Smartphone app-based interventions to support smoking cessation in smokers with mental health conditions: A systematic review

Background: Despite decades of global tobacco control efforts, tobacco smoking remains a leading cause of preventable disease, death and inequality. Smoking is particularly common in people with mental health conditions. Smartphone apps have been developed as an accessible and affordable tool to support smokers with mental health conditions to quit smoking. However, limited data exists regarding the extent to which these apps are underpinned by evidence from research. Methods: We searched for apps designed to assist smokers with mental health conditions to quit smoking, in two ways: one, from the scientic literature, and two, directly from app stores. For the apps found in app store searches, we determined the extent to which they drew on theories or empirical research evidence for their design, their features, and claims of effectiveness. We assessed and compared usage and rating scores for all apps. Results: The literature search identied eight articles with ve apps that were developed based on theories or empirical research evidence. Only two of these apps were available from an app store. Conversely, among the 22 apps found in the major app stores, only nine (41%) were built using theories or research evidence. All apps identied from app stores achieved far larger user numbers (minimum download rate = 1,000 times) and higher user rating scores (average 4.7 out of 5.0) than the apps identied in the literature search (user rating scores average 3.75). Conclusions: In general, smokers with mental health conditions are poorly served by available smoking cessation apps. Most apps developed using theories or empirical evidence are poorly used and have limited longevity. Researchers should plan for ongoing support of research-based apps, beyond the life of the research project. Developers should work in collaboration with researchers to build apps that combine theory and evidence with more engaging end-user design features. apps were found. One has functions for both smoking cessation and mental health conditions . By entering "mental health" into the Apple App store, the top ve returned apps are all focusing on mental health conditions. The most common functions among these apps are Calendar (n=3), Dairy (n=3), and Mood tracking (n=3). By entering "quit smoking" into the Apple App store, the top ve returned apps are all focusing on supporting tobacco cessation. From these apps, the most commonly developed functions include Calendar (n=4), Calculator (n=3), and Gamication (n=3).

To achieve this goal, the app market was evaluated from two perspectives: one, the health professional perspective; and two, the consumer (smokers with mental health conditions) perspective. Health professionals are more likely than consumers to review the scienti c literature. Without access to research literature, consumers are likely to rely on the recommendations of app stores when selecting healthcare apps. Exploration from this dual perspective required us to review the apps in two ways: one, starting from the literature and nding identi ed apps in the app stores and two, starting from the app stores.
To remain consistent with other published systematic reviews of smoking cessation apps [71][72][73][74][75], we followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model, and evidence-based set of criteria for reporting in systematic reviews and meta-analyses [76]. Many smoking cessation apps provide support grounded in valid, evidence-based behaviour change strategies (i.e., taxonomy of behaviour change techniques) while never being reported in a published research article. The aim of this review was to start by identifying the smoking cessation apps identi ed by the scienti c community and recommended for consumer use to form a foundation of scienti cally supported apps for smokers with mental health conditions. We elected to evaluate apps in line with traditional health interventions, which looks to the published medical literature for guidance. Therefore, for this review, only published research articles related to smoking cessation apps for smokers with mental health conditions were considered evidence of scienti c support. Apps designed to facilitate smoking cessation among smokers with mental health conditions were identi ed and evaluated in four steps.
Step 1: Identify all smoking cessation apps for smokers with mental health conditions reported in the scienti c literature We did a literature search of EMBASE, MEDLINE, APA PsycInfo, PubMed, Scopus, ACM Digital Library, and IEEE Xplore on 30 th September 2020. Table One shows the search terms used in different elds of the study. Because search engines differ between databases, search strategies were adapted to each database. Appendix 1 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. 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 [77] was developed to collect data from the identi ed articles. The data extraction sheet was adapted by adding a eld 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) 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); (3) evaluation of the app (including the assessment method, interventions, participants, duration, types of measure, ndings, 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 ndings was, therefore, not possible. Hence, identi ed literature was analysed by identifying 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 [77]. Different types of bias were assessed across studies including (1) selection bias -whether study participants are representative to the target population; (2) performance bias and detection bias -whether any types of blinding were performed; (3) attrition bias -incomplete outcome or loss of follow up; (4) other bias -speci ed 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 and SM) and agreement was reached between authors.
Step 2: Identify the apps from the literature review available in the app stores Each app identi ed in the scienti c 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 classi ed based on the taxonomy created by the National Tobacco Cessation Collaboration (NTCC) [78] and its updated version of the classi cation method developed by Abroms et al. [73].
Phase 3: Identify the top smoking cessation apps for smokers with mental health conditions, smoking cessation apps, and mental health apps available in app stores mental health, and quit smoking. Each search term was searched separately and all three were used for each of the two stores, totalling six separate searches. The top ve 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.
Step 4: Identify the apps from the app stores developed from theory or empirical evidence.
The top ve apps per search term were opened and reviewed regarding their developers, theories, development methods, smoking cessation features, mental health features, target users, categorises, charge, download rate, and user rating. Description (both in app stores and in "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 theory-related 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 ve 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 [79,80]. An app list was created by one review author (JChe) and shared with the other review authors (CB, JC and SM). All authors reviewed and analysed apps independently and discussed the key ndings of the analysis.

