Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2021 May 7.
Published in final edited form as: Drug Alcohol Depend. 2020 Nov 1;218:108394. doi: 10.1016/j.drugalcdep.2020.108394

The association between frequency of e-cigarette use and long-term smoking cessation outcomes among treatment-seeking smokers receiving a behavioral intervention

Noreen L Watson a,*, Kristin E Mull a, Jonathan B Bricker a,b
PMCID: PMC8104076  NIHMSID: NIHMS1697116  PMID: 33203525

Abstract

Introduction:

A growing body of literature suggests daily, but not non-daily, e-cigarette use is associated with greater odds of quitting combustible cigarettes in the general adult population. However, it is unknown if these findings generalize to treatment-seeking smokers who are receiving a behavioral intervention. Our primary aim was to examine whether frequency of e-cigarette use was associated with subsequent cessation among treatment-seeking smokers who are receiving a behavioral smoking cessation intervention.

Methods:

Participants (N = 2637) enrolled in a RCT of web-based smoking treatments reported their use of e-cigarettes at baseline, 3-, and 6-months. Three groups were created based on e-cigarette use: (1) non-users, (2) intermittent users, and (3) daily users. The primary outcome was complete-case, self-reported 30-day point prevalence abstinence at 12 months.

Results:

Compared to non-users, daily e-cigarette users were significantly less likely to be abstinent (21.39 % vs. 29.68 %; p = .006). Quit rates for intermittent users (24.56 %) were not significantly different from non-users (p = .092). Nicotine dependence moderated the results such that among smokers with low nicotine dependence, those who used e-cigarettes (intermittently or daily) were less likely to quit than non-users; these differences were not significant among those with high nicotine dependence. Post hoc analyses indicated that initiating daily e-cigarette use after baseline, but not daily e-cigarette use at baseline, was associated with lower odds of cessation.

Conclusions:

Daily e-cig use may be associated with lower odds of quitting smoking among treatment-seeking smokers, particularly among those with lower nicotine dependence and who initiate daily use after beginning an intervention.

Keywords: Electronic cigarettes, Cigarette smoking, Smoking cessation, Smoking

1. Introduction

In the United States, up to 78 % of adults who use e-cigarettes also smoke combustible cigarettes (Dai and Leventhal, 2019; Jaber et al., 2018; Levy et al., 2017; Mirbolouk et al., 2018; Rodu and Plurphanswat, 2017; Wei et al., 2020), and nearly 86 % indicate that a primary reason for using e-cigarettes is to help them quit smoking (Patel et al., 2016; Yong et al., 2019). Indeed, a greater proportion of smokers report using e-cigarettes than FDA-approved cessation aids such as the nicotine patch, nicotine gum, cessation medication, or behavioral interventions (Caraballo et al., 2017). However, much remains unknown about the effectiveness of e-cigarettes as a cessation aid, including the conditions under which or for which groups of smokers they may facilitate or impede cessation of combustible cigarettes (Brady et al., 2019; Glasser et al., 2017; Hajek et al., 2019; Verplaetse et al., 2019; Warner, 2019).

Although evidence from observational studies has been mixed regarding the association between e-cigarette use and smoking cessation (Hartmann-Boyce et al., 2016; Jackson et al., 2020; Kalkhoran and Glantz, 2016), a clearer picture seems to emerge when the frequency of e-cigarette use is considered (Levy et al., 2018; National Academies of Sciences, Engineering, and Medicine, 2018). Specifically, with few exceptions (Brose et al., 2015; Weaver et al., 2018), numerous cross-sectional and longitudinal, population-level studies have demonstrated that daily e-cigarette use is associated with a significantly greater likelihood of quitting combustible cigarettes than no e-cigarette use (Berry et al., 2019; Biener and Hargraves, 2015; Hitchman et al., 2015; Kalkhoran et al., 2019; Levy et al., 2018; Siegel et al., 2011; Verplaetse et al., 2019). Non-daily use of e-cigarettes, on the other hand, has been shown to either hinder cessation (Giovenco and Delnevo, 2018) or not be associated with cessation (Berry et al., 2019; Kalkhoran et al., 2019). Daily e-cigarette use is believed to be associated with greater odds of quitting because daily users may be more motivated to quit or more likely to be using e-cigarettes to help them quit; in contrast, non-daily users may be using e-cigarettes experimentally, to temporarily substitute cigarettes, or as a way to reduce the harmful effects of cigarettes (by smoking fewer) without intending to quit entirely (Amato et al., 2016; Biener and Hargraves, 2015; Kalkhoran et al., 2019). While these findings are robust, these studies have only examined the relationship between e-cigarette use and cessation in the general population of adult smokers, and it is unclear if they would generalize to smokers who are ready to quit and are receiving a behavioral intervention. Knowing the impact of e-cigarette use on cessation under these conditions is vital for informing best practices and the development of behavioral interventions.

