Utilizing Augmented Reality as an Adjunct for Smoking Cessation; Development and Initial Validation
Phase: NA
Conditions: Smoking
Sponsor: H. Lee Moffitt Cancer Center and Research Institute

 
 
 
Utilizing Augmented Reality as an Adjunct for 
Smoking Cessation; Development and Initial 
Validation 
 
NCT04101422 
 
Protocol V12  
Dated 11/23/2021 

 
 
Page 1 
 
 
 
 
 
 
 
TITLE:  Utilizing Augmented Reality as an Adjunct for Smoking Cessation; Development and Initial    
Validation 
 
 
Coordinating Center: 
 H. Lee Moffitt Cancer Center 
 
 
Principal Investigator: 
Christine Vinci, Ph.D. 
Moffitt Cancer Center 4115 E 
Fowler Ave Tampa, Fl 33617 
Phone: 813-745-5421 
Fax: 813-449-6871 
Email: christine.vinci@moffitt.org 
 
Co-Principal Investigator:  Thomas Brandon, PhD 
 
 
 
Co-Investigators:  
David Drobes, PhD 
 
Steve Sutton, PhD 
                              
                             
 
                               
                                
 
 
Protocol version 12 dated 11/23/2021 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
Page 2 
 
 
 
 
 
 
 
 
RESEARCH STRATEGY 
 
A. SIGNIFICANCE 
A.1. Smoking, Smoking Cessation, and Emerging Technology  
 
Cigarette smoking is the leading preventable cause of morbidity and mortality in the U.S. and accounts 
for 30% of all cancer deaths (ACS, 2014; USDHHS, 2014). Although tobacco use has declined over the past 
50 years, smoking prevalence was 15.5% in the U.S. among adults in 2016 (Jamal et al., 2018). As such, 
developing more effective cessation interventions remains a public health priority.   
 
There is a specific need for cessation interventions that reduce barriers to dissemination and 
implementation, and mHealth interventions hold particular promise in this regard (Abroms et al., 2012; Free et 
al., 2011; Rodgers et al., 2005; Vinci et al., in press). Moreover, harnessing recent advances in technology 
may increase the uptake of cessation treatment, while also targeting identified roadblocks to cessation. For 
example, in 2016, 77% of Americans owned a smartphone, more than double the percentage only 5 years 
earlier, with over 60% ownership even among low-income populations (PEW Research, 2018). Further, among 
all smartphone owners in 2014, 62% reported using their phone to access information about a health condition 
(Smith, 2015). Thus, novel interventions that leverage advances in technology may be an ideal way to increase 
the reach and efficacy of smoking cessation.  
 
Augmented reality (AR) is a recent and rapidly-advancing mobile technology with potential utility for 
smoking cessation. 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) to individuals 
multiple times, while not allowing them to engage in the typical drug behavior (in this case smoking), in order to 
extinguish the urge to smoke. As described in more detail below, cue-exposure treatments have demonstrated 
efficacy for decreasing craving in the laboratory, but these effects are often short-lived as they do not 
generalize well beyond the extinction setting (e.g., lab) into the real-world environment (Bouton, 2000). AR 
allows the entire cue exposure session to take place in the real-world, which could greatly enhance the efficacy 
of cue exposure treatment. (An introduction to AR from CNNMoney can be seen here: 
https://www.youtube.com/watch?v=_EfMCTOQd6A.) The current proposal aims to capitalize upon emerging 
AR technology to create and validate a novel treatment adjunct for smoking cessation. 
 
A.2. Digital Environments 
 
The last two decades have brought tremendous advances in the creation of artificial or altered 
environments via digital technology. Virtual reality creates a simulated, full digital environment. In contrast, 
augmented reality affects the perception of the real-world environment by “augmenting” it with computer-
generated digital objects. 
 
A.2.1. Virtual Reality (VR) combines 3D computer-generated graphics with motion trackers, vibration 
platforms, and audio in head-mounted displays to create immersive and interactive environments. The ability to 
create alternative, digital environments has made VR attractive for use in mental health treatments. A recent 
review of VR technology use within psychiatric disorders (Maples-Keller et al., 2017) found support for the 
efficacy of VR interventions—either alone or in conjunction with traditional therapy components—in treating 
anxiety disorders, schizophrenia, pain management, eating disorders, and addiction. VR interventions most 
often incorporate cue-exposure and social skills training, two components to which interactive, naturalistic 
virtual environments are well suited. Advantages of VR cue-exposure compared to in vivo cue-exposure 
include better stimulus control and lower risk. For example, treating a snake phobia using VR does not require 
the presence of an actual snake; and treating an addictive behavior does not require the actual presence of 
tempting and potentially illegal or harmful substances. Studies have found that VR exposure compared 
favorably to in vivo exposure (e.g., Powers & Emmelkamp, 2008). Patient acceptability of VR is high with low 
refusal (Garcia-Palacios, 2007) and treatment dropout rates (Lahiri et al., 2015).  

 
 
Page 3 
 
 
 
Thus far, VR in smoking cessation has been primarily incorporated as one element (e.g., cue-exposure 
or coping skills) in a more comprehensive treatment approach. VR smoking cue-exposure has successfully 
elicited nicotine cravings (Bordnick et al., 2004; Choi et al., 2011; Pericot-Valverde et al., 2014; Thompson-
Lake et al., 2015) and effectively reduced physiological arousal after cue-exposure trials (Choi et al., 2011). 
Cue-exposure via VR combined with nicotine replacement therapy (NRT) has been found to produce greater 
smoking cessation and reduction, better coping skills, and greater cessation self-confidence than NRT alone 
(Pericot-Valverde et al., 2015). 
Although existing research using VR suggests its potential as a smoking cessation tool, VR also has 
several disadvantages that reduce its clinical utility: (1) The development of full virtual environments is time-
consuming and costly; (2) Although constantly improving, the VR scenes are often low in realism, which may 
limit their effectiveness and generalizability; (3) Because of the limited number of VR scenes, they do not 
capture the range of smoking contexts of the typical smoker. This also limits their generalizability, as described 
below (A.3.). 
 
A.2.2. Augmented Reality (AR) is a rapidly-emerging technology that could provide a novel and exciting 
treatment strategy for smoking cessation. AR inserts virtual, digitally-created objects into the real-world 
environment as viewed on a screen (i.e., smartphone or tablet; see Figure 1), with emerging options for head-
mounted displays as well. AR is similar to VR in that the user is immersed in a particular environment.  
However, whereas VR presents 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). In summary, whereas VR brings the user into a 
digitally-created world, AR brings a digitally-created object into the user’s own world. As such, AR addresses 
the three weaknesses of VR described above: (1) Because only an object rather than an entire environment is 
digitally created, AR requires a small fraction of the time and cost to develop; (2) Because objects are much 
simpler than full environments, AR objects can be 
created with a high degree of realism, either 
through digital artistry, digital photography, or a 
combination; (3) Because VR objects are 
superimposed upon the user’s natural 
environment, an infinite number and range of 
smoking contexts are possible, with personal 
relevance to each user.   
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 been 
advancing 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, compass, and camera to superimpose primitive (by today’s standards) AR “creatures” in the 
users’ natural environment. In the two years since, the technology has improved dramatically and has 
developed more mainstream uses in education, entertainment, architecture, and retail. As an example of the 
last, the furniture store, IKEA, has an app that allows the user to place and view digital depictions of their 
products placed within the user’s own home, similar to Figure 1. 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. For example, the user can approach or view the object from any direction, distance, or 
angle while maintaining a realistic perspective. The object also maintains its apparent position even when the 
user turns away from it and then returns to it. Motion can also be included within an AR object; for example, an 
AR cigarette can include smoke actively rising from it. Note that the latest model of Apple’s iPhone (Model X) 
was designed to optimize AR applications. Indeed, the two camera lenses were switched from horizontal to 
vertical positions in part for better depth mapping and determination of planes upon which to place AR images. 
In parallel, the Apple operating system iOS 11 is optimized for AR, and Apple’s ARKit is a mobile platform for 

 
 
Page 4 
 
 
 
simplifying the development of AR apps. iOS 12 will add further AR functionality, including the ability for 
multiple users to see and interact with the same AR object. Similarly, Google’s Tango AR platform is available 
for development of AR apps on Android devices. 
Like VR, AR offers stimulus control and safety advantages over in vivo exposure for clinical use, but 
with the additional advantages listed above. Unlike VR, however, there has been relatively little clinical use of 
AR to date. The vast majority of AR application in healthcare has been in the arena of professional training, 
particularly surgical training. In contrast, a recent systematic review identified only 13 published studies of AR 
within the domain of clinical psychology (Giglioli et al., 2015). Although the potential of AR for cue-exposure 
therapies has been recognized (Baus & Bouchard, 2014), these have been mostly limited to small animal 
phobias—cockroaches, in particular (e.g., Botella et al., 2016). We believe that AR will be useful for improving 
the efficacy of cue-exposure therapies for addictive behaviors, including tobacco use. Unlike VR, it does not 
yet appear that AR has been utilized in this manner, except in superficial ways (see B. Innovation).  
 
A.3. 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 suggested that environmental CSs (e.g., drug paraphernalia) can cause cue-reactivity in 
alcoholics, opiate addicts, and smokers (Brandon et al., 1995a; Rohsenow et al., 1992), and these CRs are 
subjectively experienced as cravings to use the drug. 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). 
Cue-exposure treatment studies do indeed demonstrate post-treatment 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). However, long-term clinical outcomes remain modest. A meta-
analysis of cue-exposure treatments for addictions found that their overall effect size (d = 0.09) was small 
(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 extinction trials that take place in either a 
laboratory or clinic setting. Based on considerable animal and human research, extinction that occurs in these 
contexts does not appear to generalize to contexts in the user’s natural environment (i.e., the renewal effect; 
Bouton, 2002; Collins & Brandon, 2002). Therefore, considerable effort has been spent in testing alternative 
approaches for expanding the context of extinction to smoking cues. 
 
A.4. Expanding the Extinction Context  
The most straight-forward approach 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 have cue-exposure therapy sessions. Consequently, researchers have been creative in 
developing alternative ways to expand the context of cue-reactivity, which could then be used to expand the 
context and generalizability of extinction/cue-exposure therapies. Attempts to expand the cue-reactivity context 
have included (1) increasing the realism of the laboratory/clinic context via photos taken by smokers of their 
natural environment and large-scale projections of environmental scenes (Conklin et al., 2010); (2) having 

 
 
Page 5 
 
 
 
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 to create 
pseudo-natural exposure environments in the laboratory/clinic setting (Pericot-Valverde et al., 2015). Although 
this approach has been effective at provoking cravings and has the advantage of providing multiple, three-
dimensional and interactive environments for cue-exposure sessions, it has the limitations noted above: (1) the 
VR environments are costly to produce, so only a limited number are available for use; (2) the VR 
environments are often not graphically realistic; and (3) the VR environments do not represent each smoker’s 
actual smoking environments. 
 