Results
Step 1: The search of listed databases provided a total of 1989 articles. After adjusting publication 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. By the end of title and abstract review, 98 articles were imported into the Endnote reference management system, where 28 duplicate articles were identi ed and excluded. The full text of the remaining articles was examined in detail. Sixty-one studies did not meet the inclusion criteria as described leaving only eight studies in the systematic literature review. No additional studies were identi ed by checking the references of located, relevant papers and searching for studies that have been cited by included studies. No unpublished studies were obtained. The ow diagram of articles selection is shown in Figure 1. The following table (Table 2) summarises the details of the reviewed studies. All eight reviewed articles were published in English from the United States. Two of these articles reported pilot randomised controlled trials (RCTs), three reported qualitative studies, two reported pilot trials, one reported a full RCT and one reported a development study. The duration of studies ranged from three days to six months. The studies involved a total of only 131 participants. The main inclusion criteria entailed adults (18 years or older), smokers (smoke one or more cigarettes or using other types of tobacco products in a daily basis), with mental health issues, and using a smartphone. The studied intervention in the reviewed articles is smoking cessation app for smokers with mental health issues. Due to the diversity of study designs, participants, interventions, and outcome measures, and high risk of bias (Table 3), a meta-analysis of these studies was not appropriate [87]. Step 2: Five smoking cessation apps for smokers with mental health conditions were identi ed from the reviewed studies. Details of these apps (both from literatures and app store search) are summarised in Table 4. All were built based on theories and clinical guidelines. Three had their development methods and processes discussed in the reviewed articles. Four 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 three had only functions for smoking cessation. Only two apps found in the systematic review were available in both Apple and Google App stores, two were only available in Apple App store, and one was not mentioned (no clari cation of its availability in app stores). Two apps include the keyword "quit" in their name.
By searching these apps in app stores, three of them are unavailable in Apple or Google App store, one is available for free in Apple app store, while one is available free in both Apple and Google App stores. Only one app has its download rate available in the Google App store, the app named Stay Quit Coach has been downloaded over 10,000 times in the Google App store. The user rating scores are only available to two literature-based apps. The app named Actify! has its user rating score of 1.5 out of 5.0 in the Apple App store, while it was only rated by four users. Stay Quit Coach was rated as 3.5 out of 5.0 (No. of reviewers = 2) in the Apple App store and rated as 4.0 out of 5.0 (No. of reviewers = 21) in the Google App store.  Phase 3: Identifying the top smoking cessation apps for smokers with mental health conditions, smoking cessation apps, and mental health apps available in app stores Table 5 summarises the details of top listed apps identi ed from the app store search. The Apple App store search returned twelve 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 like "mental health" (n=5), "smok*" (n=7), and "quit" (n=8) are frequently existed in the names of the searched apps.
By entering "mental health smoking" into the Google App store, two of the top ve returned apps are focusing on mental health conditions and three are focusing on smoking cessation. The most commonly seen smoking cessation functions are Calculator (n=3) and Gami cation (n=3). By entering "mental health" into the same app store, the top ve apps are all focusing 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 ve 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), Gami cation (n=3) and information (n=3).
Ten out of twelve identi ed apps from the Apple App store require in-app purchase, which means some functions of the app are not available to users unless users pay for using. The 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 515 (ranged from 5 to 3100 reviewers).
All apps identi ed from the Google App store require in-app purchase. 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 1,000 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).
Phase 4: Identi ed which apps from the app stores were developed based on theories All searched apps from the Apple App store (n=12) were developed by individuals or commercial companies. Five out of twelve apps were developed by following theories to help their users. Two of these apps post their development methods in the app store page. All identi ed apps from the Google App store (n=10) were developed by individuals or companies. Four of them use theory-based approaches to support their users. One shows its development method in the app store page.
Six apps from the Apple App store were developed to target on smokers, ve were designed to support the general population who want to maintain good mental health, and one was developed to support construction workers. Eight of these apps are categorised as Health and Fitness apps, three as Lifestyle apps, and one as a Medical app. Five identi ed apps from the Google App store are targeting on smokers while the other ve are targeting on general population. Eight of them are categorised as Health and Fitness apps, and two as Medical apps.
Most apps (n=20 out of 22 apps from both app stores) from app stores have either functions for supporting smoking cessation or managing mental health status. By entering "mental health smoking" into the Apple App store, only two apps were found. One has functions for both smoking cessation and mental health conditions . By entering "mental health" into the Apple App store, the top ve returned apps are all focusing on mental health conditions. The most common functions among these apps are Calendar (n=3), Dairy (n=3), and Mood tracking (n=3). By entering "quit smoking" into the Apple App store, the top ve returned apps are all focusing on supporting tobacco cessation. From these apps, the most commonly developed functions include Calendar (n=4), Calculator (n=3), and Gami cation (n=3).