There are very few prospective studies that have evaluated whether e-cigarettes aid cessation among smokers who are ready to quit and are receiving a behavioral intervention (Brady et al., 2019; Bullen et al., 2013; Hajek et al., 2019; Rigotti et al., 2018; Walker et al., 2020). The results from these studies are mixed, with two demonstrating a positive association between e-cigarette use and likelihood of cessation (Hajek et al., 2019; Walker et al., 2020), one demonstrating a negative association (Rigotti et al., 2018), one suggesting no association (Brady et al., 2019), and one with inconclusive results (Bullen et al., 2013). These mixed findings can likely be attributable to the wide variation in study designs and methodologies, including that none of these studies assessed frequency of e-cigarette use. Moreover, despite the individual strengths of these studies, they collectively have two limitations. First, as mentioned, none assessed or accounted for differential effects of daily versus non-daily use of e-cigarettes. Second, three of the studies randomized some participants to receive e-cigarettes in addition to behavioral support and/or nicotine replacement therapy (Bullen et al., 2013; Hajek et al., 2019; Walker et al., 2020). While such designs are vital in answering questions about the efficacy of e-cigarettes for cessation, it is unclear if results would generalize to current “real-world” situations in which smokers self-select e-cigarette use and must acquire them on their own. The overall perception and perceived usefulness of e-cigarettes may be different for smokers who are assigned or recommended to use e-cigarettes versus those who self-select them. Given these limitations, additional research is crucial to help understand the role of e-cigarette use in smoking cessation, particularly among smokers who are ready to quit and receiving behavioral interventions.

Using data from adult smokers enrolled in a large randomized controlled trial (RCT) of two web-based smoking interventions, the purpose of this secondary analysis is to add to the small body of literature examining the relationship between e-cigarette use and subsequent smoking cessation. Specifically, our primary aim was to evaluate whether frequency of e-cigarette use from baseline, 3-, and 6-months was associated with smoking abstinence at the 12-month follow-up. We hypothesized that, relative to no e-cigarette use, daily e-cigarette use, but not non-daily use, would be associated with a greater likelihood of smoking abstinence. Additionally, to better understand the conditions under which e-cigarette use may help or hinder cessation, we also examined potential moderators of cessation outcomes (e.g., sociodemographic variables known to predict cessation).

2. Methods

2.1. Participants

Participants were 2637 adult smokers from all 50 states who were enrolled in a prior RCT of two web-delivered smoking interventions [NCT01812278] (Bricker et al., 2018). Participants had to meet the following criteria to be eligible for the trial: (1) be ≥ 18 years of age; (2) smoke ≥ 5 cigarettes per day for the last year; (3) want to quit smoking within 30 days; (4) have internet and email access; (5) not be participating in other smoking interventions or treatments; (6) have no prior use of the control intervention, Smokefree.gov; (7) have not participated in one of our previous smoking studies; (8) have no other household members in the study; (9) be willing to be randomized to treatment, complete 3 follow-up surveys, and provide contact information; (10) reside in the United States; and (11) ability and willingness to read English.

2.2. Procedures

A full description of the study methodology can be found elsewhere (Bricker et al., 2018; Watson et al., 2018). Briefly, participants were recruited from March 2014 to August 2015 via Facebook advertisements, an online survey panel, search engine results, friend/family referrals, Google advertisements, and earned media. After completing the online enrollment process (informed consent and baseline survey), participants were randomized to receive 12 months of access to one of two web-based smoking interventions based either on Acceptance and Commitment Therapy (ACT) or standard care (the National Cancer Institute’s Smokefree.gov website). Follow-up surveys were conducted at 3-, 6-, and 12-months post-randomization. Participants received up to $35 for completing each follow-up survey. The data retention rate was 88 % at 12 months and did not differ by treatment arm. All procedures were approved by the Fred Hutchinson Cancer Research Center Institutional Review Board.

2.3. Measures

2.3.1. Baseline variables

On the baseline survey, participants reported sociodemographic variables including age, gender, race, ethnicity, education, employment status, and sexual orientation. Baseline smoking-related characteristics included number of 24 -h quit attempts in the past year, nicotine dependence, and commitment to quitting. Level of nicotine dependence was assessed using the Fagerström Test for Nicotine Dependence (FTND) (Heatherton et al., 1991). Scores on the FTND range from 0 to 10, with a score ≥6 representing “high” nicotine dependence. We used the Commitment to Quitting Smoking Scale (CQSS) (Kahler et al., 2007) to assess commitment to quitting or to resisting smoking in the face of difficulties, cravings, and negative affect. Scores on the CQSS range from 1 to 5, with higher scores representing greater commitment.

The baseline survey also included commonly used, validated instruments to screen for mental health conditions including major depression, generalized anxiety, panic disorder, posttraumatic stress disorder (PTSD), social anxiety, and at-risk drinking. We used the Center for Epidemiologic Studies Depression Scale (CES-D) to screen for depression (positive screen is a score ≥16) (Radloff, 1977), the GAD-7 for generalized anxiety disorder (GAD; positive screen is a score ≥ 10) (Spitzer et al., 2006), the Autonomic Nervous System Questionnaire (ANSQ) for panic disorder (a positive screen is a report of ≥1 panic attacks within the past month, with at least one occurring in a situation in which the participant was not in danger or the center of attention) (Stein et al., 1999), the 6-item PTSD Checklist for PTSD (positive screen is a score ≥ 14) (Lang et al., 2012), the Mini-Social Phobia Inventory (Mini-SPIN) for social anxiety disorder (positive screen is a score ≥ 6) (Connor et al., 2001), and the Alcohol Use Disorders Identification Test-Concise (AUDIT-C) for risky drinking (a positive screen was indicated by drinking ≥ 4 (for women) or ≥ 5 (for men) drinks on a typical drinking day) (Bradley et al., 2007; Bush et al., 1998).

