Clinical Outcomes of a Nationwide, Naturalistic E-Cig Trial (CONNECT)
Phase: NA
Conditions: Smoking, Tobacco Use
Sponsor: Medical University of South Carolina

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Clinical Outcomes of a Nationwide, Naturalistic E-Cig Trial (CONNECT)  
 
NCT03453385 
 
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TITLE:  CONNECT (Clinical Outcomes of a Nationwide, Naturalistic E-Cig Trial) 
 
ABSTRACT  
Electronic (e-)cigarettes are by far the fastest growing segment of the non-combustible tobacco market   available 
to smokers.  Most studies of e-cigarettes are 1) lab-based studies of either pharmacokinetics or toxicant exposure 
2) cross-sectional studies of prevalence and predictors of use or, more rarely, 3) uncontrolled cohort studies.  
The three randomized studies to date, all based outside the US, are cessation-focused and thus do not say 
anything about the natural process by which smokers adopt e-cigarettes, and how such adoption changes 
smoking behavior.  Studies of naturalistic, self-determined used are important to understand real-world impact 
of e-cigarettes.  Many of the existing studies are limited by methodological issues that constrain interpretation: 
selection bias resulting from comparisons of self-selected users vs. non-users, retrospective or cross-sectional 
designs that inhibit causal inferences, absence of biomarkers of exposure, and/or insufficient sample size.  The 
absence of prospective, randomized but still ecological studies is a glaring omission in the literature.  In fact, 
there has yet to be a study that a) randomizes smokers to e-cigarettes vs. not, b) is focused on naturalistic 
outcomes that occur in real time, c) is prospective, with sufficient follow-up duration, d) provides data on 
nicotine/tobacco exposure, e) is of sufficient sample size, and f) is US-based, an important consideration given 
upcoming regulatory changes.  We know of no published study with even two of these criteria.     
We propose a nationwide randomized clinical trial in which smokers (N=660) are randomized to receive samples 
of e-cigarettes vs. not.  Randomization will be 2:1 to increase precision of uptake outcomes (Aim 1).  A subset 
of smokers (n=120) will be recruited locally, to assess the impact of e-cigarette use on biomarkers of exposure 
(Aim 2).  (For this IRB application, we are increasing the total number of study participants by ~10% from 540 to 
600 for the national sample and from 120 to 150 for the local sample, for a total of 750 study participants, to 
account for the ~10% of participants who mail us a consent back but who we never reach for the first study 
phone call – see section 2.1 Recruitment and Informed Consent on page 18 of this protocol.) 
Finally, though ours is not a cessation study, it would be a missed opportunity if we did not track cessation 
outcomes (Aim 3) or sufficiently power the trial to detect differences if they emerge.  Thus, our study is designed 
and powered to address a comprehensive set of outcomes along three themes: 1) uptake and patterns of use, 
2) biomarkers of exposure, and 3) changes in smoking.  Importantly, these, dynamic changes will be captured 
in real time as they naturally unfold.   
We believe this will be the best test to date of the naturalistic population impact of e-cigarettes. Methods are 
strengthened by 1) large sample size (the largest e-cigarette RCT), 2) nationwide recruitment, with 3) locally 
recruited subset to assess biomarkers, and 4) multiple measures of outcome. The strong investigative team, 
coupled with our success in prior studies using similar methodology, collectively enhances the probability of 
success in achieving grant aims. We believe this study will offer an important contribution to the e-cigarette 
literature, and offer considerable implications for tobacco control, including vital information for regulatory 
agencies such as the FDA.  
 
Project Narrative  
Electronic (e-)cigarettes are the fastest growing non-cigarette product available to smokers, yet the science base 
to understand these products has not kept pace.  This will be among the first prospective, large scale randomized 
studies of e-cigarette use in the U.S., with aims to examine the naturalistic course of uptake and consequence 
of e-cigarette use. 
 
SPECIFIC AIMS 
E-cigarettes have dramatically altered the tobacco control landscape, and all evidence suggests escalating rates 
of use among smokers. With the tobacco industry now fully entrenched in this market, prevalence will surely 
increase further. The e-cigarette literature is predominated by studies of three specific types: a) cross-sectional 
(usually online) surveys of prevalence and attitudes, b) short-term, lab based studies of nicotine delivery, 
withdrawal/craving, and toxicant exposure, or c) uncontrolled cohort studies, most of which are retrospective. 
Only three controlled prospective studies exist. All come from abroad and all are explicit cessation studies of 
smokers wanting to quit and using e-cigarettes to do so. Collectively, these studies suggest that a) e-cigarettes 
are likely safer than conventional cigarettes, to both individual users and those around them, and b) use of e-

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cigarettes may be associated with quitting, though causal inferences are unclear. There are a number of 
problems with the existing e-cigarette literature, however. These include i) lack of a control group, ii) crude 
estimates of use (e.g., ever vs. never), iii) use of self-selected samples of users vs. non-users, iv) limited data 
on patterns of uptake among new users, including what it means to be a dual user, and v) almost no prospective 
data on naturalistic changes in smoking. We know essentially nothing with regard to potential uptake among 
non-treatment seeking smokers naïve to e-cigarette use. 
A recurrent theme throughout the literature is a need for stronger methodology; i.e., randomized trials, particularly 
those that focus on the natural process by which smokers adopt e-cigarettes and its subsequent impact. Our 
guiding premise is that there has yet to be a study that a) randomizes smokers to e-cigarettes vs. not, b) focuses 
on naturalistic outcomes; i.e., non-treatment focused, c) is prospective, with sufficient follow-up duration, d) 
provides data on nicotine/tobacco exposure, e) is of sufficient sample size, f) includes smokers with broad range 
of motivation to quit, and g) is US-based, an important consideration given upcoming FDA regulatory changes. 
We know of no study with even two of these criteria. Our study group has a strong record of assessing the natural 
history of product sampling, including large studies of NRT and smokeless tobacco.  Our collective expertise and 
collaborative history makes us the ideal team to do the same with e-cigarettes. The FDA has articulated a clear 
agenda to understand the uptake and population impact of how smokers use e-cigarettes, and our proposed 
study offers the most methodologically sound test of these questions.  
We propose a nationwide randomized clinical trial in which smokers (N=660) receive samples of cigarette-like 
e-cigarettes (n=440) vs. not (n=220). Randomization will be 2:1 to increase precision of uptake outcomes, 
broadly but concretely defined (Aim 1). A subset of smokers (n=120) will be recruited locally, to assess the impact 
of e-cigarette use on biomarkers of exposure (Aim 2). Finally, though ours is not a cessation study, it would be 
a missed opportunity if we did not track cessation outcomes (Aim 3) or sufficiently power the trial to detect 
differences if they emerge. Thus, our study is designed and powered to address a comprehensive set of 
outcomes along three domains: 1) uptake and patterns of use, 2) biomarkers of exposure, and 3) changes in 
smoking, all captured during the switching process while it naturally occurs in real time.   
Aim1: Describe rates and patterns of naturalistic uptake of e-cigarettes among smokers. We examine a 
broad set of uptake indicators, including trial, repeat trial, regular use, and purchase, and the time course and 
predictors for each. Hypothesis 1a: Within the e-cigarette group, we hypothesize that at least 90% of smokers 
will try the product at least once, 45% will use regularly (at least 4/7 days, at any time during follow-up), 25% will 
use daily for >7 days (at any time during follow-up), 25%/10% will be using at least some/daily at final follow-up, 
and 20% will independently purchase e-cigarettes. Hyp 1b: Among smokers in the e-cigarette group, 40% will 
show a minimum of 50% reduction in cigarettes per day, and as a group, e-cigarette users will show greater 
decreases in cigs/day as compared to control. Aim2: Assess the impact of e-cigarette use on biomarkers of 
exposure, to determine if smokers self-titrate to nicotine as they switch products. Within a subset of locally 
recruited smokers, we hypothesize (Hyp 2) that use of e-cigarettes will lead to significant short-term (1 month) 
decreases in CO & NNAL, but no net change in cotinine. Aim3: Assess concurrent and downstream (over 6 
months) effects of e-cigarette use on smoking behavior. Consistent with most prior research, we hypothesize 
that provision and use of e-cigarettes (vs. control group) will lead to (Hyp 3a) higher rates of quit attempts, (Hyp 
3b) cessation, and (Hyp 3c) increases in motivation/confidence to quit. This is not a cessation trial but is 
positioned to evaluate important cessation outcomes in a naturalistic setting.           
Our methods are strengthened by randomized design, a focus on broad and detailed indicators of naturalistic 
uptake, nationwide reach but with local recruitment to allow for biomarker assessment. Our success in similar 
‘sampling’ studies, both e-cigarette focused and not, adds further strength. Our multidisciplinary and experienced 
team is ideally suited to carry out the proposed research, which is strategically aligned with the NIH and FDA’s 
goals to increase knowledge about the impact of e-cigarettes on the behavior of consumers. 

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RESEARCH PLAN: INTRODUCTION AND SIGNFICANCE 
The proliferation of electronic nicotine delivery devices, most commonly referred to as electronic cigarettes or e-
cigarettes, has dramatically altered the tobacco control landscape.  The FDA classifies e-cigarettes as tobacco 
products, and has just recently started the process of regulating these products. The three largest U.S. tobacco 
companies have each entered the e-cigarette market, and market analyses (1, 2) project continued, “disruptive” 
growth (3), highlighting the potential for the e-cigarette market to ultimately overtake the existing tobacco market.  
Research on these products has only recently escalated, but has been dominated by basic science and human 
lab studies.  A recent issue of Tobacco Control, under the title “FDA E-Cigarettes: Impact on Individual and 
Population Health” (4) was noticeably devoid of any prospective behavioral research, which is a clear need for 
this literature (5-7), particularly in light of forthcoming FDA regulation.  The public health debate on e-cigarettes 
endures (8-12), and the science needs to keep pace.   
Product Description and Safety 
E-cigarettes were originally uniform in design, but have since evolved to span the spectrum from disposable 
cartridges to highly sophisticated tank systems, including ones that allow the user to control the amount, dose, 
and power of nicotine delivery.  The basic operation of any e-cigarette involves the heating and vaporization of 
nicotine; there is no combustion, unlike a cigarette.  E-cigarettes attenuate craving to varying degree (13-15), 
but this is likely dependent upon type of e-cigarette product.  Though nicotine delivery may be less than from a 
conventional cigarette (16), this is apt to change as products evolve.  Other studies have shown comparable 
cotinine levels among sustained e-cigarette users vs. regular smokers (17).  Along with similar evidence 
elsewhere (18, 19), this suggests that, after an adjustment period, smokers learn to titrate use to manage 
cravings, or at least to maintain the psychopharmacological effects previously derived from cigarette smoking.    
Growing consensus of expert opinion (5, 20), in concert with most available lab evidence, (15, 21-30), suggests 
that e-cigarettes are safer than conventional cigarettes.  E-cigarettes deliver significantly reduced levels of 
tobacco-specific nitrosamines, and significantly reduce secondhand smoke exposure to non-smokers (23, 31).  
Three online surveys of e-cigarette users (32-34), one open label noncomparative study of at-home use (15), 
and one cross-over study (14) documented mild and tolerable side effects, the most predominant of which are 
headache, mouth/throat irritation, cough, and nausea. So while there is clear heterogeneity across products, e-
cigarettes as a group appear safer than conventional cigarettes.  
Prevalence, Patterns, and Outcomes of Use   
Awareness and use of e-cigarettes is quickly growing (35-41).  Within the U.S., recent (2013) and nationally 
representative data (42) indicate 77% awareness of e-cigarettes (89% among smokers), of whom 51% believed 
them to be less harmful than cigarettes.  A separate national survey from 2013 (41) indicate 13% ever use (47% 
and 54% among nondaily and daily smokers), and 7% current use (34% and 30% among nondaily and daily 
smokers).  More recent, still national data (2014 NHIS) document 13% ever-usage among adults, 4% current 
usage, and 1% daily usage (43).  Some (44) but not all (43) studies suggest that most e-cigarette users are daily 
users.  There are almost no data on the contextual antecedents of use; i.e., whether use is bound specifically to 
smoking restricted environments, or whether situational cues (e.g., other smokers, mood, stress) drive e-
cigarette use in ways similar to smoking.  Motives for e-cigarettes use are also unclear.  Based on our data from 
the International Tobacco Control (ITC) cohort, wherein 15% of all U.S. smokers have tried e-cigarettes, and 
37% of ever-triers were current users (45), the most common reasons for use were 1) use in smokefree settings, 
2) facilitate reduction, 3) perceptions of reduced harm, and 4) to quit smoking.  While follow-up data from this 
cohort showed greater reduction in smoking among users vs. non-users, there were no differences in quitting.  
Another cohort study from Italy (46, 47) provided e-cigarettes for ad libitum use to 40 smokers not wanting to 
quit, of whom 1/3rd reduced smoking by half, and 23% were continuously quit after six months.  Another cohort 
study from the same group but within smokers with schizophrenia found 64% of e-cigarette users to be either 
quit or have significantly reduced smoking (48).  More recent cohort studies further document a positive 
association between e-cigarette use and subsequent quitting (49, 50), particularly when e-cigarettes are used 
regularly (51-53).  As uncontrolled studies, causal effects on quitting are unclear, but these data are consistent 
with a number of other studies, almost all cross-sectional, that suggest that e-cigarette use is associated with 
motivation to quit (54) or increased abstinence (32, 33, 55-58).  Even in the absence of cessation, use of e-

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cigarettes can reduce smoking, both over the short (15) and long term (59).  It is also worth noting that at least 
three studies (60-62) and one review paper (63) show lower (either numerically or statistically) quit rates among 
self-selected user vs. non-users. On the whole however, and as the most recent review noted (64), the literature 
suggests a positive link between e-cigarette use and reduction/quitting, though this same review cites the need 
for well designed randomized trials to address this issue directly.   
A universal theme in the literature is a need for controlled trials.  Only three such trials exist (65-67), one is 
pending (68) and none are within the U.S.  The first published trial is a study of Italian smokers (66) sponsored 
by an e-cigarette manufacturer.  Smokers (N=300) were randomized to high (7.2mg cartridge), moderate (4.8mg) 
or placebo (0mg) e-cigarettes.  Rates of abstinence after 3 months (11%, 17%, 4% in each group respectively) 
and 12 months (13%, 9%, 4%) suggested higher quit rates among active e-cigarette groups. The second study, 
a non-inferiority, cessation-focused trial (N=657), came from New Zealand (67, 69).   Smokers were randomized 
to receive 1) e-cigarettes, 2) transdermal patch, or 3) placebo e-cigarettes.  Abstinence rates numerically but not 
statistically favored e-cigarettes over both patch and placebo. The last study was a small scale (N=48), 
randomized trial from Belgium that showed short term (2-month) increases in quitting as a function of e-cigarettes 
(65).  All three trials focused on prescribed and/or structured e-cigarette use, which is entirely different from real-
world uptake.  The lack of trials within the U.S., or those with a focus on naturalistic outcomes, is surprising, 
particularly given FDA interest in these products.  Indeed, a recent Cochrane review cautioned against over-
interpretation of these trials, and cited a “low” grade of overall evidence (70).   
The Glaring Literature Gap   
Most e-cigarette studies are 1) lab-based measures of pharmacokinetics or toxicant exposure 2) cross-sectional 
studies of prevalence and predictors of use or 3) uncontrolled cohort studies. The two big remaining questions 
are uptake among non-smokers (not addressed here), and the impact of e-cigarette use among smokers. This 
latter question is important to the ongoing public health debate about where these products fit within tobacco 
control.  E-cigarettes may lower individual health risk, but this may or may not translate to reduced 
population risk.  Four limitations exist within the literature. First, most studies lack a control group and as such 
they are merely descriptive in nature. Second, when studies do include a comparison group, they are almost 
invariably based on groups of self-selected users vs. non-users, which are inherently biased and limit ability to 
draw causal inferences. Third, these studies describe attitudes and patterns of use among established users, 
but say nothing about uptake, trial, and adoption of e-cigarettes among smokers who newly use these products, 
assessed concurrent in real time. Fourth, these studies provide minimal data (with appropriate control 
comparisons) on concurrent and downstream effects on behavior. There is strong need for randomized trials to 
assess these outcomes. Our guiding PREMISE is that there has yet to be a study that a) randomizes 
smokers to e-cigarettes vs. not, b) is focused on naturalistic outcomes; i.e., not cessation focused, c) is 
prospective, with sufficient follow-up duration, d) provides data on nicotine/tobacco exposure, e) is of 
sufficient sample size, and f) is US-based, an important consideration given upcoming regulation. We 
know of no study that meets even two of these criteria.  
INNOVATION 
There is strong need for prospective randomized studies that examine both uptake (trial, adoption, patterns of 
use) and consequences (dual use, quit attempts, abstinence) of e-cigarettes among smokers in the U.S. We 
searched for similar studies, either ongoing or recent, on NIH RePORTER.  We found none, with the possible 
exception of forthcoming research from the VCU TCORS P50 (Project 3: Randomized Control Trial Methods for 
MRTP Evaluation), in which 520 smokers are to be randomized into one of four groups (n=130 each): a) high 
dose e-cigarette, b) mid-dose e-cigarette, c) placebo e-cigarette, or d) non-combusted, non-nicotine cigarette 
substitute (71).  Participants will be given e-cigarettes with advice to reduce smoking by at least 50%, and thus 
is not naturalistic.  That study is primarily focused on toxicant outcomes, and secondarily on behavioral 
outcomes. Another recently funded study (R01 CA184681) does capture longitudinal patterns of e-cigarette use 
through ecological momentary assessment (EMA), but does so seemingly among self-selected users, without a 
control group.  These are clearly strong studies, and ours is similar in some ways.  However, our study will: a) 
be somewhat larger and nationwide, b) include randomized, controlled design, c) capture broad set of uptake 
data dynamically in real time, and d) be inclusive and powered on the most stringent behavioral outcomes.  In 

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fact, ours would be the largest randomized trial of e-cigarette use in the U.S.  The importance of a 
randomized design is critical in that it minimizes self-selection bias, distributing the effects of self-selection 
equally across groups.  Selection bias is well documented in the literature, which explains in part the divergent 
findings as to how e-cigarette use relates to behavior (72).  We have shown the value of randomized designs to 
test naturalistic outcomes of alternative tobacco products (see below), demonstrating the need and importance 
of large, randomized trials that may contradict prevailing indirect evidence.  
Second, our study collects a rich history of patterns of (dual) use, broadly but concretely assessed and 
operationally defined.  Most studies of e-cigarettes describe dual use in binary fashion (use vs. non-use).  A few 
more recent studies do provide broad data on contextual use (32-34, 56), but these studies are all cross-sectional 
and specific to online surveys of established users. The existing literature generally fails to capture the finer 
details of amount, frequency, context, and purpose of novel product use, and how these patterns naturally evolve 
over time.  Moreover, there is a growing sense that some smokers use e-cigarettes for purposeful use, to quit or 
cut down cigarettes, or to circumvent smoking restrictions.  Many other smokers experiment with e-cigarettes 
but never progress.  We do not know how many smokers fall into each of these “purpose buckets.”  Only a 
randomized trial will answer this question.  The three RCTs above provided some information on patterns of use 
(65-67), but were based on prescribed e-cigarette use.  Our focus is on the switching process while it naturally 
occurs, in real time.  Through collection of rich diary data, we have the ability to examine typologies of use, 
through such means as latent class analysis. Within our recently completed snus trial, we are in the process of 
analyzing use data using these methods, which will allow us to understand the varying types of dual use, 
predictors of dual user types, and how these types vary over time.  
Third, we believe this study would provide strong prospective data on biomarkers of use: carbon monoxide 
& NNAL, and cotinine. We expect both CO and NNAL to vary directly with e-cigarette use, but no overall net 
difference in cotinine. The existing literature is predominated by cross-sectional, usually online surveys. We do 
not assess other specific carcinogens, as any results herein would be specific to our e-cigarette of choice.  Here 
we use NNAL as yet another marker to discriminate between smoking vs. vaping, not as a stand-alone 
carcinogen to describe our choice of product.  
Fourth, this study will provide what we believe are the strongest data on how e-cigarettes alter smoking 
behavior, including smoking reduction, quit attempts, and cessation.  The three strongest studies to date are 
cessation studies, and do not provide information on naturalistic impact of e-cigarettes on quitting.  They are 
based outside the U.S., which leaves a gap in the domestic science that is so needed to inform regulatory policy.  
We emphasize that, though this study is powered to detect differences in abstinence, this is not a cessation 
study.  We are not specifically testing if e-cigarettes can help people quit, but will be positioned to evaluate this 
as a potential naturalistic outcome.   
A note on PATH: The Population Assessment of Tobacco and Health (PATH) study (73) is similar in some 
respects to this proposed work.  PATH will provide population-based surveillance of tobacco use and dual use.  
However, at its core, it is a cohort study and is limited by the same challenges noted above.  Furthermore, PATH 
has annual assessments only, with a limited scope of measurement.  We assess patterns of use in a much more 
fine grained and detailed manner, within a randomized design.  
Our study will add to the ongoing literature base that is slowly establishing a whole new lexicon for non-cigarette 
use, and particularly e-cigarettes.  For example, cigarettes per day is one of the most common outcome 
measures within cigarette-focused studies, but this does not translate to e-cigarettes (puffs per day?  puffing 

