) 20 (4.6%) 0.57
Race (2 categories), %Non-Hispanic White1 379 (58.4%) 285 (57.1%) 94 (62.7%) 0.23 108 (49.8%) 271 (62.7%) 0.0016
Race (4 categories)2
  White, % 393 (60.6%) 295 (59.1%) 98 (65.3%) 0.16 111 (51.2%) 282 (65.3%) <0.0001
  Black, % 180 (27.7%) 149 (29.9%) 31 (20.7%) 85 (39.2%) 95 (22.0%)
  American Indian/Alaska Native, % 53 (8.2%) 38 (7.6%) 15 (10.0%) 16 (7.4%) 37 (8.6%)
  Multi-race/Other, % 23 (3.5%) 17 (3.4%) 6 (4.0%) 5 (2.3%) 18 (4.2%)
Married/living with significant other 246 (37.9%) 195 (39.1%) 51 (34.0%) 0.26 81 (37.3%) 165 (38.2%) 0.83
Annual household income, % <$21,000 C 400 (61.6) 295 (59.1%) 105 (70.0%) 0.01 135 (62.2%) 265 (61.3%) 0.52
Health Insurance, % Uninsured/Medicaid A 97 (14.9%) 272 (54.5%) 105 (70.0%) 0.0005 117(53.9%) 260 (60.2%) 0.12
Employment, % Employed Full/Part-Time A 177 (27.3%) 136 (27.3%) 41 (27.3%) 0.95 47 (21.7%) 130(30.1%) 0.02
Baseline Tobacco Use Characteristics
EC use, % ever 432 (66.6%) - - - - - -
EC use, % past 30 days 150(23.1%) - - - - - -
  Less than once per week - - 63 (42.0%) - - -
  1-2 times per week - - 31 (20.7%) - - -
  3-4 times per week - - 18 (12.0%) - - -
  5-6 times per week - - 12 (8.0%) - - -
  Every day - - 26 (17.3%) - - -
CCs smoked per day (range 0–65) D,3 17.0 ± 10.3 16.4 (10.2) 19.0 (10.3) 0.004 17.0 ± 10.3 15.9 ± 10.2 0.04
Years of CC smoking (range 0 to 67) 30.2 ± 14.1 30.3 (14.1) 29.9 (13.9) 0.74 30.2 ± 14.1 31.1 ± 14.6 0.30
CO, ppm (range 0-83) 3 18.0 ± 12.3 18.2 ± 12.2 17.6 ± 12.7 0.48 17.4 ± 11.8 18.3 ± 12.6 0.48
Heaviness of Smoking Index (range 0-6) 3.1 ± 1.5 3.0 ± 1.5 3.4 ± 1.5 0.006 3.0 ± 1.6 3.2 ± 1.5 0.04
CC Abstinence, CO-Verified (past 7 days)
Abstinent at 4-weeks, % 133 (20.5%) 98 (19.6%) 35 (23.3%) 0.33 41 (18.9%) 92 (21.3%) 0.47
Abstinent at 12-weeks, % 116 (17.9%) 90 (18.0%) 26 (17.3%) 0.84 43 (19.8%) 73 (16.9%) 0.36
Abstinent at 26-weeks, % 90 (13.9%) 71 (14.2%) 19 (12.7%) 0.63 32 (14.8%) 58 (13.4%) 0.66
Open in a new tab

Note: EC = E-cigarettes, CC = combustible cigarettes, CO = expired carbon monoxide.

A

Missing n = 1 from analysis

B

Identify as both straight and gender conforming

C

Missing n = 31 from analysis

D

Missing n = 5 from analysis

1

The dichotomized race variable was used as covariate in adjusted models.

2

Follow-up analysis showed significant differences in EC ever-use (p<0.0001) and past 30-day EC use (p=0.03) between Black vs. Non-Black individuals.