2.3.2. E-cigarette use

We assessed frequency of e-cigarette use at baseline and each follow-up (3- and 6-month) by asking participants, “How often do you currently (i.e., within the last 30 days) use any kind of e-cigarettes?” Response options included: not at all; less than once a month; once a month or more, but less than once a week; once a week or more, but not daily; and at least daily. For the purposes of this analysis, we used frequency of e-cigarette use at baseline, 3-months, and 6-months to categorize participants into one of three groups: (1) non-users—no past 30-day use of e-cigarettes at all assessments; (2) intermittent users –used e-cigarettes on some of the past 30 days on one or more assessment, but never reported daily use; and (3) daily users –daily use in the past 30 days on one or more assessment. We considered self-reported e-cigarette use from all three assessments that occurred prior to the outcome assessment for the following reasons: (a) this approach more accurately captures patterns of e-cigarette use whereas classifying e-cigarette use using only baseline data would exclude a large proportion of smokers who initiated e-cigarette use later in the study; and (b) this approach is consistent with other studies that have classified participants based on their e-cigarette use at multiple assessments throughout a study (Brady et al., 2019; Weaver et al., 2018), used e-cigarette use at baseline as an exclusion criteria (e.g., in RCTs where some participants are randomized to receive e-cigarettes) (Hajek et al., 2019; Walker et al., 2020), or only examined post-enrollment e-cigarette use for primary analyses, not baseline or prior use (Rigotti et al., 2018).

2.3.3. Cessation outcomes

Consistent with the parent trial (Bricker et al., 2018), the primary outcome was self-reported 30-day point prevalence abstinence (PPA) (no smoking, not even a puff in the last 30 days) at 12 months post-randomization analyzed using complete-case methodology. To facilitate cross-trial comparisons, the secondary outcome was 30-day PPA with missing values imputed as smoking. We did not biochemically confirm abstinence as self-reported outcomes are recommended for large, population-level cessation trials where there is no face-to-face contact, demand characteristics for false reporting are minimal, and biochemical confirmation is not feasible due to large sample size and geographic diversity (Benowitz et al., 2002).

2.4. Statistical analyses

We first compared baseline demographics, smoking characteristics, and mental health status of the three groups of e-cigarette users (non-users, intermittent users, and daily users) using linear models for continuous variables and chi-squared tests for categorical variables. To evaluate whether frequency of e-cigarette use was associated with subsequent smoking cessation, we used logistic regression models. We initially included an interaction term to investigate moderation by treatment arm; this term was later dropped from the models because it was not significant. Model covariates included treatment arm, factors used in stratified randomization (i.e., gender, education, and smoking ≥21 cigarettes/day), and variables that both differed between e-cigarette group at baseline and were associated with the primary cessation outcome to reduce the potential for confounding (i.e., racial/ethnic minority status, nicotine dependence, and commitment to quitting).

To assess whether baseline demographics, smoking characteristics, or mental health conditions moderated the relationship between frequency of e-cigarette use and smoking cessation outcomes we assessed the interaction of each potential baseline moderator with e-cigarette group in the logistic regression outcome model. In all cases, model covariates were the same as for the primary outcome model. All statistical tests were two-sided, with α = 0.05, and p-values for pairwise comparisons between e-cigarette groups were adjusted using the Holm procedure (Holm, 1979). Analyses were carried out using R Version 3.6.1.

3. Results

3.1. Baseline characteristics between non-, intermittent, and daily e-cigarette users

Considering frequency of e-cigarette use across baseline, 3-month, and 6-month assessments, 31.89 % (841/2637) of participants were non-users, 25.10 % (662/2637) were intermittent users (i.e., never reported daily e-cigarette use), and 27.57 % (727/2637) were daily users (i.e., reported daily use at ≥ 1 assessment). Table 1 depicts comparisons of baseline variables across e-cigarette use groups and indicates a few key between-group differences. Daily e-cigarette users were significantly older and less likely to identify as a racial minority than non-users and intermittent users. No other baseline demographics differed between groups. Regarding baseline smoking characteristics, intermittent and daily users had higher levels of nicotine dependence and lower commitment to quitting scores compared to non-users. Daily e-cigarette users also reported fewer past-year quit attempts at baseline than intermittent users. No differences were found in the proportion of participants who screened positive on any mental health assessment. Overall, the participant characteristics in this study are similar to participant characteristics of treatment-seeking smokers enrolled in similar population-level (Brady et al., 2019; Bricker et al., 2020; Danaher et al., 2019; McClure et al., 2014; Taylor et al., 2017).

Table 1.

Baseline Comparisons.

Non-Users N = 841 Intermittent Users N = 662 Daily Users N = 727 Overall p-value
Demographics
Age, M(SD) 45.6 (13.0) 44.6 (13.6) 48.0 (13.0) <0.001
Female, N (%) 674 (80.14 %) 525 (79.31 %) 586 (80.61 %) 0.829
Racial minority, N (%) 263 (31.27 %) 193 (29.15 %) 164 (22.56 %) <0.001
Ethnic Minority, N (%) 72 (8.56 %) 58 (8.76 %) 53 (7.29 %) 0.543
HS or less education, N (%) 220 (26.16 %) 193 (29.15 %) 201 (27.65 %) 0.433
Employed, N (%) 438 (52.08 %) 350 (52.87 %) 366 (50.34 %) 0.623
Sexual Minority, N (%) 79 (9.39 %) 67 (10.12 %) 65 (8.94 %) 0.752
Smoking variables, M (SD)
Nicotine Dependence 5.3 (2.2) 5.6 (2.2) 5.8 (2.2) <0.001
# of past year quit attempts 1.6 (3.9), n = 809 2.0 (6.7), n = 623 1.3 (3.7), n = 686 0.036
Commitment to quitting 4.04 (0.75), n = 839 3.96 (0.76), n = 660 3.95 (0.77) 0.025
Mental health status, N (%)
Screened positive for Depression 440 (52.44 %), n = 839 370 (56.66 %), n = 653 411 (56.85 %), n = 723 0.140
Screened positive for GAD 277 (33.13 %), n = 836 229 (34.91 %), n = 656 255 (35.08 %), n = 727 0.668
Screened positive for PTSD 431 (51.43 %), n = 838 365 (55.56 %), n = 657 379 (52.20 %), n = 726 0.256
Screened positive for Panic 361 (47.50 %), n = 760 299 (50.59 %), n = 591 314 (47.65 %), n = 659 0.465
Screened positive for SAD 240 (28.64 %), n = 838 218 (33.08 %), n = 659 220 (30.26 %) 0.177
Risky drinking 80 (9.71 %), n = 824 75 (11.68 %), n = 642 75 (10.45 %), n = 718 0.472
Open in a new tab