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episodes per day? cartridges per day?).  Should abstinence definitions include or exclude e-cigarettes? What 
are the implications for either?  These are unsettled questions, and the SRNT listserv has been filled with debate 
on these issues.  We will capture the necessary information to guide the discussion of e-cigarette assessment, 
which we think will offer a significant advance upon the literature.  Smaller studies can do this, but only a large 
prospective study can do it well.  In summary, we do not believe there has yet been a study that offers the same 
innovation as ours.  This study will move the literature forward in significant ways, and is highly attentive to 
recommended NIH research agenda on e-cigarettes (74)(see Table 1 below). 
METHODS 
Relevant Experience / Preliminary Data 
Four trials of sampling NRT, including two large 
nationwide trials and two statewide trials 
  
While the current study is not a cessation trial, we 
briefly show our experience in conducting similar 
“sampling” studies.  Dr. Carpenter led a large, 
nationwide trial of smoking reduction among 
smokers not motivated to quit (N=616), demonstrating that NRT-assisted reduction leads to subsequent 
cessation (75).  We then led another large (N=849), nationwide study of NRT sampling, with less structured use 
of NRT (76).  We demonstrated clinical benefits of sampling NRT, without pressure to quit.  Evidence of 
successful recruitment and retention across both studies is in Table 2.  A third trial, again of NRT sampling (77), 
was smaller (N=150) and managed statewide.  This was a programmatic extension of our prior study, and again 
mailed NRT to smokers and again benefited from high recruitment and retention.  Lastly, we have an ongoing 
cluster randomized clinical trial of smokers (N=1160, current n=905) across 20 primary care sites in South 
Carolina, now testing the pragmatic concept of NRT sampling in real-world settings.      
Three studies of alternative tobacco products (not e-cigarettes), including one large nationwide trial 
Our group has strong experience in the assessment of novel products purported to reduce individual harm.  Dr. 
Carpenter has led two small studies and one large trial, all very similar to the current design.  The first two studies 
were small controlled pilot studies of Ariva/Stonewall (compressed nicotine lozenge) (78) and snus (79), which 
generally showed reductions in smoking and increases in motivation to quit compared to control groups.  The 
third trial was a large randomized study of smokers across the U.S. who either received Camel Snus or not 
(N=1236).  This was the largest, longest, and most direct test of snus ever conducted in the U.S., and parallels 
our intentions herein.  We provided minimal instructions on snus use, much like the current study, and smokers 
could use as much or as little as they wish.  Outcomes during the sampling period focused on uptake: trial, 
adoption, patterns of use, after which smokers were followed for one year, in which naturalistic outcomes include 
quit attempts, cessation, and additional non-cigarette use. These outcomes parallel those within the current 
study, and document our fine-tuned experience with these methods, inclusive of mailing product to individual 
participants.  Recruitment and retention are very strong; see Table 2.  Results from our snus trial are now in, and 
several manuscripts are now published (80, 81). In 
contrast to prevailing literature (mostly indirect studies), 
we were the first randomized study to demonstrate a 
negative impact of snus on quit attempts (no effect on 
cessation).  We mention results here only to 
demonstrate 
the 
value 
and 
importance 
of 
randomized trials over indirect studies to test 
alternative products.   
Ongoing / Recent studies of e-cigarettes 
Dr. 
Carpenter recently completed a small pilot study, 
internally funded, to examine short term (1wk) 
behavioral effects of active vs. placebo e-cigarettes 
(BluCig), using a double-blinded cross-over design 
(N=24). Findings were recently published (82), 

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suggesting: a) strong initial uptake of e-cigarette, b) favorable liking/satisfaction/withdrawal relief, and c) that 
these effects are not nicotine specific (few differences between active vs. placebo). We also just completed a 
R21 that is thematically very similar to the current trial: 68 participants randomized to receive e-cigarettes (again, 
BluCig) or not, with a naturalistic sampling period and prospective follow-up. The R21 used ecological 
momentary assessment (EMA) methods to capture uptake patterns among smokers who are provided an e-
cigarette. The study included a control group (Total N=68) that allows us to compare prospective changes in 
smoking behavior as compared to smokers who do not receive an e-cigarette (BluCig).  Manuscript is in 
preparation and results are to be presented at SRNT 2017. Figure 1 shows uptake and amount of usage of e-
cigarettes, during and following the sampling period.  General uptake during the 3-wk sampling period was 
strong:  100% of those receiving BluCig used at least once, 71% reported regular use during at least 1 of the 3 
sampling weeks, and 63% used daily during at least 1 week. Smokers in the e-cigarette group were 2.9x more 
likely (41 vs. 14%) to independently purchase their own e-cigarette, 1.6x more likely (44% vs. 27%) to make a 
quit attempt, and 3x more likely (15% vs. 5%) to achieve “floating abstinence” (any 7-day period of non-smoking, 
ever within study). Cessation-related outcomes were non-significant for this small study, and we caution against 
over-interpretation since confidence intervals (not shown here) are wide. We found significant reductions in both 
CPD and CO/NNAL, but no changes in cotinine. EMA data suggested clear contextual differences for e- vs. 
conventional cigarettes. EMA compliance was strong: 80% of participants completed at least 12 diaries in 3 
weeks (most comparable to herein) and 78% completed at least 3 diaries/week. These early results show: a) 
general congruence of outcomes to those we hypothesize for the proposed larger trial, b) palatability of product, 
c) uptake/use even without structured instructions to do so, and d) our strong experience in methods: recruitment, 
sampling product, EMA, biomarkers, and product evaluation. The general methods and measures from these 
studies guide our current trial, though we now turn to a 2nd generation device, which should have even fewer 
barriers to uptake and/or substitution.   
Dr. Goniewicz (co-I) has published extensively on nicotine delivery of e-cigarettes, their potential toxicity, and 
patterns of use (16, 23, 28, 31, 32, 74, 83-92).  His expertise in this area is well known.  As just one example, 
he has completed a short-term observational study (84), in which 20 smokers were provided e-cigarettes for ad 
libitum use over a 2-week period; i.e., a study very similar to current proposal. Data demonstrate that increases 
in e-cigarette use occurred in direct proportion with decreases in cigarette smoked, resulting in a non-significant 
net decrease in nicotine. Thus, smokers who engage in dual use are likely to NOT increase total nicotine intake.  
Dr. Wagener (co-I) has published several studies/reviews on e-cigarettes (8, 9, 59, 93-98).  Three studies are 
most relevant to this proposal. The first is a pilot study similarly themed to current application (94), in which  
smokers sampled three separate e-cigarettes within the lab, after which they selected one for 1 week of ad 
libitum use. E-cigarette use during the ad-libitum phase did not substantively increase total tobacco consumed, 
as there was a 44% reduction in cigarettes smoked per day. Readiness and confidence to quit increased 
significantly over the study period.  He next conducted a lab study (95) that compared 2nd vs. 3rd generation 
devices, finding that both deliver cigarette-like amounts of nicotine and reduced carcinogens.  Third, he has a 
newly funded R01 to examine 1st vs. 2nd generation e-cigarettes vs. conventional cigarettes, in terms of 
substitution, biomarkers and changes in nicotine dependence.  This will be a large study (N=453) but still smaller 
than proposed herein and local to Oklahoma (vs. national herein).  Nonetheless, we see great opportunity to 
leverage our studies together, using common assessments where possible for downstream secondary analyses.  
All tolled, he has extensive knowledge of e-cigarette devices and relevant literature. 
  
 
Overlap of Prior/Proposed Studies? The R21 was designed solely as a pilot study (N=68) in advance of a larger 
trial. The main intent of the R21 was to identify the best strategies to track the clinically relevant behaviors of use 
and dual use; i.e., to determine what questions we should be asking.  Our intent within this R01 is to do this on 
a larger scale, with longer follow-up, and with a sizable sample that will ensure adequate power for hypothesis 
testing related to a broader array of outcomes. We believe innovation is high.       
Summary of Preliminary Studies 
Across all studies described above, we have set the stage for next obvious step, i.e., to conduct a large scale, 
prospective, naturalistic study of e-cigarettes, to assess uptake/patterns of use including dual use, and to 

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rigorously determine the ultimate impact on smoking behavior. We have established all methods to a) recruit 
large numbers of participants, b) retain effectively, c) mail product to participants with minimal instructions to 
maintain naturalistic intent, and d) assess a broad range of outcomes, including multiple measures of non-
cigarette tobacco use.  If a study like this is to be done, we are the ones to do it. 
Roles 
Each investigator offers a strong, unique role. Dr. Carpenter is a clinical psychologist with an extensive history 
of conducting local and nationwide clinical trials that test alternative products and their impact on smoking 
behavior. Dr. Gray is a psychiatrist with strong experience in clinical trials for smoking and smoking cessation; 
in addition to his RCT experience, he offers primary medical oversight for the trial.  Dr. Cummings is an 
internationally respected policy expert with extensive experience in product regulation. He leads the ITC cohort 
study, which recently reported on e-cigarette use in the U.S. and abroad, briefly noted above (45), and has a 
pending P01 that focuses specifically on e-cigarettes (of which Dr. Carpenter is Co-I). Drs. Warren (radiation 
oncologist with a strong understanding of e-cigarettes, biomarkers, & nicotine health effects) and Cummings 
have extensive knowledge of e-cigarettes, each having led or co-authored policy statements for national 
organizations (87, 99). Dr. Warren is current or previous chair/member of tobacco-specific sub-committees at 
national level (ASCO, AACR, IASLC). Dr. Goniewicz is a pharmacologist and toxicologist with expertise in e-
cigarette testing and nicotine pharmacology. He has many prior/ongoing studies or reviews that focus on e-
cigarette safety (16, 23, 28, 32, 84, 86-88, 90). Dr. Wagener is a clinical psychologist who also has extensive 
experience conducting e-cigarette (9, 59, 93, 94, 97), secondhand smoke exposure, and other non-cigarette 
tobacco research, including impact on smoking. Together, our outside co-investigators are intimately familiar 
with product design and evolution, including inside knowledge of products soon to come to market.  In all, our 
group is interdisciplinary, transdisciplinary, and highly versed in both content and methods.    
Design Overview 
Eligible smokers, once consented, will be randomized to receive a sample of e-cigarettes (NJoy Pre-Filled Tank; 
n=440) or not (n=220). E-cigarette samples are inclusive of a battery and self-contained tanks of assorted flavors 
to last up to 4 weeks. Participants will be recruited nationally, but a subset (N=120) will be recruited locally to 
allow for biomarker collection (locally recruited participants will be managed via phone/mail as are all other 
participants; details below). Beginning in November 2021, all participants will be asked to complete a carbon 
monoxide breathalyzer remotely (rationale below) and send us the results.  All smokers will be asked to provide 
smoking diary data, captured electronically, daily for 4 weeks. More substantive phone assessment will track 
smoking and related behaviors at baseline (Day 0) and +10, +17, and +24 days (weekly during initial 3 weeks, 
following brief lag for delays in product mailing), and at +1, +3, and +6 months. We will not accept support from 
the e-cigarette industry and believe this independence is an additional strength. 
Naturalistic and Observational, But Randomized?  Is this a cessation trial in disguise?  
Our study is not purely naturalistic, but it comes close. We provide product for free, mailed directly to participants.  
This allows us to examine uptake in the absence of cost/access barriers. Asking smokers to pay for e-cigarettes 
might provide a truly real world test of uptake, but without large numbers of participants, does not allow for 
assessment of uptake and impact on smoking. We have very little interaction with participants other than through 
assessments, and never instruct on cigarette or e-cigarette use.  We provide referrals to cessation support 
(quitlines) for those who wish it. This is not a cessation trial, but rather an observational study of smokers over 
time, some of whom are randomized to receive e-cigarettes to use however they wish, or not at all. 
Randomization removes self-selection bias that is so common in the existing literature. It also allows for 
prospective analysis of uptake, use, trial, and adoption among smokers exposed/offered a product, with needed 
comparison to those who are not given product. Smokers both wanting to quit and not will be enrolled. Our 
primary focus in this study is the impact of e-cigarette use upon smoking behavior, with emphasis on 
uptake/usage/adoption, as well changes in smoking. However, with such a large study, among the first of its kind 
in the U.S., we believe that omission of cessation/quit attempt outcomes in this study would be a significant lost 
opportunity. Furthermore, if there are group differences in cessation, we believe it would be another lost 
opportunity to be underpowered to detect them. Thus, our trial is powered on naturalistic rates of point prevalence 

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abstinence at six-month follow-up, but we again emphasize that we are not promoting or testing e-cigarettes as 
a cessation intervention.   
Regulatory Issues 
FDA regulation of e-cigarettes is now upon us, but regulations, for now, are in regards to manufacturing, 
packaging, warnings (e.g., this product contains nicotine.  Nicotine is addictive), and retail sales (protections 
against sales to minors).  These are all consistent with the earlier (2014) notice from FDA that proposed 
regulations that would: a) ban sale of e-cigarettes to minors, b) ban vending machine sales, c) require warnings 
that e-cigarettes contain nicotine, d) require disclosure of ingredients, and e) prohibit distribution of free- samples, 
but with exemptions for research purposes. We welcome e-cigarette regulation and do not believe forthcoming 
changes will undermine our study. 
The possibility of needing an IND or ITP (investigational new device; tobacco product) weighs over investigators.  
An IND/ITP would definitely be necessary for any trial in which e-cigarettes are presented as a method to quit.  
Our study is naturalistic.  We contend that any need for an IND for a study like ours would have the immediate 
effect of extinguishing all such studies, or at least those that are independent from the e-cigarette industry.  Our 
study is methodologically very similar to our previous snus RCT, with similar outcomes.  For that study, we were 
initially told that we needed at IND, but after a discussion with the FDA in which we emphasized the naturalistic 
nature of our study, we were granted an IND exemption.  Herein, our purpose and methods are very similar: 1) 
we do not promote e-cigarettes as a cessation strategy, 2) there is no therapeutic intent, 3) we do not require 
any level of e-cigarette use (non-use is an outcome), and 4) we are primarily focused on the patterns of use 
during the sampling period, with 2:1 randomization to increase precision.   
Participant Eligibility, Consenting Process, 
Recruitment Feasibility, and Retention 
We will advertise nationally but in select number 
of cities, with separate localized recruitment of a 
subset of smokers. We will choose cities on the 
basis of exceeding national rates for minority 
and/or Hispanic populations; see Table 3 for 
potential cities, which have the added benefit to 
allow for geographic diversity.  Advertisements 
will be spread across various media outlets 
(newspapers, radio, Facebook, craigslist). We 
will now also utilize a respondent-driven (i.e., 
RDS, referral) sampling program. The RDS 
sampling methodology is based on recruiting 
the eligible friends and acquaintances of each 
participant so that the sample “snowballs”. Participants will be told that they can tell friends or acquaintances 
that they think would be interested in the study about the study. Participants will be compensated $30 for every 
person who enrolls (completes week 0 phone call) into the study. Individuals referred to the study may not reside 
in the same household as the study participant while the study participant is enrolled.  All comers, both motivated 
to quit and not, will be eligible to participate. Eligibility criteria include: a) age 21+, b) current smoker of >5 
cigarettes per day for >1 year, c) no recent history of cardiovascular distress (arrhythmia, heart attack in past 3 
months; uncontrolled hypertension) or renal disease (e.g., dialysis) that would interfere with urinary biomarker 
assessment, d) neither pregnant nor breastfeeding, e) at least some concern for health effects of smoking (>none 
at all on a Likert scale), f) no current use of cessation medication, and g) have not purchased in the past 6 months 
or ever regularly used (daily or weekly) a tank system, mechanical mod, or advanced personal vaporizer e-
cigarettes (though previous use of cig-a-like devices will be allowed), h) no regular use (daily or weekly) of any 
e-cigarette (including cig-a-like devices) in the last 6 months, i) and no current use of other tobacco products, j) 
mobile device with capacity to receive SMS text and internet access (for purposes of diary completion and remote 
carbon monoxide breathalyzer using the mobile device; see below) k) no other household members are enrolled 
in the study, and l) no history of seizure disorder.  We restrict the sample to smokers with at least some concern 

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of heath, since this is likely the target population for e-cigarettes.  We considered but rejected eligibility to be 
restricted to those with e-cigarette interest, as we believe that would artificially inflate use. Nonetheless there is 
a potential for a selection bias in that smokers interested in ecig use would be more willing to enroll. This is an 
issue of representativeness to general population (as within any smoking study), but any level of ecig interest 
will be randomized equally across groups.  We restrict our sample to smokers who have minimal use of e-
cigarettes in the past, and follow the precedent of our colleague Dr. Wagener in his ongoing R01. We recognize 
that e-cigarette use is escalating rapidly, yet the most recent, large N survey indicates 54% ever use and 30% 
current use among daily smokers (41).  We are confident in our recruitment capacity but see below for 
contingency planning. data collection.  We hope to minimize a digital divide but recognize that it may still exist.  
Over 84% of Americans have an internet connection, and almost 70% of Americans have a smartphone (Pew 
Poll 2015).  Even so, the digital divide affects the representativeness of those enrolled; it is unlikely to alter 
internal validity (i.e., cause/effect between group assignment and outcome) (100).  
In our prior studies, recruitment from national sources was based on mail-based return of consent form.  That is, 
after determination of eligibility, we mailed a consent form (2 copies) and asked the participant to sign one copy 
and return to us in a pre-addressed, pre-stamped envelope.  Receipt on our end is the start of official consent, 
and official enrollment begins with first phone call.  While this process generally worked, it was terribly inefficient.  
For one, mailing delays often meant a lag between the person expressing interest and when s/he was officially 
enrolled.  Second, there are occasional instances where the person does not fully complete the consent form 
(e.g., missing date), or does not return all necessary pages, and the process repeats itself (back and forth 
mailing) until we get all forms in full.  Third, a large number (~55%) of the consent forms go un-returned, for any 
number of reasons.  Thus if we want 660 participants we would need to identify ~1460 who would be eligible 
and interested.  Loss of interest in the study is a possibility (which we abide by) but we also believe that consents 
get lost, thrown away, regarded as junk mail (particularly if seen by someone else), etc.  For this study, we will 
continue with this procedure of mail-based consenting, but add three new design features intended to address 
this inefficiency, described below: 1) real time, personalized online consent through doxy.me (i.e, teleconsent), 
2) electronic consent (e-consent) via REDCAp and 3) nominal pre-payment sent along with mailed consent 
package.   
First, we will provide participants with the option to complete consent via MUSC’s doxy.me system (teleconsent). 
After the initial determination of study eligibility, assessed online in our secure survey, participants will be asked 
about their capacity for doxy.me procedures, including access to a computer with or without a webcam and 
speakers, and compatible internet browser (doxy.me is currently optimized for Google Chrome & Firefox, with 
planned expansion to additional browsers). For those who have the required hardware and software for doxy.me, 
we will then describe the basic mechanics of the teleconsent process and ask for their preference for mode of 
consent.  Participants can choose their preferred method of consent, and none of this is an eligibility requirement.  
Participants who do not meet doxy.me or REDCap requirements will be mailed a consent form following standard 
procedures. Thus, we are simply offering other, more immediate options for study consent that may be preferable 
for some participants. 
For those who elect teleconsent, we will follow procedures as per precedent (101) and through consultation with 
MUSC researchers (Dr. Brandon Welch).  We have consulted with 3 other MUSC investigators who are currently 
using doxy.me and their favorable impression guides our decision here.  All doxy.me signed consent forms will 
be saved as pdf files within our study records. Research staff will then have the participant complete the baseline 
questionnaire (that would otherwise be sent with mailed questionnaire; see below).  We anticipate that 10-20% 
of study participants might enroll through this method.  Signatures on the consent form may be electronically via 
REDCap/doxy.me. Participants who do not have access to the required technology to complete consent remotely 
via REDCap or doxy.me will sent a consent packet by mail.  
For those opting to receive consent packet through the mail, we will follow our prior procedures, sending a packet 
inclusive of two copies of the consent, a baseline questionnaire, and a pre-addressed, pre-stamped return 
envelope.  Participants will sign and return one consent and their completed baseline questionnaire.  We have 
a toll-free phone line to support anyone who calls with questions.  Upon return to our office, data are entered 
into our database, and these individuals comprise the consented sample.  However, the sample is reduced 

