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American Journal of Epidemiology logoLink to American Journal of Epidemiology
. 2025 Dec 5;195(5):1300–1309. doi: 10.1093/aje/kwaf270

Association of naturalistic e-cigarette use and smoking cessation in US adults

Junhan Cho 1,2,3,✉, Dae-Hee Han 4,5, Alyssa F Harlow 6,7,8, Hongying D Dai 9, Adam M Leventhal 10,11,12
PMCID: PMC12775882  NIHMSID: NIHMS2133730  PMID: 41347854

Abstract

Temporal imprecision and unaddressed confounding limit inferences whether e-cigarette vaping provides real-world benefits or harms for combustible cigarette smoking cessation. This naturalistic, multilevel longitudinal study of US adults examined whether (semi-)monthly transitions in e-cigarette vaping were associated with attaining smoking abstinence and subsequent post-quit relapse. A nationally representative panel of 1255 adults who smoked at baseline completed up to 22 biweekly and 13 monthly surveys between May 2020 and May 2022. Using multilevel longitudinal models, we assessed within-person transitions in past 7-day vaping status (none, non-daily, daily) as time-lagged, time-varying predictors of 7-day point-prevalence smoking outcomes (0 vs 1-7 days). Results showed that daily vaping was associated with higher odds of achieving smoking abstinence 2 weeks later (30.4% vs 16.3%; adjusted-RR, 2.27; 95% CI, 1.41-3.69), while non-daily vaping was not significantly associated (22.5% vs 16.3%; adjusted-RR, 1.05; 95% CI, 0.85-1.37). In 557 instances where participants had achieved at least 1 month of smoking abstinence, both daily (adjusted-HR, 1.31; 95% CI, 1.09-1.70) and non-daily (adjusted-HR, 2.82; 95% CI, 2.07-4.61) vaping were linked to increased relapse risk compared to no vaping. These findings suggest that while daily vaping may support short-term smoking cessation, it is associated with a heightened risk of relapse among individuals who have already quit.

Keywords: smoking cessation, smoking relapse, electronic cigarettes, multilevel longitudinal analysis

Introduction

Prior trends of increasing smoking cessation rates in the United States have recently stalled.1,2 Although data from the national study (2013-2021) indicate a modest increase in cessation in recent years,3 national surveillance data show that annual cessation rates have remained below 10% during 2020-2024. In 2022, 8.8% of adults who smoked successfully quit—slightly higher than in previous years—yet the majority of smokers continue to attempt quitting without success.3,4 FDA-approved smoking cessation medications remain underutilized,4 perhaps due to limited accessibility and smokers' perceptions that these medications cannot suitably replace the nicotine and sensorimotor stimulation provided by cigarettes.5

E-cigarettes have demonstrated efficacy for smoking cessation in randomized trials.6-9 However, some clinicians and public health officials remain hesitant to recommend e-cigarettes as cessation aids due to conflicting evidence from population-based observational studies regarding whether vaping provides real-world benefits or harms for quitting smoking.10-17 Recent analyses of the nationally representative US Population Assessment of Tobacco and Health cohort similarly report mixed and modest effectiveness of e-cigarettes for cessation at the population level: one study reported that daily and non-daily e-cigarette users were no more likely to quit smoking than non-users, with non-daily use linked to reduced abstinence,18 whereas another found that e-cigarette users were more likely to discontinue cigarettes compared with non-users.3 Interpretation of these findings is complicated due to methodological challenges. Previous nationally representative longitudinal studies have relied on long inter-assessment intervals (eg, annual), which fail to capture the rapid cycle of quit attempts and relapses that characterize smokers' real-world experiences.3,14,15,17 Consequently, existing observational studies on e-cigarette use and smoking cessation, though informative, remain vulnerable to several sources of confounding. Individuals with more severe, relapsing patterns of nicotine dependence and a preexisting vulnerability to cessation failure may be more likely to adopt any available cessation aid, including e-cigarettes. Conversely, those who are highly motivated to quit smoking may be more inclined to use e-cigarettes as a means to support cessation.10-13,19

Building on these limitations, this observational, naturalistic study of US adult cigarette smokers estimated associations of e-cigarette vaping with two clinically distinct smoking cessation milestones—attaining abstinence and preventing relapse. A novel, nationally representative data source with (semi-)monthly repeated within-person assessments provided temporal precision and enabled a unique application of multilevel longitudinal analyses. This approach accounts for between-person stable (ie, time-invariant) confounders that may contribute to inter-individual variance in both e-cigarette use and smoking cessation outcomes.

Methods

Data source

The Understanding America Study (UAS) regularly surveys a nationally representative online non-institutionalized adult panel on social and health issues.20 The panel, initially recruited in 2016 using address-based random probability sampling, is regularly refreshed through two-stage adaptive sampling to enhance representativeness and mitigate attrition bias.20 Recruitment was conducted via postal mail, and subsequent self-administered digital surveys were completed on respondents' own devices. Each survey wave applies updated sampling weights using post-stratification raking algorithms to improve demographic national representativeness.20

To track COVID-19 impacts, all UAS panel members were invited to repeated follow-up surveys.21 This study used 22 biweekly (May 2020 to March 2021) and 13 monthly (April 2021 to May 2022) surveys, which assessed e-cigarette vaping, smoking, and other covariates (Table S1 in the Online-Only Supplement details dates and other information about each survey). Respondents who reported past 7-day smoking at their baseline assessment (ie, first timepoint surveyed during May 2020[T1]-May 2022[T34]) and completed ≥1 post-baseline follow-up surveys were eligible (Figure S1 in the Online-Only Supplement). Participants provided written informed consent. University of Southern California's Institutional Review Board approved this study. This study followed the STROBE reporting guidelines.