Note: HS=high school; GAD=generalized anxiety disorder; PTSD=posttraumatic stress disorder; SAD=social anxiety disorder

3.2. Primary analyses: Smoking cessation outcomes by e-cigarette use groups

The primary cessation outcome of complete-case, self-reported, 30-day PPA from combustible cigarettes at 12-months post-randomization is presented in Table 2. In contrast to our hypotheses, daily e-cigarette users were 30 % less likely to be abstinent from combustible cigarettes than those who never used e-cigarettes during the study (21.39 % vs. 29.68 %; adjusted OR = 0.70, 95 % CI = 0.54 0.88, p = .006). The quit rates for intermittent users were not significantly different from non-users (24.56 % vs. 29.68 %; adjusted OR = 0.81, 95 % CI = 0.64–1.03, p = .092). This pattern of results remained unchanged in analyses where missing responses were imputed as indicating smoking (results not shown).

Table 2.

Multivariate logistic regression models of e-cigarette use categories predicting cessation.

12-month cigarette smoking abstinence AOR 95 % CI p-value
Frequency of e-cigarette use
Non-users (ref) 241 (29.68 %), n = 812 ref ref ref
Intermittent users 154 (24.56 %), n = 627 0.81 0.64–1.03 0.092
Daily users 145 (21.39 %), n = 678 0.70 0.54–0.88 0.006
Open in a new tab

Note:Model covariates included treatment arm, factors used in stratified randomization (i.e., gender, education, and smoking ≥21 cigarettes/day), and three potential confounding variables: racial/ethnic minority, nicotine dependence (FTND score), and commitment to quitting (CQSS score).

3.3. Baseline moderators of cessation outcomes

We examined all baseline characteristics in Table 1 as potential moderators of cessation. Only level of nicotine dependence moderated the effect of e-cigarette use on cessation (p = 0.042). Thus, in post hoc analyses we investigated differences in cessation by e-cigarette group separately for participants with low and high nicotine dependence (i.e., FTND < 6 and ≥6, respectively). Among participants with low nicotine dependence, those who used e-cigarettes intermittently or daily were less likely to quit combustible cigarettes than those who never used e-cigarettes during the study (p = 0.009 and 0.022, respectively; Table 3). In contrast, among participants with high nicotine dependence, there were no significant differences in quit rates of combustible cigarettes between groups.

Table 3.

Cessation outcome by nicotine dependence level.

12-month cigarette smoking abstinence AOR 95 % CI p-value
Low Nicotine Dependence
Non-users (ref) 136 (33.50 %), n = 406 ref ref ref
Intermittent users 67 (22.48 %), n = 298 0.60 0.42–0.85 0.009
Daily users 65 (23.21 %), n = 280 0.66 0.46–0.94 0.022
High Nicotine Dependence
Non-users (ref) 105 (25.86 %), n = 406 ref ref ref
Intermittent users 87 (26.44 %), n = 329 1.09 0.78–1.53 0.603
Daily users 80 (20.10 %), n = 398 0.75 0.53–1.05 0.184
Open in a new tab

3.4. Post-hoc analyses

Given the unexpected finding that daily e-cigarette users were significantly less likely to quit smoking, we conducted post-hoc tests to determine if the pattern of results differed based on whether smokers reported e-cigarette use prior to beginning an intervention (at baseline) or initiated e-cigarette use afterwards (post-baseline) (Table 4). Quit rates among smokers who reported daily or intermittent e-cigarette use at baseline were not different from those who reported non-use. However, among baseline non-users, those who initiated daily e-cigarette use after starting the intervention were significantly less likely to quit smoking than those who remained non-users (19.59 % vs. 29.68 %; adjusted OR = 0.61, 95 % CI = 0.44 – 0.85, p = .004). The quit rates for those who initiated intermittent e-cigarette use after baseline were not significantly different from those those who did not initiate use (24.72 % vs. 29.68 %, p = .130).

Table 4.

Patterns of quit rates between baseline e-cigarette users vs. those who initiated use post baseline.

