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. Author manuscript; available in PMC: 2025 Nov 1.
Published in final edited form as: Drug Alcohol Depend. 2024 Sep 19;264:112436. doi: 10.1016/j.drugalcdep.2024.112436

Understanding the Motivational Mechanisms for Smoking and Vaping Among Dual Users and Exclusive Smokers

Jennifer M Betts 1,2, Jessica W Cook 1,2, Kate H Kobinsky 1, Timothy B Baker 1, Douglas E Jorenby 1, Megan E Piper 1
PMCID: PMC11527565  NIHMSID: NIHMS2026271  PMID: 39341015

Abstract

Background:

Understanding the motivational processes that influence e-cigarette use in a laboratory setting may help elucidate mechanisms that support long-term ecigarette use, which could have significant clinical and public health consequences.

Methods:

Secondary analyses were conducted on data from exclusive smokers (N=47) and dual users (N=88) who underwent a laboratory ad lib use session. Participants were given 10 minutes to smoke (exclusive smokers) or vape (dual users) as much as they wanted. Withdrawal was assessed pre- and post-use. Smoking and vaping behavior was coded from session videos. Person-level predictors included cigarette/ecigarette craving-relief expectancies, demographics, and cigarette/e-cigarette use and dependence. Smoking and vaping status was assessed at Year 1 using self-reported 30-day point prevalence. Data were analyzed using general linear models and logistic regressions.

Results:

Both groups reported reductions in withdrawal after product use, including cigarette craving. Baseline e-cigarette craving-relief expectancies, pre-session ecigarette craving, heaviness of e-cigarette use, and relative e-cigarette dependence were significant univariate predictors of continued vaping in dual users at Year 1 (ORs>1.04, ps<.05). Dual users and exclusive smokers did not differ on use behavior (i.e., average puff duration or number of puffs, ps>.16).

Conclusions:

E-cigarette use alleviated withdrawal, including cigarette and e-cigarette craving, in dual users. Laboratory use behavior did not differ between dual users using e-cigarettes and exclusive smokers using cigarettes. Greater e-cigarette craving-relief expectancies, e-cigarette craving, heaviness of e-cigarette use, and morning product use pattern (‘relative dependence’) may reflect mechanisms that sustain e-cigarette use.

Keywords: Tobacco cigarettes, electronic cigarettes, Craving, Longitudinal Research, Withdrawal symptoms, Dual use

1. INTRODUCTION

Since their introduction in 2006, electronic cigarette (e-cigarette) use has increased (Boakye et al., 2022) and a common use pattern is the dual use of commercial combusted cigarettes and e-cigarettes. Recent reports have estimated that there are 5.6 million adult dual in the United States (Boakye et al., 2022). Many dual users first smoked tobacco cigarettes and then later initiated use of e-cigarettes to help them cut down or quit smoking (Mayer et al., 2020). However, relatively little is understood about factors that affect the likelihood that combusted tobacco or e-cigarette use will persist in these individuals.

Dual use of cigarettes and e-cigarettes may lead to one of four outcomes, which likely have very different health consequences. Some dual users, especially those who use e-cigarettes heavily, will eventually quit smoking but continue vaping (Wang, Bhadriraju and Glantz, 2021). Ideally, another group of dual users will be able to quit smoking and vaping. Other dual users will instead ultimately quit using e-cigarettes and return to exclusive smoking (Piper et al., 2020a). And, finally, other individuals may develop a pattern of persistent dual use (Coleman et al., 2022). Thus, dual use may exert a net health benefit by increasing the likelihood of smoking cessation or it may increase harm by adding e-cigarette toxicants to the toxic effects of smoking (Tattersall et al., 2023).

If e-cigarettes are used for harm reduction it would be important to ensure that they are used for a sufficient period of time to replace smoking, perhaps through the transfer of dependence or the development of new behavioral routines. However, prolonged e-cigarette use that does not increase smoking quit attempts or cessation might be harmful, making it important to help such individuals also quit their e-cigarettes. In either case, interventions might be guided by knowledge of factors that can either encourage or discourage persistent e-cigarette use in the context of dual use (Warner, 2019). Most research on persistence of e-cigarette use in dual users comes from large sample or population-based studies (Chan et al., 2019; Krishnan, Abroms and Berg, 2021; Krishnan et al., 2024; Snell, Barnes and Nicksic, 2020), which lack behavioral or ecological momentary assessments that may provide more comprehensive understanding of product use and motivational processes.

