https://orcid.org/0000-0003-3028-668X
Abstract
How nicotine dependence will be affected when current smokers initiate electronic cigarette (e-cigarette) use to reduce cigarette smoking is unknown. This study evaluated cigarette, e-cigarette, and total nicotine dependence more than 6 months among smokers reducing cigarette consumption by replacing with e-cigarettes.
Adult cigarette smokers were randomized to one of four conditions (36 mg/ml e-cigarette, 8 mg/ml e-cigarette, 0 mg/ml e-cigarette, or cigarette-substitute [CS] [provided at no cost]) and instructed to reduce their cigarette smoking by 75% at 1 month. Participants completed follow-up at 1, 3, and 6 months. The Penn State Nicotine Dependence Index (PSNDI) measured dependence on cigarettes (PSCDI) and e-cigarettes (PSECDI). Urine cotinine measured total nicotine exposure. Linear mixed effects models for each outcome were conducted and included interaction terms between visit and condition.
Participants (n = 520) were 58.8% female, 67.3% White, and 48.0 years old. At baseline, the median number of cigarettes smoked per day was 17.3 and the mean PSCDI score was 13.4, with no significant differences between conditions. Participants in the e-cigarette conditions reported significantly lower PSCDI scores, compared with baseline, and with the CS condition at all follow-up visits. Those in the 36 mg/ml e-cigarette condition reported greater PSECDI scores at 6 months, compared with baseline and the 0 mg/ml and 8 mg/ml conditions. At all follow-up visits, there were no differences in total nicotine exposure compared to baseline, nor between any conditions.
E-cigarette use was associated with reduced cigarette dependence, compared to the CS, without significant increases in total nicotine exposure.
Initiation of electronic cigarette use while continuing to smoke could potentially increase nicotine dependence. In this randomized trial aimed at helping smokers to reduce their cigarette intake, we found that use of an e-cigarette was associated with a reduction in cigarette dependence and an increase in e-cigarette dependence (in the condition with the highest nicotine concentration only), with no long term increase in total nicotine dependence or nicotine exposure.
Introduction
Cigarette smoking remains one of the leading causes of death and disease in the United States.1 While many smokers report wanting to and attempting to quit, very few smokers are successful each year,2 even when using Food and Drug Administration (FDA) approved medications.3,4 Many smokers have difficulty quitting using traditional methods, and some smokers have turned to electronic cigarettes (e-cigarettes) to help them reduce or completely quit cigarette smoking.5–7
E-cigarettes are a diverse class of battery-powered products that heat nicotine-containing liquid into an aerosol to be inhaled by the user.8 E-cigarettes have become appealing replacements for cigarette smoking because some can deliver nicotine efficiently,9–11 they mimic the behavioral aspects of smoking,7 and they deliver much lower levels of many harmful toxicants.12,13 In addition, a report from the National Academies of Science concluded that completely substituting e-cigarettes for combustible cigarettes reduced users’ exposure to many toxicants and carcinogens that are present in cigarettes. However, there is not enough available evidence to determine long-term effects of e-cigarette use.14
Many smokers report initiating e-cigarette use to reduce their cigarette smoking and associated harms,7,15–17 with many becoming dual users of both cigarettes and e-cigarettes for a period of time.18,19 There are concerns that use of two nicotine-containing products (dual use) simultaneously could increase overall exposure to nicotine20 and other toxicants21 and in turn increase nicotine dependence, actually making it harder for users to reduce or quit.17
There have been few studies that have compared nicotine dependence between exclusive cigarette smokers and dual users. Some studies have analyzed dependence using cross-sectional between-subject data. One study using data from the 2012-13 National Adult Tobacco Survey (NATS) measured nicotine dependence symptoms, such as cigarettes per day, time to first use, and craving/withdrawal symptoms, and found that exclusive cigarette smokers and dual users reported smoking a similar number of cigarettes per day, with a similar time to first use of the day. However, dual-users were more likely to report cravings and withdrawal, compared with exclusive cigarette smokers.22 Another study using data from the Population Assessment of Tobacco and Health (PATH) Wave 3 (2015-2016) survey reported that there were no differences in total cigarette dependence, as measured by the Wisconsin Inventory of Smoking Dependence Motives (WISDM-68), between exclusive cigarette smokers and dual users. However, this study found that dual users were more likely to report cravings for cigarettes, compared with exclusive cigarette smokers,23 similar to Rostron et al.22 Finally, one recent study evaluated within-subject changes in dependence pre-e-cigarette initiation (retrospectively reported) and post e-cigarette initiation. They found that both cigarettes smoked per day and cigarette dependence decreased over time, as measured by the Heaviness of Smoking Index (HSI). This study also evaluated total nicotine frequency by adding together cigarettes per day and e-cigarette use per day. They found that total frequency of nicotine use increased post e-cigarette initiation compared with exclusive cigarette smoking.24