12-month cigarette smoking abstinence AOR 95 % CI p-value
Baseline e-cigarette use
Non-users (ref) 395 (26.02 %), n = 1518 ref ref ref
Intermittent users 133 (22.32 %), n = 596 0.90 0.71–1.13 0.370
Daily users 55 (28.21 %), n = 195 1.15 0.81–1.61 0.419
E-cigarette use among baseline non-users
Non-users (ref) 241 (29.68 %), n = 812 ref ref ref
Intermittent users 67 (24.72 %), n = 271 0.78 0.56–1.07 0.130
Daily users 57 (19.59 %), n = 291 0.61 0.44–0.85 0.004
Open in a new tab

4. Discussion

To our knowledge, this was the first study to examine the relationship between frequency of e-cigarette use and future smoking cessation in large sample of treatment-seeking smokers who are receiving treatment for smoking cessation. Using data from adult smokers enrolled in a large RCT of two web-based smoking interventions, we found that compared to individuals who never reported using e-cigarettes during the study, daily e-cigarette users were significantly less likely to be abstinent from combustible cigarettes at 12-months. The quit rates observed for those who used e-cigarettes intermittently were not significantly different from non-users. Importantly, these models controlled for factors used for stratified randomization (i.e., gender, education, and smoking ≥21 cigarettes/day), and potential confounding variables (racial minority status, nicotine dependence, and commitment to quitting).

To better understand the conditions under which e-cigarette use may help or hinder cessation for this group of smokers, we also examined potential moderators of cessation outcomes. Although the groups differed on a number of baseline variables, only level of nicotine dependence moderated the results such that among participants with low nicotine dependence, those who used e-cigarettes intermittently or daily were less likely to quit smoking than non-users, but these differences were not significant among participants with high nicotine dependence. Thus, among smokers who are ready to quit smoking and receiving a behavioral smoking intervention, any e-cigarette use may reduce the chances of quitting among smokers with lower levels of nicotine dependence. Why e-cigarette use may hamper cessation for smokers with low nicotine dependence, but not those with high nicotine dependence, is unclear and warrants future research.

Our findings that daily e-cigarette users were less likely to quit smoking than non-users contrasts with our hypotheses and much of the literature examining these relationships in the general population (Berry et al., 2019; Biener and Hargraves, 2015; Giovenco and Delnevo, 2018; Kalkhoran et al., 2019; Levy et al., 2018; Siegel et al., 2011). It is unclear why a different pattern of results was found in this study. It is possible that daily e-cigarette use impedes smoking cessation among treatment-seeking smokers who are receiving a behavioral smoking intervention. For example, daily e-cigarette use may put recent-quitters at greater risk for relapse (Verplaetse et al., 2019) or undermine the effects of behavioral interventions if they are not learning other ways to cope with cravings taught by the intervention (e.g., behaviorally they are engaging in a similar behavior, which may lead them to use combustible cigarettes if they do not find that e-cigarettes satisfactory) (Brandon et al., 2019; Yong et al., 2019). It is also possible that certain patterns of daily e-cigarette use are associated with greater odds of quitting, but that we did not assess those patterns in this study. For example, there is evidence that smokers who use e-cigarettes as a complete replacement for cigarettes are more likely to achieve short-term cessation than smokers who use e-cigarettes ad-libitum (Hatsukami et al., 2019). Thus, it may be that daily use is only associated with greater odds of cessation for treatment-seeking smokers when used as a complete replacement, rather than partial replacement, for combustible cigarettes. Lastly, considering the results from our post-hoc analyses, it is also possible that the results from this study were driven by smokers who initiated daily e-cigarette use after baseline. Specifically, our post-hoc analyses indicated that initiating daily e-cigarette use after starting a behavioral intervention was associated with lower odds of quitting than those who never initiate e-cigarette use, whereas those who used them daily before beginning their intervention had similar quit rates as non-users. It may be that those who initiated daily use after beginning the intervention did so for different reasons or after failing to quit using the intervention alone and thus, may represent a group of smokers who experience greater barriers to quitting. While exploring these possibilities is beyond the scope of paper, it does suggest that future research is needed to better understand how different patterns of e-cigarette use over time may impact cessation among treatment-seeking smokers.

Our results also contrast with results from two trials conducted with treatment-seeking smokers who receiving tobacco treatment (Hajek et al., 2019; Walker et al., 2020) that suggest e-cigarettes may facilitate cessation. One trial demonstrated that, when combined with counseling, e-cigarettes were more effective for facilitating cessation than nicotine replacement therapy (Hajek et al., 2019). The other demonstrated that combining nicotine patches with nicotine-containing e-cigarettes may lead to small improvements in cessation rates than patches alone (Walker et al., 2020). An important difference between those trials and the present, observational study is that e-cigarette use in this study was self-selected (vs. an experimental group to which participants were randomized). Thus, it is possible that there are there are unmeasured factors that may account for the findings. It is also plausible, however, that for smokers who are ready to quit smoking and are receiving tobacco treatment, the effect of e-cigarettes is different when there is a perception that e-cigarettes are being recommended as a cessation aid and are provided free of charge than when e-cigarette use is self-selected. This possibility is supported by one other observational study conducted with smokers who are ready to quit smoking. Specifically, the study found that, compared to individuals who did not use e-cigarettes, e-cigarette use was associated with significantly lower odds of quitting at 6-months among patients discharged from the hospital who were interested in quitting and received a brief behavioral intervention (Rigotti et al., 2018). However, as in the current study, it may be that smokers from this study who experienced greater barriers to quitting subsequently chose to use e-cigarettes.