A.5. The Potential of Augmented Reality  
The rapid and ongoing development of AR systems presents the opportunity to harness the technical 
advances of VR, while overcoming the limitations listed above. AR requires the development of only the 
specific smoking cues (e.g., cigarettes, lighters, people smoking), rather than the entire smoking environment. 
These cues are then inserted into the smoker’s actual, naturalistic environments as seen through their 
smartphone (e.g., different locations in the home, work, outdoors, and other settings). Thus, with respect to the 
limitations of VR: (1) the production costs are much lower and an infinite number of natural environments can 
be used; (2) the environments are realistic because they are (3) the actual, real-time environments at the 
smoker’s current location.   
When compared to current cue-exposure therapies, AR has the potential to extend the short-term gains 
produced in the laboratory. Importantly, cue-exposure via AR would take place entirely in the real-world 
environment, circumventing current limitations of existing cue-exposure treatments. That is, AR provides the 
unique opportunity to conduct extinction trials in the natural environment of the smoker, which should 
dramatically increase the sustained efficacy of cue-exposure therapy, based on the contemporary models of 
extinction described above.   
We conceptualize AR-based cue-exposure as an extinction-based adjuvant to a more comprehensive 
smoking-cessation intervention. That is, we are not proposing AR-based cue-exposure per se as a sufficient 
intervention for treating tobacco dependence. However, because cue-reactivity appears to be a proximal 
determinant of smoking maintenance and post-cessation relapse, cue-extinction is a logical, theory-based 
treatment component aimed toward improving cessation and reducing risk of relapse. Moreover, because of its 
portability and presentation of stimuli in the users’ natural environments, AR has the potential to break through 
the contextual barrier associated with previous extinction-based therapies for treating addiction—i.e., the 
renewal effect. Our goal is to systematically develop and validate a smartphone app that could be used as an 
adjuvant to a wide variety of smoking cessation interventions, including traditional cessation counseling, 
telephone quitlines, online cessation websites, text messaging (SMS) interventions, and/or pharmacotherapy. 
It could also be incorporated into other mobile smoking cessation apps. Note too that AR has other therapeutic 
potential in addition to cue-exposure. For example, AR stimuli could be used to train smokers to execute 
cognitive and behavioral coping responses when confronted by smoking cues, or, alternatively, AR stimuli 
could be incorporated into mindfulness-based (e.g., Brewer et al., 2011; Davis et al., 2014; Vidrine et al., 2016) 
or Acceptance and Commitment (Bricker et al., 2014) therapies to train appropriate responses to smoking 
urges. These alternative uses would also be dependent on the ability of AR stimuli to elicit cravings to smoke 
(i.e., cue-reactivity). Thus, although in this initial stage of development, we will focus on testing AR for cue-
reactivity and extinction, this research will also be relevant to alternative therapeutic uses of the technology.   
The proposed research represents a systematic and scientifically rigorous process in the 
development of an evidence-based adjunctive therapy. Indeed, it represents Stage I of behavioral intervention 
development (Rounsaville et al., 2001); that is, the “proof of concept” research necessary to demonstrate that 
AR has potential that warrants testing in subsequent efficacy studies. Thus, the proposed research will create 
the AR images within a basic software app (Aim 1), and then test the stimuli’s ability to initially elicit and then 
extinguish cue-provoked craving responses in the laboratory (Aim 2). Finally, we will begin the translation to 
Stage II intervention development by upgrading the app and pilot testing its utility and acceptability when used 
in smokers’ natural environment (Aim 3). At the end of this project, we expect that the AR app will be ready to 
test as an adjuvant therapy for smoking cessation in a fully-powered randomized, controlled trial, representing 
Stage II and III research. 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 

 
 
Page 6 
 
 
 
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. 
 
B. INNOVATION 
The proposed research is innovative in numerous ways, capitalizing on the recent and still-emerging 
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. Although there is a small literature on AR for extinction-based treatment of phobias 
(primarily cockroaches), there are not yet any published studies of AR for treatment of addictive behaviors. The 
only uses of AR for smoking cessation that we could find include a British AR tool for physicians that, when 
pointed at a patient, illustrates organ damage caused by smoking, and a phone app created by the British 
Health Foundation that, when pointed at a cigarette pack, illustrates what the smoker could do with the money 
saved by quitting smoking (e.g., movie tickets, vacation). Thus, our proposed research would appear 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. The emergence of AR provides a new opportunity to solve the 
therapeutic dilemma of the renewal effect that has long been thought to limit the real-world efficacy of cue-
exposure therapy. 
 
C. APPROACH 
C.1. Preliminary Studies 
 
The study team (Drs. Vinci, Brandon, Drobes, and Sutton) is ideally suited for the proposed line of 
research, with relevant experience and expertise in cue-reactivity and cue-exposure among tobacco users, 
development of novel treatments for tobacco dependence, and the use of novel technologies. Drs. Vinci and 
Brandon are multiple principal investigators (MPIs) of the current application and demonstrate complimentary 
expertise that strengthens the project. Drs. Drobes and Sutton have an established history of working with Dr. 
Brandon on multiple tobacco use and cessation studies at the Tobacco Research and Intervention Program 
(TRIP) at Moffitt Cancer Center. Our primary catchment area for recruitment includes three counties 
(Hillsborough, Pinellas, and Pasco) with a population of approximately 3 million residents and a smoking 
prevalence slightly above the national average (Florida County Health Rankings, 2018). This population base 
has yielded efficient accrual of smokers for both our treatment and laboratory studies, including those involving 
multiple lab sessions (e.g., Brandon et al., 2011; Donny et al., 2015; Drobes & Tiffany, 1997).   
 
C.1.2. Cue-Reactivity and Cue-Exposure Research 
MPI, Thomas Brandon, Ph.D., has studied cue-reactivity and cue-exposure processes in the laboratory 
and has translated this research into clinical application. Innovations include: demonstrating that the pairing of 
neutral cues with smoking produced classically conditioned cravings to smoke (Lazev et al., 1999); delineating 
moderators of cue-reactivity, including drug availability (Juliano & Brandon, 1998) and medication effects 
(Brandon et al., 2011); and expanding the domain of cues that provoked craving, including affect, cognition, 
pain, and body image (Ditre & Brandon, 2008; Lopez et al., 2008; Litvin & Brandon, 2010; Heckman et al., 
2015; 2017). Of particular relevance to the proposed research, he has studied the role of context in the 
generalizability of extinction (i.e., the renewal effect) through the translation of basic animal learning research 
into human studies and applications (Brandon et al., in press; Collins & Brandon, 2002; Stasiewicz et al, 2007; 
Unrod et al., 2014). He has also contributed to the theoretical conceptualization of smoking maintenance, 
cessation, and relapse, incorporating the role of cue-reactivity (Baker et al., 2004; Brandon, 1994; Brandon et 
al., 1995b, 2007; Ditre et al., 2012).   
Co-Investigator, David Drobes, Ph.D., in addition to his collaborations with Dr. Brandon, has 
independently dedicated much of his career to understanding the mechanisms and implications of cue-
reactivity. He has used diverse laboratory methods for eliciting subjective and psychophysiological reactions to 
smoking and other drug/appetitive cues (Drobes et al., 2001; Drobes & Tiffany, 1997; Saladin et al., 2002); 
including studies of contextual learning in the development and expression of drug tolerance (Tiffany et al., 
1990, 1992); the impact of affective cues on smoking urges (Tiffany & Drobes, 1990); the relationship between 
smoking and alcohol cue-reactivity (Drobes, 2002; Oliver & Drobes, 2015); effects of medications on smoking 
cue-reactivity and cessation outcomes (Ditre et al., 2012); neural response to drug cues (Bloom et al., 2013; 

 
 
Page 7 
 
 
 
George et al., 2001; Myrick et al., 2004; Oliver et al., 2016); smoking/alcohol cue-reactivity among adolescents 
(Thomas et al., 2005; Upadhyaya et al., 2004, 2006); and use of attentional bias paradigms to capture implicit 
processes in smoking cue-reactivity (Drobes et al., 2006; Evans et al., 2011; Oliver & Drobes, 2012).   
MPI, Christine Vinci, Ph.D., has studied the effects of affect-based cue-exposure in smokers (Vinci et 
al., 2012); predictors of response to affect-based cue-exposure in smokers (Vinci et al., 2015); the role of 
mindfulness in response to negative affect cues among problematic alcohol users (Vinci et al., 2014); and the 
role of image vividness during cue-exposure treatment for posttraumatic stress disorder (Mota et al., 2015). 
 
C.1.3. Development of Treatments for Tobacco Dependence 
The investigators have considerable experience developing novel interventions for smoking cessation, 
including cue-exposure based treatments (Brandon et al., in press; Stasiewicz et al, 2007; Unrod et al., 2014), 
behavioral counseling (Brandon et al., 1987; Zelman et al., 1992), self-help interventions (e.g., Brandon et al., 
2000, 2012, 2016; Meltzer et al., 2017), and pharmacotherapy (Ditre et al., 2012). Importantly, Dr. Sutton has 
led the statistical analysis on many of these projects (e.g., Brandon et al., in press, 2012, 2016; Meltzer et al., 
2017; Unrod et al., 2014).  
 
C.1.4. Use of Emerging Technologies 
Finally, the team has embraced new technologies to advance tobacco research and treatment. Dr. 
Vinci is currently using human sensing technology (AutoSense; Ertin et al., 2011; Hovsepian et al., 2015) to 
evaluate a just-in-time adaptive intervention for smokers, by objectively and unobtrusively collecting data on 
behavioral (i.e., smoking) and physiological (e.g., heart rate) measures in the real-world environment during a 
quit attempt (R00MD010468). This project is also using ecological momentary assessment (EMA) to collect 
self-report data on response to the intervention. Dr. Brandon and his team developed the first telemetric (cable-
free) instrument for measuring smoking topography (Kashinsky et al., 1995); he tested an early mobile therapy 
device (Brandon et al., 1995a); and he has recently been using eye-tracking to study attentional processes 
among smokers (Correa & Brandon, 2016). Meanwhile, along with his history of studying psychophysiological 
responses to smoking-related cues, Dr. Drobes has recently been collaborating on innovative and influential 
research using reduced nicotine-content cigarettes (e.g., Donny et al, 2015; Tidey et al., 2017). He is currently 
conducting a trial using these cigarettes to facilitate extinction processes during smoking cessation (Florida 
Biomedical Research Program grant #6JK02). Although the proposed project will be the first use of AR 
technology by the research team, we are collaborating with an experienced AR-development firm, Haneke 
Design. Further, given our experience in using emerging technologies, we are prepared and equipped to 
handle issues that often arise when incorporating new technology into a program of research (e.g., 
communicating with inter-disciplinary teams; incorporating technological advances; iterative adaptations).    
 
In summary, the research team, led by Drs. Vinci and Brandon as MPIs, with Drs. Drobes and Sutton 
as co-investigators, has both the theoretical and research background on the mechanisms underlying the 
therapeutic potential of AR, as well as the experience and energy to capitalize upon this emerging technology. 
 
C.2. Aim 1: AR Development and Pilot Testing 
C.2.1. Project Overview 
We will collaborate with an AR software specialist to develop sets of 6 smoking and 6 neutral AR stimuli 
that are embedded within a basic digital application. These stimuli will be piloted on a small group of smokers 
(N=10) to receive their feedback and modify as needed.  
 
C.2.2. Participants 
Aim 1 will recruit 10 participants. To ensure that we are recruiting a similar population for each Aim, 
inclusion and exclusion criteria will be similar for all three aims (with a minor exceptions for Aim 2 and Aim 3). 
Inclusion criteria for Aim 1 will be ≥ 18 years of age; currently smoking ≥ 3 cigarettes per day for the past year; 
breath carbon monoxide (CO) level ≥ 5 ppm; motivated to quit smoking; valid home address in the Tampa Bay 
area; functioning telephone number; and can speak, read, and write in English. Exclusion criteria will be 
regular use (e.g., no more than 1/3 of month) of other tobacco products (cigars); and a household member 
already enrolled in the study. The criterion of ≥ 3 cigarettes per day for the past year was selected to obtain a 
wide range of smokers who would have developed both sufficient cue-reactivity and motivation to quit 
(Shiffman et al., 2014).   
 

 
 
Page 8 
 
 
 
C.2.3. Recruitment 
Research will be conducted at Moffitt Cancer Center’s Tobacco Research and Intervention Program 
(TRIP), which has exceptional facilities created specifically for human tobacco research. Participants will be 
recruited through referrals from other studies, print and media ads (e.g., radio, social media) within the Tampa 
Bay area, and flyers distributed throughout the community, such as to primary care offices and physicians. 
ResearchMatch.org will also be utilized as a recruitment tool for this protocol.  ResearchMatch.org is a national 
electronic, web-based recruitment tool that was created through the Clinical & Translational Science Awards 
Consortium in 2009 and is maintained at Vanderbilt University as an IRB-approved data repository (see IRB 
#090207). These methods mimic the successful recruitment strategies for this population in past laboratory 
studies at TRIP conducted by the investigative team (e.g., Correa & Brandon, 2016; Ditre & Brandon, 2008; 
Heckman et al., 2017).    
 
