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Addict Behav
. Author manuscript; available in PMC: 2022 Jan 1.
Published in final edited form as: Addict Behav. 2020 Aug 26;112:106616. doi:
10.1016/j.addbeh.2020.106616
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SMARTPHONE HEALTH APPS FOR TOBACCO CESSATION: A SYSTEMATIC REVIEW

Kar-Hai Chu


KAR-HAI CHU, PHD

aDepartment of Medicine, School of Medicine, University of Pittsburgh, 230 McKee
Place, Suite 600, Pittsburgh, PA 15213
Find articles by Kar-Hai Chu
a,*, Sara J Matheny


SARA J MATHENY, MS

aDepartment of Medicine, School of Medicine, University of Pittsburgh, 230 McKee
Place, Suite 600, Pittsburgh, PA 15213
Find articles by Sara J Matheny
a, César G Escobar-Viera


CÉSAR G ESCOBAR-VIERA, MD, PHD

aDepartment of Medicine, School of Medicine, University of Pittsburgh, 230 McKee
Place, Suite 600, Pittsburgh, PA 15213
Find articles by César G Escobar-Viera
a, Charles Wessel


CHARLES WESSEL, MLS

bUniversity of Pittsburgh, Health Sciences Library System, Pittsburgh, PA, 15213
Find articles by Charles Wessel
b, Anna E Notier


ANNA E NOTIER, MSW

cUniversity of Pittsburgh Medical Center, Pittsburgh, PA, 15213
Find articles by Anna E Notier
c, Esa M Davis


ESA M DAVIS, MD, MPH

aDepartment of Medicine, School of Medicine, University of Pittsburgh, 230 McKee
Place, Suite 600, Pittsburgh, PA 15213
cUniversity of Pittsburgh Medical Center, Pittsburgh, PA, 15213
Find articles by Esa M Davis
a,c
 * Author information
 * Article notes
 * Copyright and License information

aDepartment of Medicine, School of Medicine, University of Pittsburgh, 230 McKee
Place, Suite 600, Pittsburgh, PA 15213
bUniversity of Pittsburgh, Health Sciences Library System, Pittsburgh, PA, 15213
cUniversity of Pittsburgh Medical Center, Pittsburgh, PA, 15213
*

Corresponding author: Kar-Hai Chu, PhD, 230 McKee Place, Suite 600, Pittsburgh,
PA 15213, chuk@pitt.edu, 412-692-2578

Issue date 2021 Jan.

PMC Copyright notice
PMCID: PMC7572657  NIHMSID: NIHMS1623512  PMID: 32932102
The publisher's version of this article is available at Addict Behav


ABSTRACT


BACKGROUND

Given the low retention and lack of persistent support by traditional tobacco
cessation programs, evidence-based smartphone app-supported interventions can be
an important tobacco control component. The objective of this systematic review
was to identify and evaluate the types of studies that use smartphone apps for
interventions in tobacco cessation.


METHODS

We conducted a systematic review of PubMed (1946–2019), EMBASE (1974–2019), and
PsycINFO (1806–2019) databases with keywords related to smartphone-supported
tobacco cessation. Included articles were required to meet 3 baseline screening
criteria: 1) be written in English, 2) include an abstract, and 3) be a full,
peer-reviewed manuscript. The criteria for the second level of review were: 1)
primary outcome of tobacco cessation, 2) intervention study, and 3) smartphone
app as primary focus of study.


RESULTS

Of 1973 eligible manuscripts, 18 met inclusion criteria. Most studies (n = 17)
recruited adult participants (18+ years); one included teens (16+ years).
Tobacco cessation was usually self-reported (n = 11), compared to biochemical
verification (n = 3) or both (n = 4). There were 11 randomized controlled
trials, 4 of which reported statistically significant results, and 7 single-arm
trials that reported a mean abstinence rate of 33.9%.


DISCUSSION

The majority of studies that use tobacco cessation apps as an intervention
delivery modality are mostly at the pilot/feasibility stage. The growing field
has resulted in studies that varied in methodologies, study design, and
inclusion criteria. More consistency in intervention components and larger
randomized controlled trials are needed for tobacco cessation smartphone apps.

Keywords: Tobacco, Apps, Cessation, Smoking, Smartphone, Systematic literature
review


1. INTRODUCTION

Tobacco continues to be the leading cause of preventable death in the United
States (WHO, 2017). Within 10 years, smokers who successfully quit reduce their
risk of death from lung cancer by half (Yamaguchi et al., 1991); within 15
years, risk of coronary heart disease is equal to that of a nonsmoker (World
Health Organization, 2016). Although 30 million smokers contact a healthcare
provider each year (Fiore et al., 2008), and more than half attempt to quit,
most are not successful (Shiffman et al., 2008). After one year, the success
rates of cessation were 5% when patients tried to quit without support, 16% with
behavioral intervention, and 24% when the patient used pharmacological treatment
in addition to behavioral support (Castaldelli-Maia et al., 2013; Khan et al.,
2012; Laniado-Laborín, 2010). Limitations in traditional cessation interventions
include low utilization (Fiore et al., 2008; Soon et al., 2014) and intermittent
interactions between treatment experts (e.g., cessation counselors) and smokers
(Martinez et al., 2018). It is also difficult to provide real-time responses to
smoking urges and related cues, which are known to be important factors in
relapse (Fiore et al, 2008).

