Aim 1: Augmented Reality as an Adjunct to Quitline Counseling for Smoking Cessation
Phase: NA
Conditions: Smoking
Sponsor: H. Lee Moffitt Cancer Center and Research Institute

 
 
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TITLE:  Augmented Reality as an Adjunct to Quitline Counseling for Smoking Cessation 
 
Coordinating Center:              H. Lee Moffitt Cancer Center 
 
Principal Investigator: 
Christine Vinci, Ph.D. 
 12902 Magnolia Drive 
MRC – TRIP 
Tampa, FL 33612Phone: 813-745-5421 
Fax: 813-449-6871 
Email: christine.vinci@moffitt.org 
  
Co-Principal Investigator:     Thomas Brandon, PhD 
 
 
Co-Investigators: 
Margaret Byrne, PhD 
 
Kea Turner, PhD 
 
Damon Vadrine, DRPH 
 
Lee Ritterbrand, PhD 
 
Kara Wiseman, PhD, MPH 
 
Katrina Vickerman, PhD 
 
Ursula Martinez, PhD 
 
Statistician: 
Steve Sutton, Ph.D. 
 
Study Coordinators: 
Skye Dougan, M.P.H. 
 
Helen Yates, M.P.H. 
 
 
 
Protocol Version #8 Date: 02/06/2025 
USF IRB Approval: 
 
Protocol Type: 
_X_ external grant application 
 
 
___ internal grant application 
 
 
___ … continue the list 
 
 
 
 
 
 
other ___________ 
 

 
 
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TABLE OF CONTENTS 
 
 
Page 
1. SPECIFIC AIMS  ................................................................................................................ 4 
 
2. BACKGROUND AND SIGNIFICANCE ................................................................................. 5 
 
3. PARTICIPANT SELECTION ................................................................................................ 8 
 
4. STUDY DESIGN AND METHODS ........................................................................................ 9 
 
5. STATISTICAL CONSIDERATIONS ................................................................................... 16 
 
6. STUDY TIMELINE and ACCRUAL ESTIMATES ............................................................... 19 
 
7. REGULATORY AND REPORTING REMINDERS .............................................................. 20 
 
8. REFERENCES ................................................................................................................... 20 
 
APPENDICES (e.g., non-standard self-report measures, other materials) 

 
 
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1. SPECIFIC AIMS  
Smoking remains the leading preventable cause of mortality and morbidity, accounting for nearly 
500,000 annual deaths in the United States alone. Developing effective cessation interventions remains a 
public health priority. mHealth offers the opportunity to reduce barriers to dissemination and implementation of 
cessation treatment and improve efficacy. Mobile technology offers the ability to conduct certain intervention 
components in the user’s natural environment. Recent advances in augmented reality (AR) on smartphones 
provide opportunities for improving extinction-based therapies, including those for tobacco dependence. 
AR inserts virtual, digitally-created objects into the real-world environment as viewed on a screen (i.e., 
smartphone or tablet) or headset. As such, AR provides the opportunity to conduct extinction-based treatment 
in the real-world. Cue-exposure treatments have demonstrated efficacy for decreasing tobacco craving in the 
laboratory or clinic, but these effects do not appear to generalize beyond the extinction setting. This “renewal 
effect” has been demonstrated in both animal and human studies. With AR, it is now possible to conduct 
extinction trials across multiple environments throughout smokers’ own natural smoking settings.  
The study team received R34 funding to develop and test AR stimuli, with the long-term goal of 
integrating this technology into a smartphone application for real-world use as an adjuvant to existing smoking 
cessation treatment. This research has shown that (1) AR smoking stimuli (e.g., cigarettes, lighter) produce 
substantial cue-provoked cravings to smoke, approximating in vivo stimuli, and (2) repeated exposure to these 
stimuli reduces craving responses, consistent with extinction. These proof-of-concept findings support the 
clinical use of AR for cue-exposure therapy to reduce the risk of smoking relapse in response to naturally-
encountered “triggers.”  
The proposed study will test the efficacy of the AR app and assess key constructs related to optimizing 
future implementation. Utilizing a hybrid design in partnership with the Florida Tobacco Quitline, the study will 
test if the addition of extinction trials via the AR app improves cessation outcomes compared to quitline alone. 
Quitlines are a low-cost, easily scalable, standard of care treatment option for smoking cessation, employing 
both behavioral counseling and pharmacotherapy. Nonetheless, smoking abstinence via quitline treatment is 
modest (~15%) and would likely increase if paired with other theoretically-based treatment components.  
Current smokers (N=2072) will be recruited via the quitline and randomized to one of two intervention 
arms: quitline only (QLO) or quitline plus the AR app (QL+AR). After achieving 48 hours of continuous self-
reported abstinence, participants in the QL+AR condition will begin cue-exposure treatment. Although the AR 
app is designed to be sufficient, participants will have the option of contacting an App coach for app 
assistance. Abstinence, craving, and nicotine replacement therapy (NRT) will be monitored via daily diary 
across both conditions. The primary outcome is biochemically-confirmed abstinence at 6 months after baseline 
survey completion.   
 
Aim 1. Update the existing AR app to be an engaging, user-friendly treatment tool, and verify user satisfaction.  
Aim 2. Test efficacy of QL+AR over QLO & explore potential moderators and predictors of treatment effects.     
a. Hypothesis: QL+AR participants will demonstrate higher tobacco abstinence rates six months post-
baseline than will QLO participants.  
b. Potential moderators of interest include sex, nicotine dependence, trait cue reactivity, smoking status 
during treatment, quitline use, and NRT use. Potential predictors within the QL+AR arm include initial 
cue reactivity, extinction magnitude and latency, indices of treatment engagement, and App coach use. 
Aim 3. Evaluate treatment implementation to guide future quitline adoption, including:  
a. Cost-assessment and incremental cost-effectiveness analyses comparing QL+AR with QLO.  
b. Examine treatment engagement and acceptability among those in QL+AR. This will include evaluation 
of the App coach.  
c. Identify facilitators and barriers to incorporating AR treatment into quitline services via qualitative 
interviews with quitline supervisors, App coaches, and participants.   
 
If participants who receive the AR app demonstrate higher rates of tobacco abstinence than those in QLO, this 
would indicate that AR is a suitable adjunct to quitline treatment. Results of Aim 3 will guide implementation of 
the AR app into state quitlines and other smoking cessation services. This theoretically-based AR app has the 
potential to improve a range of existing treatments for dependence on tobacco as well as other substances.  

 
 
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2. BACKGROUND AND SIGNIFICANCE  
Tobacco Use and Leveraging Scalable Smoking Cessation Treatment Options 
 
Cigarette smoking is the leading preventable cause of morbidity and mortality in the U.S. (USDHHS, 
2020). Smoking prevalence was 13.7% in the U.S. among adults in 2018 (Creamer et al., 2019). Whereas the 
majority of those who smoke report interest in quitting smoking, only 7.4% successfully quit each year (CDC, 
2017; Fiore et al., 2000). Thus, improving the effectiveness and reach of cessation interventions remains a 
public health priority.   
A significant advance in public health has been the development of evidence-based interventions for 
tobacco cessation, including those delivered via telephone quitlines (North American Quitline Consortium, 
2016). Quitlines typically provide free smoking cessation counseling and nicotine replacement therapy (NRT). 
They have consistently demonstrated efficacy, cost-effectiveness, and high scalability, with quitlines now 
available in all 50 states, Puerto Rico, DC, and Guam (Abrams et al., 2010; Fiore, 2008; Glasgow et al., 2004; 
Matkin et al., 2019). However, abstinence rates via quitlines remain modest (10-15%; Fiore et al., 2008; Matkin 
et al., 2019; Pinerio et al., 2020) and would likely increase if paired with other, theory-based treatment 
components that maintain high scalability and low cost. Our study aims to leverage quitline services by pairing 
them with a novel theory-based, smartphone application that addresses a unique contributor, conditioned cue 
reactivity, to smoking cessation outcomes.   
 
Mobile health (mHealth) interventions hold particular promise in reducing barriers to dissemination and 
implementation (Abroms et al., 2012; Free et al., 2011; Rodgers et al., 2005; Vinci et al., 2018; Vinci et al., 
2020a). The advantages of mHealth have been magnified by the COVID-19 pandemic, which has accentuated 
the need for remote treatment delivery. Moreover, harnessing recent advances in technology may increase the 
uptake of cessation treatment, while also targeting identified roadblocks to cessation. In 2019, 81% of 
Americans owned a smartphone, with 70% ownership among low-income populations (PEW Research, 2019). 
Thus, novel interventions that leverage advances in technology hold great potential for improving the reach 
and effectiveness of smoking cessation interventions.    
 