C.2.4. Procedures 
Based on our discussion with the AR developer, Haneke Design, we anticipate the development of the 
AR stimuli and app to be completed within 4-8 weeks. We will base the stimuli on the existing cue-reactivity 
literature and commonly-used cues in these types of studies. We have chosen to focus on proximal smoking 
cues for this study (e.g., cigarettes, lighters, ashtrays), whereas future research will also likely extend to distal 
AR cues (e.g., coffee, alcohol; Conklin & Perkins, 2002). Examples of neutral cues are a pencil, eraser, and 
keys (Carpenter et al., 2009; Conklin et al., 2010; Conklin & Perkins, 2002; LaRowe et al., 2007; Wray et al., 
2011).  
After creating the stimuli, we will recruit interested participants as described above, who will complete 
an initial phone screen to assess eligibility. Those eligible based on the phone screen will be invited to attend 
an in-person session. At this session, study personnel will provide a detailed description of the study, answer 
questions, obtain written informed consent, and finalize eligibility. Individuals who are ineligible will be thanked 
and compensated for their time, and offered smoking cessation resources.  
Eligible participants will then be brought to a private room, where they will provide a breath CO sample 
in order to confirm smoking status, complete a brief survey of demographics, smoking history, and familiarity 
with augmented reality, and view and provide feedback on each of the AR stimuli. Likert scale questions will 
assess urge and reality/co-existence (how realistic the item looks, and its integration into the environment), 
along with general feedback on the stimuli (discussed in further detail below). We anticipate this session taking 
approximately 1 hour, and participants will be compensated $35 for their time. We will then work with the AR 
developer to make any needed adjustments to the stimuli or the app. We anticipate that these adjustments will 
be minor and not require further piloting. However, we will have the flexibility to conduct additional piloting, if 
deemed necessary. Once the app and stimuli are finalized, we will begin recruiting for Aim 2 (see C.3.).   
 
C.2.5. Measures 
C.2.5.a. Demographics, tobacco use, and AR familiarity. Participants will complete a survey of 
demographics (age, Ethnicity, Race, sex, gender identity, sexual orientation, marital status, education, and 
income), Tobacco use and motivation (frequency and quantity, other types of tobacco use, dependency, 
motivation to quit smoking and confidence in quitting), and two questions about familiarity with augmented 
reality.  
C.2.5.b. Urge. Participants will rate their urge (craving) to smoke on a 10-point Likert scale from 1 
(absolutely no urge to smoke) to 10 (strongest urge to smoke). Single-item scales, such as this one, reduce 
participant burden when multiple ratings are required by the protocol, and they have been found to be reliable 
and valid in assessing urge to smoke (Kozlowski et al., 1996). They have also been used in prior work by the 
investigators (e.g., Brandon et al., 2011; Drobes & Tiffany, 1997; Vinci et al., 2012).  
C.2.5.c. Reality/Co-existence. Three items are recommended to assess the quality of the AR 
experience (Baus & Bouchard, 2014). Reality refers to the perceived realism of the AR object (“How real did 
the object seem to you?”). Environment co-existence refers to the integration of the object into the surrounding 
environment, as seen on the smartphone screen (“How well did the object appear to be part of the scene?).  
User co-existence refers to the perceived degree to which the user feels in the presence of the object (“How 
much did you feel the object was right there in front of you?”). Of the three, user co-existence, is likely to 
improve the most with the development of AR headsets, which will eliminate the need to view the environment 
through handheld screens. Nevertheless, we will assess all three using 10-point Likert scales.  

 
 
Page 9 
 
 
 
C.2.5.d. Additional Feedback on Stimuli. Participants will be asked for their feedback on a variety of 
other features of the stimuli including color, size, texture, as well as any characteristics that elicited strong 
positive or negative reactions, and any other images that we did not include that might elicit cravings (e.g., “Did 
anything about the object immediately turn you off or bother you?”).  
 
C.2.6. Data Management and Statistical Considerations 
See Statistical Design and Power attachment, as per new instructions for Clinical Trial applications. 
 
C.3. Aim 2: Laboratory Validation of AR Stimuli 
C.3.1. Project Overview1 
 
The primary goal of Aim 2 is to validate the AR 
stimuli developed in Aim 1 with respect to eliciting cue-
reactivity and extinction in the laboratory. Pre-Covid-19 
participants (N=17) will attend two lab-based sessions 
(Session 1 and Session 2). Although the same 
participants will attend both sessions, each session is 
independent with its own study design. Post-Covid-19 
participants will attend only Session 2. Session 1 will test 
cue-reactivity, and Session 2 will test extinction. Session 
1 will utilize a cue-reactivity paradigm using a 2x2 within-
subjects factorial design crossing stimuli type (AR vs in 
vivo) with stimuli content (smoking vs neutral) as shown 
in Figure 2. The primary outcome will be urge to smoke, 
with reality/co-existence scales as secondary measures 
in the AR cells. Session 2 (N = 128) will evaluate extinction of urge to smoke through repeated presentations of 
the AR smoking stimuli (vs AR neutral stimuli). A secondary outcome will be latency to smoke an available 
cigarette following the presentation of AR stimuli.  
 
C.3.2. Participants and Recruitment 
Recruitment strategies and inclusion/exclusion criteria for the 17 pre-Covid-19 participants and the 128 
post-Covid-19 participants will be identical to those outlined in Aim 1 (C.2.2., C.2.3.) with the additional 
exclusion criteria of self-reported pregnancy or lactation (due to expected smoking in a laboratory session) and 
currently not actively trying to quit smoking (e.g., enrolled in a cessation program). We have allotted 15 months 
for recruitment, which would result in consenting an average of about 2 participants per week. As noted above 
(C.1.), our population base and smoking prevalence allow for efficient accrual of smokers to a range of studies. 
For example, for the multi-site Donny et al. (2015) study of reduced-nicotine cigarettes, our site recruited 84 
smokers within 10 months (2 per week) to a more complex and extensive project with multiple visits. We also 
recently recruited 130 adult e-cigarette users (a much lower prevalence group) over 6 months (5 per week) for 
a behavioral lab study (Palmer & Brandon, 2018). Thus, we are confident that our proposed accrual goal is 
feasible.   
 
 
C.3.3. Phone Screening 
Potentially eligible participants will be assessed via phone to determine whether they meet initial 
eligibility requirements. If eligible and interested, participants will be invited to Session 1 (pre Covid-19) or 
Session 2 (post Covid-19). They will be asked to abstain from smoking and all nicotine products (e.g., e-
cigarettes) for 3 hours prior to attending this session, and told that confirmation of smoking status will be taken 
via a breath carbon monoxide (CO) measurement. There is no validated CO cut-off to confirm 3 hours of 
abstinence, so this is a “bogus pipeline” procedure, which has been found to improve accuracy of reported 
smoking behavior (Adams et al., 2008; Aguinis et al., 1993). 
 
 
1 Due to Covid-19 disruptions, after 3/13/20 additional safety precautions including minimizing participant contact were implemented.  
Aim 2 was powered for Session 2 (extinction), and cue reactivity was sufficiently demonstrated with the 17 participants who 
completed session 1 before the Covid-19 disruption in participant involvement. Thus, pre-Covid-19, participants attended both 
Sessions 1 and 2.  Post-Covid-19, participants attend only Session 2 (extinction) and procedures were modified to accommodate a 
single session. 

 
 
Page 10 
 
 
 
C.3.4. Procedure Overview for Session 1 and Session 2 
At the beginning of Session 1 for pre-Covid-19 participants or Session 2 for post-covid participants, 
study staff will answer any questions the participants may have and the informed consent process will be 
completed. Participants will provide a breath CO sample and asked when they smoked their last cigarette, to 
confirm that they had abstained for 3 hours. Eligible participants will then complete a series of self-report 
questionnaires described below. We anticipate the informed consent and questionnaire phase will take 
approximately 45 minutes. Next, Session 1 pre-Covid-19 participants will complete the cue-reactivity 
procedures (about 45-60 minutes). Thus, Session 1 should take under 2 hours. Following this session, 
participants will be scheduled for Session 2 (extinction) within 120 days. Participants will be given similar pre-
session abstinence instructions and asked to bring a pack of their own cigarettes. Again, this session will begin 
with self-report and CO assessment, and the full session is anticipated to last approximately 1.25 hours for pre-
Covid-19 participants and 1.5-2.5 hours for post-Covid-19 participants.   
 
If, at the beginning of either session, a participant self-reports smoking within the past 3 hours, s/he will 
be rescheduled. Further, smoking status will be assessed at the beginning of Session 2; any participant who 
indicates that s/he has quit smoking for 24 hours or more will be ineligible for Session 2 (as participants will be 
asked to smoke during this session). Otherwise, participants will then complete each respective session as 
described below. Participants will complete all procedures in a private laboratory room at TRIP. The 
procedures will be video-recorded from a central control room. The recordings will be reviewed to confirm 
study fidelity, and they will be used to score latency to smoke after Session 2.   
 
C.3.5. Randomization 
Session 1 will utilize a 2 x 2 within-subject design. Session 2 participants will be randomly assigned to 
either the extinction or control condition after stratifying by sex and cigarettes per day (cut point = 10). Block 
size will be 8 given the target sample size of 64 per condition (see below) and the 4 stratification cells.   
 
C.3.6. Lab-based sessions: Descriptions of AR and In Vivo Cues  
C.3.6.a. AR Cues. All AR cues will be presented via a smartphone provided to participants during the 
lab session. Six AR smoking cues and six AR neutral cues will be presented to participants. Although these 
cues can be presented individually (e.g., a single cigarette), we will have the ability to combine cues (e.g., 
cigarette in an ashtray). Further, movement of components of the cues (e.g., smoke coming up out of a 
cigarette, ash falling) will also be utilized whenever possible. Finally, participants will be encouraged to move 
around the stimuli to interact with the cue, as AR allows for the individual to see a stimulus from various 
angles. Urge to smoke and reality/co-existence questions will appear on the smartphone screen with response 
options. The AR app will allow for experimenters to select the order of the cues, as described below. 
C.3.6.b. In vivo Cues. Presentation of in vivo cues will generally follow procedures used in the existing 
cue-reactivity literature (e.g., Wray et al. 2011; LaRowe et al., 2007). To match the AR cues for complexity and 
overall content, we will use 6 smoking and 6 neutral cues with matching content (e.g., if we use a pink eraser 
as a neutral cue for AR, a similar eraser will be used as an in vivo cue). Cues will be placed under numbered, 
opaque covers, and participants will be instructed when to uncover and re-cover each one during the trials. To 
control for assessment modality across Stimulus Type (AR vs in vivo), participants will answer urge questions 
via a smartphone app, similar to the AR app. Participants will also be encouraged to interact with the in vivo 
cues in a similar manner to the AR cues (e.g., walk around the cues to view from different distances and 
angles).  
 
C.3.7. Session 1: Cue-reactivity  
 
Session 1 will utilize a within-subjects design, so all participants will receive all cue presentations (see 
Figure 2 above). Because it would be difficult for participants to switch repeatedly between AR and in vivo 
cues, Stimulus Type (AR vs in vivo) will be presented in counterbalanced blocks. Within each block, we will 
randomize the presentation of smoking vs neutral cues. Figure 3 is a visual depiction of the stimulus-
presentation strategy. Each cue will be presented for 15 seconds. Participants will complete the urge question 
at baseline and will then complete the urge question and reality/co-existence questions (for AR only) 
immediately following each cue presentation. To reduce carryover effects, each stimulus presentation will be 

 
 
Page 11 
 
 
 
separated by 1 minute, during which participants will engage 
in a neutral activity (e.g., solving easy word search puzzles). 
The general procedures are similar to those commonly found 
within the literature (e.g., Carpenter et al., 2009; LaRowe et 
al., 2007; Wray et al., 2011). In sum, participants will complete 
baseline urge questions, view a cue for 15 seconds, complete 
urge and/or reality/co-existence questions, complete the word 
search for 1 minute, view another cue, and so on, until all 24 
cues have been presented. The cue-reactivity procedures 
should last under 1 hour.    
   
