Smartphone App-Based Interventions to Support Smoking Cessation in Smokers with Mental Health Conditions: A Systematic Review

Abstract

Background—Despite global efforts to control tobacco use, smoking remains a leading cause of preventable diseases, mortality, and disparities, especially among individuals with mental health conditions. Smartphone apps have emerged as cost-effective tools to aid smokers in quitting; however, their evidence-based foundation remains understudied. This research conducted two searches to identify relevant apps: one through the scientific literature and the other from app stores. Methods—The study sought apps designed to assist smokers with mental health conditions in quitting. Searches were conducted in the scientific literature and major app stores. The apps found were evaluated for their basis in theory, features, and claimed effectiveness. Usage and rating scores were compared. Results—Among 23 apps found from app store search, only 10 (43%) were evidence-based and none had explicit reference to theory, while all apps identified in the literature were developed by applying theory. However, app store apps had significantly higher user numbers and ratings than those identified in the literature (mean rating 4.7 out of 5.0). Conclusion—Smokers with mental health conditions have limited support from currently available smoking cessation apps. Apps identified in the scientific literature lack sufficient use and longevity. Sustained support beyond research projects is crucial for enabling theoretically informed evidence-based apps to be available for people with mental health conditions, as is greater collaboration between developers and researchers to create apps that engage with end-user design.

Full text

Citation: Chen, J.; Chu, J.; Marsh, S.;
Shi, T.; Bullen, C. Smartphone
App-Based Interventions to Support
Smoking Cessation in Smokers with
Mental Health Conditions:
A Systematic Review. Psych 2023,5,
1077–1100. https://doi.org/10.3390/
psych5040072
Academic Editor: Mosad Zineldin
Received: 29 July 2023
Revised: 12 September 2023
Accepted: 19 September 2023
Published: 8 October 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Systematic Review
Smartphone App-Based Interventions to Support Smoking
Cessation in Smokers with Mental Health Conditions:
A Systematic Review
Jinsong Chen 1, Joanna Chu 1, Samantha Marsh 1, Tianyi Shi 2and Chris Bullen 1,*
1The National Institute for Health Innovation, School of Population Health, The University of Auckland,
Auckland 1023, New Zealand; jinsong.chen@auckland.ac.nz (J.C.); jt.chu@auckland.ac.nz (J.C.);
sam.marsh@auckland.ac.nz (S.M.)
2School of Population Health, The University of Auckland, Auckland 1023, New Zealand
*Correspondence: c.bullen@auckland.ac.nz
Abstract:
Background—Despite global efforts to control tobacco use, smoking remains a leading
cause of preventable diseases, mortality, and disparities, especially among individuals with mental
health conditions. Smartphone apps have emerged as cost-effective tools to aid smokers in quitting;
however, their evidence-based foundation remains understudied. This research conducted two
searches to identify relevant apps: one through the scientific literature and the other from app
stores. Methods—The study sought apps designed to assist smokers with mental health conditions
in quitting. Searches were conducted in the scientific literature and major app stores. The apps
found were evaluated for their basis in theory, features, and claimed effectiveness. Usage and rating
scores were compared. Results—Among 23 apps found from app store search, only 10 (43%) were
evidence-based and none had explicit reference to theory, while all apps identified in the literature
were developed by applying theory. However, app store apps had significantly higher user numbers
and ratings than those identified in the literature (mean rating 4.7 out of 5.0). Conclusion—Smokers
with mental health conditions have limited support from currently available smoking cessation apps.
Apps identified in the scientific literature lack sufficient use and longevity. Sustained support beyond
research projects is crucial for enabling theoretically informed evidence-based apps to be available for
people with mental health conditions, as is greater collaboration between developers and researchers
to create apps that engage with end-user design.
Keywords:
smoking cessation; mental health; mHealth; smartphone app; tobacco control; quit smoking
1. Introduction
Tobacco smoking is a leading cause of preventable death in the world [
1
]. Although
millions of smokers receive advice from their healthcare providers each year for quitting
smoking, and over half of them attempt to quit, the success rate is low [
2
]. Without
any support, the success rate from a quit attempt is about 5% to 7% [
3
], but it can be
raised to over 20% with behavioural intervention even without pharmacotherapy [
4
,
5
].
Unfortunately, 85% of tobacco users in the world have no access to cessation support [6].
Mental health conditions and smoking are strongly correlated; people with mental
health conditions are far more likely to smoke tobacco than those without mental health
conditions, and smoking amplifies the negative impacts of their medication and physical
co-morbidities on their mental wellbeing [
7
–
10
]. Nevertheless, there is good evidence that
smokers with mental health issues are just as interested in and able to quit smoking as others,
even more so when support is provided [
7
–
10
]. For this reason, people with mental illness
and other addictions often carry a greater burden of disease due to smoking. Poor lung
health, smoking, and poor mental health co-occur: 50% of Chronic Obstructive Pulmonary
Disease (COPD) patients have depressive symptoms, and over one in five COPD patients
Psych 2023,5, 1077–1100. https://doi.org/10.3390/psych5040072 https://www.mdpi.com/journal/psych

Psych 2023,51078
experience anxiety [
7
,
11
,
12
]. Smokers are more likely to develop depressive symptoms than
non-smokers [
10
]. Smokers with mental health issues are also more likely to (1) keep on
smoking; (2) consume more tobacco products; (3) die on average 10 to 20 years earlier; and
(4) need higher doses of antipsychotic medicines and antidepressants [
10
]. Smoking also
increases socioeconomic and ethnic disparities [
7
,
10
,
11
]. However, by quitting smoking,
people may experience a reduction in anxiety, depression, and stress levels; improvements
in quality of life and mood; and decreases in use of mental health medicines [7,10,11].
The most recent Cochrane Review found moderate-certainty evidence, limited by
inconsistency, that mobile smoking cessation (text message-based) interventions were more
effective than minimal smoking cessation support (risk ratio “RR” = 1.54,
95% CI = 1.19
to 2.00; 13 studies, 14,133 participants) [
13
]. There was also moderate-certainty evidence,
limited by imprecision, that text messaging added to other smoking cessation interven-
tions was more effective than the other smoking cessation interventions alone (RR = 1.59,
95% CI = 1.09
to 2.33; four studies, 997 participants) [
13
]. Compared to traditional in-person
interventions, mobile smoking cessation interventions have been shown to improve user
engagement with a cessation programme by expanding communication through real-time
messaging with support networks, and by reducing barriers to access, such as cost, location,
or timing conflicts [14–16].
However, text-based programmes have limited functionality, whereas smartphone
apps offer more interactive and customisable tools to support smokers throughout the
multi-stage process of quitting smoking, such as tools for self-monitoring, progress tracking,
urges overcoming, daily reminders, and social support [
17
]. A growing number of apps
purporting to be effective at helping smokers quit are available for downloading [18–66].
In recent years, apps have been developed to support individuals with mental health
issues to quit smoking [
45
,
47
,
67
]. Research suggests that smoking cessation apps can
engage smokers with mental health issues, [
27
] including those who are not already re-
ceiving nor seeking professional help [
47
], may promote smoking cessation or reduction of
tobacco consumption [
67
], and improve mental health status [
45
]. With the proliferation
of smartphones, mobile health tools are uniquely positioned to reach and influence the
smoking populations that need both smoking cessation and mental health support [
68
].
However, there has been little assessment of the quality of content, engagement, and reach
of apps that are underpinned by research or theory, compared with apps commonly used
in the marketplace [
45
,
58
–
67
] that purport to assist smokers with mental health issues to
quit [69,70].
The aim of this review was to identify all available apps designed to support smoking
cessation of smokers with mental health conditions, identify apps developed from theory
and/or empirical scientific evidence, and apps without such a basis, available from app
stores and to determine and compare the usage, user ratings, and availability of such apps.
2. Methods
We assessed the app market from two distinct viewpoints: firstly, that of health
professionals; and secondly, that of consumers, specifically smokers with mental health
conditions. Health professionals typically consult scientific literature, whereas consumers,
often without access to such literature, tend to rely on app store recommendations when
choosing healthcare apps. This dual approach necessitated our exploration of apps in two
ways: firstly, by beginning with the literature and then locating the identified apps in the
app stores; and secondly, by directly starting from the app stores.
In alignment with established systematic review practices [
69
–
73
], we adhered to the
Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model [
74
].
Whilst numerous smoking cessation apps incorporate evidence-based behaviour change
strategies, many have not been featured in published research articles. We prioritised
apps that have been recognised and recommended by the scientific community, focusing
on published research articles as evidence of their scientific support. We identified and

