Mobile applications (apps) for tobacco cessation: Behaviour change potential and heuristic analysis using the Mobile Application Rating Scale (MARS)

TL;DR

This study evaluates mobile apps for quitting tobacco in India, finding that newer apps focused on behavior change are more effective than older ones.

Contribution

The study identifies high-quality tobacco cessation apps in India and highlights the shift toward behavior change-focused mHealth solutions.

Findings

Top-rated apps like Quitsure and Craving to Quit! scored highest on the MARS scale.

Newer apps emphasize behavior change, while older apps focus on goal setting or substance use.

Behavior change-focused apps offer higher quality content and functionality.

Abstract

Given the high prevalence of tobacco use, India presents a significant challenge in tobacco control. Yet, the support received for tobacco cessation is suboptimal. Hence, the aim was to identify and heuristically evaluate ‘high-quality’ and ‘engaging’ tobacco cessation mobile apps using the Mobile Application Rating Scale (MARS). Also, to categorize and analyse their features with respect to engagement, functionality, aesthetics, and information quality.

A systematic search of tobacco cessation apps was done within app stores of prominent smartphone platforms developed by Apple and Android. The following search terms: ‘quit smoking,’ ‘smoking cessation, ‘stop smoking,’ ‘smoking therapy,’ ‘quit tobacco,’ ‘cigarette cessation,’ ‘cold turkey,’ and ‘quit cigarette.’ Pearson’s correlations were used to analyse correlations between app scores (Total score app-quality/mean score) and downloads/ratings and number of downloads with the overall MARS score. A Chi-square test was performed to analyse any association between app focus and app release dates.

Total MARS scores ranged from 3.1 to 4.9. Quitsure app (4.9), Craving to Quit! app (4.8) and Stop Tobacco Mobile Trainer: Quit Smoking App (4.74) were ranked the highest according to the MARS overall mean score. Older apps focussed more on mere goal setting or substance use, as compared to behaviour change; whereas, recently developed apps are now focussing more on behaviour change.

The content and functionality of behaviour change-focused apps were of higher quality than those of other app categories. These recently developed mHealth apps can effectively supersede the traditional smoking cessation methods.

Figures

Mobile Application Rating Scale (MARS) description.

Flowchart for identification of Apps through systematic screening of the available tobacco cessation apps.

Pie diagram depicting the distribution of apps based on their primary focus.

Tables

Table 1.. Basic attributes of mHealth apps for tobacco cessation.

Characteristics of the app		Apps n (%)
Platform	Android	17
	iOS	3
Cost	Free upgraded versions	6
	Paid upgraded versions	14
Year of release	2010-2014	5
	2015-2018	5
	2019-2023	10
Number of downloads	Up to ten thousand	4
	Up to one million	12
	More than one million	4
Primary focus	Substance use	2
	Goal setting	14
	Behaviour change	4

Table 2.. General attributes of apps included in the study.

No.	App name	Rating	Last update	Cost
1	Smoke Free- Stop Smoking Now	4.6	17-06-2023	Paid
2	Quitsure	4.7	26-07-2023	Paid
3	Quit Now: Quit Smoking for Good	4.6	17-07-2023	Paid
4	Stop Tobacco Mobile Trainer. Quit Smoking App	4.5	22-06-2022	Paid
5	Easy Quit Stop Smoking	4.7	24-11-2022	Paid
6	Smokefree – stop smoking now	4.6	17-06-2023	Paid
7	Smoking Log	4.4	15-07-2022	Free
8	Quit Now: My Quitbuddy	4.7	17-07-2023	Free
9	Craving To Quit!	4.4	09-05-2022	Paid
10	Quit It- Stop Smoking Today	4.6	17-06-2019	Paid
11	Quit Tracker: Stop Smoking	4.7	24-03-2022	Paid
12	Smokefree: Quit smoking slowly	4.1	13-11-2020	Paid
13	Kwit- Quit Smoking for Good	4	28-07-2023	Paid
14	Quitstart	4.3	16-05-2023	Free
15	Stop smoking - Stay sober	4.2	05-02-2023	Paid
16	No Smoking	4.4	20-11-2023	Paid
17	Qwit	4.1	10-08-2023	Free
18	Quit Smoking- Stop Tobacco Mobile Trainer	5	5 years ago	Paid
19	Quit Smoking- Get Healthy	4.7	3 years ago	Free
20	Quit Smoking	4.5	10 months ago	Free

Table 3.. Mean app quality ratings scored under engagement, functionality, aesthetics and information domains.

