RESEARCH REPORT
`
`doi:10.1111/add.12917
`
`Is the use of electronic cigarettes while smoking
`associated with smoking cessation attempts, cessation
`and reduced cigarette consumption? A survey with
`a 1-year follow-up
`
`Leonie S. Brose1, Sara C. Hitchman1, Jamie Brown2, Robert West2 & Ann McNeill1
`
`Department of Addictions, UK Centre for Tobacco and Alcohol Studies (UKCTAS), Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London,
`UK1 and Health Behaviour Research Centre, University College London, London, UK2
`
`ABSTRACT
`
`Aims To use a unique longitudinal data set to assess the association between e-cigarette use while smoking with
`smoking cessation attempts, cessation and substantial reduction, taking into account frequency of use and key potential
`confounders. Design Web-based survey, baseline November/December 2012, 1-year follow-up in December 2013.
`Setting Great Britain. Participants National general population sample of 4064 adult smokers, with 1759 (43%)
`followed-up. Measurements Main outcome measures were cessation attempt, cessation and substantial reduction
`(≥50% from baseline to follow-up) of cigarettes per day (CPD). In logistic regression models, cessation attempt in the last
`year (analysis n = 1473) and smoking status (n = 1656) at follow-up were regressed on to baseline e-cigarette use (none,
`non-daily, daily) while adjusting for baseline socio-demographics, dependence and nicotine replacement (NRT) use.
`Substantial reduction (n = 1042) was regressed on to follow-up e-cigarette use while adjusting for baseline socio-
`demographics and dependence and follow-up NRT use. Findings Compared with non-use, daily e-cigarette use at
`baseline was associated with increased cessation attempts [odds ratio (OR) = 2.11, 95% confidence interval (CI) = 1.24–3.58,
`P = 0.006], but not with cessation at follow-up (OR = 0.62, 95% CI = 0.28–1.37, P = 0.24). Non-daily use was not
`associated with cessation attempts or cessation. Daily e-cigarette use at follow-up was associated with increased odds of
`substantial reduction (OR = 2.49, 95% CI = 1.14–5.45, P = 0.02), non-daily use was not. Conclusions Daily use of
`e-cigarettes while smoking appears to be associated with subsequent increases in rates of attempting to stop smoking
`and reducing smoking, but not with smoking cessation. Non-daily use of e-cigarettes while smoking does not appear
`to be associated with cessation attempts, cessation or reduced smoking.
`
`Keywords
`quit attempts.
`
`Electronic cigarettes, electronic nicotine delivery systems, harm reduction, smoking cessation, tobacco,
`
`Correspondence to: Leonie Brose, Department of Addictions, UK Centre for Tobacco and Alcohol Studies (UKCTAS), Institute of Psychiatry, Psychology and Neu-
`roscience, King’s College London, 4 Windsor Walk, London SE5 8BB, UK. E-mail: leonie.brose@kcl.ac.uk
`Submitted 22 July 2014; initial review completed 28 October 2014; final version accepted 4 March 2015
`
`INTRODUCTION
`
`In electronic cigarettes, a battery-powered heating element
`heats a solution, usually containing nicotine, to produce a
`aerosol. The use of e-cigarettes has increased dramatically
`in the last few years; users are almost exclusively smokers
`or former smokers, with fewer than 1% of never-smokers
`using them regularly [1–8]. The vast majority of e-cigarette
`users report using them to stop smoking tobacco [6,9] and
`in England,
`for example, smokers attempting to stop
`smoking now use e-cigarettes more often than any other
`
`aid, including nicotine replacement therapy (NRT) [10].
`Smoking prevalence in England has been declining from
`20% in 2012 to 18.4% in 2014 (up to October), and in
`2014 smoking cessation rates were the highest since at least
`2008 [10,11]. This simultaneous increase in e-cigarette use
`and cessation may be coincidental, and it is therefore vitally
`important for longitudinal studies to be conducted to assess
`the impact of e-cigarette usage on quitting behaviour.