Alleviation of nicotine withdrawal through negative reinforcement, especially craving reduction, may be a relevant motivator, consistent with theories of nicotine dependence (Piasecki et al., 2000). Laboratory research has shown that e-cigarettes can significantly relieve nicotine withdrawal in individuals who exclusively vape (Dawkins and Corcoran, 2014; Hiler et al., 2020). Withdrawal-related craving, a facet of withdrawal most consistently related to smoking occurrence (e.g., lapses; Baker et al., 2012b; Piasecki et al., 2000) may therefore be particularly significant in motivating tobacco product use. While e-cigarettes have been found to relieve cigarette craving (Bullen et al., 2010; Dawkins and Corcoran, 2014; Maloney et al., 2020), the motivational significance of this effect is unknown. Thus, the limited research in this area (Dawkins and Corcoran, 2014) has not determined whether this effect, or expectancies of this effect (that e-cigarettes would reduce cigarette craving), predict sustained use of e-cigarettes. The present research assessed both e-cigarette effects on cigarette craving and expectancies of those effects and uniquely determined whether these variables predicted long-term dual use (i.e., continued smoking and vaping).

Other factors such as individual and product characteristics may also motivate e-cigarette use amongst dual users (Pacek, Wiley and McClernon, 2019). Relevant individual-level characteristics may include nicotine dependence, as well as transdiagnostic psychological constructs, such as anhedonia and distress tolerance, which have been shown to index motivation to smoke cigarettes (Leventhal and Zvolensky, 2015) and are related to tobacco withdrawal (Cook et al., 2017; Cook et al., 2015; Schlam et al., 2019). In particular, anhedonia has received support as a tobacco withdrawal symptom (Cook et al., 2017; Cook et al., 2015; Hughes, 2007; Kenny and Markou, 2005; Paterson, Balfour and Markou, 2007) but it is unclear how anhedonia relates to nicotine dependence in dual users and to e-cigarette motivation. Product characteristics that affect e-cigarette abuse liability, product satisfaction, and withdrawal relief may include e-liquid flavor (Baker et al., 2021; Cheney, Gowin and Wann, 2016; Peterson et al., 2021), nicotine content (Bullen et al., 2010; Peterson et al., 2021), and e-cigarette device generation (Dawkins et al., 2015; Soule et al., 2017).

In addition to individual and product characteristics, outcome expectancies have been shown to index motivation to use e-cigarettes (Peraza et al., 2020; Pokhrel et al., 2015; Subica et al., 2020). However, many e-cigarette expectancy assessments have not evaluated expectancies that vaping will produce craving-relief (although see (Harrell et al., 2015)) and have not compared expectancies that vaping would relieve cigarette vs. e-cigarette craving. Since craving reduction appears to index smoking dependence (Baker et al., 2012b; Bolt et al., 2012), it seems important to determine whether it predicts continued vaping alone or continued dual use. Understanding the effects of expectancies that e-cigarettes will reduce combusted cigarette craving may be particularly relevant for dual users, who often report using e-cigarettes as a substitute for combusted cigarettes and using them to reduce or quit smoking (Yong et al., 2019). Of course, motivational influences are likely to be inter-related, perhaps sharing mediational or interactive relationships.

The present study used data from an observational longitudinal study of non-treatment seeking dual users and exclusive smokers (Piper et al., 2020a; Piper et al., 2019) and assessed potential motivational mechanisms that might account for combusted tobacco and e-cigarette use among dual users. This study assessed cigarette and e-cigarette motivational factors, including self-report and self-administration variables, and related these to long-term cigarette and e-cigarette use. Through combining laboratory assessments and longitudinal outcomes, these secondary analyses explored hypothesized motivational mechanisms that might support sustained combusted cigarette and e-cigarette use in people who dual use.

2. METHODS

2.1. Participants

Eligibility criteria for the original study included: ≥18 years old, fluent in English, not interested in quitting smoking and/or vaping in the next month, not currently engaged in treatment for smoking cessation, no current diagnosis of psychosis or bipolar disorder. Exclusive smokers had to smoke ≥5 cigarettes/day for the past six months with no e-cigarette use in the last three months. Dual users had to use e-cigarettes containing nicotine at least once per week and smoke tobacco cigarettes daily in the last three months. See Piper and colleagues (2019) for full inclusion/exclusion criteria. Participants were recruited in Madison and Milwaukee, WI through television and social media advertising. Of the 184 participants recruited from Madison, where the ad lib session took place, 73.4% (88 dual users and 47 exclusive smokers) completed the ad lib session at baseline, provided laboratory use and withdrawal data, and were included in these analyses.

2.2. Procedures

Participants completed a phone screen to determine study eligibility. Participants who were eligible and consented to participate attended a baseline session, during which their exhaled carbon monoxide (CO) was measured. They then attended the ad lib session, before which they were instructed to remain abstinent from all tobacco and nicotine products overnight. If CO taken at the start of the session did not indicate overnight abstinence from combusted cigarettes, they were not excluded from the study or analyses. At the start of the session, participants completed assessments of withdrawal and craving-relief expectancies. Participants then underwent a 10-minute ad lib session, which was video recorded. Exclusive smokers were allowed to smoke their preferred cigarettes and dual users were allowed to vape their e-cigarettes as much as they wanted. After the ad lib session, participants completed assessments of withdrawal and individual characteristics.