No.	App name	Engagement mean score	Functionality mean score	Esthetics mean score	Information mean score
1	Smoke Free- Stop Smoking Now	4.4	4.75	4.67	4.4
2	Quitsure	5	4.75	5	4.8
3	Quit Now: Quit Smoking for Good	4.2	4.75	5	4.2
4	Stop Tobacco Mobile Trainer. Quit Smoking App	4.6	4.75	5	4.6
5	Easy Quit Stop Smoking	3.6	3.75	3.67	3.6
6	Smokefree-stop smoking now	3.6	4	4	4
7	Smoking Log	3.4	4	3.67	3.4
8	Quit Now: My Quitbuddy	4.6	4.5	4.67	4.6
9	Craving To Quit!	4.8	4.75	5	4.8
10	Quit It- Stop Smoking Today	3.4	3.75	3.33	3.6
11	Quit Tracker: Stop Smoking	4.2	4.25	4.67	4
12	Smokefree: Quit smoking slowly	3.5	3.5	3.33	3
13	Kwit- Quit Smoking for Good	4.8	4.5	4.67	4.8
14	Quitstart	4.2	3.75	4	3.8
15	Stop smoking - Stay sober	4	4	4	3.6
16	No Smoking	3.8	3.75	3.33	3.6
17	Qwit	3.4	3.5	3.67	3.4
18	Quit Smoking- Stop Tobacco Mobile Trainer	4.4	4.5	4.67	4.2
19	Quit Smoking- Get Healthy	4	4	4	3.6
20	Quit Smoking	3.8	4	4	3.6

Table 4.. The scores of each Mobile Application Rating Scale (MARS) domain and overall MARS score of the studied apps.

No.	Android app name	App quality mean score	App subjective mean score	App specific mean score	MARS overall mean score
1	Smoke Free- Stop Smoking Now	4.55	4.25	4.33	4.37
2	Quitsure	4.88	5	4.83	4.9
3	Quit Now: Quit Smoking for Good	4.53	4	4.16	4.23
4	Stop Tobacco Mobile Trainer. Quit Smoking App	4.73	5	4.5	4.74
5	Easy Quit Stop Smoking	3.65	3	3.5	3.38
6	Smokefree-stop smoking now	3.9	3	3.66	3.52
7	Smoking Log	3.61	3	3.66	3.42
8	Quit Now: My Quitbuddy	4.59	4.25	4.66	4.5
9	Craving To Quit!	4.83	4.75	4.83	4.8
10	Quit It- Stop Smoking Today	3.52	3	3.33	3.28
11	Quit Tracker: Stop Smoking	4.27	4.25	4.16	4.23
12	Smokefree: Quit smoking slowly	3.35	3	3	3.11
13	Kwit- Quit Smoking for Good	4.69	4.75	4.66	4.7
14	Quitstart	3.93	3.5	3.83	3.75
15	Stop smoking - Stay sober	3.9	3.5	3.66	3.68
16	No Smoking	3.62	3.5	3.33	3.48
17	Qwit	3.49	3	3.16	3.21
18	Quit Smoking- Stop Tobacco Mobile Trainer	4.44	4.75	4.5	4.56
19	Quit Smoking- Get Healthy	3.9	3.75	3.66	3.77
20	Quit Smoking	3.85	3.75	3.83	3.81

Full text

Introduction

Tobacco use is the world’s most significant threat that is responsible for numerous preventable morbidities, most of which have the potential to kill consumers prematurely. Six of the top eight causes of mortality have smoking as a risk factor. The 20th century has seen an estimated 100 million fatalities worldwide as a result of the tobacco epidemic. ^ 1 ^ India is currently experiencing a second-stage tobacco pandemic. Each year, 800,000 to 900,000 people in India die from diseases linked to tobacco use. It is crucial to act swiftly to stop this preventable disease. ^ 2 ^

Given the high prevalence of tobacco use, India represents a significant challenge in tobacco control. According to the latest Global Adult Tobacco Survey (GATS) India (2016–17), 28.6% of smokers in India use tobacco, with male smokers having a greater prevalence (42.4%) than female smokers (14.2%). According to this nationally representative survey, 52% of tobacco users are ready to give up the habit. ^ 3 ^ ^,^ ^ 4 ^ Tobacco cessation programs run at hospitals and rehabilitation centres provide face-to-face and telephone-based behavioural interventions (e.g., brief motivational advice, group or individual counselling, telephone counselling and quit lines) and pharmacotherapy (e.g., nicotine replacement therapies, bupropion) to support smoking cessation. Yet, tobacco cessation support is suboptimal owing to the lack of access to smoking cessation resources, lack of clinician knowledge or skills, and lack of clinician time. The majority of people making independent attempts fail to maintain their quit status for even one month. Hence, use of mHealth intervention for smoking cessation represents one of the best methods to curb the global public health threat of the tobacco epidemic. ^ 5 ^