Augmented reality (AR) is a recent and rapidly advancing mobile technology with potential utility for 
smoking cessation treatment. Through the display of virtual smoking cues superimposed upon smokers’ 
natural environment, AR appears ideal for improving the long-term effects of extinction-based intervention 
strategies (i.e., cue exposure). Cue exposure typically consists of presenting a drug cue (e.g., cigarette, glass 
of beer) to individuals multiple times, while not allowing them to engage in the typical drug behavior (smoking, 
drinking), so as to extinguish the cravings. As described in more detail below, cue-exposure treatments have 
demonstrated efficacy for decreasing craving in the clinic/laboratory, but these effects are short-lived, and have 
not generalized well beyond the extinction setting (e.g., clinic) into the real world (Bouton, 2000). AR allows the 
entire cue-exposure session to take place in the real world, which should greatly enhance the efficacy of cue-
exposure treatment. The current proposal aims to capitalize upon emerging AR technology to test a novel 
treatment adjuvant for smoking cessation in conjunction with quitline services.  
Augmented Reality (AR) 
  
 
AR inserts virtual, digitally-created objects into the real-world environment as viewed on a 
screen (i.e., smartphone or tablet; see Figure 1 for AR smoking image developed and tested by 
our team), with emerging options for head-mounted displays. AR is similar to virtual reality (VR) 
in that the user is immersed in a digitally-enhanced environment. However, whereas VR attempts 
to insert the user fully into an artificial, computer-generated environment, AR inserts digital 
objects into the user’s actual environment in real-time. To be considered AR, a system must (1) 
combine real and virtual objects in a real environment; (2) run interactively, and in real-time; and 
(3) align real and virtual objects with each other (Azuma et al., 2001; Baus & Bouchard, 2014).  
Rudimentary forms of AR have been developed long before the term “augmented reality” was coined in 
1990. However, with the exponential increase in computer power in mobile devices, AR hardware and software 
have advanced particularly fast over the past decade (Baus & Bouchard, 2014). The first mass awareness of 
AR occurred with the Pokémon Go gaming app, released in 2016, which used smartphones’ GPS, gyroscopes, 

 
 
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compass, and camera to superimpose primitive (by today’s standards) AR “creatures” in the users’ natural 
environment. In the five years since, the technology has improved dramatically and has developed more 
mainstream uses in education, entertainment, architecture, and retail. For instance, many furniture retailers 
now have apps that allow the user to place and view digital depictions of products within the user’s own home. 
Once a digital AR object has been placed within a user’s natural environment, the digital object closely 
approximates the perceptual qualities of a real object in that location (e.g., the user can view the object from 
any direction, distance, or angle while maintaining a realistic perspective). Motion can also be included; for 
example, an AR cigarette can have smoke actively rising from it (as shown in Figure 1). Both Apple and 
Android devices support AR capabilities, and they continue to advance as AR becomes a more integral part of 
day-to-day life. For instance, the Apple iPhone 12 has a 3D triple lens camera system that includes a laser to 
enhance AR capabilities. Importantly, Apple’s ARKit is a mobile platform that allows for simplifying the 
development of AR apps for iOS, and Google’s ARCore platform is available for development of AR apps on 
Android and iOS. 
Aside from retail and entertainment, the vast majority of AR application has been in healthcare (e.g., 
surgical training; Barsom et al., 2016). In contrast, a systematic review identified only 13 published studies of 
AR within the domain of mental health (Giglioli et al., 2015). Perhaps ARs greatest potential is to advance 
extinction-based cue-exposure therapies, which have primarily been used to treat disorders conceptualized as 
originating from or maintained through conditioning processes, most notably substance use and anxiety 
disorders. We believe that AR will improve the efficacy of cue-exposure therapies for addictive behaviors, 
including tobacco use. Although the potential of AR for cue-exposure therapies has been recognized (Baus & 
Bouchard, 2014), research has been mostly limited to small animal phobias—cockroaches, in particular (e.g., 
Botella et al., 2016). AR was successful in such studies in reducing anxiety and avoidance behaviors. 
Cue-Reactivity and Cue-Exposure Therapy  
Most theories of addiction include a prominent role for conditioned cue reactivity—the idea, based on 
Pavlovian conditioning, that during substance self-administration otherwise neutral stimuli become paired or 
associated with the unconditioned, pharmacological effects of the drug (e.g., tachycardia or euphoria). Over 
time, the previously neutral conditioned stimuli (CSs) develop the capacity to elicit physiological conditioned 
responses (CRs) in the absence of drug ingestion (Stewart et al., 1984; Siegel, 1983; Wikler, 1965). Data have 
consistently shown that environmental CSs (e.g., drug paraphernalia) can cause cue reactivity in those 
addicted to alcohol, opioids, and cigarettes (Brandon et al., 1995a; Rohsenow et al., 1992), and these CRs are 
subjectively experienced as cravings to use the drug. In fact, as described below, our team recently 
demonstrated that smoking-related AR cues produce significantly greater cue reactivity than neutral AR cues 
with very large effect sizes (Brandon et al., 2020). CRs (craving in particular) contribute to drug use 
maintenance and relapse, and cue reactivity is predictive of future smoking cessation (Brandon et al., 2007; 
Conklin et al., 2012; Ditre et al., 2012). The role of conditioned cue reactivity in addictive behaviors has been 
incorporated into most of the major contemporary influential models of addiction, including models with primary 
perspectives that are psychosocial (e.g., Witkiewitz & Marlatt, 2004), cognitive (Tiffany, 1990), and neurological 
(Kalivas & Volkow, 2005; Robinson & Berridge, 1993). 
The logical clinical implication of cue-reactivity theory and research is that cue reactivity can be 
extinguished through the repeated presentation of drug-related cues (CSs) in the absence of the unconditioned 
stimulus (UCS; the ingested drug). To the extent that post-cessation relapse is initiated by conditioned craving, 
this treatment—called cue-exposure with response prevention, or cue-exposure therapy—should have clinical 
efficacy (Monti & Rohsenow, 1999). 
Although cue-exposure treatments produce declines in drug cravings or consumption (Childress et al., 
1986; Drummond et al., 1994; McLellan et al., 1986; Monti et al., 1993; Sitharthan et al., 1997; Unrod et al., 
2014), long-term clinical outcomes are modest. A meta-analysis of cue-exposure treatments for addictions 
found that their overall effect size was small (d=0.09; Conklin & Tiffany, 2002). Learning and addiction theorists 
have argued that the limited efficacy of cue-exposure therapies can be attributed to the minimal attention paid 
to context effects (Bouton, 2000; Brandon, 2001; Brandon et al., 1995a; Childress et al., 1986; Conklin & 
Tiffany, 2002; Powell, 1995; Rodriguez et al., 1999). That is, cue-exposure therapy is typically provided via 

 
 
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extinction trials that occur in either a laboratory or clinic. Based on extensive animal and human research, 
extinction that occurs in these contexts does not appear to generalize to contexts in the participant’s natural 
environment (i.e., the “renewal effect”; Bouton, 2002; Collins & Brandon, 2002). Thus, considerable effort has 
been spent testing alternative approaches for expanding the context of extinction to smoking cues. 
Expanding the Extinction Context  
The most straight-forward approach to overcome the renewal effect would be to provide cue-exposure 
therapy in multiple contexts within the smoker’s natural environment. However, smoking takes place in a 
multitude of locations, and it is not feasible for a smoking cessation counselor to accompany the smoker 
throughout his/her naturalistic environments to conduct cue-exposure sessions. Consequently, researchers 
have developed ways to expand the context of cue reactivity, which could then be used to expand the context 
and generalizability of cue-exposure therapies. These include (1) increasing the realism of the laboratory/clinic 
context via large-scale projections of photos taken by smokers of their natural environment (Conklin et al., 
2010); (2) having smokers bring video images of smoking cues into their natural environment (Wray et al., 
2011); and (3) providing smokers with “extinction cues” for them to bring into their natural environment (Unrod 
et al., 2014; Collins & Brandon, 2002). More recently, researchers have attempted to harness advances in VR 
(Pericot-Valverde et al., 2015), and although this approach has been effective at provoking cravings, limitations 
of VR environments include being (1) costly to produce, limiting availability; (2) graphically unrealistic, although 
this is improving; and (3) unrepresentative of individuals’ unique smoking environments.  
The Potential of Augmented Reality  
In contrast to VR, AR requires the development of only the specific smoking cues (e.g., cigarettes, 
lighters, ashtrays), not the entire smoking environment. These cues are then inserted into the smoker’s actual, 
naturalistic environments via smartphone (e.g., different locations in the home, work, outdoors). Our team has 
already developed and tested an initial set of AR smoking stimuli.   
AR has the potential to extend the short-term gains produced in the laboratory. Importantly, cue 
exposure via AR can occur entirely in the smoker’s natural environment, circumventing limitations of existing 
cue-exposure treatments. This should dramatically increase the sustained efficacy of cue-exposure therapy, 
based on contemporary models of extinction described above. AR also offers stimulus control and safety 
advantages over in vivo exposure for clinical use (i.e., the user is not exposed to the actual substance). 
Theoretical and therapeutic advantages aside, the value of conducting interventions remotely has been 
amplified by COVID-19 and the need to minimize direct social contact.  
 The Current Study  
We conceptualize AR-based cue exposure as an extinction-based adjuvant to a more comprehensive 
smoking cessation intervention. Thus, we are not proposing AR-based cue exposure per se as a stand-alone 
intervention for treating tobacco dependence. However, because cue reactivity appears to be a proximal 
determinant of smoking maintenance and post-cessation relapse, cue exposure is a logical, theory-based 
treatment component aimed at improving cessation and reducing risk of relapse. Moreover, AR has the 
potential to break through the contextual barrier associated with previous extinction-based therapies for 
treating addiction—i.e., the renewal effect—because of its portability and presentation of stimuli in the users’ 
natural environments. After demonstrating that AR cues elicit both cue reactivity and extinction in controlled 
experiments, the ideal next step is to test an AR app in conjunction with a telephone quitline. Not only do 
quitlines provide broad reach with validated counseling and pharmacotherapy, but cue exposure via AR should 
complement the potent elements of quitline counseling (e.g., general coping skills for managing craving, social 
support). If demonstrated as an effective adjuvant to quitlines, AR would likely be similarly effective with a 
variety of smoking cessation interventions, including traditional cessation counseling, online cessation 
websites, text messaging (SMS) interventions, other mobile cessation apps, and/or pharmacotherapy.  
The proposed research represents a systematic and scientifically rigorous process in the evaluation 
of an evidence-based adjunctive therapy to quitline treatment. Indeed, it represents a hybrid type-I design 
(Curran et al., 2012), such that we aim to test the efficacy of AR when combined with quitline treatment, while 
also considering key questions relevant to future implementation. We will build off our team’s preliminary data 
and initial app development to test the efficacy of the quitline plus the AR app (QL+AR) against usual care, 