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further to those with whom we are able to establish first phone contact (Day 0); i.e., the enrolled sample.  This 
enrolled sample is the intent-to-treat sample.  We have used this procedure in 3 other nationwide trials, and, as 
noted above, consistently get about ~45% which we think is an inefficient use of resources, both financial and 
personnel.  As a second strategy to attempt to increase recruitment efficiency, we will insert $5 cash within the 
packet of consent/baseline questionnaire for a subset of participants.    This is based on findings elsewhere (102-
104) that show the effectiveness of pre-payment to increase survey response in large, nationwide mail-based 
studies.  This pre-payment is NOT tied to any behavior, and is independent of whether the individual returns the 
consent form, or not.  The amount is kept low to decrease any coercive impression.  We simply wish to know 
whether such pre-payment increases the rate of returned mailings (above 45%).  Toward that end, and merely 
because we aim to pilot these procedures, we will randomize these mailings 1:4, such that 20% will get this $5 
pre-payment, and 80% will not.  The outcome is merely the return rate, and will be compared to doxy.me 
completion rates above, all with the intent to optimize efficiency of recruitment (proportion of eligible and 
interested individuals who complete consent).  We do not offer a second consent for these procedures because 
the single aggregate outcome is % enrolled (i.e., no individual level outcomes).  This itself becomes an outcome 
we can report in the literature.   
Finally, though we cannot fully ensure that this will be a representative sample, we make efforts toward that goal.  
One strategy is to pick cities with large, diverse populations, noted above in Table 3.  But within a city, we are 
unsure how fast or slow recruitment might take, and we run the possibility that several hundred participants could 
come from one locale.  We wish to avoid that and will impose an upper limit of 80 participants per city.  As we 
approach this upper limit, we can limit further enrollment by either removing the ad or temporarily removing the 
screening link.  We had to do this in one prior national survey when 300+ survey respondents joined over a 3-
day weekend.  Given the demands of phone calls and mailings on our end, we may have to take more explicit 
measures to titrate our flow if this should happen again.  As a last resort, and if more than 80 participants enroll 
from one city, we may need to turn eligible people away, and will send them a letter to such effect (“thank you 
for your interest but our study has reached full capacity in your geographic area”) and will include cessation 
referral information specific to that area. 
Recruitment & Retention Feasibility Our primary concern with regard to recruitment is the increasing use of e-
cigarettes nationally (38, 105), balanced by our focus on minimal users. While e-cigarette use is increasingly 
common, non-use is still the norm by far (45).  We initially considered eligibility to be based on never-users, but 
decided to follow recent precedent (94) to restrict to allow prior experimenters. We will review this issue prior to 
study start; our guiding philosophy is a focus on smokers new to e-cigarettes, or at least with minimal history.  In 
our prior local studies of alternative tobacco, with almost no recruitment budget, we recruited 34 participants in 
2 months (78), and 65 participants in 11 months (79).  In our prior statewide study of mailed NRT, we recruited 
165 participants in 6 months (77).  Rates of recruitment for our national studies are noted above within 
Preliminary Studies.  For the current study, we anticipate an overall recruitment period of 42 months (60 minus 
8 months setup, minus 6 months of final follow-up, minus 4 months of data analyses at study end).  We are 
confident in our ability to recruit 16 participants per month (660/42).  Retention:  The same table above 
demonstrates our consistently strong rates of retention, as measured by % of scheduled contacts completed.  
One subset of the current study will be recruited locally, in which we are also still strong with retention: across 
264 scheduled in-person visits in our prior studies of Ariva/Stonewall and Camel Snus (collapsed), we completed 
>95%.  Within our recent R21, we completed 89% of all scheduled follow-up visits.   
Local Subset / Biomarkers 
One strength of our study is the collection of exposure biomarkers: CO, cotinine, and NNAL (the latter to be 
tested at Roswell Park).  We do this based on the targeted recruitment of smokers local in our area who complete 
a baseline in-person lab visit (N=120).  This subset will be managed via phone and email as are all other 
participants, with the sole exception that they will be asked/incentivized to provide biospecimen samples 4 times: 
a) baseline, b) +10 days (first sampling week; CO only), c) +1 month, and d) +6 months. We will accommodate 
biospecimen collection at local participants’ home or work, or visit to our lab, in order to minimize participant 
burden and research office contact. Furthermore, we will provide transportation via Yellow Cab of Charleston 
(paying them directly through Addiction Sciences Division account) for participants who are eligible, but do not 

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have reliable transportation to in-person visits. Power and sample size estimations for this subset are presented 
below. 
Beginning November 2021, we will ask all participants to complete a carbon monoxide breathalyzer remotely 
four times (at Weeks 0, 1, 4, and 24 – the same time points we were doing in person) and send us the results. 
Our rationale for doing so is to ensure that we reach our target sample size within the funding time, especially 
given COVID constraints, recognizing that biospecimen collection is critically dependent upon CO breath 
samples (which can now be collected remotely; not available at the outset of the study).   
We will mail an iCO Smokerlyzer to all participants after they complete the week 0 call.  The device connects to 
a smartphone (both Android and iOS); thus, we require this as an eligibility criterion. The iCO can detect CO 
concentrations of 0-100 ppm, and has strong test-retest reliability, as well as concordance with traditional device.  
We will include instructions to complete the iCO breathalyzer, and all CO readings will be time/date stamped, 
providing objective recordings. The $70 iCO device can be used repeatedly within a person, but it is not returned 
at end of study as it is not transferable to another user. A portion of participant compensation is contingent upon 
iCO compliance (not abstinence). We define abstinence as 7-day point prevalence (no smoking in prior week), 
with CO <4ppm. 
Randomization 
We considered a host of potential moderators on which to stratify randomization, but ultimately the sample size 
should be sufficient to equalize groups (if incorrect, analyses will include covariates).  However, outcomes might 
very well be moderated by readiness to quit, and thus randomization will be stratified based on desire to quit in 
next 30 days (0-6 vs. 7-10 on a VAS scale).  We will also stratify on locally recruited vs. not. Participants will be 
randomized via a stratified, mixed block design, overseen by our statistician. 
E-cigarette Group    
Our general aim within this group is to approximate the real-world scenario in which 
smokers are exposed to e-cigarette products and decide on their own if and how they will use them.  There are 
numerous products on the market offering a range of nicotine delivery. Recent evidence suggests that any 
association between e-cigarettes and cessation is restricted to users of tank systems, as opposed ciga-like 
devices (53). Our guiding intent is to use the most efficient yet non-adulterable product available, optimizing user 
satisfaction. We previously evaluated a 1st Generation product (BluCig), with generally strong patterns of uptake.  
We were hesitant to move to a 2nd generation system because we could find none that were pre-filled, closed, 
disposable (“ego style”) tank systems, a necessary requirement given our methods (we do not want home 
consumers adulterating the tank [spilling, adding more] in any way, for both safety and scientific reasons). 
Recently a new product came to market that meets this requirement: NJoy’s pre-filled tank & battery.  This is a 
closed tank system, sufficiently powered (1000MAH), and delivering sufficient nicotine (95, 106), even among 
smokers naïve to e-cigarettes (107). NJoy comes in 10 flavors, including traditional tobacco and menthol. Given 
the popularity of flavors within these devices, and potential role they may have for helping to reduce smoking, 
we will offer an assortment of each: top 5 selling flavors only; none of which contain diacetyl which some reports 
link to “popcorn lung” (108).  We believe that a range of flavors more closely mimics the real world scenario of 
multiple options from which to choose; if e-cigarette uptake within our study is low, we do not want that to be an 
artifact of unrealistic range of flavors.  If menthol or any other flavors are banned by FDA, as has been discussed, 
we will abide.  We will offer participants 3ml pre-filled tanked, 15mg/ml of nicotine. NJoy does come in 25mg 
dosage but we opt for the 15mg to conform to SREC comparability (see below).   
One clear advantage of NJoy system is that is the basis for the newly released NIDA Standardized Research E-
Cigarette (SREC), which now provides a naturalistic test of a standardized product that will be used in other lab-
based studies (http://www.e-cigarette-summit.us.com/files/2017/05/Kevin-Walton.pdf).  In fact the only 
difference between our product and the SREC will be packaging; ours will be provided in original NJOY 
packaging.  A menu of several e-cigarette products would better reflect our naturalistic intent, but this would 
introduce too much variability, as we would need to account for switching between products and a range of 
product characteristics. While this study is based on one product alone, we view it as a test of the concept of e-
cigarettes in general, and thus more broadly generalizable.  We recognize that products change regularly and 
what we choose now may not be the best option once this study starts.  There are other options that may meet 
our criteria (e.g., V2, LogicPro) and if a more efficient, closed tank-based system becomes available, we will 

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consider it. Throughout this decision process, we will work within our investigative team prior to study onset to 
verify product and will discuss changes with NIH.  NJoy availability or design might also change after the study 
starts, and if so the same guiding principles will apply.  We expect product changes during the course of the 
study, as occurred within our R21.  We view this as a strength, not a weakness, in that a) it gives us an opportunity 
to inventory a small number of each product for lab testing and product comparison, b) allows us to examine 
product evolution over time, and c) allows for sub-analyses of each product, recognizing limited power to do so. 
The overall comparison of e-cigarette vs. control group will control for any product changes as a covariate. 
Finally, to authenticate key biological variables, we will verify the nicotine content (through sub-contract with 
Dr. Goniewicz) on a regular basis (e.g., xx times per year, or every Nth product; depending on participant flow).  
Samples will be provided in two 2-week shipments, with sufficient number of tanks to last ~4 weeks. We will 
send 2nd shipment only after request for more product is initiated by participant, either through response to diary 
question, call to our 1-800 number, or return of a mailback card sent along with 1st shipment.  Flavors for 2nd 
shipment will be participant-driven.  No more than 30 tanks will be sent to any one participant, no more than 20 
for first shipment (slightly more than half of sampling period, to account for lag time after 2nd mailing) and 10 for 
second shipment.  Each shipment will contain instructions not to allow anyone else to use the product, to store 
in a location out of reach of minors, and how to safely charge/clean product, per FDA guidelines.  Based on a 
recent experience from Dr. Wagener and also through personal communications with both he and Dr. Goniewicz, 
we anticipate that ~25% of the sample will use daily; 50% will use for about 2weeks, and 25% will use less than 
that; these are all conservative estimates, as we do not want to artificially suppress uptake through insufficient 
sampling. Driven largely by our prior studies on product sampling, we believe this 4-wk period allows for a 
sufficient period of acclimation, in which smokers determine if they like it, and how to use it to manage cravings.  
This period also allows sufficient time to observe naturalistic changes in cigarette smoking, dynamic and 
concurrent in time with uptake of e-cigarette use. Smokers will not be asked to return product, but we will assess 
sustained and/or intermittent use over extended duration (6 months).  Our group has a strong record of this 
sampling “intervention,” in which we give smokers the opportunity to try new products over a short period, testing 
their impact both during and beyond the sampling period (see above).  
We carefully deliberated the instructions we would provide for e-cigarette use.  On one hand, providing minimal 
instruction is most reflective of the real-world scenario in which smokers self-determine amount of use. It is also 
consistent with our primary aim, in which we examine usage under natural conditions.  On the other hand, we 
are concerned that a total absence of suggested use could result in minimal usage, which would undermine the 
study’s secondary aim to examine prospective changes in smoking behavior.  Thus, we will provide minimal 
suggestion, guided in large part by our previous sampling studies (NRT, snus, e-cigarettes; see preliminary 
studies above) ~“studies e-cigarettes are likely much safer than conventional cigarettes, but they are not entirely 
safe.  We encourage you to use them as much as you can, either to  cut down or quit smoking, or to help manage 
smoking restrictions. You are not required to use e-cigarettes and how you use it is completely up to you.  As 
with all tobacco products, you should keep these out of reach from children.” Product will be provided in original 
packaging, inclusive of instructions and marketing themes (reduced individual harm as compared to smoking). 
At the end of the 4-week sampling period, we discontinue any further e-cigarette administration but allow 
participants to keep any existing product, which we believe is most consistent with the real world experience of 
sampling a new product.  
Control Group Smokers in this group will smoke their own brand of cigarettes, as they wish, for the duration of 
the sampling period, but will engage in all study assessments including diary assessments.  We considered a 
placebo-controlled study, but our interest is not in the pharmacological intake of nicotine (vs. not), but rather the 
use (inclusive of behavioral substitution) vs. non-use of e-cigarettes.  Blinding of active vs. placebo e-cigarettes 
is easily managed for lab-studies, but less so for nationwide, naturalistic studies.  To control for cost differential 
with e-cigarette group, participants in the control group will receive extra reimbursement; we do not want changes 
in behavior to be a function of free product.  We debated whether to provide same general message of e-
cigarettes as above. On one hand, doing so would decrease study-induced demand to try e-cigarettes, making 
this equivalent across groups.  However, this would artificially inflate e-cigarette use within the non-sampling 
control group, which impinges on internal validity.  We opt not to include such messaging, but will acknowledge 

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this as a potential confound.  We do anticipate that some control participants will use e-cigarettes, and this too 
is an outcome we assess (along with all other uptake variables).  Within our R21, 1/21 control participants (4.7%) 
used an e-cigarette (see Figure above). E-cig use is escalating, but still low and we expect nominal initiation 
within control group. Primary analyses will be based on ITT principles (i.e., group assignment as the independent 
variable, not product use).  
Smokers in both groups will be assessed at screening for interest in cessation support.  All comers will be 
accepted, but smokers actively using cessation medication at baseline will be excluded.  At all contacts, we will 
assess motivation to quit and, as necessary, offer referral information to state quitlines.  Such transitions to 
cessation support are included as study outcomes.   
Diary of E-cigarette Uptake  
All smokers will be asked to complete a brief diary to assess smoking behavior, both conventional and e-
cigarettes.  We make no claim that this purely follows ecological momentary assessment procedures.  Rather, 
ours is more accurately described as regular, periodic diary assessment, which we believe still captures our 
intent to understand uptake behavior as it happens.  Diaries will be electronically collected daily for 28 days.  We 
have established procedures to auto-send an email or SMS text (their choice) on a set schedule for intended 
diary days (first 28 days of sampling).  SMS text messaging is possible via Twilio, embedded within our RedCap 
system, which allows participants to complete a survey directly from their phone, without having to access a 
webpage. Note that diaries are not completed via texting, just prompted via text with link to RedCap.  Participants 
can choose to receive their diary invitations via email if they wish, again with a link to an secure RedCap survey.  
Thus, per eligibility criteria above, we require participants to have either 1) SMS text capacity with internet access 
OR 2) regular (at least daily) use of email.  Diaries consist of a brief (2-5 minutes) survey, directly entered into 
an online protected database (REDCap) through secure encryption; all PHI is protected.  Participants will be 
compensated for compliance based on % diaries completed (e.g., $100 for 90-100% compliance across all 28 
entries, $70 for 75% compliance, etc).  Diary data include items on I) conventional smoking behavior (amount, 
frequency, time to first cigarette), II) e-cigarettes (puffing episodes, puffs per episode and per hour, time to fist 
puff), and III) general mood & stress. There is some debate as to whether EMA/diary procedures alter behavior, 
but recent evidence suggests that assessment is unrelated to smoking (109).  We considered a non-diary control 
group, but believe a third group is unwise; our trial is not a test of EMA/diary methods and any diary-specific 
effect, if it exists, is uniform across both groups. See preliminary studies above for documentation of diary 
compliance using similar methods.   
Other Assessments 
Assessments will occur at 7 points: baseline, weekly x4 for first month, and at both 3 and 6 months.  Our baseline 
demographic questionnaire will include common items from NHIS and Adult Tobacco Survey. We will assess 
prior quit history (#, duration of attempts, medications used), motivation to quit, dependence, using standardized 
surveys were possible (110-113).  We also assess withdrawal (114), and attitudes towards novel products (115-
119). Follow-up assessments (~20-25min phone call, based on our prior experience) include:     
Uptake  
Based in part on marketing theory that describes product adoption/diffusion (120), we will report 
incidence rates for uptake of use, based on participants who engage in: a) first time use, b) repeat use, c) first 
time buyer, d) repeat buyer, and e) advocacy; i.e., promoting/suggesting to others.  We will also be poised to 
assess the time course for each.  We will also track transitions to other e-cigarette systems, e.g., tanks, flavors.   
Patterns of E-Cigarette Use Through both diaries and phone-based assessments, we will determine the 
duration, quantity, and frequency of e-cigarette use, as well ascertain the context in which it is used (e.g., places 
with smoking restrictions vs. other) using prior methods (121). We will specifically ask if e-cigarettes are used 
“not to quit, but to reduce the amount you smoke” or “not to quit or reduce smoking, but to get you through times 
when you cannot smoke.”  Some research (122) suggests a distinction between purpose-driven use vs. mere 
experimentation, with different trajectories/consequences of each, and we will assess this as well.  We will also 
assess any use of other tobacco products, and can determine patterns of dual use as well. 
Other e-cigarette outcomes  We will also assess likeability/preference of e-cigarettes through items derived 
from the Cigarette Evaluation Scale (123) and related acute effects from smoking (e.g., calming, relaxing, 
arousing) (124), and select items from the Product Evaluation Scale (125) that is specifically meant for evaluation 

Protocol Version Date: 11/1/2021 
 
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of novel products.  Lastly, though assessment of e-cigarette dependence is not well understood (126), we will 
use the recently developed scale from Foulds et al (127).  
Motivation and Confidence to Quit will be assessed separately for conventional and e-cigarettes, using our 
modification of the Contemplation Ladder (128) to measure readiness to quit in the next 1 month and readiness 
to quit in the next 6 months. Our ladders have adequate test-retest stability and predictive validity (129). 
Use of Other Cessation Resources We will assess use of other cessation resources, including use of NRT and 
non-NRT pharmacotherapy, behavioral counseling, quitlines, and consultation with other health professionals. 
We will also clarify the intensity (i.e., duration) and timing of use of these cessation resources. 
Changes in Smoking We have established procedures to assess: a) smoking reduction, b) self-defined quit 
attempts, c) CDC-defined 24hr quit attempts (130).  Abstinence will be based on established definitions (131) 
and will include: d) floating abstinence (any 7-day period of non-smoking throughout the course of the study), 
which may be more appropriate for non-cessation trials (132), e) point prevalence, and f) sustained abstinence.   
Adverse Events  Prior e-cigarette literature suggests that adverse events are rare and mild: headache, 
mouth/throat irritation, and nausea (24, 32, 56).  Dr. Gray provides medical oversight throughout.   
Biomarkers  
Within the locally recruited subset who complete the baseline in-person lab visit, urine cotinine 
and NNAL level will serve as a biomarker for nicotine exposure, both to conventional and e-cigarettes.  Four 
samples will be collected per person: a) baseline, b) after 1st wk of sampling, c) 1 month follow-up, and d) 6 
month follow-up.  Samples will be assayed locally by our Department of Psychiatry’s Clinical Neurobiology lab 
(cotinine) and remotely at Roswell Park Cancer Institute (NNAL).  Collection of NNAL (tested beyond week 1, so 
as to not be confounded with baseline cigarette smoking), as well CO, will allow us to discriminate between use 
of e- vs. conventional cigarettes.  We do not expect cotinine to change as smokers switch from one product to 
another, but we do expect decreases in CO and NNAL, to the extent that smokers switch from cigarettes to e-
cigarettes (issues of compensation notwithstanding).    
CAPTURE (Chronic Obstructive Pulmonary Disease Assessment in Primary Care to Identify Undiagnosed 
Respirator Disease and Exacerbation Risk):  To be asked at Baseline only, to ascertain the distribution of 
capture scores among a general community-based sample of smokers.  
 