In the recent times, mobile phones, the Internet and mobile communication have become an inevitable part of our lives. According to estimates, 3.5 billion smartphones were in use worldwide as of June 2020, providing a sizable number of users to help coordinate and conduct mobile interventions to distribute information. ^ 6 ^ An “app” is the most frequent term for a mobile application that is created to run on portable wireless mobile phones like smartphones. ^ 7 ^ ^,^ ^ 8 ^ With over three billion downloads of mHealth apps worldwide in 2015, an effective, high-quality smoking cessation app can be an easy-to-use resource that can provide personalized smoking cessation support to thousands of people, including remote smokers, at the right time and location. Past reviews show that mobile apps have tremendous behavioural change potential. ^ 6 ^ ^,^ ^ 7 ^ ^–^ ^ 9 ^

The utilization of mobile applications in healthcare can offer a range of benefits, including ease of access to information and the potential for enhanced patient engagement and adherence to treatment. However, there are some drawbacks to the use of mobile applications, particularly as the information on apps may not be regulated. As a result, some of the apps that patients access may contain substantial inaccuracies. ^ 8 ^

The use of smoking cessation interventions utilizing smartphone technology is on the rise globally. However, the effects on cessation rate and prevention of relapses are not often evaluated. ^ 10 ^ Moreover, few studies are reporting the effectiveness of smartphone apps for tobacco cessation. What’s more, rankings within the leading app stores are highly volatile and primarily based on the usability of apps. Hence, the behaviour change potential of apps cannot be solely judged based on app ‘ratings’ or ‘popularity’ in app stores.

Recently many evaluation criteria/scales have been developed to evaluate app efficacy. Evaluation based on addressing the 5 As (Ask, Advise, Assess, Assist and Arrange) of tobacco cessation, App Behaviour Change Scale (ABACUS), adherence to clinical practice guidelines for tobacco control, Mobile Application Rating Scale (MARS) have been commonly used in recent studies. ^ 11 ^ ^–^ ^ 15 ^ The latter evaluation method, MARS was developed by Stoyanov SR, is widely used and validated by many studies ( Figure 1). ^ 15 ^ ^–^ ^ 18 ^ Hence, we aimed to identify and heuristically evaluate ‘high-quality’ and ‘engaging’ tobacco cessation mobile apps using MARS. The objectives were to identify tobacco cessation apps on Apple and Android app stores and to categorize and analyse their features as regards to engagement, functionality, aesthetics, and information quality. Thereby recommending directions for the improvement of these apps.

Mobile Application Rating Scale (MARS) description.

Methods

Ethics statement

The study was registered with Kasturba Hospital ethics committee, approval number [IEC2 – 19 (2022), dated 21/06/2022.

Search strategy

A systematic search of tobacco cessation apps was done within the Apple and Android app stores. The following search terms were used: ‘smoking cessation, ‘quit smoking,’ ‘stop smoking,’ ‘smoking therapy,’ ‘quit tobacco,’ ‘cigarette cessation,’ ‘cold turkey,’ and ‘quit cigarette.’ All the apps rated 4* and above, with downloads of more than 10,000 were included in the study. Apps that did not target tobacco cessation and those not in English were excluded from the study. We also eliminated the duplicate apps, including earlier versions. To begin with, we identified all apps based on their focus area:

• •Substance use: information about the ill-effects of tobacco and thereby motivating users to quit.
• •Goal setting: monitoring and tracking the process of quitting.
• •Behaviour change: using therapeutic techniques such as cognitive behaviour therapy (CBT), acceptance commitment therapy (ACT) and mindfulness.

Subsequently, extracted data from each app were assessed using the MARS rating scale. The MARS evaluation tool is divided into three broad sections: App overall quality, App subjective quality, and App specific quality. It consists of 23 questions designed to help analyse the mobile applications' engagement, functionality, aesthetics, information, and subjective quality. In addition, there are six additional app-specific questions that can be customized to represent the goal health behaviour/function of the application/study. The average scores for each of these four sub-schemes were calculated, and the average scores of these sub-quotas used to evaluate the overall quality score of the application ranged from 0 to 5. Therefore, the total score of these sub-categories would be 0 to 5 and the average score would be calculated by dividing the total score by 4, i.e., four domains. The subjective quality section of the App and the App-specific section of the App were calculated similarly. These, however, were considered as separate from the app quality score. ( Figure 1)

Data regarding the App’s features were entered in a Microsoft Excel sheet v 2019 and subjected to statistical analysis to conclude. The two reviewers were trained in the use of MARS using a 37-minute video on YouTube. Subsequently, they conducted an independent assessment of a standard application and discussed their scores.

Statistical analysis

Data were analysed using descriptive and analytical statistics. Quantitative variables were expressed as means and standard deviations. Kendall’s coefficient of concordance was used to calculate the interrater agreement between two raters. Pearson’s correlations were used to analyse correlations between app scores (Total score app-quality/mean score) and downloads/ratings and number of downloads with the overall MARS score. A Chi-square test was performed to analyse any association between app focus and app release dates with a p-value of <0.05 considered as significant.