Smartphones are ubiquitous in the United States with 77% of Americans owning a
smartphone (Pew Research Center, 2018). Providing real-time help and information
to patients can be achieved with the use of mobile health (mHealth) smartphone
apps. While short message service (SMS), or text-messages, successfully provide
support for cessation (Scott-Sheldon et al., 2016), apps afford the
functionality (e.g., visual aids, interactive components, advanced multimedia)
to develop more complex interventions based on health behavioral theories that
might not be implementable in an SMS-only platform. Given that smartphone apps
are able to reach and engage a larger population of individuals, it is necessary
to identify evidence-based tobacco cessation apps, understand the theories that
support the intervention components they deliver, and collect information on
their effectiveness.

Therefore, the objective of this systematic review was to identify and evaluate
the current evidence on smartphone apps used as an intervention for tobacco
cessation. The most important factors considered were a primary outcome of
tobacco cessation and the explicit use of a smartphone app as the source of
intervention. Previous related reviews were content analyses that focused on
apps that were commercially available in Android, Apple, Blackberry, and Windows
online stores (Abroms et al., 2011; Hoeppner et al., 2016; Ubhi et al., 2016);
they reported on the lack of scientific evidence for most commercially available
apps, and all tobacco cessation outcomes were self-reported. Those reviews rated
the apps based on strength of evidence, which showed that only one commercially
available app was supported by high quality evidence. Rather than identify
potential apps through commercial venues (e.g., Apple app store), this review
searched exclusively through several scientific databases. Previous reviews are
also out-of-date; given the speed of changes in mobile technologies, there have
been many new advances, developed by both industry and academic institutions,
since 2016. This review focused on all smartphone apps reported in the
scientific literature, including those outside commercial app stores, and
include more information on the effectiveness of each study.


2. METHODS


2.1. DESIGN

The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)
guidelines were followed to report the results of this systematic review (see
Appendix A). The protocol was registered with PROSPERO International Prospective
Register of systematic reviews (CRD42018096567).


2.2. DATA SOURCES

Studies were located from PubMed/MEDLINE (1946–2019), EMBASE.com (1974–2019) and
PsycINFO, Ovid® (1806–2019) using formulated search strategies developed by a
medical librarian. Search strategies used appropriate and broad controlled
vocabulary, phrases and keywords representative for the concepts of smartphones
(e.g., smartphone, cellphone, iPhone), smoking (e.g., smoking, tobacco,
nicotine), and smoking cessation (e.g., abstinence, quitting, intervention).
Searches have no specific restrictions and include all languages and publication
years (see Appendix B for search strategy details). Downloading database
records, importing into EndNote, and de-duplicating records occurred on February
26, 2019. The web-based software DistillerSR aided in screening and data
extraction.


2.3. ELIGIBILITY CRITERIA

This systematic review proceeded through two levels of eligibility screening.
First, articles were required to meet the following three baseline criteria: the
articles 1) were written in English, 2) included text in the abstract, and 3)
published as a full manuscript from a peer-reviewed journal. These requirements
disqualified any dissertations, abstracts without manuscripts, book chapters,
and other similar publications.

Articles then underwent a second level of review and had to meet the following
criteria: the study must 1) have had tobacco cessation as a primary outcome, 2)
was an intervention, and 3) primarily focused on a smartphone. For the purposes
of this review, an app-based intervention was defined as using a smartphone
application to support the user in stopping the use of tobacco. Studies that
only used tablets (e.g., iPads) or websites accessed by smartphones—techniques
that provide a different user experience (e.g., smartphone interventions might
support outdoor locations where smokers feel the urge to have a cigarette,
addressing a scenario that is not suitable for tablets)—were not considered.
Given the ubiquity of smartphones, this systematic review excluded tablet apps
in favor of smartphone apps that reach a more widespread audience.


2.4. ANALYSIS

Before conducting this review, 5 researchers independently reviewed a
preliminary group of 500 abstracts to establish a baseline understanding of the
screening criteria. One adjudication meeting was held per level of review to
better establish definitions and guidelines for including and excluding
articles. If coders could not agree, a final determination was made by the lead
author. Following adjudication, researchers began to independently review
articles from the final dataset. An assessment of bias was also conducted for
each study. Because there were a wide range of study designs as well as an
expectation of small or underpowered studies, we applied several metrics from
STROBE and CONSORT guidelines that could be considered for multiple types of
studies (e.g., single and multiple arms). The data assessed included: 1) setting
and location of study, 2) eligibility criteria, 3) recruitment source, 4) study
size justification, 5) baseline participant demographics, 6) discussion of loss
of participants, 7) discussion and justification on generalizability, and 8)
funding source. Inter-rater reliability (Cohen’s Kappa) for the basic data
extraction from two coders (CGEV and SJM) ranged from κ=0.78 to κ=1.0.


3. RESULTS

The searches retrieved 2835 database records containing 826 duplicate records,
leaving 1973 records to screen (duplicates can occur more than once). In the
initial screening, if the text was not written in English, did not have an
abstract, or was not a full peer-reviewed manuscript, the record was excluded (n
= 219). In the second screening, if tobacco cessation was not an outcome, the
study was not an intervention, or if a smartphone app was not used, the record
was excluded (n = 1733). Finally, after 21 manuscripts were selected for full
text review, two additional manuscripts were deemed ineligible because they did
not use an actual smartphone app (e.g., use of a smartphone to access a
website); a third was disqualified because it was a secondary analysis of an app
already included. A total of 18 manuscripts were included in the final review
(Figure 1). There were two studies that used a different version of a similar
app, but as the trial designs were also different, both studies were
independently considered. Almost every study (n = 17) recruited adult
participants (18+); 1 app included teens as young as 16 years old. Confirmation
of cessation was usually self-reported (n = 11), compared to biochemical
verification (n = 3) or both (n = 4).