3

Used Fisher's exact test due to non-normal distribution

3.2. Prevalence and Correlates of Baseline EC Use.

Of the 649 participants, 66.6% (n=432) reported that they had ever-used ECs, and 23.1% (n=150) reported past 30-day EC use. Among past 30-day EC users, 17.3% (n=26) were daily users, 40.7% (n=61) reported using ECs 1-6 times per week, and 42.0% (n=63) reported using ECs < once per week. Past 30-day EC users were younger (p=0.003) and more likely to report an annual household income of <$21,000 (p=0.01) than non-users. A higher proportion of EC users than non-users were uninsured or had Medicaid (p=0.0005). Finally, participants who reported past 30-day EC use reported smoking more CCs per day at baseline (p=0.004) and they had higher HSI scores (p=0.006) than non-users. Although past 30-day EC use did not differ by race across all 4 categories, follow-up analyses were conducted to determine whether Blacks differed from non-Blacks on EC use given apparent differences within this racial group specifically. Findings indicated that Blacks were less likely to have used ECs in the past 30-days than non-Blacks (p=.03). With regard to EC ever-use, ever-users were younger (p<0.0001), more likely to be female (p<0.0001), more likely to report White/non-Hispanic race/ethnicity (p=0,0016), less likely to report Black race (p<0.0001), more likely to be employed (p=0.02), and they smoked fewer cigarettes per day (p=0.04) and had higher HSI scores (p=0.04) than never-users. See Table 1 for details.

3.3. Switching and Dual-Use during Treatment.

Of participants who completed the 4-, 12-, and 26-week follow-ups, dual-use of ECs and CCs was reported by 6.4% (26/405), 7.4% (28/379), and 8.1% (27/334) of participants. Switching from CCs to ECs at each follow-up was endorsed by 2.7% (11/405), 3.4% (13/379), and 2.7% (9/334) of participants, respectively. See Table 2.

Table 2.

CC/EC use status by post-quit week among visit completers.

Week 4 (n=405) Week 12 (n=379) Week 26 (n=334)
Exclusive CC user 246 (60.7%) 235 (62.0%) 217 (65.0%)
Dual CC/EC user 26 (6.4%) 28 (7.4%) 27 (8.1%)
Dual CC/EC abstainer 122 (30.1%) 103 (27.2%) 81 (24.3%)
Switched from CCs to ECs 11 (2.7%) 13 (3.4%) 9 (2.7%)
Open in a new tab

Note: EC = E-cigarettes, CC = combustible cigarettes

3.4. Impact of Baseline EC use on CC Cessation.

CC abstinence rates at the 4-, 12, and 26-week follow-ups were 20.5% (n=133), 17.9% (n=116) and 13.9% (n=90). After adjustment for covariates, neither past 30-day or ever-use of ECs were significantly associated with CC abstinence at the 4-, 12, or 26-week follow-up visits. However, among past 30-day EC users (n=150), adjusted analyses indicated that frequency of EC use was associated with CC abstinence at 26-week follow-up (aOR 0.346, 95% CI: 0.120, 0.997). Specifically, those who reported using ECs ≥ once per week were less likely to be CC abstinent relative to those who used ECs < once per week. EC use frequency was not significantly associated with CC abstinence at the 4- or 12-week follow-ups. Unadjusted models were also evaluated, and the findings were similar to the adjusted analyses.