The current study had several methodological strengths: a large sample of treatment-seeking smokers from all 50 states, high rates of outcome data retention, controlling for possible confounders assessed at baseline, and assessment of frequency of e-cigarette use at multiple time points. Additionally, there are limitations to the present study worth noting. First, given the observational nature of the present study, our conclusions should be considered preliminary and conclusions regarding causality or directionality cannot be ascertained. As discussed above, although daily e-cigarette use may hinder cessation for some treatment-seeking smokers, it is also possible that smokers who have difficulty quitting or experience greater barriers to quitting subsequently begin using e-cigarettes. Second, the item we used to assess frequency of e-cigarette use did not specifically ask about nicotine-containing devices. Thus, it is possible that some respondents who reported e-cigarette use were not using their device to consume nicotine. Third, we did not assess three potentially important variables that may help elucidate the relationship between frequency of e-cigarette use and cessation among treatment-seeking smokers—reasons for using e-cigarettes, type of e-cigarette (e.g., cigalike vs tank), and whether or not e-cigarettes were used as a complete replacement for combustible cigarettes. Each of these has been shown to predict cessation in past research (Berry et al., 2019; Hatsukami et al., 2019; Hitchman et al., 2015). Finally, we did not biochemically verify smoking abstinence. However, verifying abstinence is often considered unnecessary in population-level intervention studies with no face-to-face contact as doing so is often not feasible and can bias results (Benowitz et al., 2020; Benowitz et al., 2002). Moreover, biochemical confirmation of abstinence from all nicotine products is particularly impractical in remotely-conducted, geographically-diverse population-level studies.

4.1. Conclusions

This secondary analysis of data from a large trial of two web-based smoking interventions was the first to examine the relationship between frequency of e-cigarette use and future smoking cessation in large sample of adult, treatment-seeking smokers who are receiving treatment for smoking cessation. In contrast with our hypotheses, relative to no e-cigarette use, daily e-cigarette use was associated with a lower likelihood of smoking abstinence at 1-year. Our exploratory and post-hoc analyses also suggest that daily e-cigarette use may especially hinder cessation for smokers with lower levels of nicotine dependence and those who initiate e-cigarette use after beginning a behavioral intervention.

Acknowledgments

We would like to thank those who volunteered as participants in this study.

Role of funding source

This work was supported by a grant from the National Cancer Institute at the National Institutes of Health (R01CA166646 to JBB). The National Institutes of Health had no involvement in the conduct of the study or preparation of the manuscript.

Footnotes

Declaration of Competing Interest

JBB has served as a consultant for GlaxoSmithKline and serves on the advisory board for Chrono Therapeutics. None of the other authors have competing interests to disclose.