Psych 2023,51079
assessed apps designed to help smokers with mental health issues quit smoking through a
four-step process.
2.1. Identify the Scientific Literature
We performed a literature search of EMBASE, MEDLINE, APA PsycInfo, PubMed,
Scopus, ACM Digital Library, and IEEE Xplore on 30 September 2020. A second round
of the literature search was conducted on 23 July 2023. The gap is based on two reasons:
(1) we aimed for a comprehensive capture of all pertinent studies, especially given the
swift evolution and introduction of new apps in the market, and (2) the dynamic nature
of the app market and the continuous release of new research articles led us to believe
that a more extended gap would yield a richer and more current dataset for our review.
Table 1shows the search terms used in different fields of the study. Because search engines
differ between databases, search strategies were adapted to each database. Appendix A
shows the search strategies used in the different databases. Only peer-reviewed articles on
the topic of smoking cessation apps for smokers with mental health conditions that were
published in English before the search date were included for the review.
Table 1. Search terms used.
Fields Terms
Smoking
Smoking OR “smoking cessation” OR “quit smoking” OR “stop
smoking” OR cigarette OR “cigarette cessation” OR tobacco OR
“tobacco cessation”.
Smartphone Smartphone OR “mobile phone” OR phone OR iPhone OR iOS
OR Android OR “smartphone” OR “cell phone”.
App
App* OR application OR “mobile app*” OR “mobile software”
OR “mobile program*” OR “smartphone app*” OR
“smartphone software” OR “smartphone program*”.
Mental Health
Anxiety OR depression OR stress OR emotion* OR mental OR
“mental health” OR “mental health wellbeing” OR “mental
disorder*” OR “mental illness*” OR “psychiatric disorder*”.
A data extraction sheet based on the review of mobile phone-based interventions
for smoking cessation published in the Cochrane Database of Systematic Reviews [
14
]
was developed to collect data from the identified articles. The data extraction sheet was
adapted by adding a field on the impact of the intervention on the mental health statuses
of users. One review author (JChe) from the team extracted the data, and the other
authors (CB, JC, SM, TS) checked the data. Disagreements were resolved by consensus.
Information included: (1) information about the study (including country and year of
the study’s implementation); (2) characteristics of the app (including the name of the
app, underpinning theories, app development methods, functions, and target users); and
(3) evaluation of the app (including the assessment method, interventions, participants,
duration, types of measure, findings, bias, and limitations).
No gold standard exists against which to evaluate a smoking cessation app for smokers
with mental health conditions. A meta-analysis of study findings was, therefore, not
possible. Hence, the identified literature was analysed by identifying the availability,
validity, user experience, and effectiveness of current smoking cessation apps for mental
health smokers. Analysis of the features of current apps, their potential for improvement,
and the feasibility of evaluation to validate these apps’ effectiveness and acceptability was
also included.
We assessed risk of bias and obtained methodological details using a standardised
form applied by a Cochrane Review on mobile smoking cessation interventions [
14
]. Differ-
ent types of bias were assessed across studies including: (1) selection bias—whether study
participants are representative to the target population; (2) performance bias and detec-

Psych 2023,51080
tion bias—whether any types of blinding were performed; (3) attrition bias—incomplete
outcome or loss of follow-up; and (4) other bias—specified as small sample size, short
follow-up, and confounding factors. Each type of bias was rated for each study by JChe
with one of the following three risk levels: “high risk”; “low risk”; and “unclear”. The
results of the rating were reviewed by other authors (CB, JC, SM, TS) and agreement was
reached between authors.
2.2. Identify the Literature-Based Apps from App Stores
Each app identified in the scientific literature during phase 1 was searched for in each
of the following online app stores: the Apple App Store and the Google Play Store. Apps
with the same name and developer of those listed in the literature were considered a match.
Functions of smoking cessation apps were classified based on the taxonomy created by
the National Tobacco Cessation Collaboration (NTCC) [
75
] and its updated version of the
classification method developed by Abroms et al. [71].
2.3. Identify Market-Based Smoking Cessation Apps from App Stores
We searched the two main online app stores (Apple App Store and the Google Play
Store) in January 2021 using the following terms: mental health smoking, mental health,
and quit smoking. Each search term was searched separately and all three were used for
each of the two stores, totaling six separate searches. The top five apps returned per search
were documented for each store. Apps not relevant to the support of smoking cessation or
improving of users’ mental health status were removed.
2.4. Identify Market-Based Apps Developed from Theory or Empirical Evidence
The top five apps per search term were opened and reviewed regarding their develop-
ers, theories, development methods, smoking cessation features, mental health features,
target users, categories, charges (pay for download or free), download rate, and user rating.
The description (both in app stores and in the “About” section of the apps) of the apps was
reviewed to determine if the reviewed apps were developed based on theories. An app
with explicit theory or theoretical functions or components mentioned in its description
was considered as theory-based. Apps that had been tested in a methodologically robust
way were considered as based on empirical evidence. Only the top five apps were chosen
to best represent real search behaviours of focusing on the top apps of search results, which
is unlikely to include all apps available for the given health concerns [
76
,
77
]. An app list
was created by one review author (JChe) and shared with the other review authors (CB, JC,
SM, and TS). All authors reviewed and analysed apps independently and discussed the
key findings of the analysis.
3. Results
3.1. Literature Search Results
The search of listed databases provided a total of 1989 articles. After adjusting pub-
lication types and written languages, 447 articles remained. Of these, 349 articles were
discarded because after reviewing the abstracts, these articles did not meet the inclusion
criteria. The review article inclusion criteria include: (1) studies that focus on smartphone
app-based interventions for smoking cessation; (2) research articles that specifically tar-
get smokers with mental health conditions; (3) studies published in English or Chinese;
(4) peer-reviewed articles published between January 2010 and July 2023, and (5) articles
that provide clear outcomes related to the efficacy or effectiveness of the app interventions.
By the end of the title and abstract review, 98 articles were imported into the Endnote
reference management system, where 28 duplicate articles were identified and excluded.
The full text of the remaining articles was examined In detail. Two additional articles
were identified by the search conducted on 23 July 2023. Sixty-one studies did not meet the
inclusion criteria as described, leaving only ten studies in the systematic literature review.
No additional studies were identified by checking the references of located, relevant papers