 
 
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quitline only (QLO). To address implementation questions, we will determine the cost-effectiveness of the 
intervention, as well as measure indices of treatment engagement and acceptability. We will also collect 
feedback from quitline supervisors, study staff, and participants regarding potential barriers to implementation 
and dissemination. Although the AR app is designed to be self-contained (i.e., to not require additional 
explanation or assistance from live operators), we will also monitor the degree to which such live assistance is 
sought. Importantly, the proposed research is not driven solely by the availability of new technology, but by a 
sound scientific premise derived from both the theory-based and empirically-supported role of cue reactivity 
in the maintenance and cessation of addictive behaviors, and consequently, the potential value of extinction-
based cue-exposure approaches in the treatment of addiction.  
INNOVATION 
The proposed research is innovative in numerous ways, capitalizing on advances in AR technology. Searches 
on NIH Reporter and PubMed revealed that AR has begun to be utilized for training purposes (e.g., for training 
physicians), but there is little literature and no ongoing grants utilizing AR psychotherapeutically aside from the 
work conducted by our team. Although there is a small literature on AR for extinction-based treatment of 
phobias, our team is the only group that has published on AR for the treatment of addictive behaviors. The only 
uses of AR for tobacco we could find include a British AR tool for physicians that illustrates organ damage 
caused by smoking, and a phone app by the British Health Foundation that, when pointed at a cigarette pack, 
illustrates how the smoker could spend money saved by quitting. Aside from the app itself, we are leveraging 
quitline treatment, an existing evidence-based treatment for tobacco cessation, and will examine 
implementation questions that will be key to future quitline adoption. Thus, adding such a novel, scalable 
adjuvant therapy to the quitline in and of itself is innovative. In sum, our proposed research appears to be 
among the first to use established scientific methods to capitalize upon AR technology to: (a) develop 
therapeutic uses of any kind; (b) including extinction-based approaches; (c) particularly for treating addictive 
behaviors; (d) notably tobacco dependence. AR provides a new opportunity to solve the therapeutic dilemma 
of the renewal effect that limits the real-world efficacy of cue-exposure therapy, and our team is uniquely 
situated to advance this innovative approach to smoking cessation treatment. 
 
3. 
PARTICIPANT SELECTION 
3.1  AIM 1: For Aim 1, we will recruit 10 adult participants who have quit smoking within the last 3 months and 
are currently abstaining from smoking for usability testing. Inclusion criteria will be: ≥ 18 years of age; were 
daily smokers that have quit smoking within the past 3 months; functioning telephone number; owns a 
smartphone capable of supporting AR and willing to download the study app (94% of Android phones and 
>99% of iPhones in the U.S. have operating systems that support AR; Statcounter, 2021); and can speak, 
read, and write in English. Exclusion criteria for both aims will include another household member already 
enrolled in the study. 
Recruitment and Consent 
 
The primary goal of recruiting participants for Aim 1 is to conduct field-based usability testing on the 
enhanced AR app with respect to the new smoking cues and general app functionality, building upon the lab-
based data collected on the basic app via our preliminary research. This approach permits identification of 
major issues by participants or staff, while preparing staff to problem-solve in real time in preparation for Aim 2.  
All Aim 1 procedures will be conducted remotely. Recruitment will take place on social media and other 
websites, as our team has successfully used this approach to recruit large samples of smokers (Martinez et al., 
in press). Interested individuals will either directly call the study phone line or complete online contact forms 
and be called back, and additional information about the study will be provided. Interested participants will be 
screened for eligibility and complete the verbal informed consent process, with a waiver of written consent. 
Once consented, participants are enrolled in the study. Recruitment will take place over 3 months. Participants 
may withdraw at any time for any reason by contacting the staff and informing staff of their desire to withdraw 
from the study. Participants will be removed from the study if they do not download the app within one week. 
 
3.2 AIM 2: Participants and Recruitment 

 
 
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For Aim 2, inclusion criteria include: ≥ 18 years of age; currently 
smoking ≥ 3 cigarettes per day for the past year; functioning telephone 
number; owns a smartphone capable of supporting AR and willing to 
download the study app; and can speak, read, and write in English. The 
smoking criteria were selected to obtain a wide range of smokers who 
would have developed both sufficient cue reactivity and cessation 
motivation (Shiffman et al., 2014).  Recruitment (N=2072) will take place 
via Tobacco Free Florida’s Quitline. Upon calling the quitline and 
enrolling in quitline services, participants will be provided with 
information about the study by quitline enrollment agents to determine 
initial interest. A brief eligibility screening will be conducted (e.g., 
confirm English-speaking, smoking status); eligible callers will be asked 
for their consent for the quitline to send their contact information to the 
study team. Enrollment agents will send referrals to study staff daily 
either via a secure email or secure file transfer protocol. Upon referral 
receipt, study staff will call participants to complete eligibility screening 
and conduct the full informed consent process. Then, participants will 
be randomized to one of the two study conditions. A baseline survey will 
be sent to participants and study staff will textlinks to participants to 
download the study app after survey completion, provided the 
participant reports not being currently abstinent for more than 7 days. Participants are considered enrolled in 
the study following app download. Participants may withdraw at any time for any reason by contacting the staff 
and informing staff of their desire to withdraw from the study. These procedures have already been developed 
in conjunction with the Tobacco Free Florida Quitline. We have allotted 29 months for recruitment, which 
requires consenting an average of 17 participants per week. Figure 5 presents the expected flow of 
participants. Thus, the recruitment process is feasible with respect to sample size, quitline burden, research 
staff burden, and funding duration.  
 
4. 
STUDY DESIGN AND METHODS 
4.1  Design 
 
The proposed study will test the efficacy of the AR app while also assessing key constructs 
related to optimizing future implementation. Utilizing a hybrid design in partnership with the Florida Tobacco 
Quitline, we will recruit and randomize participants to one of two treatment arms: (1) quitline only (QLO) or (2) 
quitline plus the AR app (QL+AR). 
4.2 
Intervention 
Participants in the QL + AR condition will download and use a smoking cue extinction app that includes the 
features outlined in Table 1. 
 Table 1. AR App Features 
FEATURE 
DESCRIPTION 
RATIONALE 
Situation Menu 
Participants will choose what situations/times of day are most 
appropriate for their AR sessions, although the app will 
provide general recommendations. 
Tailoring treatment content to the individual and 
supporting choice;1 supporting autonomy.2 
Tutorials 
Video tutorial on correct app use (e.g., placing AR stimuli), 
with unlimited interactive practice sessions.  
Supporting competence; identified as a needed 
feature in preliminary work by the investigative team. 
Rationale for 
Cue Exposure 
Rationale for cue exposure to help quit smoking/support 
participant goals (video). 
Supporting autonomy;2 providing purpose of the app 
and improving knowledge of benefits.1 
Best Practices 
for AR Sessions  
Specific guidelines for using the app (e.g., no smoking during 
sessions; no use of nicotine products before sessions, patch 
use ok).  
“Expert advice” is an ideal feature for quit smoking 
apps.3 
Reminder 
Notifications 
Participants will select frequency of reminder notifications to 
complete AR sessions. 
Supporting autonomy;2 participants prefer to choose 
timing of reminders.3  
Progress 
Tracker  
Participants will be able to view 1) their progress through the 
program overall, 2) changes in urge ratings over time, 3) daily 
cigarette use, and 4) use of NRT.  
Supporting competence;2 consistent with mHealth 
frameworks;1 preferred feature in quit smoking 
apps.3,4  

 
 
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Troubleshooting  
Available any time to support use of the app.  
Quick access for app support. 
Rewards 
Linked app engagement and progress towards quitting (e.g., 
visual presentation of money saved by not smoking). 
Rewards for quitting smoking commonly requested 
feature.3 
App Coach 
Link to live App coaching by phone call. 
Offers additional explanation of extinction goals as 
well as technical assistance for app. 
    1Floryan et al., 2016; 2Ryan & Deci, 2000; 3Hartzler et al., 2016; 4McClure et al., 2016 
 
App Use 
Initial use of the app will occur in stages: Assess, Setup, Recommendations for Use, and Extras. Once 
users complete the stages, they will be able to use the app as needed on a daily basis. We describe all 
aspects of the app below, including those only relevant to Aim 2.  
Assess. Each morning, participants will be prompted to complete a daily diary to assess the following 
from the previous day: number of cigarettes smoked, NRT use, and tobacco craving. Because ongoing 
smoking during cue-exposure sessions will be counter-productive to extinction of urges, participants will need 
to self-report 48 hours of abstinence to advance to Setup. Achieving such short-term abstinence with quitline 
support/NRT should be feasible for at least 70% of participants (Shiffman et al., 1996; Shiffman et al., 2007). 
Setup. Once the abstinence requirement is met, participants will personalize the app, learn the purpose 
of the app, and practice using AR. First, they will watch a video tutorial explaining the use of cue exposure. 
They will select their most relevant AR cues and situations in which they most commonly smoke and/or 
experience high cravings (e.g., upon waking, after meals, when stressed, with alcohol, with coffee, 
celebrating). They will receive a list of “best practices” for completing AR sessions (e.g., no smoking during 
sessions). Participants will next receive instruction on using the app for cue-exposure sessions, comprising a 
short video describing how to place the AR objects, followed by an interactive tutorial with real-time practice.  
Recommendations for Use. Recommendations for daily use will be proactively provided, but also 
available for later review on demand. The app will recommend that participants complete 2-5 AR sessions per 
day. It will emphasize that the more often they use the app when experiencing craving, the more successful 
they will likely be in maintaining abstinence.  
Extras. Participants will be introduced to all other features of the app: setting up reminder notifications, 
accessing the progress tracker, access to the App coach, and viewing of rewards.  
 