2.3. Measures

2.3.1. Withdrawal

Withdrawal was assessed using items from the Wisconsin Smoking Withdrawal Scale (WSWS; Smith et al., 2021) Participants were asked to rate how they felt “right now” on a scale of 1 (not at all) to 7 (extremely) on 13 items assessing negative affect (3 items), cigarette craving (4 items), concentration difficulties (1 item), and hunger (1 item). We also added 4 items assessing e-cigarette craving (which paralleled cigarette craving items). Although these items were assessed for all participants, e-cigarette craving was included in the average WSWS score only for dual users to more accurately characterize dual users’ withdrawal experiences. This WSWS score including both tobacco and e-cigarette craving for dual users generated larger correlation coefficients to e-cigarette dependence than the WSWS score without e-cigarette craving, although the differences in correlation magnitude were not statistically significant. Change in withdrawal was calculated by subtracting post-session scores from pre-session scores, with higher values representing greater reductions in withdrawal after the ad lib session.

2.3.2. Smoking and Vaping Craving-Relief Expectancies

Participants rated three items assessing craving-relief expectancies on a scale of 1 (not at all) to 7 (extremely). Cigarette craving expectancies were assessed with the question “How effective are cigarettes at reducing or getting rid of strong urges?” Vaping craving expectancies were assessed with the questions “How effective are e-cigarettes at reducing or getting rid of strong urges?” and “E-cigarettes are as good as regular cigarettes when I have a strong urge to smoke.” The two e-cigarette expectancies are highly correlated (r=.69, p<.001). They are also associated with WISDM e-cigarette dependence (rs>.32, ps<.007), pre-session average withdrawal (r=.27, p=.01) and pre-session e-cigarette craving (rs>.31, ps<.003), suggesting appropriate associations with related dependence constructs.

2.3.3. Individual Characteristics

Participants’ demographics, and smoking/vaping histories were collected at baseline. Cigarette dependence was assessed with the Wisconsin Index of Smoking Dependence Motives (WISDM; Smith et al., 2010), Fagerström Test of Nicotine Dependence (FTND; Heatherton et al., 1991), and the Penn State Cigarette Dependence Index (Foulds et al., 2015). Cigarette use was assessed using cigarettes per day (CPD). Dual users completed the e-WISDM, e-FTND, and Penn State E-Cigarette Dependence Index to assess e-cigarette dependence (PSECDI; Piper et al., 2020b). E-cigarette use frequency was defined as either daily or nondaily based on self-reported frequency in the month prior to the baseline assessment. The PSECDI definition of an episode of e-cigarette use (“15 puffs or around 10 minutes”) was used to characterize vaping use within days. Participants also reported their e-cigarette device characteristics (used to determine device generation based on Center for Disease Control guidelines), e-liquid flavor, and nicotine concentration. A validated measure of relative dependence on e-cigarettes compared to cigarettes was calculated based on the percentage of all days in a two-week ecological momentary assessment (EMA) period where e-cigarettes were the first product used in the morning (Piper et al., 2022). Anhedonia was assessed using the Snaith-Hamilton Pleasure Scale (Snaith et al., 1995) and distress tolerance was assessed using the Distress Tolerance Scale (Simons and Gaher, 2005).

2.3.4. Laboratory Use Behavior

The number of puffs during the ad lib session were obtained from video recordings. Each video was coded by two independent and trained coders; discrepancies were coded by a third coder and then resolved with the final author.

2.3.5. Smoking and Vaping Outcomes

One year after baseline, data on participants’ smoking and vaping status were collected based on self-reported 30-day point prevalence of cigarette and e-cigarette use. This was used to categorize the smoking and vaping status of participants.

2.4. Data Analysis Plan

Differences in baseline characteristics between exclusive smokers and dual users included in these secondary analyses were tested using independent t-tests and chi-square analyses.