Psych 2023,51081
and searching for studies that have been cited by included studies. No unpublished studies
were obtained. The flow diagram of articles selection is shown in Figure 1. Appendix A
summarises the details of the reviewed studies.
Psych 2023, 5, FOR PEER REVIEW 5
the inclusion criteria as described, leaving only ten studies in the systematic literature re-
view. No additional studies were identified by checking the references of located, relevant
papers and searching for studies that have been cited by included studies. No un-
published studies were obtained. The flow diagram of articles selection is shown in Figure
1. Appendix A summarises the details of the reviewed studies.
Figure 1. Flow Diagram of Study Selection.
All ten reviewed articles were published in English. Two of these articles reported pilot
randomised controlled trials (RCTs), four reported qualitative studies, two reported pilot
trials, one reported a full RCT, one reported a development study, and one reported a
mixed-method study. The duration of studies ranged from three days to six months. The
studies involved a total of only 160 participants. The main inclusion criteria entailed adults
(18 years or older), smokers (smoke one or more cigarettes or using other types of tobacco
products on a daily basis), with mental health issues (10 out of 160 studied participants were
not diagnosed with psychotic disorders) and using a smartphone. The studied intervention
in the reviewed articles is a smoking cessation app for smokers with mental health issues.
The primary outcome of six reviewed studies was smoking status of participants.
Smoking status was measured in different ways, including self-reported smoking absti-
nence and biochemical verification of smoking abstinence with different follow-up. Four
studies reported smoking abstinence with biochemical verification [45,67,78,79]. The du-
ration of abstinence ranged from 7-day to 30-day. Secondary outcomes of reviewed stud-
ies included adherence, user experience, participant mental health status, acceptability,
and feasibility of the intervention.
Six studies concluded that smoking cessation apps support smokers with mental
health conditions to quit smoking [29,45,67,78,80,81]. However, none of these studies had
conclusive findings on the effectiveness of smoking cessation apps. User experience of
smoking cessation apps varied across different studies [25,29,45,47,67,81]. The majority
indicated smoking cessation apps achieved positive user experience, while one study
stated that the smoking cessation app scored five points below industry standard (65.5 out
of 100) on the user experience measuring scale [25]. The same study found that some fea-
tures of smoking cessation app are redundant and rarely used [25]. Eight studies meas-
ured participants’ adherence to intervention activities. Most of these studies reported a
high compliance level of participants to intervention [25,45,47,67,78], while one reported
low compliance [81]. Only one study measured participants’ mental health status [45].
Figure 1. Flow Diagram of Study Selection.
All ten reviewed articles were published in English. Two of these articles reported
pilot randomised controlled trials (RCTs), four reported qualitative studies, two reported
pilot trials, one reported a full RCT, one reported a development study, and one reported
a mixed-method study. The duration of studies ranged from three days to six months.
The studies involved a total of only 160 participants. The main inclusion criteria entailed
adults (18 years or older), smokers (smoke one or more cigarettes or using other types
of tobacco products on a daily basis), with mental health issues (10 out of 160 studied
participants were not diagnosed with psychotic disorders) and using a smartphone. The
studied intervention in the reviewed articles is a smoking cessation app for smokers with
mental health issues.
The primary outcome of six reviewed studies was smoking status of participants.
Smoking status was measured in different ways, including self-reported smoking absti-
nence and biochemical verification of smoking abstinence with different follow-up. Four
studies reported smoking abstinence with biochemical verification [
45
,
67
,
78
,
79
]. The dura-
tion of abstinence ranged from 7-day to 30-day. Secondary outcomes of reviewed studies
included adherence, user experience, participant mental health status, acceptability, and
feasibility of the intervention.
Six studies concluded that smoking cessation apps support smokers with mental
health conditions to quit smoking [
29
,
45
,
67
,
78
,
80
,
81
]. However, none of these studies had
conclusive findings on the effectiveness of smoking cessation apps. User experience of
smoking cessation apps varied across different studies [
25
,
29
,
45
,
47
,
67
,
81
]. The majority
indicated smoking cessation apps achieved positive user experience, while one study stated
that the smoking cessation app scored five points below industry standard (65.5 out of 100)
on the user experience measuring scale [
25
]. The same study found that some features
of smoking cessation app are redundant and rarely used [
25
]. Eight studies measured
participants’ adherence to intervention activities. Most of these studies reported a high
compliance level of participants to intervention [
25
,
45
,
47
,
67
,
78
], while one reported low
compliance [
81
]. Only one study measured participants’ mental health status [
45
]. This
study indicated that the use of smoking cessation app led to a significant decrease in
depressive symptoms among app users [45].