Measures

E-cigarette use

At each timepoint, the number of days used an e-cigarette or vaping device to vape e-liquids with nicotine in the past 7 days was assessed and recoded into a time-varying trichotomous exposure variable (none [0 days], non-daily [1-6 days], daily [7 days]) per previous research showing divergent smoking outcomes for non-daily vs daily vaping.13,21-24

Cigarette smoking abstinence and relapse

At T2-T35, days smoked cigarettes in the past 7 days were coded as repeated-measures binary outcomes for abstinence (0 vs 1-7 days) and relapse (1-7 vs 0 days), following a 7-day point prevalence abstinence conventions.25,26

Covariates

To account for confounders influencing the hypothesized associations, the following covariates, previously implicated in vaping and smoking, were included in both analytic models examining smoking abstinence and relapse outcomes.11,14,16 Time-invariant baseline covariates were self-reported sex, ethnicity/race, age, marital status, poverty level, highest education, residential state, past 7-day cannabis, alcohol, or other drug use status (yes/no),2,27 and anxiety and depressive symptoms (each coded as yes/no based on the two-item Patient Health Questionnaire cutoff).28,29 Additionally, survey timepoint (weeks since baseline timepoint) was included as a time-varying continuous covariate. For the models examining smoking abstinence only, past 7-day smoking frequency (0-7 days) was included as a time-varying, time-lagged covariate contemporaneous with respective e-cigarette exposure timepoints. For the smoking relapse analyses, participants' baseline past 7-day smoking frequency (continuous; 1-7 days) prior to establishing smoking abstinence was adjusted for.

Data analysis

After descriptive analyses, we fitted multilevel mixed-effects log-binomial regressions to estimate time-lagged, time-varying associations of e-cigarette use with attaining smoking abstinence at the subsequent wave (2 weeks later), using an analytic dataset involving the biweekly T1-T22 timepoints only. Biweekly data were also used to calculate unadjusted differences in attaining abstinence, by preceding 2-week lagged vaping statuses. To investigate associations of e-cigarette use with smoking relapse after abstinence was established, we created an analytic dataset that included all unique instances of sustained abstinence defined as two consecutive (non-missing) survey waves with no smoking during T2-T34 and ≥ 1 subsequent follow-up timepoints. Because relapse cannot occur prior to meeting this criterion, follow-up for relapse began immediately after these two abstinent waves, thereby excluding any person-time not at risk for relapse. Individual respondents could have multiple discrete instances of abstinence over time if they achieved ≥1 month of sustained abstinence, relapsed, and later re-established abstinence for another ≥1 month (Figure S2).

Consequently, the relapse dataset contained repeated vaping/smoking observations nested within abstinence instances, which were in turn nested within individuals. To account for this hierarchical structure, we estimated discrete-time relapse hazards using multilevel mixed-effects models in Mplus with the TWOLEVEL COMPLEX option,32 specifying observations nested within abstinence instances and instances nested within respondents. This approach appropriately accounts for within-person correlation across multiple abstinence cycles and avoids treating repeated abstinence episodes as independent or exchangeable. For abstinence instances without relapse events, right censoring was applied at T35 or at the last available observation.

Confounders that contribute to between-person variability reflect factors that are time-invariant (ie, stable), differ across individuals, and may contribute to variation in both e-cigarette use and smoking outcomes. To account for such biases, our multilevel longitudinal models included each respondent’s mean e-cigarette use across timepoints (the “between-person” exposure) as a fixed-effect regressor, along with baseline sociodemographic, psychopathological, and smoking-related covariates. This specification addressed potential static (time-invariant) confounding using established multilevel longitudinal methods.30,31 The models simultaneously incorporated a time-invariant fixed-effect regressor capturing respondents' average e-cigarette use (“between-person” exposure) and a time-varying random-effect regressor capturing fluctuations in e-cigarette use over time (“within-person” exposure). Between-person estimates indicate how average inter-individual differences in abstinence odds or relapse hazards vary among respondents who tended to vape daily or non-daily compared with those who tended not to vape, whereas within-person estimates reflect intra-individual changes in these outcomes following transitions in vaping status over time.

We tested unadjusted models with between- and within-person vaping exposures and time only, and adjusted models that additionally incorporated other time-invariant covariates. These covariates accounted for between-person differences that may be associated with smoking abstinence and relapse (eg, sociodemographic characteristics, baseline smoking frequency, and baseline substance use and mental health problems). The multilevel log-binomial regression models for abstinence outcomes also controlled for time-varying smoking frequency to account for variation in smoking levels at the time-lagged waves. This approach allowed us to model variance in e-cigarette exposure associated with abstinence odds resulting from different changes in smoking behavior across inter-wave intervals (eg, infrequent smoking to abstinence, daily smoking to abstinence), while accounting for the preceding smoking level. Because episodes of frequent smoking may temporarily reduce the likelihood of subsequent abstinence, this dynamic was explicitly adjusted for in the models.

To assess the robustness of our findings, we pre-specified three categories of sensitivity analyses: (1) alternative model specifications that did not disaggregate within- and between-person effects; (2) conservative analyses imputing missing outcomes as non-abstinent or relapse events to reduce potential bias in estimates; and (3) tests of effect modification by survey design, time in the study, and cigarette use. More details are provided in “Sensitivity Analyses” and the Online-Only Supplement.

Analyses used Mplus Version 8 with complex design options accounting for clustering and nationally-representative sampling weights.32,33 Missing data were managed using full information maximum likelihood estimation.34 RRs and HRs with 95% CIs were estimated for log-binomial and discrete-time hazard regression models, respectively, with Benjamini–Hochberg multiple test corrections applied to two-sided P values to maintain 0.05 false discovery rate.35 Additional sensitivity analyses are detailed below, with methodological specifics provided in the Online-Only Supplement.

Results

Descriptive results

Among 9330 UAS participants from May 2020 to May 1, 2022, 255 reported ≥1 past 7-day smoking days at their baseline during T1-T34 and completed ≥1 subsequent follow-up, which constituted the pool eligible for one or both analytic datasets (median surveys completed; 27; IQR, 13-34]; median follow-up weeks, 48; IQR, 32-60). Table S1 in the Online-Only Supplement details number surveyed and descriptive smoking and vaping data at each timepoint. The dataset used to examine biweekly associations between vaping and attaining smoking abstinence included 1121 baseline smokers identified during T1-T21 (ie, exposure waves in biweekly data) who completed ≥1 subsequent follow-up surveys between T2-T22. This was drawn from a total of 1255 baseline smokers across both biweekly (T1-T22) and monthly (T23-T35) survey timepoints, contributing total 17 767 observations (surveys completed: median, 20; IQR, 10-22; follow-up: median, 38 weeks; IQR, 18-42 weeks). The relapse analysis dataset included 557 sustained abstinence instances from 355 respondents who attained smoking abstinence (227 respondents had 1 instance, 79 had 2 instances, and 49 had ≥3 instances), contributing 5240 observations. Both samples were slight majority female, demographically heterogenous, and included sizeable proportions reporting cannabis, alcohol, or other drug use, as well as baseline anxiety or depressive symptoms (Table 1).

Table 1.

Descriptive statistics in two analytic datasets.