Details of AR Sessions. Participants will be encouraged and reminded to complete 2-5 AR sessions per 
day. Each session will present 3-8 cues, and each cue will be presented for 20-40 seconds (average amount of 
extinction per day will be about 10 minutes). We have included variability in the number and length of cue 
presentations to decrease predictability and maintain general interest in the sessions. Participants will be 
encouraged to conduct the AR sessions when high cravings are expected. Before beginning an AR session, 
participants will indicate their current situation/environment (e.g., after meal), and current urge to smoke and 
then a series of AR cues will be presented. Participants will rate their urge to smoke following the first and last  
cue in the session. Data will be automatically uploaded to the cloud.  
 
4.3 Procedure 
 
Aim 1: AR App Updates and Usability Testing  
Aim 1 Overview 
Aim 1 will include updates/modifications to the AR app, along with participant usability testing to finalize 
the product for use in Aim 2. In collaboration with Haneke Design, we will develop additional AR smoking-
related stimuli, totaling 12 to 20 AR images. Given our previous experience developing smoking and neutral 
AR stimuli, we believe this process will take relatively little time to complete. We will also update the AR app 
with additional content for Aim 2 (e.g., menu options) and make the app functional for both Apple and Android 
devices. We will field-test the revised app with 10 participants, and adjust software and methodology as 
needed prior to Aim 2. Concurrently, Dr. Vickerman will work with her team at RVO Health to modify the 
existing software of the Tobacco Free Florida Quitline to facilitate recruitment for Aim 2, and train quitline 
enrollment agents on caller screening and study referral. Aim 1 will take 12 months to complete (see attached 
Timeline).  

 
 
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Development of Additional Stimuli and App-based Content  
 
Currently, the AR app is only available on iOS, Apple’s operating 
system, so Aim 1 will update the app to be compatible with both Apple and 
Android operating systems. We already developed 6 AR smoking cues 
(Figure 4), and we will create 6-14 additional images. We anticipate a 
subset of these cues to include motion (e.g., smoke rising) and some 
combined cues (e.g., lighter, ashtray, and pack of cigarettes). The process 
for developing these cues will mimic the procedures from our R34 project.  
 
Although the app will function as an adjuvant to quitline phone 
counseling, we will include specific functionality and features within the app 
that complement the AR sessions. These features are based on: (1) 
current recommendations for mHealth interventions, guided by both self-
determination theory (SDT) and the behavioral intervention technologies (BIT) model (Floryan et al., 2016; 
Mohr et al., 2014); (2) recommendations for smoking cessation apps (Hartzler et al., 2016; Heffner et al., 2015; 
McClure et al., 2016; Ortis et al., 2020); and (3) our experience developing the first iteration of the app. To 
increase intrinsic motivation and engagement, we utilized SDT as a guiding framework when deciding on 
features to include, with a primary focus on supporting autonomy and competence (Deci et al., 1994; Ryan & 
Deci, 2000; Williams et al., 2006). Given modest rates of treatment adherence for existing smoking cessation 
apps (Abroms et al., 2013), it is important to integrate features that enhance engagement. Social support is 
often recommended for smoking cessation apps (e.g., Hartzler et al., 2016; Heffner et al., 2015), but because 
this app will be an adjuvant to quitline counseling, we will not include a comprehensive support feature. That 
said, we are interested in knowing whether participants require additional support for the AR and cue-exposure 
components, and we have incorporated the option of utilizing an App coach via two routes.   
 
Table 1 (above) includes primary app features and rationale for inclusion. Consistent with SDT and 
general recommendations for mHealth apps (e.g., Floryan et al., 2016), we have incorporated participant 
choice into the features whenever possible. We expand on those features that require additional rationale here. 
First, “Rationale for Cue Exposure” will provide a foundation for the use of cue exposure to aid in quitting 
smoking and will offer information on why cue exposure is important for their personal goal to quit. Second, 
“Best Practices for AR Sessions” will provide guidelines for using the app that are specific to tobacco (e.g., no 
smoking during cue-exposure sessions; no use of acute-delivery nicotine products before the sessions). 
Content from “experts” has been identified as a desirable app feature (Hartzler et al., 2016).  
 
 
Participants (see 3.1 above) 
Informed Consent and Assent 
All consenting procedures described here apply to both smokers and quitline supervisors. Prior to 
enrolling in the study, interested participants will speak with study staff at TRIP to learn more about the study. 
At that time they will be provided with a detailed overview of the study. Potential participants will be screened 
for inclusion/exclusion criteria. If eligible, the informed consent process will take place, which includes study 
personnel providing a detailed description of the study, answering questions, and obtaining verbal consent to 
participate. We will apply for a waiver of written consent from our IRB. If this is not approved, we are prepared 
to collect electronic consent via REDCap, which our institution has setup to be a secure, HIPAA-compliant way 
to document consent from participants. Participants will be given as much time as needed to ask questions 
during the consenting process, prior to agreeing to participate. Assent is not applicable to our study, as all 
participants must be over the age of 18. 
Recruitment and Procedures 
 
All Aim 1 procedures will be conducted remotely. Recruitment will take place on social media and other 
websites. Interested individuals will either directly call the study phone line or complete online contact forms 
and be called back, and additional information about the study will be provided. Interested participants will be 
screened for eligibility and complete the informed consent process. Staff will provide an overview of general 
issues about which we will seek feedback (e.g., functionality). Participants will be sent a REDCap survey to 

 
 
11 
 
 
 
complete when the call is over. When the survey has been completed,  participants will then be texted a link to 
download the app onto their personal device and use the app in their natural environments for 1 week. A 
second survey will be sent at the end of the week, followed by a phone interview. Participants will be paid $10 
for completing the baseline survey, $20 for the follow-up survey  and $15 for completing the phone interview. 
 
App Use (see 4.2 above) 
 
Aims 2 and 3: Randomized Controlled Trial  
Project Overview 
 
The primary goal of Aim 2 is to test the incremental efficacy of the AR app when added to standard 
tobacco quitline services. We will recruit and randomize participants to one of two treatment arms: (1) quitline 
only (QLO) or (2) quitline plus the AR app (QL+AR). In the interest of rapid dissemination and implementation 
pending positive findings, we have designed procedures with future scalability in mind. Participants will be 
recruited via the Tobacco Free Florida Quitline and referred to the study team via quitline enrollment agents. 
Participants who return a baseline questionnaire will be directed to download the app on their personal device 
and will complete the study. The primary outcome will be biochemically-confirmed tobacco abstinence 
assessed at around 6 months in a subset of participants.  
 
Participants and Recruitment (see 3.2 above) 
 
 
Randomization 
Block randomization (block sizes=8) will be used to assign 2072 enrolled participants to one of the two 
conditions. The randomization order will be generated by Dr. Sutton and implemented within REDCap. 
 
Quitline Services 
 
All study procedures have been developed in close coordination with Tobacco Free Florida, the Florida 
Department of Health, and RVO Health, the quitline contractor will oversee all aspects of the quitline, including 
supervising staff to coordinate project activities, modification of quitline software to support the screening and 
referral of callers, and management of data collection by quitline staff.  
The Florida Tobacco Quitline is operated by RVO Health, the largest provider of tobacco cessation 
quitlines in the U.S. RVO Health’s Quit For Life® program is an evidence-based tobacco cessation treatment 
delivered over phone, web, and text, and is grounded in social cognitive theory and the U.S. Public Health 
Service clinical guidelines (PHS Guideline, 2008). Quitline coaches use cognitive behavioral therapy, 
reinforcement, and principles of self-efficacy to promote effective behavior change. The quitline approach 
includes 5 key elements: setting a quit date, coping with triggers, effectively using medications, tobacco 
proofing, and social support. Phone counseling for cessation has been evaluated in randomized trials and 
effectiveness studies (Hollis et al., 2007; Orleans et al., 1991; Stead et al., 2013; Zhu et al., 2002). Individuals 
who enroll in the quitline’s multi-call program receive three phone calls. They are mailed two weeks of NRT 
after the first phone call, to begin using on their quit date (coinciding with the second call). Participants will be 
provided with information about the study during their first phone call, and study staff will reach out to enroll 
participants in the study after receiving their contact information from the Quitline.   
 
Study Arms 
Quitline Only (QLO). Participants randomized to the QLO condition will receive an app that administers 
the daily diary questions for 5 weeks and shows their app and abstinence related progress. They will not 
receive any treatment via the app. Thus, they will receive usual care via the quitline and complete minimal daily 
assessments.   
Quitline plus AR (QL+AR). Participants randomized to QL+AR will receive the AR app described in Aim 
1. Initially, the app will only administer the daily diary questions. Once 48 hours of self-reported tobacco 
abstinence is achieved, the AR elements of the app will become available (see 4.2). Daily diary questions will 
continue for 5 weeks. AR extinction trials will occur daily until (1) the participant reports 3 consecutive days of 
smoking, in which case the AR trials will be suspended until another smoke-free 48 hours are reported, or (2) a 

 
 
12 
 
 
 
total of 5 weeks have elapsed since the start of app use.  
The integration of cue exposure and quitline treatment is important. Quitline coaches encourage 
removing all smoking paraphernalia prior to the quit date, which will facilitate tobacco-free AR sessions and 
reduce risk of post-AR relapse. Notably, the use of AR stimuli for exposure sessions is completely consistent 
with the instructions to avoid in vivo cues to smoke. Other aspects of quitline treatment (setting a quit date, 
coping with triggers, using NRT, social support) are well-established tobacco treatment components and 
should facilitate abstinence.  
Although the app is designed to be self-contained, it will include access to an App coach who can 
provide additional instruction and explanation of the extinction goals and process, as well as technical 
assistance with the app. App coaching can be initiated through two routes: on demand and triggered. The on 
demand route allows participants to contact an App coach at any time (9am – 6pm, 7 days/week) via a 
selection within the app. The triggered route is activated based on non-engagement with the app within the first 
48-hour post-abstinence period. If AR trials have not been initiated within this period, the app will first send 
reminder notifications. If these do not yield AR usage within 24 hours, the app will send a notification offering 
the option of immediate or scheduled connection to an App coach (opt-in). If this is declined and AR disuse 
continues for another 24 hours, the app will send a notification that an App coach will reach out to them that 
day. This process may be repeated one additional time. App coaching sessions will focus on the following 
topics: reinforcing the rationale for cue exposure; addressing any issues related to smoking or NRT use 
before/during/after cue-exposure sessions; guidance on how to be fully immersed in the exposure sessions; 
and encouragement to maintain connection with the stimuli during exposures (vs use of any avoidance 
strategies). Technology-related assistance will also be provided as needed, along with any other concerns 
brought up by the participant. It should be noted that these topics will be available to view in the app itself. We 
will monitor the frequency, content, and time required of App coaching to guide future implementation. Data 
may inform improvements to the app and whether App coaches should be retained—perhaps via specialized 
training of quitline coaches. 
 
End of Treatment and Follow-up  
 
Follow-up questionnaires will be sent to all participants via REDCap around 5 weeks and 6 months 
after-baseline completion.  A subset of participants (40%, approximately 100) who self-report cigarette smoking 
abstinence at around 6 months will be mailed a saliva collection kit for cotinine-based biochemical confirmation 
of abstinence. A subset of participants (n=40) from QL+AR will complete a phone interview at the end of 
treatment, as will a subset of quitline supervisors (n=10) and all App coaches.  
 
Compensation and Retention 
 
For completion of measures at baseline, and around 5 weeks and 6 months after baseline survey 
completion, participants will receive a $20 gift card (up to $60 total). For each week they complete at least 70% 
of the daily diary questions (5 days), they will receive a $10 gift card (up to $50 total). Thus, most participants 
will be compensated up to $110. Participants who complete the interview, will be compensated $20. 
Participants who return a saliva sample will be compensated $25.  
To aid in retention, the following procedures will take place: collection of full contact information; text 
reminders and phone calls to follow-up on incomplete surveys; and collection of names and contact information 
of at least 2 collaterals (i.e., relatives, friends) who can help us reach lost participants. 
 
Treatment Delivery and Fidelity for AR and Quitline Coaching 
Although we expect minimal use of App coaches based on the comprehensive app content, we will 
monitor and assess coaching fidelity. App coaches will be part of the study team. With an eye toward 
scalability, qualifications will mimic those of quitline coaches (e.g., Bachelor’s degree, tobacco abstinent). 
Training will be manualized and conducted by Drs. Vinci and Brandon, given their experience with exposure-
based treatments. Training (8-10 hours) will be a combination of reading, didactics, and role playing. We will 
develop a measure to assess adherence and competence to be used during the training period. Weekly 
supervision will occur to ensure App coaching is delivered consistently. Coaches falling below performance 

 
 
13 
 
 
 
criteria will receive additional training. To monitor treatment adherence, sessions will be recorded and a 
random sample of 50% will be rated by the investigators using the measure developed during the training 
period. Fidelity of the screening and referral process will be conducted by Dr. Vickerman’s team on a weekly 
basis by reviewing a subset of call recordings of the study offer and screening, and providing regular feedback 
to enrollment agents.        
 
4.4  Measures  
AIM 1 Measures 
Baseline Measures. Participants will complete a series of baseline measures, administered via 
REDCap link, to assess demographics, experience with AR, and smoking history.  
Daily Diary Questions. Each morning, participants will report via the app: number of cigarettes smoked 
from previous day; previous day’s use of NRT, e-cigarettes, and other tobacco products; and current urge to 
smoke, assessed on a 10-point Likert scale (see appendices).  
Urge and Situation during AR Sessions. Participants will rate their urge (craving) to smoke on a single 
10-point Likert scale. Single-item scales reduce participant burden when multiple ratings are required, and they 
have been found to be reliable and valid in assessing urge to smoke (Kozlowski et al., 1996). The 
circumstances surrounding app use (e.g., at home, alone, with alcohol, etc.) will be assessed.  
Feedback and App Usage. The System Usability Scale (SUS) measures participants’ perception of 
usability and learnability (Brooke et al., 1996; Lewis & Sauro, 2009). We will develop a questionnaire with 
Likert scale items to capture the following areas: reality/co-existence of the AR images (e.g., How realistic did 
the AR images appear?), usefulness (e.g., How useful was this app for smoking craving this past week?), ease 
of use (e.g., How easy/difficult did you find using the app?); ease of learning (e.g., How many days did it take 
to get comfortable using the app?); and satisfaction (e.g., Would you recommend this app to a friend or family 
member to help them quit smoking?; Overall, how satisfied were you with the app?). During the phone 
interview, participants will be asked open-ended questions on their perceptions of the app. Interviews will be 
tailored to participants’ app use to understand challenges and barriers among those with low app use 
(completed <5 AR sessions) and to receive feedback from those with high app use (completed 5 or more AR 
session). Examples of questions include: “What changes would you make to the app to increase the likelihood 
of using it, or, to make it more enjoyable?; What was the easiest part of using the app?; What was most 
difficult?; What was most interesting? (see appendices).  We will also have access to the real-time app use 
metadata, including proportion of sessions started, completed, etc.; number of unfinished sessions; time spent 
in the app; days of use; engagement with app features (e.g., tracking, rewards). We will query during the 
phone interview why any app feature was not used.  
 
AIM 2 Measures 
Measures assessed via the app were described for Aim 1. Additional measures unique to Aims 2 and 3 are 
described below. REDCap will be programmed to email and text participants a survey link to complete 
measures at baseline, around 5 weeks post initial app engagement, and around 6 months post-baseline.  
Questionnaires. Demographics and Smoking History will collect data on variables including gender, 
age, race, marital status, income, education, and smoking history (e.g., number of quit attempts, years 
smoked). Heaviness of Smoking Indiex (HSI; Heatherton et al., 1989) is a widely used measure of nicotine 
dependence. We will also administer the subscales of Automaticity and Affective Engagement of the Brief 
Wisconsin Inventory of Smoking Dependence Motives (WISDM-38), a multidimensional assessment of 
motivational factors associated with smoking behavior (Smith et al., 2010). We are particularly interested in the 
Cue-Exposure/Associative Processes scale, which assesses the degree to which smoking is driven by cue 
reactivity. Therefore, we will include the full subscale from the original WISDM-68 (Piper et al., 2004), due to its 
superior psychometrics (Smith et al., 2010). We will use the Self-efficacy Scale – Smoking to assess level of 
confidence for not smoking in various situations (Velicer et al., 1990). Positive and Negative Affect Schedule 
short form (PANAS; Thomson, 2007) will assess affect. Aside from their descriptive value, these measures will 
allow for testing moderators of the study outcomes. These measures will be administered at each assessment.  
App Use, Evaluation, and Extinction. Similar to Aim 1, we will collect metadata on app use as an 

 
 
14 
 
 
 
indicator of treatment engagement. Primary treatment engagement variables of interest that are linked to our 
theoretical model of extinction and that are related to increased abstinence include: greater completion of the 
AR sessions (e.g., multiple times per day over several days), conducting the AR sessions in several different 
locations (to increase generalizability of extinction), and less use of avoidance strategies during the AR 
sessions (e.g., using distraction or other behaviors to avoid experiencing craving). Avoidance strategies will be 
assessed at the 5 week survey via a checklist of avoidance strategies and the frequency that they occurred 
during AR sessions. We will also collect self-reported treatment satisfaction using a modified version of the 
Client Satisfaction Questionnaire (Attkisson & Greenfield, 1994), the SUS (Brooke et al., 1996; Lewis & Sauro, 
2009), and unique items to assess features and functions of the app and the AR experience, including the App 
coach. Extinction magnitude (degree of craving reduction over time) and latency (time to meeting extinction 
criteria) will also be derived. For participants who never achieved abstinence during the 5 weeks of app use, 
thus, never completed AR sessions, versions of the follow-up surveys will omit items specifically related to AR 
use. For participants who achieved abstinence, but did not complete more than 4 AR sessions, there will be 
questions at the 5-week survey regarding barriers to completing the AR sessions.  
Quitline Usage and App Coaching. Basic information on participant interactions will be provided to the 
study team from the quitline, including call completion dates, quit status (planned and actual quit dates), and 
NRT that was shipped. For those in QL+AR, we will track calls completed with an App coach.  
Tobacco Abstinence. The primary outcome measure will be 7-day point prevalence abstinence (defined 
as self-report of no combustible tobacco use in the past 7 days; Piper et al., 2020) around 6 months post-
baseline. Reported abstinence will be confirmed in approximately 40% of self-reported abstinent participants 
using a saliva cotinine level cut-off of <10 ng/ml (Benowitz et al., 2020). Cotinine analysis using ELISA assay 
will be conducted by Salimetrics (Carlsbad, CA).  
Cost-assessment and Cost-effectiveness. An important step in evaluating feasibility of interventions is 
showing that the intervention can be performed at a (relatively) low cost while improving outcomes. We will 
assess the resource utilization associated with both arms of the intervention, allowing us to determine the 
relative costs and cost-effectiveness of the arms. Our cost-assessment methodology will be done initially from 
the perspective of the health care system and based on collecting information on all resources used in the 
intervention and affixing a standardized “price” to those resources. This approach, long recommended as the 
most appropriate means of calculating true resource costs (Sanders et al., 2016; Dranove, 1996; Weinstein et 
al., 1996), results in “costs” that are comparable both among participants within an intervention and between 
interventions. We have successfully used this methodology in previous studies (e.g., Byrne et al., 2006; 2014; 
Martinez et al., in press; Medina-Ramirez et al., 2019; 2020; Morgan et al., 2008).  
Quality of Life. To assess potential improvements in quality of life, we will assess smoking-related 
health items from the Tobacco Quality Of Life Impact Tool™ (TQOLIT v.1; Ware JE; Gandek B; Kulasekaran 
A; Guyer R, 2015) which includes 8 items that measure the frequency of smoking-related symptoms (e.g., bad 
breath, difficulty breathing). We will also use the EQ-5D-5L  which consists of the EQ-5D descriptive system 
that measures health using five levels of severity in five dimensions (mobility, self-care, usual activities, 
pain/discomfort, anxiety/depression) and the EQ visual analogue scale (EQ VAS) that records the individual’s 
self-rated health on a vertical visual analogue scale, where the endpoints are labelled ‘The best health you can 
imagine’ and ‘the worst health you can imagine (Herdman, et al. 2011). Each of these measures will be 
included in the Baseline and 6 Month surveys. 
Implementation Barriers and Facilitators. To identify barriers and facilitators of incorporating AR 
treatment into the quitline, interviews will be conducted with a subset of quitline supervisors, all App coaches, 
and a subset of participants. The interview guide will be structured around the Consolidated Framework for 
Implementation Research and assess multi-level barriers and facilitators for implementation. For example, the 
guide will assess information about intervention characteristics (e.g., technical challenges with AR app), 
organizational characteristics (e.g., compatibility of the app with quitline workflow), individual characteristics 
(e.g., beliefs about the AR app, self-efficacy for app use), process (e.g., sufficiency of training and engagement 
strategy), and external context (e.g., how well the app fits with quitline external incentives and policies). ). A 
brief survey will capture demographic information.  

 
 
15 
 
 
 
 
Questions will be asked to quitline supervisors (e.g., What training is needed to train quitline coaches to 
train callers in using the AR app?; What concerns do you have about quitline callers using the app?) and App 
coaches (e.g., What additional training would be useful as an App coach?; What were some common issues 
participants reported having and do you have any thoughts on how to resolve those more effectively?; What 
challenges did you encounter as a coach?). A small subset of 20 participants who used the AR app feature 
(AR use = 10+ times) will be randomly selected and asked about their experiences (e.g., What features of the 
AR app could be improved?; What challenges did you encounter with the app?; How might we improve the 
App coaching you received?). To learn how to improve engagement, we will also contact 10 participants who 
failed to engage with the AR app feature (AR use = 0 times), and 10 who engaged but prematurely 
discontinued using the AR app feature (AR use = 1-4 times). Thus, a total of 40 participants will be included.   
 
Data Safety and Monitoring Plan 
The data and safety monitoring plan development for this project is commensurate with the expected minimal 
risks posed by the project. Data and safety monitoring will be ongoing by the Principal Investigators, Moffitt 
Cancer Center’s Protocol Review and Monitoring Committee, the Data and Safety Monitoring Board (DSMB), 
and the Institutional Review Board (IRB). Overall, the plan for monitoring includes: 1) Monitoring the progress 
of the studies and safety of participants; 2) Assuring compliance with the requirements for reporting adverse 
events that may occur during the study; 3) Assuring any action resulting in a temporary or permanent 
suspension of a study is reported to the NIH official who is responsible for the grant; and 4) assuring data 
accuracy and protocol compliance. Participant data will be associated with participant study ID and not directly 
associable with names or other identifying information. Questionnaire/interview data will be collected and 
recorded via computer and will only be identified with the participant’s study ID. The PIs will keep the codes 
that link the name of the participant and the study ID confidential in a password protected electronic file. Data 
accuracy will be subject to random audit. Monthly data management reports will be made by the project 
manager (with oversight by the study statistician), including data entry progress, error rates, range checks, and 
general descriptive statistics. The investigators and research staff will conduct all data analyses using primarily 
SPSS and SAS software. Participants who elect to be informed of study findings will be sent summaries of the 
research findings upon the completion of data analysis. 
 
Research staff will report any potential AEs immediately to the PIs. Any SAEs will be reported within two days 
to the IRB and NIH using the required reporting format. We will use the FDA definitions of AE (any untoward 
medical occurrence associated with the [intervention] in humans, whether or not considered [intervention] 
related) and SAE (an AE that results in any of the following outcomes: death, a life-threatening adverse event, 
inpatient hospitalization or prolongation of existing hospitalization, a persistent or significant incapacity or 
substantial disruption of the ability to conduct normal life functions, or a congenital anomaly/birth defect). Given 
the very low-risk nature of our behavioral intervention, the likelihood of suspected SAEs is extremely low. Any 
IRB actions in relation to this protocol will also be reported to NIH. 
 
As noted above, this project will have a DSMB comprised of individuals with expertise in the necessary content 
areas (e.g., mHealth, tobacco, statistics). We have already identified three individuals with expertise in these 
areas who have agreed to serve on the DSMB, assuming we receive funding – Michael Sayette, Ph.D. 
(University of Pittsburgh; tobacco use and cue exposure), Michael Businelle, Ph.D. (University of Oklahoma; 
smoking cessation interventions and mHealth), and Brent Small, Ph.D. (University of South Florida; statistician 
with expertise in longitudinal data analysis). The primary role of the DSMB will be to evaluate the safety and 
efficacy of the study as it is ongoing, and to provide recommendations as needed on the protocol. Data on 
participant progress through the study (e.g., recruitment, retention), protocol compliance, and any protocol 
deviations and AEs/SAEs will be monitored. DSMB meetings will occur twice per year, with a report provided to 
the committee in advance of the meeting for review. A final report will be drafted following the meeting and will 

 
 
16 
 
 
 
be forwarded to the necessary stakeholders (e.g., NIDA, IRB). Additional meetings, phone calls, and emails 
will be exchanged between meetings as needed.  
 
Drs. Vinci and Brandon will implement standardized procedures for each Aim. This will include weekly 
meetings to discuss any issues related to the progression of the project and factors that may affect the 
outcome, including a review of data quality and security, recruitment, and retention. For any problems that may 
arise, including adverse events, a discussion among the investigators will be conducted. 
 
4.5  Adverse Reactions 
 
Minimal risks are anticipated for this study and are described below. Risk mitigation 
strategies are described in the Adequacy of Protection Against Risks. This study involves no investigational 
drugs or devices. 
• 
Data including self-report, interview (psychological and medical), and/or biological samples (e.g., saliva) 
involve risk of breaches in confidentiality. Participants will always be given the option to refuse to 
answer any questions on the measures that may be distressing. This is the only risk that also applies to 
quitline supervisors. 
• 
For data collected via smartphone, there are always risks associated with privacy when collecting data 
in this format. 
• 
There may also be an increased risk of using cigarettes due to increased craving experienced during 
some of the AR sessions. 
• 
Successful abstinence may cause irritability, anxiety, general distress, and difficulty concentrating. 
• 
Nicotine replacement therapy (NRT) will be provided via the quitline per their usual treatment 
procedures and will be the appropriate dose based on level of smoking and should aid in the 
management of withdrawal symptoms. NRT and smoking cessation counseling have been shown to be 
safe and effective for smokers attempting to quit (e.g., NRT is available over-the-counter). However, 
side effects of NRT may occur and include skin irritation/ rash (for the patch), nausea, dizziness, dry 
mouth, diarrhea, nervousness, headache, vivid dreams or sleep disturbances, irritability, and irregular 
heartbeat. 
 
5.  STATISTICAL CONSIDERATIONS 
 
Data Management and Statistical Considerations for Aim 1 
Compliance will be calculated for the completion of AR urge and daily diary questions. We will also 
observe the use of various features to determine general use/engagement. The SUS will be scored according 
to scale instructions, with an expected mean above 68. Interview responses will be reviewed for common 
themes. Modifications to the app will be made based on these findings. In the unlikely event that major 
changes need to be made based on participant feedback, we will repeat the procedures for this aim to gather 
additional feedback on the revised app. Any major changes will field tested in a similar manner prior to the 
onset of the RCT in Aim 2. 
 
Data Management and Statistical Considerations for Aims 2 and 3 
Overview of Data Management and Statistical Considerations for Aim 2 
 
Based on abstinence rates from quitline/telephone counseling (Fiore et al., 2008; Matkin et al., 2019; 
Pinerio et al., 2020), the estimated abstinence rate around 6 months for the QLO condition is 10%. With 1036 
randomized to each condition, power was calculated for the 725 participants expected to achieve 48-hours of 
abstinence in each condition (to activate the AR app). Power ≥ .80 to detect an abstinence rate of at least 15% 
(OR = 1.59) in the QL+AR condition using alpha = .05, a 2-sided test, and allowing 5% of variance in 
abstinence rates to be accounted for by other variables. Treatment group differences in baseline measures will 
be assessed and any variable with a group difference of p < .10 will be incorporated into analyses focusing on 

 
 
17 
 
 
 
intervention effects. To manage missing data, multiple imputation under the Missing at Random assumption 
will be applied using a Markov Chain Monte Carlo method. The primary statistical analysis for Aim 2 will use 
logistic regression to compare biochemically-confirmed abstinence rates at the 6-month assessment between 
conditions. A parallel analysis will be performed to evaluate efficacy for all enrolled (N=2072; i.e., beyond those 
who exhibit the required 48-hour abstinence to engage the AR intervention). A second test of effectiveness will 
use generalized estimating equations (GEE) with both the 5-week and 6-month abstinence as the outcomes. 
Please see the Statistical Design and Power attachment for additional details on our statistical approach.   
 