Psych 2023,51082
Due to the diversity of study designs, participants, interventions, and outcome mea-
sures, and a high risk of bias (Appendix B), a meta-analysis of these studies was not
appropriate [82].
Four studies had a high risk of selection bias—specific population groups (e.g., mental
health smokers from high socioeconomic status) were invited to take part in these studies,
leading to a lack of generalisability of the study results [
25
,
47
,
67
,
83
]. All reviewed trials
had a high risk of performance bias because blinding was not performed by researchers or
trial participants in any of these trials [
29
,
45
,
67
,
78
,
79
]. The majority of reviewed studies
had small sample sizes and failed to detect a statistically significant smoking cessation
outcome [
29
,
45
,
67
,
78
,
79
]. All studies had a short to medium length of follow-up. Two
studies had a six-month follow-up, while the other studies had less than a three-month
follow-up. The reviewed studies could not detect the long-term impacts of smoking
cessation apps on smokers with mental health conditions. Two reviewed studies have no
post-treatment assessment to the control group, which made it impossible to identify the
size of the effect of the evaluated interventions [45,79].
3.2. Apps Identified from the Literature Search
Five smoking cessation apps for smokers with mental health conditions were identified
from the reviewed studies. Details of these apps (both from the literatures and app store
search) are summarised in Appendix C. All were built based on theories and clinical
guidelines. Six had their development methods and processes discussed in the reviewed
articles. Seven focused on smokers with mental health conditions while one targeted
all smokers. Two apps include functions for both smoking cessation and mental health
management, but six had only functions for smoking cessation. Four apps include the
keyword “quit” in their name.
Three were unavailable in both the Apple and the Google App store, one was only
available at no cost in the Apple app store, while four were available (free of charge) in
both Apple and Google App stores. Four apps have their download rates available in the
Google App store. The user rating scores are available for five literature-based apps. The
app named Actify! has a user rating score of 1.5 out of 5.0 in the Apple App store, but
it was only rated by four users. The app quitSTART was rated as 4.5 out of 5.0 (No. of
reviewers = 1500
) in the Apple App store and rated as 3.4 out of 5.0 (No. of
reviewers = 276
)
in the Google App store.
3.3. Smoking Cessation Apps in App Stores
Appendix Dsummarises the details of the top listed apps identified from the app
store search. The Apple App store search returned 13 apps and the Google App store
returned ten apps (some apps exist more than once when searching by different keywords).
Three apps (Wysa: Mental Health Support, MindDoc: Depression & Anxiety, and Smoke
Free—Stop Smoking Now) existed multiple times in both app stores and for different
keywords. Some keywords, such as “mental health” (n= 5), “smok*” (n= 8), and “quit”
(n= 8) are common in the names of the apps.
By entering “mental health smoking” into the Google App store, two of the top five
returned apps focused on mental health conditions and three on smoking cessation. The
most commonly seen smoking cessation functions are Calculator (n= 3) and Gamification
(n= 3). By entering “mental health” into the same app store, the top five apps all focus
on supporting mental health management. The most common functions of these apps are
Mood tracking (n= 4), Mental health practice (n= 4), and Diary (n= 3). By entering “quit
smoking” into the app store, four of the top five returned apps are focusing on supporting
users to quit smoking, and one is focusing on both supporting smoking cessation and
mental health management. The most common functions among these apps are Calendar
(n= 4), Gamification (n= 3), and Information (n= 3).
Eleven out of the 13 apps from the Apple App store require in-app purchases, which
means some functions of the app are not available to users unless users pay for using. The

Psych 2023,51083
price of these features ranged from $1.69 to $159.99 NZD (approx. $1.21 to $114.81 USD).
All download rates were for apps in the Apple App store. Ten apps have their user rating
scores available in the Apple App store. The average user rating score of these ten apps is
4.7 out of 5.0 (ranged from 4.5 to 5.0 out of 5.0). The average number of reviewers is 606
(ranged from 5 to 3100 reviewers).
All apps identified from the Google App store require in-app purchases. The price of
these features ranged from NZD $1.69 to $239.99 (approx. $1.21 to $172.22 USD). Six of these
apps were downloaded over 1,000,000 times, two were downloaded over
500,000 times,
one was downloaded over 100,000 times and one was downloaded over 1000 times. The
average user rating score of these apps is 4.7 out of 5.0 (ranged from 4.5 to 4.8 out of 5.0).
The average number of reviewers is 41,109 (ranged from 94 to 100,413 reviewers).
3.4. Apps from the App Stores Developed Based on Theories
Six apps from Apple App Store were developed by following theories to help their
users. Two of these apps post their development methods in the app store page. Four
identified apps from the Google App Store use theory-based approaches to support their
users. One shows its development method in the app store page. Seven apps from the
Apple App Store were developed to target smokers, five were designed to support the
general population who want to maintain good mental health, and one was developed to
support construction workers. Nine of these apps are categorised as Health and Fitness
apps, three as Lifestyle apps, and one as a Medical app. Five identified apps from the
Google App Store are targeting smokers while the other five are targeting the general
population. Eight of them are categorised as Health and Fitness apps, and two as Medical
apps. Most apps (n= 21 out of 23) from app stores have either functions for supporting
smoking cessation or managing mental health status.
4. Discussion
This systematic review aimed to identify smoking cessation apps for smokers with
mental health conditions. A search of studies from seven databases identified only ten
studies, 75% of which provide some supportive evidence of positive impacts of smoking
cessation apps on helping smokers with mental health conditions to quit smoking. Most of
the reviewed articles were small pilot studies. It was impossible to conduct a meaningful
meta-analysis with such heterogeneous measures [
82
]. Nevertheless, the narrative syn-
thesis of evidence about mHealth app-based interventions for smoking cessation enables
researchers to make several observations, as follows.
4.1. Findings from Reviewed Studies
Based on the reviewed studies, there are no standard methods to develop or evaluate
smoking cessation apps for smokers with mental health conditions. In general, the sample
sizes of current studies are small. Most reviewed studies were unable to detect statistically
significant results.
The effectiveness of supporting smoking cessation and user experience are the two
most common outcome measures of the reviewed studies. Most studies indicated that
smoking cessation apps had some positive impacts on supporting smokers with mental
health conditions to quit smoking. In contrast, two studies show that smoking cessation
apps may weaken the effectiveness of another smoking cessation programme when used
together [
29
,
79
]. Other literature reviews [
70
] on smoking cessation apps and app content
analysis [
72
,
73
,
84
–
92
] also draw the same conclusion: that the evidence for the effectiveness
of smoking cessation apps in helping smokers to quit smoking is limited.
User satisfaction and perceived effectiveness were used to reflect the user experience
of apps. Most reviewed studies found that participants perceived smoking cessation apps
as a helpful tool to support smoking cessation. Two studies indicate that smoking cessation
apps have an average user satisfaction. For instance, the study conducted by Vilardaga
et al. found that the app “QuitPal” was five points below the industry standard based