Attaining abstinence analytic dataset a Post-abstinence relapse analytic dataset b
n (%)/M (SD) c n (%)/M (SD) c
Time-invariant baseline variablesd
 No. respondents, n (%) 1121 (100.0) 355 (100.0)
 Female (vs male), n (%) 680 (51.0) 218 (55.5)
 Ethnicity/race, n (%)
  Hispanic/any race 143 (13.3) 73 (18.1)
  Non-Hispanic/Asian 26 (2.5) 18 (5.2)
  Non-Hispanic/Black 114 (14.4) 49 (20.2)
  Non-Hispanic/White 753 (65.8) 206 (54.5)
  Non-Hispanic/another racee 84 (4.1) 15 (2.0)
 Age, y, n (%)
  18-39 374 (43.6) 147 (50.5)
  40-50 276 (21.2) 80 (16.8)
  51-64 343 (25.4) 94 (23.4)
  65+ 128 (9.8) 40 (9.3)
 Received college degree, n (%) 385 (20.6) 146 (26.0)
 Below federal poverty level, n (%) 300 (34.0) 101 (39.7)
 Working, n (%) 595 (53.1) 200 (53.7)
 Currently married (vs other), n (%)f 446 (40.1) 139 (35.7)
 Used cannabis in past week, n (%)g 329 (32.2) 120 (35.2)
 Used alcohol in past week, n (%)g 571 (52.0) 192 (52.1)
 Used other drugs in past week, n (%)g 160 (14.7) 71 (20.4)
 Anxiety symptoms, n (%)h 296 (24.9) 97 (27.4)
 Depressive symptoms, n (%)h 246 (21.4) 76 (22.0)
 No. past-week smoking days, M (SD)i 5.89 (1.98) 4.03 (2.44)
Time-varying variables
 No. observations, n (%) 17 767 (100.0)j 5240 (100.0)k
 Past-week e-cigarette use
  None (0 days) 14 948 (90.1)j 3287 (86.6)k
  Non-daily (1-6 days) 961 (5.4)j 203 (5.2)k
  Daily (7 days) 729 (4.5)j 313 (8.2)k
 No. past-week smoking days, M (SD) 5.26 (2.76)j
a

Respondents with baseline cigarette use at T1 (May 2020)—T21 (March 2021). Baseline smokers who reported ≥1 smoking days in the past 7 days at their baseline during T1-T21 (exposure waves) and completed ≥1 subsequent follow-up surveys in the biweekly T1-T22 data.

b

Respondents with baseline cigarette use at T1 (May 2020)-T34 (May 2022) and later reported smoking abstinence over 2 consecutive timepoints.

c

Unweighted N and weighted % are shown for categorical variables. Weighted mean (M) and standard deviation (SD) are shown for continuous variables.

d

Assessed at each respondent's baseline survey timepoint. Person-level statistics presented.

e

Native American (American Indian/Alaska Native), Native Hawaiian, or Pacific Islander.

f

“ Other” category includes separated, divorced, widowed, and never married.

g

Any use (≥1 days) in the past week.

h

Sum score was calculated using 2-item of Patient Health Questionnaire (PHQ-2) for generalized anxiety and depression (each item: Not at all = 0, Several days = 1, More than half the days = 2, Nearly every day = 3). Cutoff score ≥ 3.

i

Number of smoking days (range = 0-7) assessed each respondent’s initial survey assessment.

j

Observation-level statistics combined across T1-T21.

k

Observation-level statistics combined across T3-T34.

Primary results

Attaining smoking abstinence

Smoking abstinence was attained in 17.6% of the 21 biweekly follow-ups overall. Unadjusted prevalence of smoking abstinence was higher 2 weeks after timepoints with past-week daily vaping (30.4%; 95% CI, 21.9-39.0) than timepoints with no vaping (16.3%; 95% CI, 14.3-18.3; unadjusted difference, 14.1%; 95% CI, 5.5-22.8; Figure 1). Multilevel longitudinal models, controlling for between-person variance in vaping, found that within-person daily vaping (vs no vaping) was associated with higher odds attaining smoking abstinence 2 weeks later (unadjusted-RR, 2.61; 95% CI, 1.77-4.15; covariate adjusted-RR, 2.27; 95% CI, 1.41-3.69; Table 2). Between-person daily vs no vaping estimates were not significantly associated with abstinence (P values≥0.22; Table 2).

Figure 1.

Figure 1

Unadjusted prevalence of smoking abstinence overall and stratified by e-cigarette use status, 2-week earliera. a Total 17 767 observations (obs) nested within 1121 respondents of baseline smokers from the biweekly T1-T22 data. Estimated marginal prevalence with 95% confidence intervals were estimated, adjusting for standard errors due to nesting of data within respondents.

Table 2.

Associations of e-cigarette use with odds of subsequently attaining smoking abstinence 2 weeks later from multilevel longitudinal modelsa.

Regressors Unadjusted b Adjusted c
RR (95%CI) P RR (95%CI) P
E-cigarette use (within-person)
 No use REF REF
 Non-daily use 1.11 (0.94-1.48) 0.36 1.05 (0.85-1.37) 0.69
 Daily use 2.61 (1.77-4.15) <.001d 2.27 (1.41-3.69) <.001d
E-cigarette use (between-person)
 No use REF REF
 Non-daily use 1.54 (1.03-2.27) 0.03 1.28 (0.94-1.82) 0.14
 Daily use 1.16 (0.95-1.53) 0.22 1.07 (0.86-1.59) 0.66
a

Multilevel mixed-effects log-binomial regression models to estimate time-lagged and time-varying association of e-cigarette use with attaining smoking abstinence at the subsequent wave 2 weeks later using biweekly T1-T22 data including total 17 767 observations nested within 1121 respondents.

b

Results from time-varying and time-lagged mixed-effects log-binomial regression model including e-cigarette use within-person time-varying exposure, e-cigarette use between-person time-invariant exposure, and time covariate as simultaneous regressors (fit statistics: AIC = 194171.63, BIC = 194248.82).

c

Additionally includes baseline time-invariant covariates (ie, sex, ethnicity/race, age, college degree, poverty level, working status, marital status, residential state, cannabis use, alcohol use, drug use, anxiety symptoms, depressive symptoms) as well as time and time-varying past-week cigarette use frequency covariate at exposure waves (fit statistics: AIC = 5040.63, BIC = 5199.69). Covariate estimates of association presented in Table S2.

d

Statistically significant after Benjamini–Hochberg corrections for multiple testing to control false-discovery rate at 0.05 (based on two-tailed corrected P value).