While these studies can provide some insight into differences in dependence between cigarette and dual users, the cross-sectional studies all have a potential flaws if smokers who choose to use e-cigarettes are different in some ways from those who do not. Additional studies are required in which smokers are randomly allocated to different products and followed prospectively in order to understand how experience with e-cigarettes impacts cigarette dependence over time. In addition, the previous studies reported on dependence when participants were using their own e-cigarette device, with unknown nicotine delivery. Studies of e-cigarette devices with known and consistent nicotine delivery are necessary to explore the impact of nicotine delivery on changes in cigarette dependence. The aim of this study was to evaluate self-reported cigarette, e-cigarette, and total nicotine dependence among a sample of smokers who participated in a longitudinal randomized controlled trial to reduce their cigarette smoking over a 6-month period by using a well-characterized, empirically evaluated e-cigarette (0 mg/ml, 8 mg/ml, or 36 mg/ml) or a cigarette substitute (CS).25
Methods
Study Procedures
Methods have been previously published in greater detail.25–27 The study was designed as a two-site, four-arm, six-month, parallel condition randomized controlled trial lasting 6 months. Both sites received human subjects’ approval from their respected institutional review boards. Tobacco cigarette smokers interested in reducing their cigarette intake, but with no immediate plans or interest to quit tobacco within the next six months, were recruited in the greater Richmond, Virginia and Hershey, Pennsylvania regions.
Study eligibility included the following criteria: aged 21-65, smoking > 9 regular filtered cigarettes per day for at least one year, had an expired-air carbon monoxide (CO) reading of > 9 parts per million at baseline, had made no serious quit attempts in the past month (including any use of an FDA-approved smoking cessation medication such as varenicline, bupropion used specifically as a quitting aid, nicotine patch, gum, lozenge, inhaler, or nasal spray), be willing and able to attend study visits weekly and monthly over a 9-month period, and be able to read and understand English in order to provide informed consent and respond to study surveys. Participants were excluded if they reported past 7 days use of other tobacco products (cigars, pipes, smokeless tobacco, hookah) or reported e-cigarette use in the past 7 days or e-cigarette use on more than 5 days in the past 28 days.
Following a screening questionnaire completed over the phone to determine their eligibility, interested potential participants were invited to attend an in-person visit to confirm eligibility prior to obtaining informed consent and ending with baseline data collection. All individuals were instructed to continue smoking cigarettes at their normal rate. One week later, participants returned and were randomized to their study condition. At their baseline visit participants were randomized to receive, at no cost, one of the four study conditions: cigarette substitute (CS), 0, 8, or 36 mg/ml nicotine concentration e-cigarette. The CS is a plastic tube containing no nicotine, tobacco, or aerosol, and it is designed to resemble a tobacco cigarette and provide the same draw resistance as a real cigarette (QuitSmart, Inc., North Carolina). The e-cigarette device consisted of a 3.3 V, 1100 mAh battery with a 1.5 Ohm dual-coil, 510-style cartomizer (SmokTech; China). Assignment to e-cigarette conditions was double-blind. Study staff with no participant contact pre-filled cartomizers with 1 ml of a flavored (tobacco or menthol) 70% propylene glycol/30% vegetable glycerin liquid containing the specific nicotine concentration for the condition assigned for each participant. The e-cigarette liquid, purchased locally from an e-cigarette retailer (AVAIL; Richmond, VA), was verified by an independent laboratory analysis for liquid nicotine concentration with an acceptable error range of +/- 2 mg/ml. In a study to measure the increase in plasma nicotine concentrations from the products used in this study results showed that the 8mg/ml concentration produced an increase in nicotine concentration of 8.2 ng/ml, while the 36mg/ml concentration produced an increase in plasma nicotine of 17.9 ng/ml.10 For comparison, the increase in plasma nicotine obtained from smoking one conventional cigarette ad libitum is about 21.0 ng/ml (range 12.9-27.9 ng/ml depending on the study).28,29
After randomization, participants received their study product and instructions for use via a product manual and verbal instruction/demonstration from study staff. All e-cigarette participants were initially provided with a supply of 3 cartomizers per day, which was adjusted at each visit throughout the study based on use. All participants were instructed to reduce their cigarette smoking by 50% by supplementing this decrease with study product use (After Visit 2 at week 0). Participants aimed to reach the 50% reduction goal for a two-week period (Visit 4 at week 2), and were then instructed to reduce by 75% of their cigarette consumption for the next two weeks (Visit 5 at 1 month). After one month, participants were instructed to maintain the 75% reduction and continue to reduce cigarette use if desired (from Visit 5 at 1 month to Visit 10 at 6 months in a one-month interval).