General linear models (GLM) were used to look at associations between predictors and outcomes (withdrawal, laboratory use variables) with PROC GLM in SAS 9.4. Additionally, the main effect of group (dual user, exclusive smoker) and group by predictor interactions were evaluated. For withdrawal outcomes, time (pre-session, post-session) was added as a within-subjects factor. Models were run to separately examine the associations between withdrawal outcomes and craving-relief expectancies and ad lib use. Individual-level characteristics (i.e., gender, education, CPD, cigarette dependence, anhedonia, and distress tolerance) were examined in the same model. Additional models with only dual users included e-cigarette characteristics as predictors (i.e., vaping frequency, e-cigarette dependence, relative dependence, e-cigarette generation, and e-liquid nicotine concentration). These variables were selected a priori based on theories of nicotine product use and prior research indicating these factors would influence results. However, the analyses reported in this paper are exploratory in nature and were not pre-registered as part of the primary study aims. Continuous variables were mean-centered, and model estimates reported are unstandardized betas. To address the exploratory nature of these analyses, Benjamini-Hochberg corrections for multiple comparisons were used within each model to identify significant omnibus effects, which were then probed using Tukey post-hoc pairwise comparisons. We conducted a series of sensitivity analyses that used percent reduction in CO as a covariate, but this did not change the basic pattern of findings (results not presented).

Individual-level characteristics, craving-relief expectancies, and withdrawal scores (pre-session and pre-to-post change controlling for pre-session) were used to predict smoking and vaping status at Year 1 using univariate binary logistic regressions with PROC LOGISTIC. To account for missing data for smoking and vaping status at Year 1, we conducted sensitivity analyses with the SPSS multiple imputation procedure, using methods previously reported with this dataset (Piper et al., 2020a) to categorize the smoking and vaping status of participants at follow-up.

3. RESULTS

3.1. Sample Characteristics

Among the 135 participants included in these analyses, participants were a mean of 38.0 years old, 56.6% female, 77.0% White, and smoked a mean of 12.3 CPD (see Table 1 for sample demographics and group difference tests). Dual users reported vaping 5.8 days per week on average with a mean 10.5 vaping events per day; 54.6% reported vaping daily. Most dual users (78.2%) vaped third generation e-cigarette devices (e.g., refillable tank devices) and sweet/fruit flavored e-liquid (65.8%). All participants vaped e-liquid with freebase nicotine. See Table 2 for e-cigarette use among dual users.

Table 1.

Group differences in baseline demographic characteristics

Overall Mean (SD) Dual user Mean (SD) Exclusive Smoker Mean (SD) Group Difference p-value
N 135 88 47
Age 38.04 (13.09) 35.51 (12.36) 40.89 (14.04) .06
CPD 12.26 (6.58) 11.16 (6.51) 14.40 (6.25) .007
Pre-session CO (ppm) 9.96 (7.01) 9.69 (7.12) 10.47 (6.86) .54
Age 1st cigarette 13.78 (2.99) 13.80 (3.05) 13.75 (2.92) .93
Age smoking daily 16.70 (3.95) 16.84 (3.95) 16.43 (3.97) .58
WISDM Total 46.08 (11.69) 45.54 (11.98) 47.10 (11.16) .46
WISDM PDM 4.47 (1.41) 4.34 (1.48) 4.68 (1.25) .19
WISDM SDM 4.04 (1.02) 4.02 (1.03) 4.07 (1.01) .78
FTND 4.05 (2.29) 3.78 (2.33) 4.55 (2.13) .06
Penn State 10.89 (3.89) 10.69 (4.07) 11.28 (3.56) .41
Anhedonia 21.45 (6.05) 21.01 (5.78) 22.28 (6.53) .25
Distress Tolerance 2.98 (0.97) 2.92 (1.01) 3.08 (0.90) .36
N (%)
Female 75 (55.56%) 54 (61.36%) 21 (44.68%) .06
White 104 (77.04%) 69 (78.41%) 35 (74.47%) .50
Hispanic/Latino 9 (6.67%) 7 (7.95%) 2 (4.26%) .41
Education .003
 Less than high school 4 (2.99%) 0 (0.00%) 4 (8.51%)
 High school or GED 47 (35.07%) 26 (29.89%) 21 (44.68%)
 More than high school 83 (61.94%) 61 (70.11%) 22 (46.81%)
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Note: Group differences = p-value associated with independent samples t-test or chi-square analyses testing for group differences.

CPD=Cigarettes Per day, CO=carbon monoxide, FTND=Fagerström Test of Nicotine Dependence, WISDM=Wisconsin Index of Smoking Dependence Motives, WISDM PDM=WISDM Primary Dependence Motives, WISDM SDM=WISDM Secondary Dependence Motives

Table 2.