C.3.8. Session 2: Extinction  
The extinction session will use only the AR cues (smoking and neutral). As noted above, participants 
will be randomized to either the extinction or control group. Twenty-eight trials of cues will be presented for 
each group. Both groups will receive the same neutral cue in Trial 1 (to establish baseline urge) and the same 
smoking cue in Trial 2 (for pre-test cue-reactivity). Then, the extinction group will receive smoking cues for 
trials 3-26, whereas the control group will receive neutral cues. Both groups will receive matched smoking cues 
for the second to last trial (27) followed by matched neutral cues for the final trial (28), for post-test cue-
reactivity. Each cue will be presented for 1 minute and will be shown 4 times in trials 3-26. Figure 4 shows a 
visual representation of cue presentation for each group. With respect to the order of the cues, we will create 4 
quasi-random orders for each condition (each comprising 4 blocks of 6 images, with no image repeated twice 
over a series of three images), which will be varied across participants. We derived these procedures from the 
existing cue-exposure literature (e.g., 
Carpenter et al., 2009; Collins et al., 2011; 
Conklin & Tiffany, 2002; Kamboj et al., 
2012; LaRowe et al., 2007; Vinci et al., 
2012), and although no specific guidelines 
exist, these studies typically exposed 
participants to cues for up to 60 minutes 
total (54 minutes in the current study); 
utilize one to nine cues (6 smoking and 6 
neutral in the current study); and utilize 
various amounts of time for exposure to 
each individual cue, often ranging from 10 
seconds to 5 minutes (1 minute in the 
current study). For this study, we wanted 
each cue to be presented multiple times to 
allow sufficient time and exposure for extinction to occur, which ultimately resulted in 28 trials.  
Following each cue, participants will complete the single-item measure of urge (primary outcome). 
Following the final trial (28) for both groups, participants will be presented with one of their own cigarettes and 
asked to take at least one puff. Latency to smoke will later be determined (using time stamps on the video-
recording) and used as a secondary outcome as a behavioral measure of smoking motivation (e.g., Ditre et al., 
2010; Kovacs et al., 2014; Shiffman et al., 2013; Stevenson et al., 2017). TRIP is equipped with 8 “negative 
pressure” rooms, compliant with state law regarding indoor smoking for research purposes that allow for the 
efficient removal of smoke between participants. Session 2 is expected to last 1.25 hours for pre-Covid-19 
participants, and 1.5 to 2.5 hours for post-Covid-19 participants.   
 
 
C.3.9. Compensation and Retention 
Pre-Covid-19 participants will be compensated for Session 1 ($40) and Session 2 ($50). Post-Covid-19 
participants will be compensated $60 for Session 2. To aid in retention, the following procedures will take 
place: reminder phone calls prior to all study visits, flexible scheduling for sessions, reminder cards, requiring a 
functioning phone number and home address to contact participants by phone or mail as needed, and 
obtaining the name, address, and phone number of at least 2 collaterals (i.e., relatives, friends) who can 
provide contact information for the participants, should we be unable to reach them during the study. 

 
 
Page 12 
 
 
 
 
C.3.10. Additional Measures 
Aside from those already described, the following measures will be administered at Session 1 for pre-
Covid-19 participants and at Session 2 for post-Covid-19 participants . 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). Fagerström Test for Nicotine Dependence (FTND; Heatherton 
et al., 1991) is a widely used measure of nicotine dependence. Wisconsin Smoking of Withdrawal Scale 
(WSWS; Welsch et al., 1999) is a well-validated measure used to assess various facets of nicotine withdrawal. 
Positive and Negative Affect Schedule (PANAS; Watson et al., 1988) is a self-report measure that assesses an 
individual’s positive and negative affect at a given point in time. We will also administer 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 a participant’s smoking is driven 
by cue-reactivity. Therefore, we will include the full subscale from the original WISDM-68 (Piper et al., 2014), 
due to its superior psychometrics (Smith et al., 2010). Aside from their descriptive value, these additional 
measures will allow for testing moderators of cue-reactivity and extinction.  
 
Summary of Changes Pre and Post-Covid-19 
 
Pre-Covid-19  
 Post-Covid-19  
N 
17 
128 
Session(s) 
Session 1 and Session 2 
Session 2 only 
Consent and Survey administration 
(demographics, smoking history, etc.) 
 
Session 1 
 
Session 2 
Length of Session(s) 
1.25 hours 
1.5-2.5 hours 
Compensation 
$40 Session 1; $50 Session 2 
$60 
 
 
C.3.11. Data Management and Statistical Considerations 
See Statistical Design and Power attachment, as per new instructions for Clinical Trial applications. 
 
C.4. Aim 3: Initial Translation into Intervention-Ready App 
C.4.1. Project Overview 
Aim 3 will consist of the beginning phases of Stage II intervention development by (1) refining the app 
for intervention (rather than laboratory) use, (2) pilot testing the app for utility and acceptability in the smokers’ 
natural environment, and (3) examining proxies of behavior change for quitting smoking and smoking status. 
Although we will use the same general components of the app developed for Aim 2, we anticipate that 
improvements to the app will be necessary to enhance its sophistication and usability by smokers attempting to 
quit in the field. When Aim 3 is complete, we expect the app and study team to be ready to conduct a fully-
powered, randomized controlled trial using AR as an adjuvant therapy for smoking cessation. Finally, given the 
rapid pace of technology, it is very possible that by the time of Aim 2 completion, AR headsets/eyewear will be 
available at a reasonable cost. (Note that AR contact lenses are also under development.) If so, we will also 
pilot test these AR systems for utility and acceptability as part of Aim 3.  
 
C.4.2. App Refinement 
 
We will work with our AR software developers to refine the app based on (1) experimenter observation 
and participant feedback from Aim 2; (2) advances in AR capabilities since Aim 1; and (3) additional 
requirements for field use. In terms of the last, the app will be adjusted to deliver the cues over a longer period 
of time, as described below, and modified to allow bidirectional data transfer between the device in the field 
and the lab. This will allow the research team to alter or correct stimulus presentation, if necessary, and to 
download data on app use and response variables at regular intervals. We will also be able to add additional 
AR smoking cues, including distal cues (e.g., alcohol, coffee), which have been shown to be potent 
precipitators of smoking urge and relapse (e.g., Brandon et al., 1990; Conklin et al., 2008). Other user-friendly 
modifications will also be made (e.g., colors, fonts). Finally, versions of the app will be created for both IOS and 
Android operating systems, which will capture the vast majority of smokers.  
 

 
 
Page 13 
 
 
 
C.4.3. Participants and Recruitment 
We will recruit a small group of participants (N=50) using similar strategies and inclusion/exclusion 
criteria as Aim 1. However, additional inclusion 
criteria will be having a smartphone that they are 
willing to use during the study, and being 
motivated to quit smoking within the next month 
(i.e., “preparation” stage of the Transtheoretical 
Model; Prochaska et al., 1992). Because 
participation will be entirely remote, participants 
will not need to have a local address. If deemed 
eligible, they will be asked to provide verbal 
informed consent via telephone or video 
conferencing.   
 
 
C.4.4 Procedures 
After consenting, enrolled participants will 
be sent a link to complete an online baseline 
survey.  Once the completed baseline survey has been returned, the participants will be sent a code or 
instructions to download the free app. They will also be sent a user/training manual for the AR app use. Given 
that all participants will be motivated to quit smoking, basic cessation assistance will be provided, including 
Tobacco Free Florida’s Quit Tips smoking cessation manual and the FL state tobacco quitline phone number 
and website. A few days after the app and quitting materials are sent, research staff will call participants to 
explain the materials and answer questions. Participants will be instructed to use the AR app over the next 7 
days in locations and situations where they smoke (e.g., home, bar; see Figure 5), with the goal of at least 5 
uses per day. Usage and rating data will be collected in real-time. After the 7-day app use period, participants 
will be sent a link to an online follow-up survey that includes feedback on the app, smoking within the prior 7 
days and smoking cessation motivation. Within approximately 7-14 days of completing the 7 day app use, 
participants will complete a brief telephone interview with study staff on their perceptions of the app as a 
potential cessation adjuvant and cigarette use over the course of the study. Participants will be compensated 
with electronic gift cards with value totaling up to $80. Participants will receive $10 for each of the completed 
baseline and follow-up surveys and the follow up phone interview. Participants can also receive a bonus of $5 
for each online survey completed within 24 hours of being sent the link and a bonus of $5 if the follow up 
phone interview is completed within 10 days of completing the app use period. Participants will also receive $5 
for each day that they use the AR app at least one time (for up to $35). Participants must use the app on at 
least four occasions to be eligible for the follow-up survey and interview.    
 
 
C.4.5. Measures 
To assess usability and acceptability, we will use both an established questionnaire, as well as 
questions developed for this study. Questions developed for this Aim are derived from Dr. Vinci’s experience 
with implementing mHealth procedures among smokers, as well as the existing literature on usability testing 
(e.g., Harrison et al., 2013; Nayebi et al., 2012). We will also track how often participants use the app over the 
course of the week. The System Usability Scale measures participants’ perception of usability and learnability 
(Brooke et al., 1996; Lewis & Sauro, 2009). To assess other components of usability and acceptability, we will 
develop a questionnaire with Likert scale items to capture the following areas: reality/co-existence (same items 
as in Aims 1 and 2), usefulness (Would this app appeal to you if you were currently attempting to quit 
smoking?), ease of use (How easy/difficult did you find using the app?;), ease of learning (How many days did 
it take to get comfortable using the app?); and satisfaction (Would you recommend this app to a friend or family 
member to help them quit smoking?; Overall, how satisfied were you with the app?).  
Participants will also be asked questions by a trained interviewer on their perceptions of the phone app 
as a potential adjuvant for smoking cessation. Specifically, participants will be queried with open-ended 
questions such as, “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?; What features would you add to the app?; What was the easiest part of 
using the app?; What was the most difficult part of using the app?; What was the most interesting part of using 

 
 
Page 14 
 
 
 
the app?”. These interviews will be recorded, transcribed and coded for analysis (described in more detail in 
the Statistical Design attachment).  
Proxies of behavior change will be collected pre- and post-app testing, including motivation to quit and 
self-efficacy for quitting. For motivation, we will use the Contemplation Ladder, which provides a visual image 
of a 10-rung ladder for participants to indicate their current motivation to change their smoking behavior (Biener 
& Abrams, 1991). We will also use the short form of the Abstinence-Related Motivational Engagement scale 
(ARME; Simmons et al., 2010), which provides a more sensitive index of current motivation. For self-efficacy, 
we will use the short form of the Self-efficacy Scale–Smoking, which is a 9-item measure that determines an 
individual’s level of confidence for not smoking in positive/social situations, negative affect situations, and out 
of habit (Velicer et al., 1990).  
Data on smoking urges and smoking status will also be collected. Throughout testing, participants will 
rate their urge to smoke each time they use the app, which will allow us to observe any changes over time in 
this variable. Smoking status will be collected daily on the app and at follow-up, and abstinence will be defined 
as a self-report of no smoking in the past seven days (Perkins et al., 2013). Given the limited-time frame of this 
grant, we will only have short-term abstinence outcomes (7 days post-quit).  
 
C.4.6. Statistical Considerations 
See Statistical Design and Power attachment, as per new instructions for Clinical Trial applications. 
 
C.5. Design Considerations  
 
Choice of Comparison Stimuli Type for Aim 2, Session 1. Laboratory studies have historically used a 
variety of modalities for presenting cues to participants including in vivo, imaginal, video, photos, and audio 
(Conklin & Perkins, 2002). For this proof-of-concept study, we chose to compare the AR cues to in vivo cues 
for two reasons. First, in vivo cues are one of the most commonly used cues in the cue-reactivity literature 
(Conklin & Perkins, 2002). Second, using in vivo smoking cues sets the bar high for the AR comparison group, 
as they are the cues that smokers would typically see in their natural environment. Indeed, all other cue 
modalities are attempts at approximating in vivo cues. Thus, we believe comparing AR to real-world stimuli is 
ideal. Evidence that AR cues produce similar reactivity as do in vivo cues will provide strong support for this 
program of research.  
 