FIGURE 1.



Open in a new tab

PRISMA Flow Diagram.

The length of study ranged from 1 to 12 months from enrollment to the final
survey or assessment (mean=4.18), participants from 11 to 28,112 (mean=1953,
median=235), and retention from 8% to 100% (mean=71.55%, median=81%).
Participant eligibility criteria was highly variable in the inclusion criteria
which comprised a mixture of smoking habits, location, smartphone ownership,
gender, health status, and literacy. The behavioral theories applied in each
study also showed heterogeneity, with 12 different theories or techniques
applied (Table 1). Study designs split between randomized 2-arm (n = 11) or
1-arm (n = 7) trials. In the 2-arm trials, most had a different control
condition: relapse training, counseling and medication, a different app, a
text-messaging intervention, a self-help guide, experience training, brief
advice, and a yoked condition (participants in the experimental arm were
compensated specifically for low carbon monoxide (CO) readings, while
participants in the control/yoked arm were compensated for any CO reading). In 3
of the 2-arm trials, the control arm was a reduced or downgraded version of the
app (Table 2). Primary outcomes of point prevalence abstinence (PPA) for each
study differed (e.g., 7-day PPA or 30-day PPA) based on study design as listed
in Table 2. In the trials with a comparison group, 4 reported significantly
higher rates of abstinence in the experimental app arm, 5 reported
non-significant higher abstinence than control, and 2 reported no difference. In
the 1-arm trials, the mean abstinence rate was 33.9%. In randomized-control
trials, the mean abstinence rate was 22.9% for the app group. In total, the 18
studies reported an average of 6.1 out of the 8 categories that were assessed
for bias (Table 3). The most common underreported categories were justification
of sample size (39% of studies reported) and discussing loss of participants
(39%).


TABLE 1.

Tobacco Cessation Smartphone App Studies, Overall Descriptions.

Author & Year App Name Theories or Techniques Phone Platforms Participants
Completed % Retained Smoking Status Location Other Conditions BinDhim 2018
Smartphone Smoking Cessation Application (SSC App) Ottawa decision support Apple
684 583 85% Daily smoker US, UK, Australia, Singapore Bricker 2014 SmartQuit
Acceptance and commitment therapy (ACT) Apple + Android 340 196 58% 5 cigs/day
>1 year willing to quit in next 30 days English speaking Bricker 2017 SmartQuit
2.0 Acceptance and commitment therapy (ACT) Apple + Android 99 84 85% 5 cigs/day
>1 year willing to quit in next 30 days Not using other cessation Buller 2014
REQ_mobile Quit Coach theoretical framework Android + Windows 102 68 67% Smokers
English proficient Businelle 2016 Smart-T EMA/tailored messages Android 61 59
97% 5 cigs/day current smokers willing to quit within 7 days 6th grade literacy,
clinic visit once/week for 6 months Gordon 2017 See me smoke-free Guided imagery
Android 151 73 48% Smoked in last 30 days US Female, English speaking Hassandra
2017 Physical Activity over Smoking (PhoS) Multiple Android 44 34 77% Regular
smokers (10+ years) motivated to quit No mental health conditions Hertzberg 2013
Mobile Contingency Management (mCM) Contingency management Apple + Android 22 22
100% 10 cigs/day >1 year, no other tobacco products PTSD, not pregnant or
schizophrenic Hicks 2017 Stay Quit Coach Contingency management Apple + Android
11 11 100% 10 cigs/day >1 year willing to quit US VA PTSD patients, speak/write
English Iacoviello 2017 Clickotine Motivational interviewing Apple 416 365 88% 5
cigs/day wants to quit US English speaking Ubhi 2015 SmokeFree28 (SF28) PRIME
theory Apple 1977 1170 59% Smoked at time of registration, set quit date Tombor
2019 SmokeFree Baby Behavioral Change Theories Apple + Android 565 247 44%
Weekly smoker Pregnant, 18+, interested in cessation and set a quit date in the
app Masaki 2019 CureApp Smoking Cessation (CASC) Cessation Counseling Apple +
Android 56 51 91% Nicotine dependence patients Marler 2019 Pivot Multiple Apple
+ Android 319 272 85% 5+ cigs/day current smokers 18–65 years old Baskersville
2018 Crush the Crave Contingency Management Apple + Android 1599 725 45% Smoker
considering quitting in the next 30 days Canada Crane 2019 Smoke Free Behavioral
change techniques Apple + Android 28112 2114 8% Current smokers App users, 18+,
set one quit date Krishnan 2019 Coach2Quit Biomarker Feedback Android 102 89 87%
Daily smoker US Willing to set a quit date within 2 weeks Garrison 2020 Craving
to Quit Mindfulness Apple + Android 505 325 64% 5+ cigs/day current smokers
18–65, less than 3 months past-year abstinence, 8 or great on the contemplation
ladder and 4 or greater on the readiness to change questionnaire

Open in a new tab


TABLE 2.

Tobacco Cessation Smartphone App Studies, Study Designs, and Outcomes.