References

  1. 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. 10.1136/tobaccocontrol-2015-052236. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Benowitz NL, Bernert JT, Foulds J, Hecht SS, Jacob P, Jarvis MJ, Piper ME, 2020. Biochemical verification of tobacco use and abstinence: 2019 update. Nicotine Tob. Res 22 (7), 1086–1097. 10.1093/ntr/ntz132. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Berry KM, Reynolds LM, Collins JM, Siegel MB, Fetterman JL, Hamburg NM, Stokes A, 2019. E-cigarette initiation and associated changes in smoking cessation and reduction: The Population Assessment of Tobacco and Health Study, 2013–2015. Tob. Control 28 (1), 42–49. 10.1136/tobaccocontrol-2017-054108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Biener L, Hargraves JL, 2015. A longitudinal study of electronic cigarette use among a population-based sample of adult smokers: association with smoking cessation and motivation to quit. Nicotine Tob. Res 17 (2), 127–133. 10.1093/ntr/ntu200. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bradley KA, DeBenedetti AF, Volk RJ, Williams EC, Frank D, Kivlahan DR, 2007. AUDIT-C as a brief screen for alcohol misuse in primary care. Alcohol. Clin. Exp. Res 31 (7), 1208–1217. 10.1111/j.1530-0277.2007.00403.x. [DOI] [PubMed] [Google Scholar]
  6. Brady BR, Crane TE, O’Connor PA, Nair US, Yuan NP, 2019. Electronic cigarette use and tobacco cessation in a state-based quitline. J. Smok. Cessat 14 (3), 176–185. 10.1017/jsc.2019.2. [DOI] [Google Scholar]
  7. Brandon KO, Simmons VN, Meltzer LR, Drobes DJ, Martinez U, Sutton SK, Palmer AM, Bullen CR, Harrell PT, Brandon TH, 2019. Vaping characteristics and expectancies are associated with smoking cessation propensity among dual users of combustible and electronic cigarettes. Addiction 114 (5), 896–906. 10.1111/add.14551. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bricker JB, Mull KE, Mcclure JB, Watson NL, Heffner JL, 2018. Improving quit rates of web-delivered interventions for smoking cessation: full-scale randomized trial of WebQuit.org versus Smokefree.gov. Addiction 113 (5), 914–923. 10.1111/add.14127. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Bricker JB, Watson NL, Mull KE, Sullivan BM, Heffner JL, 2020. Efficacy of smartphone applications for smoking cessation: a randomized clinical trial. JAMA Intern. Med 10.1001/jamainternmed.2020.4055 [published online ahead of print, 2020 Sep 21]. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Brose LS, Hitchman SC, Brown J, West R, Mcneill A, 2015. Is the use of electronic cigarettes while smoking associated with smoking cessation attempts, cessation and reduced cigarette consumption? A survey with a 1-year follow-up. Addiction 110 (7), 1160–1168. 10.1111/add.12917. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Bullen C, Howe C, Laugesen M, Mcrobbie H, Parag V, Williman J, Walker N, 2013. Electronic cigarettes for smoking cessation: a randomised controlled trial. Lancet 382, 1629–1637. 10.1016/j.addbeh.2017.08.002. [DOI] [PubMed] [Google Scholar]
  12. Bush K, Kivlahan DR, McDonell MB, Fihn SD, Bradley KA, 1998. The AUDIT alcohol consumption questions (AUDIT-C). Arch. Intern. Med 158, 1789–1796. [DOI] [PubMed] [Google Scholar]
  13. Caraballo RS, Shafer PR, Patel D, Davis KC, McAfee TA, 2017. Quit methods used by US adult cigarette smokers, 2014–2016. Prev. Chronic Dis 14, E32. 10.5888/pcd14.160600. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Connor KM, Kobak Ka., Churchill LE, Katzelnick D, Davidson JR, 2001. Mini-SPIN: a brief screening assessment for generalized social anxiety disorder. Depress. Anxiety 14 (2), 137–140. 10.1002/da.1055. [DOI] [PubMed] [Google Scholar]
  15. Dai H, Leventhal AM, 2019. Prevalence of e-cigarette use among adults in the United States. 2014-JAMA 322 (18), 1824–1827. 10.1001/jama.2019.15331. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Danaher BG, Tyler MS, Crowley RC, Brendryen H, Seeley JR, 2019. Outcomes and device usage for fully automated internet interventions designed for a smartphone or personal computer: the MobileQuit smoking cessation randomized controlled trial. J. Med. Internet Res 21 (6), e13290. 10.2196/13290. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Giovenco DP, Delnevo CD, 2018. Prevalence of population smoking cessation by electronic cigarette use status in a national sample of recent smokers. Addict. Behav 76, 129–134. 10.1016/j.addbeh.2017.08.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Glasser AM, Collins L, Pearson JL, Abudayyeh H, Niaura RS, Abrams DB, Villanti AC, 2017. Overview of electronic nicotine delivery systems: a systematic review. Am. J. Prev. Med 52 (2), e33–e66. 10.1016/j.amepre.2016.10.036. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Hajek P, Phillips-Waller A, Przulj D, Pesola F, Myers Smith K, Bisal N, McRobbie HJ, 2019. A randomized trial of e-cigarettes versus nicotine-replacement therapy. N. Egnl. J. Med 380 (7), 629–637. 10.1056/NEJMoa1808779. [DOI] [PubMed] [Google Scholar]
  20. Hartmann-Boyce J, McRobbie H, Bullen C, Begh R, Stead LF, Hajek P, 2016. Electronic cigarettes for smoking cessation. Cochrane Database Syst. Rev 9 (9) 10.1002/14651858.CD010216.pub3. Cd010216. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Hatsukami D, Meier E, Lindgren BR, Anderson A, Reisinger S, Norton K, 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 10.1093/ntr/ntz233 [published online ahead of print, 2019 Dec 12]. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Heatherton TF, Kozlowski LT, Frecker RC, Fagerström KO, 1991. The Fagerström Test for Nicotine Dependence: a revision of the Fagerström Tolerance Questionnaire. Br. J. Addict 86 (9), 1119–1127. 10.1111/j.1360-0443.1991.tb01879.x. [DOI] [PubMed] [Google Scholar]
  23. Hitchman SC, Brose LS, Brown J, Robson D, Mcneill A, 2015. Original investigation associations between e-cigarette type, frequency of use, and quitting smoking: findings from a longitudinal online panel survey in Great Britain. Nicotine Tob. Res 17 (10), 1187–1194. 10.1093/ntr/ntv078. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Holm S, 1979. A simple sequentially rejective multiple test procedure. Scand. J. Stat 6 (2), 65–70. [Google Scholar]