Unadjusted abstinence prevalence was higher 2 weeks after non-daily vaping (22.5%; 95% CI, 18.8-26.3) than no vaping (16.3%; 95% CI, 14.3-18.3; difference, 6.2%; 95% CI, 1.4-11.0) timepoints (Figure 1). However, longitudinal regression models found that neither within- nor between-person variation in non-daily vaping (vs no vaping) was significantly associated with attaining abstinence 2 weeks later after multiple test corrections (Table 2). Adjusted models included baseline and time-varying covariates to account for potential confounding (see Table S2 in the Online-Only Supplement for full covariate estimates).

Smoking relapse after sustained abstinence

Among 557 sustained smoking abstinence instances, 327 (58.5%) subsequent smoking relapse events were recorded during the 2- to 102-week follow-up; mean time to relapse was 15.3 (95% CI, 13.3-17.4) weeks. As depicted in Figure 2, estimated unadjusted cumulative relapse rate throughout follow-up was 79.2% after daily vaping, 88.7% after non-daily vaping, and 52.3% after no past-week vaping. Table S3 in the Online-Only Supplement details relapse events by timepoint. Multilevel longitudinal models controlling for between-person variance in vaping found higher smoking relapse hazards after timepoints involving daily vaping (unadjusted HR, 1.77; 95% CI, 1.14-2.57; adjusted HR, 1.31; 95% CI, 1.09-1.70) or non-daily vaping (unadjusted HR, 3.36; 95% CI, 2.47-5.42; adjusted HR, 2.82; 95% CI, 2.07-4.61), compared to no vaping (Table 3 within-person estimates). No significant differences in relapse hazards were observed for between-person effects comparing individuals who tended to vape (non-daily or daily) vs those who did not, when aggregated across timepoints (see Table 3 between-person estimates). Adjusted model covariate estimates indicated increased relapse hazards with later survey dates, non-Hispanic White (vs Hispanic) race/ethnicity, and baseline drug use (Table S4). Baseline past-week smoking frequency prior to establishing smoking abstinence was not significantly associated with the hazards of smoking relapse (Table S4).

Figure 2.

Figure 2

Unadjusted estimated cumulative probability of smoking relapse following sustained smoking abstinence, by e-cigarette use statusa. aTotal 5240 observations nested within 557 sustained abstinence instances from 355 respondents of baseline smokers at T1 (May 2020)—T34 (May 2022) who later reported smoking abstinence over two consecutive timepoints (≥1 month). Unweighted number of smoking relapse events and weighted smoking relapse rates are presented in Table S3. Detailed information about the estimation procedure is provided in the Appendix S1 of the online-only supplement.

Table 3.

Associations of e-cigarette use after sustained abstinence with subsequent smoking relapse hazards from multilevel longitudinal modelsa

Predictors Unadjusted b Adjusted c
HR (95%CI) P HR (95%CI) P
E-cigarette use (within-person)
No use REF REF
Non-daily use 3.36 (2.47-5.42) <.001d 2.82 (2.07-4.61) <.001d
Daily use 1.77 (1.14-2.57) .006d 1.31 (1.09-1.70) .01d
E-cigarette use (between-person)
No use REF REF
Non-daily use 1.22 (0.95-1.57) 0.12 1.13 (0.84-1.49) 0.40
Daily use 0.89 (0.61-1.09) 0.43 0.93 (0.68-1.15) 0.58
a

Multilevel mixed-effects discrete-time hazard models to estimate associations of time-lagged, time-varying e-cigarette use with subsequent smoking relapse hazards using 5240 observations nested within 557 sustained abstinence instances, which were nested within respondents. Total 355 respondents of baseline smokers at T1 (May 2020)—T34 (May 2022) who later reported smoking abstinence over two consecutive timepoints. Abbreviations: HR = Hazard ratio; CI=Confidence interval.

b

Results from time-varying and time-lagged mixed-effects discrete-time hazard models including e-cigarette use within-person time-varying exposure, e-cigarette use between-person time-invariant exposure, and time covariate as simultaneous regressors (fit statistics: AIC = 8491.51, BIC = 8851.36).

c

Additionally includes baseline time-invariant covariates (ie, sex, ethnicity/race, age, college degree, poverty level, working status, marital status, residential state, cannabis use, alcohol use, drug use, anxiety symptoms, depressive symptoms, baseline past-week cigarette use frequency) and time covariate at exposure waves (fit statistics: AIC = 2996.17, BIC = 3142.54). Covariate estimates of association presented in Table S4.

d

Statistically significant after Benjamini–Hochberg corrections for multiple testing to control false-discovery rate at 0.05 (based on two-tailed corrected P-value).

Sensitivity analyses

First, to evaluate whether disaggregating within-person and between-person effects influenced the results, we fit multilevel mixed-effects models that did not disaggregate within- and between-person estimates. Results were consistent with the primary within-person estimates—daily vaping was associated with greater odds of smoking abstinence attainment, and both daily and non-daily vaping were associated with higher post-abstinence relapse hazards (Tables S5 and S6 in the Online-Only Supplement). Second, given the possibility that missing outcome data could bias estimates of abstinence and relapse, we tested models under conservative assumptions by imputing missing outcomes as non-abstinent (for abstinence) or relapse (for post-abstinence follow-ups). This approach guards against potential overestimation of cessation rates. Findings remained consistent with the primary analyses, supporting the robustness of our conclusions (Tables S7, S8, and S9). Finally, to examine potential effect modification, we tested whether associations between vaping and smoking outcomes varied by survey design (biweekly [T1-T22] vs monthly [T23-T35] inter-assessment intervals; Table S10), time (ie, number of weeks) since the first exposure wave (Table S11), or smoking frequency as a key covariate (Tables S12 and S13). None of these moderators significantly altered the associations (Tables S10-S13).

Taken together, these sensitivity analyses—including alternative model specifications without within- and between-person disaggregation, conservative imputations for missing outcomes, and tests of potential time-related effect modification—consistently indicate that the associations between e-cigarette use and smoking outcomes are robust to different analytic assumptions. These findings reinforce the interpretation that daily vaping is associated with short-term cessation benefits but also greater vulnerability to relapse, thereby supporting the overall robustness of our primary conclusions.