Temporality of Session 1 (cue-reactivity test) and Session 2 (extinction test). We considered two 
options for these two critical proof-of-concept tests of AR stimuli. We could have conducted these as two 
separate, sequential studies, using different participants, with the second study dependent on findings from the 
first. The primary advantage would be the possibility of making major revisions to the AR app based on 
findings from the first study prior to conducting the second. However, sequential studies would have required a 
much longer time frame and the recruitment of many more participants, which presents challenges with respect 
to this grant mechanism. Therefore, we opted to conduct the two tests simultaneously for each participant (as 
two sessions), using the same research participants. This decision was bolstered by our high degree of 
confidence based on the existing cue-reactivity literature that AR stimuli will produce significant cue-reactivity. 
Despite this confidence, we believe it is necessary in a systematic line of research to subject this confidence to 
an empirical, documented test. Moreover, using the same participant sample allows us to use data collected in 
Session 1 (e.g., cue-reactivity, reality/in-presence) as moderator variables in Session 2.   
Three-Hour Period of Abstinence. For both studies in Aim 2, we will ask participants to refrain from 
smoking for 3 hours prior to the session and will rely on self-report of no smoking to gauge compliance, along 
with “bogus-pipeline” collection of breath CO. These decisions were made for multiple reasons. First, because 
participants will have a range of smoking dependence, there are no valid CO cut-points to verify abstinence 
within the past 3 hours. We considered adding an initial session to collect baseline CO, but this would entail 
participants attending 3 sessions, which would increase attrition. Second, we could have asked for overnight 
abstinence and confirmed abstinence with CO the following day. However, again, due to the variability in 
smoking dependence, CO would not be accurately captured among light smokers. Compliance with overnight 
abstinence would likely be lower, and we would have less flexibility for scheduling the experimental sessions. 
Further, the bogus pipeline of CO should further increase compliance. Third, cue-reactivity is often elevated 
during acute deprivation, and 3 hours of abstinence should demonstrate substantial increases in 
craving/withdrawal (Hendricks et al., 2006).  

 
 
Page 15 
 
 
 
 
Modality for AR presentation. The current study will evaluate cue-reactivity and extinction via a 
smartphone. We also considered using headgear/eyewear (i.e., AR glasses) as another modality. On the one 
hand, eyewear should allow the participant to be more immersed in his/her environment with greater co-
existence. However, at this time AR eyewear is extremely expensive and less scalable than if we present AR 
via smartphone. As such, we decided to begin this line of research using a smartphone, with the intention of 
testing eyewear in the future, as the technology advances and the price drops.  
 
Go vs No-Go Thresholds and Alternative Strategies. Aims 1 and 3 are developmental in nature and 
already have a built-in iterative process that allows us to incorporate feedback from participants and address 
potential issues as they arise. It is possible that Aim 2 might not result in the hypothesized cue reactivity and 
extinction, limiting our ability to advance to Aim 3. Although we do not anticipate this occurring due to the 
strong theoretical and empirical foundation of Aim 2, if this does happen, we will explore potential reasons for 
the unexpected outcomes (e.g., number of trials, reality/co-existence outcomes), including examination of the 
potential moderator variables. Based on those findings, we will modify the protocol (e.g., increase number of 
trials) and retest the key elements on new participants. Although we believe this is unlikely, if necessary we 
would defer Aim 3 in favor of resolving problems with Aim 2.    
  
C.6. Timeline 
 
Please see Study Timeline attachment, as per new instructions for Clinical Trial applications. 
 
C.7. Implications and Future Directions 
 
The primary goal of this study is to validate AR as an effective way to present smoking stimuli to elicit 
cue-reactivity and subsequent extinction. If successful, these findings have immediate implications for the use 
of cue-exposure as an adjunct to smoking cessation treatment. Indeed, AR has the potential to solve the 
extinction generalizability challenge presented by the renewal effect, which has limited the potential of cue-
exposure treatments to date. Next steps would include taking our extinction paradigm into the real-world and 
conducting a larger, randomized controlled trial testing the efficacy of adding AR cue-exposure to an existing 
evidence-based smoking cessation program. Although the current study examines smoking stimuli (i.e., 
proximal cues), future research can also evaluate the role of AR for extinction of urge provoked by distal cues 
(e.g., alcohol, coffee; Conklin et al., 2008), which are also potent precipitants of smoking relapse. Beyond cue-
exposure treatment, AR stimuli could be incorporated into other intervention protocols (e.g., coping skills 
training; mindfulness). This research could also inform the development of treatments for other mental health 
conditions that utilize extinction as part of treatment (e.g., other substance use disorders, social anxiety, 
PTSD). Exploring potential moderators (e.g., sex, nicotine dependence, initial cue-reactivity) will also allow us 
the ability to determine for whom this type of treatment may be most efficacious, consistent with a personalized 
medicine approach for treatment.  
 
 
C.8. Participant Confidentiality 
In the event, and only in the event, that study staff must work from home due to mandated orders (e.g., 
COVID-19 stay home orders), documents that contain protected health information may be temporarily kept at 
a personal residence for data entry and analysis. Documents will be secured in such a manner that they will be 
protected from being accessed by other individuals and pets who live in or visit the home. Any remote 
meetings (e.g., zoom meeting) or conference calls where protected health information may be discussed will 
be performed in a location that does not have listening devices (e.g., amazon echo) and are not likely to be 
overheard by those who live in or visit the personal residence. 
 
REFERENCES 
 
Adams, J., Parkinson, L., Sanson-Fisher, R. W., & Walsh, R. A. (2008). Enhancing self-report of adolescent 
smoking: The effects of bogus pipeline and anonymity. Addictive Behaviors, 33(10), 1291-1296. 
Abroms, L. C., Ahuja, M., Kodl, Y., Thaweethai, L., Sims, J., Winichoff, J., & Windsor, R. A. (2012). Text2Quit: 
Results from a pilot test of a personalized, interactive mobile health smoking cessation program. 
Journal of Health Communications, 2, 44-53. 

 
 
Page 16 
 
 
 
Aguinis, H., Pierce, C. A., & Quigley, B. M. (1993).  Conditions under which a bogus pipeline procedure 
enhances the validity of self-reported cigarette smoking: a meta-analytic review. Journal of Applied 
Social Psychology, 23, 352-373. 
American Cancer Society (2014). Cancer Facts and Figures 2014. Atlanta: American Cancer Society. 
Azuma, R. T., Baillot, Y., Behringer, R., Feiner, S., Julier, S., & MacIntyre, B. (2001). Recent advances in 
Augmented Reality. IEEE Computer Graphics and Applications, 21, 34–47. 
Baker, T. B., Piper, M. E., McCarthy, M. E., Majeskie, M. R., & Fiore, M. C., (2004). Addiction motivation 
reformulated: An affective processing model of negative reinforcement. Psychological Review, 111, 33-
51. 
Baus, O., & Bouchard, S. (2014). Moving from virtual reality exposure-based therapy to augmented reality 
exposure-based therapy: A review. Frontiers in Human Neuroscience, 8, 1-15. 
Biener, L., & Abrams, D. B. (1991). The Contemplation Ladder: Validation of a measure of readiness to 
consider smoking cessation. Health Psychology, 10(5), 360-365.  
Bloom, E.L., Potts, G.F., Evans, D.E., & Drobes, D.J. (2013). Cue reactivity in smokers: An event-related 
potential study. International Journal of Psychophysiology, 90, 258-264. PMCID: PMC4282775 
Bordnick, P. S., Graap, K. M., Copp, H., Brooks, J., Ferrer, M., & Logue, B. (2004). Utilizing virtual reality to 
standardize nicotine craving research: A pilot study. Addictive Behaviors, 29(9), 1889-1894. 
Botella, C., Pérez-Ara, M. Á., Bretón-López, J., Quero, S., García-Palacios, A., & Baños, R. M. (2016). In vivo 
versus augmented reality exposure in the treatment of small animal phobia: A randomized controlled 
trial. PloS one, 11(2), 1-22. 
Bouton, M. E. (2000). A learning theory perspective on lapse, relapse, and the maintenance of behavior 
change. Health Psychology, 19(1S), 57-63. 
Bouton, M. E. (2002). Context, ambiguity, and unlearning: Sources of relapse after behavioral extinction. 
Biological Psychiatry, 52(10), 976-986. 
Brandon, T. H. (1994). Negative affect as motivation to smoke. Current Directions in Psychological Science, 3, 
33-37. 
Brandon, T. H. (2001). Behavioral tobacco cessation treatments: Yesterday’s news or tomorrow’s headlines? 
Journal of Clinical Oncology, 19, 64s-68s. 
Brandon, T. H., Drobes, D. J., Unrod, M., Heckman, B. W., Oliver, J. A., Roetzheim, R. C., …Small, B. J. 
(2011). Varenicline effect on craving, cue reactivity, and smoking reward. Psychopharmacology, 218, 
391-403. PMCID: PMC4667942 
Brandon, T. H., Collins, B. N., Juliano, L. M., & Lazev, A. B.  (2000). Preventing relapse among former 
smokers:  A comparison of minimal interventions via telephone and mail.  Journal of Consulting & 
Clinical Psychology, 68, 103-113. 
Brandon, T. H., Copeland, A. L., & Saper, Z. L. (1995a). Programmed therapeutic messages as a smoking 
treatment adjunct: Reducing the impact of negative affect. Health Psychology, 14(1), 41. 
Brandon, T. H., Piasecki, T., Quinn, E. P., & Baker, T. B. (1995b). Cue exposure treatment in nicotine 
dependence. In D. C. Drummond, S. Glautier, B. Remington, & S. Tiffany (Eds.), Addictive Behaviour: 
Cue exposure theory and practice (pp. 211-227). New York: Wiley.  
Brandon, T. H., Simmons, V. N., Meade, C. D., Quinn, G. P., Khoury, E. L., Sutton, S. K., &  Lee, J-H. (2012). 
Self-help booklets for preventing postpartum smoking relapse: A randomized trial.  American Journal of 
Public Health, 102, 2109-2115. PMCID: PMC3477952 
Brandon, T. H., Simmons, V. N., Sutton, S. K., Unrod, M., Harrell, P. T., Meade, C. D., Craig, B. M., Lee, J. H., 
& Meltzer, L. R. (2016).  Extended self-help for smoking cessation: A randomized controlled trial. 
American Journal of Preventive Medicine, 51, 54-62. PMCID: PMC4914420 
Brandon, T. H., Tiffany, S. T., Obremski, K. M., & Baker, T. B.  (1990). Postcessation cigarette use: The 
process of relapse.  Addictive Behaviors, 15, 105-114. 
Brandon, T. H., Unrod, M., Drobes, D. J., Sutton, S. K., Hawk, L. W., Simmons, V. N.,…Cahill, S. P. (in press). 
Facilitated extinction training to improve pharmacotherapy for smoking cessation: A pilot feasibility trial.  
Nicotine & Tobacco Research. 
Brandon, T. H., Vidrine, J. I., & Litvin, E. B. (2007). Relapse and relapse prevention. Annual Review of Clinical 
Psychology, 3, 257-284. 

 
 
Page 17 
 
 
 
Brandon, T. H., Zelman, D. C., & Baker, T. B.  (1987). Effects of maintenance sessions on smoking relapse: 
Delaying the inevitable?  Journal of Consulting and Clinical Psychology, 55, 780-782. 
Brewer, J. A., Mallik, S., Babuscio, T. A., Nich, C., Johnson, H. E., Deleone, C. M., ... & Carroll, K. M. (2011). 
Mindfulness training for smoking cessation: Results from a randomized controlled trial. Drug & Alcohol 
Dependence, 119(1), 72-80. 
Bricker, J. B., Bush, T., Zbikowski, S. M., Mercer, L. D., & Heffner, J. L. (2014). Randomized trial of telephone-
delivered acceptance and commitment therapy versus cognitive behavioral therapy for smoking 
cessation: A pilot study. Nicotine & Tobacco Research, 16(11), 1446-1454. 
Brooke, J. (1996). SUS: A “quick and dirty” usability scale. In: Jordan, P. W., Thomas, B., Weerdmeester, B. 
A., McClelland (eds.) Usability Evaluation in Industry, pp. 189-194. Taylor & Francis, London, UK. 
Carpenter, M. J., Saladin, M. E., DeSantis, S., Gray, K. M., LaRowe, S. D., & Upadhyaya, H. P. (2009). 
Laboratory-based, cue-elicited craving and cue reactivity as predictors of naturally occurring smoking 
behavior. Addictive Behaviors, 34(6-7), 536-541. 
Carter, B. L., & Tiffany, S. T. (1999). Meta‐analysis of cue‐reactivity in addiction research. Addiction, 94(3), 
327-340. 
Carter, B. L., & Tiffany, S. T. (2001). The cue-availability paradigm: The effects of cigarette availability on cue 
reactivity in smokers. Experimental and Clinical Psychology, 9, 183-190. 
Charmez, K. (2006). Conducting Grounded Theory: A Practical Guide through Qualitative Analysis. Sage 
Publications. 
Childress, A. R., McLellan, A. T., & O’Brien, C. P. (1986). Abstinent opiate abusers exhibit conditioned craving, 
conditioned withdrawal and reductions in both through extinction. British Journal of Addictions, 81, 655-
660. 
Choi, J. S., Park, S., Lee, J. Y., Jung, H. Y., Lee, H. W., Jin, C. H., & Kang, D. H. (2011). The effect of 
repeated virtual nicotine cue exposure therapy on the psychophysiological responses: A preliminary 
study. Psychiatry Investigation, 8(2), 155-160. 
Collins, B. N., & Brandon, T. H. (2002). Effects of extinction context and retrieval cues on alcohol cue reactivity 
among nonalcoholic drinkers. Journal of Consulting & Clinical Psychology, 70, 390-397. 
Conklin, C. A., Parzynski, C. S., Salkeld, R. P., Perkins, K. A., & Fonte, C. A. (2012). Cue reactivity as a 
predictor of successful abstinence initiation among adult smokers. Experimental and Clinical 
Psychopharmacology, 20(6), 473. 
Conklin, C. A., Perkins, K. A., Robin, N., McClernon, F. J., & Salkeld, R. P. (2010). Bringing the real world into 
the laboratory: personal smoking and nonsmoking environments. Drug and Alcohol Dependence, 
111(1), 58-63. 
Conklin, C. A., Robin, N., Perkins, K. A., Salkeld, R. P., & McClernon, F. J. (2008). Proximal versus distal cues 
to smoke: The effects of environments on smokers’ cue reactivity. Experimental and Clinical 
Psychopharmacology, 16, 207-214. 
Conklin, C. A., & Tiffany, S. T. (2002). Applying extinction research and theory to cue-exposure addiction 
treatments. Addiction, 97, 155-167. 
Correa, J. B., & Brandon, T. H. (2016). Exposure to in-vivo stimuli and attentional bias among female smokers. 
Experimental and Clinical Psychopharmacology, 24, 331-340. 
Corty, E., & McFall, R. M. (1984). Response prevention in the treatment of cigarette smoking. Addictive 
Behaviors, 9, 405-408. 
Davis, J. M., Goldberg, S. B., Anderson, M. C., Manley, A. R., Smith, S. S., & Baker, T. B. (2014). Randomized 
trial on mindfulness training for smokers targeted to a disadvantaged population. Substance Use & 
Misuse, 49(5), 571-585. 
Ditre, J. W., & Brandon, T. H. (2008). Pain as a motivator of smoking: Effects of pain induction on smoking 
urge and behavior. Journal of Abnormal Psychology, 117(2), 467-472. PMCID: PMC4391507  
Ditre, J. W., Heckman, B. W., Butts, E. A., & Brandon, T. H. (2010). Effects of expectancies and coping on 
pain-induced motivation to smoke. Journal of Abnormal Psychology, 119, 524-533. PMCID: 
PMC2917270 
Ditre, J. W., Oliver, J. A., Myrick, H., Henderson, S., Saladin, M. E., & Drobes, D. J. (2012). Effects of 
divalproex on smoking cue reactivity and cessation outcomes among smokers achieving initial 
abstinence. Experimental and Clinical Psychopharmacology, 20(4), 293-301. PMCID: PMC4421892 

 
 
Page 18 
 
 
 
Donny, E. C., Denlinger, R. L., Tidey, J. W., Koopmeiners, J. S., Benowitz, N. L., Vandrey, R. G., ... & Drobes, 
D. J. (2015). Randomized trial of reduced-nicotine standards for cigarettes. New England Journal of 
Medicine, 373(14), 1340-1349. 
Drobes, D.J.  (2002). Cue reactivity in alcohol and tobacco dependence.  Alcoholism: Clinical and 
Experimental Research, 26, 1928-1929. 
Drobes, D.J., Elibero, A., & Evans, D.E. (2006).  Attentional bias for smoking and affective stimuli: A Stroop 
task study.  Psychology of Addictive Behaviors, 20, 490-495. 
Drobes, D.J., Miller, E.J., Hillman, C.H., Bradley, M.M., Cuthbert, B.N., & Lang, P.J. (2001).  Food deprivation 
and emotional reactions to food cues: Implications for eating disorders.  Biological Psychology, 57, 153-
177. 
Drobes, D.J., & Tiffany, S.T. (1997). Induction of smoking urge through imaginal and in vivo procedures: 
Physiological and self-report manifestations. Journal of Abnormal Psychology, 106, 15-25. 
Drummond, D. C., & Glautier, S. (1994). A controlled trial of cue exposure treatment in alcohol dependence. 
Journal of Consulting and Clinical Psychology, 62, 809-817. 
Ertin, E., Stohs, N., Kumar, S., Raij, A., Al'Absi, M., & Shah, S. (2011). AutoSense: unobtrusively wearable 
sensor suite for inferring the onset, causality, and consequences of stress in the field. In Proceedings of 
the 9th ACM Conference on Embedded Networked Sensor Systems (pp. 274-287). ACM. 
Evans, D.E., Craig, C., Oliver, J.A., & Drobes, D.J. (2011).  The Smoking N-Back: A measure of biased cue 
processing at varying levels of cognitive load.  Nicotine & Tobacco Research, 13, 88-93. 
Florida County Health Rankings (2018). Adult Smoking. Retrieved from: 
http://www.countyhealthrankings.org/app/florida/2018/measure/factors/9/map  
Free, C., Knight, R., Robertson, S., Whittaker, R., Edwards, P., Zhou, … Roberts, I. (2011). Smoking cessation 
support delivered via mobile phone text messaging (txt2stop): A single-blind randomized trial. The 
Lancet, 378, 49-55. 
Garcia-Palacios, A., Botella, C., Hoffman, H., & Fabregat, S. (2007). Comparing acceptance and refusal rates 
of virtual reality exposure vs. in vivo exposure by patients with specific phobias. Cyberpsychology & 
Behavior, 10(5), 722-724. 
George, M.S., Anton, R.F., Bloomer, C., Teneback, C., Drobes, D.J., Lorberbaum, J.P., Nahas, Z., & Vincent, 
D.J.  (2001). Activation of prefrontal cortex and anterior thalamus in alcoholics on exposure to alcohol 
specific cues.  Archives of General Psychiatry, 58, 345-352. 
Giglioli, I. A., Pallavicini, F., Pedroli, E., Serino, S., & Riva, G. (2015). Augmented reality: A brand new 
challenge for the assessment and treatment of psychological disorders. Computational and 
Mathematical Methods in Medicine, 2015, 1-12. 
Gotestam, K. G., & Melin, L. (1983). An experimental study of covert extinction on smoking cessation. 
Addictive Behaviors, 8, 27-31. 
Harrison, R., Flood, D., & Duce, D. (2013). Usability of mobile applications: Literature review and rationale for a 
new usability model. Journal of Interaction Science, 1, 1-16.  
Heatherton, T. F., Kozlowski, L. T., Frecker, R. C., & Fagerström, K. O. (1991). The Fagerström test for 
nicotine dependence: A revision of the Fagerström Tolerance Questionnaire. British Journal of 
Addictions, 86, 1119-1127. 
Heckman, B. W., Carpenter, M. J., Correa, J. B., Wray, J. M., Saladin, M. E., Froeliger, B., ... & Brandon, T. H. 
(2015). Effects of experimental negative affect manipulations on ad libitum smoking: A meta‐analysis. 
Addiction, 110(5), 751-760. PMCID: PMC4398635  
Heckman, B. W., MacQueen, D. A., Marquinez, N. S., MacKillop, J., Bickel, W. K., & Brandon, T. H. (2017). 
Self-control depletion and nicotine deprivation as precipitants of smoking cessation failure: A human 
laboratory model. Journal of Consulting and Clinical Psychology, 85, 381-396. PMCID: PMC5364802  
Hendricks, P.S., Ditre, J.W., Drobes, D.J., & Brandon, T.H. (2006). The early time course of smoking 
withdrawal effects.  Psychopharmacology, 187, 385-396.  
Hovsepian, K., al’Absi, M. Ertin, E., Kamarck, T., Nakajima, M., & Kumar, S. (2015). cStress: Towards a gold 
standard for continuous stress assessment in the mobile environment. Paper presented at the 
Proceedings of the International Conference on Ubiquitous Computing. 

 
 
Page 19 
 
 
 
Jamal, A., Phillips, E., Gentzke, A. S., Homa, D. M., Babb, S. D., King, B. A., & Neff, L. J. (2018). Current 
Cigarette Smoking Among Adults—United States, 2016. Morbidity and Mortality Weekly Report, 67(2), 
53-59. 
Juliano, L. M., & Brandon, T. H. (1998). Reactivity to instructed smoking availability and environmental cues: 
Evidence with urge and reaction time. Experimental and Clinical Psychopharmacology, 6, 45-53. 
Kamboj, S. K., Joye, A., Das, R. K., Gibson, A. J. W., Morgan, C. J. A., & Curran, H. V. (2012). Cue exposure 
and response prevention with heavy smokers: A laboratory-based randomized placebo-controlled trial 
examining the effects of D-cycloserine on cue-reactivity and attentional bias. Psychopharmacology, 
221, 273-284.  
Kashinsky, W., Collins, B. N., & Brandon, T. H. (1995). A telemetric device for measuring smoking topography. 
Behavior Research Methods, Instruments, & Computers, 27(3), 375-378. 
Kalivas, P. W., & Volkow, N. D. (2005). The neural basis of addiction: A pathology of motivation and choice. 
American Journal of Psychiatry, 162(8), 1403-1413. 
Kovacs, M. A., Correa, J. B., & Brandon, T. H. (2014). Smoking as alternative to eating among restrained 
eaters: Effect of food prime on young adult female smokers. Health Psychology, 33, 1174-1184. 
Kozlowski, L. T., Pillitteri, J. L., Sweeney, C. T., Whitfield, K. E., & Graham, J. W. (1996). Asking questions 
about urges or cravings for cigarettes. Psychology of Addictive Behaviors, 10, 248 –260.  
Lahiri, U., Bekele, E., Dohrmann, E., Warren, Z., & Sarkar, N. (2015). A physiologically informed virtual reality 
based social communication system for individuals with autism. Journal of Autism and Developmental 
Disorders, 45(4), 919-931.  
LaRowe, S. D., Saladin, M. E., Carpenter, M. J., & Upadhyaya, H. P. (2007). Reactivity to nicotine cues over 
repeated cue reactivity sessions. Addictive Behaviors, 32, 2888-2899. 
Lazev, A. B., Herzog, T. A., & Brandon, T. H.  (1999). Classical conditioning of environmental cues to cigarette 
smoking.  Experimental and Clinical Psychopharmacology, 7, 56-63. 
Lee, J., Lim, Y., Graham, S. J., Kim, G., Wiederhold, B. K., Wiederhold, M. D., Kim, I. Y., & Kim, S. I. (2004). 
Nicotine craving and cue exposure therapy by using virtual environments. CyberPsychology and 
Behavior, 7, 705-713. 
Lewis J.R., & Sauro J. (2009). The Factor Structure of the System Usability Scale. In: Kurosu M. (eds) Human 
Centered Design. Lecture Notes in Computer Science, vol 5619. Springer, Berlin, Heidelberg.  
Lowe, M., Green, L., Kurtz, S., Ashenberg, Z., & Fisher, E. (1980). Journal of Behavioral Medicine, 3, 357-372.  
Litvin, E. B., & Brandon, T. H. (2010). Testing the influence of external and internal cues on smoking motivation 
using a community sample. Experimental and Clinical Psychopharmacology, 18, 61-70. 
Lopez, E. N., Drobes, D. J., Thompson, J. K., & Brandon, T. (2008).  Effects of a body image challenge on 
smoking motivation among college females. Health Psychology, 27, S243-251. PMCID: PMC3989068 
Maples-Keller, J. L., Bunnell, B. E., Kim, S. J., & Rothbaum, B. O. (2017). The use of virtual reality technology 
in the treatment of anxiety and other psychiatric disorders. Harvard Review of Psychiatry, 25(3), 103-
113. 
McLellan, A. T., Childress, A. R., Ehrman, R., O’Brien, C. P., & Pashko, S. (1986). Extinguishing conditioned 
responses during opiate dependence treatment: Turning laboratory findings into clinical procedures. 
Journal of Substance Abuse Treatment, 3, 33-40. 
Meltzer, L. R., Simmons, V. N., Sutton, S. K., Drobes, D. J., Quinn, G. P., Meade, C. D., ... & Bullen, C. R. 
(2017). A randomized controlled trial of a smoking cessation self-help intervention for dual users of 
tobacco cigarettes and E-cigarettes: Intervention development and research design. Contemporary 
Clinical Trials, 60, 56-62. PMCID: PMC5559662  
Miranda, R., Rohsenow, D. J., Monti, P. M., Tidey, J., Ray, L. (2008). Effects of repeated days of smoking cue 
exposure on urge to smoke and physiological reactivity. Addictive Behaviors, 33, 347-353. 
Monti, P. M., & Rohsenow, D. J. (1999). Coping-skills training and cue-exposure therapy in the treatment of 
alcoholism. Alcohol Research & Health, 23(2), 107-107. 
Monti, P. M., Rohsenow, D. J., Rubonis, A. V., Niaura, R. S., Sirota, A. D., Colby, S. M., Goddard, P., & 
Abrams, D. B. (1993). Cue exposure with coping skills treatment for male alcoholics: A preliminary 
investigation. Journal of Consulting and Clinical Psychology, 61, 1011-1018. 