Added Assessments: Charleston/Local Sample 
For the local study sample who complete the baseline in-person lab visit (N=120), we previously included a 
number of in-depth mechanistic and outcome measures that are not possible with phone-based assessment of 
a national sample. These outcomes were considered exploratory and were not vital to the main aims of the 
project. In the context of COVID-19 and the ongoing need to minimize unnecessary in-person study 
procedures, we will now send a REDCap link to these questionnaires to local participants via text/email after 
the corresponding study phone call. The Go/No-Go task (Task-based measure of Inhibitory Control), which is 
not able to be completed via REDCap survey, will be eliminated.  The in-person lab visit will be used ONLY to 
collect CO and urine samples, for biomarker testing (which are vital to the main aims of the study). These 
revised procedures will require ~5 minutes of lab time only and will incur minimal interaction between study 
staff and participants. We will follow all SOPs for appropriate social distancing that are applicable to our lab 
studies as a whole. Participant remuneration for these lab visits remain unchanged.  The only change for them 
is a shorter visit.  
 
Thus, for this sample alone, we include additional measures to capture the following, all of which will be sent as 
a REDCap survey after the following phone calls: 1) baseline, 2) week 4 (end of sampling), and 3) 6 month (final 
follow-up). 
  
a) Task-based measure of Inhibitory Control: This task will be eliminated, as it is not able to be asked 
via REDCap survey and is not a main outcome of the study.   
b) Task-based measure of Inhibitory Control: Delay Discounting.  Delay discounting is a behavioral 
economic construct that helps explain addictive behaviors. It is well established that smokers have 
higher levels of discounting (impulsivity) than do non-smokers. However, no prospective study has 
examined discounting as a predictor of vaping uptake or how transitioning to vaping may alter 
discounting (135). The current study will fill these gaps by testing whether discounting at baseline 

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predicts vaping uptake/substitution during the sampling period and at follow-up, and how sampling 
e-cigarettes changes discounting (relative to control). Participants will undergo a 5-trial (136) 
adjusting delay discounting task, with delay timeframes ranging from 1hr to 18yrs, with magnitudes 
of either $500 now or $1000 at the specified delay. This task is used to determine the preference for 
smaller magnitude immediate rewards compared to larger magnitude delayed rewards, or k value. 
To efficiently determine k values, the task uses a progressive forced choice sequence over five 
choice trials.  A higher k value indicates steeper discounting; i.e., impulsivity.   
c) Subjective measure of impulsivity: Barrett Impulsivity Scale (BIS).  The BIS is the most widely cited 
instrument to assess impulsiveness, and complements above objective measures of the same (137). 
The 30-item BIS captures 6 sub-factors: attention, cognitive instability, motor, perseverance, self-
control, and cognitive complexity.   
d) Experimental Tobacco Marketplace (ETM): The ETM (138) allows us to address questions of 
whether e-cigarette sampling changes demand for cigarettes; i.e., substitutability, in ways that 
complement or our other outcomes.  During the ETM (~10 minutes), participants make hypothetical 
purchases of cigarettes and other tobacco products at various prices. This task can be used to 
understand how demand for cigarettes changes as price is increased and which products are likely 
to substitute for cigarettes as higher prices. During each of these sessions, participants will view an 
online store of tobacco products, and be instructed to make purchases, with access to an account 
balance consistent with weekly expenditure on cigarettes/e-cigarettes. Participants will be told that 
purchases are hypothetical, but to complete the task as if purchases are real. A picture, description, 
and price will be displayed for each tobacco products. Products may include cigarettes, e-liquid, 
nicotine gum, nicotine lozenges, snus, and dip. Participants will complete five trials, and across trials 
prices will vary for cigarettes (1/2x, 1x, 2x, 4x, 8x market price). Nicotine replacement therapies 
(gum/lozenge), smokeless tobacco (snus, dip), and e-cigarettes / e-liquids will be available at a 
constant price across all purchase decisions. 
 
 
Statistical Considerations – Sample Size & Power 
Our primary focus in this study is the short and long term 
behavioral impact of e-cigarettes. We considered a trial that 
was powered on e-cigarette uptake or substitution of 
conventional cigarettes (Specific Aim 1).  These questions 
alone are important outcomes that would inform the public 
health debate. However, it would be a missed opportunity to 
not ask about overall exposure (Specific Aim 2) and quitting 
(Specific Aim 3).  We also believe that, if group differences in 
quitting do exist, it would be entirely shortsighted if we lacked 
sensitivity (i.e., power) to demonstrate them.  So while we focus on uptake, adoption, and patterns of dual use, 
we power our study on the most stringent outcome: point prevalence abstinence at 6 months (continuous 
abstinence is more stringent, but inappropriate here since there is no quit date to commence cessation). Table 
4 depicts various sample size scenarios, under the guiding belief that e-cigarettes will facilitate quitting (see 
Intro). These scenarios are all based on proposed 2:1 randomization, in an effort to be have better precision 
(i.e., tighter confidence intervals) for our primary outcome, involving only the e-cigarette group. Our base rates 
for quitting come from our three prior/ongoing nationwide studies, in which 6-month abstinence rates within 
control group ranged 4-13% (75, 76). We also recognize other population estimates (~5%) of population-based 
quitting (133), and ultimately take an estimate within this range: 8%. As for quit rates in the e-cigarette group, 
we note outcomes from most relevant study yet published: an uncontrolled observational cohort (i.e., naturalistic) 
study of smokers given e-cigarettes for up to 6 months (47), in which 23% (ITT, based on full N) and 33-39% 
(respondent only analyses) were quit at 6 months. We think that is a bit high.  Given that we provide e-cigarettes 
for only ~1month, we expect somewhat lower rates of quitting.  We power on a RR of 2.0: 8% vs. 16%. We would 
like to detect even smaller increases in quit rates (5% vs. 10%; RR still 2.0), but clearly that is not feasible.  We 
thus decide on 600 participants, but inflate this by 10% to account for attrition: 660 participants to be enrolled 

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(440 e-cigarette vs. 220 control). With this sample, we have 80% power to detect differences of 10% vs. 18.5%, 
with a two-sided α=0.05 chi-square test. If provision of e-cigarettes should undermine quitting, we are able to 
detect a decrement of 4% (8% vs. 4%; OR = .48; 95% CI: 0.23 - 0.98).   
Precision of Proportional estimates within Aim 1  With 440 e-cigarette participants, a two-sided 95.0% confidence 
interval will have a maximum half-width of 0.047 (for any outcome in Aim1) (e.g., 95% CI on an observed rate of 
50% would range from 0.453 to 0.547). As the observed proportion deviates from 50% (either higher or lower), 
the 95% confidence interval will decrease in width, providing more precision on the estimate (e.g., 95% CI on 
90% would range from 0.872 to 0.928). A confidence interval as wide as +/- 5% is a relatively small width and 
one we are willing to accept for our estimates on uptake, adoption, and patterns of dual use. 
Sample size estimate for local subset, to capture biomarkers of exposure We searched for prospective e-
cigarette studies (controlled or not) that were NOT cessation based but reported on biomarkers, either CO or 
cotinine.  We found only one (47) but acute decreases (within 1 month) in CO were only indirectly reported (in 
graph, separated by smoking group outcomes).  Consistent with those data, we expect an overall decrease in 
CO within 1month of 35%, compared with no such decreases within the control group.  A sample size of 80 in 
the e-cigarette local subset group (total of n=120 for the local subset who complete the baseline in-person lab 
visit) will have at least 80% power to detect a 35% decrease in CO (23.5ppm to 15.3ppm in the e-cigarette group 
vs. no change in the control group, assuming a standard deviation of 15.0 in the control group and 10.0 in the e-
cigarette group (standard deviations were estimated from the interquartile ranges reported in the Polosa study 
(47) using standard procedures (134)). We expect no group differences in cotinine.  We recognize that this 
technically implies a test of equivalence, and we are willing to accept up to +/-3% difference to conclude 
equivalence.  With a local subset sample size of n=120 (40 control and 80 e-cigarette), α=0.05, a two-sided t-
test has >90% power to reject the null hypothesis that the two groups are not equivalent (1400ng/ml +/- 45ng/ml) 
in favor of the alternative hypothesis that the mean cotinine levels of the two groups are equivalent (assuming 
an expected difference in means of 0ng/ml and a common standard deviation of 70.0ng/ml) (135).  
Statistical Considerations – Data Analyses 
All analyses are based on an intent-to-treat approach, and are nearly identical to analyses from our prior studies. 
Exploratory analysis will be performed on all outcomes, and both graphical displays and summary statistics will 
help determine if transformations will be needed. Any significant baseline differences between groups will be 
included in regression analyses described below. Missing Data: The most conservative approach for handling 
missing data (136) is to substitute them with baseline values, assuming outcomes have all returned to baseline, 
with no quit attempts made. If this assumption is incorrect, it biases the results towards the null by reducing 
between group differences (136). SRNT recommendations (136) suggest that when there are small amounts of 
missing data (< 10%) the conservative approach can be used with little bias in outcome.  If missing data >10%, 
we will calculate results not only using this conservative method but also using methods in which missing data 
are imputed as described in the SRNT guidelines. We will also assess whether dropout is differential by study 
group. In our current and recent studies of cessation induction, this has not been the case, and we expect similar 
results here.  Finally, though sex/gender is not an explicit focus, we are well poised, given our sample size, to 
address potential sex/gender influences on study outcomes.    
Hyp 1a: Within the e-cigarette group, we hypothesize that at least 90% of smokers will try at least once, 
45% will use regularly (at least 4/7 days, at any time during follow-up), 25% will use daily for >7 days (at any 
time during follow-up), 25%/10% will be using at least some/daily at final follow-up, and 20% will independently 
purchase e-cigarettes.  Within the e-cigarette group (N=440), we will analyze prevalence of each definition of 
uptake. We will estimate each of these parameters, and the 95% confidence interval for each proportion.  
 
Hyp1b:  Among smokers in the e-cigarette group, 40% will show a minimum of 50% reduction in cigarettes 
per day, and as a group, e-cigarette users will show greater decreases in cigs/day as compared to control.  
Reduction of cigarettes per day will be calculated based on the TLFB at baseline and 6 months for both groups, 
with “reducers” defined as those that reduce cigarettes per day by >50%. We will estimate the mean reduction, 
95% confidence interval on the reduction, and the proportion of those that are considered “reducers” in both 
groups, the latter tested via a chi-square test with α=0.05 two-sided significance level. 

Protocol Version Date: 11/1/2021 
 
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Hyp2:  Within a subset of locally recruited smokers, we hypothesize that use of e-cigarettes will lead to 
significant decreases in CO and NNAL, but no net change in overall nicotine exposure.  Changes in CO, NNAL 
and cotinine will be calculated based on the baseline, 1, and 6-month measurements. Mean change in CO/NNAL 
(thru 1 and 6 months) will be compared via a two-sample t-test with α=0.05 significant level. In order to test 
equivalence for change in cotinine between the two groups, the TOST procedure (137) will be followed where 
two one-sided tests will be used to test that the difference in the change between groups (Δ) is within the +/-3% 
bounds that we are willing to accept (H01: Δ < -3%; H02: Δ < +3%). If Δ is contained completely within the +/-3% 
bounds, then equivalence will be concluded. We will use an α=0.05 significance level.  
 
Hyp 3:  Provision and use of e-cigarettes will lead to increased incidence of (Hyp3a) quit attempts, (Hyp 
3b) cessation, and (Hyp 3c) motivation/confidence to quit.  Logistic regression analyses will be performed with 
(1) any quit attempt [QA] within 6 months (1=one or more quit attempts, 0=none), (2) any 24hr QA (same), (3) 
point prevalence abstinence at 6 months (1=7 day abstinence at 6 months, 0=not abstinent), and (4) floating 
abstinence (1=any 7 day period of abstinence during study, 0=no abstinence) as the outcomes and group (e-
cigarette vs. control) as the covariate. The coefficient on group provides a log odds ratio for measuring the odds 
of (1) any self-defined QA’s, (2) 24hr QA’s, (3) point prevalence abstinence, and (4) floating abstinence. 
Statistical significance of these coefficients will be assessed using a p-value with a two-sided α=0.05, and both 
the odds ratio and its 95% confidence interval (CI) will be reported. Motivation and confidence to quit are 
evaluated on a continuous scale (0-10), and linear regression models will be used for both, with group (e-
cigarette vs. control) as the covariate (along with any other baseline differences as covariates). To fully capitalize 
on the longitudinal nature of our data, secondary analyses will include a) regression analyses where number of 
QA’s are compared across groups using poisson or linear regression (depending on the distribution of QA’s), b) 
latency to any quit attempt, and c) stability of e- and conventional cigarette use over time. We will also repeat 
above analyses: a) based on those who do vs. do not use e-cigarettes, and b) using quit attempts and abstinence 
of all tobacco products, including e-cigarettes.  
Ancillary Analyses:   
Moderator analyses We can examine if uptake and/or consequences are moderated by motivation to quit, 
dependence, concerns of health, and/or any potential changes in e-cigarette product. Mediation of “intervention” 
effects: For each potential mediator (e.g., changes in harm perception/likeability of e-cigarettes), we will fit logistic 
models to assess each individually for each outcome. Latent class analyses: Patterns of use may be explored 
using latent class or other latent variable models to investigate if subjects separate into “classes’ of use, which 
may be defined via reduction in cigarette use, uptake patterns in e-cigarettes and/or patterns of quit attempts.  
Adverse Events (AE’s): We will determine the incidence of AEs and the associated 95% CI. A chi-square test 
will determine if the rate of serious adverse events (i.e., requiring treatment or life threatening) is greater than 
5%.   Other: 1) We will examine the longest duration of non-smoking between groups via a two-sample t-test.  2) 
If sufficient data are available, we will also conduct statewide comparisons on outcomes of interest to determine 
the influence of policy variables like a) level of excise tax, or b) % of state that is smokefree.  3) Urine samples 
will be frozen, with options for future secondary data analyses of both anabasine/anatabine, two tobacco specific 
biomarkers, but currently very costly to analyze.  4) Our local lab is developing an assay for 3’OH, which when 
compared to cotinine, provides an index of nicotine metabolite ratio (138, 139) which could be used as a predictor 
of e-cigarette uptake, something never before assessed.  We are unsure when this assay will be ready, and 
consider this only ancillary. 
Conclusion 
We believe this will be the best, most 
rigorous 
test 
to 
date 
of 
the 
naturalistic population impact of e-
cigarettes. Methods are strengthened 
by 1) large sample size (the largest e-
cigarette 
RCT), 
2) 
nationwide 
recruitment, with 3) locally recruited subset to assess biomarkers, and 4) multiple measures of outcome. The 
strong investigative team, coupled with our success in prior studies using similar methodology, collectively 
enhances the probability of success in achieving grant aims.  The NIH and FDA have each articulated a clear 

Protocol Version Date: 11/1/2021 
 
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agenda to understand how smokers use e-cigarettes and what impact such use may have on behavior.  The 
proposed study offers the most methodologically rigorous test of these questions.  

Protocol Version Date: 11/1/2021 
 
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Human Subjects Research 
1.  
 
Risks to Human Subjects 
1.1 
 
Human Subjects Involvement and Characteristics  
General Inclusion / exclusion criteria are as follows:  
a) 
age 21+, 
b) 
current smoker of >5 cigarettes per day for >1 year,  
c) 
no recent history of cardiovascular distress (heart attack in past 3 months; arrhythmia, 
uncontrolled hypertension), or renal disease (e.g., dialysis) that would interfere with urinary 
biomarker assessment 
d) 
neither pregnant nor breastfeeding,  
e) 
at least some concern for health effects of smoking (>none at all on a Likert scale),  
f) 
no current use of cessation medication,  
g)  
have not purchased in the past 6 months or ever regularly used (daily or weekly) a tank 
system, mechanical mod, or advanced personal vaporizer e-cigarettes  
h)  
no regular use (daily or weekly) of any e-cigarette (including cig-a-like devices) in the last 6 
months,  
i)  
no current use of other tobacco products,  
j)  
capacity to receive SMS text and internet access (for purposes of diary completion and iCO 
completion),  
k) 
no other household members are enrolled in the study, and 
l) 
no history of seizure disorder. 
 
 
1.2 
 
Sources of Materials  
Research material obtained from the participants include responses to phone-based surveys, collected directly 
by our research team and entered directly within secure databases. We will also collect urine samples among a 
subset of participants, locally recruited, to assess biomarkers of smoking (CO and cotinine).  Research data will 
be obtained specifically for research purposes. Every effort will be made to maintain subject confidentiality, in 
accordance with HIPAA. 
 
1.3 
 
Potential Risks 
The research protocol calls for non-treatment seeking smokers to either use e-cigarettes or not.  Use of e-
cigarettes is entirely self-chosen, since this is one outcome, rather than prescribed.  E-cigarettes are no more 
harmful than conventional cigarettes, and various studies suggest that they may offer reduced harm. 
Questionnaires and interviews are all non-invasive and involve minimal risk to study participants. Potential risks 
are as follows:  
1) risk of using e-cigarettes 
 
 
2) concurrent use of e-cigarettes & smoking 
3) potential for undermining cessation 
4) non-smokers in the home (children) experimenting with e-cigarettes 
5) loss of confidentiality 
 
1.3.1  E-Cigarettes  
E-Cigarettes are not combusted, and therefore levels of carcinogens are markedly 
reduced, if not eliminated, comparable to trace levels seen in nicotine replacement products (26).  Nicotine liquid 
is based on a solution containing propylene glycol, which some suggest may be harmful.  Propylene glycol is an 
FDA approved food additive, but with uncertain effects upon inhalation.    The detailed NIDA-guided DSMP 
(Section 4 below) presents data on tobacco-specific nitrosamines (TSNAs), within both e-cigarettes and their 
vapor.    
 
As for adverse events, the majority of e-cigarette studies are based within on-line surveys.  We report here on 
three moderate to large such surveys, 2 recently published (2013) and one from 2011 but is the largest survey 
to date.  In the first (32), three side effects were reported by >20% of respondents: headaches (21%), cough 

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(27%), and increased phlegm (25%).  In the second (33), the most common negative effect of e-cigarette use 
was throat and mouth irritation, and fewer than 3% “reported a high level of side effects.”  Finally, the largest 
online survey to date (56) did not fully assess adverse events, but reported that 26% of e-cigarette users 
reported burning in throat. In a cross-over study of 40 smokers given e-cigarette for four days (14), the four 
most common adverse events (within highest dosage group) were mouth/throat irritation (38%), nausea (29%), 
vertigo (21%) and headache (22%).  All other adverse events were rare (<5%).  There have also been a small 
number of reports to the FDA of people using e-cigarettes and experiencing seizures, with most reports 
involving youth or young adult users. These are rare, but we will inform participants of this risk and monitor. 
 
In the recent RCT from New Zealand (67), there was a higher number and proportion of adverse events among 
active e-cigarette group, but the event rate did not significantly differ as compared to nicotine patches.  In the 
RCT from Italy (66) there was no differential rate of adverse events among high, medium, or placebo e-cigarette 
groups.  The five most common adverse events were dry cough, mouth irritation, shortness of breath, throat 
irritation, and headache, with no serious adverse events.  The study also reported no significant changes in body 
weight, resting heart rate, or blood pressure.   
 