Author & Year Study Design Comparison or Control Length Abstinence Report
Primary Outcome Difference with Control Other Outcomes BinDhim 2018 Automated
double blind RCT Downgraded app (no motivational messages) 6 months Self-report
28.5% abstinent Significant difference (p<.001) Quit attempts, app feasibility
Bricker 2014 Double blind RCT pilot trial QuitGuide 2 months Self-report 13%
abstinent Significant difference (p<.001) Receptivity to app, smoking reduction
Bricker 2017 Single arm pilot NA 2 months Self-report 21% 7-day point-prevalence
abstinent (PPA) NA Receptivity to app, smoking reduction Buller 2014 Randomized
pre/post feasibility onQ text messaging 3 months Self-report 37% 7-day PPA
Significant difference (p=.04) App usage Businelle 2016 Nonrandomized
feasibility NA 12 weeks Biochemical 20% abstinent NA Acceptability Gordon 2017
Single arm pilot NA 90 days Self-report 47% 7-day PPA NA Weight, diet, exercise,
app satisfaction and acceptability Hassandra 2017 RCT feasibility Relapse
prevention training 6 months Both 36% 7-day PPA No significant difference App
engagement, physical activity Hertzbeg 2013 Randomized feasibility Yoked
condition 3 months Biochemical 50% abstinent No significant difference Hicks
2017 Two arm RCT feasibility Counseling and medication 6 months Both 40% 7-day
PPA 100% abstinent in Combined Contact 60% abstinent in QUIT4EVER Participant
satisfaction, app compliance Iacoviello 2017 Single arm feasibility NA 8 weeks
Self-report 45% 7-day PPA NA App engagement, medical monitoring of other health
events Ubhi 2015 Single arm observational prospective cohort NA 28 days
Self-report 19% 28-day PPA NA App usage Tombor 2019 RCT Different versions of
the app 6 weeks Self-report N/A No difference App engagement Masaki 2019
Single-arm N/A 52 weeks Both 58% continues abstinence rate at 52 weeks NA
Withdrawal and craving symptoms Marler 2019 Open-label single arm N/A 18.5 weeks
Both 27.6% 30-day PPA NA Module completion rate, increased confidence to quit
Baskersville 2018 Parallel double-blind RCT Evidence informed self-help guide
OnRQ (usual care) 6 months Self-report 14.4% 30-day PPA at six months No
significant difference Frequency of use Crane 2019 Two-arm exploratory RCT
Reduced version (no daily ‘missions’) 3 months Self-report 19.3% abstinent
Significant difference (p<.001) CPD, quit date designation Krishnan 2019 RCT
Brief advice only 30 days Self-report 3% 30 day PPA No difference Carbon
monoxide levels, CPD Garrison 2020 RCT Experience training 6 months Biochemical
11.1% 7 day PPA No significant difference Craving strength and frequency,
mindfulness

Open in a new tab


TABLE 3.

Assessment of bias, including: aselection bias, breporting bias, cattrition
bias.

Author & Year Study settinga Eligibility criteriaa Source of recruitmenta
Justify sample size Baseline data in resultsb Discuss loss of participantsc
Discuss generalizability Funding source BinDhim 2018 ✓ ✓ ✓ ✓ ✓ ✓ ✓ Bricker 2014
✓ ✓ ✓ ✓ ✓ ✓ ✓ Bricker 2017 ✓ ✓ ✓ ✓ ✓ Buller 2014 ✓ ✓ ✓ ✓ ✓ ✓ ✓ Businelle 2016 ✓
✓ ✓ ✓ ✓ ✓ Gordon 2017 ✓ ✓ ✓ ✓ ✓ ✓ Hassandra 2017 ✓ ✓ ✓ ✓ ✓ Hertzberg 2013 ✓ ✓ ✓
✓ Hicks 2017 ✓ ✓ ✓ Iacoviello 2017 ✓ ✓ ✓ ✓ ✓ ✓ Ubhi 2015 ✓ ✓ ✓ ✓ ✓ Tombor 2019 ✓
✓ ✓ ✓ ✓ ✓ ✓ Masaki 2019 ✓ ✓ ✓ ✓ ✓ ✓ Marler 2019 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ Baskersville
2018 ✓ ✓ ✓ ✓ ✓ ✓ ✓ Crane 2019 ✓ ✓ ✓ ✓ ✓ ✓ ✓ Krishnan 2019 ✓ ✓ ✓ ✓ ✓ ✓ Garrison
2020 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

Open in a new tab


4. DISCUSSION

This systematic review identified 18 articles that focused on smartphone apps
for delivering theory-driven tobacco cessation intervention. Overall, there was
heterogeneity in the design of testing each app, for example, many of the 2-arm
trials had a different control condition. Additionally, studies varied in length
of study duration, participant eligibility criteria, and behavioral theories.
Unfortunately, the amount of variability in the number of theories and models
applied by the different studies prevented identifying patterns to understand if
any particular model or design can be more effective for tobacco cessation.
Similarly, given the amount of variation in protocols, measurements of outcome,
and control groups, a meta-analysis was not feasible. However, this review
identified new apps and research developments since the most recently available
analyses (Hoeppner et al., 2016; Ubhi et al., 2016). Additionally, this review
searched exclusively for studies available through scientific databases rather
than commericial app stores, allowing us to provide more detailed information on
developing projects.

An important challenge to conducting mHealth studies is participant attrition
(Lane et al., 2015; O’Connor et al., 2015; Pfammatter et al., 2017). This might
be explained in part by app user fatigue or limited resources; unfortunately,
only 39% (n=7) studies included any discussion regarding participant retention
(Table 3). Gordan (2017) suggested the pragmatic nature of their trial, with
little to no contact with participants, contributed to the high attrition;
Hassandra (2017) found that the most common reason for dropping out was relapse
back to smoking; Garrison (2020) believed their study was able to achieve high
retention with proactive methods that included obtaining contact information for
three additional referrals. Overall, there was no pattern of app features,
participant compensation, or other retention strategies that demonstrated
consistently high retention. It would also be beneficial to disentangle the
issue of attrition with user engagement, leaving an opportunity for future
research. For example, mHealth studies in other fields have found that
gamification and interactive components help improve engagement and retention
(Muessig et al., 2013), which could be applied for tobacco cessation. Another
potential effect of scope and resources is that the majority of studies (61%)
relied on self-report as the source of the cessation outcome, even though
biochemical validation is the gold-standard for traditional tobacco cessation
studies. However, mHealth studies are inherently based on remote use and/or
monitoring. It is possible that participants could be more truthful reporting
the amount they smoke to an app rather than a person that could be perceived as
judgmental. Further studies are needed to understand whether the same reporting
standards are necessary.