In addition, because unmeasured factors may have influenced the associations observed in our primary analyses, we calculated E values to evaluate the robustness of these findings to potential unmeasured confounding.36 For daily versus no vaping, the adjusted within-person estimate for smoking abstinence corresponded to an E value of 3.98, with a lower limit (LL) confidence interval of 2.17 (Table 2). For relapse, the E value was 1.95 (LL = 1.40) for daily vs no vaping and 5.09 (LL = 3.56) for non-daily vs no vaping. These results suggest that relatively modest unmeasured confounding could explain the daily vaping–relapse association, whereas substantially stronger confounding would be needed to account for the non-daily vaping–relapse association.

Discussion

In this naturalistic observational study of US adult smokers, associations of within-person changes in e-cigarette use with smoking outcomes diverged across unique smoking cessation milestones. Transitioning to daily vaping (vs no vaping) increased the odds of attaining smoking abstinence 2 weeks later. Conversely, among individuals who had achieved sustained abstinence, timepoints with no vaping were associated with lower relapse hazards than timepoints involving daily or non-daily vaping.

This study’s design addresses methodological concerns that might have contributed to inconsistent results in prior naturalistic studies, which have found positive,10,16,17,19 null,11,14 and inverse12,13,15 associations between vaping and smoking cessation. Annual inter-assessment intervals in most nationally-presentative datasets lack the temporal precision required to capture cases in which vaping promotes quitting smoking but fails to sustain abstinence. Instances of e-cigarette adoption, smoking cessation, and relapse occurring within a few months may be entirely missed between annual assessments.10-13,19 Additionally, previous observational studies are prone to confounders that contribute to between-person variability. Individuals at high risk for chronic nicotine dependence are more likely to adopt vaping, while those most committed to quitting may seek out any product to help them stop smoking.37 High exposure to cigarette and e-cigarette marketing in certain neighborhoods may enable access to e-cigarettes while simultaneously tempting individuals to relapse back to smoking.38,39 Although some studies have applied propensity score matching to mitigate confounding, matching approaches cannot fully eliminate bias.14

This study implemented multiple strategies to address confounding variables. Specifically, to account for the potential effects of smoking dependence severity—a factor closely linked to both smoking abstinence and relapse—we adjusted for participants’ past-week smoking frequency in our analyses. For smoking abstinence analyses, this adjustment was applied as a time-varying, time-lagged covariate across exposure timepoints. For smoking relapse analyses, it was incorporated as a baseline covariate measured prior to establishing smoking abstinence. Our models additionally included key factors influencing smoking cessation, such as demographics that are proxies for exposure to social determinants and cultural norms (eg, ethnicity/race), developmental stage (eg, age), familial support (eg, marital status), and behavioral health (eg, depression symptoms, drug use).2,27 Chronological time was also adjusted for, accounting for the dynamic health behavior trends during the first two years of the COVID-19 pandemic.40 While covariate adjustment partially attenuated estimates, associations between vaping and cessation outcomes remained statistically significant. Importantly, multilevel longitudinal analysis controlled for between-person variance in vaping to account for unmeasured stable confounders.41 Statistically significant within-person associations with abstinence and relapse, combined with null between-person estimates, suggest these findings were not driven by inherent differences between vapers and non-vapers. The associations are unlikely explained by time-varying confounders influencing short-term vaping and smoking decisions, as they persisted after adjusting for time-varying smoking frequency. Sensitivity analyses showed no evidence of effect modification by smoking frequency, indicating the associations are consistent across circumstances and populations that vary along the continuum of smoking.

Although time-varying residual confounding cannot be ruled out, it is plausible that e-cigarettes may support initial quit attempts but hinder long-term success when used after achieving sustained abstinence. Approximately half of US adult smokers do not attempt to quit each year.42 Some of these individuals might be motivated to stop but demoralized by past cessation failures or the perception that FDA-approved cessation medications are unappealing, ineffective, burdensome, or inaccessible.43 Others might enjoy inhaling nicotine too much to consider quitting smoking.43 For both groups, FDA-approved nicotine replacement therapies may fail to replicate the sensorimotor experience and rapid pulmonary nicotine delivery of combustible cigarettes.44 E-cigarettes, when used daily, may provide enough satisfaction to motivate quit attempts in individuals who might not otherwise try. Similar to this study, some prior research has shown that daily (but not non-daily) vaping is associated with smoking abstinence.10,13,16,19 A randomized trial also found that vaping significantly increases quit attempts, particularly in those initially less motivated to quit.8

Vaping after extended smoking abstinence may undermine long-term cessation by sustaining addiction mechanisms that typically diminish when all tobacco products are discontinued. Unlike nicotine replacement therapies, which deliver nicotine slowly via buccal mucosa or transdermally,45 e-cigarettes replicate cigarettes’ addictive pharmacokinetics by rapidly delivering nicotine to the brain through pulmonary absorption.46 This rapid delivery may prolong cycles of fluctuating blood nicotine levels that perpetuate tolerance, withdrawal, and compulsive nicotine-seeking behaviors.47 E-cigarettes are often designed to mimic cigarettes, with aerosols resembling smoke and inhalation sounds imitating the crackling of drawing puffs from lit cigarettes.48 These sensory attributes may maintain conditioned associations between smoking-related stimuli and nicotine reward, which typically fade during extended cessation. Post-quit vaping could prolong the conditioned urge to smoke and increase relapse risk when encountering smoking-related cues. Some observational studies suggest post-cessation vaping is associated with higher residual cigarette dependence symptoms48 and long-term smoking relapse risk.49 Although randomized clinical trials with intensive behavioral support show favorable long-term cessation outcomes with e-cigarettes,9 a “real-world” trial without such support appeared to find that the cessation-promoting effect of vaping weakened over time.8 Taken together, these mixed findings underscore the complexity of evaluating the public health impact of e-cigarettes. While our study separately examined their associations with abstinence and relapse, future research that integrates both initiation of abstinence and risk of relapse within a unified analytic framework will be important to more directly assess the overall net impact of e-cigarette use on smoking cessation outcomes.

Randomized trials with random assignments, free e-cigarette provisions, and narrow participant samples, do not replicate the self-motivated adoption of e-cigarettes in real-world commercial settings. The FDA Center for Tobacco Products has cited insufficient rigorous, nationally representative observational evidence of vaping’s cessation-promoting effects as a reason for denying e-cigarette marketing applications.50 This study’s findings suggest that e-cigarettes—when used naturalistically within the unrestricted commercial market—may increase quit attempts among US adults. Clinicians and healthcare agencies, previously hesitant to promote vaping due to the limited ecological validity of randomized trials, may find this real-world evidence compelling.51,52 This study offers new real-world evidence supporting e-cigarettes as a potential cessation aid for individuals who have not succeeded with established treatments. However, clinicians should advise patients that vaping after prolonged smoking abstinence may increase the risk of unfavorable long-term outcomes compared to complete discontinuation of all tobacco products.