There have been a number of reports of respiratory illness from individuals using e-devices.  No specific 
substance or product has been linked to these cases. Many of but not all of the cases involved users vaping 
THC, which is the psychoactive ingredient in marijuana.  Some of the instances are specific to nicotine alone.  
We advise all participants not to add any substances to the devices we give them.  We advise against using 
any e-device or e-liquid that is obtained by questionable or unknown sources (such as off the street or on the 
black market). While we believe that e-cigarettes are less harmful than combustible cigarettes on a long term 
basis, there may still be acute (i.e., short term) risks of using e-cigarettes.  We will continue to monitor the 
participant’s health in our study.   
 
1.3.2  Concurrent use of e-cigarettes and smoking  
If smokers engage in dual use, the major concern will be intake of nicotine, and too much of it.  Symptoms of 
nicotine intoxication include nausea, dizziness, headache, and stomachache (140). In our two prior studies 
wherein participants who used nicotine gum/lozenge and smoked concurrently, there was no evidence of nicotine 
intoxication (75, 76), nor have we seen serious adverse events in our current e-cigarette studies.  We recently 
completed a literature review (141) that showed combined NRT, as well as concurrent use of NRT and smoking, 
were both safe.   
 
Dr. Goniewicz (co-I) recently completed a short term observational study, in which 20 smokers were provided e-
cigarettes for ad libitum use over a 2 week period; i.e., a study very similar to current proposal.  Data below 
demonstrate that changes (increases) in e-cigarette use increased in direct proportion with changes (decreases) 
in cigarette smoked (1st and 2nd panel), resulting in no net change in nicotine (3rd panel).  Thus, smokers who 
engage in dual use are likely to NOT increase total nicotine intake, a finding replicated elsewhere (17).  These 
findings are very much consistent with our own prior work on smokeless tobacco, wherein we provided smokers 
snus to use ad libitum over two weeks, finding no net increase in nicotine intake.    
 

Protocol Version Date: 11/1/2021 
 
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1.3.3 Undermining Cessation 
Another potential risk is that the sampling intervention will decrease rather 
than increase future cessation.  We are aware of one recent longitudinal study (61) that showed numerically 
lower but still statistically similar rates of non-smoking among e-cigarette users vs. non-users, but this study did 
not assess for timing of e-cigarette use.  This is the only study to show this that we are aware of.  However, most 
of the available data available suggest that e-cigarettes either do not affect cessation or increase it (32, 44-46, 
56, 66, 67).  
 
1.3.4  Use of E-cigarettes among non-participants and non-smokers, including children 
Whenever a product is given to a smoker to take home and use, there is potential that the product will be used 
by someone else, inclusive of non-smokers and even children.  In our ongoing snus trial in which we mail tins of 
smokeless tobacco to smokers all over the country, such “diversion” (tracked by our team) was not a problem. 
We will advise participants who receive e-cigarettes to keep them out of reach of children. The fact that we are 
using pre-loaded cartridges of e-cigarettes (NJoy), rather than refillable tank systems, minimizes the potential 
for adulterating the system by adding additional substances.    
 
1.3.5  Confidentiality A final risk is breach of confidentiality. 
 
2. 
 
Adequacy of Protection Against Risks 
2.1 
 
Recruitment and Informed Consent 
All research personnel have up to date CITI Certification for Protection of Human Subjects, and will keep this 
training current throughout the course of the study.  Study participants will be recruited through local media 
outlets.  Those who call expressing interest in study participation will be screened for all inclusion criteria.  We 
will provide participants with the option to complete consent 1) via MUSC’s doxy.me system (teleconsent), 2) via 
REDCap electronic consent (e-consent),combined with a discussion over the phone, or 3) by mail.  
 
For those who have the required hardware and software for doxy.me, we will describe the basic mechanics of 
the teleconsent process and ask for preference for mode of consent.  Participants who do not have access to 
the required technology to complete consent remotely via doxy.me or REDCap will be mailed a consent form. 
Signatures on the consent form may be obtained electronically via REDCap/doxy.me. 
For those who elect teleconsent, we will follow procedures as per precedent (101) and through consultation with 
MUSC researchers (Dr. Brandon Welch).  All doxy.me signed consent forms will be saved as pdf files within our 
study records. Research staff will then have the participant complete the baseline questionnaire (that would 
otherwise be sent with mailed questionnaire; see below).  We anticipate that 10-20% of study participants might 
enroll through this method.   
For those opting to receive consent packet through the mail, we will follow our prior procedures, sending a packet 
inclusive of two copies of the consent, a baseline questionnaire, and a pre-addressed, pre-stamped return 
envelope.  Participants will sign and return one consent and their completed baseline questionnaire.  We have 

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a toll-free phone line to support anyone who calls with questions.  Upon return to our office, data are entered 
into our database, and these individuals comprise the consented sample.  
  
In the past, about 45-50% of individuals who are mailed a consent return it; we do not badger those who decline.  
However, not all consenting individuals are able to be reached (~10% from previous studies) and those that are 
(~90%) will comprise the intent-to-treat enrolled sample. We will abide by all HIPAA regulations as set forth by 
our institution.  The PI will supervise all aspects of the recruiting process.      
 
2.2 
 
Protection Against Risk 
2.2.1   
Use of E-cigarettes 
 Participants will be screened for general medical precautions (pregnancy, 
cardiovascular disease), and all participants will be monitored for adverse events during the study period. We 
will clearly advise against use of e-cigarettes during pregnancy and breast-feeding and will verify non-pregnancy 
at study onset.  Two study physicians (Drs. Gray and Warren) are available for consult for all adverse events. 
Participants will be educated about potential risks of e-cigarette use, including concurrent use with cigarettes. 
Any adverse events will be reported to the IRB. The most likely adverse event (potential for nicotine overdose) 
is anticipated to be rare (~5%) and mild (mouth/throat irritation, headache, nausea, headache), and will be 
handled quickly (i.e., advice to participant to reduce or stop e-cigarettes). Lab studies of toxin exposure (above) 
suggest that e-cigarettes confer no greater risk to health than do conventional cigarettes. It is unlikely that e-
cigarette users will become addicted to the product in the 3-week sampling period, though that is one outcome 
we will track.  All participants will be provided with cessation information (referrals to Quitline) as part of this 
study. 
 
2.2.2   
Concurrent Use of E-cigarettes & Smoking Per above, the most common effects from too much 
nicotine are nausea, headache, and disturbed sleep. The sampling period is up to a month in duration, and thus 
we do not expect sustained patterns of dual use.  We will track adverse events at every study contact, and will 
have a toll-free number available for participants to call if they experience an adverse event (AE).  All study 
contacts will remind participants of this number. A physician will be on call throughout the study for questions 
about AEs, etc. Participants will be encouraged to contact the Study PI as soon as possible for serious events. 
If they wish, they may contact their local MD or give the study physician permission to do so. We will withdraw 
participants who have a serious AE, become pregnant or begin breast-feeding. For other AEs, if the study 
physician, the participant’s physician or the participant wishes it, the participant will be withdrawn from the study. 
If interim checks show the percent of serious or severe AEs to be greater than 5%, our Data Safety and 
Monitoring Board will be notified to make a decision on early termination of the study.   
 
2.2.3 Undermining Cessation 
We emphasize that this is not a cessation trial, though we will collect 
various cessation outcomes.  Nonetheless, it is possible that use of e-cigarettes will undermine quitting (though 
this would be contrary to existing literature).  To protect against this outcome, we will form a Data Safety and 
Monitoring Board (DSMB; see below) and have them conduct an interim analysis after 33%, 50% of the sample 
has completed 6 month follow-up. If the rate of quit attempts or abstinence within the e-cigarette group is >10% 
below that within the control group, the DSMB will independently decide on whether to stop the study. 
 
2.24  Diversion of e-cigarettes 
 We now plan to send e-cigarette samples in multiple shipments instead of 
one.  We will ask at each visit if the product has been diverted to anyone in the house.  Any affirmed response 
to this question, or suspicion of it by our team, will result in discontinuation of product for that individual.  We 
cannot directly assess any diversion/uptake from the perspective of adolescents, since that would require 
separate consent, and is a separate research question.  We are aware e-cigarette flavoring might hold appeal 
to youth, but believe this risk is mitigated by: a) childproof packaging from manufacturer, b) providing no more 
than 30 tanks (no more than 20/10 for first/second shipment), and c) repeated instruction (oral and written) to 
study participants to keep this and all tobacco products out of reach of children.   Our protocol follows our prior 
study of mailed sampling of snus (also offered with multiple flavors), and also the sampling experience of our co-
investigators, all without such problems of diversion. The fact that we are using pre-loaded cartridges of e-

Protocol Version Date: 11/1/2021 
 
25 
 
cigarettes (NJoy), rather than refillable tank systems, minimizes the potential for adulterating the system by 
adding additional substances.   
 
2.25  Confidentiality We will use the participant’s name only on the screening and informed consent documents 
and these will be kept in a locked file, to be kept centrally at our study office.  Copies of informed consent will be 
kept by research personnel under lock and key. The research materials will become part of the modern record 
keeping facility of the Institute of Psychiatry, which will minimize risks to the privacy of participants. All interviews, 
records, charts, rating scales, and other patient information will be kept in locked files at the Cancer Control 
Program, with limited access to the study personnel. All database files will include password protection to further 
ensure confidentiality. 
 
3.  Potential Benefits of the Proposed Research to the Participants and Others / Importance of the 
Knowledge to be Gained 
In an ever-changing marketplace of tobacco-produces and nicotine-delivery devices, e-cigarettes are arguably 
the most popular new products available to smokers.  The two most important questions about e-cigarettes are 
1) uptake among non-smokers, and 2) impact among smokers.  This study is poised to answer the latter, and is 
a strength upon existing research that is predominated by cross-sectional surveys and short term lab studies.  
There is a universal call for randomized trials of e-cigarettes, but these trials do not exist, at least in the U.S.  It 
is important to test e-cigarettes in advance of potential FDA role in regulation of them.  E-cigarettes are classified 
as a tobacco product and in all likelihood will eventually be regulated by the FDA.  The FDA will need high-level 
science to assess overall population impact.  
This study is not without benefit to individual participants.  However, this is not a treatment trial, and will not be 
advertised as such.  We assess at screening for interest in cessation support.  All comers will be accepted, but 
smokers actively seeking cessation support, or currently using cessation medication at baseline will be excluded.  
At all contacts, we will assess motivation to quit and uniformly offer referral information to state quitlines.  Such 
transitions to cessation support are included as study outcomes.  Two-thirds of the participants in the trial will 
receive samples of e-cigarettes, which most evidence to date suggests is a) safer than conventional cigarettes, 
and b) possibly related to quitting.  
 
 

Protocol Version Date: 11/1/2021 
 
26 
 
Data and Safety Monitoring Plan  
This fully detailed section is based on the recommendations in NCI’s “Guidelines for Developing a Data and 
Safety Monitoring Plan” as well as NIDA’s “Guidelines for Developing a Data and Safety Monitoring Plan.”  
 
1.  Summary of the Protocol   
Eligible smokers, once consented (process described below), will be randomized to receive a onetime sample 
of e-cigarettes (NJoy; n=440) or not (n=220). E-cigarette samples are inclusive of a battery and self-contained 
tanks of assorted flavors to last up to 4 weeks.  Participants will be recruited nationally, but a subset (N=120) will 
be recruited locally to allow for biomarker collection (locally recruited participants will be managed via phone/mail 
as are all other participants; details below).  All smokers will be asked to provide smoking diary data, captured 
electronically, daily for 4 weeks.  More substantive phone assessment will track smoking and related behaviors 
at baseline (Day 0) and +10, +17, and +24 days (weekly during initial 3 weeks, following brief lag for delays in 
product mailing), and at +1, +3, and +6 months (7 phone assessments total). Major outcomes during both the 
sampling and follow-up periods are noted below.   
 
2.  Primary and secondary outcomes 
During the sampling period and beyond, uptake of e-cigarettes is the primary outcome.  Uptake is defined in 
several ways, including a) first time use, b) repeat use, c) first time buyer, d) repeat buyer, and e) advocacy; i.e., 
promoting/suggesting to others.  Following a brief sampling period, smokers are then followed for six months, in 
which outcomes include subsequent e-cigarette use, biomarkers of exposure, quit attempts, and cessation.   
 
3.  Inclusion/exclusion criteria  
General Inclusion / exclusion criteria are as follows:  
a) 
age 21+, 
b) 
current smoker of >5 cigarettes per day for >1 year,  
c) 
no recent history of cardiovascular distress (heart attack in past 3 months; arrhythmia, 
uncontrolled hypertension), or renal disease (e.g., dialysis) that would interfere with urinary 
biomarker assessment 
d) 
neither pregnant nor breastfeeding,  
e) 
at least some concern for health effects of smoking (>none at all on a Likert scale),  
f) 
no current use of cessation medication,  
g)  
have not purchased in the past 6 months or ever regularly used (daily or weekly) a tank 
system, mechanical mod, or advanced personal vaporizer e-cigarettes  
h)  
no regular use (daily or weekly) of any e-cigarette (including cig-a-like devices) in the last 6 
months,  
i)  
no current use of other tobacco products, 
j)  
capacity to receive SMS text and internet access (for purposes of diary completion and iCO 
completion) 
k) 
 
no other household members are enrolled in the study, and 
 
 
l) 
 
no history of seizure disorder.   
 
4.  Sample Size 
The sample size is 660 participants. (For this IRB application, we are increasing the total number of study 
participants by ~10% from 540 to 600 for the national sample and from 120 to 150 for the local sample, for a 
total of 750 study participants, to account for the ~10% of participants who mail us a consent back but who we 
never reach for the first study phone call – see section 2.1 Recruitment and Informed Consent on page 18 of this 
protocol.) 
 
 
5.  List of participating / enrolling sites 

Protocol Version Date: 11/1/2021 
 
27 
 
The study will recruit nationally, in pre-determined cities with high percentages of minorities and/or latinos.  A 
subset of smokers will be recruited locally in the Charleston SC area, to collect biomarkers of smoking behavior.  
Locally-recruited participants will count toward the local/Charleston sample of n=120 only after they complete 
the baseline in-person lab visit.  If a participant is recruited in the Charleston area but does not complete the 
baseline lab visit, they will be counted toward the national sample enrollment total and not eligible for any further 
lab visits. 
 

Protocol Version Date: 11/1/2021 
 
28 
 
6.  Projected Timetable 
The timetable is as follows: 
 
 
Year 1 
(months) 
Year 2 
(months) 
Year 3 
(months) 
Year 4 
(months) 
Year 5 
(months) 
Refine all procedures / IRB 
1-6 
 
 
 
 
Procure supplies 
5-6 
 
 
 
 
Hire and Train Personnel 
5-6 
 
 
 
 
Study Enrollment 
 
 
 
 
 
   Cumulative N to start** 
(105) 
(305) 
(505) 
(615) 
(660) 
   First Participant Starts 
 7 
 
 
 
 
   First Participant Completes 
 
13 
 
 
 
   Last Participant Starts 
 
 
 
 
50 
   Last Participant Completes 
 
 
 
 
56 
Data Analysis 
 
 
 
 
57-60 
Manuscript Preparation 
 
 
 
 
57-60 
All numbers reflect months within total study duration (**with the exception of cumulative N)  
 
 
7.  Target Population 
Women will be included in this protocol. Currently, women are about 50% of smokers (slightly less among African 
American women) and thus it is estimated that 50% of the study sample will be women. If there are discrepancies 
in terms of gender, efforts will be made to improve recruitment of women into the study through oversampling 
via online recruitment. 
Minorities are also included in this study, at proportions (aggregate 22%) generalizable to US Census data, 
wherein 78% of US residents are White, 13% Black, 5% Asian, 1% Native American, and <1% native Hawaiian 
or Pacific Islander.  These same census data indicate that 17% of the US residents are of Hispanic origin.  The 
Targeted Enrollment Table reflects these proportions accordingly.  We have chosen recruitment cities that meet 
or exceed national estimates proportions of minorities and/or Hispanics, which should facilitate recruitment goals.   
 
8.  Data Acquisition and Transmission 
Participants are recruited nationally, and contacted via mail and phone.  The study will be managed centrally 
from the Division of Clinical Neuroscience within the Department of Psychiatry and Behavioral Sciences at the 
Medical University of South Carolina (MUSC), but physically based within the Cancer Control Program of the 
Hollings Cancer Center, where the PI is based.  A subset of study visits will occur in lab space within our study 
group.  Urine assays will be managed locally within our departmental lab.   
 
9.  Data Analysis Plan 
Data will be entered centrally by research assistants directly in real time, into a computer-assisted telephone 
REDCap (Research Electronic Data Capture) database.  RedCAP is a secure, web-based application designed 
exclusively to support data capture for research studies.  Our group has been using REDCap for all studies since 
2009, reducing errors with data entry.  All databases are password protected. Cotinine and carbon monoxide 
data will be managed through the Department of Psychiatry’s Clinical Neurobiology Lab.  Samples will be stored 
in deidentified fashion, with unique SID numbers alone.   
 
Hyp 1a: Within the e-cigarette group, we hypothesize that at least 90% of smokers will try at least once, 
45% will use regularly (at least 4/7 days, at any time during follow-up), 25% will use daily for >7 days (at any 
time during follow-up), 25%/10% will be using at least some/daily at final follow-up, and 20% will independently 
purchase e-cigarettes.  Within the e-cigarette group (N=440), we will analyze prevalence of each definition of 
uptake. Each of these outcomes will be estimated, and 95% confidence intervals will be calculated for each.  

Protocol Version Date: 11/1/2021 
 
29 
 
 
Hyp1b:  Among smokers in the e-cigarette group, 40% will show a minimum of 50% reduction in cigarettes 
per day, and as a group, e-cigarette users will show greater decreases in cigs/day as compared to control.  
Reduction of cigarettes per day will be calculated based on the TLFB at baseline and 6 months for both groups, 
with “reducers” defined as those that reduce cigarettes per day by >50%. We will estimate the reduction, 95% 
confidence interval on the reduction, and the percent of those that are considered “reducers” in both groups, the 
latter tested via a chi-square test with an α=0.05 two-sided significance level. 
 
Hyp2:  Within a subset of locally recruited smokers, we hypothesize that use of e-cigarettes will lead to 
significant decreases in CO, but no net change in overall nicotine exposure.  Changes in CO and cotinine will be 
calculated based on the baseline, 1, and 6-month measurements. Change in CO (thru 1 and 6 months) will be 
compared via a two-sample t-test with α=0.05 significant level. In order to test equivalence for change in cotinine 
between the two groups, the TOST procedure (137) will be followed where two one-sided tests will be used to 
test that the difference in the change between groups (Δ) is within the +/-3% bounds that we are willing to accept 
(H01: Δ < -3%; H02: Δ < +3%). If Δ is contained completely within the +/-3% bounds, then equivalence will be 
concluded. We will use an α=0.05 significance level.  
 
Hyp 3:  Provision and use of e-cigarettes will lead to increased incidence of (Hyp3a) quit attempts, (Hyp 
3b) cessation, and (Hyp 3c) motivation/confidence to quit.  Logistic regression analyses will be performed with 
(1)any quit attempt within 6 months (1=one or more quit attempts, 0=none), (2) any 24hr quit attempt (1=one or 
more 24 hour quit attempt, 0=none), (3) point prevalence abstinence at 6 months (1=7 day abstinence at 6 month 
call, 0=not abstinent), and (4) floating abstinence (1=any 7 day period of abstinence during study, 0=no 
abstinence) as the outcomes and group (e-cigarette vs. control) as the covariate. The coefficient on group 
provides a log odds ratio for measuring the increased odds of (1) any self-defined quit attempts, (2) 24hr quit 
attempts, (3) point prevalence abstinence, and (4) floating abstinence in the e-cigarette group compared to the 
control group. Statistical significance of these coefficients will be assessed using a p-value with a two-sided 
alpha of 0.05, and both the odds ratio and its 95% confidence interval (CI) will be reported. Motivation and 
confidence to quit are evaluated on a continuous scale (0-10), and linear regression models will be used with 
motivation/confidence to quit at 6 months as the outcomes with group (e-cigarette vs. control) as the covariate 
(along with baseline motivation/confidence and any other variables that are different between groups at baseline 
treated as covariates). Secondary analyses will include regression analyses where number of quit attempts are 
compared across groups using poisson or linear regression (depending on the distribution of number of quit 
attempts). We will also repeat this analysis: a) based on those who do vs. do not use e-cigarettes, and b) using 
quit attempts and abstinence of all tobacco products, including e-cigarettes. 
 