Single-arm trials had a mean abstinence rate of 33.9%, showing promise compared
to rates of existing recommended therapies; as a frame of reference, 24% of
patients successfully quit smoking when using pharmacological treatment in
addition to behavioral support (Laniado-Laborín, 2010). Of the 11 trials with a
control group, only 4 demonstrated significant improvement in intervention group
over the control group. Only the Bindhim study (BinDhim, McGeechan, & Trevena,
2018) was both sufficiently powered and detected a significant difference in
cessation outcomes (80% power at alpha=0.05).

Most of the studies underreported information or data that could lead to
potential biases (Table 3). As most of the studies were limited in sample size
and power, acknowledgement and discussion of sample sizes and retention rates
would be beneficial to inform future trials and larger studies. Overall, a
stronger adherence to reporting guidelines such as CONSORT or STROBE could help
smaller pilot studies have a larger impact.

Only 10 studies (56%) had developed versions of their app for both Android and
Apple, limiting their potential reach. Researchers likely constrained by time
and budget focused their development time on a single platform. These design
decisions affect their reach because only releasing the app on a single major
platform limits the generalizability of the study to only users of that platform
(StatCounter Global Stats, 2018). Additionally, the average price of an iPhone
is more than double an average Android phone (The Washington Post, 2018),
potentially limiting the reach of different apps based on socioeconomics.

Although traditional tobacco cessation studies often report outcomes at 12
months (Lemmens et al., 2008), the study periods in this review had a mean of
4.18 months, with only a single study reaching 12 months; this is likely due to
the nature of pilot studies and limited resources. While there was a variation
in participant eligibility criteria, we did not find studies focused on specific
groups. Research has identified important disparities in tobacco products use
among racial, ethnic, and sexual and gender minorities (Bränström and Pachankis,
2018; Drope et al., 2018; Margerison-Zilko and Cubbin, 2013; McQuoid et al.,
2018). Future mHealth research should have a focus on studying feasibility,
acceptability, and effectiveness of apps for all tobacco users, with an
awareness for design features that are tailored and culturally appropriate for
minority groups.


5. LIMITATIONS

This work has several limitations. We included studies with different methods
and settings; there was also no scoring or review of the methodological quality
of the included studies. However, these differences were explicitly reported,
and a meta-analysis was not conducted due to the heterogeneity in study designs.
The studies chosen for review were limited by the selected search terms
(Appendix B). We also did not include studies that were not published, possibly
due to null findings, raising the risk of publication bias. Despite these
limitations, this systematic review was able to identify and summarize the
peer-review literature of smartphone apps for delivering theory-driven tobacco
cessation interventions. Using DistillerSR also provided rigor and consistency
to the review. These findings are important for the future of research in this
field. There is a need for more consistency in intervention components, which
could be supported by a registry or consistent set of guidelines for mHealth
tobacco trials. Larger, randomized controlled trials are also needed to better
elucidate how smartphone apps can be effective for tobacco cessation.


6. CONCLUSIONS

Studies have consistently shown a significant decrease in cancer and heart
disease mortality for people who quit tobacco use (World Health Organization;
2016; Yamaguchi et al., 1991). However, given the low retention and lack of
consistent support in traditional evidence-based treatment programs (Fiore et
al., 2008; Martinez et al., 2018; Soon et al., 2014), theory-driven
app-supported interventions serve as a promising component in tobacco control
strategies. Over the past several years, tobacco researchers have leveraged the
engagement of smartphone users to develop and deliver app-supported cessation
efforts. This study shows that more effort is needed to design studies that can
be generalizable as well as better retain participants. Mobile apps provide the
real-time, persistent, and cost-effective ability to support tobacco cessation
and cancer prevention. If effective, they can reduce various negative health
outcomes by providing prevention practitioners with a valuable tool to reduce
smoking.


SUPPLEMENTARY MATERIAL

1
NIHMS1623512-supplement-1.xml (263B, xml)
App Table 1
NIHMS1623512-supplement-App_Table_1.docx (14.9KB, docx)
App Table 2
NIHMS1623512-supplement-App_Table_2.docx (11.2KB, docx)


HIGHLIGHTS.

 * Identified 18 studies using apps for evidence-based tobacco cessation
   intervention

 * Most interventions using tobacco cessation apps were pilot/feasibility
   studies

 * Studies had varied methodology, study design, inclusion criteria

 * More effort is needed to design studies that can be generalizable as well as
   retain participants

 * Consistent app interventions and larger randomized controlled trials needed


7.

ROLE OF THE FUNDING SOURCE

Research in this publication was supported by the National Cancer Institute
(K07CA222338, PI: Chu). The funders had no role in the design and conduct of
this study; collection, management, analysis, or interpretation of the data; or
preparation, review, or approval of the manuscript.


FOOTNOTES

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has
been accepted for publication. As a service to our customers we are providing
this early version of the manuscript. The manuscript will undergo copyediting,
typesetting, and review of the resulting proof before it is published in its
final form. Please note that during the production process errors may be
discovered which could affect the content, and all legal disclaimers that apply
to the journal pertain.