E-cigarette use characteristics for dual users

Mean (SD)
Age 1st vape 34.02 (12.59)
Vaping days per week 5.76 (1.99)
Vaping events per day 10.5 (16.44)
Puffs per day 23.44 (35.25)
e-WISDM 33.35 (13.05)
e-WISDM PDM 2.91 (1.57)
e-WISDM SDM 3.10 (1.04)
e-FTND 2.88 (2.12)
PS-ECDI 6.88 (4.94)
N (%)
Vaping Frequency
 Daily Vaping 48 (54.55%)
 Nondaily Vaping 40 (45.45%)
E-cigarette generationa
 1st generation 9 (10.34%)
 2nd generation 10 (11.49%)
 3rd generation 68 (78.16%)
E-liquid Flavor
 Sweet/fruit 52 (65.82%)
 Mint/menthol 12 (15.19%)
 Unflavored 9 (11.39%)
 Tobacco 4 (5.06%)
 Other 2 (2.53%)
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e-FTND=e-cigarette Fagerström Test of Nicotine Dependence, e-WISDM=e-cigarette Wisconsin Index of Smoking Dependence Motives, e-WISDM PDM=e-WISDM Primary Dependence Motives, e-WISDM SDM=e-WISDM Secondary Dependence Motives

Note. Vaping events are defined using the PS-ECDI question defining an event as 15 puffs or 10 minutes.

a

Device generation determined based on Center for Disease Control recommendations. 1st generation = cig-a-like disposables, 2nd generation = cartridge-based devices, and 3rd generation = tank devices.

3.2. Associations Between Withdrawal Pre- and Post-Session with Laboratory Use Behavior and Individual Characteristics

3.2.1. Change in withdrawal

Across groups, withdrawal scores decreased from pre- to post-session for total WSWS scores, negative affect, and cigarette craving subscales (ps<.001); and in dual users, e-cigarette craving also significantly decreased (p<.001). See Supplemental Table 2 for summary of significant effects controlling for covariates (CPD, dependence, gender, education, anhedonia, and distress tolerance). The time effect varied by group for cigarette craving such that both exclusive smokers and dual users had comparable cigarette craving levels pre-session, t(120)= −0.13, p=.90, but exclusive smokers reported lower post-session cigarette craving than did dual users, t(120)=3.86, p<.001. Dual users reported significantly higher post-session total withdrawal scores compared to exclusive smokers (b=0.51, p=.007). Additionally, dual users reported higher pre- and post-session hunger scores (bs>0.76, ps<.04), although these were non-significant after adjusting for multiple comparisons.

3.2.2. Predictors of Withdrawal

Across both groups, cigarette craving-relief expectancies were related to elevated pre-session total withdrawal (b=0.34, p<.0001), negative affect (b=0.42, p<.0001), and cigarette craving (b=0.55, p<.0001). Also, across both groups, greater cigarette dependence (bs>0.02, ps<.006) and anhedonia (bs>0.03, ps<.05) were each related to higher pre- and post-session average withdrawal, negative affect, cigarette craving, and concentration scores. Lower distress tolerance was related to greater pre-session total withdrawal, as well as pre- and post-session negative affect and concentration difficulty scores (bs<−0.25, ps<.02). Women scored higher than men on pre-session total withdrawal (b=0.50, p=.008) and negative affect (b=0.72, p=.006). Among women dual users, e-cigarette craving was also elevated compared to men (b=0.83, p=.048) but was non-significant after Benjamini-Hochberg adjustments. Lastly, CPD was associated with lower pre-session concentration scores (b=−0.07, p=.01) across both groups.

Regarding the relations between withdrawal and laboratory use behavior across both groups, taking more puffs during the ad lib session was significantly associated with lower post-session average withdrawal (b=−0.03, p=.006) and cigarette craving (b=−0.06, p<.001). Gender moderated the effect of the number of puffs taken on post-session negative affect, F(1,134)=5.38, p=.02, and concentration, F(1,134)=4.39, p=.04. However, post-hoc tests did not generate significant effects of the number of puffs taken within males and females for both post-session negative affect and concentration scores (ps>.08).

3.2.3. Dual users

Vaping significantly reduced craving for both combusted cigarettes, t(87)=11.39, p<.001, paired Cohen’s d=1.21, and e-cigarettes, t(87)=11.77, p<.001, paired Cohen’s d=1.26, to similar extents. Examination of effects amongst dual users revealed that e-cigarette dependence was significantly and positively associated with all pre-session withdrawal scores apart from cigarette craving and hunger (bs>0.05, ps<.002, see Supplemental Table 3 for significant results). After adjustments for multiple comparisons, e-cigarette dependence remained significantly related to only e-cigarette craving and concentration scores. Elevated e-cigarette craving-relief expectancies were significantly associated with elevated pre-session e-cigarette craving (bs>0.44, ps<.001). Greater e-cigarette craving-relief expectancies were also associated with greater pre-session hunger (b=0.29, p=.01) and with lower post-session cigarette craving scores (b=−0.29, p=.007), neither of which remained significant with Benjamini-Hochberg adjustments. See Supplemental Text 1 for discussion of effects regarding e-cigarette device generation and e-liquid flavors.