10. Quality Assurance Plan  
The REDCap system has validation options to not accept outliers, illogical response patterns, etc. The PI will 
have weekly meetings with the research assistants to discuss qualitative comments received during data 
collection and any problems in data collection. The statistician will periodically examine the database to look for 
irregularities. Initial data analyses will examine distributions of variable scores, comparability of baseline 
characteristics, follow-up rates and use of extra-study cessation treatment across conditions in case analyses 
need to be adjusted for these. 
 
11.  Reporting mechanisms of AEs/SAEs to IRB, FDA, NIH 
Prior to the start of the study, the protocol will be registered on the clinicaltrials.gov registry.  This study closely 
resembles three of our prior trials on alternative products, but the regulatory paths for these 3 studies varied.  
Two studies were small pilot studies (N=34, 57), much like herein (proposed N=60).  One was of a test of 
Ariva/Stonewall, and the other was based on Camel Snus (both smokeless tobacco products).  Both were 
prospective (2 weeks each).  In neither case did we ask for, nor were we required by our IRB, to seek an IND 
from the FDA.  The third study, ongoing (N=1236; year long RCT of Camel Snus) had a very different regulatory 
process, mired in months of debate.  In seeking an IND exemption from the FDA, we were initially denied 
exemption, much to our surprise.  We challenged that ruling, with months of back and forth emails and even a 
trip to the FDA.  In the end, the FDA recognized the study for what it was: a naturalistic study of uptake of snus 
and its consequences.  Though we assess quit attempts and cessation, and even a priori hypothesized increased 

Protocol Version Date: 11/1/2021 
 
30 
 
quitting among snus users, this hypothesis was based entirely on the literature at the time.  Like the snus trial, 
we are NOT seeking any new claims for e-cigarettes within the current trial.  Ultimately, we were granted an IND 
exemption for the snus study, and, after charting those murky waters for almost one year, have every reason to 
believe that the current trial will also be exempt from needing an IND.  
 
Serious Adverse Events (SAEs) are defined as any even that is fatal or life threatening, is permanently or 
significantly disabling (physically or psychologically), requires inpatient hospitalization or prolongation of 
hospitalization, contributes to a congenital anomaly/birth defect or is any medical event that requires treatment 
to prevent one of the medical outcomes listed above.    
 
For a study of this size and duration, we do expect some deaths to naturally occur, all of which will be reported 
to IRB, but given that we are providing a mere few weeks of e-cigarettes, we do not anticipate any study-related 
SAEs.  In our ongoing trial of now 1200 smokers followed for 1 year, there have been <5 deaths total.   
 
All serious AEs, study related or not, will be reported to the MUSC Committee on Human Research within 48 
hrs. A summary of these SAE will be submitted annually to NIH via progress report.  Follow-up of all unexpected 
and serious AEs will also be reported. All AEs are reviewed weekly by the PI and yearly by the IRB. Any 
significant actions taken by the local IRB, protocol changes will be relayed to the funding agency. We estimate 
the significant AE rate to be 5% or less. If the monthly monitoring indicates the rate is above this, we will convene 
a special meeting of the DSMB.  
 
12.  Reporting mechanisms of IRB actions to NIH & Report of changes or amendments to the protocol 
We will a) discuss with NIH in advance any need for major protocol changes (e.g., change in aims, significant 
(+/-10%) change in sample size), and b) provide IRB approval to NIH once these major protocol changes have 
been local approved.  We will report any IRB-actions within 5 business days.  Notice of annual continuing 
approval will be included within each annual progress report to NIH. 
 
13.  Trial stopping rules 
There are two potential reasons to stop the trial prematurely: a) undermining of cessation, and b) significant rate 
of adverse events.  Each is addressed below. 
 
a) To protect against undermining of cessation, we will form a Data Safety and Monitoring Board (DSMB) and 
have them conduct an interim analysis after 50% of the sample has completed 6 month follow-up. We chose this 
number as this is the minimum necessary given our base rate is projected to be 10% and one has to see clear 
trend toward an even lower rate. To do this, the statistician will provide a copy of the dataset to the board. If this 
occurs, this board will independently decide on whether to stop the study. 
 
b) The research staff will report any unexpected AEs or any scores of “severe” on the side-effect symptom rating 
form or any FDA-defined serious AEs to the PI within 24 hrs so that the PI can decide on the appropriate action. 
All unexpected AEs will be monitored while they are active to determine if treatment is needed. Since a maximum 
three week supply of e-cigarettes, adverse events will be rare. Nonetheless, they will be coded on a weekly basis 
using the FDA’s COSTART rules (142) and entered into a database. For each weekly study meeting, the 
research assistants will prepare a summary of all AEs, including their severity, whether they occurred during 
smoking or abstinence, caused a dropout, required treatment and presumed relation to drug intake. The PI will 
review this at the weekly study meeting (or before if more urgent). At the weekly meeting (or before if urgent), 
research assistants will report any premonitory symptoms of emergence of a mental disorder such as depression 
or alcohol dependence. Dr. Gray, a board-certified psychiatrist, Dr. Warren, a radiation oncologist (both with 
expertise in nicotine dependence and treatment) will be available for medical supervision.  Any study-related 
SAE will be reported to the DSMB immediately, which may convene a special meeting.  Aggregate summary of 
all AEs will be provided quarterly to the DMSB and at each regularly scheduled DSMB meeting. 
 
14.  Conflicts of Interest 

Protocol Version Date: 11/1/2021 
 
31 
 
The Medical University of South Carolina is fully compliant with federal laws in reporting of conflicts of interest.  
All key personnel listed within this application have complied with this policy.  Any conflicts of interest will be 
acknowledged in any publications or conference proceedings. 
15.  Potential risks and benefits to participants 
The research protocol calls for non-treatment seeking smokers to either use e-cigarettes or not.  Use of e-
cigarettes is entirely self-chosen, since this is one outcome, rather than prescribed.  E-cigarettes are no more 
harmful than conventional cigarettes, and various studies suggest that they may offer reduced harm. 
Questionnaires and interviews are all non-invasive and involve minimal risk to study participants. Potential risks 
are as follows:  
1) risk of using e-cigarettes 
 
 
2) concurrent use of e-cigarettes & smoking 
3) potential for undermining cessation 
4) non-smokers in the home (children) experimenting with e-cigarettes 
5) loss of confidentiality 
 
15.1  E-Cigarettes Tables I and II below provide, respectively, levels of tobacco-specific nitrosamines across a 
number of nicotine delivery devices, and levels of toxicants within cigarette smoke and e-cigarette vapor.  Table 
I data show that TSNAs within e-cigarettes are much lower than tobacco products, including low-nitrosamine 
smokeless tobacco.  Table II shows that e-cigarette vapor includes significantly lower levels of a number of 
toxicants as compared to cigarette smoke.  
 
Table I: Tobacco-Specific Nitrosamines within Nicotine-Delivery Products 
Tobacco-Specific Nitrosamines 
Product 
NNN 
NNK 
NAT 
NAB 
Total 
Nicorette gum 
2.00 
ND 
ND 
ND 
2.00 
Nicoderm CQ patch 
ND 
8.00 
ND 
ND 
8.00 
E-cigarettes 
3.87 
1.46 
2.16 
0.69 
8.18 
Swedish Snus 
980 
180 
790 
60 
2010 
Marlboro (Ultra-light) 
2900 
750 
1100 
58 
4808 
Marlboro (full) 
2900 
960 
2300 
100 
6260 
Source: Cahn 2010  
 
Among human studies, the majority of e-cigarette studies are based within on-line surveys.  We report here on 
three moderate to large such surveys, 2 recently published (2013) and one from 2011 but is the largest survey 
to date.  In the first (32), three side effects were reported by >20% of respondents: headaches (21%), cough 
(27%), and increased phlegm (25%).  In the second (33), the most common negative effect of e-cigarette use 
was throat and mouth irritation, and fewer than 3% “reported a high level of side effects.”  Finally, the largest 
online survey to date (56) did not fully assess adverse events, but reported that 26% of e-cigarette users reported 
burning in throat. In a cross-over study of 40 smokers given e-cigarette for four days (14), the four most common 
adverse events (within highest dosage group) were mouth/throat irritation (38%), nausea (29%), vertigo (21%) 
and headache (22%).  All other adverse events were rare (<5%).   
 
Table II:  Toxicant Levels within E-cigarette Vapor vs. Cigarette Smoke 
Toxic compound 
Conventional cigarette 
[µg in mainstream smoke] 
Electronic cigarette 
[µg per 15 puffs] 
Average ratio (conventional vs. 
electronic cigarette) 
Formaldehyde 
0.85-10 
0.20-5.61 
2 
Acetaldehyde 
52-140 
0.11-1.36 
130 

Protocol Version Date: 11/1/2021 
 
32 
 
Acrolein 
4.6-14 
0.07-4.19 
4 
Toluene 
6.4-9.0 
0.02-0.63 
23 
NNN 
0.012-0.37 
0.00008-0.00043 
145 
NNK 
0.009-0.08 
0.00011-0.00283 
30 
Cd 
0.03-0.35 
0.001-0.022 
16 
Ni 
0.003-0.60 
0.011-0.029 
15 
Source: Goniewicz 2014 
 
15.2  Concurrent use of e-cigarettes and smoking  
If smokers engage in dual use, the major concern will be intake of nicotine, and too much of it.  Symptoms of 
nicotine intoxication include nausea, dizziness, headache, and stomachache (140). In our two prior studies 
wherein participants who used nicotine gum/lozenge and smoked concurrently, there was no evidence of nicotine 
intoxication (75, 76), nor have we seen serious adverse events in our current e-cigarette studies.  We recently 
completed a literature review (141) that showed combined NRT, as well as concurrent use of NRT and smoking, 
were both safe.   
 
15.3 
Undermining Cessation 
Another potential risk is that the sampling intervention will decrease rather 
than increase future cessation. However, the limited data available suggest this is unlikely (32, 44, 45, 56).  
 
15.4  Use of E-cigarettes among non-participants and non-smokers, including children 
Whenever a product is given to a smoker to take home and use, there is potential that the product will be used 
by someone else, inclusive of non-smokers and even children.  In our ongoing snus trial in which we mail tins of 
smokeless tobacco to smokers all over the country, such “diversion” (tracked by our team) has not been a 
problem. In the rare possibility that someone in the household takes an e-cigarette, either knowingly or 
unknowingly from study participants, this risk would be no greater than if that same individual took the 
participant’s cigarettes.  The fact that we are using self-contained cartridges of e-cigarettes (NJoy), rather than 
refillable tank systems, minimizes the potential for adulterating the system by adding additional substances.    
 
15.5  Confidentiality A final risk is breach of confidentiality. 
 
 
Benefits: 
In an ever-changing marketplace of tobacco-produces and nicotine-delivery devices, e-cigarettes 
are arguably the most popular new products available to smokers.  The two most important questions about e-
cigarettes are 1) uptake among non-smokers, and 2) impact among smokers.  This study is poised to answer 
the latter, and is a strength upon existing research that is predominated by cross-sectional surveys and short 
term lab studies.  There is a universal call for randomized trials of e-cigarettes, but these trials do not exist.  It is 
important to test e-cigarettes before they become more popular, in advance of potential FDA role in regulation 
of them.  E-cigarettes are classified as a tobacco product but in all likelihood will be subject to FDA regulation. 
The FDA will need high-level science to assess overall population impact.  While this small pilot study alone will 
surely not address that need, it will certainly guide forthcoming research in this area.   
This study is not without benefit to individual participants.  However, this is not a treatment trial, and will not be 
advertised as such.  We assess at screening for interest in cessation support.  All comers will be accepted, but 
smokers actively seeking cessation support, or currently using cessation medication at baseline will be excluded.  
At all contacts, we will assess motivation to quit and uniformly offer referral information to state quitlines.  Such 
transitions to cessation support are included as study outcomes.  Two-thirds of the participants in the trial will 
receive samples of e-cigarettes, which most evidence to date suggests is a) safer than conventional cigarettes, 
and b) possibly related to quitting.  
 
16.  Collection, reporting and management of AEs and SAEs 

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33 
 
Adverse events will be tracked and rated as mild, moderate or severe by the patient and rated as related to e-
cigarettes by the research assistant using guidelines. We will determine if any adverse events result in dropouts 
or are serious according to FDA guidelines. A DSMB will assist in determining if the rate or severity of adverse 
events exceeds expectations. 
The research staff will report any unexpected AEs or any scores of “severe” on the side-effect symptom rating 
form or any FDA-defined serious AEs to the PI within 24 hrs so that the PI can decide on the appropriate action. 
All unexpected AEs will be monitored while they are active to determine if treatment is needed. Since the e-
cigarette sampling period is for a few weeks only, adverse events will be rare. Nonetheless, they will be coded 
on a weekly basis using the FDA’s COSTART rules and entered into a database. For each weekly study meeting, 
the research assistants will prepare a summary of all AEs, including their severity, whether they occurred during 
smoking or abstinence, caused a dropout, required treatment and presumed relation to drug intake. The PI will 
review this at the weekly study meeting (or before if more urgent). At the weekly meeting (or before if urgent), 
research assistants will report any premonitory symptoms of emergence of a mental disorder such as depression 
or alcohol dependence. Dr. Gray, a board-certified psychiatrist, and Dr. Warren, a radiation oncologist, will be 
available for on-site medical supervision.  
 
17.  Plans for Interim Analyses of efficacy data 
We plan to examine the data halfway through enrollment to determine if observed differences in quit rates are 
so large that the trial should be stopped early. Using the method of O’Brien and Fleming (143) the significance 
level for this interim analysis is α = 0.005. According to this same reference, conducting this analysis decreases 
our power for detecting our projected differences only from 0.80 to 0.79. 
 
18.  Responsibility for data and safety monitoring 
The PI will be responsible for monitoring the trial. The statistician will monthly examine the outcomes database 
for missing data, unexpected distributions or responses, and outliers. The PI will weekly check the AE database 
prepared by the research assistants immediately prior to the lab meeting a) to see if any particular COSTART 
categories are being endorsed more frequently than normal and b) to determine if any side-effect symptom 
checklist scores are higher than expected. A DSM report will be filed with the IRB and funding agency on a yearly 
basis, unless greater than expected problems occur. The report will include participant characteristics, retention 
and disposition of study participants, quality assurance issues and reports of AEs, significant/unexpected AEs 
and serious AEs. We will report efficacy at the end of the trial. 
 
19.  Frequency of DSM reviews 
The DSMP will be reviewed annually. 
 
20.  Content of DSM report 
The DSM report, which will be provided to all DSMB members, will include: a) enrollment data, in aggregate and 
split by gender and race, b) retention (% of all scheduled contacts that are completed), and c) adverse event 
data.  Adverse event data will be presented in aggregate but will also include a detailed listing of all serious 
adverse events (SAEs). 
 
21.  DSMB   
We will create a Data Safety and Monitoring Board to monitor both the rate and severity of adverse events, and 
any decremented rate of quitting in the e-cigarette-group. This panel will include 3 clinicians with expertise in 
smoking cessation trials, and a statistician. Potential conflicts of interest will be discussed jointly by the PI and 
the Chair of the DSMB; at least 1 member of the DSMB will be from outside the PI’s home department.  The 
DSMB will meet yearly upon anniversary of starting of recruitment (i.e. 1 year following first participant) to review 
any adverse events related to the study, as well as review any data management related errors. The board may 
be called at any point if needed for unexpected AEs, etc. Modification will be made in the procedures and/or the 
protocol if necessary based on the findings of the board. We will update the DSMB quarterly with a listing of all 

Protocol Version Date: 11/1/2021 
 
34 
 
Adverse Events, and we will update them in real-time with any substantive information we may learn about the 
new reports of vaping illness, be it from CDC, FDA, NCI, or any other credible scientific/regulatory source. 
 
Inclusion of Women 
Women will be included in this protocol, expected to be approximately 50% of study sample.   
 
Inclusion of Minorities 
Minorities are included in this study, at proportions (aggregate 22%) generalizable to US Census data, wherein 
78% of US residents are White, 13% Black, 5% Asian, 1% Native American, and <1% native Hawaiian or Pacific 
Islander.  These same census data indicate that 17% of the US residents are of Hispanic origin.  The Targeted 
Enrollment Table reflects these proportions accordingly.  We have chosen recruitment cities that meet or exceed 
national estimates proportions of minorities and/or Hispanics, which should facilitate recruitment goals.   
 
For the locally recruited subset, we will target recruitment proportional to US Census data for our state:  68% 
white, 28% black or African American, <1% Asian, Native American, or Hawaiian, and 5% Latino.   
 
Inclusion of Children 
Children under 21 will be excluded.  Though e-cigarettes are not regulated by the FDA, it seems reasonable to 
believe that, if they ever were, they would approved for adults.  

Protocol Version Date: 11/1/2021 
 
35 
 
REFERENCES 
 
1. 
Azer V, Small G. E-cigarettes: Early adopters and the per capita conundrum. In: Equities CR, editor.: 
Citigroup Global Markets; 2013. 
2. 
Herzog B, Gerberi J. E-cigs revolutionizing the tobacco industry. In: Securities WF, editor. 2013. 
3. 
Stimson GV, Thom B, Costall P. Disruptive innovations: The rise of the electronic cigarette. International 
Journal of Drug Policy. 2014;25:653-5. doi: 10.1016/j.drugpo.2014.05.003. 
4. 
Chen IL, Husten CG. Introduction to tobacco control supplement. Tobacco Control. 2014;23:ii1-ii3. doi: 
10.1136/tobaccocontrol-2013-051504; PMCID: 3995278. 
5. 
Benowitz NL. Emerging nicotine delivery products: Implications for public health. Annals of the American 
Thoracic Society. 2014;11:231-5. doi: doi: 10.1513/AnnalsATS.201312-433PS. 
6. 
Etter JF, Bullen C, Flouris AD, Laugesen M, Eissenberg T. Electronic nicotine delivery systems: A 
research agenda. Tobacco Control. 2011;20(3):243-8. Epub 2011/03/19. doi: tc.2010.042168 [pii] 
10.1136/tc.2010.042168. PubMed PMID: 21415064; PMCID: 3215262. 
7. 
Henningfield JE, Zaatari GS. Electronic nicotine delivery systems: Emerging science foundation for 
policy. Tobacco Control. 2010;19:89-90. 
8. 
Etter JF. Commentary on Wagener et al. (2012): Electronic cigarettes – the Holy Grail of nicotine 
replacement? Addiction. 2012;107:1550-2. doi: 10.1111/j.1360-0443.2012.03909.x. 
9. 
Wagener TL, Siegel M, Borrelli B. Electronic cigarettes: Achieving a balanced perspective. Addiction. 
2012;107:1545-8. doi: 10.1111/j.1360-0443.2012.03826.x. 
10. 
Bell K, Keane H. Nicotine control: E-cigarettes, smoking and addiction. International Journal of Drug 
Policy. 2012;23:242-7. doi: 10.1016/j.drugpo.2012.01.006. 
11. 
Cobb NK, Abrams DB. E-cigarette or drug-delivery device? Regulating novel nicotine products. New 
England Journal of Medicine. 2011;365:193-5. doi: 10.1056/NEJMp1105249. 
12. 
Grana R, Benowitz N, Glantz SA. E-Cigarettes: A scientific review. Circulation. 2014; 129:1972-86. doi: 
10.1161/CIRCULATIONAHA.114.007667; PMCID: 4018182. 
13. 
Eissenberg T. Electronic nicotine delivery devices: Ineffective nicotine delivery and craving suppression 
after acute administration. Tobacco Control. 2010;19:87-8. doi: 10.1136/tc.2009.033498; PMCID: 3208854. 
14. 
Bullen C, McRobbie H, Thornley S, Glover M, Lin R, Laugesen M. Effect of an electronic nicotine delivery 
device (e cigarette) on desire to smoke and withdrawal, user preferences and nicotine delivery: Randomised 
cross-over trial. Tobacco Control. 2010;19:98-103. doi: 10.1136/tc.2009.031567. 
15. 
Nides MA, Leischow SJ, Bhatter M, Simmons M. Nicotine blood levels and short-term smoking reduction 
with an electronic nicotine delivery system. American Journal of Health Behavior. 2014;38:265-74. doi: 
10.5993/AJHB.38.2.12. 
16. 
Goniewicz ML, Kuma T, Gawron  M, Knysak  J, Kosmider L. Nicotine levels in electronic cigarettes. 
Nicotine & Tobacco Research. 2013;15:158-66. doi: 10.1093/ntr/nts103. 

