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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 inuence 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 identied 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 signicantly
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 identier 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, LoriPbert1, ChengwuYang3 and Rajani S.Sadasivam2
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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 [1–4]. Studies have
shown that digital interventions can promote smoking
cessation (e.g., web-based, mobile phone text messaging)
[5–7]. 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 [11–14]. Evidence on
the effectiveness of adults’ e-cigarette use on their smok-
ing cessation efforts is mixed [10–14]. 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
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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
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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 signicant for age and race, but not signicant for other demographic characteristics (gender, ethnicity, education,
and perceived nancial diculty). P-values represent statistically signicant dierences 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 acic Islander
b Perceived diculty of accessing medical care was collapsed into hard (very hard, hard, somewhat hard), not very hard, and don’t know
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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 signicant for ciga rettes smoked per day, rea diness to quit, and livi ng with others who smo ke. P-values represent
statistic ally signicant dier 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 signicant. P-v alues represent st atistically signi cant dierences 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
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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 [38–40], 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
Tables1, 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 ocial
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
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