Measures
At baseline, participants completed a series of questions about demographic characteristics including their age, gender, race, ethnicity, education, and income level. Participants self-reported their cigarette dependence at baseline and all follow-up visits using two widely used dependence scales: the Penn State Cigarette Dependence Index (PSCDI) and the Fagerström Test for Nicotine Dependence (FTND). The PSCDI is a 10 item scale with a total score ranging from 0 (not at all dependent), to 20 (highly dependent). The FTND is a 6 item scale, with total scores ranging from 0 (not at all dependent), to 10 (highly dependent). Because the study protocol instructed participants to reduce the number of cigarettes smoked per day, we also generated a modified version of these measures with the cigarettes per day item removed from the total score (modified PSCDI and FTND) to ensure that changes in dependence were not simply due to study instructions. In addition, the Penn State Electronic Cigarette Dependence Index (PSECDI) was used to measure dependence on e-cigarettes throughout the trial, and a modified score was also calculated without the e-cigarette times per day variable. This modified scale was generated to account for the study instructions telling participants to use their device as a replacement for their cigarette smoking as well as to account for the conditions using a product that did not contain any nicotine.
Three outcomes were assessed: cigarette dependence (using the PSCDI and FTND), e-cigarette dependence (using PSECDI), and total dependence. To measure total self-reported nicotine dependence, we summed the total dependence scores of the PSCDI and PSECDI (PSCDI + PSECDI) at all follow-up visits. The baseline summed score included only the PSCDI items. Total nicotine exposure via urine cotinine (ng/mg creatinine) was measured with a single spot urine sample collected at baseline and follow-up visits. Samples were analyzed using liquid chromatography tandem mass spectrometry techniques. Due to skewness, we log-transformed cotinine to better approximate a normal distribution.
Cigarette dependence and total dependence outcomes were assessed at four timepoints (baseline, 1 month, 3 months, and 6 months), while e-cigarette dependence outcomes were assessed at three timepoints (1 month, 3 months, and 6 months). For each outcome, we evaluated 1) whether there was change from baseline to 1, 3, and 6 months within each condition and 2) whether there was a change between conditions at each follow-up (1, 3, and 6 months) adjusted for baseline outcome (except for e-cigarette dependence as e-cigarette dependence was not collected at baseline).
Data Analysis
We first examined the overall characteristics of the sample and compared baseline demographics and participant characteristics between randomized conditions. For subgroup analysis, participants were categorized into groups based on whether they reduced their cigarette consumption by 75% at 1 month per protocol instructions. Categorical variables were summarized with frequencies and compared across groups using the Chi-squared tests. Continuous variables were summarized with median and interquartile range (IQR) and compared across groups using the nonparametric Kruskal–Wallis tests. Spearman correlation coefficients were calculated to measure correlation between the two different measures of cigarette dependence, PSCDI and FTND. Also, to measure the test–retest reliability on the e-cigarette dependence measure, PSECDI, we calculated the Spearman correlation between the pairwise measures at 5 months (Visit 9) and 6 months (Visit 10) in the subpopulation who stated they used their e-cigarette in the prior 7 days at both visits 9 and 10.