Psych 2023,51084
on the rating given by the study participants on the system usability scale (SUS) [
25
].
As mentioned by some smoking cessation app studies and reviews, limited evidence is
available about the factors that increase a smoking cessation app’s user experience. Some
potential positive factors may include providing multi-media information (e.g., audio,
video) to users and being built by following theories [20,23,28,42,49,50,88].
Although all studies targeted smokers with mental health conditions, only one study
measured the mental health status of the participants. In Heffner’s study, a significant
decrease in Patient Health Questionnaire (PHQ) score was found in participants who used
the smoking cessation app to achieve smoking abstinence (mean change in PHQ–9 scores
were
−
4.5, 95% CI
−
7.7 to
−
1.3; p= 0.01) [
45
]. However, the mechanism between quitting
smoking and improving mental health status was not explained. The reason why mental
health status was not included as an outcome measurement by most of the reviewed studies
is not made clear, but what is clear is that mental health status improvement should be an
essential parameter to include in studies, given the strong correlation between tobacco use
and mental health conditions [7–10].
4.2. Findings from App Review
Overall, 31 apps were discussed in this review (eight reviewed literature-based apps,
and twenty-three apps identified from the Apple and Google App stores). Key words, such
as “mental health”, “smok*”, and “quit” are very common in apps from app stores than
apps introduced in the reviewed studies. This may be the reason why the literature-based
apps were difficult to find in app stores (three out of eight apps were unavailable). Instead
of typing the names of the research-based apps, these apps were out of the top 50 searching
results when typing terms, such as “mental health smoking”, “mental health”, and “quit
smoking”. It is very unlikely for smartphone users to download an app that requires too
many scrolls or swipes [
76
,
77
]. It will be worthwhile for researchers who are developing
smoking cessation apps for smokers with mental health smokers to understand the logic of
app stores for exhibiting apps in response to search. One technique is the use of the hyphen
between the app name and the aims of the app. For example, the apps’ names like “What’s
Up?—A Mental Health App”, “Stop Smoking—EasyQuit free”, and “Flamy—quit smoking
& become a non-smoker” make them easy to navigate when typing the correct keywords.
All literature-based apps use approaches developed based on theories to support
smoking cessation and mental health management. In comparison, less than half of the
apps searched from app stores apply theory-based approaches. The finding of lack of
theory-based approaches in health-related commercial apps is similar to other studies on
health-related apps targeting other conditions [
93
,
94
]. The application of theory-based
approaches to support smoking cessation and mental health management should be used
as a marketing highlight to promote the literature-based apps or other research-based apps.
Supportive evidence has been found from an existing smoking cessation app analysis.
Cheng et al. found that a smoking cessation app’s theories and guideline adherence
level is positively related to its rating in app stores [
88
]. Abroms et al. also found that
a smoking cessation app’s user experience rating is positively associated with its score
on the application of theory and guideline-based approaches [
71
]. Although there are
some exceptions, the application of theory-based approaches secures the safety, rigour, and
potentially, the effectiveness of the apps.
Two literature-based apps have both smoking cessation and mental health manage-
ment functions, while two apps searched from the app stores have functions from both
categories. However, the functions of app store-searched apps are relatively easier com-
pared to the literature-based apps. For instance, the app named “Construction Industry
Helpline” has both smoking cessation and mental health-related functions, but the smoking
cessation approach it uses is just providing smoking harm information to users. This situa-
tion is less common among research-based apps. For example, the app called “Stay Quit
Coach” introduced by reviewed studies has multiple functions related to both smoking
cessation and mental health management [
29
,
47
,
81
]. As introduced, smoking and mental

Psych 2023,51085
health conditions are two strongly connected health conditions [
7
–
10
]. It is important to un-
derstand the mechanism between how these two factors affect one another before designing
an appropriate smoking cessation app for smokers with mental health conditions.
The majority of apps searched from app stores only provide free trial versions for
their users. The cost of these apps is varied. It shows the business potentials of smoking
cessation and/or mental health management apps, but also reflects the potential cost for
maintaining these products. Although the download rate is unavailable for apps from
Apple App Store, the identified apps from the Google App Store provide some information
about how popular these apps are. Based on the ten identified apps from the Google
App Store, six of them were downloaded over 1 million times. The massive difference
in download rates reflects a vast difference between the literature-based and commercial
apps to reach their target users. Commercial apps also achieve higher user rating scores
and are more likely to be rated by their users than the literature-based apps. They provide
some excellent examples for researchers about designing and developing an attractive
and engaging app. Collaboration between research teams and commercial companies or
applying app design standards from commercial companies can be a method to improve
the attractiveness and engagement of research-based apps [95,96].
4.3. Limitations
There are a number of limitations in this systematic review. First, there was an incon-
sistency of interventions and study settings, making a meta-analysis inappropriate [
13
].
Without a meta-analysis, no conclusive statements can be made about the impacts of
smoking cessation apps on supporting smokers with mental health conditions. More
standardised approaches to the research design and evaluation would enable greater com-
parability between studies. Second, the quality of studies varied widely. Six trials had a
small sample size (n< 50 participants) and failed to detect statistically significant results.
Most studies had a short follow-up (<3 months) and were unable to measure long-term
impacts. Only one study measured changes in participants’ mental health status. Studies
with larger sample sizes, longer follow-up, and measures of a range of impacts are needed.
A third limitation of this study is the limited number of apps included in our analysis
(n= 31,
eight based on reviewed literature and 23 from app stores). Commercial apps were
the top five apps in the two major app stores but there are hundreds of apps available in
both app stores that were not reviewed.
5. Conclusions
In this systematic review, we meticulously examined the current literature on smoking
cessation apps tailored for smokers with mental health conditions. Our findings underscore
a notable gap: there is limited evidence to conclusively determine the efficacy of these apps
in assisting individuals with mental health challenges to quit smoking. While the impact
of these apps on users’ mental health remains largely uncharted, it is evident that apps
grounded in research are generally perceived as effective by their users, often employing
theory-driven strategies.
However, a stark contrast emerges when comparing research-based apps with their
commercial counterparts. The former, despite their evidence-based foundations, often fall
short in terms of user engagement and appeal. Enhancing the marketability of research-
based apps is crucial. Adopting effective naming conventions and aligning with industry
design standards can significantly elevate their appeal.
Moving forward, there is a compelling case for the creation of smoking cessation apps
for this demographic, drawing from both scientific evidence (including established theories
and pertinent clinical guidelines) and the best practices observed in popular commercial
apps. Future research, particularly randomised controlled trials, should aspire for more
robust methodologies, encompassing larger participant cohorts, extended monitoring
durations, and a broader spectrum of outcome metrics.

Psych 2023,51086
Author Contributions:
Conceptualization, C.B. and J.C. (Jinsong Chen); methodology, C.B. and J.C.
(Jinsong Chen); literature search, J.C. (Jinsong Chen); formal analysis, C.B., J.C. (Joanna Chu), S.M.
and J.C. (Jinsong Chen); writing—original draft preparation, J.C. (Jinsong Chen); writing—review
and editing, C.B., J.C. (Joanna Chu), S.M., J.C. (Jinsong Chen) and T.S.; visualization, J.C. (Jinsong
Chen); supervision, C.B.; project administration, J.C. (Jinsong Chen). All authors have read and
agreed to the published version of the manuscript.
Funding: This research was funded by The Ember Korowai Takitini, grant number 20201214.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data presented in this study are available in Appendices A–C.
Acknowledgments:
We would like to acknowledge our study funder The Ember Korowai Takitini
for providing us with an opportunity to work on such an interesting and meaningful topic.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Table A1. Details of the Reviewed Studies.
Source Study Design
Participant Interventions and Follow-Up Outcomes and Measures
Hertzberg et al., 2013 [78]Pilot RCT
Smokers with PTSD (n= 22)
Intervention: mCM (app) and
two smoking cessation
counselling sessions, nicotine
replacement, and bupropion
(n= 11)
Control: non-app conditions
(n= 11)
Adherence: Compliance with
trial activities
Smoking status: CO-verified
7-day PPA at 4-week and
3-month follow-up
Vilardaga et al., 2016 [25]
Qualitative study
Individuals with a history of
serious mental illness (n= 5)
Day 1: introduced participants
to QuitPal (app) with a brief
hands-on demonstration and
explained its functions
Day 2–3: participants
field-tested QuitPal and
interacted with the app to
gain a more in-depth
user experience
Adherence: App usage logs
User experience: Interview
and SUS questionnaire
Hicks et al., 2017 [29]Pilot RCT
Smokers with PTSD (n= 11)
Intervention: QUIT4EVER, an
intervention combining
mobile contingency
management smoking
cessation counselling and
medications, and the SCQ app
(n= 5)
Control: a contact control
condition that was identical to
QUIT4EVER except SCQ app
was not included (n= 6)
Smoking status: Self-reported
prolonged smoking abstinence
User experience: Perceived
effectiveness on a Likert
scale questionnaire