`Evidence on NRT supports the possibility of a link be-
`tween using e-cigarettes that deliver nicotine and attempts
`to stop smoking. Use of NRT while smoking is associated
`
`Addiction, 110, 1160–1168
`© 2015 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.
`This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,
`provided the original work is properly cited.
`
`JLI Ex. 2022, Page 1 of 9
`
`

`

`with a small reduction in cigarette consumption and a sig-
`nificant increase in the likelihood of subsequent smoking
`cessation even in smokers without intentions to stop
`smoking [12,13]. Very little evidence is available to evalu-
`ate whether a similar pattern is observed with use of
`e-cigarettes by smokers and only a handful of studies have
`used any longitudinal data on e-cigarette use and smoking
`behaviour. A trial in smokers not intending to quit com-
`pared e-cigarettes with no nicotine with e-cigarettes with
`two different nicotine strengths and found that all led to
`significant reduction in tobacco consumption, and that sig-
`nificantly more smokers using the e-cigarettes with nicotine
`quit smoking [14]. In a web-based survey of a national sam-
`ple of current smokers in the United States who were
`followed-up 1 year later, e-cigarette use at baseline did not
`predict smoking cessation 1 year later [15]. Data from two
`waves of the International Tobacco Control survey showed
`that smokers who were using e-cigarettes at follow-up were
`more likely to have reduced their cigarette consumption
`than non-users, but cessation did not differ [9]. Among a
`cohort of young adults in the United States, those who
`had used e-cigarettes at least once in the month before
`baseline had a similar likelihood of quitting smoking 1 year
`later to those who had never used e-cigarettes [16]. Unfor-
`tunately, none of these analyses distinguished frequency of
`use and many defined any trial or experimentation, even
`if just once, as use, so it is unclear what proportion were
`actually using e-cigarettes with any regularity. Regular
`use is likely to have a stronger effect on smoking behaviour
`than trial or infrequent use. When separating regular from
`intermittent use, respondents who had used e-cigarettes
`daily for at least a month were far more likely to have quit
`smoking than those who had not used them, whereas there
`was no such association of quitting with intermittent
`e-cigarette use [17]. This highlights the importance of
`disentangling use from trial; however, the intensity of
`e-cigarette use had to be determined retrospectively. Because
`use is more common in smokers making quit attempts and
`all those who had quit must have made a quit attempt, this
`method confounds e-cigarette use with quit attempts.
`To address the question as to whether use of e-cigarettes
`by smokers is associated with smoking behaviour change,
`this study used a web-based national sample from the
`general population in Great Britain with a 1-year follow-up.
`We used the two waves of survey data to assess the
`association of:
`1. daily, non-daily and non-use of e-cigarettes in
`smokers at baseline with smoking cessation at-
`tempts during follow-up (quit attempt analysis);
`2. daily, non-daily and non-use of e-cigarettes in
`smokers at baseline with smoking cessation at
`follow-up (cessation analysis); and
`3. daily, non-daily and non-use of e-cigarette use at
`follow-up with substantial reduction in tobacco
`
`E-cigarettes, cessation, attempts, reduction
`
`1161
`
`cigarette consumption from baseline to follow-up. First
`(primary reduction analysis), we excluded those using
`e-cigarettes at baseline because, if use of e-cigarettes is
`associated with reduction in tobacco consumption,
`respondents may already have reduced their consump-
`tion at baseline, making detection of reduction from base-
`line to follow-up less likely. As it could also be argued that
`e-cigarette using smokers should be reducing further, we
`then also included smokers using e-cigarettes at both
`time-points (secondary reduction analysis).
`
`METHODS
`
`Design
`
`This was a web-based longitudinal survey, with baseline data
`collected in November/December 2012 and follow-up in
`December 2013. University College London ethics commit-
`tee confirmed that specific approval was not required. Data
`were anonymized before being passed to the research team.
`
`Sample
`
`The study sample was recruited from an online panel man-
`aged by Ipsos MORI. Ipsos MORI is the second largest mar-
`ket research organization in the United Kingdom. Members
`were invited by e-mail to participate in an online study
`about smoking. By completing the survey respondents
`would earn points which could be redeemed against high
`street vouchers or used to enter a prize draw. Each respon-
`dent logged into their Ipsos MORI online account and was
`asked a screening question about their past-year smoking
`status. Between November and December 2012, a total of
`23 785 respondents were asked the screening question of
`whom 25.9% (n = 6165) had smoked in the past year. This
`proportion was similar to that identified by a face-to-face
`survey of representative samples of the population in
`England during 2012 [10]. Five thousand respondents
`completed the survey (4064 current smokers). They were
`re-contacted 1 year later for follow-up. Follow-up achieved
`a response rate of 43.6% overall (n = 2182) and of 43.3%
`among baseline smokers (n = 1759). Figure 1 shows the
`selection of analyses samples for the three main outcomes.
`The secondary reduction analysis included smokers
`using e-cigarettes at both time-points (n = 1005).
`
`Measures
`
`Baseline and follow-up surveys included a range of ques-
`tions on socio-demographic and smoking characteristics,
`nicotine use, quit attempts and health status. The current
`analyses included the following measures, fully presented
`in the Supporting information, Appendix.
`
`© 2015 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.
`
`Addiction, 110, 1160–1168
`
` 13600443, 2015, 7, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/add.12917, Wiley Online Library on [27/02/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
`
`JLI Ex. 2022, Page 2 of 9
`
`

`

`1162
`
`Leonie S. Brose et al.
`
`Figure 1 Sample flowchart. Grey boxes indicate exclusions. Bold numbers in brackets indicate the three different outcomes. CPD = cigarettes per day
`
`Outcome measures
`
`1. Quit attempts: smokers and recent ex-smokers were
`asked about the number of attempts to stop they
`had made in the previous year. Those reporting at
`least one attempt and 37 respondents who did not
`report an attempt but had stopped smoking be-
`tween baseline and follow-up were coded as having
`made an attempt.
`2. Cessation: smoking status was assessed at baseline
`and follow-up in all respondents. Change from being
`a smoker at baseline to being an ex-smoker at
`follow-up was coded as cessation.
`3. Substantial reduction: smoking characteristics in-
`cluded the number of cigarettes smoked per day
`(CPD) for daily smokers and the number of cigarettes
`per week for non-daily smokers. Number of cigarettes
`per week were divided by seven to calculate CPD. Sub-
`stantial reduction was defined as a reduction by at
`least 50% from baseline CPD to follow-up CPD [13].
`
`Socio-demographic characteristics, dependence and nicotine use
`
`All characteristics were measured at baseline and follow-
`up; the Analysis section explains which time-points were
`used in each analysis. Respondents provided their age, gen-
`der and highest level of formal education (see Supporting
`information, Appendix for questions and response options).
`Level of education was collapsed into those with any uni-
`versity education (including ‘some university’) and those
`without university education.
`
`Strength of urges to smoke (SUTS) can be used as a
`measure of dependence and is a strong predictor of suc-
`cessful cessation in population samples [18,19]. The
`SUTS was included rather than the Fagerstrom Test of
`Nicotine Dependence (FTND [20]) or the subset of FTND
`questions used for the Heaviness of Smoking Index (HSI
`[21]) for two reasons. One reason was that the SUTS
`has outperformed the FTND in predicting failure of quit
`attempts [18]; the second was that we hypothesized
`e-cigarette use to have an effect on smoking behaviour,
`specifically on the number of cigarettes smoked, one of
`the two components of the HSI, which would limit the
`comparability of scores across users and non-users of
`e-cigarettes.
`Smokers and recent ex-smokers also reported if they
`were using NRT for any reason (not necessarily for a quit
`attempt), and how frequently they used NRT products. Re-
`spondents who had heard of e-cigarettes were asked
`whether they had ever tried one and, if they had, how often
`they were currently using an e-cigarette. For the main
`analyses, frequency of use of NRT and e-cigarettes were
`each collapsed into daily, non-daily and none.
`
`Analysis
`
`Respondents who completed the follow-up were compared
`with those who did not respond to the invitation in terms of
`socio-demographic characteristics, nicotine use and depen-
`dence using t-tests or analyses of variance (ANOVAs) for
`continuous data and χ2 statistics for categorical data.
`
`© 2015 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.
`
`Addiction, 110, 1160–1168
`
` 13600443, 2015, 7, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/add.12917, Wiley Online Library on [27/02/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
`
`JLI Ex. 2022, Page 3 of 9
`
`

`

`In the main logistic regression models, reports of at
`least one quit attempt in the last year and smoking status
`at follow-up were regressed onto baseline e-cigarette use
`(none, non-daily, daily) while adjusting for baseline age,
`gender, education, dependence (SUTS) and NRT use. Simi-
`lar logistic regression models were used to analyse substan-
`tial reduction in CPD, but using NRT and e-cigarette use at
`follow-up, not baseline. Because only a small number of re-
`spondents overall had reduced substantially and 26.1%
`(n = 322) of the sample for the primary reduction analysis
`had increased consumption, the quantitative change in
`CPD was analysed using multiple linear
`regression,
`adjusting for the same characteristics as in the logistic re-
`gressions but dummy-coding NRT and e-cigarette use.
`As sensitivity analyses, we collapsed daily and non-daily
`e-cigarette use categories and conducted logistic regres-
`sions using the collapsed variable while adjusting as in
`the main models.
`SPSS version 21 was used for all analyses.
`
`RESULTS
`
`Prevalence and characteristics of users of e-cigarettes in
`the baseline survey have been reported previously [22].
`In brief, more than 90% of current smokers and recent
`ex-smokers were aware of e-cigarettes, approximately a
`third had ever used e-cigarettes and a fifth was currently
`using them. Daily use was more common in recent ex-
`smokers (46% of current users) than in current smokers
`(23%). Age and gender split did not differ between users
`and non-users. Among smokers, e-cigarette users had a
`higher socio-economic status than non-users and were
`more likely to have made a quit attempt in the past year.
`Users reported higher tobacco cigarette consumption than
`non-users [22].
`Follow-up respondents differed from respondents lost
`to follow-up on some baseline characteristics. Those lost
`to follow-up were younger and women were more likely
`to be lost to follow-up than men. Frequency of NRT use
`differed; those who used NRT less than daily were more
`probably lost
`to follow-up (Supporting information,
`Table S1). Education, dependence and frequency of
`e-cigarette use did not differ.
`A range of e-cigarettes were used and will be reported
`in a separate publication [23]; briefly, a majority used‘ first
`generation’ e-cigarettes that were cigarette-like in appear-
`ance (‘cigalikes’).
`
`Quit attempts
`
`Overall, 46.2% (n = 680) of respondents in the analysis
`made a quit attempt; 43.7% (n = 508) of non-users of
`e-cigarettes, 52.5% (n = 124) of non-daily e-cigarette users
`and 64.9% (n = 48) of daily users. Sample characteristics
`
`E-cigarettes, cessation, attempts, reduction
`
`1163
`
`are presented in Table 1. In unadjusted analysis, both daily
`[odds ratio (OR) = 2.38, 95% confidence interval (CI)
`= 1.46–3.89, P = 0.001] and non-daily e-cigarette use
`(OR = 1.43, 95% CI = 1.08–1.89, P = 0.013) were associ-
`ated with increased likelihood of quit attempts compared
`with non-use.
`While adjusting for socio-demographic characteristics,
`dependence and NRT use, daily e-cigarette use at baseline
`was associated with increased odds of making an attempt
`to stop smoking compared with non-use. Non-daily
`e-cigarette users did not differ significantly from non-users
`(Table 1). There was a strong association of quit attempts
`with daily and non-daily NRT use. In the sensitivity analysis
`that collapsed daily and non-daily use, e-cigarette use
`remained associated with quit attempts (OR = 1.35, 95%
`CI = 1.03–1.77, P = 0.03).
`
`Smoking cessation
`
`Among smokers not using e-cigarettes at baseline, 168
`(12.9%) quit smoking, compared with 25 non-daily
`users (9.5%) and seven daily users (8.1%). Sample char-
`acteristics are presented in Table 1. Unadjusted results
`showed no significant association with cessation for daily
`(OR = 0.60, 95% CI = 0.27–1.32, P = 0.21) or non-daily
`e-cigarette use (OR = 0.71, 95% CI = 0.46–1.11, P = 0.13)
`compared with non-use.
`While adjusting for baseline characteristics, neither
`daily nor non-daily use of e-cigarette at baseline was asso-
`ciated with cessation at follow-up and nor was NRT use
`(Table 1). Considering any e-cigarette use (daily and
`non-daily), we found non-significantly reduced cessation
`(adjusted OR = 0.73, 95% CI = 0.48–1.09, P = 0.13).
`
`Reduction in tobacco cigarette consumption
`
`Overall, 6.2% (n = 65) of respondents reduced their con-
`sumption substantially. Forty-four (5.7%) smokers not
`using e-cigarettes at
`follow-up, 11 (5.5%) non-daily
`e-cigarette users and 10 (13.9%) daily users reduced sub-
`stantially. Sample characteristics are included in Table 2.
`In unadjusted analysis of substantial reduction, daily use
`of e-cigarettes at follow-up compared with non-use was as-
`sociated with increased likelihood of reduction (OR = 2.66,
`95% CI = 1.28–5.54, P = 0.009); non-daily use was not as-
`sociated with substantial reduction (OR = 0.96, 95%
`CI = 0.48–1.89, P = 0.90).
`In the primary reduction analysis and while
`adjusting for other relevant characteristics, daily use of
`e-cigarettes remained associated with increased likeli-
`hood of reduction while non-daily use was not associ-
`ated significantly with substantial reduction (Table 2).
`Neither daily nor non-daily NRT use was associated with
`substantial reduction (Table 2).
`
`© 2015 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.
`
`Addiction, 110, 1160–1168
`
` 13600443, 2015, 7, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/add.12917, Wiley Online Library on [27/02/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
`
`JLI Ex. 2022, Page 4 of 9
`
`

`

`1164
`
`Leonie S. Brose et al.
`
`Table 1 Logistic regression analyses of association of baseline socio-demographics, dependence [strength of urges to smoke (SUTS)] and
`non-cigarette nicotine intake with quit attempts and smoking cessation during follow-up.
`
`Quit attempt (n = 1473, of whom n = 680
`made attempt)
`
`Cessation (n = 1656, of whom n = 200
`stopped smoking)
`
`n(%)/mean (SD)
`
`OR
`
`95% CI
`
`P
`
`n(%)/mean (SD)
`
`OR
`
`95% CI
`
`P
`
`Agea
`Gender
`
`Female
`Male
`Level of education No HE
`Some HE
`
`SUTSb
`NRT use
`
`E-cig use
`
`None
`Non-daily
`Daily
`None
`Non-daily
`Daily
`
`46.6 (15.2)
`642 (43.6)
`831 (56.4)
`958 (65.0)
`515 (35.0)
`2.2 (1.1)
`1212 (82.3)
`161 (10.9)
`100 (6.8)
`1163 (79.0)
`236 (16.0)
`74 (5.0)
`
`0.83
`1
`0.84
`1
`0.83
`1.06
`1
`4.21
`9.43
`1
`1.18
`2.11
`
`0.77–0.90
`
`0.67–1.05
`
`0.66–1.05
`0.96–1.18
`
`0.12
`
`0.12
`0.25
`
`<0.001 45.7 (15.3)
`720 (43.5)
`936 (56.5)
`1074 (64.9)
`582 (35.1)
`2.2 (1.1)
`1339 (80.9)
`193 (11.7)
`124 (7.5)
`1307 (78.9)
`263 (15.9)
`86 (5.2)
`
`<0.001
`2.89–6.14
`5.17–17.23 <0.001
`
`0.87–1.60
`1.24–3.58
`
`0.29
`0.006
`
`0.88
`1
`0.86
`1
`0.76
`0.74
`1
`1.39
`1.67
`1
`0.77
`0.62
`
`0.79–0.97
`
`0.009
`
`0.64–1.16
`
`0.32
`
`0.55–1.05
`0.099
`0.64–0.86 <0.001
`
`0.88–2.21
`0.98–2.84
`
`0.16
`0.062
`
`0.49–1.21
`0.28–1.37
`
`0.25
`0.24
`
`aMean and standard deviation (SD) presented, odds ratios (OR) for single year raised to the power of 10 to present per 10-year increase.
`bStrengths of urges to smoke, possible range 0 ‘no urges’ to 5 ‘extremely strong urges’, mean and SD presented, OR per unit increase. HE = higher education;
`NRT = nicotine replacement therapy.
`
`Table 2 Logistic regression analyses of association of socio-demographics, dependence (SUTS) and non-cigarette nicotine intake at follow-
`up with substantial reduction in cigarettes per day (CPD).
`
`Reduction (n = 1042, of whom n = 65 reduced CPD by ≥50% of baseline)
`
`Baseline agea
`Gender
`
`Baseline level of education
`
`Baseline SUTSb
`Follow-up NRT use
`
`Follow-up e-cig use
`
`n(%) /mean (SD)
`
`Female
`Male
`No HE
`Some HE
`
`None
`Non-daily
`Daily
`None
`Non-daily
`Daily
`
`46.7 (15.3)
`455 (43.7)
`587 (56.3)
`706 (67.8)
`336 (32.3)
`2.1 (1.1)
`909 (87.2)
`83 (8.0)
`50 (4.8)
`769 (73.8)
`201 (19.3)
`72 (6.9)
`
`OR
`
`0.99
`1
`0.51
`1
`0.90
`0.76
`1
`1.50
`1.66
`1
`0.85
`2.49
`
`95% CI
`
`0.78 to 1.08
`
`0.30 to 0.86
`
`0.52 to 1.57
`0.59 to 0.98
`
`0.61 to 3.70
`0.58 to 4.70
`
`0.43 to 1.71
`1.14 to 5.45
`
`P
`
`0.30
`
`0.012
`
`0.71
`0.031
`
`0.38
`0.34
`
`0.66
`0.022
`
`aMean and standard deviation (SD) presented, odds ratio (OR) for single year raised to the power of 10 to present per 10-year increase.
`bStrengths of urges to smoke, possible range 0 ‘no urges’ to 5 ‘extremely strong urges’, mean and SD presented, OR per unit increase. HE = higher education
`NRT = nicotine replacement therapy.
`
`When daily and non-daily e-cigarette use were
`collapsed, this was not significantly different from non-use
`(OR = 1.23, 95% CI = 0.70–2.15, P = 0.48). Secondary
`analysis in those using e-cigarettes at both time-points, ad-
`justed for the same variables as the primary analysis,
`showed that compared with non-use at
`follow-up
`(n = 769), daily e-cigarette use (n = 79) was again associ-
`ated with substantial
`reduction (OR = 4.19, 95%
`CI = 2.13–8.24, P < 0.001), while non-daily use (n = 157)
`was not (OR = 1.02, 95% CI = 0.48–2.19, P = 0.96).
`
`Linear regression on quantitative change in CPD indi-
`cated that the difference in change between those using
`e-cigarettes daily and those not using them at follow-up
`(Table 3) was significant while adjusting for baseline age,
`gender, education, dependence and follow-up NRT use
`(SE)] = –1.55 (0.65), β = –0.08,
`{[B [standard error
`P = 0.02}. The difference in change between non-daily
`users and non-users was not significant [B (SE) = 0.28
`(0.41), β = 0.02, P = 0.50] Secondary analysis in those
`using e-cigarettes at both time-points suggested a larger
`
`© 2015 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.
`
`Addiction, 110, 1160–1168
`
` 13600443, 2015, 7, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/add.12917, Wiley Online Library on [27/02/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
`
`JLI Ex. 2022, Page 5 of 9
`
`

`

`Table 3 Cigarettes per day by frequency of e-cigarette use.
`
`Mean (SD) cigarettes per day
`
`Follow-up e-cigarette use
`
`Baseline
`
`Follow-up
`
`Change
`
`None
`
`13.3 (8.9)
`
`13.5 (8.9)
`
`0.2 (4.7)
`
`Primary analysis, use initiated after baseline
`
`Non-daily
`Daily
`
`13.5 (7.9)
`14.3 (9.8)
`
`13.9 (8.9)
`13.0 (9.4)
`
`0.4 (5.9)
`–1.4 (6.8)
`
`Secondary analysis, some use at baseline
`
`Non-daily
`Daily
`
`14.9 (8.9)
`14.1 (7.9)
`
`15.0 (8.0)
`11.5 (7.2)
`
`0.09 (5.4)
`–2.5 (6.1)
`
`SD = standard deviation.
`
`difference between changes for daily users and non-users
`[Table 3, B (SE) = –2.58 (0.61), β = –0.14, P < 0.001,
`adjusted as before], whereas the difference in change
`between non-daily users and non-users remained small
`[B (SE) = –0.08 (0.44), β = –0.01, P = 0.85].
`
`DISCUSSION
`
`In a web-based national sample of smokers from the gen-
`eral population, those using e-cigarettes daily at baseline
`were more likely to have attempted to stop smoking when
`followed-up a year
`later
`than smokers not using
`e-cigarettes, but neither non-daily nor daily e-cigarette
`use was associated with smoking cessation during follow-
`up. Smokers using e-cigarettes daily when followed-up were
`more likely to have achieved at least 50% reduction in
`tobacco cigarette consumption from baseline. Less frequent
`e-cigarette use did not have a significant effect on consump-
`tion. Using e-cigarettes every day while smoking increased
`the prevalence of substantial reduction in tobacco con-
`sumption, and this was not restricted to smokers who had
`recently taken up e-cigarettes, suggesting that persistent
`users continue to reduce consumption over time. Reduc-
`tion in consumption has been reported previously [14].
`This increase in substantial reduction was reflected in a
`small overall reduction in the number of cigarettes smoked
`in daily e-cigarette users. The size of the reduction was
`similar to that seen in smokers using NRT [12]. NRT itself
`showed a similar size of positive association with subse-
`quent cessation to that found in previous studies [12], but
`in this case it was not statistically significant using a
`conventional alpha (P = 0.067 two-tailed).
`The use of NRT while smoking is supported as a harm
`reduction approach by national guidance in the United
`Kingdom [24]. It reduces tobacco harm not only by
`increasing cessation and reducing consumption but also
`by reducing the amount of nicotine taken in from each
`cigarette [25], which is likely to be accompanied by a re-
`duction in intake of toxins [26,27]. Although it remains
`
`E-cigarettes, cessation, attempts, reduction
`
`1165
`
`to be tested, it appears possible that the use of e-cigarettes
`while smoking similarly reduces intake from each cigarette,
`thus supporting tobacco harm reduction. Although long-
`term data on safety of e-cigarettes are not yet available,
`toxicology testing suggests that they will be considerably
`safer than tobacco cigarettes [28], although they may be
`less safe than NRT, which is licensed as medicine.
`Smoking cessation rates in England were higher in
`2014 than in previous years. Generally, cessation rates in
`a population can be increased by encouraging as many
`smokers as possible to make quit attempts and to use the
`most effective support in each of these attempts. The cur-
`rent data indicate that e-cigarettes were associated with
`more smokers attempting to stop smoking. We found no
`evidence that e-cigarette use while smoking increased sub-
`sequent smoking cessation. This is in line with previous
`findings [9,15,16], although in one recent study intense
`long-term use was associated with increased cessation
`[17]. The present analyses extend the evidence by
`assessing use prospectively, thus avoiding confounding
`with quit attempts (otherwise e-cigarette use may be
`mainly a marker of having made a quit attempt) and by
`assessing quit attempts, cessation and reduction separately.
`Further research on the link between smoking cessation
`rates and e-cigarette use is warranted.
`Importantly, the current sample used e-cigarettes for
`any reason, not necessarily to stop smoking, so the results
`cannot be used to derive statements on their effectiveness
`as cessation aids. Few studies have looked at e-cigarettes
`as cessation aids. One randomized controlled trial indicated
`that the particular e-cigarette used in the trial was of
`similar effectiveness as nicotine patches in supporting
`abstinence [29]. Use and effects of different devices in the
`general population are likely to differ from those in con-
`trolled trials and samples of dedicated e-cigarette users
`may differ from other users in the general population. A re-
`cent study using a representative population sample found
`that smokers who used e-cigarettes in an attempt to stop
`smoking were more likely to report continued abstinence
`than those using NRT without prescription or no aids
`[30]. Further high-quality longitudinal studies are needed
`on e-cigarettes as cessation aids. Future research should
`also evaluate the impact of continued use of e-cigarettes
`on smoking behaviour, as we were only able to provide
`snapshots of use at two time-points.
`Further evidence is needed on differences between the
`numerous types of e-cigarettes, as products vary widely
`in their appearance, function, content, marketing and nic-
`otine delivery [31–34], and use and effects on smoking will
`vary considerably across different types. In this sample, the
`majority were using cigarette-like products. These have
`been found to deliver less nicotine than more recently
`developed products [22,32,35], and in a sample of ex-
`smokers who had quit using e-cigarettes all had used more
`
`© 2015 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.
`
`Addiction, 110, 1160–1168
`
` 13600443, 2015, 7, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/add.12917, Wiley Online Library on [27/02/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
`
`JLI Ex. 2022, Page 6 of 9
`
`

`

`1166
`
`Leonie S. Brose et al.
`
`recently developed products [36], indicating that cigarette-
`like e-cigarettes may be less helpful.
`Several limitations of the study should be noted. Follow-
`up rate was 43%, resulting in small sample sizes for some
`analysis. Respondents who were followed-up differed from
`those not followed-up on some demographic variables, spe-
`cifically age and gender, potentially reducing the generaliz-
`ability to younger and female smokers. However, key
`smoking characteristics and e-cigarette use were not asso-
`ciated with follow-up. The survey did not include questions
`on the duration of use, so non-daily e-cigarette users will
`have included people who had just tried e-cigarettes once
`or twice, as well as occasional users. This also means that
`we did not assess if respondents continued to use
`e-cigarettes throughout the follow-up period and not all
`baseline users may have continued to use them. Also, those
`initiating e-cigarette use during the follow-up period were
`included with baseline non-users. Any short-term use of
`e-cigarettes around baseline and uptake during follow-up
`will therefore have led to an underestimation of their effects
`on quit attempts and cessation. Additionally, the baseline
`sample including only smokers would have excluded any
`ex-smokers who had used e-cigarettes and successfully
`quit, thus potentially biasing the sample in favour of ‘treat-
`ment failures’. The definition of cessation did not include a
`minimum time of abstinence, but relied upon respondents’
`self-report. However, this method avoids recall bias, and in
`population surveys the risk of misreporting is reduced, as
`there is no expectation to report cessation [37]. The online
`recruitment method is likely to have led to some selection
`bias, as internet use is linked to socio-economic status
`and age; however, the socio-economic divide has narrowed
`considerably between 2011 and 2013 [38]. The sample
`was self-selected in so far as participants had volunteered
`for a market research company web panel; nevertheless,
`the overall sample characteristics were broadly similar to
`those of representative samples from a national household
`survey [22,39].
`The recruitment method also represents a strength, as
`in contrast to many early studies of e-cigarettes that
`recruited from e-cigarette interest groups (e.g. [33,40]),
`recruitment was not from self-selected populations with
`decidedly positive attitudes towards the devices. Thus, the
`association between their use and changes in smoking be-
`haviour found in this study is expected to be more widely
`generalizable. The present survey has overcome another
`limitation of the very small number of previous longitudi-
`nal studies by separating regular and occasional use. More
`frequent use showed an effect on smoking behaviour
`wh