 
 
Page 20 
 
 
 
Mota, N. P., Schaumberg, K., Vinci, C., Sippel, L. M., Jackson, M., Schumacher, J. A., & Coffey, S. F. (2015). 
Imagery vividness ratings during exposure treatment for posttraumatic stress disorder as a predictor of 
treatment outcome. Behaviour Research and Therapy, 69, 22-28. PMCID: PMC4425990 
Myrick, H., Anton, R.F., Li, X., Henderson, S., Drobes, D.J., Voronin, K., & George, M.S.  (2004). Differential 
brain activity in alcoholics and social drinkers to alcohol cues:  Relationship to craving.  
Neuropsychopharmacology, 29, 393-402. 
Nayebi, F., Desharnais, J., & Abran, A. (2012). The state of the art of mobile application usability evaluation. 
Electrical and Computer Engineering (CCECE), 1-4.   
Niaura, R., Abrams, D. B., Shadel, W. G., Rohsenow, D. J., et al. (1999). Cue exposure treatment for smoking 
relapse prevention: A controlled trial. Addiction, 94, 685-695. 
Oliver, J.A., & Drobes, D.J. (2012). Visual search and attentional bias for smoking cues: The role of familiarity. 
Experimental and Clinical Psychopharmacology, 20, 489-496. 
Oliver, J.A., & Drobes, D.J. (2015). Cognitive manifestations of drinking-smoking associations: A cross-primed 
Stroop task. Drug and Alcohol Dependence, 147, 81-88. PMCID: PMC4297695 
Oliver, J.A., Jentink, K.G., Drobes, D.J., & Evans, D.E. (2016). Smokers exhibit biased neural processing of 
smoking and affective images. Health Psychology, 35, 866-869. PMCID: PMC5796754 
Palmer,  A. M. & Brandon, T. H.  (2018).  How do electronic cigarettes affect cravings to smoke or vape?  
Parsing the influences of nicotine and expectancies using the balanced-placebo design.  Journal of 
Consulting and Clinical Psychology, 86, 486-491. PMID: 29683704 
PASS 15 Power Analysis and Sample Size Software (2017). NCSS, LLC. Kaysville, Utah, USA, 
ncss.com/software/pass. 
Perkins, K. A., Karelitz, J. L., & Joa, N. C. (2013). Optimal carbon monoxide criteria to confirm 24-hr smoking 
abstinence. Nicotine and Tobacco Research, 15, 978-982. 
Pericot-Valverde, I., Germeroth, L. J., & Tiffany, S. T. (2015). The use of virtual reality in the production of cue-
specific craving for cigarettes: A meta-analysis. Nicotine & Tobacco Research, 18(5), 538-546. 
Pericot-Valverde, I., Secades-Villa, R., Gutiérrez-Maldonado, J., & García-Rodríguez, O. (2014). Effects of 
systematic cue exposure through virtual reality on cigarette craving. Nicotine & Tobacco Research, 
16(11), 1470-1477. 
Pew Research Center (2018).  Mobile Fact Sheet.  Available at: http://www.pewinternet.org/fact-sheet/mobile/  
Piper, M. E., Piasecki, T. M., Federman, E. B., Bolt, D. M., Smith, S. S., Fiore, M. C., & Baker, T. B. (2004). A 
multiple motives approach to tobacco dependence: The Wisconsin Inventory of Smoking Dependence 
Motives (WISDM-68). Journal of Consulting and Clinical Psychology, 72(2), 139-154. 
Powell, J. (1995). Conditioned responses to drug-related stimuli: Is context crucial? Addiction, 90, 1089-1095. 
Powers, M. B., & Emmelkamp, P. M. (2008). Virtual reality exposure therapy for anxiety disorders: A meta-
analysis. Journal of Anxiety Disorders, 22(3), 561-569. 
Preacher, K. J., & Hayes, A. F. (2008a). Asymptotic and resampling methods for estimating and comparing 
indirect effects. Behavior Research Methods, 40, 879-891.  
Preacher, K. J., & Hayes, A. F. (2008b). Contemporary approaches to assessing mediation in communication 
research. In A.F. Hayes, M.D. Slater, & L.B. Snyder (Eds.), The Sage sourcebook of advanced data 
analysis methods for communication research (pp. 13-54). Thousand Oaks, CA. Sage Publications.   
Prochaska, J. O., DiClemente, C. C. & Norcross, J. C. (1992). In search of how people change. Applications to 
addictive behavior. American Psychologist, 47, 1102–1104. 
Raw, M., & Russell, M. A. H. (1980). Rapid smoking, cue exposure and support in the modification of smoking. 
Behaviour Research and Therapy, 18, 363-372. 
Robinson, T. E., & Berridge, K. C. (1993). The neural basis of drug craving: An incentive-sensitization theory of 
addiction. Brain Research Reviews, 18(3), 247-291. 
Rodgers, A., Corbett, T., Bramley, D., Riddell, T., Wills, M., Lin, R. B., & Jones, M. (2005). Do u smoke after 
txt? Results of a randomized trial of smoking cessation using mobile phone text messaging. Tobacco 
Control, 15, 255-261. 
Rodriguez, B. I., Craske, M. G., Mineka, S., & Hladek, D. (1999). Context-specificity of relapse: Effects of 
therapist and environmental context on return of fear. Behavior Research Therapy, 37, 845-862. 

 
 
Page 21 
 
 
 
Rohsenow, D. J., Monti, P. M., Abrams, D. B., Rubonis, A. V., Niaura, R. S., Sirota, A. D., & Colby, S. M. 
(1992). Cue elicited urge to drink and salivation in alcoholics: Relationship to individual differences. 
Advances in Behavior Research & Therapy, 14, 195-210. 
Rounsaville, B. J., Carroll, K. M., & Onken, L. S. (2001). A stage model of behavioral therapies research: 
Getting started and moving on from stage I. Clinical Psychology: Science and Practice, 8(2), 133-142. 
Saladin, M.E., Drobes, D.J., Coffey, S.F., & Libet, J.M.  (2002). The human startle reflex and alcohol cue 
reactivity: Effects of early versus late abstinence.  Psychology of Addictive Behaviors, 16, 98-105. 
Sandelowski, M., & Barroso, J. (2003). Classifying the findings in qualitative studies. 
Qualitative Health Research, 13, 905-923. 
Shiffman, S., Dunbar, M., Kirchner, T., Li, X., Tindle, H., Anderson, S., & School, S., (2013). Smoker reactivity 
to cues: Effects on craving and on smoking behavior. Journal of Abnormal Behavior, 122, 264-280. 
Shiffman S, Dunbar MS, Li X, Scholl, S. M., Tindle, H. A., Anderson, S. J., & Ferguson, S. G.  (2014).   
Smoking patterns and stimulus control in intermittent and daily smokers. PLoS One, 9, e89911. 
Siegel, S. (1983). Classical conditioning, drug tolerance, and drug dependence. In Y. Isreal, F. B. Glaser, H. 
Kalant, R. E. Popham, W. Schmidt, & R. G. Smart (Eds.), Research advances in alcohol and drug 
problems, Vol. 7 (pp. 207-246). New York: Plenum. 
Simmons, V. N., Heckman, B. W., Ditre, J. W., & Brandon, T. H. (2010). A measure of smoking abstinence-
related motivational engagement: Development and initial validation.  Nicotine and Tobacco Research, 
12(4), 432-437. PMCID: PMC2847080. 
Sitharthan, T., Sitharthan, G., Hough, M. J., & Kavanagh, D. J. (1997). Cue exposure in moderation drinking: A 
comparison with cognitive–behavior therapy. Journal of Consulting and Clinical Psychology, 65(5), 878. 
Smith, A. (2015). U.S. Smartphone Use in 2015. Retrieved from http://www.pewinternet.org/2015/04/01/us-
smartphone-use-in-2015/ 
Smith, S. S., Piper, M. E., Bolt, D. M., Fiore, M. C., Wetter, D. W., Cinciripini, P. M., & Baker, T. B. (2010). 
Development of the brief Wisconsin inventory of smoking dependence motives. Nicotine & Tobacco 
Research, 12(5), 489-499. 
Stasiewicz, P. R., Brandon, T. H., & Bradizza, C. M.  (2007).  Effects of extinction context and retrieval cues on 
alcohol cue reactivity among alcohol dependent outpatients. Psychology of Addictive Behaviors, 21, 
244-248. 
Stevenson, J. G., Oliver, J. A., Hallyburton, M. B., Sweitzer, M. M., Conklin, C. A., & McClernon, F. J. (2017). 
Smoking environment cues reduce ability to resist smoking as measured by a delay to smoking task. 
Addictive Behaviors, 67, 49-52. 
Stewart, J., de Wit, H., & Eikelboom, R. (1984). Role of unconditioned and conditioned drug effects in the self 
administration of opiates and stimulants. Psychological Review, 91, 251-268. 
Thompson-Lake, D. G., Cooper, K. N., Mahoney III, J. J., Bordnick, P. S., Salas, R., Kosten, T. R., ... & De La 
Garza, R. (2014). Withdrawal symptoms and nicotine dependence severity predict virtual reality craving 
in cigarette-deprived smokers. Nicotine & Tobacco Research, 17(7), 796-802. 
Thomas, S., Drobes, D.J., & Dias, D.  (2005). Alcohol cue reactivity in alcohol dependent adolescents.  Journal 
of Studies on Alcohol, 66, 354-360. 
Tidey, J.W., Pacek, L.R., Koopmeiners, J.S., Vandrey, R.G., Nardone, N., Drobes, D.J., … Donny, E.C. (2017). 
Effects of 6-week use of reduced-nicotine cigarettes in smokers with and without elevated depressive 
symptoms. Nicotine & Tobacco Research, 19, 59-67. 
Tiffany, S. T. (1990). A cognitive model of drug urges and drug-use behavior: Role of automatic and 
nonautomatic processes. Psychological Review, 97(2), 147-168. 
Tiffany, S.T., & Drobes, D.J. (1990). Imagery and smoking urges: The manipulation of affective content. 
Addictive Behaviors, 15, 531-539. 
Tiffany, S.T., Drobes, D.J., & Cepeda-Benito, A. (1992). Contribution of associative and nonassociative 
processes to the development of morphine tolerance. Psychopharmacology, 109, 185-190. 
Tiffany, S.T., Maude-Griffin, P.M., & Drobes, D.J. (1990). The effect of inter-dose interval on the development 
of associative tolerance to morphine in the rat: A dose-response analysis. Behavioral Neuroscience, 
105, 49-61. 
U.S. Department of Health and Human Services (2014). The Health Consequences of Smoking—50 Years of 
Progress: A Report of the Surgeon General. Atlanta: U.S. Department of Health and Human Services, 