Psych 2023,51087
Table A1. Cont.
Source Study Design
Participant Interventions and Follow-Up Outcomes and Measures
Minami et al., 2018 [67]
Pilot trial
Smokers with psychiatric
disorders (have a depressive
disorder or bipolar disorder)
(n= 8)
A smartphone intervention
app that prompts participants
to practice mindfulness
(listening to an audio
recording on the smartphone
five times per day), complete
EMA reports five times per
day, and submit CO videos
twice per day
Follow-up: Three months
Adherence: Compliance with
trial activities
Smoking status: CO-verified
7-day PPA at 2-, 4-week, and
3-month post-quit follow-ups,
and Cigarettes use reduction.
User experience: Satisfaction
to the programme
questionnaire
Heffner et al., 2019 [45]
Pilot trial
Daily smokers with mild to
moderate depressive
symptoms (n= 16)
Smokefree TXT along with
Actify! (app) to provide
cessation content that had not
yet been built into the app for
this pilot testing
Follow-up: Six weeks
Adherence: Number of log-ins
per participants and reported
usability challenges
Mental health status:
Depressive symptoms
measured by the PHQ
Smoking status: CO-verified,
7-day and 30-day PPA at
6-week follow-up
User experience: Interview
Herbst et al., 2019 [47]
Qualitative study Use of the SQC app
Adherence: Retention
User experience: Comfort
levels with mobile technology
(baseline measure)—the
PMPIQ-P and Interview
US military veterans with
PTSD who smoked at least
five cigarettes per day for 15
of the past 30 days and stated
an interested in cessation
(n= 20)
Follow-up: Three months
Klein et al., 2019 [80]
Qualitative study Intervention: view prototype
of the Kick.it app Acceptability: Explored
participants’ smoking-related
experiences and perceptions
of social support for
smoking cessation
Feasibility: Participants’
perceptions of the feasibility,
utility, and acceptability of the
app features for
SMI populations
Smokers with severe mental
illness (SMI) (n= 12)
Follow-up: Two consecutive
semi-structured in-depth
interviews (Stage 1 = 1 h,
Stage 2 = 1.5 h)
Wilson et al., 2019 [81]
Qualitative study
The intervention included
mobile contingency
management (i.e., financial
compensation for confirmed
abstinence from smoking),
pharmacotherapy for
smoking cessation,
cognitive–behavioural
counselling sessions, and the
use of SQC app for
relapse prevention
Adherence: Compliance with
treatment
Smoking status: Self-reported
smoking abstinence
User experience: Interview
Perception of usefulness
Smokers with schizophrenia,
schizoaffective, or psychotic
disorders) two cohorts
(Cohort 1 n= 5, Cohort 2 n= 8,
total n= 13)
Follow-up: Three months

Psych 2023,51088
Table A1. Cont.
Source Study Design
Participant Interventions and Follow-Up Outcomes and Measures
Alyssa et al., 2020 [79]
RCT
Intervention: mobile CM (i.e.,
monetary compensation for
bio-verification of abstinence
through using a phone app),
CBT, and pharmacotherapy
for smoking cessation (n= 21)
Acceptability: A questionnaire
assessing eight self-reported
items
Feasibility: A questionnaire
that was completed by the
therapist about the participant
Knowledge of treatment: A
tailored questionnaire
Smoking status: Self-reported
prolonged abstinence,
Bio-verified (including saliva
and CO verification)
prolonged abstinence at
6-month follow-up, 7-day, and
30-day self-reported PPA
Smokers with diagnosed
mental health illness (n= 34)
Control: ITC, which contained
all components except the CM
(n= 13)
Follow-up: Six months
Gowarty et al., 2021 [83]
Mixed-method (descriptive
statistics and analysis of app
utilisation data and
semi-structured interview)
Intervention: QuitGuide and
quitSTART. Participants were
randomly assigned to one of
the two apps.
Adherence: backend app
usage data
Acceptability: perceptions of
the acceptability assessed
using a modified version of
the Acceptability of
Intervention Measure (AIM)
User experience: assessed
using the System Usability
Scale (SUS)
Daily smokers (n= 17, and 7
of them were diagnosed with
psychotic disorders)
Follow-up: The follow-up
period lasted for 2 weeks,
during which participants
were instructed to use their
assigned app independently.
SUS: System Usability Scale; PPA: Point Prevalence Abstinence; PHQ: Patient Health Questionnaire; PTSD:
Post-Traumatic Stress Disorder; PMPIQ-P: Perceptions of Mobile Phone Interventions Questionnaire–Patient
(version); EMA: Ecological Momentary Assessment; CO: Carbon Monoxide; CBT: Cognitive Behavioural Therapy;
ITC: Intensive Treatment Comparison.

Psych 2023,51089
Appendix B
Table A2. Risk of bias of the reviewed studies.
Source Selection
Bias
Performance
Bias Attrition Bias Other Bias
Hertzberg et al.,
2013 [78]Low High Low
1. Small sample size
2. Short follow-up period
3. Did not ask participants if they were receiving any
PTSD treatment
4. Did not measure PTSD symptoms change
Vilardaga et al.,
2016 [25]High NA Low
1. Limited understanding of participants’ psychiatric
status (before and after intervention)
2. Limited time using the app
3. Interviewer’s characteristics (e.g., gender,
investigator role) could have influenced study
procedures and interview results
Hicks et al.,
2017 [29]Low High Low
1. Small sample size
2.
Aspects of the current study underscore the potential
for behavioural mobile health apps to promote
long-term abstinence in smokers with PTSD
3. Smoking status relied on self-reported data
Minami et al.,
2018 [67]High High Low
1. Small sample size
2. The generalisation of these findings to other
populations may be limited as all participants in this
study were of low socioeconomic status
Heffner et al.,
2019 [45]High High Low
1. Small sample size
2. Study design: lack of a control group
3.
Potential confounding effect: did not track non-study
treatment use
Herbst et al.,
2019 [47]High NA Low
1. Sample men exclusively from urban and
suburban areas
2. The study did not examine the efficacy of the app
3. The study relied on self-report data of app usage
4. Potential of the therapeutic alliance that makes
biased answers in interviews
5. The study did not include specific questions about
the use of the app to cope with PTSD symptoms
Klein et al.,
2019 [80]Low High NA NA
Wilson et al.,
2019 [81]Low NA High 1. Smoking status relied on self-reported data
Alyssa et al.,
2020 [79]Low High Low
1. Small sample size
2. Unable to detect which treatment components
determined the smoking cessation effect
3. There was no post-treatment assessment to the
control group, while the intervention group had
bio-verified data at post-treatment
Gowarty et al.,
2021 [83]High High Low 1. Results based on self-reported data, might create
social desirability bias or recall bias