Regarding the covariates, treatment adherence, race, other TTRP research study participation, and HSI were associated with CC cessation at follow-up. Specifically, in the analyses evaluating the associations of past 30-day EC use at baseline with CC cessation at follow-up, better treatment adherence was associated with a greater likelihood of CC cessation at 4-, 12-, and 26-week follow-ups (all p’s<0.0004). White/Non-Hispanic race/ethnicity was associated with a greater likelihood of CC cessation at 4- and 12-week follow-ups (all p’s<0.04), while participating in another TTRP research study during treatment was associated with a greater likelihood of CC cessation at the 4-week follow-up only (p=0.002). In the analyses of EC ever-use as a predictor of CC cessation, better treatment adherence was associated with a greater likelihood of CC cessation at 4-, 12-, and 26-week follow-ups (all p’s<0.0003). Non-Hispanic Whites were more likely to achieve CC cessation at 4- and 12-week follow-ups (all p’s<0.02), and participating in another TTRP research study during treatment was associated with a greater likelihood of cessation at the 4-week follow-up (p=0.002). In the analyses of EC use frequency among past 30-day EC users (i.e., used EC≥ once per week vs. < than weekly) as a predictor of CC cessation at follow-up, higher HSI was associated with a lower likelihood of CC cessation at the 12-week follow-up only (p=0.05).

4. Conclusion

The prevalence of past 30-day EC use among treatment-seeking CC users (23%) was far higher than estimates of recent use in the general adult population (3-7%; Creamer et al., 2019; Dai and Leventhal, 2019; Wilson and Wang, 2017), though measurement differences across studies limit direct comparisons (e.g., see Amato et al., 2016; Boyle et al., 2020). EC users were younger than non-users in the current study, though older than EC users in nationally representative samples (Creamer et al., 2019; Dai and Leventhal, 2019; Wilson and Wang, 2017). Consistent with previous reports (Creamer et al., 2019; Dai and Leventhal, 2019; Wilson and Wang, 2017), Black participants were less likely to use ECs than other racial groups. Harlow et al. (2019) reported that this may be due to greater perceptions of harm from ECs, and more positive perceptions and social norms surrounding tobacco use among Blacks relative to other racial/ethnic groups (Harlow et al., 2019). In contrast with other studies (Creamer et al., 2019; Wilson and Wang, 2017), past 30-day EC use was associated with socioeconomic disadvantage, which may reflect differences in EC use patterns between the general U.S. population of adults and the more disadvantaged population of CC users entering cessation treatment. Finally, similar to other research among U.S. adults nationally (Wang et al., 2018), treatment-seeking dual CC/EC users at baseline smoked more CCs and reported greater CC dependence than non-users.

Notably, ECs replaced CCs for only a small number of participants (≈3%) at follow-up, with a larger proportion (6-8%) reporting dual use of CCs/ECs. Overall, while EC use was not associated CC cessation outcomes at follow-up, using ECs at a higher frequency (> once per week) in the subsample of past 30-day EC users appeared to have an adverse impact on longer-term CC cessation. Plausibly, those who used ECs more frequently prior to making a CC cessation attempt may have been more dependent on CCs/nicotine, thereby increasing the difficulty of CC cessation (e.g., see Osibogun et al., 2020). Importantly, participants were not instructed to replace CCs with ECs as part of their treatment, and perhaps with instruction switching rates might increase. Providing advice about the adverse health effects of dual CC/EC use among those making a CC cessation attempt is warranted given evidence that dual-use might be more harmful than using either product alone (e.g., see Wang et al., 2018).

Limitations of the present study include the relatively small number of past 30-day EC users, as well as low rates of switching and dual EC and CC use at follow-up, which limited our ability to evaluate potential predictors of these outcomes. In addition, attrition limited our ability to draw strong conclusions about the impact of EC use on CC cessation. In general, findings indicated that EC use did not adversely impact a CC cessation among adults entering treatment. However, greater CC/nicotine dependence among treatment-seeking CC smokers who use ECs may need to be addressed in treatment. In addition, more frequent EC use (≥ once per week) prior to quitting CCs may interfere with CC cessation and may also warrant attention during treatment (e.g., counseling/education regarding dual EC/CC use, higher NRT dosing). Future research should evaluate the circumstances and characteristics associated with EC switching and dual use among CC users to inform intervention approaches, and also explore the impact of instructing CC users to switch to ECs on outcomes such as CC cessation and dual use. Findings may inform approaches to smoking cessation and EC switching as a harm reduction strategy among adult CC smokers.