Data Management. Baseline, 5-week, and 6-month survey data will be exported from REDCap as .CSV 
files, which will be read into SAS version 9.4 (SAS Institute, Cary, NC) on a weekly basis during data 
collection. Data review and primary data analyses will be performed using SAS version 9.4. Dr. Sutton will 
oversee the data management, provide the PIs with reports and summaries on a regular basis, and perform 
the final analyses. 
Sample Size and Study Power Analysis. The primary statistical analysis for Aim 2 will use logistic 
regression to compare abstinence rates at the 6-month assessment between QL+AR and QLO conditions. 
Preliminary analyses will be used to identify demographics and tobacco history variables that predict either 
missing surveys or abstinence around 5 weeks and 6 months. These variables will be used in a multiple 
imputationmodel to manage missing data. Based on abstinence rates from quitline/telephone counseling (Fiore 
et al., 2008; Matkin et al., 2019; Pinerio et al., 2020), the estimated 7-day point prevalence abstinence rate 
around 6 months for the QLO condition is 10%. With 1036 randomized to each condition, power was calculated 
for the 725 participants expected to achieve 48-hours of abstinence in each condition (to activate the AR app). 
Power ≥ .80 to detect an abstinence rate of at least 15% (OR = 1.59) in the QL+AR condition using alpha = 
.05, a 2-sided test, and allowing 5% of variance in abstinence rates to be accounted for by other variables. A 
comparable analysis using all 2072 randomized participants (the 1450 who do achieve 48-hours of abstinence 
plus the 622 who do not) will have lower expected abstinence rates. Estimating that 7% of all QLO 
participants will be abstinent, power > .80 to detect an abstinence rate of at least 10.6% for QL+AR (OR = 
1.57)using alpha = .05, a 2-sided test, and allowing 5% of variance in abstinence rates to be accounted for by 
other variables. 
Data Analysis Overview. Descriptive statistics will be used to review all variables in the study prior to 
hypothesis testing. Data transformations will be made as needed. Treatment group differences in baseline 
measures will be assessed and any variable with a group difference of p < .10 will be incorporated into 
analyses focusing on intervention effects. All analyses described below will be performed for those who 
achieved 48 hours of self-reported abstinence (n=1450) as well as the full sample of those enrolled (N=3590). 
The former is relevant to assessing the efficacy of the app specifically, whereas the latter will reflect the overall 
incremental effectiveness of the app from the perspective of the quitline. Full-sample versus AR-sample 
differences in baseline measures will be evaluated. 
To manage missing data, multiple imputation under the Missing at Random assumption will be applied 
using a Markov Chain Monte Carlo method (Schafer, 1997) via PROC MI in SAS, given the expected 
nonmonotonicmissing data patterns and the expected large number of auxiliary variables (e.g., baseline 
measuresthat predict smoking status at follow-up) to be determined by preliminary analyses. The imputation 
model will 
include smoking status around 5 weeks, which is expected to be a primary predictor of smoking status around 
6 months. 
In addition, any other measures acquired around 5 weeks that are unique predictors of missingness or 
smokingstatus around 6 months will be included in the imputation model. Finally, a post hoc approach (Rubin, 
1987) willaddress the possible influence of Missing Not at Random. As done in recent studies (e.g., Brandon et 
al., 
2016; Martinez et al., in press), the post hoc adjustment will be small-to-medium in size (akin to Cohen’s 
d=0.35) in accordance with missing implies smoking. Twenty data sets will be generated. Therefore, all 
analyses of the primary outcome will be performed on complete data sets (i.e., intent-to-treat) following multiple 
imputation. 

 
 
18 
 
 
 
 
Analytic Plan for Addressing Specific Aims. 
Aim 2a. The primary test of the efficacy of QL+AR over QLO will use logistic regression on 
biochemically-confirmed abstinence around 6 months after baseline survey completion. This will be performed 
on the 20 imputed data sets usingthose participants who exhibited 48 hours of abstinence (n=1450). The 
model will include demographics and 
smoking-related variables that were found to uniquely predict self-reported abstinence at 6 months based on 
preliminary analyses. A parallel analysis will be performed to evaluate efficacy for all enrolled (N=2072; i.e., 
beyond those who exhibit the required 48-hour abstinence to engage the AR intervention). A second test of 
effectiveness will use generalized estimating equations (GEE) with both the 5-week and 6-month abstinence 
as the outcomes. This approach permits evaluation of a change in abstinence from around 5 weeks to 6 
months andan assessment of the intervention at around 5 weeks in the context of the primary outcome (using 
a timespecificcontrast). 
Aim 2b. Prospective moderators of the QL+AR intervention will be explored, separately, by extending 
the analyses described above through the inclusion of the moderator and the moderator x intervention as 
predictors of the dependent measure. A significant interaction term supports the variable as a moderator, 
which will be explored by assessing condition differences for each level of categorical moderators and for 
levels of continuous moderators created via median splits, tertiles, or quartiles. The following baseline 
measures will be evaluated: sex, nicotine dependence, trait cue reactivity, smoking status during treatment, 
quitline use, and NRT use. 
Prospective predictors of the AR intervention will be evaluated using only participants from the QL+AR 
arm. Similar to the analyses described above, AR-related variables will be added to a logistic regression model 
of abstinence at around 6 months that includes demographics and baseline smoking-related variables found to 
bepredictors of abstinence among QL+AR participants. The following variables will be explored: initial cue 
reactivity, extinction magnitude and latency, indices of treatment engagement, and App coach use. 
 
Aim 3a. Our preliminary cost-effectiveness assessment will use the primary trial endpoint of smoking 
abstinence at around 6 months as the main effectiveness/outcome measure. Using this outcome, we will 
calculate theincremental cost-effectiveness of the QL+AR arm compared to the QLO arm in increasing 
smoking abstinence 
(e.g., cost per quitter). Finally, we will also use quality adjusted life years (QALYs) and lifetime medical costs to 
estimate more inclusive, life-long effect and cost-effectiveness of the intervention. We will use the most recent 
estimates in the literature (e.g., Feirman et al., 2016; Kaplan et al., 2007) to estimate QALYs and lifetime 
medical costs associated with smokers and smoking cessation. We will assess the robustness of incremental 
results between the two study arms using probabilistic sensitivity analysis. Cost-assessments and cost 
effectiveness will be calculated on the basis of both all enrolled participants as well as only those who qualified 
for the AR intervention by achieving 48 hours of abstinence. 
Aims 3b and 3c. Descriptive statistics will be used to evaluate treatment engagement (measured via 
greater completion of the AR sessions, conducting the AR sessions in several different locations, and less use 
of avoidance strategies during the AR sessions), acceptability (measured via the Client Satisfaction 
Questionnaire, System Usability Scale, and unique items that assess app features, functions, and the AR 
experience including the App coach). Variables derived from engagement with the app will be explored as 
predictors in multivariable models that already include baseline predictors. 
Qualitative methods will be used to identify facilitators and barriers to app usage. We will use thematic 
analysis to analyze the key stakeholder interviews (quitline supervisors, App coaches, subset of participants; 
Creswell, 2007). We will develop a list of a priori codes based on the domains identified in the interview guide 
to develop an initial codebook. Two trained qualitative analysts from Moffitt’s Participant Research 
Interventions and Measurement Core will independently code an initial set of transcripts (n=5). After the first 
round of coding is complete, they will discuss their notes on possible new codes (e.g., themes that emerge 
from the data) and refine the codebook. The analysts will code another initial set of transcripts (n=5) and 

 
 
19 
 
 
 
compare coding through Kappa coefficients (the lines of text on a code transcript divided by the number of 
coding agreements between the two coders; Ulin et al., 2004). If k ≥ .80 is achieved, the analysts will continue 
coding the rest of the transcripts. If sufficient inter-rater agreement is not achieved, the analysts will refine the 
codebook and recode the transcripts. Coding and analyses will be conducted in NVivo qualitative software 
package. We anticipate 10 interviews with quitline supervisors and 40 participant interviews (20 high AR app 
users, 10 low AR app users  
, and 10 no AR app users) will be sufficient to achieve data saturation or the point at which no 
new themes emerge (ensures minimum of 10 in each group; Guest et al., 2006). All App coaches will be 
interviewed, as we anticipate having fewer than 10 across the course of the study. 
 
6. 
STUDY TIMELINE and ACCRUAL ESTIMATES 
 
 
Estimate for  
AIM 1 
AIM 2 
25% Accrual 
Month 8 
Month 20 
50% Accrual 
Month 9 
Month 27 
75% Accrual 
Month 10 
Month 34 
100% Accrual 
Month 10 
Month 41 
 
7.  REGULATORY AND REPORTING REMINDERS 
 
 
7.1 
Institutional Review Board 

 
 
20 
 
 
 
No subject is to be enrolled on this protocol until the Center’s Institution Review Board has approved it. 
 
7.2 
Informed Consent 
The investigators and the researchers associated with the study are responsible for obtaining consent 
by the participants in a manner approved by the Institutional Review Board. 
 