Psych 2023,51090
Appendix C
Table A3. Details of apps identified from the reviewed studies.
Source App Names Grounding
Theories
Development
Methods Target Users Smoking Cessation Features Mental Health
Features
No. of Downloads
App Store Rating
(n= No. of User Rated)
Cost in
NZD
1 2 3 4 5 6 7 Other
Vilardaga
et al.,
2016 [25]
QuitPal
(Apple and
Google)
USPHSCPG Iterative
development
Smokers X X X X X 1. Social
support
2. Contact to
Quitline
NA NA NA
Minami,
et al.,
2018 [67]
NA CM and
EMA
NA Smokers with
psychiatric
disorders
XBio-
verification on
quitting data
1. Mental health
practice
2. Prompt to
complete EMA
reports
NA NA
Heffner
et al.,
2019 [45]
Actify!
(Apple)
BAT-D and
BATS
User-centred design
process including
competitive
analysis, focus
groups, and
usability testing of
low- and
high-fidelity
prototypes
Smokers with
depressive
symptoms
X X Quitting plan NA Download rate
unavailable
User rating:
Apple-1.5/5.0 (n= 4)
Free
Hicks
et al.,
2017 [29]
Stay Quit
Coach (SQC)
(Apple and
Google)
CBT and
USPHSCPG
The app is part of
the integrated care
(IC) treatment
protocol, which
consists of
combined
behavioural and
pharmacotherapy
treatment
Smokers with
PTSD X X
1. Quitting
plan
2. Contact to
Quitline
1. Mental health
practice (reduce
anxiety
sensitivity and
hyperarousal)
2. Help users to
cope with
negative
emotions
Download rate:
Apple-unavailable;
Google-10,000+
User rating:
Apple-3.5/5.0 (n= 2);
Google-4.0/5.0 (n= 21)
Free
Herbst
et al.,
2019 [47]
Wilson
et al.,
2019 [81]

Psych 2023,51091
Table A3. Cont.
Source App Names Grounding
Theories
Development
Methods Target Users Smoking Cessation Features Mental Health
Features
No. of Downloads
App Store Rating
(n= No. of User Rated)
Cost in
NZD
1 2 3 4 5 6 7 Other
Klein
et al.,
2019 [80]
Kick.it
(Apple and
Google)
IM and TDF The development
method for the
Kick.it was
co-design, which
involved consumer
involvement and
collaboration in the
tailoring of the app
Individuals with
SMI who are
seeking to quit
smoking
X X NA NA Download rate:
Apple-unavailable;
Google-10,000+
User rating:
Apple-3.8/5.0 (n= 9);
Google-NA
Renamed as “No Butts”
Free
Hertzberg
et al.,
2013 [78]
mCM
(Apple) CM -Smokers with
PTSD
1. Bio-verification
on quitting data
2. Compensation
to quitting
behaviours
NA NA NA
Alyssa
et al.,
2020 [79]
Gowarty
et al.,
2021 [83]
QuitGuide BCTs and
CPGs
Mixed usability
reviews
Young adults
with SMI who
were current
smokers
X X X NA NA Download rate:
Apple-unavailable;
Google-50,000+
User rating:
Apple-4.2/5.0 (n= 17);
Google-NA
Free
quitSTART BCTs and
CPGs
Mixed usability
reviews
X X X NA NA Download rate:
Apple-unavailable;
Google-50,000+
User rating:
Apple-4.5/5.0
(n= 1500);
Google-3.4/5.0
(n= 276)
Free
USPHCPG—US Public Health Service’s Clinical Practice Guideline for Treating Tobacco Use and Dependence; EMA—Ecological momentary assessment; CM—Contingency management;
BAT-D—Behavioural Activation Treatment for Depression; BATS—Behavioural Activation Treatment for Smoking; CBT—Cognitive Behaviour Therapy; IM—Intervention Mapping;
TDF—Theoretical Domains Framework; PTSD—Posttraumatic Stress Disorder; BCTs—Behaviour Change Theories; CPGs—Clinical Practice Guidelines; 1—Calculator; 2—Calendar;
3—Gamification; 4—Hypnosis; 5—Information; 6—Lung Health Tester; 7—Rationing.

Psych 2023,51092
Appendix D
Table A4. Details of apps found from the Apple and Google App Stores.
App Names
Developer
Grounding Theories
Development Methods
Target Users
Category
Smoking Cessation Features Mental Health
Features
No. of Downloads
App Store Rating
(n= No. of User Rated)
Cost in NZD $
1 2 3 4 5 6 7 Other
Apple App Store—“Mental health smoking”.
LIFEGIFT
HERE4U
LifeGift Pte. Ltd.
NA
NA
Smokers
Health and fitness
XDownload rate: NA
User Rating: NA
$1.69 (in-app
purchase)
Construction
Industry Helpline
Construction Industry
Solutions Limited
NA
NA
Construction
worker with any
of the four areas
of need: mental,
physical, financial
and social health
Lifestyle
X1. An assessment
tool to evaluate
conditions
2. Self-help tools
to cope with
conditions
Download rate: NA
User Rating: NA
NA
Smoke Free—Stop
Smoking Now
Smoke Free, Inc. UK
Transtheoretical model
of behaviour change
NA
People who are
trying to quit
smoking
Health and fitness
XXXXXXX Quit
Planning
Community
Chat
1. Mood tracker Download rate: NA
User Rating: 4.7/5.0
(n= 1700)
$1.99 per week or
$5.99 per month
(in-app purchase)
Apple App Store—“Mental health”.
Daylio Journal Relaxio s.r.o.
NA
Principles: help users
being mindful, identify
the influence of new
hobby, easy-to-use
Everyone
Lifestyle
1. Calendar
2. Diary
3. Mood tracking
4. Prompt goals
setting (include
mood and health
behaviours)
NA
4.7 out of 5.0 (n= 452)
$4.99 to $39.99
(in-app purchase,
depending on the
package)
Morning!—A 5
Minute Journal
Adriana Padilla
NA
NA
Everyone
Lifestyle
1. Calendar
2. Daily quotes
3. Diary
4. Mood tracking
5. Reminders
NA
4.9 out of 5.0 (n= 12)
$9.99 (in-app
purchase)