We conducted linear mixed-effects models for both unadjusted and adjusted analysis. The interaction term between categorical time (eg, baseline, 1 month follow-up, etc.) and condition (eg, cigarette substitute, 0 mg/ml nicotine e-cigarette, etc.) was included in all analyses as a fixed effect. A random-effect term with unstructured variance–covariance matrix was included to capture the within-subject correlation across repeated measures analysis. To investigate the change from baseline to follow-up visits within each condition, we applied a constrained longitudinal data analysis (cLDA) strategy which assumes a common mean across treatment conditions at baseline and a different mean for each treatment at each of the post-baseline timepoints.30 The cLDA model accounts for the variability of the baseline outcomes and increases efficiency.
For the adjusted analysis, we performed multiple imputation to impute missing values in the baseline measurements of the three outcomes (PSCDI, FTND, and cotinine) and all participant characteristics were included in the adjusted analysis models (see Supplementary Table S1). Imputation was not used for the PSECDI or the PSCDI + PSECDI measure because e-cigarette use was not measured at baseline as the participants were not yet randomized to their conditions.
We invoked a fully conditional regression model as the imputation model to generate 50 imputed datasets: logistic regression for binary and categorical variables and linear regression for continuous variables, as implemented in the SAS procedure MI via the FCS statement. It assumed a separate conditional distribution for each imputed variable and demonstrated comparable performance as another popular multiple imputation method with multivariate normal distribution.31 The covariates included in the fully conditional regression model are shown in Supplementary Table 1.
After missing value imputation, we used linear mixed-effects models for adjusted analysis for each imputed dataset to evaluate the changes within-group from baseline to each follow-up and differences between conditions at each follow-up. In the adjusted model, study site, gender, age, and race/ethnicity were always included in the multivariable model. We used stepwise model selection procedure via Akaike's information criterion (AIC) to select the additional covariates included in the adjusted models for each of the outcomes. The covariates included in the adjusted models are shown in Supplementary Table S1.
Pairwise comparisons with Bonferroni adjustment were conducted (1) between conditions at each timepoint (FTND, PSCDI, cotinine, and log-transformed cotinine: 6 comparisons, alpha = 0.008; PSECDI and PSECDI + PSCDI: 3 comparisons, alpha = 0.017) and (2) within conditions relative to week 0 (FTND, PSCDI, cotinine, and log-transformed cotinine: 3 comparisons, alpha = 0.017; PSECDI: 2 comparisons, alpha = 0.025).
The primary analyses presented and discussed in this paper utilize intention-to-treat (ITT) methodology, unadjusted, and adjusted, where all available data were considered with no imputation of missing values. Supplementary Table S2 displays the sample size for each analysis overall and by randomized condition and group. Sensitivity analyses were also performed in both unadjusted and adjusted analysis and are included in the supplementary materials. The per-protocol (PP) analysis included only participants who completed the respective outcome measured at weeks 0, 4, 12, and 24. In the baseline-carried-forward (BCF) and the last-observation-carried-forward (LCF) analyses, we imputed missing values with the week 0 observation and the last available observation of the participant, respectively. Each model in the adjusted analysis was based on 50 imputed datasets.
Results
Participants (n = 520) were 58.8% female, 67.3% White, and had a median age of 48 years (IQR = [37.0, 56.0]). There were no significant differences between randomized conditions at baseline (Supplementary Table S3).
Cigarette Use
At baseline, the mean number of cigarettes smoked was 19.01 (SD = 7.75) (n = 520). All groups significantly reduced their CPD from baseline to all follow-up visits within condition, unadjusted and adjusted for baseline covariates (Supplementary Figures S1–S3). At 6 months, the mean CPD was 6.69 (SD = 5.58) for the 36 mg/ml condition, 8.22 (SD = 6.33) for the 8 mg/ml condition, 8.46 (SD = 7.67) for the 0 mg/ml condition, and 11.84 (SD = 9.16) for the cig sub condition. The e-cigarette conditions had significantly lower CPD than the cig sub condition at all follow-ups and there were no differences in CPD between the e-cigarette conditions at any follow-up (Supplementary Figures S1 and S2). The adjusted analyses produced the same result (Supplementary Figures S2 and S3).
Among those who attended the follow-up visits and reported cigarette smoking, 7.4% of participants reduced their smoking by 75% per protocol at 1 month (n = 32/432), while 17.9% of participants reached the intended 75% reduction at 3 months (n = 62/347), and 27.4% of participants reached the 75% reduction at 6 months (n = 86/314).