Limitations

This study has limitations. First, it did not collect detailed data on smoking history, intensity, dependence, quit intentions, e-cigarette product characteristics (eg, flavors, nicotine concentration, liquid volume), or use of other cessation aids (eg, nicotine replacement therapy, other pharmacotherapies for smoking cessation). The potential influence of these unmeasured factors on our findings remains unclear and may introduce residual confounding. For example, successful quitting associated with daily e-cigarette use could partly reflect unmeasured individual characteristics, such as stronger motivation or commitment to cessation, rather than causal effects of vaping itself. Relatedly, because e-cigarette users were more likely to achieve short-term quitting, the relapse sample may have been systematically different, including a greater proportion of individuals with higher relapse risk.15 Furthermore, restricting the relapse analysis to individuals who had achieved sustained abstinence could induce selection bias (ie, collider stratification) if abstinence is a common effect of both e-cigarette use and another unmeasured variable that is a cause of relapse.53 While our analytic approach adjusted for a number of variables associated with subsequent relapse (eg, mental health, other drug use), collider stratification bias may still occur due to unmeasured common causes of abstinence and relapse. In addition, without measures of smoking intensity, variations in cigarettes per day among daily smokers were also not addressed, and it remains uncertain whether some instances of cessation were unintentional. Second, some baseline covariates such as non-nicotine substance use and mental health may vary over time, and residual time-varying effects of these factors cannot be fully ruled out. Future research should further explore their dynamic roles in shaping the association between vaping and smoking cessation. Third, the within-participant association does not clarify the sequence of vaping onset and offset relative to smoking cessation. Moreover, the dataset did not capture discrete vaping cessation events; vaping was modeled solely as a past-week, time-varying exposure at each wave. As a result, our relapse analyses compared relapse risk across observed vaping states rather than the effect of a specific discontinuation event. Future research should distinguish between outcomes associated with post-quit vaping onset and those associated with sustained vaping initiated prior to quit attempts, ideally using designs that more precisely capture the timing of vaping initiation and cessation. Fourth, while the UAS data source uses probability sampling and weights to address biases, non-response may still limit generalizability. Finally, the COVID-19 pandemic and unique e-cigarette market and policy conditions during the study period may have influenced the results, although chronological time did not moderate results suggesting consistency of findings while the pandemic began to abate.

Conclusion

This observational naturalistic study in US adults who smoke found that daily vaping was associated with achieving smoking abstinence. However, among individuals who attained smoking abstinence, vaping during the post-abstinence period was associated with an increased risk of returning to smoking. Policymakers and clinicians should consider that vaping might be associated with different quit smoking outcomes depending on the stage of the smoking cessation process.

Supplementary Material

Web_Material_kwaf270
web_material_kwaf270.zip (157.2KB, zip)

Acknowledgments

We thank the Understanding America Study (UAS) for collecting and providing the data used in this research. This study was conducted independently, and all interpretations and conclusions are solely those of the authors and do not represent the views of UAS.

Contributor Information

Junhan Cho, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90033, United States; Institute for Addiction Science, University of Southern California, Los Angeles, CA 90032, United States; USC Norris Comprehensive Cancer Center, Los Angeles, CA 90033, United States.

Dae-Hee Han, Department of Behavioral, Social, and Health Education Sciences, Emory University, Atlanta, GA 30322, United States; Department of Health Policy and Management, Emory University, Atlanta, GA 30322, United States.

Alyssa F Harlow, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90033, United States; Institute for Addiction Science, University of Southern California, Los Angeles, CA 90032, United States; USC Norris Comprehensive Cancer Center, Los Angeles, CA 90033, United States.

Hongying D Dai, Department of Biostatistics, University of Nebraska College of Public Health, Omaha, NE 68198, United States.

Adam M Leventhal, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90033, United States; Institute for Addiction Science, University of Southern California, Los Angeles, CA 90032, United States; USC Norris Comprehensive Cancer Center, Los Angeles, CA 90033, United States.

Supplementary material

Supplementary material is available at the American Journal of Epidemiology online.

Funding

This research was supported by the National Institute on Drug Abuse (NIDA) and the US Food and Drug Administration (FDA) under award R21DA058343; by the National Cancer Institute (NCI) and FDA under award U54180905; and by NIDA under awards R01DA058992, K24DA048160, K99DA058241, and K01DA058084. The funding agencies had no role in the design, conduct, analysis, interpretation, or writing of the study.

Conflict of interest

The authors have no conflicts of interest relevant to this article to disclose.

Disclaimer

The views expressed in this article are solely those of the authors and do not represent the views of the funding agencies or the Understanding America Study.

Data availability

Data from the Understanding America Study (UAS) are available to researchers upon request through the UAS data portal, subject to registration and approval of the UAS data use agreement.