3.3. Laboratory Use Behavior

There were no significant differences between exclusive smokers’ use of cigarettes and dual users’ use of e-cigarettes in terms of the number of puffs taken during the ad lib session (see Table 3 for means). When examined across both cigarettes and e-cigarettes, greater anhedonia was associated with taking fewer puffs (b =−0.30, p=.004), but this was no longer significant after adjusting for multiple comparisons. Craving-relief expectancies, withdrawal, e-cigarette characteristics, and baseline characteristics were not significantly related to number of puffs taken during the ad lib session. Group status and gender did not moderate any of the associations between predictors and use behavior outcomes (ps > .09).

Table 3.

Means and standard deviations for ad lib session dependent variables separated by group

Dual User Exclusive Smoker
Mean (SD) Mean (SD)
Puff count 13.39 (4.34) 14.51 (4.50)
Pre Post Pre Post
Expectancy that combusted cigarettes relieve cravings 5.85 (1.36) 5.70 (1.51) 5.34 (1.45) 5.43 (1.54)
Expectancy that e-cigarettes relieve cravings 4.97 (1.51) 4.61 (1.47) 1.52 (1.12) 1.44 (0.94)
Expectancy that vaping is as effective as smoking to relieve cravings 4.49 (1.76) 4.18 (1.97) 1.40 (0.95) 1.47 (1.01)
Total WSWS* 4.25 (1.34) 2.54 (1.11) 3.88 (1.08) 2.10 (0.86)
WSWS Negative Affect 3.20 (1.72) 2.16 (1.25) 2.84 (1.64) 2.05 (1.33)
WSWS Combusted cigarette craving 5.26 (1.69) 3.03 (1.78) 5.29 (1.31) 2.12 (1.03)
WSWS E-cigarette Craving 4.51 (1.87) 2.36 (1.27) 1.14 (0.47) 1.10 (0.50)
WSWS Concentration 2.80 (1.88) 1.92 (1.24) 2.43 (1.81) 2.00 (1.54)
WSWS Hunger 3.78 (1.90) 3.19 (1.85) 2.85 (1.77) 2.26 (1.61)
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WSWS = Wisconsin Smoking Withdrawal Scale.

*

Total WSWS includes E-cigarette craving only for the Dual User group.

3.4. Associations with Smoking and Vaping Behavior at Year 1

Of the 47 exclusive smokers, 32 (68%) reported continued smoking at Year 1, while one (2%) transitioned to exclusive vaping and one (2%) reported no smoking or e-cigarette use. At Year 1, 44 (50%) dual users continued to dual use, 25 (28%) returned to exclusive smoking, 8 (9%) transitioned to exclusive vaping, while one (1%) reported no smoking or vaping. Thirteen exclusive smokers and 10 dual users did not provide Year 1 smoking and vaping data.

When controlling for group, only smoking more CPD at baseline was associated with increased likelihood of smoking at Year 1 (OR=1.16, p=.03, 95% CI [1.02, 1.31]), but this was not significant after controlling for multiple comparisons. Results remained consistent when Year 1 smoking status was imputed. Neither group (dual user, exclusive smoker) nor gender significantly moderated these results (ps>. 07).

3.4.1. Dual users

Analysis of dual users’ smoking status at Year 1 showed that greater belief that e-cigarettes are as effective as cigarettes at relieving urges was associated with reduced likelihood of smoking (OR=0.60, p=.046, 95% CI [0.36,0.99]), which was non-significant after controlling for multiple comparisons.

Among dual users, multiple scores gathered prior to the ad lib vaping session were associated with Year 1 vaping status after controlling for multiple comparisons: positive vaping craving-relief expectancies, e-cigarette craving, vaping frequency, and relative dependence (type of product initially used in the morning) (see Table 4). With regard to expectancies, higher scores on both expectations that e-cigarettes effectively reduce urges (OR=1.68, p=.004) and that e-cigarettes are as effective as cigarettes at coping with urges (OR=1.61, p=.003) were associated with vaping one year later. Greater e-cigarette craving prior to the ad lib session was associated with higher likelihood of vaping after one year (OR=1.41, p=.01). Daily vaping, compared to nondaily vaping (OR=6.11, p<.001) and greater relative e-cigarette dependence (OR=1.04, p=.006) were both associated with greater likelihood of vaping at follow-up. Although non-significant after controlling for multiple comparisons, greater reduction in negative affect pre-to-post ad lib session was significantly associated with greater likelihood of vaping at Year 1, controlling for pre-session levels (OR=1.97, p=.03). Gender did not moderate these results (ps>.06). When limiting analyses to dual users who were either exclusively smoking or dual using at follow-up, this pattern of results remained consistent for predicting increased odds of dual use compared to returning to exclusive smoking. Results from analyses imputing missing values for Year 1 vaping status were largely consistent with the results reported above, with the exception that change in negative affect became non-significant (p=.14).

Table 4.

Significant logistic regression results predicting vaping status at Year 1 in dual users

Predictor OR 95% CI Wald X2 p-value Benjamini-Hochberg p-value
Expectancy that e-cigarettes relieve cravings 1.70 1.19 – 2.43 8.42 .004 .02
Expectancy that vaping is as effective as smoking to relieve cravings 1.62 1.18 – 2.23 9.06 .003 .02
Pre WSWS E-cigarette Craving 1.42 1.09 – 1.87 6.53 .01 .04
Change WSWS Negative Affect 1.87 1.04 – 3.37 4.40 .04 .11
 Pre WSWS Negative Affect 0.92 0.64 – 1.32 0.23 .63
Vaping frequency 6.11 2.14 – 17.41 11.46 <.001 .01
Relative dependence 1.04 1.01 – 1.07 8.02 .005 .02
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CI = Confidence interval, OR = odds ratio, WSWS = Wisconsin Smoking Withdrawal Scale.

4. DISCUSSION

This study identified variables that may influence laboratory and long-term e-cigarette use among dual users, including relative dependence on e-cigarettes vs. cigarettes, withdrawal symptoms, and craving-relief expectancies regarding e-cigarette effects. This study indicates that e-cigarettes can alleviate symptoms of nicotine withdrawal in dual users, contributing to the evidence that supports their use as a substitute for combusted cigarettes. Although vaping significantly reduced cigarette craving amongst dual users, it did not ameliorate it to the same extent as did smoking by exclusive smokers. However, results showed that vaping reduced combusted cigarette craving and e-cigarette craving by similar amounts in dual users, providing further evidence of the harm reduction potential of e-cigarettes fur dual users. E-cigarette effects on combusted cigarette craving may be highly important for substitution given the important role of craving in sustaining and triggering smoking (Bolt et al., 2012; Sayette, 2016). However, craving reduction during the ad lib session did not predict sustained vaping or dual use, whereas expectancies that e-cigarettes can reduce smoking urges, and do so as effectively as combusted cigarettes, did significantly predict vaping or dual use outcomes.

This research joins other work in suggesting the existence of a network of related dependence indicators, some of which may interact with one another (Baker et al., 2012a; Bekiroglu et al., 2017; Lydon-Staley et al., 2020; Rhemtulla et al., 2016). For instance, positive combusted cigarette craving-relief expectancies were associated with elevated pre-session total withdrawal, negative affect, and combusted cigarette craving across groups. Also, across both groups, greater cigarette dependence was related to higher pre-session average withdrawal, negative affect, cigarette craving, and concentration difficulties. In addition, ad lib product use across both groups resulted in decreases in total withdrawal and cigarette craving. Moreover, expectations that e-cigarettes would reduce craving were associated with elevated pre-session e-cigarette craving in dual users. Anhedonia, a potential tobacco withdrawal symptom, was positively associated with average withdrawal, negative affect, cigarette craving, and concentration scores. Lower distress tolerance, a key individual characteristic and transdiagnostic factor, was associated with greater total withdrawal, negative affect, and concentration difficulties. These findings suggest that e-cigarette and cigarette dependence may be highly similar in terms of the basic motivational forces, which include withdrawal symptoms such as craving and negative affect, craving-relief expectations, anhedonia, and distress tolerance. This study also produced evidence that some of these factors are predictors of the likelihood that dual users will continue versus discontinue their e-cigarette use.

Identifying factors that influence duration of e-cigarette use may help us understand constructs that might be targeted in efforts to either promote harm reduction (substituting e-cigarettes for cigarettes) or for helping individuals stop vaping. Amongst dual users, continued Year 1 vaping was predicted by multiple baseline measures: greater vaping craving-relief expectancies, greater pre-session e-cigarette craving, vaping frequency, and relatively greater dependence on e-cigarettes versus combusted cigarettes. ‘Relative dependence,’ as indexed by the proportion of days dual users vaped first in the morning during a baseline EMA measurement, was associated with continuing to vape. While this measure was a significant predictor of continued vaping and dual use, questionnaire measures of e-cigarette dependence were not, nor was actual craving reduction due to ad lib e-cigarette use in the laboratory. The superiority of the expectancy measures vs. craving reduction experienced during an ad lib use episode could reflect the fact that the former integrates experience over multiple use episodes rather than just a single laboratory use. Also, since this relative dependence measure reflects multiple product use decisions in a person’s daily life, this measure may possess strong external validity.