Cigarette Dependence
At baseline, the mean PSCDI score was 13.4 (SD = 3.0; n = 495) with no significant differences between randomized conditions (Supplementary Table S3). PSCDI score decreased for all conditions from baseline to each follow-up visit (Supplementary Figures S1 and S4). Those in the e-cigarette conditions (0 mg/ml, 8 mg/ml, and 36 mg/ml) reported significantly lower PSCDI scores at all follow up visits, compared with the cigarette substitute condition, except for the 8 mg/ml condition at 6 months (Figure 1; Supplementary Figures S1 and S4). These differences remained significant after adjusting for baseline covariates (Supplementary Figures S3 and S4). There were no significant differences in PSCDI score between e-cigarette conditions at any of the follow-up visits for both the unadjusted and adjusted analyses (Supplementary Figure S4).
Total Penn State Cigarette Dependence Index (PSCDI) score and Fagerström Test for Nicotine Dependence (FTND) score, by condition and follow-up visit (unadjusted-ITT). Error bars: bounds of 95% CI (estimated mean ± 1.96 × standard error) for outcome means at each condition and follow-up visit.
When the times per day variable (CPD) was not included in the total PSCDI score (modified baseline mean = 10.0, SD = 2.5), significant differences between the 36 mg/ml condition and the cigarette substitute condition remained at 1, 3, and 6 months for both unadjusted and adjusted analyses. There were also significant differences between the 8 mg/ml and cig sub group at 1 and 3 months across both adjusted and unadjusted analyses (Supplementary Figures S1, S3, and S5).
Among the subgroup of participants who did not reduce by 75% at 1 month (n = 400), PSCDI total scores were significantly lower for the 36 mg/ml condition, compared with the cigarette substitute condition, at all follow-up visits, for both unadjusted and adjusted analysis (Supplementary Figures S6, S7, and S8). These differences remained when evaluating the modified PSCDI score (Supplementary Figures S6, S7, and S9). There were no significant differences in PSCDI total or modified score between any pairwise conditions for those who did reduce their smoking by 75% at 1 month (n = 32) in the unadjusted analyses (Supplementary Figures S10 and S11). Adjusted analyses were not completed for this subgroup due to lack of convergence due to small sample size.
Findings were similar for the FTND to evaluate changes in self-reported dependence, which was not surprising given that the FTND and PSCDI were highly positively correlated (r > 0.7 for all visits, baseline through Visit 10 at 6 months). The mean FTND score at baseline was 5.9 (SD = 1.9; n = 510) with no significant differences between conditions (Supplementary Table S3). Similar to the PSCDI, all conditions experienced a significant reduction in FTND total score from baseline to all follow-up. There was a significant difference between the 36 mg/ml condition and CS condition at all follow-ups, as well as significant differences between the 0 mg/ml e-cigarette condition and the CS conditions at 1, 3, and 6 months, and the 8 mg/ml and CS condition at 1 and 3 months, in both the unadjusted and adjusted models (Supplementary Figures S1, S3, and S12). These differences remained significant when evaluating the modified FTND score, except that there were not significant differences between the 0 mg/ml and CS conditions at 6 months or between the 8 mg/ml and CS conditions at 1 month using the unadjusted models (Supplementary Figures S1, S3, and S13).
Electronic Cigarette Use
At 1 month, participants in the 0 mg/ml condition reported using their e-cigarette 11.84 (SD = 9.16) times per day, participants in the 8 mg/ml condition participants reported using their e-cigarette 10.45 (SD = 6.44) times per day, and participants in the 36 mg/ml condition reported using their e-cigarette 9.46 (SD = 5.35) times per day. Those in the 36 mg/ml condition reported significantly greater times per day at 1 month in both the unadjusted and adjusted models compared to the 8 mg/ml condition (Supplementary Figures S1, S3, and S14). In the adjusted model only, those in the 8 mg/ml condition reported greater puffs per day than the 0 mg/ml condition at 1 month (Supplementary Figures S3 and S14).