  25. Jaber RM, Mirbolouk M, DeFilippis AP, Maziak W, Keith R, Payne T, Nasir K, 2018. Electronic cigarette use prevalence, associated factors, and pattern by cigarette smoking status in the United States from NHANES (National Health and Nutrition Examination Survey) 2013–2014. J. Am. Heart Assoc 7 (14), e008178. 10.1161/JAHA.117.008178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Jackson SE, Shahab L, West R, Brown J, 2020. Associations between dual use of e-cigarettes and smoking cessation: a prospective study of smokers in England. Addict. Behav 103, 106230 10.1016/j.addbeh.2019.106230. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Kahler CW, Lachance HR, Strong DR, Ramsey SE, Monti M, Brown RA, 2007. The commitment to quitting smoking scale: initial validation in a smoking cessation trial for heavy social drinkers. Addict. Behav 32, 2420–2424. 10.1016/j.addbeh.2007.04.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Kalkhoran S, Glantz SA, 2016. E-cigarettes and smoking cessation in real-world and clinical settings: a systematic review and meta-analysis. Lancet Respir. Med 4 (2), 116–128. 10.1016/s2213-2600(15)00521-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Kalkhoran S, Chang Y, Rigotti NA, 2019. Electronic cigarette use and cigarette abstinence over two years among U.S. Smokers in the Population Assessment of Tobacco and Health Study. Nicotine Tob. Res 22 (5), 728–733. 10.1093/ntr/ntz114. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Lang AJ, Wilkins K, Roy-Byrne PP, Golinelli D, Chavira D, Sherbourne C, Stein MB, 2012. Abbreviated PTSD Checklist (PCL) as a guide to clinical reseponse. Gen. Hosp. Psychiatry 34 (4), 332–338. 10.1016/j.genhosppsych.2012.02.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Levy DT, Yuan Z, Li Y, 2017. The prevalence and characteristics of e-cigarette users int eh U.S. Int. J. Environ. Res. Public Health 14 (10), 1200. 10.3390/ijerph14101200. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Levy DT, Yuan Z, Luo Y, Abrams DB, 2018. The relationship of e-cigarette use to cigarette quit attempts and cessation: insights from a large, nationally representative U.S. Survey. Nicotine Tob. Res 20 (8), 931–939. 10.1093/ntr/ntx166. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. McClure JB, Peterson D, Derry H, Riggs K, Saint-Johnson J, Nair V, Shortreed SM, 2014. Exploring the “active ingredients” of an online smoking intervention: a randomized factorial trial. Nicotine Tob. Res 16 (8), 1129–1139. 10.1093/ntr/ntu057. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Mirbolouk M, Charkhchi P, Kainoush S, Uddin I, Orimoloye OA, Jaber R, Blaha MJ, 2018. Prevalence and distribution of e-cigarette use among U.S. adults: behavioral Risk Factor Surveillance System, 2016. Ann. Intern. Med 169 (7), 429–438. 10.7326/M17-3440. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. National Academies of Sciences, Engineering, and Medicine, 2018. Public Health Consequences of E-Cigarettes. The National Academies Press, Washington, DC. 10.17226/24952. [DOI] [PubMed] [Google Scholar]
  36. Patel D, Davis KC, Cox S, Bradfield B, King BA, Shafer P, Bunnell R, 2016. Reasons for current e-cigarette use among U.S. adults. Prev. Med 93, 14–20. 10.1016/j.ypmed.2016.09.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Radloff LS, 1977. The CES-D Scale: a self-report depression scale for research in the general population. Appl. Psychol. Meas 1, 385–401. 10.1177/014662167700100306. [DOI] [Google Scholar]
  38. Rigotti NA, Chang Y, Tindle HA, Kalkhoran SM, Levy DE, Regan S, Singer DE, 2018. Association of e-cigarette use with smoking cessation among smokers who plan to quit after a hospitalization: A prospective study. Ann. Intern. Med 168 (9), 613–620. 10.7326/m17-2048. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Rodu B, Plurphanswat N, 2017. E-cigarette use among US adults: population Assessment of Tobacco and Health (PATH) study. Nicotine Tob. Res 20 (8), 940–948. 10.1093/ntr/ntx194. [DOI] [PubMed] [Google Scholar]
  40. Siegel MB, Tanwar KL, Wood KS, 2011. Electronic cigarettes as a smoking-cessation tool: results from an online survey. Am. J. Prev. Med 40 (4), 472–475. 10.1016/j.amepre.2010.12.006. [DOI] [PubMed] [Google Scholar]
  41. Spitzer RL, Kroenke K, Williams JBW, Lowe B, 2006. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch. Intern. Med 166 (10), 1092–1097. 10.1001/archinte.166.10.1092. [DOI] [PubMed] [Google Scholar]
  42. Stein MB, Roy-Byrne PP, Mcquaid JR, Laffaye C, Russo J, Mccahill ME, Sherbourne CD, 1999. Development of a brief diagnostic screen for panic disorder in primary care. Psychosom. Med 61 (3), 359–364. [DOI] [PubMed] [Google Scholar]
  43. Taylor GMJ, Dalili MN, Semwal M, Civljak M, Sheikh A, Car J, 2017. Internet-based interventions for smoking cessation. Cochrane Database Syst. Rev 9 (9) 10.1002/14651858.CD007078.pub5. CD007078. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Verplaetse TL, Moore KE, Pittman BP, Roberts W, Oberleitner LM, Peltier MKR, McKee SA, 2019. Intersection of e-cigarette use and gender on transitions in cigarette smoking status: Findings across Waves 1 and 2 of the Population Assessment of Tobacco and Health Study. Nicotine Tob. Res 21 (10), 1423–1428. 10.1093/ntr/nty187. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Walker N, Parag V, Verbiest M, Laking G, Laugesen M, Bullen C, 2020. Nicotine patches used in combination with e-cigarettes (with and without nicotine) for smoking cessation: a pragmatic, randomised trial. Lancet Respir. Med 8 (1), 54–64. 10.1016/S2213-2600(19)30269-3. [DOI] [PubMed] [Google Scholar]
  46. Warner KE, 2019. How to think–not feel–about tobacco harm reduction. Nicotine Tob. Res 21 (10), 1299–1309. 10.1093/ntr/nty084. [DOI] [PubMed] [Google Scholar]
  47. Watson NL, Mull KE, Heffner JL, McClure JB, Bricker JB, 2018. Participant recruitment and retention in remote eHealth intervention trials: methods and lessons learned from a large RCT of two web-based smoking interventions. J. Med. Internet Res 20 (8), e10351. 10.2196/10351. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Weaver SR, Huang J, Pechacek TF, Heath JW, Ashley DL, Eriksen MP, 2018. Are electronic nicotine delivery systems helping cigarette smokers quit? Evidence from a prospective cohort study of U.S. Adult smokers, 2015–2016. PLoS One 13 (7), e0198047. 10.1371/journal.pone.0198047. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Wei L, Mahammad-Kah RS, Hannel T, Pithawalla YB, Gogova M, Chow S, Black RA, 2020. The impact of cigarette and e-cigarette use history on transition patterns: A longitudinal analysis of the population assessment of tobacco and health (PATH) study, 2013–2015. Harm Reduct. J 17, 45. 10.1186/s12954-020-00386-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Yong HH, Borland R, Cummings KM, Gravely S, Thrasher JF, McNeill A, Fong GT, 2019. Reasons for regular vaping and for its discontinuation among smokers and recent ex-smokers: findings from the 2016 ITC Four Country Smoking and Vaping Survey. Addiction 114, 35–48. 10.1111/add.14593. [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES