ArticlePDF Available

Stopping use of E-cigarettes and smoking combustible cigarettes: findings from a large longitudinal digital smoking cessation intervention study in the United States

Authors:
  • University of Massachusetts Chan Medical School
  • UMass Chan Medical School

Abstract and Figures

Objective Digital interventions have been widely implemented to promote tobacco cessation. However, implementations of these interventions have not yet considered how participants’ e-cigarette use may influence their quitting outcomes. We explored the association of e-cigarette use and quitting smoking within the context of a study testing a digital tobacco cessation intervention among individuals in the United States who were 18 years and older, smoked combustible cigarettes, and enrolled in the intervention between August 2017 and March 2019. Results We identified four e-cigarette user groups (n = 990) based on the participants’ baseline and six-month e-cigarette use (non-users, n = 621; recently started users, n = 60; sustained users, n = 187; recently stopped users, n = 122). A multiple logistic regression was used to estimate the adjusted odds ratios (AOR) of six-month quit outcome and the e-cigarette user groups. Compared to e-cigarette non-users, the odds of quitting smoking were significantly higher among recently stopped users (AOR = 1.68, 95% CI [1.06, 2.67], p = 0.03). Participants who were most successful at quitting combustible cigarettes also stopped using e-cigarettes at follow-up, although many sustained using both products. Findings suggest that digital tobacco cessation interventions may carefully consider how to promote e-cigarette use cessation among participants who successfully quit smoking. Trial registration ClinicalTrials.gov identifier NCT03224520 (July 21, 2017).
This content is subject to copyright. Terms and conditions apply.
RESEARCH NOTE Open Access
© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use,
sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and
the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this
article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included
in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Lee et al. BMC Research Notes (2024) 17:276
https://doi.org/10.1186/s13104-024-06939-w BMC Research Notes
*Correspondence:
Donghee N. Lee
donghee.lee10@umassmed.edu
1Department of Population and Quantitative Health Sciences, Division
of Preventive and Behavioral Medicine, UMass Chan Medical School, 368
Plantation Street, Worcester, MA, USA01605
2Department of Population and Quantitative Health Sciences, Division
of Health Informatics and Implementation Science, UMass Chan Medical
School, 368 Plantation Street, Worcester, MA, USA01605
3Department of Population and Quantitative Health Sciences, Division of
Biostatistics and Health Services Research, Measurement and Outcome
Section, Department of Obstetrics and Gynecology, UMass Chan Medical
School, 368 Plantation St., Worcester, MA, USA01605
Abstract
Objective Digital interventions have been widely implemented to promote tobacco cessation. However,
implementations of these interventions have not yet considered how participants’ e-cigarette use may inuence their
quitting outcomes. We explored the association of e-cigarette use and quitting smoking within the context of a study
testing a digital tobacco cessation intervention among individuals in the United States who were 18 years and older,
smoked combustible cigarettes, and enrolled in the intervention between August 2017 and March 2019.
Results We identied four e-cigarette user groups (n = 990) based on the participants’ baseline and six-month
e-cigarette use (non-users, n = 621; recently started users, n = 60; sustained users, n = 187; recently stopped users,
n = 122). A multiple logistic regression was used to estimate the adjusted odds ratios (AOR) of six-month quit outcome
and the e-cigarette user groups. Compared to e-cigarette non-users, the odds of quitting smoking were signicantly
higher among recently stopped users (AOR = 1.68, 95% CI [1.06, 2.67], p = 0.03). Participants who were most successful
at quitting combustible cigarettes also stopped using e-cigarettes at follow-up, although many sustained using
both products. Findings suggest that digital tobacco cessation interventions may carefully consider how to promote
e-cigarette use cessation among participants who successfully quit smoking.
Trial registration ClinicalTrials.gov identier NCT03224520 (July 21, 2017).
Keywords Smoking Cessation, Digital intervention, Electronic cigarettes, Cessation Aid, Smoking characteristics
Stopping use of E-cigarettes and smoking
combustible cigarettes: ndings from a
large longitudinal digital smoking cessation
intervention study in the United States
Donghee N.Lee1*, Jamie M.Faro2, Elise M.Stevens1, LoriPbert1, ChengwuYang3 and Rajani S.Sadasivam2
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 2 of 7
Lee et al. BMC Research Notes (2024) 17:276
Introduction
Smoking is a leading cause of preventable death, disabil-
ity, and serious illnesses worldwide [14]. Studies have
shown that digital interventions can promote smoking
cessation (e.g., web-based, mobile phone text messaging)
[57]. Real-world programs have adopted these inter-
ventions, including as an adjunct to Quitlines [8] or as a
standalone program (smokefree.gov) [9].
Individuals who smoke have used electronic cigarettes
(e-cigarettes) to quit [10], and the medical and public
health community has increasingly accepted the harm
reduction benefits of e-cigarettes [1114]. Evidence on
the effectiveness of adults’ e-cigarette use on their smok-
ing cessation efforts is mixed [1014]. A review of clinical
trials demonstrated that substituting combustible ciga-
rettes with e-cigarettes has increased smoking quit rates
compared to nicotine replacement therapy (NRT) or
e-cigarettes without nicotine [15]. A national U.S. cohort
study revealed that adults who used e-cigarettes on their
own were less successful in quitting or preventing relapse
[10]. To our knowledge, no study has explored how e-cig-
arette use would influence quit outcomes among adult
participants of a digital smoking cessation intervention.
Our paper describes a secondary analysis of a large ran-
domized controlled trial for a digital smoking cessation
intervention (Smoker-to-Smoker (S2S)). We examined
the association of e-cigarette use and quitting smoking
among U.S. adults who participated in a six-month digi-
tal smoking cessation intervention. We explored: (1) the
demographic characteristics of e-cigarette users, (2) the
smoking characteristics of e-cigarette users, and (3) was
e-cigarette use associated with quitting smoking? Our
results have implications for the design of digital inter-
ventions in the context of increasing e-cigarette use.
Methods
Study design, setting, and participants
e study was approved by the UMass Chan Medical
School’s Institutional Review Board (H00012329) and
informed consent was obtained from each participant in
accordance with the Declaration of Helsinki.
We examined a cohort of adults who participated in the
S2S digital smoking cessation intervention [16] between
August 2017 and March 2019 (funded by Patient-Cen-
tered Outcomes Research Institute (PCORI; award: CDR-
1603-34645). Eligibility for the S2S trial included: (1)
speaking English, (2) currently smoking (as determined
by a self-report question, “Do you currently smoke?”),
and (3) aged 18 years. e research protocol and main
outcomes have been published [16, 17]. e total ana-
lytic sample for the current study was n = 990 after only
including participants who self-reported their e-cigarette
use status at baseline.
The original smoker-to-smoker (S2S) intervention
In the S2S trial, participants were randomly assigned to
the intervention (machine-learning recommender mes-
saging, which incorporated participants’ feedback to
improve the message selection in addition to their base-
line readiness to quit) or comparison (standard motiva-
tional messaging, which only incorporated participants’
baseline readiness to quit) group and received smoking
cessation messages that were selected from the same
messaging database [18]. ese messages were emailed
for six months post-registration in the S2S trial for the
same frequency (four messages in the first two weeks
followed by two messages each week). e messages
exclusively discussed combustible cigarettes and did not
include information about e-cigarettes. Data were col-
lected using an online survey form at baseline and at six
months [19] (See “Additional file 2”).
Data collection and measures
At baseline, we collected (1) demographic data: age,
gender, race, ethnicity, education level, and perceived
difficulty of accessing medical care, and (2) smoking
characteristics: the number of cigarettes smoked per day,
readiness to quit, and living with others who smoke. At
six-months, quit outcome was assessed using the fol-
lowing question: “Do you currently smoke cigarettes
(smoked even 1 puff in the last 7 days)?” with answer
choices of yes or no [20].
E-cigarette use was assessed using one question: “How
many days have you used an e-cigarette within the past
30 days?” with answer choices of “every day,” “some
days,” “not at all,” “don’t know/not sure” [21] at baseline
(assessed at one week) and follow-up (assessed at six
months). We also collected data on participants’ reasons
for using e-cigarettes: “Why did you use an e-cigarette?”
with a single-choice answers of “every day to quit smok-
ing,” “some days to cut down on my smoking,” “to use in
places where I was not allowed to smoke cigarettes,” and
“others.” [21] (For the survey questions, see “Additional
file 1”).
Statistical analysis
Four e-cigarette user groups were created based on
participants’ response to the baseline and at follow-up
e-cigarette use question: non-users (not at all at base-
line, not at all at follow-up), recently started users (not
at all at baseline, every day/some days at follow-up),
sustained users (every day/some days at baseline, every
day/some days at follow-up), and recently stopped users
(every day/some days at baseline, not at all at follow-
up). Some of these users had missing e-cigarette values
at follow-up. We treated the follow-up missing values for
e-cigarette use as continuing the baseline e-cigarette use
status. ese users with missing follow-up values were all
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 3 of 7
Lee et al. BMC Research Notes (2024) 17:276
assigned to the non-users and sustained groups only, as
by definition, the recently stopped and recently started
group had to have e-cigarette values at baseline and fol-
low-up. We performed chi-square tests to compare the
baseline measures of demographic and smoking charac-
teristics for three e-cigarette user groups with those of
the non-user group (reference group).
We applied multiple logistic regression to estimate the
adjusted odds ratios (AOR) of smoking cessation asso-
ciated with e-cigarette user groups. In this analysis, the
dependent variable was quit smoking (point prevalence
smoking cessation at 6 months). Consistent with the
smoking cessation literature and statistically significant
baseline characteristics, we controlled for demograph-
ics (age, gender, race/ethnicity, education, and perceived
financial difficulty) and random assignment. We reported
both completed cases and penalized imputation where
we assigned missing values for quitting smoking outcome
as smoking. We used SPSS v28 [22] for all analyses.
Results
Of 990 participants, 31.2% (n = 309) reported using e-cig-
arettes every day or some days at baseline, while 68.8%
(n = 681) reported not using them at baseline. Our six-
month follow-up survey completion rate was 66.7%. Of
those who completed the follow-up survey, 24.3% of the
participants (n = 157) reported using e-cigarettes every
day or some days, and 75.0% (n = 484) reported not using
them at follow-up. Four groups were identified: non-
users (n = 621; n = 242 missing), recently started users
(n = 60), sustained users (n = 187; n = 88 missing), and
recently stopped users (n = 122).
Demographic and smoking characteristics
Table 1 presents the baseline measures of demographic
characteristics of the four e-cigarette user groups
(n = 990). Differences in age were statistically significant
across the e-cigarette user groups (p < 0.001). Compared
to e-cigarette non-users (3.9%, n = 24), a higher propor-
tion of recently started users (10.0%, n = 6, p = 0.04),
sustained users (16.0%, n = 30, p < 0.001), and recently
stopped users (14.8%, n = 18, p < 0.001) were younger
(19–24 years). Differences in race were statistically signif-
icant across the e-cigarette user groups (p = 0.035). Com-
pared to non-users (14.7%, n = 89), a higher proportion
of recently stopped users identified as African American
(20.4%, n = 23, p = 0.04). Gender, ethnicity, education, and
perceived financial difficulty of accessing medical care
were not statistically different across e-cigarette user
groups.
Table 2 presents the baseline measures of smok-
ing characteristics of the four e-cigarette user groups
(n = 990). Differences in the number of cigarettes smoked
per day, readiness to quit smoking, and living with
others who smoke cigarettes were not statistically dif-
ferent across the e-cigarette user groups. Differences in
e-cigarette use reasons were statistically significant across
the e-cigarette user groups (p < 0.001). Compared to non-
users (35.7%, n = 136), a higher proportion of recently
stopped users (61.2%, n = 74, p < 0.001) reported that they
used e-cigarettes on some days to reduce smoking ciga-
rettes. Compared to non-users (19.4%, n = 74), sustained
users (31.0%, n = 57, p < 0.001) reported that they used
e-cigarettes to replace smoking in the prohibited areas.
Smoking cessation outcomes
Table 3 presents the six-month follow-up quit outcome
(yes vs. no) of the four e-cigarette user groups. Differ-
ences in the quit outcomes were statistically significant
across the e-cigarette user groups (p < 0.001). Compared
to non-users (35.6%, n = 135), a higher proportion of
recently stopped users reported quitting smoking at fol-
low-up (56.6%, n = 69, p < 0.001).
Figure 1 presents the adjusted odds ratio (AOR) of
the six-month follow-up quit outcome by e-cigarette
user groups. Compared to non-users, the odds of quit-
ting smoking were significantly higher among recently
stopped users (AOR = 1.68, 95% CI [1.06, 2.67], p = 0.03).
Discussion
We examined the association of e-cigarette use and
quitting smoking among participants of a six-month
digital smoking cessation intervention. Participants’
demographic characteristics differed across e-cigarette
user groups. More participants who used e-cigarettes
both at baseline and follow-up were younger (19–24
years old) than those who did not use e-cigarettes at all.
More participants who used e-cigarettes at baseline but
stopped at follow-up identified as African Americans
than those who did not use e-cigarettes at all. Overall,
56.6% of participants who stopped using e-cigarettes at
follow-up also quit smoking.
Younger participants’ e-cigarette use indicates their
possible e-cigarette exposure from their peers [23], mar-
keting influence [24], or their lack of awareness of the
health harms and addictiveness of e-cigarettes [25, 26].
is is concerning for those between 19 and 24 years who
use e-cigarettes, as nicotine in e-cigarettes can harm their
brain development [27]. Furthermore, young adults who
used e-cigarettes at follow-up without successfully quit-
ting smoking engaged in dual use of cigarettes and e-cig-
arettes. is is concerning, as dual use can pose greater
health risk than exclusively using combustible cigarettes
[28]. us, more intervention work is needed to help
young adults quit using both e-cigarettes [29] and ciga-
rettes. Additionally, more participants who stopped using
e-cigarettes at follow-up identified as African American
than white (20.4%), which differ from other findings that
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 4 of 7
Lee et al. BMC Research Notes (2024) 17:276
African American participants had higher e-cigarette use
rates compared to their white counterpart [30], despite
their generally lower overall e-cigarette use [31, 32].
Participants who initially used e-cigarettes but stopped
at follow-up were more successful in quitting smok-
ing than those who did not use e-cigarettes at all during
the intervention. Differences in participants’ e-cigarette
use reasons may explain this difference. More partici-
pants who recently stopped using e-cigarettes were more
likely to report using e-cigarettes on some days to reduce
smoking, whereas more participants who initiated or
sustained using e-cigarettes reported using e-cigarettes
in smokefree areas. ese findings raise questions about
the role of e-cigarette use in quit outcomes in the con-
text of a digital smoking cessation intervention, as it may
potentially lead to dual use [33], suggesting the need to
examine challenges and motivations of those who use
both products when designing interventions targeting
this group. Prior randomized controlled trials that pro-
vided and encouraged e-cigarette use have shown that
e-cigarettes can be used as a smoking cessation aid [15,
34]. However, many participants of these trials contin-
ued using e-cigarettes after quitting smoking. While we
did not include messages about e-cigarettes in our study,
digital smoking cessation interventions may also consider
including messages promoting quitting both e-cigarettes
Table 1 Comparison of baseline demographic characteristics across e-cigarette user groups
Participant characteristics Non-usersaRecently Started Users Sustained Users Recently
Stopped
Users
621 (62.7%) 60 (6.1%) 187 (19.1%) 122
(12.1%)
Age***
19–24 24 (3.9%) 6 (10.0%)* 30 (16.0%)*** 18
(14.8%)***
25–34 108 (17.4%) 15 (25.0%) 54 (28.9%) 38 (31.1%)
35–44 110 (17.7%) 9 (15.0%) 37 (19.8%) 38 (31.1%)
45–54 99 (15.9%) 13 (21.7%) 20 (10.7%) 10 (8.2%)
55–64 213 (34.3%) 14 (23.3%) 34 (18.2%) 14 (11.5%)
65+ 67 (10.8%) 3 (5.0%) 12 (6.4%) 4 (3.3%)
Gender
Female 475 (76.5%) 48 (80.0%) 127 (67.9%) 87 (71.3%)
Male 146 (23.5%) 12 (20.0%) 60 (32.1%) 35 (28.7%)
Race*
White 488 (80.8%) 48 (81.4%) 146 (84.4%) 80
(70.8%)*
African American 89 (14.7%) 5 (8.5%) 20 (11.6%) 23 (20.4%)
Other race#27 (4.5%) 6 (10.2%) 7 (4.0%) 10 (8.8%)
Ethnicity
Not Hispanic/Latino 549 (93.1%) 53 (91.4%) 159 (91.4%) 106
(92.2%)
Hispanic 41 (6.9%) 5 (8.6%) 15 (8.6%) 9 (7.8%)
Education
Never attended/some high school 29 (4.7%) 1 (1.7%) 17 (9.1%) 5 (4.2%)
High school graduate 153 (24.6%) 20 (33.9%) 43 (23.2%) 31 (25.8%)
Some college/technical school 270 (43.5%) 30 (50.8%) 74 (40.0%) 51 (42.5%)
College graduate 169 (27.2%) 8 (13.6%) 51 (27.6%) 33 (27.5%)
How hard is it for you (and your family) to pay for
medical care?b
Hard 424 (68.3%) 48 (80.0%) 119 (63.6%) 88 (72.1%)
Other 183 (29.5%) 11 (18.3%) 60 (32.1%) 30 (24.6%)
Don’t know 14 (2.3%) 1 (1.7%) 8 (4.3%) 4 (3.3%)
*p < 0.05, **p < 0.01, ***p < 0.0 01
Note: Overall chi-square tests were statistically signicant for age and race, but not signicant for other demographic characteristics (gender, ethnicity, education,
and perceived nancial diculty). P-values represent statistically signicant dierences between the left column (non-users) and each column (recently started
users, sus tained users, recen tly stopped users)
a Indicates the reference group for comparison
# Other race i ncludes Asian, Americ an Indian or Alaska Native , Native Hawaiian or Other P acic Islander
b Perceived diculty of accessing medical care was collapsed into hard (very hard, hard, somewhat hard), not very hard, and don’t know
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 5 of 7
Lee et al. BMC Research Notes (2024) 17:276
and cigarettes. Research needs to identify the appropriate
timing to discuss e-cigarettes as a cigarette substitution
for harm reduction and eventually to quit e-cigarettes.
Overall, our study provides insights into how e-ciga-
rette use may affect quitting smoking among adult par-
ticipants of a digital smoking cessation intervention. It is
possible that individuals who stopped using e-cigarettes
at the end of the intervention had greater motivation
and efficacy to adopt a healthier lifestyle, as has been
shown in other trials [35, 36]. However, this conclusion
requires a further investigation into how individuals’
e-cigarette use may interact with their stages of change
in smoking to influence their quit outcomes. erefore,
careful consideration of how to promote both smoking
and e-cigarette cessation may help improve effectiveness
of future digital tobacco cessation interventions.
Limitations
We are unable to make a causal association between
e-cigarette use and participants’ quit outcomes, as e-ciga-
rette use was not part of our intervention. Specific infor-
mation about e-cigarettes (types, flavors, intensity of use),
or intermediate e-cigarette use outcomes, or cigarette
dependence measures were not collected, although more
information on e-cigarette and cigarette use could pro-
vide more insights. e main outcome was self-reported
Table 2 Comparison of baseline smoking characteristics across e-cigarette user groups
Smoking characteristics Non-usersa (n = 621) Recently Started Users
(n = 60)
Sustained Users
(n = 187)
Recently
Stopped
Users
(n = 122)
Cigarettes smoked per day
0–10 199 (32.0%) 18 (30.0%) 65 (34.8%) 40 (32.8%)
> 10 and < = 20 294 (47.3%) 32 (53.3%) 81 (43.3%) 58 (47.5%)
> 20 128 (20.6%) 10 (16.7%) 41 (21.9%) 24 (19.7%)
Readiness to quit
Not thinking of quitting 23 (3.7%) 3 (5.0%) 11 (5.9%) 3 (2.5%)
Thinking of quitting 361 (58.1%) 33 (55.0%) 110 (58.8%) 62 (50.8%)
Set a quit date 153 (24.6%) 16 (26.7%) 39 (20.9%) 36 (29.5%)
Quit today 40 (6.4%) 6 (10.0%) 13 (7.0%) 11 (9.0%)
E-cigarette use reasons***
Every day to quit smoking 100 (26.2%) 8 (15.7%)* 32 (17.4%)*** 21
(17.4%)***
Some day to cut down on smoking 136 (35.7%) 14 (27.5%) 84 (45.7%) 74 (61.2%)
To use in smoking prohibited areas 74 (19.4%) 19 (37.3%) 57 (31.0%) 20 (16.5%)
Others 71 (18.6%) 10 (19.6%) 11 (6.0%) 6 (5.0%)
Does anyone else living in your home smoke
cigarettes?
Yes 248 (39.9%) 26 (43.3%) 78 (41.7%) 59 (48.4%)
No 373 (60.1%) 34 (56.7%) 109 (58.3%) 62 (51.6%)
Note: Overa ll chi-square tests we re not statistical ly signicant for ciga rettes smoked per day, rea diness to quit, and livi ng with others who smo ke. P-values represent
statistic ally signicant dier ences between the le ft column (non-user s) and each colu mn (recently started us ers, sustained use rs, recently stoppe d users)
a Indicates the reference group for comparison
Table 3 Comparison of six-month quitting smoking across e-cigarette user groups
Non-usersa (n = 621) Recently Started Users (n = 60) Sustained Users (n = 187) Recently Stopped Users (n = 122)
Quit smokingb***
Complete Cases n/N (%)
Yes 135 (35.6%) 15 (25.0%) 40 (40.4%) 69 (56.6%)***
No 244 (64.4%) 45 (75.0%) 59 (59.6%) 53 (43.4%)
Missing = Smoking c
n/N (%) 242 (39.0%) 0 (0.0%) 88 (46.6%) (0.0%)
Notes: The percentages of quit smoking responses were reported based on the total complete cases. Overall chi-square test results of the e-cigarette user group
and quit smok ing were statistic ally signicant. P-v alues represent st atistically signi cant dierences be tween the lef t column (non-users) an d each column (recently
starte d users, sustained us ers, and recently stop ped users). Due to attrit ion, quit outcomes of n = 330 partic ipants are missing (comple tion rate at the follow-u p was
66.7%)
a Indicates the reference group for comparison
b Quit smok ing was assessed by revers e coding responses to the qu estion on “Do you curre ntly smoke cigarettes?”
c Indicates mi ssing data for six-month qu it outcomes due to sample at trition. *p < 0.05, **p < 0.01, ***p < 0.0 01
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 6 of 7
Lee et al. BMC Research Notes (2024) 17:276
smoking status. Future analysis should incorporate bio-
chemical measures [37], to reduce the potential report-
ing bias. Additionally, our findings have limited statistical
power from the small sample size. We did not adjust
for multiple comparisons, as it may lead to false nega-
tive findings and reduce statistical power [3840], thus
not recommended for exploratory studies. Finally, our
findings may not fully apply to the current tobacco mar-
ketplace, as the e-cigarette landscape has evolved (e.g.,
emergence of novel product types and regulations) since
our data collection.
Abbreviations
S2S Smoker-to-smoker
Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s13104-024-06939-w.
Supplementary Material 1
Supplementary Material 2
Acknowledgements
We would like to thank the S2S program sta, IT team as well as the patient
panel members for their contributions to the project.
Author contributions
DNL wrote the original draft and conducted statistical analysis, and prepared
Tables1, 2 and 3; Fig.1. JMF revised and edited writing, conducted statistical
analysis, and prepared Fig.1. EMS revised and edited writing, LP revised
and edited writing, CY reviewed statistical analysis, RSS acquired funding,
conceptualized the study, conducted statistical analysis and supervised. All
authors reviewed the manuscript.
Funding
This work was supported through a Patient-Centered Outcomes Research
Institute (PCORI) Program Award (CDR-1603-34645). This manuscript was
additionally supported by NCI PRACCTIS Grant (2T32CA172009), NHLBI
(1K12HL138049-01), and NIDA (R00DA046563). The content is solely the
responsibility of the authors and does not necessarily represent the ocial
views of PCORI or the National Institutes of Health.
Data availability
Data are available upon request (PI: Sadasivam) at rajani.sadasivam@
umassmed.edu.
Declarations
Ethics approval and consent to participate
The study was approved by the UMass Chan Medical School’s institutional
review board (H00012329). Informed consent to participate was obtained
from all of the individual participants in the study.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Received: 30 April 2024 / Accepted: 9 September 2024
References
1. Murray CJ, Lopez AD. Alternative projections of mortality and dis-
ability by cause 1990–2020: global burden of Disease Study. Lancet.
1997;349(9064):1498–504.
2. Critchley JA, Capewell S. Mortality risk reduction associated with smoking
cessation in patients with coronary heart disease: a systematic review. JAMA.
2003;290(1):86–97.
3. Fiore MC, Croyle RT, Curry SJ, Cutler CM, Davis RM, Gordon C, et al. Preventing
3 million premature deaths and helping 5 million smokers quit: a national
action plan for tobacco cessation. Am J Public Health. 2004;94(2):205–10.
Fig. 1 Adjusted model estimating association of quitting smoking and e-cigarette user groups
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 7 of 7
Lee et al. BMC Research Notes (2024) 17:276
4. Centers for Disease Control and Prevention. Smoking & Tobacco Use. 2021
[cited 2021 Dec 9]. https://www.cdc.gov/tobacco/data_statistics/fact_
sheets/fast_facts/index.htm
5. Whittaker R, McRobbie H, Bullen C, Rodgers A, Gu Y, Dobson R. Mobile
phone text messaging and app-based interventions for smoking ces-
sation. Cochrane Database of Systematic Reviews. 2019 [cited 2022
Mar 9];(10). https://www.cochranelibrary.com/cdsr/doi/https://doi.
org/10.1002/14651858.CD006611.pub5/full
6. Ubhi HK, Michie S, Kotz D, Wong WC, West R. A Mobile App to Aid Smoking
Cessation: preliminary evaluation of SmokeFree28. J Med Internet Res.
2015;17(1):e3479.
7. Rajani NB, Mastellos N, Filippidis FT. Self-Ecacy and Motivation to quit of
smokers seeking to quit: quantitative Assessment of Smoking Cessation
Mobile apps. JMIR mHealth uHealth. 2021;9(4):e25030.
8. Web-Assisted Tobacco Inter ventions (WATI). - North American Quitline
Consortium. [cited 2022 Apr 4]. https://www.naquitline.org/page/wati
9. Prutzman YM, Wiseman KP, Grady MA, Budenz A, Grenen EG, Vercammen LK,
et al. Using Digital Technologies to Reach Tobacco users who want to quit:
evidence from the National Cancer Institute’s Smokefree.gov Initiative. Am J
Prev Med. 2021;60(3):S172–84.
10. Chen R, Pierce JP, Leas EC, Benmarhnia T, Strong DR, White MM et al.
Eectiveness of e-cigarettes as aids for smoking cessation: evidence from
the PATH Study cohort, 2017–2019. Tobacco Control. 2022 Jan 11 [cited
2022 Feb 10]; https://tobaccocontrol.bmj.com/content/early/2022/01/11/
tobaccocontrol-2021-056901.
11. Wang RJ, Bhadriraju S, Glantz SA. E-Cigarette use and adult cigarette Smoking
Cessation: a Meta-analysis. Am J Public Health. 2021;111(2):230–46.
12. Zhu SH, Zhuang YL, Wong S, Cummins SE, Tedeschi GJ. E-cigarette use and
associated changes in population smoking cessation: evidence from US cur-
rent population surveys. BMJ. 2017;358:j3262.
13. Levy DT, Yuan Z, Luo Y, Abrams DB. The relationship of E-Cigarette use to
cigarette quit attempts and Cessation: insights from a large. Nationally Repre-
sentative U S Surv Nicotine Tob Res. 2018;20(8):931–9.
14. Biener L, Hargraves JL. A longitudinal study of electronic cigarette Use among
a Population-based sample of adult smokers: Association with Smoking Ces-
sation and Motivation to quit. Nicotine Tob Res. 2015;17(2):127–33.
15. Har tmann-Boyce J, Lindson N, Butler AR, McRobbie H, Bullen C, Begh R, et
al. Electronic cigarettes for smoking cessation. Cochrane Database Syst Rev.
2022;11(11):CD010216.
16. Faro JM, Orvek EA, Blok AC, Nagawa CS, McDonald AJ, Seward G, et al.
Dissemination and eectiveness of the peer marketing and messaging of a
web-assisted Tobacco intervention: protocol for a hybrid eectiveness trial.
JMIR Res Protoc. 2019;8(7):e14814.
17. Faro J, Chen J, Flahive J, Nagawa C, Orvek E, Houston T, et al. Eects of a
machine learning recommender system and viral peer marketing interven-
tion on smoking cessation. JAMA Netw Open. 2023;6(1):e2250665.
18. Coley HL, Sadasivam RS, Williams JH, Volkman JE, Schoenberger YM, Kohler
CL, et al. Crowdsourced peer- versus expert-written smoking-cessation mes-
sages. Am J Prev Med. 2013;45(5):543–50.
19. Sadasivam RS, Kinney RL, Delaughter K, Rao SR, Williams JH, Coley HL, et al.
Who participates in web-assisted tobacco interventions? The QUIT-PRIMO
and National Dental Practice-Based Research Network Hi-Quit studies. J Med
Internet Res. 2013;15(5):e77.
20. Jar vis MJ, Tunstall-Pedoe H, Feyerabend C, Vesey C, Saloojee Y. Comparison
of tests used to distinguish smokers from nonsmokers. Am J Public Health.
1987;77(11):1435–8.
21. Peters EN, Harrell PT, Hendricks PS, O’Grady KE, Pickworth WB, Vocci FJ. Elec-
tronic cigarettes in adults in outpatient substance use treatment: awareness,
perceptions, use, and reasons for use. Am J Addict. 2015;24(3):233–9.
22. IBM. IBM SPSS Statistics for Window. IBM Corp: Armonk, NY, USA, 2017.
23. Kong G, Morean ME, Cavallo DA, Camenga DR, Krishnan-Sarin S. Reasons for
electronic cigarette Experimentation and Discontinuation among adoles-
cents and Young adults. Nicotine Tob Res. 2015;17(7):847–54.
24. Pokhrel P, Herzog TA, Fagan P, Unger JB, Stacy AW. E-cigarette advertis-
ing exposure, explicit and implicit harm perceptions, and E-cigarette
use susceptibility among nonsmoking young adults. Nicotine Tob Res.
2019;21(1):127–31.
25. Russell C, K atsampouris E, Mckeganey N. Harm and addiction percep-
tions of the JUUL E-Cigarette among adolescents. Nicotine Tob Res.
2020;22(5):713–21.
26. Sutn EL, McCoy TP, Morrell HER, Hoeppner BB, Wolfson M. Electronic ciga-
rette use by college students. Drug Alcohol Depend. 2013;131(3):214–21.
27. U.S. Department of Health and Human Services. E-Cigarette Use Among
Youth and Young Adults: A Report of the Surgeon General. Atlanta, GA: U.S.
Department of Health and Human Services, Centers for Disease Control and
Prevention, National Center for Chronic Disease Prevention and Health Pro-
motion, Oce on Smoking and Health; 2016 [cited 2021 Dec 15]. https://e-
cigarettes.surgeongeneral.gov/documents/2016_sgr_full_report_non-508.
pdf
28. Pisinger C, Rasmussen SKB. The Health eects of Real-World Dual Use of
Electronic and Conventional cigarettes versus the Health eects of Exclusive
Smoking of Conventional cigarettes: a systematic review. Int J Environ Res
Public Health. 2022;19(20):13687.
29. Owusu D, Massey Z, Popova L. An experimental study of messages
communicating potential harms of electronic cigarettes. PLoS ONE.
2020;15(10):e0240611.
30. Webb Hooper M, Kolar SK. Racial/Ethnic dierences in electronic cigarette
use and reasons for Use among current and former smokers: ndings from a
community-based sample. Int J Environ Res Public Health. 2016;13(10):1009.
31. Schoenborn CA, Gindi RM. Electronic cigarette Use among adults: United
States, 2014. National Center for Health Statistics (U.S.). Division of Vital Statis-
tics. Reproductive Statistics Branch., editor. 2015;(217). https://stacks.cdc.gov/
view/cdc/76858
32. Carroll DM, Cole A. Racial/ethnic group comparisons of quit ratios and
prevalences of cessation-related factors among adults who smoke with a
quit attempt. Am J Drug Alcohol Abuse. 2022;48(1):58–68.
33. Glantz SA, Nguyen N, Oliveira da Silva AL. Population-based Dis-
ease odds for E-Cigarettes and dual use versus cigarettes. NEJM Evid.
2024;3(3):EVIDoa2300229.
34. Hajek P, Phillips-Waller A, Przulj D, Pesola F, Smith KM, Bisal N et al. A Random-
ized Trial of E-Cigarettes versus Nicotine-Replacement Therapy. New England
Journal of Medicine. 2019 Jan 30 [cited 2022 Jan 16]; https://www.nejm.org/
doi/https://doi.org/10.1056/NEJMoa1808779
35. Piñeiro B, López-Durán A, del Río EF, Martínez Ú, Brandon TH, Becoña E.
Motivation to quit as a predictor of smoking cessation and abstinence main-
tenance among treated Spanish smokers. Addict Behav. 2016;53:40–5.
36. Elshatarat RA, Yacoub MI, Khraim FM, Saleh ZT, Afaneh TR. Self-ecacy in
treating tobacco use: A review article. Proceedings of Singapore Healthcare.
2016;25(4):243–8.
37. Abrams DB, Follick MJ, Biener L, Carey KB, Hitti J. Saliva cotinine as a measure
of smoking status in eld settings. Am J Public Health. 1987;77(7):846–8.
38. Nak agawa S. A farewell to Bonferroni: the problems of low statistical power
and publication bias. Behav Ecol. 2004;15(6):1044–5.
39. O’Keefe DJ. Colloquy: should Familywise Alpha be adjusted? Hum Commun
Res. 2003;29(3):431–47.
40. Perneger TV. What’s wrong with Bonferroni adjustments. BMJ.
1998;316(7139):1236–8.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional aliations.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com
ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
Importance: Novel data science and marketing methods of smoking-cessation intervention have not been adequately evaluated. Objective: To compare machine learning recommender (ML recommender) computer tailoring of motivational text messages vs a standard motivational text-based intervention (standard messaging) and a viral peer-recruitment tool kit (viral tool kit) for recruiting friends and family vs no tool kit in a smoking-cessation intervention. Design, setting, and participants: This 2 ×2 factorial randomized clinical trial with partial allocation, conducted between July 2017 and September 2019 within an online tobacco intervention, recruited current smokers aged 18 years and older who spoke English from the US via the internet and peer referral. Data were analyzed from March through May 2022. Interventions: Participants registering for the online intervention were randomly assigned to the ML recommender or standard messaging groups followed by partially random allocation to access to viral tool kit or no viral tool kit groups. The ML recommender provided ongoing refinement of message selection based on user feedback and comparison with a growing database of other users, while the standard system selected messages based on participant baseline readiness to quit. Main outcomes and measures: Our primary outcome was self-reported 7-day point prevalence smoking cessation at 6 months. Results: Of 1487 participants who smoked (444 aged 19-34 years [29.9%], 508 aged 35-54 years [34.1%], 535 aged ≥55 years [36.0%]; 1101 [74.0%] females; 189 Black [12.7%] and 1101 White [78.5%]; 106 Hispanic [7.1%]), 741 individuals were randomly assigned to the ML recommender group and 746 individuals to the standard messaging group; viral tool kit access was provided to 745 participants, and 742 participants received no such access. There was no significant difference in 6-month smoking cessation between ML recommender (146 of 412 participants [35.4%] with outcome data) and standard messaging (156 of 389 participants [40.1%] with outcome data) groups (adjusted odds ratio, 0.81; 95% CI, 0.61-1.08). Smoking cessation was significantly higher in viral tool kit (177 of 395 participants [44.8%] with outcome data) vs no viral tool kit (125 of 406 participants [30.8%] with outcome data) groups (adjusted odds ratio, 1.48; 95% CI, 1.11-1.98). Conclusions and relevance: In this study, machine learning-based selection did not improve performance compared with standard message selection, while viral marketing did improve cessation outcomes. These results suggest that in addition to increasing dissemination, viral recruitment may have important implications for improving effectiveness of smoking-cessation interventions. Trial registration: ClinicalTrials.gov Identifier: NCT03224520.
Article
Full-text available
Background: A high prevalence of dual use of e-cigarettes and conventional cigarettes has been reported across the world. Methods: A systematic search was carried out. We included original articles on any topic relevant to health, excluding mental health, in all languages. The PRISMA guidelines were followed. Both reviewers independently screened and read all publications. We compared dual use with exclusive smoking of conventional cigarettes (ESCC). Results: Fifty-two publications (49 studies) were included. Thirteen papers/10 studies were prospective. There was great heterogeneity across studies. Many methodological weaknesses, such as inaccurate exposure measurement, lack of adjustment for former tobacco consumption, and lack of significance testing were identified. Most prospective studies found dual use to be at least as harmful as ESCC. The longest follow-up was six years. Most of the best available cross-sectional studies found dual use associated with the same and, in several studies, significantly higher risk of self-reported symptoms/disease than in ESCC. The intensity of cigarette smoking seems associated with worse health. Conclusion: Existing studies indicate that dual use is at least as, or probably even more, harmful than ESCC. Due to the predominance of cross-sectional studies and the methodological weaknesses we judged the overall certainty of the evidence as "low certainty".
Article
Full-text available
Objective To assess the effectiveness of e-cigarettes in smoking cessation in the USA from 2017 to 2019, given the 2017 increase in high nicotine e-cigarette sales. Methods In 2017, the PATH Cohort Study included data on 3578 previous year smokers with a recent quit attempt and 1323 recent former smokers. Respondents reported e-cigarettes or other products used to quit cigarettes and many covariates associated with e-cigarette use. Study outcomes were 12+ months of cigarette abstinence and tobacco abstinence in 2019. We report weighted unadjusted estimates and use propensity score matched analyses with 1500 bootstrap samples to estimate adjusted risk differences (aRD). Results In 2017, 12.6% (95% CI 11.3% to 13.9%) of recent quit attempters used e-cigarettes to help with their quit attempt, a decline from previous years. Cigarette abstinence for e-cigarette users (9.9%, 95% CI 6.6% to 13.2%) was lower than for no product use (18.6%, 95% CI 16.0% to 21.2%), and the aRD for e-cigarettes versus pharmaceutical aids was −7.3% (95% CI −14.4 to –0.4) and for e-cigarettes versus any other method was −7.7% (95% CI −12.2 to –3.2). Only 2.2% (95% CI 0.0% to 4.4%) of recent former smokers switched to a high nicotine e-cigarette. Subjects who switched to e-cigarettes appeared to have a higher relapse rate than those who did not switch to e-cigarettes or other tobacco, although the difference was not statistically significant. Conclusions Sales increases in high nicotine e-cigarettes in 2017 did not translate to more smokers using these e-cigarettes to quit smoking. On average, using e-cigarettes for cessation in 2017 did not improve successful quitting or prevent relapse.
Article
Full-text available
Background Decreasing trends in the number of individuals accessing face-to-face support are leaving a significant gap in the treatment options for smokers seeking to quit. Face-to-face behavioral support and other interventions attempt to target psychological factors such as the self-efficacy and motivation to quit of smokers, as these factors are associated with an increased likelihood of making quit attempts and successfully quitting. Although digital interventions, such as smoking cessation mobile apps, could provide a promising avenue to bridge the growing treatment gap, little is known about their impact on psychological factors that are vital for smoking cessation. Objective This study aims to better understand the possible impact of smoking cessation mobile apps on important factors for successful cessation, such as self-efficacy and motivation to quit. Our aim is to assess the self-efficacy and motivation to quit levels of smokers before and after the use of smoking cessation mobile apps. Methods Smokers seeking to quit were recruited to participate in a 4-week app-based study. After screening, eligible participants were asked to use a mobile app (Kwit or Quit Genius). The smoking self-efficacy questionnaire and the motivation to stop smoking scale were used to measure the self-efficacy and motivation to quit, respectively. Both were assessed at baseline (before app use), midstudy (2 weeks after app use), and end-study (4 weeks after app use). Paired sample two-tailed t tests were used to investigate whether differences in self-efficacy and motivation between study time points were statistically significant. Linear regression models investigated associations between change in self-efficacy and change in motivation to quit before and after app use with age, gender, and nicotine dependence. ResultsA total of 116 participants completed the study, with the majority being male (71/116, 61.2%), employed (76/116, 65.6%), single (77/116, 66.4%), and highly educated (87/116, 75.0%). A large proportion of participants had a low to moderate dependence on nicotine (107/116, 92.2%). A statistically significant increase of 5.09 points (95% CI 1.83-8.34) from 37.38 points at baseline in self-efficacy was found at the end of the study. Statistically significant increases were also found for the subcomponents of self-efficacy (intrinsic and extrinsic self-efficacies). Similarly, a statistically significant increase of 0.38 points (95% CI 0.06-0.70) from 5.94 points at baseline in motivation to quit was found at the end of the study. Gender, age, and nicotine dependence were not statistically significantly associated with changes in self-efficacy and motivation to quit. Conclusions The assessed mobile apps positively impacted the self-efficacy and motivation to quit of smokers making quit attempts. This has important implications on the possible future use of digitalized interventions and how they could influence important psychological factors for quitting such as self-efficacy and motivation. However, further research is needed to assess whether digital interventions can supplement or replace traditional forms of therapy.
Article
Full-text available
The rapid growth of smartphone ownership and broadband access has created new opportunities to reach smokers with cessation information and support using digital technologies. These technologies can both complement and be integrated with traditional support modalities such as telephone quitlines and 1-on-1 clinical cessation counseling. The National Cancer Institute's Smokefree.gov Initiative provides free, evidence-based cessation support to the public through a multimodal suite of digital interventions, including several mobile-optimized websites, text messaging programs, and 2 mobile applications. In addition to digital resources directed at the general population, the Smokefree.gov Initiative includes population-specific resources targeted to adolescents, women, military veterans, Spanish speakers, older adults, and other populations. This paper describes the reach and use of the Smokefree.gov Initiative's resources over a 5-year period between 2014 and 2018, including how users interact with the program's digital content in ways that facilitate engagement with live counseling support. Use of Smokefree.gov Initiative resources has grown steadily over time; in 2018 alone, approximately 7-8 million people accessed Smokefree.gov Initiative web- and mobile-based resources. Smokefree.gov Initiative utilization data show that people take advantage of the full range of technology tools and options offered as part of the Smokefree.gov Initiative's multiplatform intervention. The Smokefree.gov Initiative experience suggests that offering different, complementary technology options to meet the needs and preferences of smokers has the potential to meaningfully expand the reach of cessation treatment.
Article
Abstract BACKGROUND E-cigarettes are promoted as less harmful than cigarettes. There has not been a direct comparison of health effects of e-cigarettes or dual use (concurrently using e-cigarettes and cigarettes) with those of cigarettes in the general population. METHODS Studies in PubMed, EMBASE, Web of Science, and PsychINFO published through October 1, 2023, were pooled in a random-effects meta-analysis if five or more studies were identified with a disease outcome. We assessed risk of bias with Risk Of Bias In Non-randomized Studies of Exposure and certainty with Grading of Recommendations, Assessment, Development, and Evaluations. Outcomes with fewer studies were summarized but not pooled. RESULTS We identified 124 odds ratios (94 cross-sectional and 30 longitudinal) from 107 studies. Pooled odds ratios for current e-cigarette versus cigarette use were not different for cardiovascular disease (odds ratio, 0.81; 95% confidence interval, 0.58 to 1.14), stroke (0.73; 0.47 to 1.13), or metabolic dysfunction (0.99; 0.91 to 1.09) but were lower for asthma (0.84; 0.74 to 0.95), chronic obstructive pulmonary disease (0.53; 0.38 to 0.74), and oral disease (0.87; 0.76 to 1.00). Pooled odds ratios for dual use versus cigarettes were increased for all outcomes (range, 1.20 to 1.41). Pooled odds ratios for e-cigarettes and dual use compared with nonuse of either product were increased (e-cigarette range, 1.24 to 1.47; dual use, 1.49 to 3.29). All included studies were assessed as having a low risk of bias. Results were generally not sensitive to study characteristics. Limited studies of other outcomes suggest that e-cigarette use is associated with additional diseases. CONCLUSIONS There is a need to reassess the assumption that e-cigarette use provides substantial harm reduction across all cigarette-caused diseases, particularly accounting for dual use.
Article
Background: Electronic cigarettes (ECs) are handheld electronic vaping devices which produce an aerosol by heating an e-liquid. Some people who smoke use ECs to stop or reduce smoking, although some organizations, advocacy groups and policymakers have discouraged this, citing lack of evidence of efficacy and safety. People who smoke, healthcare providers and regulators want to know if ECs can help people quit smoking, and if they are safe to use for this purpose. This is a review update conducted as part of a living systematic review. Objectives: To examine the effectiveness, tolerability, and safety of using electronic cigarettes (ECs) to help people who smoke tobacco achieve long-term smoking abstinence. Search methods: We searched the Cochrane Tobacco Addiction Group's Specialized Register, the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, and PsycINFO to 1 July 2022, and reference-checked and contacted study authors. SELECTION CRITERIA: We included randomized controlled trials (RCTs) and randomized cross-over trials, in which people who smoke were randomized to an EC or control condition. We also included uncontrolled intervention studies in which all participants received an EC intervention. Studies had to report abstinence from cigarettes at six months or longer or data on safety markers at one week or longer, or both. Data collection and analysis: We followed standard Cochrane methods for screening and data extraction. Our primary outcome measures were abstinence from