Discussion
This systematic review aimed to identify smoking cessation apps for smokers with mental health conditions. A search of studies from seven databases identi ed only eight 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 [87]. Nevertheless, the narrative synthesis of evidence about mHealth app-based interventions for smoking cessation enables researchers to make several observations, as follows.

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 signi cant results.
Effectiveness of supporting smoking cessation and user experience are the two most commonly applied 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 [83,86]. Other literature reviews [72] on smoking cessation apps and app content analysis [74,75,[88][89][90][91][92][93][94][95][96], also draw the same conclusion: that the evidence for the effectiveness of smoking cessation apps on helping smokers to quit smoking is limited.
User satisfaction and perceived effectiveness were used to re ect 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 average user satisfaction.
For instance, the study done by Vilardaga et al. found that the app "QuitPal" was ve points below the industry standard based on the rating done by the study participants on the SUS [82]. As mentioned by some smoking cessation app studies and reviews, limited evidence is available about the factors those 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 built by following theories [21,24,29,43,50,51,92].
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 signi cant decrease in PHQ score was found in participants who used the smoking cessation app to achieve smoking abstinence (mean change in PHQ-9 scores was -4.5, 95% CI -7.7 to -1.3; P=.01) [84]. 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 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][8][9][10].

Findings from App Review
Overall, 27 apps were discussed in this review ( ve reviewed literature-based apps, and twenty-two apps identi ed from the Apple and Google App stores). Key words like "mental health" (n=5), "smok*" (n=7), and "quit" (n=8) are very common in apps from app stores than apps introduced in the reviewed studies. This may be the reason of why the literature-based apps were di cult to nd in app stores (three out of ve 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 like "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 [79,80]. 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 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 can be easily navigated when typing the correct key words.
All literature-based apps use approaches developed based on theories to support smoking cessation and mental health management. In comparison, less than half (41%) of apps searched from app stores apply theory-based approaches. The nding of lack of theory-based approaches in health-related commercial apps is similar to other studies on health-related apps target on other conditions [97,98]. 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 existing smoking cessation app analysis. Cheng et al. found that an smoking cessation app's theories and guideline adherence level is positively related to its rating in app stores [92]. 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 [73]. Although there are some exception scenarios, the application of theory-based approaches secure the safety, rigorousness and potentially the effectiveness of the apps.
Two literature-based apps have both smoking cessation and mental health management functions, while two apps searched from the app stores have functions from the both categories. However, the functions of app store searched apps are relatively easier in compared to 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 harms information to users. The situation 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 [68, 83, 85]. As introduced, smoking and mental health conditions are two strongly connected health conditions [7][8][9][10]. It is important to understand the mechanism between how these two factors affect one to the other 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 re ect to the potential cost for maintaining these products. Although download rate is unavailable for apps from Apple App store, the identi ed apps from the Google App store provide some information about how popular these apps are. Based on the ten identi ed apps from the Google App store, six of them were downloaded over 1 million times. The massive difference in download rates re ects a vast difference between 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 literature-based apps. They provide some good examples and reference to research team about designing and developing an attractive and engaging app. Collaboration between research teams and commercial companies or applying app design standards [99,100] from commercial companies can be a method to improve the attractiveness and engagement of research-based apps.

Limitations
There are a number of limitations in this systematic review. First, there was inconsistency of interventions and study settings, making a meta-analysis inappropriate [14]. 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 research design and evaluation would enable greater comparability between studies. Second, the quality of studies varied widely. Five trials had a small sample size (n<50 participants) and failed to detect statistically signi cant 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 size, 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=27, ve based on reviewed literature and 22 from app stores). Commercial apps were the top ve apps in the two major app stores but there are hundreds of apps available in both app stores that were not reviewed.

Conclusion
This study used a systematic method to identify and review current studies of smoking cessation apps for smokers with mental health conditions. There is currently insu cient evidence that smoking cessation apps are effective at supporting people with mental health conditions to quit smoking. There is a lack of data on the size of the effect and the impact of these apps on users' mental health conditions. Research-based apps were usually perceived as effective by their users, and usually have theory-based approaches to support their users. However, compared to commercial-based apps, research-based apps have a lack of attractiveness and engagement. The marketing strategies for research-based apps need to be improved. Using a better naming strategy and following industrial standards to design apps would be a good starting point. The logical next step for future study would thus be developing a smoking cessation app for smokers with mental health conditions based on scienti c evidence (including theories and relevant clinical guidelines), as well as learning from popular commercial-based apps. Future randomised controlled trials of such apps should aim for larger sample sizes, longer follow up periods and use more comprehensive outcome measures. Availability of data and materials Data used to draw the ndings of literature review of the current study are available from the reviewed articles, data used to draw the ndings of app review of the current study are available from the app stores and the corresponding author on reasonable request.

Competing interests
The authors declare that they have no competing interests.

Funding
The current study is funded by the Ember Korowai Takitini Research Grant. The funding body did not take part in the design of the study, or the collection, analysis, and interpretation of data, or in writing the manuscript.

Authors' contribution
Jche ran the literature and app search, extracted and analysed the data, and wrote the manuscript. CB, JC and SM reviewed the data analysis and co-wrote the manuscript. All authors read and approved the nal manuscript.