Electronic Cigarette Dependence
One month after e-cigarette initiation, the mean PSECDI score was 4.3 (SD = 3.1; n = 305). PSECDI score statistically significantly increased from 1 month to 6 months for only the 36 mg/ml condition (Supplementary Figures S1 and S15). In addition, those in the 36 mg/ml condition had a significantly greater PSECDI score, compared with the 0 mg/ml condition at 6 months, for both unadjusted and adjusted analyses (Figure 2; Supplementary Figures S1, S3, and S15). In the adjusted model only, there was also a significant difference between the 36 mg/ml and the 8 mg/ml conditions at 6 months (Supplementary Figures S3 and S15). These differences were the same when utilizing the modified PSECDI score, however, there was also a significant difference at 3 months between the 36 mg/ml and 0 mg/ml conditions in the adjusted model (Supplementary Figures S1, S3, and S16).
Penn State Electronic Cigarette Dependence Index (PSECDI) Score, Electronic Cigarette Use by condition from 1 to 6 months (Unadjusted—ITT). Error bars: bounds of 95% CI (estimated mean ± 1.96 × standard error) for outcome means at each condition and follow-up visit.
The test–retest reliability of PSECDI is strong in the subpopulation who used the e-cigarettes in the prior 7 days at both visits 9 (at 5 months) and 10 (at 6 months), which includes n = 41 in the 0 mg/ml group, n = 43 in the 8 mg/ml group, and n = 56 in the 36 mg/ml group with the Spearman correlation coefficient between the PSECDI at visits 9 and 10 = +0.78.
Total Dependence
Total dependence (PSCDI + PSECDI) across the trial was assessed by examining scores after initiating e-cigarette use, as baseline scores did not include any e-cigarette use. Compared with 1 month after initiating e-cigarette use, the 0 mg/ml condition reported lower total dependence at 3 and 6 months, the 36 mg/ml condition reported lower total dependence at 3 months, while the 8 mg/ml condition reported no differences from 1 to 3 or 6 months, in the unadjusted model (Figure 3; Supplementary Figures S1 and S17). The adjusted model had a similar result, except that within the 8 mg/ml group, there was a significant difference at 6 months compared with 1 month (Supplementary Figures S3 and S17). There were no between-condition differences for total dependence score across all analyses (Supplementary Figures S1, S3, and S17).
Total Nicotine Dependence Score (PSCDI + PSECDI), Urine Cotinine by condition from baseline to 6 months (Unadjusted—ITT). Error bars: bounds of 95% CI (estimated mean ± 1.96 × standard error) for outcome means at each condition and follow-up visit.
Urine Cotinine Concentration
At baseline, mean cotinine was 2218.9 pg/ng per mg creatinine (SD = 1914.8), with no significant differences between randomized conditions (Supplementary Table S3). At 6 months, the unadjusted mean cotinine level was 1973.73 pg/ng per mg creatinine for the 0 mg/ml condition, 1857.36 pg/ng per mg creatinine for the 8 mg/ml condition, 2155.99 pg/ng per mg creatinine for the 36 mg/ml per mg for the 36 mg/ml condition, and 2169.74 pg/ng per mg creatinine for the CS condition. There were no significant changes from baseline within-condition, and there were no significant between-condition differences at any follow-ups in both the unadjusted and adjusted analyses (Supplementary Figures S1, S3, and S18). However, the log-cotinine of the 36 mg/ml condition at 1 month was significantly higher than that of the 8 mg/ml and 0 mg/ml conditions, unadjusted and adjusted for baseline covariates (Supplementary Tables S1, S3, and S19).
Discussion
Overall, results suggest that participating in a trial to reduce cigarette smoking by using e-cigarettes or a cigarette substitute was associated with significant reductions in self-reported cigarette dependence from baseline to all follow-ups for all conditions using two well-known dependence scales, even when the cigarettes per day item was removed from the scale. According to the PSCDI, participants’ scores changed from high cigarette dependence to moderate cigarette dependence from baseline to follow-up. Participants in the e-cigarette conditions (0, 8, and 36 mg/ml) reported significantly greater reductions in cigarette dependence at all follow-up visits, compared with participants in the CS condition. This result is similar to another study in which cigarette dependence decreased after initiating e-cigarette use.24 Importantly, we did not find differences in cigarette dependence by the e-cigarette nicotine concentration used, suggesting that using an e-cigarette in general promotes changes in smoking behaviors that result in lower self-reported cigarette dependence.