Protocol Version Date: 11/1/2021 
 
36 
 
17. 
Etter JF, Bullen C. Saliva cotinine levels in users of electronic cigarettes. Eur Respir J. 2011;38(5):1219-
20. Epub 2011/11/03. doi: 10.1183/09031936.00066011. PubMed PMID: 22045788. 
18. 
Vansickel AR, Eissenberg T. Electronic cigarettes: Effective nicotine delivery after acute administration. 
Nicotine & Tobacco Research. 2013;15:267-70. doi: 10.1093/ntr/ntr316; PMCID: 3524053. 
19. 
Dawkins L, Corcoran O. Acute electronic cigarette use: nicotine delivery and subjective effects in regular 
users. Psychopharmacology. 2014;231:401-7. doi: 10.1007/s00213-013-3249-8. 
20. 
Polosa R, Rodu B, Caponnetto P, Maglia M, Raciti C. A fresh look at tobacco harm reduction: The case 
for the electronic cigarette. Harm Reduction Journal. 2013;10:19. doi: 10.1186/1477-7517-10-19; PMCID: 
3850892. 
21. 
Westenberger BJ. Evaluation of e-cigarettes. FDA Center for Drug Evaluation and Research  2009. 
22. 
Trehy ML, Ye W, Hadwiger ME, Moore TW, Allgire JF, Woodruff JT, Ahadi SS, Black JC, Westenberger 
BJ. Analysis of electronic cigarette cartridges, refill solutions, and smoke for nicotine and nicotine related 
impurities. 
Journal 
of 
Liquid 
Chromatography 
& 
Related 
Technologies. 
2011;34:1442-58. 
doi: 
10.1080/10826076.2011.572213. 
23. 
Goniewicz ML, Knysak  J, Gawron  M, Kosmider L, Sobczak A, Kurek J, Prokopowicz A, Jablonska-
Czapla M, Rosik-Dulewska C, Havel C, Jacob P, Benowitz N. Levels of selected carcinogens and toxicants in 
vapour from electronic cigarettes. Tobacco Control. 2014;23:133-9. doi: 10.1136/tobaccocontrol-2012-050859; 
PMCID: 4154473. 
24. 
Hua M, Alfi M, Talbot P. Health-related effects reported by electronic cigarette users in online forums. 
Journal of Medical Internet Research. 2013;15:e59. doi: 10.2196/jmir.2324; PMCID: 3636314. 
25. 
Vansickel AR, Weaver MF, Eissenberg T. Clinical laboratory assessment of the abuse of liability of an 
electronic cigarette. Addiction. 2012;107:1493-500. doi: 10.1111/j.1360-0443.2012.03791.x; PMCID: 3330136. 
26. 
Cahn Z, Siegel M. Electronic cigarettes as a harm reduction strategy for tobacco control: A step forward 
or a repeat of past mistakes? Journal of Public Health Policy. 2011;32:16-31. doi: 10.1057/jphp.2010.41. 
27. 
Regan AK, Promoff G, Dube SR, Arrazola R. Electronic nicotine delivery systems: adult use and 
awareness of the 'e-cigarette' in the USA. Tobacco Control. 2013;22:19-23. doi: 10.1136/tobaccocontrol-2011-
050044. 
28. 
Goniewicz ML, Hajek P, McRobbie H. Nicotine content of electronic cigarettes, its release in vapour and 
its consistency across batches: Regulatory implications. Addiction. 2014;109:500-7. doi: 10.1111/add.12410. 
29. 
Weaver M, Breland A, Spindle T, Eissenberg T. Electronic cigarettes: A review of safety and clinical 
issues. Journal of Addiction Medicine. 2014;8:234-40. doi: 10.1097/ADM.0000000000000043; PMCID: 4123220. 
30. 
Hecht SS, Carmella SG, Kotandeniya D, Pillsbury ME, Chen M, Ransom BW, Isaksson Vogel R, 
Thompson E, Murphy SE, Hatsukami DK. Evaluation of toxicant and carcinogen metabolites in the urine of e-
cigarette users verses cigarette smokers. Nicotine and Tobacco Research. 2015;17:704-9. doi: 
10.1093/ntr/ntu218; PMCID: 4481723. 
31. 
Goniewicz ML, Gawron M, Smith DM, Peng M, Jacob P, Benowitz NL. Exposure to nicotine and selected 
toxicants in cigarette smokers who switched to electronic cigarettes: A longitudinal within-subjects observational 
study. Nicotine & Tobacco Research. in press. doi: 10.1093/ntr/ntw160. 

Protocol Version Date: 11/1/2021 
 
37 
 
32. 
Goniewicz ML, Lingas EO, Hajek P. Patterns of electronic cigarette use and user beliefs about their safety 
and benefits: An internet survey. Drug and Alcohol Review. 2013;32:133-40. doi: 10.1111/j.1465-
3362.2012.00512.x; PMCID: 3530631. 
33. 
Dawkins L, Turner J, Roberts A, Soar K. 'Vaping' profiles and preferences: An online survey of electronic 
cigarette users. Addiction. 2013;108:1115-25. doi: doi: 10.1111/add.12150. 
34. 
Farsalinos KE, Romagna G, Tsiapras D, Kyrzopoulos S, Voudris V. Characteristics, perceived side 
effects and benefits of electronic cigarette use: A worldwide survey of more than 19,000 consumers. International 
Journal of Environmental Research and Public Health. 2014;14:4356-73. doi: 10.3390/ijerph110404356; PMCID: 
4025024. 
35. 
Ayers JW, Ribisl KM, Brownstein JS. Tracking the rise in popularity of electronic nicotine delivery systems 
(electronic cigarettes) using search query surveillance. American Journal of Preventive Medicine. 2011;40:448-
53. doi: 10.1016/j.amepre.2010.12.007. 
36. 
Dockrell M, Morrison R, Bauld L, McNeill A. E-cigarettes: Prevalence and attitudes in Great Britain. 
Nicotine & Tobacco Research. 2013;15:1737-44. doi: 10.1093/ntr/ntt057; PMCID: 3768337. 
37. 
Grana RA, Ling PM. “Smoking Revolution”: A content analysis of electronic cigarette retail websites. 
American Journal of Preventive Medicine. 2014;46:395-403. doi: 10.1016/j.amepre.2013.12.010; PMCID: 
3989286. 
38. 
Pearson JL, Richardson A, Niaura RS, Vallone DM, Abrams DB. e-Cigarette awareness, use, and harm 
perceptions in US adults. American Journal of Public Health. 2012;9:1758-66. doi: 10.2105/AJPH.2011.300526; 
PMCID: 3474361. 
39. 
Pokhrel P, Fagan P, Little MA, Kawamoto CT, Herzog TA. Smokers who try e-cigarettes to quit smoking: 
Findings from a multiethnic study in Hawaii. American Journal of Public Health. 2013;103:e57-e62. doi: 
10.2105/AJPH.2013.301453; PMCID: 3780693. 
40. 
Kralikova E, Novak J, West O, Kmetova A, Hajek P. Do e-cigarettes have the potential to compete with 
conventional cigarettes?: A survey of conventional cigarette smokers' experiences with e-cigarettes. Chest. 
2013;144:1609-14. doi: 10.1378/chest.12-2842. 
41. 
McMillen RC, Gottlieb MA, Shaefer RM, Winickoff JP, Klein JD. Trends in electronic cigarette use among 
U.S. Adults: Use is increasing in both smokers and nonsmokers. Nicotine & Tobacco Research. 
2015;17(10):1195-202. Epub 2014/11/09. doi: 10.1093/ntr/ntu213. PubMed PMID: 25381306. 
42. 
Tan AS, Bigman CA. E-cigarette awareness and perceived harmfulness: Prevalence and associations 
with smoking-cessation outcomes. American Journal of Preventive Medicine. 2014;47:141-9. doi: 
10.1016/j.amepre.2014.02.011; PMCID: 4107147. 
43. 
Delnevo CD, Giovenco DP, Steinberg MB, Villanti AC, Pearson JL, Niaura RS, Abrams DB. Patterns of 
electronic cigarette use among adults in the United States. Nicotine & Tobacco Research. 2016;18:715-9. doi: 
10.1093/ntr/ntv237. 
44. 
Siegel MB, Tanwar KL, Wood KS. Electronic cigarettes as a smoking-cessation tool: results from an 
online survey. American Journal of Preventive Medicine. 2011;40:472-5. doi: 10.1016/j.amepre.2010.12.006. 
45. 
Adkison SE, O'Connor RJ, Bansal-Travers M, Hyland A, Borland R, Yong H-H, Cummings KM, McNeill 
A, Thrasher JF, Hammond D, Fong GT. Electronic nicotine delivery systems: International tobacco control four-

Protocol Version Date: 11/1/2021 
 
38 
 
country survey. American Journal of Preventive Medicine. 2013;44:207-15. doi: 10.1016/j.amepre.2012.10.018; 
PMCID: 3627474. 
46. 
Polosa R, Caponnetto P, Morjaria JB, Papale G, Campagna D, Russo C. Effect of an electronic nicotine 
delivery device (e-Cigarette) on smoking reduction and cessation: A prospective 6-month pilot study. BMC Public 
Health. 2011;11. doi: 10.1186/1471-2458-11-786; PMCID: 3203079. 
47. 
Polosa R, Morjaria JB, Caponnetto P, Campagna D, Russo C, Alamo A, Amaradio M, Fisichella A. 
Effectiveness and tolerability of electronic cigarette in real-life: A 24-month prospective observational study. 
Internal and Emergency Medicine. 2014;9:537-46. doi: 10.1007/s11739-013-0977-z. 
48. 
Caponnetto P, Auditore R, Russo C, Cappello GC, Polosa R. Impact of an electronic cigarette on smoking 
reduction and cessation in schizophrenic smokers: A prospective 12-month pilot study. International Journal of 
Environmental Research and Public Health. 2013;10:446-61. doi: 10.3390/ijerph10020446; PMCID: 3635154. 
49. 
Etter JF, Bullen C. A longitudinal study of electronic cigarette users. Addictive Behaviors. 2014;39:491-
4. doi: 10.1016/j.addbeh.2013.10.028. 
50. 
Zhuang Y-L, Cummins SE, Sun JY, Zhu S-H. Long-term e-cigarette use and smoking cessation: A 
longitudinal study with US population. Tobacco Control. 2016;25:i90-i5. doi: 10.1136/tobaccocontrol-2016-
053096. 
51. 
Biener L, Hargraves JL. A longitudinal study of electronic cigarette use in a population-based sample of 
adult smokers: Association with smoking cessation and motivation to quit. Nicotine & Tobacco Research. 
2015;17:127-33. doi: 10.1093/ntr/ntu200 PMCID: 4375383. 
52. 
Brose LS, Hitchman SC, Brown J, West R, McNeill A. 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. Addiction. 2015;110:1160-8. doi: 10.1111/add.12917; PMCID: 4862028  
53. 
Hitchman SC, Brose LS, Brown J, Robson D, McNeill A. Associations between e-cigarette type, 
frequency of use, and quitting smoking: Findings from a longitudinal online panel survey in great britain. Nicotine 
& Tobacco Research. 2015;17:1187-94. doi: 10.1093/ntr/ntv078; PMCID: 4580313. 
54. 
Popova L, Ling PM. Alternative tobacco product use and smoking cessation: A national study. American 
Journal of Public Health. 2013;103:923-30. doi: 10.2105/AJPH.2012.301070; PMCID: 3661190. 
55. 
Etter JF. Electronic cigarettes: A survey of users. BMC Public Health. 2010;10. doi: 10.1186/1471-2458-
10-231; PMCID: 2877672. 
56. 
Etter JF, Bullen C. Electronic cigarette: Users profile, utilization, satisfaction and perceived efficacy. 
Addiction. 2011;106:2017-28. doi: 10.1111/j.1360-0443.2011.03505.x. 
57. 
Barbeau AM, Burda J, Siegel M. Perceived efficacy of e-cigarettes versus nicotine replacement therapy 
among successful e-cigarette users: A qualitative approach Addiction Science and Clinical Practice. 2013;8. doi: 
10.1186/1940-0640-8-5; PMCID: 3599549. 
58. 
Brown J, Beard E, Kotz D, Michie S, West R. Real-world effectiveness of e-cigarettes when used to aid 
smoking cessation: A cross-sectional population study. Addiction. 2014;109:1531-40. doi: 10.1111/add.12623; 
PMCID: 4171752. 
59. 
Lechner WV, Tackett AP, Grant DM, Tahirkheli NN, Driskill LM, Wagener TL. Effects of duration of 
electronic cigarette use. Nicotine & Tobacco Research 2015;17:180-5. doi: 10.1093/ntr/ntu061. 

Protocol Version Date: 11/1/2021 
 
39 
 
60. 
Vickerman KA, Carpenter KM, Altman T, Nash CM, Zbikowski SM. Use of electronic cigarettes among 
state tobacco cessation quitline callers. Nicotine & Tobacco Research. 2013;15:1787-91. doi: 10.1093/ntr/ntt061. 
61. 
Grana RA, Popova L, Ling PM. A longitudinal analysis of electronic cigarette use and smoking cessation. 
JAMA Internal Medicine. 2014;174:812-3. doi: 10.1001/jamainternmed.2014.187; PMCID: 4122246. 
62. 
Al-Delaimy WK, Myers MG, Leas EC, Strong DR, Hofstetter CR. E-Cigarette use in the past and quitting 
behavior in the future: a population-based study. American Journal of Public Health. 2015;105:1213-9. doi: 
10.2105/AJPH.2014.302482. 
63. 
Kalkhoran S, Glantz SA. E-cigarettes and smoking cessation in real-world and clinical settings: A 
systematic review and meta-analysis. Lancet. 2016;4:116-28. doi: 10.1016/S2213-2600(15)00521-4. 
64. 
Malas M, van der Tempel J, Schwartz R, Minichiello A, Lightfoot C, Noormohamed A, Andrews J, 
Zawertailo L, Ferrence R. Electronic cigarettes for smoking cessation: A systematic review. Nicotine & Tobacco 
Research. 2016;18:1926-36. doi: 10.1093/ntr/ntw119. 
65. 
Adriaens K, Van Gucht D, Declerk P, Baeyens F. Effectiveness of the electronic cigarette: An eight-week 
Flemish study with six-month follow-up on smoking reduction, craving, and experienced benefits and complaints. 
International 
Journal 
of 
Environmental 
Research 
and 
Public 
Health. 
2014;11:11220-48. 
doi: 
10.3390/ijerph111111220; PMCID: 4245610. 
66. 
Caponnetto P, Campagna D, Cibella F, Morjaria JB, Caruso M, Russo C, Polosa R. Efficiency and safety 
of an electronic cigarette (ECLAT) as tobacco cigarettes substitute: A prospective 12-month randomized control 
design study. PLoS ONE. 2013;8:e66317. doi: 10.1371/journal.pone.0066317; PMCID: 3691171. 
67. 
Bullen C, Howe C, Laugesen M, McRobbie H, Parag V, Williman J, Walker N. Electronic cigarettes for 
smoking cessation: A randomised controlled trial. Lancet. 2013;382:1629-37. doi: 10.1016/S0140-
6736(13)61842-5. 
68. 
Fraser D, Borland R, Gartner C. Protocol for a randomised pragmatic policy trial of nicotine products for 
quitting or long-term substitution in smokers. BMC Public Health. 2015;15:1-9. doi: 10.1186/s12889-015-2366-
1; PMCID: 4596390. 
69. 
Bullen C, Williman J, Howe C, Laugesen M, McRobbie H, Parag V, Walker N. Study protocol for a 
randomised controlled trial of electronic cigarettes versus nicotine patch for smoking cessation. BMC Public 
Health. 2013;13. doi: 10.1186/1471-2458-13-210; PMCID: 3602285. 
70. 
McRobbie H, Bullen C, Hartmann-Boyce J, Hajek P. Electronic cigarettes for smoking cessation and 
reduction.  Cochrane Database of Systematice Reviews. Oxford: John Wiley & Sons; 2014. 
71. 
Lopez AA, Cobb CO, Yingst JM, Veldheer S, Hrabovsky S, Yen MS, Foulds J, Eissenberg T. A 
transdisciplinary model to inform randomized clinical trial methods for electronic cigarette evaluation. BMC Public 
Health. 2016;16:217. doi: 10.1186/s12889-016-2792-8; PMCID: 4778292. 
72. 
Pearson JL, Stanton CA, Cha S, Niaura RS, Luta G, Graham AL. E-cigarettes and smoking cessation: 
Insights and cautions from a secondary analysis of data from a study of online treatment-seeking smokers. 
Nicotine & Tobacco Research. 2015;17:1219-27. doi: 10.1093/ntr/ntu269; PMCID: 4592337. 
73. 
Hyland A, Ambrose BK, Conway KP, Borek N, Lambert E, Carusi C, Taylor K, Crosse S, Fong GT, 
Cummings KM, Abrams D, Pierce JP, Sargent J, Messer K, Bansal-Travers M, Niaura R, Vallone D, Hammond 
D, Hilmi N, Kwan J, Piesse A, Kalton G, Lohr S, Pharris-Ciurej N, Castleman V, Green VR, Tessman G, Kaufman 
A, Lawrence C, van Bemmel DM, Kimmel HL, Blount B, Yang L, O'Brien B, Tworek C, Alberding D, Hull LC, 