Highlights.

  • Baseline dual CC/EC users were more CC dependent than CC-only users.

  • EC use prior to a CC quit attempt was not associated with CC cessation.

  • More frequent baseline EC use predicted a lower likelihood of CC cessation.

Acknowledgements

This research was supported by the Oklahoma Tobacco Settlement Endowment Trust (TSET; Grant #R21-02) and NCI Cancer Center Support Grant P30CA225520 awarded to the Stephenson Cancer Center.

Role of Funding Source

The funders had no role in any aspect of the study design, data collection, data analysis, decision to publish, or preparation of the manuscript.

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 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.

Conflict of Interest

No conflict declared.

References

  1. Abrams DB, Glasser AM, Pearson JL, Villanti AC, Collins LK, Niaura RS, 2018. Harm Minimization and Tobacco Control: Reframing Societal Views of Nicotine Use to Rapidly Save Lives. Annu Rev Public Health 39, 193–213. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Amato MS, Boyle RG, Levy D, 2016. How to define e-cigarette prevalence? Finding clues in the use frequency distribution. Tob Control 25(e1), e24–29. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Andresen EM, Malmgren JA, Carter WB, Patrick DL, 1994. Screening for depression in well older adults: evaluation of a short form of the CES-D (Center for Epidemiologic Studies Depression Scale). Am J Prev Med 10(2), 77–84. [PubMed] [Google Scholar]
  4. Benowitz NL, Bernert JT, Foulds J, Hecht SS, Jacob P III, Jarvis MJ, Joseph A, Oncken C, Piper ME, 2019. Biochemical Verification of Tobacco Use and Abstinence: 2019 Update. Nicotine & Tobacco Research. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Boyle RG, Richter S, St Claire AW, 2020. Defining adult e-cigarette prevalence: comparing a categorical definition with days of use. Tob Control. [DOI] [PubMed] [Google Scholar]
  6. Bullen C, Howe C, Laugesen M, McRobbie H, Parag V, Williman J, Walker N, 2013. Electronic cigarettes for smoking cessation: a randomised controlled trial. The Lancet 382(9905), 1629–1637. [DOI] [PubMed] [Google Scholar]
  7. Creamer M, Wang T, Babb S, Cullen K, Day H, Willis G, Jamal A, Neff L, 2019. Tobacco Product Use and Cessation Indicators Among Adults - United States, 2018. MMWR Morb Mortal Wkly Rep 65(52), 1457–1464. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Czogala J, Goniewicz ML, Fidelus B, Zielinska-Danch W, Travers MJ, Sobczak A, 2014. Secondhand exposure to vapors from electronic cigarettes. Nicotine Tob Res 16(6), 655–662. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Dai H, Leventhal AM, 2019. Prevalence of e-Cigarette Use Among Adults in the United States, 2014-2018. JAMA Pediatr 173(3), 1824–1827. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Drummond MB, Upson D, 2014. Electronic cigarettes. Potential harms and benefits. Ann Am Thorac Soc 11(2), 236–242. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Fairchild AL, Lee JS, Bayer R, Curran J, 2018. E-Cigarettes and the Harm-Reduction Continuum. N Engl J Med 378(3), 214–216. [DOI] [PubMed] [Google Scholar]
  12. Farsalinos KE, Barbouni A, 2020. Association between electronic cigarette use and smoking cessation in the European Union in 2017: analysis of a representative sample of 13 057 Europeans from 28 countries. Tob Control. [DOI] [PubMed] [Google Scholar]
  13. Farsalinos KE, Niaura R, 2020. E-cigarettes and Smoking Cessation in the United States According to Frequency of E-cigarette Use and Quitting Duration: Analysis of the 2016 and 2017 National Health Interview Surveys. Nicotine Tob Res 22(5), 655–662. [DOI] [PubMed] [Google Scholar]