7.3 
Investigator Study Files 
Research records are the responsibility of the investigator.   
They will be available for review by the sponsors of the trial, health care personnel involved in this 
study, the IRB, and the SRC. 
The Principal Investigator will maintain study files for a period of years.  
 
8. DATA SHARING 
Data obtained from interviews, including both audio recordings and transcripts, will be shared with other 
investigators and institutions as approved to facilitate data analysis. These data could contain PHI.  
   
 
   
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Appendix of non-standard self-report surveys for AIM 1). 
 
 
 
AIM 1 Augmented Reality Questions 
  
 1. Have you ever used any kind of augmented reality (AR) app before? (e.g., AR feature in Pokemon Go, 
IKEA or other furniture app, Snapchat filters, Google Sky Map, etc.) 
o Yes  
o No  
o Don't know  
 
 
2. How frequently do you use augmented reality (AR) apps? 
o 7 days a week  
o 4-6 days a week  
o 1-3 days a week  
o 1-5 times a month  
o Less than once a month  
o Never  
o Don't know  
 
3. How interested are you in using an augmented reality smartphone app? 
1-----------2------------3-----------4-----------5-----------6-----------7-----------8-----------9-----------10 
       Not at All 
 
 
 
 
 
 
                                                  Very Interested 
 
 
 
 
 
 
 
 

 
 
30 
 
 
 
IN App Daily Diary Questions: 
URGE:  
 
 
 
Cigarettes smoked prior day: 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Did you use any of the following products yesterday? (check all that apply). 
Nicotine Gum 
O 
Nicotine Patch 
O 
Nicotine Nasal Spray 
O 
Prescription Nicotine Inhaler 
O 
Nicotine Lozenge 
O 
Zyban or Wellbutrin 
O 
Chantix or Varenicline 
O 
E-cigarette (e.g., vape pen, personal vaporizer, etc.) 
O 
Other treatments or medications 
Specify: ___________________________ 
O 
 
 
 

 
 
31 
 
 
 
AIM 1: Feedback and app usage self-report measures 
 
Reality/Coexistence items 
Instructions: Please mark the number from 1-10 that represents how you feel about each question. 
 
1. How real did the objects seem to you? 
1-----------2------------3-----------4-----------5-----------6-----------7-----------8-----------9-----------10 
       Not at All 
 
 
 
 
 
 
                                                  Very Real  
 
2. How well did the objects appear to be a part of the scene? 
1-----------2------------3-----------4-----------5-----------6-----------7-----------8-----------9-----------10 
        Not at All  
 
 
 
 
 
 
 
                    Very Well  
 
3. How much did you feel the objects were right there in front of you? 
1-----------2------------3-----------4-----------5-----------6-----------7-----------8-----------9-----------10 
       Not at All 
 
 
 
 
 
 
 
 
                  Very Much  
 
App Usefullness/Ease of learning/Satisfaction 
1. Would this app appeal to you if you were currently attempting to quit smoking? 
1  
2  
3 
4  
No, definitely not  
No, I don’t think so  
Yes, I think so  
Yes, definitely  
 
2. At the beginning of the week, how easy or difficult was it to use the app? 
 1  
2  
3  
4  
5  
  
Very Difficult  
                Very Easy  
 
3. At the end of the week, how easy or difficult was it to use the app? 
 1  
2  
3  
4  
5  
  
Very Difficult  
                Very Easy  
 
4. How many days did it take to get comfortable using the app? 
Choices are from 1day -7 days 
 
5. Would you recommend this app to a friend or family member to help them quit smoking? 
 1  
2  
3 
4  
No, definitely not  
No, I don’t think 
so  
Yes, I think so  
Yes, definitely  
 
6. Overall, how satisfied were you with the app? 
 1  
2  
3  
4  
Quite dissatisfied  
Indifferent or  mildly 
dissatisfied 
Mostly satisfied  
Very satisfied  

 
 
32 
 
 
 
 
 
ARC AIM 1 Follow-up Phone Interview (APP USE [AR ≥ 5]) 
“Hello, this is ________ from Project ARC. How are you today?” 
“Thank you for setting time aside to give us feedback. I just have a few questions for you today.” 
General: 
1. What did you think of the augmented reality smartphone app? 
a. [probe for specifics if give general answers,] e.g., “It was good/weird/frustrating”.   
Rationale: 
2.  What do you think is the purpose of this app? 
3. [TAM – perceived usefulness] Based on your recent experience quitting smoking, how do you think viewing 
smoking related images might be used while someone is trying to quit smoking? 
Ease of use: 
4. “What were the issues you encountered when navigating the app (e.g., …)?”  
5. [TAM – perceived ease of use] What was the easiest part of using the app? 
6. [TAM – perceived ease of use] What was the most difficult part of using the app? 
7. [TAM – attitude toward using] What was the most interesting part of using the app? 
8. [TAM – behavioral intention to use] How would you tell your friends/family about this app if you were going to 
have them use the app to quit smoking? 
New Features:  I’m going to ask you about some specific features of the app. 
9. In the app, there are three videos that explain how the AR sessions work, how they will help you stop smoking, 
and tips for the best AR experience. Did you watch these videos?  
a. If yes, What did you find most helpful after watching these videos? What aspects of the videos can be 
improved? 
b. If no, why did you not watch them? What recommendations may you have for improvement?  
10. What are your thoughts on the instructions for doing the AR sessions?  
a. What would have been helpful to know about doing AR sessions?  
11. [TAM – perceived usefulness] After the three videos, there was a Practice Tutorial with a Rubber Ducky and the 
chance to practice an AR session. Did you practice?  
a. If yes, what was helpful?  
b. If no, why did you not practice? 
c. What could we change to improve the practice experience? [Probe] 
12. [TAM – perceived ease of use] When you started using the app, you had the chance to select 0, 1, or 2 reminder 
notifications. What did you select? Can you tell us why you choose this amount?  
a. [TAM – perceived usefulness] If > 0, How did you feel about that number of reminders? Did they help 
remind you to complete AR sessions? 
b. [TAM – perceived usefulness] If 0, Why did you opt out of reminder notifications?  
c. What would you change about the reminder notifications?  
13. In the app, there is a feature to track your progress (e.g., money and time saved, days abstinent, number of AR 
sessions completed).   
a. I will list each progress feature, and I’d like you to tell me if you ever viewed it.   
i. Days Remaining / Weekly Streak 

 
 
33 
 
 
 
ii. # AR sessions Completed 
iii. Days Abstinent 
iv. Daily use of Nicotine Replacement Therapy 
v. Change in Urge  
vi. Money Saved 
vii. Time Saved 
b. [If Never used,] Why not?  
c. [For each progress feature ask;]  
i. What about [feature] did you like?  
 
ii. What about [feature] didn’t you like? Why? 
d. [TAM – perceived usefulness] Did you find the progress feature useful as related to your quitting 
smoking? 
e. [TAM – perceived usefulness] Did you find the progress feature useful for your continued use of the 
app? (if question unclear, rephrase: In other words, did it make you more likely to continue using the 
app?)   
14. After completing a certain amount of AR sessions, you had the ability to receive a reward.  Did you ever receive 
a reward (as a reminder, the first one was a flower pot)? If no, skip a-d.  
a. [TAM – attitude toward use] What did you like about the rewards? 
b. [TAM – attitude towards use] What did you NOT like about them? 
c. [TAM – perceived usefulness] How did the rewards motivate you to do more AR sessions? 
d. [TAM – perceived usefulness or attitude towards use] What aspects of the AR rewards could be 
improved? 
 
Improvements: 
15. [TAM – behavioral intention to use OR attitude toward using] What type of changes would you make to the app 
to increase the likelihood of using it, or, to make it more enjoyable to use?  
16. [TAM – behavioral intention to use] What other features could we include in the app to help someone continue 
using the app for quitting smoking? 
17. What aspects would you change about the app? 
18. What are the key points that you think we should talk to smokers about when explaining this app for smoking 
cessation? 
 
Study team/Support: 
19. [TAM – perceived ease of use] Did you ever reach out to the study team for help with the app? 
If YES: What did you find helpful?  
IF NO: Why not? 
What other feedback do you have for us?  
 
 
 
 
 
 
 
 

 
 
34 
 
 
 
ARC AIM 1 Follow-up Phone Interview (NO/Little APP USE [AR < 5]) 
“Hello, this is ________ from Project ARC. How are you today?” 
“Thank you for setting time aside to give us feedback. I just have a few questions for you today.” 
 
General: 
1. We noticed that although you downloaded the app, you did not use the app/only used the app X times.  It would 
help us to understand why you did not use the app (much). Can you tell me why you didn’t use the app? Probe 
reasons.  
 
2. What did you think about the description of the app during the study screening call and directions for use?  
a. Were the descriptions and directions clear? 
i. If not clear, what could have made it clearer?  
b. What do you wish you were told before starting the ARC app? 
c. When we first introduce this study to smokers in the future, what else should we tell them about the 
app to make sure they fully understand what they are being asked to do? 
Rationale: 
20. What do you think is the purpose of this app? 
 
21. [TAM – perceived usefulness] How do you think viewing smoking related images might be used while someone 
is trying to quit smoking? 
 
22. [TAM – behavioral intention to use] How would you tell your friends/family about this app if you were going to 
have them use the app to quit smoking? 
Barriers: 
23. [TAM – perceived ease of use] Can you tell me what made it difficult to use the app? (Probe all) 
a. What would make it easier to use the app 
 
24. [TAM – behavioral intention to use] What kind of strategies do you think would help motivate someone to 
continue using the app to help them quit smoking? 
 
Study team/Support: 
25. Did you ever reach out to the study team for help with the app? 
a. If YES: Did you find it helpful? Why or why not? 
 
26. Do you have any other feedback for the study team?