REFERENCES

 1.  Abroms LC, Padmanabhan N, Thaweethai L, & Phillips T (2011). iPhone apps
     for smoking cessation: a content analysis. American Journal of Preventive
     Medicine, 40(3), 279–285. 10.1016/j.amepre.2010.10.032 [DOI] [PMC free
     article] [PubMed] [Google Scholar]
 2.  Baskerville NB, Struik LL, Guindon GE, Norman CD, Whittaker R, Burns C,
     Hammond D, Dash D, & Brown KS (2018). Effect of a mobile phone intervention
     on quitting smoking in a young adult population of smokers: Randomized
     controlled trial. JMIR MHealth and UHealth, 6(10), e10893 10.2196/10893
     [DOI] [PMC free article] [PubMed] [Google Scholar]
 3.  BinDhim NF, McGeechan K, & Trevena L (2018). Smartphone Smoking Cessation
     Application (SSC App) trial: a multicountry double-blind automated
     randomised controlled trial of a smoking cessation decision-aid “app”. BMJ
     Open, 8(1), e017105 10.1136/bmjopen-2017-017105 [DOI] [PMC free article]
     [PubMed] [Google Scholar]
 4.  Bränström R, & Pachankis JE (2018). Sexual orientation disparities in the
     co-occurrence of substance use and psychological distress: a national
     population-based study (2008–2015). Social Psychiatry and Psychiatric
     Epidemiology, 53(4), 403–412. 10.1007/s00127-018-1491-4 [DOI] [PMC free
     article] [PubMed] [Google Scholar]
 5.  Bricker JB, Copeland W, Mull KE, Zeng EY, Watson NL, Akioka KJ, & Heffner
     JL (2017). Single-arm trial of the second version of an acceptance &
     commitment therapy smartphone application for smoking cessation. Drug and
     Alcohol Dependence, 170(2017), 37–42. 10.1016/j.drugalcdep.2016.10.029
     [DOI] [PMC free article] [PubMed] [Google Scholar]
 6.  Bricker JB, Mull KE, Kientz JA, Vilardaga R, Mercer LD, Akioka KJ, &
     Heffner JL (2014). Randomized, controlled pilot trial of a smartphone app
     for smoking cessation using acceptance and commitment therapy. Drug and
     Alcohol Dependence, 143, 87–94. 10.1016/j.drugalcdep.2014.07.006 [DOI] [PMC
     free article] [PubMed] [Google Scholar]
 7.  Buller DB, Borland R, Bettinghaus EP, Shane JH, & Zimmerman DE (2014).
     Randomized Trial of a Smartphone Mobile Application Compared to Text
     Messaging to Support Smoking Cessation. Telemedicine and E-Health, 20(3),
     206–214. 10.1089/tmj.2013.0169 [DOI] [PMC free article] [PubMed] [Google
     Scholar]
 8.  Businelle MS, Ma P, Kendzor DE, Frank SG, Vidrine DJ, & Wetter DW (2016).
     An Ecological Momentary Intervention for Smoking Cessation: Evaluation of
     Feasibility and Effectiveness. Journal of Medical Internet Research,
     18(12), e321 10.2196/jmir.6058 [DOI] [PMC free article] [PubMed] [Google
     Scholar]
 9.  Castaldelli-Maia JM, Carvalho CFC, Armentano F, Frallonardo FP, Alves TC de
     TF, Andrade, de AG, … Nicastri S (2013). Outcome predictors of smoking
     cessation treatment provided by an addiction care unit between 2007 and
     2010. Brazilian Journal of Psychiatry, 35(4), 338–346.
     10.1590/1516-4446-2012-0907 [DOI] [PubMed] [Google Scholar]
 10. Crane D, Ubhi HK, Brown J, & West R (2019). Relative effectiveness of a
     full versus reduced version of the ‘smoke free’ mobile application for
     smoking cessation: An exploratory randomised controlled trial.
     F1000Research, 7 10.12688/f1000research.16148.2 [DOI] [PMC free article]
     [PubMed] [Google Scholar]
 11. Drope J, Liber AC, Cahn Z, Stoklosa M, Kennedy R, Douglas CE, … Drope J
     (2018). Who’s still smoking? disparities in adult cigarette smoking
     prevalence in the United States. CA: A Cancer Journal for Clinicians,
     68(2), 106–115. 10.3322/caac.21444 [DOI] [PubMed] [Google Scholar]
 12. Fiore MC, Jaen CR, Baker TB, Bailey WC, Bennett G, Benowitz N, … Williams C
     (2008). A clinical practice guideline for treating tobacco use and
     dependence: 2008 update: a U.S. public health service report. American
     Journal of Preventive Medicine, 35(2), 158–176.
     10.1016/j.amepre.2008.04.009 [DOI] [PMC free article] [PubMed] [Google
     Scholar]
 13. Garrison K, O’Malley S, Pittman B, Gueorguieva R, Rojiani R, Scheinost D,
     Dallery J, & Brewer J (2020). Craving to Quit: A Randomized Controlled
     Trial of Smartphone App-Based Mindfulness Training for Smoking Cessation.
     Nicotine & Tobacco Research, 22(3), 324–331. 10.1093/ntr/nty126 [DOI] [PMC
     free article] [PubMed] [Google Scholar]
 14. Gordon JS, Armin J, Hingle D, M., Giacobbi P, Cunningham JK, Johnson T,
     Abbate K, Howe CL, & Roe DJ (2017). Development and evaluation of the See
     Me Smoke-Free multi-behavioral mHealth app for women smokers. Translational
     Behavioral Medicine, 7(2), 172–184. 10.1007/s13142-017-0463-7 [DOI] [PMC