  14. Goniewicz ML, Knysak J, Gawron M, Kosmider L, Sobczak A, Kurek J, Prokopowicz A, Jablonska-Czapla M, Rosik-Dulewska C, Havel C, Jacob P 3rd, Benowitz N, 2014. Levels of selected carcinogens and toxicants in vapour from electronic cigarettes. Tob Control 23(2), 133–139. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Hajek P, Etter JF, Benowitz N, Eissenberg T, McRobbie H, 2014. Electronic cigarettes: review of use, content, safety, effects on smokers and potential for harm and benefit. Addiction 109(11), 1801–1810. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Hajek P, Phillips-Waller A, Przulj D, Pesola F, Myers Smith K, Bisal N, Li J, Parrott S, Sasieni P, Dawkins L, Ross L, Goniewicz M, Wu Q, McRobbie HJ, 2019a. A Randomized Trial of E-Cigarettes versus Nicotine-Replacement Therapy. N Engl J Med 380(7), 629–637. [DOI] [PubMed] [Google Scholar]
  17. Hajek P, Phillips-Waller A, Przulj D, Pesola F, Smith KM, Bisal N, Li J, Parrott S, Sasieni P, Dawkins L, Ross L, Goniewicz M, Wu Q, McRobbie HJ, 2019b. E-cigarettes compared with nicotine replacement therapy within the UK Stop Smoking Services: the TEC RCT. Health Technol Assess 23(43), 1–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Harlow AF, Stokes A, Brooks DR, 2019. Socioeconomic and Racial/Ethnic Differences in E-Cigarette Uptake Among Cigarette Smokers: Longitudinal Analysis of the Population Assessment of Tobacco and Health (PATH) Study. Nicotine Tob Res 21(10), 1385–1393. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Hatsukami D, Meier E, Lindgren BR, Anderson A, Reisinger S, Norton K, Strayer L, Jensen J, Dick L, Murphy S, Carmella S, Tang MK, Chen M, Hecht SS, O'Connor RJ, Shields PG, 2019. A Randomized Clinical Trial Examining the Effects of Instructions for Electronic Cigarette Use on Smoking-Related Behaviors, and Biomarkers of Exposure. Nicotine Tob Res. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Heatherton TF, Kozlowski LT, Frecker RC, Rickert W, Robinson J, 1989. Measuring the heaviness of smoking: using self-reported time to the first cigarette of the day and number of cigarettes smoked per day. Br J Addict 84(7), 791–799. [DOI] [PubMed] [Google Scholar]
  21. Hiscock R, Bauld L, Amos A, Fidler JA, Munafo M, 2012. Socioeconomic status and smoking: a review. Ann N Y Acad Sci 1248, 107–123. [DOI] [PubMed] [Google Scholar]
  22. HPRC, 2020. Tobacco Treatment Research Program (TTRP). https://otrc.stephensoncancercenter.org/Free-Help-to-Quit. (Accessed October 11 2020).
  23. Kviz FJ, Clark MA, Crittenden KS, Warnecke RB, Freels S, 1995. Age and Smoking Cessation Behaviors. Preventive Medicine 24(3), 297–307. [DOI] [PubMed] [Google Scholar]
  24. Levy DT, Cummings KM, Villanti AC, Niaura R, Abrams DB, Fong GT, Borland R, 2017. A framework for evaluating the public health impact of e-cigarettes and other vaporized nicotine products. Addiction 112(1), 8–17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Martinez U, Martinez-Loredo V, Simmons VN, Meltzer LR, Drobes DJ, Brandon KO, Palmer AM, Eissenberg T, Bullen CR, Harrell PT, Brandon TH, 2020. How Does Smoking and Nicotine Dependence Change After Onset of Vaping? A Retrospective Analysis of Dual Users. Nicotine Tob Res 22(5), 764–770. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Morisky DE, Green LW, Levine DM, 1986. Concurrent and predictive validity of a self-reported measure of medication adherence. Medical care 24(1), 67–74. [DOI] [PubMed] [Google Scholar]