smoking after at least six months follow-up, adverse events (AEs), and serious adverse events (SAEs). Secondary outcomes included the proportion of people still using study product (EC or pharmacotherapy) at six or more months after randomization or starting EC use, changes in carbon monoxide (CO), blood pressure (BP), heart rate, arterial oxygen saturation, lung function, and levels of carcinogens or toxicants, or both. We used a fixed-effect Mantel-Haenszel model to calculate risk ratios (RRs) with a 95% confidence interval (CI) for dichotomous outcomes. For continuous outcomes, we calculated mean differences. Where appropriate, we pooled data in meta-analyses. Main results: We included 78 completed studies, representing 22,052 participants, of which 40 were RCTs. Seventeen of the 78 included studies were new to this review update. Of the included studies, we rated ten (all but one contributing to our main comparisons) at low risk of bias overall, 50 at high risk overall (including all non-randomized studies), and the remainder at unclear risk. There was high certainty that quit rates were higher in people randomized to nicotine EC than in those randomized to nicotine replacement therapy (NRT) (RR 1.63, 95% CI 1.30 to 2.04; I2 = 10%; 6 studies, 2378 participants). In absolute terms, this might translate to an additional four quitters per 100 (95% CI 2 to 6). There was moderate-certainty evidence (limited by imprecision) that the rate of occurrence of AEs was similar between groups (RR 1.02, 95% CI 0.88 to 1.19; I2 = 0%; 4 studies, 1702 participants). SAEs were rare, but there was insufficient evidence to determine whether rates differed between groups due to very serious imprecision (RR 1.12, 95% CI 0.82 to 1.52; I2 = 34%; 5 studies, 2411 participants). There was moderate-certainty evidence, limited by imprecision, that quit rates were higher in people randomized to nicotine EC than to non-nicotine EC (RR 1.94, 95% CI 1.21 to 3.13; I2 = 0%; 5 studies, 1447 participants). In absolute terms, this might lead to an additional seven quitters per 100 (95% CI 2 to 16). There was moderate-certainty evidence of no difference in the rate of AEs between these groups (RR 1.01, 95% CI 0.91 to 1.11; I2 = 0%; 5 studies, 1840 participants). There was insufficient evidence to determine whether rates of SAEs differed between groups, due to very serious imprecision (RR 1.00, 95% CI 0.56 to 1.79; I2 = 0%; 8 studies, 1272 participants). Compared to behavioural support only/no support, quit rates were higher for participants randomized to nicotine EC (RR 2.66, 95% CI 1.52 to 4.65; I2 = 0%; 7 studies, 3126 participants). In absolute terms, this represents an additional two quitters per 100 (95% CI 1 to 3). However, this finding was of very low certainty, due to issues with imprecision and risk of bias. There was some evidence that (non-serious) AEs were more common in people randomized to nicotine EC (RR 1.22, 95% CI 1.12 to 1.32; I2 = 41%, low certainty; 4 studies, 765 participants) and, again, insufficient evidence to determine whether rates of SAEs differed between groups (RR 1.03, 95% CI 0.54 to 1.97; I2 = 38%; 9 studies, 1993 participants). Data from non-randomized studies were consistent with RCT data. The most commonly reported AEs were throat/mouth irritation, headache, cough, and nausea, which tended to dissipate with continued EC use. Very few studies reported data on other outcomes or comparisons, hence evidence for these is limited, with CIs often encompassing clinically significant harm and benefit. Authors' conclusions: There is high-certainty evidence that ECs with nicotine increase quit rates compared to NRT and moderate-certainty evidence that they increase quit rates compared to ECs without nicotine. Evidence comparing nicotine EC with usual care/no treatment also suggests benefit, but is less certain. More studies are needed to confirm the effect size. Confidence intervals were for the most part wide for data on AEs, SAEs and other safety markers, with no difference in AEs between nicotine and non-nicotine ECs nor between nicotine ECs and NRT. Overall incidence of SAEs was low across all study arms. We did not detect evidence of serious harm from nicotine EC, but longest follow-up was two years and the number of studies was small. The main limitation of the evidence base remains imprecision due to the small number of RCTs, often with low event rates, but further RCTs are underway. To ensure the review continues to provide up-to-date information to decision-makers, this review is a living systematic review. We run searches monthly, with the review updated when relevant new evidence becomes available. Please refer to the Cochrane Database of Systematic Reviews for the review's current status.
Article
Background Smoking-related disparities exist among racial/ethnic minoritized groups. Objective We compared quit ratios and smoking cessation-related protective and risk factors by race/ethnicity to inform approaches to reduce disparities. Methods Among adults who smoke with a quit attempt from Wave 4 (2016–2017) Population Assessment of Tobacco Use and Health Study, the following factors were examined by racial/ethnic group (American Indians/Alaska Native [AI/AN;n = 165], Black/African American [AA;n = 526], Asian [n = 38], Hispanic/Latino/Latina/Spanish [n = 475], or White [n = 1,960]), wherein each were nearly gender-balanced: cessation medications, counseling/self-help materials, home smoking ban, social support, e-cigarette use, sleep, and mental health. Results Quit ratio was lower for AI/AN (adjusted odds ratio[aOR]:0.61) and Black/AA (aOR:0.49) and higher for Asian (aOR:1.90) and Hispanic/Latino/Latina/Spanish (aOR:1.30) than White adults. Medication use was low among all and lower among Black/AA (aOR:0.70) and Hispanic/Latino/Latina/Spanish (aOR:0.56) than White adults. Use of counseling/self-help materials were low among all and higher in AI/AN (aOR:1.85), Black/AA (aOR:1.87), and Hispanic/Latino/Latina/Spanish (aOR:1.49) than White adults. Presence of a smoking ban was lower among Black/AA (aOR:0.40) and higher in Hispanic/Latino/Latina/Spanish (aOR:1.59) than White adults. E-cigarette use was lower in Black/AA (aOR:0.53) and Hispanic/Latino/Latina/Spanish (aOR:0.43) than White adults. Sadness, anxiety, and sleep difficulties were higher in AI/AN (aORs:1.57, 1.50, 1.64) than White adults. Conclusions All racial/ethnic groups would benefit from policies and programs that increase cessation medications and counseling. Quit ratios were particularly low among Black/AA and AI/AN adults. Black/AA adults may benefit from efforts to increase smoking bans, while AI/AN adults may benefit from cessation approaches that simultaneously target sleep and mental health.
Article
Objectives. To determine the association between e-cigarette use and smoking cessation. Methods. We searched PubMed, Web of Science Core Collection, and EMBASE and computed the association of e-cigarette use with quitting cigarettes using random effects meta-analyses. Results. We identified 64 papers (55 observational studies and 9 randomized clinical trials [RCTs]). In observational studies of all adult smokers (odds ratio [OR] = 0.947; 95% confidence interval [CI] = 0.772, 1.160) and smokers motivated to quit smoking (OR = 0.851; 95% CI = 0.684, 1.057), e-cigarette consumer product use was not associated with quitting. Daily e-cigarette use was associated with more quitting (OR = 1.529; 95% CI = 1.158, 2.019) and less-than-daily use was associated with less quitting (OR = 0.514; 95% CI = 0.402, 0.665). The RCTs that compared quitting among smokers who were provided e-cigarettes to smokers with conventional therapy found e-cigarette use was associated with more quitting (relative risk = 1.555; 95% CI = 1.173, 2.061). Conclusions. As consumer products, in observational studies, e-cigarettes were not associated with increased smoking cessation in the adult population. In RCTs, provision of free e-cigarettes as a therapeutic intervention was associated with increased smoking cessation. Public Health Implications. E-cigarettes should not be approved as consumer products but may warrant consideration as a prescription therapy. (Am J Public Health. Published online ahead of print December 22, 2020: e1–e17. https://doi.org/10.2105/AJPH.2020.305999 )