     free article] [PubMed] [Google Scholar]
 15. Hassandra M, Lintunen T, Hagger MS, Heikkinen R, Vanhala M, & Kettunen T
     (2017). An mHealth App for Supporting Quitters to Manage Cigarette Cravings
     With Short Bouts of Physical Activity: A Randomized Pilot Feasibility and
     Acceptability Study. JMIR MHealth and UHealth, 5(5), e74
     10.2196/mhealth.6252 [DOI] [PMC free article] [PubMed] [Google Scholar]
 16. Hertzberg JS, Carpenter VL, Kirby AC, Calhoun PS, Moore SD, Dennis MF,
     Dennis PA, Dedert EA, & Beckham JC (2013). Mobile Contingency Management as
     an Adjunctive Smoking Cessation Treatment for Smokers With Posttraumatic
     Stress Disorder. Nicotine & Tobacco Research, 15(11), 1934–1938.
     10.1093/ntr/ntt060 [DOI] [PMC free article] [PubMed] [Google Scholar]
 17. Hicks TA, Thomas SP, Wilson SM, Calhoun PS, Kuhn ER, & Beckham JC (2017). A
     Preliminary Investigation of a Relapse Prevention Mobile Application to
     Maintain Smoking Abstinence Among Individuals With Posttraumatic Stress
     Disorder. Journal of Dual Diagnosis, 13(1), 15–20.
     10.1080/15504263.2016.1267828 [DOI] [PMC free article] [PubMed] [Google
     Scholar]
 18. Hoeppner BB, Hoeppner SS, Seaboyer L, Schick MR, Wu GWY, Bergman BG, &
     Kelly JF (2016). How smart are smartphone apps for smoking cessation? a
     content analysis. Nicotine & Tobacco Research : Official Journal of the
     Society for Research on Nicotine and Tobacco, 18(5), 1025–1031.
     10.1093/ntr/ntv117 [DOI] [PMC free article] [PubMed] [Google Scholar]
 19. Iacoviello BM, Steinerman JR, Klein DB, Silver TL, Berger AG, Luo SX, &
     Schork NJ (2017). Clickotine, A Personalized Smartphone App for Smoking
     Cessation: Initial Evaluation. JMIR MHealth and UHealth, 5(4), e56
     10.2196/mhealth.7226 [DOI] [PMC free article] [PubMed] [Google Scholar]
 20. Khan N, Anderson JR, Du J, Tinker D, Bachyrycz AM, & Namdar R (2012).
     Smoking cessation and its predictors: results from a community-based
     pharmacy tobacco cessation program in New Mexico. Annals of
     Pharmacotherapy, 46(9), 1198–1204. 10.1345/aph.1P146 [DOI] [PubMed] [Google
     Scholar]
 21. Krishnan N, Elf J, Chon S, & Golub J (2019). COach2Quit: A Pilot Randomized
     Controlled Trial of a Personal Carbon Monoxide Monitor for Smoking
     Cessation. Nicotine & Tobacco Research, 21(11), 1573–1577.
     10.1093/ntr/nty182 [DOI] [PMC free article] [PubMed] [Google Scholar]
 22. Lane TS, Armin J, & Gordon JS (2015). Online recruitment methods for
     web-based and mobile health studies: a review of the literature. Journal of
     Medical Internet Research, 17(7), e183 10.2196/jmir.4359 [DOI] [PMC free
     article] [PubMed] [Google Scholar]
 23. Laniado-Laborín R (2010). Smoking cessation intervention: an evidence-based
     approach. Postgraduate Medicine, 122(2), 74–82. 10.3810/pgm.2010.03.2124
     [DOI] [PubMed] [Google Scholar]
 24. Lemmens V, Oenema A, Knut IK, & Brug J (2008). Effectiveness of smoking
     cessation interventions among adults: a systematic review of reviews.
     European Journal of Cancer Prevention, 17(6), 535–544.
     10.1097/CEJ.0b013e3282f75e48 [DOI] [PubMed] [Google Scholar]
 25. Margerison-Zilko C, & Cubbin C (2013). Socioeconomic disparities in
     tobacco-related health outcomes across racial/ethnic groups in the United
     States: national health interview survey 2010. Nicotine & Tobacco Research,
     15(6), 1161–1165. 10.1093/ntr/nts256 [DOI] [PubMed] [Google Scholar]
 26. Marler JD, Fujii CA, Utley DS, Tesfamariam LJ, Galanko JA, & Patrick H
     (2019). Initial assessment of a comprehensive digital smoking cessation
     program that incorporates a mobile app, breath sensor, and coaching: Cohort
     study. Journal of Medical Internet Research, 21(2), e12609 10.2196/12609
     [DOI] [PMC free article] [PubMed] [Google Scholar]
 27. Martinez E, Tatum KL, Weber DM, Kuzla N, Campbell K, Ridge JA, … Schnoll RA
     (2018). Issues related to implementing a smoking cessation clinical trial
     for cancer patients. Cancer Causes & Control, 20(1), 97–104.
     10.1007/s10552-008-9222 [DOI] [PMC free article] [PubMed] [Google Scholar]
 28. Masaki K, Tateno H, Kameyama N, Morino E, Watanabe R, Sekine K, Ono T,
     Satake K, Suzuki S, Nomura A, Betsuyaku T, & Fukunaga K (2019). Impact of a
     novel smartphone app (cureapp smoking cessation) on nicotine dependence:
     Prospective single-arm interventional pilot study. Journal of Medical
     Internet Research, 21(2), e12694 10.2196/12694 [DOI] [PMC free article]
     [PubMed] [Google Scholar]
 29. McQuoid J, Thrul J, & Ling P (2018). A geographically explicit ecological
     momentary assessment (GEMA) mixed method for understanding substance use.