References

  • 1. Babb  S, Malarcher  A, Schauer  G, et al.  Quitting smoking among adults—United States, 2000-2015. MMWR Morb Mortal Wkly Rep. 2017;65(52):1457-1464. 10.15585/mmwr.mm6552a1 [DOI] [PubMed] [Google Scholar]
  • 2. Leventhal  AM, Dai  H, Higgins  ST. Smoking cessation prevalence and inequalities in the United States: 2014-2019. J Natl Cancer Inst. 2022;114(3):381-390. 10.1093/jnci/djab208 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3. Kasza  KA, Tang  Z, Seo  YS, et al.  Divergence in cigarette discontinuation rates by use of electronic nicotine delivery systems (ENDS): longitudinal findings from the United States PATH study waves 1–6. Nicotine Tob Res. 2025;27(2):236-243. 10.1093/ntr/ntae027 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. VanFrank  B, Malarcher  A, Cornelius  ME, et al.  Adult smoking cessation—United States, 2022. MMWR Morb Mortal Wkly Rep. 2024;73(29):633-641. 10.15585/mmwr.mm7329a1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Vogt  F, Hall  S, Marteau  TM. Understanding why smokers do not want to use nicotine dependence medications to stop smoking: qualitative and quantitative studies. Nicotine Tob Res. 2008;10(8):1405-1413. 10.1080/14622200802239280 [DOI] [PubMed] [Google Scholar]
  • 6. Eisenberg  MJ, Hébert-Losier  A, Windle  SB, et al.  Filion KB; E3 investigators. Effect of e-cigarettes plus counseling vs counseling alone on smoking cessation: a randomized clinical trial. JAMA.  2020;324(18):1844-1854. 10.1001/jama.2020.18889 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Tattan-Birch  H, Kock  L, Brown  J, et al.  E-cigarettes to augment stop smoking in-person support and treatment with Varenicline (E-ASSIST): a pragmatic randomized controlled trial. Nicotine Tob Res. 2023;25(3):395-403. 10.1093/ntr/ntac149 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Carpenter  MJ, Wahlquist  AE, Dahne  J, et al.  Effect of unguided e-cigarette provision on uptake, use, and smoking cessation among adults who smoke in the USA: a naturalistic, randomised, controlled clinical trial. E Clinical Medicine. 2023;63(63):102142. 10.1016/j.eclinm.2023.102142 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Hajek  P, Phillips-Waller  A, Przulj  D, et al.  A randomized trial of e-cigarettes versus nicotine-replacement therapy. N Engl J Med. 2019;380(7):629-637. 10.1056/NEJMoa1808779 [DOI] [PubMed] [Google Scholar]
  • 10. Benmarhnia  T, Pierce  JP, Leas  E, et al.  Can e-cigarettes and pharmaceutical aids increase smoking cessation and reduce cigarette consumption? Findings from a nationally representative cohort of American smokers. Am J Epidemiol. 2018;187(11):2397-2404. 10.1093/aje/kwy129 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Watkins  SL, Thrul  J, Max  W, et al.  Real-world effectiveness of smoking cessation strategies for young and older adults: findings from a nationally representative cohort. Nicotine Tob Res. 2020;22(9):1560-1568. 10.1093/ntr/ntz223 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Kalkhoran  S, Chang  Y, Rigotti  NA. Electronic cigarette use and cigarette abstinence over 2 years among U.S. smokers in the population assessment of tobacco and health study. Nicotine Tob Res. 2020;22(5):728-733. 10.1093/ntr/ntz114 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Berry  KM, Reynolds  LM, Collins  JM, et al.  E-cigarette initiation and associated changes in smoking cessation and reduction: the Population Assessment of Tobacco and Health Study, 2013-2015. Tob Control. 2019;28(1):42-49. 10.1136/tobaccocontrol-2017-054108 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Chen  R, Pierce  JP, Leas  EC, et al.  Use of electronic cigarettes to aid long-term smoking cessation in the United States: prospective evidence from the PATH Cohort Study. Am J Epidemiol. 2020;189(12):1529-1537. 10.1093/aje/kwaa161 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Pierce  JP, Chen  R, Kealey  S, et al.  Incidence of cigarette smoking relapse among individuals who switched to e-cigarettes or other tobacco products. JAMA Netw Open. 2021;4(10):e2128810. 10.1001/jamanetworkopen.2021.28810 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. 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. 10.1136/tobaccocontrol-2017-054174 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Glasser  AM, Vojjala  M, Cantrell  J, et al.  Patterns of E-cigarette use and subsequent cigarette smoking cessation over 2 years (2013/2014-2015/2016) in the Population Assessment of Tobacco and Health Study. Nicotine Tob Res. 2021;23(4):669-677. 10.1093/ntr/ntaa182 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Quach  NE, Pierce  JP, Chen  J, et al.  Daily or nondaily vaping and smoking cessation among smokers. JAMA Netw Open. 2025;8(3):e250089. 10.1001/jamanetworkopen.2025.0089 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Han  DH, Lee  SH, Seo  DC. Within-person longitudinal associations between electronic nicotine delivery systems use and smoking cessation efforts among US continuing adult cigarette smokers. Nicotine Tob Res. 2022;24(4):590-597. 10.1093/ntr/ntab232 [DOI] [PubMed] [Google Scholar]
  • 20. Alattar  L, Messel  M, Rogofsky  D. An introduction to the understanding America study internet panel. Soc Sec Bull. 2018;78:13. [Google Scholar]
  • 21. Kapteyn  A, Angrisani  M, Bennett  D, et al.  Tracking the effect of the COVID-19 pandemic on the lives of American households. Survey Research Methods. 2020;14(2):179-186. [Google Scholar]
  • 22. Biener  L, Hargraves  JL. 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. 2015;17(2):127-133. 10.1093/ntr/ntu200 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Harlow  AF, Stokes  AC, Brooks  DR, et al.  Prospective association between e-cigarette use frequency patterns and cigarette smoking abstinence among adult cigarette smokers in the United States. Addiction.  2022;117(12):3129-3139. 10.1111/add.16009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Levy  DT, Yuan  Z, Luo  Y, et al.  The relationship of e-cigarette use to cigarette quit attempts and cessation: insights from a large, nationally representative U.S. survey. Nicotine Tob Res. 2018;20(8):931-939. 10.1093/ntr/ntx166  Erratum in: Nicotine Tob Res. 2024 Oct 15:ntae236 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25. Cohn  A, Brandon  T, Armeli  S, et al.  Real-time patterns of smoking and alcohol use: an observational study protocol of risky-drinking smokers. BMJ Open. 2015;5(1):e007046. 10.1136/bmjopen-2014-007046 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26. Piper  ME, Bullen  C, Krishnan-Sarin  S, et al.  Defining and measuring abstinence in clinical trials of smoking cessation interventions: an updated review. Nicotine Tob Res. 2020;22(7):1098-1106. 10.1093/ntr/ntz110 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Han  B, Volkow  ND, Blanco  C, et al.  Trends in prevalence of cigarette smoking among US adults with major depression or substance use disorders, 2006-2019. JAMA.  2022;327(16):1566-1576. 10.1001/jama.2022.4790 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Manea  L, Gilbody  S, Hewitt  C, et al.  Identifying depression with the PHQ-2: a diagnostic meta-analysis. J Affect Disord. 2016;203:382-395. 10.1016/j.jad.2016.06.003 [DOI] [PubMed] [Google Scholar]