Protocol Version Date: 11/1/2021 
 
40 
 
Cheng YC, Maklan D, Backinger CL, Compton WM. Design and methods of the population assessment of 
tobacco and health (PATH) study. Tobacco Control. In Press. doi: 10.1136/tobaccocontrol-2016-052934. 
74. 
Walton KM, Abrams DB, Bailey WC, Clark D, Connelly GN, Djordjevic MV, Eissenberg TE, Fiore MC, 
Goniewicz ML, Haverkos L, Hecht SS, Henningfield JE, Hughes JR, Oncken CA, Postow L, Rose JE, Wanke 
KL, Yang L, Hatsukami DK. NIH electronic cigarette workshop: Developing a research agenda. Nicotine & 
Tobacco Research. 2015;17:259-69. doi: 10.1093/ntr/ntu214; PMCID: 4311173. 
75. 
Carpenter MJ, Hughes JR, Solomon LJ, Callas PW. Both smoking reduction with nicotine replacement 
therapy and motivational advice increase future cessation among smokers unmotivated to quit. Journal of 
Consulting and Clinical Psychology. 2004;72:371-81. doi: 10.1037/0022-006X.72.3.371. PubMed PMID: 
15279521. 
76. 
Carpenter MJ, Hughes JR, Gray KM, Wahlquist AE, Saladin ME, Alberg AJ. Nicotine therapy sampling 
to induce quit attempts among smokers unmotivated to quit: A randomized clinical trial. Archives of Internal 
Medicine. 2011;171:1901-7. doi: 10.1001/archinternmed.2011.492; PMCID: 3372898. 
77. 
Jardin BF, Cropsey KL, Wahlquist AE, Gray KM, Silvestri GA, Cummings KM, Carpenter MJ. Evaluating 
the effect of access to free medication to quit smoking: A clinical trial testing the role of motivation. Nicotine & 
Tobacco Research. 2014;16:992-9. Epub 2014/03/13. doi: 10.1093/ntr/ntu025. PubMed PMID: 24610399; 
PMCID: 4133568. 
78. 
Carpenter MJ, Gray KM. A pilot randomized study of smokeless tobacco use among smokers not 
interested in quitting: changes in smoking behavior and readiness to quit. Nicotine & Tobacco Research. 
2010;12(2):136-43. Epub 2010/01/08. doi: 10.1093/ntr/ntp186. PubMed PMID: 20053788; PMCID: 2816197. 
79. 
Burris JL, Carpenter MJ, Wahlquist AE, Cummings KM, Gray KM. Brief, instructional smokeless tobacco 
use among cigarette smokers who do not intend to quit: a pilot randomized clinical trial. Nicotine & Tobacco 
Research. 2014;16(4):397-405. Epub 2013/10/17. doi: 10.1093/ntr/ntt161. PubMed PMID: 24130144; PMCID: 
3954419. 
80. 
Carpenter MJ, Wahlquist AE, Burris JL, Gray KM, Garrett-Mayer E, Cummings KM, Alberg AJ. Snus 
undermines quit attempts but not abstinence: A randomized clinical trial among U.S. smokers. Tobacco Control. 
in press. doi: 10.1136/tobaccocontrol-2015-052783; PMCID: 5061602. 
81. 
Burris JL, Wahlquist AE, Alberg AJ, Cummings KM, Gray KM, Garrett-Mayer E, Carpenter MJ. A 
longitudinal, naturalistic study of U.S. smokers’ trial and adoption of snus. Addictive Behaviors. 2016;63:82-8. 
doi: 10.1016/j.addbeh.2016.07.008; PMCID: 4993634  
82. 
Meier E, Wahlquist AE, Heckman BW, Cummings KM, Froeliger B, Carpenter MJ. A pilot randomized 
crossover trial of e-cigarette sampling among smokers. Nicotine & Tobacco Research. in press. 
83. 
Czogala J, Goniewicz ML, Fidelus B, Zielinska-Danchm W, Travers MJ, Sobczak A. Secondhand 
exposure to vapors from electronic cigarettes. Nicotine & Tobacco Research. 2014;16:655-62. doi: 
10.1093/ntr/ntt203. 
84. 
Goniewicz ML, Gawron M, Peng M, Jacob P, Benowitz N. Electronic cigarettes deliver similar levels of 
nicotine and reduce exposure to combustion toxicants after switching from tobacco cigarettes.  18th Annual 
Meeting for the Society for Research on Nicotine and Tobacco; Houston, TX2012. 
85. 
Jacob P, Goniewicz ML, Havel CM, Schick SF, Benowitz NL. Nicotelline: A proposed biomarker and 
environmental tracer for particulate matter derived from tobacco smoke. Chemical Research in Toxicology. 
2013;26:1615-31. doi: 10.1021/tx400094y; PMCID: 3929594. 

Protocol Version Date: 11/1/2021 
 
41 
 
86. 
Shahab L, Goniewicz M. Electronic cigarettes are at least as effective as nicotine patches for smoking 
cessation. Evidence-Based Medicine. 2014;19:133. doi: 10.1136/eb-2013-101690. 
87. 
Brandon TH, Goniewicz ML, Hanna NH, Hatsukami DK, Herbst RS, Hobin JA, Ostroff JS, Shields PG, 
Toll BA, Tyne CA, Viswanath K, Warren GW. Electronic nicotine delivery systems: A policy statement from the 
American Association for Cancer Research and the American Society of Clinical Oncology. Clinical Cancer 
Research. 2015;21:514-25. doi: 10.1158/1078-0432.CCR-14-2544. 
88. 
Hajek P, Goniewicz ML, Phillips A, Myers Smith K, West O, McRobbie H. Nicotine intake from electronic 
cigarettes on initial use and after 4 weeks of regular use. Nicotine & Tobacco Research. 2015;17:175-9. doi: 
10.1093/ntr/ntu153. 
89. 
Lee YH, Gawron M, Goniewicz ML. Changes in puffing behavior among smokers who switched from 
tobacco to electronic cigarettes. Addictive Behaviors. 2015;48:1-4. doi: 10.1016/j.addbeh.2015.04.003; PMCID: 
4457608. 
90. 
Nelson VA, Goniewicz ML, Beard E, Brown J, Sheals K, West R, Shahab L. Comparison of the 
characteristics of long-term users of electronic cigarettes versus nicotine replacement therapy: A cross-sectional 
survey of English ex-smokers and current smokers. Drug and Alcohol Dependence. 2015;153:300-5. doi: 
10.1016/j.drugalcdep.2015.05.005; PMCID: 4528068. 
91. 
Czoli CD, Goniewicz M, Islam T, Kotnowski K, Hammond D. Consumer preferences for electronic 
cigarettes: 
results 
from 
a 
discrete 
choice 
experiment 
Tobacco 
Control. 
2016;25:e30-e6. 
doi: 
10.1136/tobaccocontrol-2015-052422. 
92. 
Saddleson ML, Kozlowski LT, Giovino GA, Goniewicz ML, Mahoney MC, Homish GG, Arora A. 
Enjoyment and other reasons for electronic cigarette use: Results from college students in New York. Addictive 
Behaviors. 2016;54:33-9. doi: 10.1016/j.addbeh.2015.11.012. 
93. 
Meier E, Tackett AP, Wagener TL. Effectiveness of electronic aids for smoking cessation. Current 
Cardiovascular Risk Reports. 2013;7:464-72. doi: 10.1007/s12170-013-0343-8; PMCID: 3851041. 
94. 
Wagener TL, Meier E, Hale JJ, Oliver ER, Warner ML, Driskill LM, Gillaspy SR, Siegel MB, Foster S. 
Pilot investigation of changes in readiness and confidence to quit smoking after e-cigarette experimentation and 
1 week of use. Nicotine & Tobacco Research. 2014;16:108-14. doi: 10.1093/ntr/ntt138. 
95. 
Wagener TL, Floyd EL, Stepanov I, Driskill LM, Frank SG, Meier E, Leavens EL, Tackett AP, Molina N, 
Queimado L. Have combustible cigarettes met their match? The nicotine delivery profiles and harmful constituent 
exposures of second-generation and third-generation electronic cigarette users. Tobacco Control. In Press. doi: 
10.1136/tobaccocontrol-2016-053041. 
96. 
Busch AM, Leavens EL, Wagener TL, Buckley ML, Tooley EM. Prevalence, reasons for use, and risk 
perception of electronic cigarettes among post-acute coronary syndrome smokers. Journal of Cardiopulmonary 
Rehabilitation and Prevention. 2016;36:352-7. doi: 10.1097/HCR.0000000000000179; PMCID: 5001913. 
97. 
Meier EM, Tackett AP, Miller MB, Grant DM, Wagener TL. Which nicotine products are gateways to 
regular use?: First-tried tobacco and current use in college students. American Journal of Preventive Medicine. 
2015;48:S86-S93. doi: 10.1016/j.amepre.2014.09.018. 
98. 
Lechner WV, Meier E, Wiener JL, Grant, D.M., Gilmore J, Judah MR, Mills AC, Wagener TL. The 
comparative efficacy of first- versus second-generation electronic cigarettes in reducing symptoms of nicotine 
withdrawal. Addiction. 2015;110:862-7. doi: 10.1111/add.12870. 

Protocol Version Date: 11/1/2021 
 
42 
 
99. 
Cummings KM, Dresler CM, Field JK, Fox J, Gritz ER, Hanna NH, Ikeda N, Jassem J, Mulshine JL, 
Peters MJ, Yamaguchi NH, Warren G, Zhou C. E-cigarettes and cancer patients. Journal of Thoracic Oncology. 
2014;9:438-41. doi: 10.1097/JTO.0000000000000129; PMCID: 4040965. 
100. 
Etter JF, Perneger TV. A comparison of cigarette smokers recruited through the internet or by mail. 
International Journal of Epidemiology. 2001;30:521-5. doi: 10.1093/ije/30.3.521. PubMed PMID: 11416075. 
101. 
Welch BM, Marshall E, Qanungo S, Aziz A, Laken M, Lenert L, Obeid J. Teleconsent: A novel approach 
to obtain informed consent for research. Contemporary Clinical Trials Communications. 2016;3:74-9. doi: 
10.1016/j.conctc.2016.03.002; PMCID: 5096381  
102. 
Newby R, Watson J, Woodliff D. SME survey methodology: Response rates, data quality, and cost 
effectiveness. Entrepreneurship Theory and Practice. 2003;28:163-72. doi: 10.1046/j.1540-6520.2003.00037.x. 
103. 
Edwards PJ, Roberts I, Clarke MJ, DiGuiseppi C, Wentz R, Kwan I, Cooper R, Felix LM, Pratap S. 
Methods to increase response to postal and electronic questionnaires (review). Cochrane Database of 
Systematic Reviews. 2009;3. doi: 10.1002/14651858.MR000008.pub4. 
104. 
White E, Carney PA, Kolar AS. Increasing response to mailed questionnaires by including a pencil/pen. 
American Journal of Epidemiology. 2005;162:261-6. doi: 1093/aje/kwi194. 
105. 
King BA, Alam S, Promoff G, Arrazola R, Dube SR. Awareness and ever-use of electronic cigarettes 
among U.S. adults, 2010-2011. Nicotine & Tobacco Research. 2013;15:1623-7. doi: 10.1093/ntr/ntt013; PMCID: 
4570561. 
106. 
Farsalinos KE, Spyrou A, Tsimopoulou K, Stefopoulos C, Romagna G, Voudris V. Nicotine absorption 
from electronic cigarette use: Comparison between first and new-generation devices. Scientific Reports. 
2014;5:11269. doi: 10.1038/srep04133; PMCID: 4469966. 
107. 
Lopez AA, Hiler MM, Soule EK, Ramoa CP, Karaoghlanian NV, Lipato T, Breland AB, Shihadeh A, 
Eissenberg T. Effects of electronic cigarette liquid nicotine concentration on plasma nicotine and puff topography 
in tobacco cigarette smokers: A preliminary report. Nicotine & Tobacco Research. 2016;18:720-3. doi: 
10.1093/ntr/ntv182. 
108. 
Farsalinos KE, Kistler KA, Gillman G, Voudris V. Evaluation of electronic Cigarette liquids and aerosol 
for the presence of selected inhalation toxins. Nicotine & Tobacco Research. 2015;17:168-74. doi: 
10.1093/ntr/ntu176; PMCID: 4892705  
109. 
McCarthy DE, Minami H, Yeh VM, Bold KW. An experimental investigation of reactivity to ecological 
momentary assessment frequency among adults trying to quit smoking. Addiction. 2015;110(10):1549-60. Epub 
2015/05/27. doi: 10.1111/add.12996. PubMed PMID: 26011583; PMCID: 4565778. 
110. 
Shiffman S, Waters AJ, Hickcox M. The Nicotine Dependence Syndrome Scale: A multidimensional 
measure of nicotine dependence. Nicotine & Tobacco Research. 2004;6:327-47. 
111. 
Heatherton TF, Kozlowski LT, Frecker RC, Fagerström K-O. The Fagerström test for nicotine 
dependence: A revision of the Fagerstrom Tolerance Questionnaire. British Journal of Addiction. 1991;86:1119-
27. doi: 10.1111/j.1360-0443.1991.tb01879.x. 
112. 
Piper ME, McCarthy DE, Bolt DM, Smith SS, Lerman C, Benowitz N, Fiore MC, Baker TB. Assessing 
dimensions of nicotine dependence : An evaluation of the Nicotine Dependence Syndrome Scale (NDSS) and 
the Wisconsin Inventory of Smoking Dependence Motives (WISDM). Nicotine & Tobacco Research. 
2008;10:1009-20; PMCID: 2614360. 

Protocol Version Date: 11/1/2021 
 
43 
 
113. 
Shiffman S, Sayette MA. Validation of the nicotine dependence syndrome scale (NDSS): A criterion-
group design contrasting chippers and regular smokers. Drug and Alcohol Dependence. 2005;79:45-52; PMCID: 
2659852. 
114. 
Hughes JR, Hatsukami D. Signs and symptoms of tobacco withdrawal. Archives of General Psychiatry. 
1986;43:289-94. 
115. 
Biener L, Bogen K, Connolly G. Impact of corrective health information on consumers' perceptions of 
"reduced exposure" tobacco products. Tobacco Control. 2007;16:306-11; PMCID: 2598559. 
116. 
O'Connor RJ, Ashare RL, Fix BV, Hawk LW, Cummings KM, Schmidt WC. College students' 
expectancies for light cigarettes and potential reduced exposure products. American Journal of Health Behavior. 
2007;31:402-10. 
117. 
O'Connor RJ, Hyland A, Giovino GA, Fong GT, Cummings KM. Smoker awareness of and beliefs about 
supposedly less harmful tobacco products. American Journal of Preventive Medicine. 2005;29:85-90. 
118. 
Parascandola M, Hurd AL, Augustson E. Consumer awareness and attitudes related to new potential 
reduced-exposure tobacco products. American Journal of Health Behavior. 2008;32(4):431-7. Epub 2007/12/21. 
doi: 10.5555/ajhb.2008.32.4.431 [doi]. 
119. 
Parascandola M, Augustson E, O’Connell ME, Marcus S. Consumer awareness and attitudes related to 
new potential reduced-exposure tobacco product brands. Nicotine & Tobacco Research. 2009;11:886-95. doi: 
10.1093/ntr/ntp082; PMCID: 2722238. 
120. 
Rogers EM. New product adoption and diffusion. Journal of Consumer Research. 1976;2:290-301. 
121. 
Hughes JR, Adams EH, Franzon MA, Maguire MK, Guary J. A prospective study of off-label use of, abuse 
of and dependence on nicotine inhaler. Tobacco Control. 2005;14:49-54; PMCID: 1747971. 
122. 
Pepper JK, Ribisl KM, Emery SL, Brewer NT. Reasons for starting and stopping electronic cigarette use. 
International Journal of Environmental Research and Public Health. 2014;11(10):10345-61. Epub 2014/10/07. 
doi: 10.3390/ijerph111010345. PubMed PMID: 25286168; PMCID: 4210982. 
123. 
Cappelleri JC, Bushmakin AG, Baker CL, Merikle E, Olufade AO, Gilbert DG. Confirmatory factor 
analyses and reliability of the modified cigarette evaluation questionnaire. Addictive Behaviors. 2007;32:912*23. 
doi: 10.1016/j.addbeh.2006.06.028. 
124. 
Westman EC, Behm FM, Rose JE. Dissociating the nicotine and airway sensory effects of smoking. 
Pharmacology Biochemistry and Behavior. 1996;53:309-15. doi: 10.1016/0091-3057(95)02027-6. 
125. 
Hatsukami DK, Zhang Y, O'Connor RJ, Severson HH. Subjective responses to oral tobacco products: 
Scale validation. Nicotine & Tobacco Research. 2013;15:1259-64. doi: 10.1093/ntr/nts265; PMCID: 3682844. 
126. 
Etter JF. A comparison of the content-, construct- and predictive validity of the cigarette dependence 
scale and the Fagerstrom test for nicotine dependence. Drug and Alcohol Dependence. 2005;77:259-68. doi: 
10.1016/j.drugalcdep.2004.08.015. 
127. 
Foulds J, Veldheer S, Yingst J, Hrabovsky S, WIlson SJ, Nichols TT, Eissenberg T. Development of a 
questionnaire for assessing dependence on electronic cigarettes in a large sample of ex-smoking E-cigarette 
users. Nicotine & Tobacco Research. 2015;17:186-92. doi: 10.1093/ntr/ntu204. 

Protocol Version Date: 11/1/2021 
 
44 
 
128. 
Biener L, Abrams DB. The contemplation ladder: Validation of a measure of readiness to consider 
smoking cessation. Health Psychology. 1991;10:360-5. 
129. 
Hughes JR, Keely JP, Fagerstrom K-O, Callas PW. Intentions to quit smoking change over short periods 
of time. Addictive Behaviors. 2005;30:353-662. 
130. 
USDHS. Current cigarette smoking among adults — United States, 2011. MMWR. 2012;61:889-94. 
131. 
Hughes JR, Keely JP, Niaura RS, Ossip-Klein DJ, Richmond RL, Swan GE. Measures of abstinence in 
clinical trials: Issues and recommendations. Nicotine & Tobacco Research. 2003;5:13-25. doi: 
10.1093/ntr/5.1.13. 
132. 
Aveyard P, Wang D, Connock M, Fry-Smith A, Barton P, Moore D. Assessing the outcomes of prolonged 
cessation-induction and aid-to-cessation trials: Floating prolonged abstinence. Nicotine & Tobacco Research. 
2009;11:475-80. doi: 10.1093/ntr/ntp035; PMCID: 2671463. 
133. 
Hughes JR, Keely JP, Naud S. Shape of relapse curve and long-term abstinence among untreated 
smokers. Addiction. 2004;99:29-38. 
134. 
Dixon WJ, Massey FJ.  Introduction to Statistical Analysis: McGraw-Hill; 1983. p. 142. 
135. 
Shiffman S, Dunbar MS, Benowitz NL. A comparison of nicotine biomarkers and smoking patterns in daily 
and non-daily smokers. Cancer Epidemiology, Biomarkers and Prevention. 2014;23:1264-72. doi: 10.1158/1055-
9965.EPI-13-1014. 
136. 
Hall SM, Delucchi KL, Velicer WF, Kahler CW, Ranger-Moore J, Hedeker D, Tsoh JY, Niaura R. Statistical 
analysis of randomized trials in tobacco treatment: longitudinal designs with dichotomous outcome. Nicotine & 
Tobacco Research. 2001;3(3):193-202. Epub 2001/08/17. doi: 10.1080/14622200110050411. PubMed PMID: 
11506764. 
137. 
Schuirmann DJ. A comparison of the two one-sided tests procedure and the power approach for 
assessing the equivalence of average bioavailability. Journal of Pharmacokinetics and Biopharmaceutics. 
1987;15:657-80. 
138. 
Lerman C, Schnoll RA, Hawk LWJ, Cinciripini P, George TP, Wileyto EP, Swan GE, Benowitz NL, Heitjan 
DF, Tyndale RF, Group P-PR. Use of the nicotine metabolite ratio as a genetically informed biomarker of 
response to nicotine patch or varenicline for smoking cessation: a randomised, double-blind placebo-controlled 
trial. The Lancet Respiratory Medicine. 2015;3:131-8. doi: 10.1016/S2213-2600(14)70294-2; PMCID: 4480925. 
139. 
Schnoll RA, Patterson F, Wileyto EP, Tyndale RF, Benowitz N, Lerman C. Nicotine metabolic rate 
predicts successful smoking cessation with transdermal nicotine: A validation study. Pharmacology, 
Biochemistry and Behavior. 2009(92):6-11. doi: 10.1016/j.pbb.2008.10.016; PMCID: 2657225. 
140. 
Benowitz NL. Nicotine Safety and Toxicity. New York: Oxford University Press; 1998. 
141. 
Carpenter MJ, Jardin BF, Burris JL, Mathew AR, Schnoll RA, Rigotti NA, Cummings KM. Clinical 
strategies to enhance the efficacy of nicotine replacement therapy for smoking cessation: A review of the 
literature. Drugs. 2013;73(5):407-26. Epub 2013/04/11. doi: 10.1007/s40265-013-0038-y. PubMed PMID: 
23572407; PMCID: 3662024. 
142. 
Center for Drug Evaluation and Research. COSTART Coding Symbols for Thesaurus of Adverse 
Reaction Terms, Fourth Edition. Rockville, MD: Public Health Services; 1993. 

Protocol Version Date: 11/1/2021 
 
45 
 
143. 
O'Brien PC, Fleming TR. A multiple testing procedure for clinical trials. Biometrics. 1979;35:549-56.