  27. Nutt DJ, Phillips LD, Balfour D, Curran HV, Dockrell M, Foulds J, Fagerstrom K, Letlape K, Milton A, Polosa R, Ramsey J, Sweanor D, 2014. Estimating the harms of nicotine-containing products using the MCDA approach. Eur Addict Res 20(5), 218–225. [DOI] [PubMed] [Google Scholar]
  28. Osibogun O, Bursac Z, McKee M, Li T, Maziak W, 2020. Cessation outcomes in adult dual users of e-cigarettes and cigarettes: the Population Assessment of Tobacco and Health cohort study, USA, 2013–2016. Int J Public Health. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Piper ME, Cook JW, Schlam TR, Jorenby DE, Smith SS, Bolt DM, Loh WY, 2010. Gender, race, and education differences in abstinence rates among participants in two randomized smoking cessation trials. Nicotine Tob Res 12(6), 647–657. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Raupach T, Brown J, Herbec A, Brose L, West R, 2014. A systematic review of studies assessing the association between adherence to smoking cessation medication and treatment success. Addiction 109(1), 35–43. [DOI] [PubMed] [Google Scholar]
  31. Rojewski AM, Tanner NT, Dai L, Ravenel JG, Gebregziabher M, Silvestri GA, Toll BA, 2018. Tobacco Dependence Predicts Higher Lung Cancer and Mortality Rates and Lower Rates of Smoking Cessation in the National Lung Screening Trial. Chest 154(1), 110–118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Smith PH, Bessette AJ, Weinberger AH, Sheffer CE, McKee SA, 2016. Sex/gender differences in smoking cessation: A review. Prev Med 92, 135–140. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Trtchounian A, Williams M, Talbot P, 2010. Conventional and electronic cigarettes (e-cigarettes) have different smoking characteristics. Nicotine Tob Res 12(9), 905–912. [DOI] [PubMed] [Google Scholar]
  34. USDHHS, 2016. E-Cigarette Use Among Youth and Young Adults: A Report of the Surgeon General. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health. [Google Scholar]
  35. Wagener TL, Floyd EL, Stepanov I, Driskill LM, Frank SG, Meier E, Leavens EL, Tackett AP, Molina N, Queimado L, 2017. Have combustible cigarettes met their match? The nicotine delivery profiles and harmful constituent exposures of second-generation and third-generation electronic cigarette users. Tob Control 26(e1), e23–e28. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Wang JB, Olgin JE, Nah G, Vittinghoff E, Cataldo JK, Pletcher MJ, Marcus GM, 2018. Cigarette and e-cigarette dual use and risk of cardiopulmonary symptoms in the Health eHeart Study. PLoS One 13(7), e0198681–e0198681. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Weinberger AH, Kashan RS, Shpigel DM, Esan H, Taha F, Lee CJ, Funk AP, Goodwin RD, 2017. Depression and Cigarette Smoking Behavior: A Critical Review of Population-based Studies. The American Journal of Drug and Alcohol Abuse 43(4), 416–431. [DOI] [PubMed] [Google Scholar]
  38. Wetter DW, Kenford SL, Smith SS, Fiore MC, Jorenby DE, & Baker TB, 1999. Gender differences in smoking cessation. Journal of consulting and clinical psychology 67(4). [DOI] [PubMed] [Google Scholar]
  39. Wilson FA, Wang Y, 2017. Recent Findings on the Prevalence of E-Cigarette Use Among Adults in the U.S. Am J Prev Med 52(3), 385–390. [DOI] [PubMed] [Google Scholar]
  40. Zawertailo L, Voci S, Selby P, 2018. The Cigarette Dependence Scale and Heaviness of Smoking Index as predictors of smoking cessation after 10weeks of nicotine replacement therapy and at 6-month follow-up. Addict Behav 78, 223–227. [DOI] [PubMed] [Google Scholar]

RESOURCES