 
 
Page 22 
 
 
 
Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and 
Health Promotion, Office on Smoking and Health. 
Unrod, M., Drobes, D. J., Stasiewicz, P. R., Ditre, J. W., Heckman, B., Miller, R. R. …Brandon, T. H. (2014). 
Decline in cue-provoked craving during cue exposure therapy for smoking cessation. Nicotine and 
Tobacco Research, 16, 306-315. PMCID: PMC4830224 
Velicer, W.F., DiClemente, C.C., Rossi,J.S., & Prochaska, J.O. (1990). Relapse situations and self-efficacy: An 
integrative model. Addictive Behaviors, 15, 271-283. 
Vidrine, J. I., Spears, C. A., Heppner, W. L., Reitzel, L. R., Marcus, M. T., Cinciripini, P. M., ... & Tindle, H. A. 
(2016). Efficacy of mindfulness-based addiction treatment (MBAT) for smoking cessation and lapse 
recovery: A randomized clinical trial. Journal of Consulting and Clinical Psychology, 84(9), 824-838.  
Vinci, C., Copeland, A. L., & Carrigan, M. H. (2012). Exposure to negative affect cues and urge to smoke. 
Experimental and Clinical Psychopharmacology, 20(1), 47-55. 
Vinci. C., Haslam, A., Lam, C. Y., Kumar, S., & Wetter D. W. (in press). The use of ambulatory assessment in 
smoking cessation. Addictive Behaviors. 
Vinci, C., Kinsaul, J., Carrigan, M. H., & Copeland, A. L. (2015). The relationship between smoking motives 
and smoking urges experienced in response to a negative affect induction. Addictive Behaviors, 40, 96-
101. 
Vinci, C., Peltier, M. R., Shah, S., Kinsaul, J. Waldo, K., McVay, M. A., & Copeland, A. L. (2014). Effects of a 
brief mindfulness intervention on negative affect and urge to drink among college student drinkers. 
Behaviour Research and Therapy, 59, 82-93. 
Watson, D., Clark, L. A., Tellegen, A. (1988). Development and validation of brief measures of positive and 
negative affect: The PANAS Scales. Journal of Personality and Social Psychology, 54, 1063-1070. 
Welsch, S. K., Smith, S. S., Wetter, D. W., Jorenby, D. E., Fiore, M. C., & Baker, T. B. (1999). Development 
and validation of the Wisconsin Smoking Withdrawal Scale. Experimental and Clinical 
Psychopharmacology, 7, 354- 361. 
Wikler, A. (1965). Conditioning factors in opiate addiction and relapse. In D. I. Wilner, & G. G. Kassenbaum 
(Eds.), Narcotics (pp. 85-100). New York: Plenum. 
Witkiewitz, K., & Marlatt, G. A. (2004). Relapse prevention for alcohol and drug problems: That was Zen, this is 
Tao. American Psychologist, 59(4), 224-235. 
Wray, J. M., Godleski, S. A., & Tiffany, S. T. (2011). Cue-reactivity in the natural environment of cigarette 
smokers: the impact of photographic and in vivo smoking stimuli. Psychology of Addictive Behaviors, 
25(4), 733-737. 
Zelman, D. C., Brandon, T. H., Jorenby, D. E., & Baker, T. B. (1992).  Measures of affect and nicotine 
dependence predict differential response to smoking cessation. Journal of Consulting and Clinical 
Psychology, 60, 943-952. 
 
 
 
STATISTICAL DESIGN AND POWER 
Aim 1.  
For urge and reality/co-existence questions, descriptive analyses will be conducted to confirm that 
responses are in the expected direction (e.g., smoking stimuli result in higher urge reports than neutral stimuli; 
co-existence for smoking stimuli are rated ≥6 on the Likert scale). For questions asked in C.2.5.c., responses 
will be written down by an experimenter to be later analyzed. Using hand coding, the key themes/textual units 
related to the stimuli will be identified from these responses. Content analysis (via hand coding) will be 
conducted using an “intuitive” or “immersion/crystallizing” analysis plan, whereby the investigators review all 
the data and pull out those aspects most relevant to the research questions (Sandelowski & Barroso, 2003). 
Modifications to the stimuli will then be made based on these findings.  
 
Aim 2.  

 
 
Page 23 
 
 
 
The 2x2 within-subject design for Session 1 will provide measures of urge to smoke and reality/co-
existence (for AR only) for analysis. Smoking cue-reactivity will be computed for both the AR and the in vivo 
conditions by subtracting the average urge to smoke (1-10 scale) following smoking cues from the average 
urge to smoke following neutral cues; higher scores represent greater smoking cue-reactivity. Based on the 
existing literature (Carter & Tiffany, 1999), we expect in vivo smoking cue-reactivity equivalent to a Cohen’s d = 
1.0, a very large effect. In terms of urge to smoke, this approximates an average difference score of 2.0 units 
with a standard deviation of 2.0.  
Support for the effectiveness of AR to generate smoking cue-reactivity will be evaluated in two ways. 
First, average AR smoking cue-reactivity will be tested against a minimal threshold of Cohen’s d = 0.5 (i.e., 
expected 1 unit on the urge to smoke scale) using a single-sample t-test. Second, the equivalence of the AR 
and in vivo stimuli toward generating smoking cue-reactivity will be evaluated using two one-sided tests 
(TOST) for equivalence.  
The 142 participants completing Session 1 will be randomly assigned to the extinction or control 
condition in Session 2. With an estimated attrition of 10%, 128 are expected to complete Session 2. One 
measure of extinction will be the difference in urge to smoke at Trial 27 from urge to smoke at Trial 2, such that 
more positive values represent greater extinction of AR smoking cue-reactivity. Extinction based on this 
measure will be evaluated using a between-groups t-test. In addition, growth curve modeling will be used to 
compare the trajectories of urge to smoke using each self-report during the presentation of stimuli with the 
hypothesis of a greater negative slope for the extinction condition. Finally, the secondary measure of latency to 
smoke following the extinction trials will also be evaluated using a between groups t-test. 
Sample size was driven by the power required for Session 2. Power was estimated using PASS 15 
(2017). With 128 participants in Session 2 (64 per condition), alpha = .05, and a two-sided test, Power will ≥ 
.80 to detect a Cohen’s d ≥ .50 (a medium effect size) using the t-test. A medium effect size is consistent with 
most of the existing cue-exposure treatment literature on cravings to smoke despite variations in population, 
types of cues (e.g., visual, audio, in vivo), number of exposures, massed or spaced exposures and number of 
participants. Cohen’s ds = .20,  .45, .52, .75, 1.15 for studies with reported effect sizes or with effect sizes that 
could be easily calculated for smoking urges (Niaura et al, 1999; Corty and McFall, 1984; Lowe et al., 1980; 
Kamboj et al., 2012; Unrod et al, 2014). Although an effect size was not reported, Collins et al. (2011) found 
significant reductions in cravings following repeated cue exposure despite having a substantially smaller 
sample size (N = 42).  Studies that did not find an exposure effect were severely underpowered with Ns 
ranging from 7 to 32 (Gotestam & Melin, 1983; Raw & Russel, 1980; Lee et al 2004; Vinci et al, 2012; Miranda 
et al. 2008; and LaRowe et al. 2007). Growth curve modeling of urge to smoke will have greater power 
presuming reliable measurement. The 142 participants completing Session 1 will yield power > .95 to detect a 
small-medium (d =.35) AR smoking cue-reactivity effect that is greater than the minimal threshold (1 scale unit, 
d = 0.5). This N will also yield power > .80 using TOST with alpha = .05 to detect an average AR reactivity 
within 0.25 standard deviations of the average in vivo reactivity (expected 2 scale units, d = 1.0).  
 
 The target sample size is not adequate for fully-powered tests of moderators or mediators. 
Nevertheless, we will conduct exploratory analyses attempting to identify potential influences on or boundary 
conditions of AR smoking cue-reactivity and extinction. Key theory-based moderators are sex, nicotine 
dependence, and cue-reactivity (self-reported via the WISDM subscale). For Session 2, cue-reactivity and 
reality/co-existence measures from Session 1 also will be evaluated. Each moderator will be tested individually 
using the interaction term in a regression model (e.g., Cue Type x Sex in Session 1 and Group x Sex in 
Session 2). For example, if sex were a small-medium moderator of extinction in Session 2 (i.e., β ~ .2), the 
current sample size provides Power = .67 to detect an interaction of Group x Sex. The key theory-based 
mediators will be the reality/co-existence measures within the same session. Mediation will be tested using 
methods developed by Preacher and Hayes (e.g., 2008a, 2008b). 
 
Aim 3.  
For Aim 3, we will calculate descriptive statistics to examine a priori benchmarks regarding the usability 
and acceptability of the app. If any of these benchmarks are not achieved, the app will be adjusted accordingly 
and re-tested in additional field studies (beyond the present proposal). For motivation to quit, self-efficacy for 
quitting, smoking urge, and quit status, we will also derive descriptive outcomes. Given the small sample size, 
we will not be powered to detect significant changes over time. Nonetheless, we will examine these data for 

 
 
Page 24 
 
 
 
trends in the expected directions (e.g., increases in motivation and self-efficacy). Smoking status will be 
collected post-app testing to determine what percentage of the sample successfully quit smoking. A priori 
benchmarks for each measure are presented in Table 1. 
Qualitative analysis of interviews will be conducted. Interviews will be audio-recorded and verbatim 
transcripts created for content analysis (Charmez, 2006). Using hand coding, the goal will be to identify key 
themes/textual units related to the phone app. Content analysis will be conducted using an “intuitive” or 
“immersion/crystallizing” analysis plan, whereby the researcher reviews all data and pulls out those aspects 
most relevant to the research questions (Sandelowski & Barroso, 2003). The research team will identify key 
themes as they read through the interview transcripts. These findings will supplement quantitative data in order 
to identify potential changes/improvements for future iterations of the app.   
 
Item 
Scale 
A Priori Benchmark 
Usability and Acceptability Outcomes 
Daily use  
From real-time data 
≥ 80% of participants will use the app on ≥ 80% of days 
System Usability 
Scale 
Likert 
≥ 80% will “strongly agree” that the app is usable (i.e., 
ratings ≥ 4 on 5-point scale) 
Reality/co-
existence  
Likert 
≥ 75% will rate these variables as ≥ 8 on 10-point scale 
Usefulness 
Dichotomous (Y/N) 
≥ 75% will respond “yes” 
Ease of Use 
Likert 
≥ 75% will indicate the app was easy to use 
Ease of Learning  
Days to learn (ratio); 
Dichotomous (Y/N) 
≥ 75% will indicate 1-3 days for learning to use the app;  
≤ 30% will contact staff for additional help using the app 
Satisfaction  
Dichotomous (Y/N); 
Likert 
≥ 75% would recommend the app;  
≥ 75% will indicate being very satisfied 
Smoking Behavior 
Motivation to Quit 
Contemplation 
Ladder/ARME 
≥ 80% of the sample will increase on motivation to quit 
smoking  
Cessation Self-
Efficacy Scale 
Likert Scale 
≥ 80% of the sample will increase on self-efficacy for quitting  
Urge to Smoke 
Likert 
≥ 75% of the sample will have lower urges to smoke in the 
last 2 days of app testing, when compared to the first 2 days 
of app testing 
Smoking 
Abstinence  
Dichotomous (Y/N) 
None. Collected for descriptive purposes only.  
 Table 1. Benchmarks of Aim 3 Usability and Acceptability Testing.