     Social Science & Medicine (1982), 202, 89–98.
     10.1016/j.socscimed.2018.02.014 [DOI] [PMC free article] [PubMed] [Google
     Scholar]
 30. Muessig KE, Pike EC, LeGrand S, & Hightow-Weidman LB (2013). Mobile phone
     applications for the care and prevention of HIV and other sexually
     transmitted diseases: a review. Journal of Medical Internet Research,
     15(1). 10.2196/JMIR.2301 [DOI] [PMC free article] [PubMed] [Google Scholar]
 31. O’Connor S, Mair FS, McGee-Lennon M, Bouamrane M-M, & O’Donnell K (2015).
     Engaging in large-scale digital health technologies and services. what
     factors hinder recruitment? Studies in Health Technology and Informatics,
     210, 306–310. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/25991155
     [PubMed] [Google Scholar]
 32. Pew Research Center. (2018). Mobile fact sheet. Retrieved June 12, 2019,
     from http://www.pewinternet.org/fact-sheet/mobile/
 33. Pfammatter AF, Mitsos A, Wang S, Hood SH, & Spring B (2017). Evaluating and
     improving recruitment and retention in an mHealth clinical trial: an
     example of iterating methods during a trial. MHealth, 3, 49
     10.21037/mhealth.2017.09.02 [DOI] [PMC free article] [PubMed] [Google
     Scholar]
 34. Scott-Sheldon LAJ, Lantini R, Jennings EG, Thind H, Rosen RK,
     Salmoirago-Blotcher E, & Bock BC (2016). Text messaging-based interventions
     for smoking cessation: a systematic review and meta-analysis. JMIR MHealth
     and UHealth, 4(2), e49 10.2196/mhealth.5436 [DOI] [PMC free article]
     [PubMed] [Google Scholar]
 35. Shiffman S, Brockwell SE, Pillitteri JL, & Gitchell JG (2008). Use of
     smoking-cessation treatments in the United States. American Journal of
     Preventive Medicine, 34(2), 102–111. 10.1016/j.amepre.2007.09.033 [DOI]
     [PubMed] [Google Scholar]
 36. Soon P, Bastian B, Anderson RN, Collins JL, & Jaffe HW (2014). Potentially
     preventable deaths from the five leading causes of death — United States,
     2008–2010. Morbidity and Mortality Weekly Report, 63(17), 369–374. [PMC
     free article] [PubMed] [Google Scholar]
 37. StatCounter Global Stats. (2018). Mobile operating system market share
     United States of America. Retrieved June 12, 2019, from StatCounter Global
     Stats website:
     http://gs.statcounter.com/os-market-share/mobile/united-states-of-america/2016
     [Google Scholar]
 38. The Washington Post. (2018). Are Apple products overpriced? The Washington
     Post. [Google Scholar]
 39. Tombor I, Beard E, Brown J, Shahab L, Michie S, & West R (2019). Randomized
     factorial experiment of components of the SmokeFree Baby smartphone
     application to aid smoking cessation in pregnancy. Translational Behavioral
     Medicine, 9(4), 583–593. 10.1093/tbm/iby073 [DOI] [PMC free article]
     [PubMed] [Google Scholar]
 40. Ubhi HK, Michie S, Kotz D, Wong WC, & West R (2015). A mobile app to aid
     smoking cessation: preliminary evaluation of SmokeFree28. Journal of
     Medical Internet Research, 17(1), e17 10.2196/jmir.3479 [DOI] [PMC free
     article] [PubMed] [Google Scholar]
 41. Ubhi HK, Kotz D, Michie S, van Schayck OCP, Sheard D, Selladurai A, & West
     R (2016). Comparative analysis of smoking cessation smartphone applications
     available in 2012 versus 2014. Addictive Behaviors, 58, 175–181.
     10.1016/j.addbeh.2016.02.026 [DOI] [PMC free article] [PubMed] [Google
     Scholar]
 42. World Health Organization. (2016). Tobacco fact sheet. Retrieved October
     18, 2018, from http://www.who.int/mediacentre/factsheets/fs339/en/
 43. World Health Organization. (2017). WHO report on the global tobacco
     epidemic 2017. In World Health Organization. Retrieved from World Health
     Organization website:
     https://apps.who.int/iris/bitstream/handle/10665/255874/9789241512824-eng.pdf;jsessionid=E4B96422129BEF7744D335D5705D0217?sequence=1
     [Google Scholar]
 44. Yamaguchi N, Tamura Y, Sobue T, Akiba S, Ohtaki M, Baba Y, … Watanabe S
     (1991). Evaluation of cancer prevention strategies by computerized
     simulation model: an approach to lung cancer. Cancer Causes & Control,
     2(3), 147–155. [DOI] [PubMed] [Google Scholar]


ASSOCIATED DATA

This section collects any data citations, data availability statements, or
supplementary materials included in this article.


SUPPLEMENTARY MATERIALS

1
NIHMS1623512-supplement-1.xml (263B, xml)
App Table 1
NIHMS1623512-supplement-App_Table_1.docx (14.9KB, docx)
App Table 2
NIHMS1623512-supplement-App_Table_2.docx (11.2KB, docx)


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 * Abstract
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 * 5. LIMITATIONS
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