The results of this research could have some clinical applications. For instance, the results suggest a possible precision medicine application. The expectancy that e-cigarettes are as effective as cigarettes at relieving urges was associated with increased likelihood of sustained vaping and may be related to quitting smoking, albeit the latter effect was not statistically significant after controlling for multiple comparisons. If individuals who smoke were given a pre-cessation period of vaping, resulting expectancies of craving reduction by e-cigarette use might identify individuals who are most likely to be successful with an e-cigarette-based cessation treatment. Individuals who do not develop such expectancies might be given other smoking cessation interventions. High levels of expectations that e-cigarettes will reduce urges might also identify individuals who are especially in need of intervention to stop vaping. A strong pattern of morning ‘vaping-before-smoking’ (relative dependence) might also prove useful for this purpose. Our results showed that e-cigarettes reduced craving for combusted cigarettes a similar amount as it did for e-cigarette craving, which is potential evidence of effectiveness as a cessation aid in treating a central component of nicotine dependence.

The pattern of results described above suggests that persistent vaping amongst dual users is more a function of the attitudes about e-cigarettes per se than it is about the level of dependence on combusted cigarettes (Shafie-Khorassani et al., 2023). This pattern of findings may also reflect the fact that virtually all dual users were highly dependent on cigarettes, which might have suppressed associations between cigarette motivational factors and product use. In sum, the results suggest that e-cigarette craving and craving-relief expectancies regarding e-cigarettes may be key variables to focus on in attempts to understand or predict persistent e-cigarette use amongst dual users. Also, the relative dependence on cigarettes vs. e-cigarettes may identify individuals for whom e-cigarettes will increase smoking cessation. The likely importance of such factors is buttressed by evidence that craving, expectancies, and morning patterns of product use, are highly important to the development and maintenance of smoking (Baker et al., 2007; Brandon et al., 2004; Colvin and Mermelstein, 2010). However, it is the case that we know relatively little about how these variables and other dependence indicators develop and work together to influence e-cigarette use, perhaps interacting with one another over time in complex ways (Lydon-Staley et al., 2020).

The results of these exploratory analyses should be interpreted cautiously given their limitations. Differences between smoking and vaping ad lib use behavior and withdrawal were not tested using a within-person design for dual users, thereby limiting the conclusions we can draw when comparing smoking to vaping among dual users. Data collection occurred prior to the introduction of nicotine salt products (e.g., Juul) to the marketplace, which are now the most frequently used type of e-cigarettes and may be the more likely to induce dependence (Leventhal et al., 2021). Power to detect statistically significant effects related to e-cigarette variables may have been limited by sample size, and attrition may have biased analyses and weakened the detection of longitudinal outcomes. Some of the significant findings obtained did not remain significant when statistical control for multiple testing was applied. Thus, they should be viewed as providing evidence for hypothesis generation rather than a basis for strong inference. We did not exclude participants who were unable to maintain overnight abstinence from participating in the laboratory session, which may have attenuated pre-session withdrawal for some participants. Also, the product expectancies examined in this study were limited to craving-relief outcomes. Further, smoking and vaping status at follow-up was assessed with self-report. Finally, dual users are heterogenous (Buu et al., 2023) and results should be replicated in broader and more diverse populations of dual users and with individuals using more current e-cigarette devices.

In summary, secondary analyses of this observational longitudinal study identified putative motivational mechanisms that may affect combusted and e-cigarette use. Such mechanisms predicted whether dual users would continue their e-cigarette use over a one-year follow-up, contributing novel information to our understanding of e-cigarette dependence and sustained use one year later. Relatively strong predictors of continued vaping over one year were positive craving-relief expectancies regarding e-cigarette effects, e-cigarette craving, vaping frequency, and relative e-cigarette dependence. Such relations may inform efforts to treat e-cigarette use and dependence or to enhance the harm reduction effects of e-cigarettes.

Supplementary Material

1

Highlights:

  • Laboratory e-cigarette use alleviated nicotine withdrawal symptoms

  • Tobacco cigarette craving reduction was more pronounced for smoking than vaping

  • Sustained vaping in dual users was related to baseline motivational factors

  • Expectancies, craving, dependence, and use were associated with sustained vaping

Role of Funding Source:

This research was funded by the National Cancer Institute Grant R01CA190025 to the University of Wisconsin Center for Tobacco Research and Intervention. Clinical Trials Registration Identifier NCT02527980. JMB is supported by an Advanced Fellowship from the U.S. Department of Veterans Affairs. JWC is also supported by Merit Review Award 101CX00056 from the U.S. Department of Veterans Affairs.

Footnotes

Conflict of Interests: The funding bodies had no part in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The authors have no conflicts of interest to report.

Declaration of Competing Interest

The funding bodies had no part in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The authors have no conflicts of interest to report.

Author Disclosures

Data Statement: The data underlying this article will be shared on reasonable request to the corresponding author. Participants did not provide informed consent for public sharing of their data. Qualified researchers or other individuals may send data access requests to the study principal investigator (Megan Piper), which may be approved under a Data Use Agreement with the University of Wisconsin-Madison.

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