For e-cigarette dependence, participants randomized to an e-cigarette condition reported low dependence on e-cigarettes according to the PSECDI. While it could be hypothesized that the low dependence on e-cigarettes could be attributed to poor delivery of nicotine, previous analysis of the nicotine delivery of the device, and liquids used in the current study showed that the devices were capable of delivering nicotine, with the 36 mg/ml e-cigarette capable of delivering nicotine similar to conventional cigarettes.10 Not surprisingly, those in the 36 mg/ml condition reported significantly greater e-cigarette dependence than those in the 0 mg/ml condition by the 6 month follow-up visit.
Total self-reported dependence (cigarettes and e-cigarettes) significantly increased from baseline to one month after e-cigarette initiation for all e-cigarette conditions. By the 3 and 6 month follow-ups, once participants had adjusted to using two products, total self-reported dependence decreased, and was no longer different than baseline dependence (when participants were only using cigarettes). Importantly, using a high concentration e-cigarette (36 mg/ml) did not result in greater total dependence, suggesting that users adjust their use of cigarettes and e-cigarettes to obtain their preferred amount of nicotine. This is supported by the cotinine data, which did not significantly change from baseline to follow-up and did not differ between conditions at any timepoint.
Strengths of this study included a randomized design including longitudinal data collection over a 6-month period after e-cigarette initiation with a device with known nicotine delivery. In addition, this study used two different self-reported measures of nicotine dependence and found consistent findings across these measures, which could be attributed to the strong correlation between measures. Also, while the study reported on the ITT analyses, sensitivity analyses were performed and the broadly similar results are presented in the supplementary data. Finally, the study analyzed urine cotinine as a biomarker for total dependence. This study was limited by the use of one type of e-cigarette with free-base nicotine. These results may not translate to newer e-cigarettes with protonated nicotine.
In conclusion, while it has been hypothesized that initiating use of an e-cigarette in addition to cigarette smoking could increase overall nicotine dependence, in this study we found that over a 6-month period, participants initiating e-cigarette use to reduce cigarette smoking reported reductions in cigarette dependence, while exhibiting low dependence on e-cigarettes, with no increases in total self-reported dependence.
Supplementary Material
A Contributorship Form detailing each author’s specific involvement with this content, as well as any supplementary data, are available online at https://academic.oup.com/ntr.
Funding
This research was supported by P50DA036105 and U54DA036105 from the National Institute on Drug Abuse of the National Institutes of Health and the Center for Tobacco Products of the U.S. Food and Drug Administration. Data collection was supported by UL1TR002649 at Virginia Commonwealth University and by UL1TR002014 at Penn State University from the National Center for Advancing Translational Sciences of the National Institutes of Health. Funding sources had no other role other than financial support. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Food and Drug Administration.
Declaration of Interests
JF reports a research grant, receipt of study medication, personal fees and non-financial support from Pfizer Inc., outside the submitted work. He has also purchased ENDS products for use in clinical trials. CB has previously undertaken trials of e-cigarettes for smoking cessation, requiring purchase of ENDS products and nicotine patches, outside the submitted work. None of the above (non-author) parties had any role in the design, conduct, analysis or interpretation of the trial findings, or writing of the resulting publication. TE is a paid consultant in litigation against the tobacco industry and also the electronic cigarette industry and is named on one patent for a device that measures the puffing behavior of electronic cigarette users and on another patent for a smartphone app that determines electronic cigarette device and liquid characteristics and a third patent application for a smoking cessation intervention.
Availability of Data
With publication, requests for de-identified individual participant data and/or study documents (data dictionary, protocol, statistical analysis plan, measures/manuals/informed consent documentation) will be considered. The requestor must submit a 1-page abstract of their proposed research, including purpose, analytical plan, and dissemination plans. The Executive Leadership Committee will review the abstract and decide based on the individual merits. Review criteria and prioritization of projects include potential of the proposed work to advance public health, qualifications of the applicant, the potential for publication, the potential for future funding, and enhancing the scientific, geographic, and demographic diversity of the research portfolio. Following abstract approval, requestors must receive institutional ethics approval or confirmation of exempt status for the proposed research. An executed data use agreement must be completed prior to data distribution. Contact is through Dr. Caroline Cobb ([email protected]).
References
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