Psych 2023,51093
Table A4. Cont.
App Names
Developer
Grounding Theories
Development Methods
Target Users
Category
Smoking Cessation Features Mental Health
Features
No. of Downloads
App Store Rating
(n= No. of User Rated)
Cost in NZD $
1 2 3 4 5 6 7 Other
Wysa: Mental
Health Support
Touchkin
CBT, DBT, Yoga and
meditation
NA
Everyone
Health and
Fitness
1. Chatbot
communication
supports
2. Contact to
therapists
3. Self-help tools
to cope with
conditions
NA
4.7 out of 5.0 (n= 34)
$8.49 to $119.99
(in-app purchase,
depending on
package)
MindDoc:
Depression &
Anxiety
MindDoc Health GmbH
NA
Developed with
psychotherapists and
scientists
Everyone
Medical
1. Information
2. Mental health
practice (course)
3. Mood tracking
NA
4.7 out of 5.0 (n= 281)
$7.49 to $76.99
(in-app purchase,
depending on the
package)
Stoic. Mental
health journal
Maciej Lobodzinski
Stoicism
NA
Everyone
Health and
Fitness
1. Calendar
2. Diary
3. Daily quotes
NA
4.7 out of 5.0 (n= 93)
$10.99 to $159.99
(in-app purchase,
depending on the
package)
Apple App Store—“Quit smoking”.
Quit smoking Dennis Ebbinghaus
NA
NA
Smokers
Health and
Fitness
X X X X 1. Craving
supports
2. Goal
setting
NA
4.5 out of 5.0 (n= 28)
$1.99 to $46.99
(in-app purchase,
depending on the
package)
Smoke-Free—Stop
Smoking Now
David Crane
30+ proven quit
smoking techniques and
the most reliable
quitting methods science
NA
Smokers
Health and
Fitness
X X X X Goal
setting
NA
4.7 out of 5.0 (n= 1125)
$1.69 to $49.99
(in-app purchase,
depend on
package)

Psych 2023,51094
Table A4. Cont.
App Names
Developer
Grounding Theories
Development Methods
Target Users
Category
Smoking Cessation Features Mental Health
Features
No. of Downloads
App Store Rating
(n= No. of User Rated)
Cost in NZD $
1 2 3 4 5 6 7 Other
My QuitBuddy Australian National
Preventive Health
Agency
NA
NA
Smokers
Health and
Fitness
X X Social
support
NA
5.0 out of 5.0 (n= 22)
NA
Quit Genius—quit
smoking
Digital Therapeutics Ltd.
CBT
NA
Smokers
Health and
Fitness
X1. Re-
minders
2. Quitting
exercise
NA
4.5 out of 5.0 (n= 3100)
$10.99 to $149.99
(in-app purchase,
depend on
package)
Kwit—Quit
smoking and
vaping
KWIT
CBT
NA
Smokers
Health and
Fitness
X X X
Motivational
messages
NA
4.6 out of 5.0 (n= 5)
$2.49 to $89.99
(in-app purchase,
depend on
package)
Google App Store—“Mental Health Smoking”
Quit Tracker: Stop
Smoking
despDev
NA
NA
Smokers
Health and
Fitness
XXX 1,000,000+
4.7 out of 5.0
(n= 100,413)
$3.99 (in app
purchase)
Smoke Free, stop
smoking now and
quit for good
David Crane
30+ proven quit
smoking techniques and
the most reliable
quitting methods science
NA
Smokers
Health and
Fitness
X X X X Goal
setting
1,000,000+
4.8 out of 5.0 (n= 52,631)
$1.69 to $47.99
(in-app purchase,
depend on
package)
Guided Mental
Health
Journal—Iona
Mind
Iona Mind—Mental
Health Support
CBT and performance
psychology
NA
Everyone
Health and
Fitness
1. Diary
2. Information
(CBT)
3. Mood tracking
4. Mental health
practice
5. Goal setting
1000+
4.8 out of 5.0 (n= 94)
$7.99 to $89.99
(in-app purchase,
depending on the
package)

Psych 2023,51095
Table A4. Cont.
App Names
Developer
Grounding Theories
Development Methods
Target Users
Category
Smoking Cessation Features Mental Health
Features
No. of Downloads
App Store Rating
(n= No. of User Rated)
Cost in NZD $
1 2 3 4 5 6 7 Other
Stop Smoking—
EasyQuit
free
Mario Herzberg (Hanna)
NA
NA
Smoker
Health and
Fitness
XXXXQuitting
plan
1,000,000+
4.8 out of 5.0 (n= 78,056)
$7.99 to $89.99
(in-app purchase,
depend on
package)
Wysa: stress,
depression &
anxiety therapy
chatbot
Touchkin
CBT, DBT, Yoga and
meditation
NA
Everyone
Health and
Fitness
1. Chatbot
communication
supports
2. Contact to
therapists
3. Self-help tools
to cope with
conditions
1,000,000+
4.8 out of 5.0 (n= 72,643)
$2.49 to $239.99
(in-app purchase,
depend on
package)
Google App Store—“Mental health”.
Guided Mental
Health
Journal—Iona
Mind
Wysa: stress,
depression &
anxiety therapy
chatbot
Mind journal:
anxiety relief &
mental health
diary
Bazimo
CBT
NA
Everyone
Health and
Fitness
1. Diary
2. Mood tracking
3. Mental health
practice
100,000+
4.8 out of 5.0 (n= 4873)
$12.99 to $32.99
(in-app purchase,
depend on
package)

Psych 2023,51096
Table A4. Cont.
App Names
Developer
Grounding Theories
Development Methods
Target Users
Category
Smoking Cessation Features Mental Health
Features
No. of Downloads
App Store Rating
(n= No. of User Rated)
Cost in NZD $
1 2 3 4 5 6 7 Other
What’s Up?—A
Mental Health App
Jackson Tempra
CBT and ACT
NA
Everyone
Health and
Fitness
1. Behaviour
tracking (habit)
2. Diary
3. Gamification
4. Mental health
practice
5. Mood tracking
6. Daily quotes
500,000+
4.8 out of 5.0 (n= 3354)
$1.34 to $5.62
(in-app purchase,
depend on
package)
MindDoc:
Depression &
Anxiety
MindDoc Health GmbH
NA
Developed with
psychotherapists and
scientists
Everyone
Medical
1. Information
2. Mental health
practice (course)
3. Mood tracking
1,000,000+
4.5 out of 5.0 (n= 35,355)
$8.49 to $129.99
(in-app purchase,
depend on
package)
Google App Store—“Quit smoking”
Quit Tracker: Stop
Smoking
XXX
Smoke Free, stop
smoking now and
quit for good
X X X X Goal
setting
Stop Smoking—
EasyQuit
free
XXXXQuitting
plan
Flamy—quit
smoking & become
a non-smoker
Offlinefirst
NA
NA
Smoker
Health and
Fitness
X X 1. Social
support
2. Craving
supports
500,000+
4.8 out of 5.0 (n= 10,815)
$0.99 to $13.99
(in-app purchase,
depend on
package)
QuitNow! Fewlaps
NA
NA
Smoker
Medical
X X Quitting
plan
Mental health
practice
1,000,000+
4.6 out of 5.0 (n= 52,860)
$6.99 (in-app
purchase)
CBT—Cognitive Behaviour Therapy; DBT—Dialectical Behaviour Therapy; 1—Calculator; 2—Calendar; 3—Gamification; 4—Hypnosis; 5—Information; 6—Lung Health Tester;
7—Rationing.

Psych 2023,51097
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