  • 29. Plummer  F, Manea  L, Trepel  D, et al.  Screening for anxiety disorders with the GAD-7 and GAD-2: a systematic review and diagnostic metaanalysis. Gen Hosp Psychiatry. 2016;39:24-31. 10.1016/j.genhosppsych.2015.11.005 [DOI] [PubMed] [Google Scholar]
  • 30. Asparouhov  T, Muthén  B. Comparison of models for the analysis of intensive longitudinal data. Struct Equ Model Multidiscip J. 2020;27(2):275-297. 10.1080/10705511.2019.1626733 [DOI] [Google Scholar]
  • 31. Berli  C, Inauen  J, Stadler  G, et al.  Understanding between-person interventions with time-intensive longitudinal outcome data: longitudinal mediation analyses. Ann Behav Med. 2021;55(5):476-488. 10.1093/abm/kaaa066 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Muthén  LK, Muthén  BO. Mplus: Statistical Analysis with Latent Variables: User’s Guide (Version 8). Los Angeles, CA: Authors; 2017. [Google Scholar]
  • 33. Asparouhov  T, Muthén  B. Testing for informative weights and weights trimming in multivariate modeling with survey data. In: Proceedings of the Joint Statistical Meetings, Section on Survey Research Methods; August 2007; Alexandria, VA. Alexandria, VA: American Statistical Association; 2007:3456-3463. [Google Scholar]
  • 34. Schminkey  DL, von  Oertzen  T, Bullock  L. Handling missing data with multilevel structural equation modeling and full information maximum likelihood techniques. Res Nurs Health. 2016;39(4):286-297. 10.1002/nur.21724 [DOI] [PubMed] [Google Scholar]
  • 35. Benjamini  Y, Hochberg  Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol. 1995;57(1):289-300. 10.1111/j.2517-6161.1995.tb02031.x [DOI] [Google Scholar]
  • 36. VanderWeele  TJ, Arah  OA. Unmeasured confounding for general outcomes, treatments, and confounders: bias formulas for sensitivity analysis. Epidemiology.  2011;22(1):42-52. 10.1097/EDE.0b013e3181f74493 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37. Centers for Disease Control and Prevention . Tobacco Use and Dependence: Patient Care Settings [Internet]. Atlanta (GA): CDC; [2024]. Available from:  https://www.cdc.gov/tobacco/hcp/patient-care-settings/clinical.html [Google Scholar]
  • 38. Siahpush  M, Shaikh  RA, Cummings  KM, et al.  The association of point-of-sale cigarette marketing with cravings to smoke: results from a cross-sectional population-based study. Tob Control. 2016;25(4):402-405. 10.1136/tobaccocontrol-2015-052253 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Siahpush  M, Shaikh  RA, Smith  D, et al.  The association of exposure to point-of-Sale tobacco marketing with quit attempt and quit success: results from a prospective study of smokers in the United States. Int J Environ Res Public Health. 2016;13(2):203. 10.3390/ijerph13020203 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40. Nelson  BW, Pettitt  A, Flannery  JE, et al.  Rapid assessment of psychological and epidemiological correlates of COVID-19 concern, financial strain, and health-related behavior change in a large online sample. PloS One. 2020;15(11):e0241990. 10.1371/journal.pone.0241990 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41. Quintana  R. Thinking within-persons: using unit fixed-effects models to describe causal mechanisms. Methods in Psychology. 2021;5(5):100076. 10.1016/j.metip.2021.100076 [DOI] [Google Scholar]
  • 42. U.S. Department of Health and Human Services . Smoking Cessation: A Report of the Surgeon General – Key Findings [Internet]. Washington (DC): HHS; 2020. Available from:  https://www.hhs.gov/sites/default/files/2020-cessation-sgr-factsheet-key-findings.pdf [Google Scholar]
  • 43. Counselor Magazine . Smoking Cessation Treatments: What Are Young Adult Smokers Thinking? [Internet]. [2024. Available from:  https://counselormagazine.com/article/smoking-cessation-treatments-what-are-young-adult-smokers-thinking/
  • 44. Shahab  L, Brose  LS, West  R. Novel delivery systems for nicotine replacement therapy as an aid to smoking cessation and for harm reduction: rationale, and evidence for advantages over existing systems. CNS Drugs. 2013;27(12):1007-1019. 10.1007/s40263-013-0116-4 [DOI] [PubMed] [Google Scholar]
  • 45. Wadgave  U, Nagesh  L. Nicotine replacement therapy: an overview. Int J Health Sci (Qassim). 2016;10(3):407-416. 10.12816/0048737 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46. Voigt  K. Smoking norms and the regulation of E-cigarettes. Am J Public Health. 2015;105(10):1967-1972. 10.2105/AJPH.2015.302764 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47. Glasser  AM, Collins  L, Pearson  JL, et al.  Overview of electronic nicotine delivery systems: a systematic review. Am J Prev Med. 2017;52(2):e33-e66. 10.1016/j.amepre.2016.10.036  Erratum in: Am J Prev Med 2024;67(6):988. doi:https://doi.org/10.1016/j.amepre.2024.07.026 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48. Russell  C, Dickson  T, McKeganey  N. Advice from former-smoking e-cigarette users to current smokers on how to use e-cigarettes as part of an attempt to quit smoking. Nicotine Tob Res. 2018;20(8):977-984. 10.1093/ntr/ntx176 [DOI] [PubMed] [Google Scholar]
  • 49. Schwamm  E, Noubary  F, Rigotti  NA, et al.  Longitudinal transitions in initiation, cessation, and relapse of cigarette smoking and e-cigarette use among US youth and adults: validation of a microsimulation model. PloS One. 2023;18(4):e0284426. 10.1371/journal.pone.0284426 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50. National Cancer Institute . FDA Regulation of E-Cigarettes: An Interview with Mitch Zeller, JD [Internet]. Bethesda (MD): National Cancer Institute; 2022. Available from:  https://www.cancer.gov/news-events/cancer-currents-blog/2022/ecigarettes-zeller-fda-regulation [Google Scholar]
  • 51. Delnevo  CD, Jeong  M, Teotia  A, et al.  Communication between US physicians and patients regarding electronic cigarette use. JAMA Netw Open. 2022;5(4):e226692. 10.1001/jamanetworkopen.2022.6692 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52. Nickels  AS, Warner  DO, Jenkins  SM, et al.  Beliefs, practices, and self-efficacy of US physicians regarding smoking cessation and electronic cigarettes: a national survey. Nicotine Tob Res. 2017;19(2):197-207. 10.1093/ntr/ntw194 [DOI] [PubMed] [Google Scholar]
  • 53. Hernán  MA, Hernández-Díaz  S, Robins  JM. A structural approach to selection bias. Epidemiology.  2004;15(5):615-625. 10.1097/01.ede.0000135174.63482.43 [DOI] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Web_Material_kwaf270
web_material_kwaf270.zip (157.2KB, zip)

Data Availability Statement

Data from the Understanding America Study (UAS) are available to researchers upon request through the UAS data portal, subject to registration and approval of the UAS data use agreement.


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