Results

A total of 670 apps were retrieved from within the Apple and Android App stores (Android 482; iOS 188). After screening the description available on the web page, 48 apps were excluded as they were duplicate apps present in both the Play stores. [19] Of the 622 apps filtered, 558 apps didn’t meet the inclusion criteria and were excluded from the study. After all the exclusions, 20 apps (Android,17; iOS, 3) were subjected to analysis. A flowchart illustrating the selection and exclusion of apps at various stages of the study is shown in the Figure 2. Interrater agreement between two raters showed high reliability (0.804).

Flowchart for identification of Apps through systematic screening of the available tobacco cessation apps.

All apps were free in their basic version, but 14 required payments to view their upgraded versions. As per their primary focus area, two apps targeted only substance use, while 14 apps focused on goal setting and four apps were aimed at behaviour change ( Figure 3). Five apps had an app-based community that allowed virtual help. All apps were available for general age groups ( Table 1). Fifteen apps had second or higher versions and the last update of most apps was after June 2023. The mean app rating by consumers in the Google Play store was more than 4 (according to the inclusion criteria) ( Table 2).

Pie diagram depicting the distribution of apps based on their primary focus.

Table 3 gives the mean app quality ratings scored under engagement, functionality, aesthetics and information domains. The Craving To Quit! and QuitSure apps recorded a score of 4.75 and above in all four domains. Whereas, the SmokeFree: Quit smoking slowly and Qwit apps had scores of 3.7 or below in all the domains. The average score of the above four domains, that is the App quality mean score, was highest for the QuitSure app (4.88) and Craving To Quit! app (4.83).

The MARS overall mean score was then calculated by calculating an average of App quality mean score, App subjective mean score and App specific mean score. We observed that the QuitSure app (4.9), Craving To Quit! app (4.8) and Stop Tobacco Mobile Trainer: Quit Smoking App (4.74) were ranked the highest according to the MARS overall mean score. Whereas, Smokefree: Quit smoking slowly (3.1) and Qwit (3.2) scored the least ( Table 4).

We examined if there was any correlation between average review scores and overall MARS scores. There was a moderate, positive correlation seen, which was statistically significant (r = 0.395, n = 20, p = 0.038). Also, a moderate, positive correlation between the number of downloads and the overall MARS score was seen (r = 0.480, n = 20, p = 0.013).

A Chi-square test was performed to analyse any association between app focus and app release dates. Older apps focussed more on mere goal setting or substance use, as compared to behaviour change. Apps that have been recently developed are now focussing more on behaviour change and these results were statistically significant (P value = 0.000).

Discussion

This study reviewed mobile applications for quitting smoking and used the MARS scale to rate the apps' quality. It is one of the most popular scales for evaluating the value and content of mHealth apps. It is a multidimensional tool for assessing the quality using semantic analysis and synthesis of existing literature. ^ 15 ^ Engagement, functionality, aesthetics, and information quality are the four main dimensions that are assessed ( Figure 1). The initial validation study demonstrated strong objectivity and reliability for the overall scale and subscales. ^ 16 ^ ^,^ ^ 17 ^ The MARS' applicability for quality assessment was proved by a validation study with metric evaluation. ^ 17 ^ As a result, MARS might be utilised to make the quality of apps transparent to patients and healthcare stakeholders.

Our study found more than 622 smoking cessation apps in both platforms. It is concerning that the app stores for smoking cessation applications is saturated with apps of low quality, perhaps making it difficult for users who aren't the most selective to find higher-quality apps. This study reviewed 20 of these apps aimed at helping people quit smoking in order to evaluate their quality and distinguishing characteristics.

In this study, we classified apps based on their primary goals: goal setting, substance use, and behaviour change. Most of apps with the primary focus as goal setting functioned as trackers for cravings, calendars, number of cigarettes not smoked and the amount of money saved. These results are similar to the study conducted by Vilardaga et al., ^ 9 ^ and Hoeppner et al., ^ 11 ^

The results of our study are consistent with the results of earlier reviews. Education and behavioural methods are the two areas that most frequently appear in mHealth apps. ^ 5 ^ ^,^ ^ 9 ^ ^,^ ^ 18 ^ By changing their behaviour, people can better their prognosis, lessen pain or suffering, and take control of their health. According to the findings of our study, the apps that focus on behaviour modification had higher user ratings and overall MARS scores with many features.

Goal-setting and substance use are two additional crucial components of tobacco cessation applications. These apps function by asking users to enter their cigarette consumption before creating their own objective goals for quitting. The app also offers resources and data to help users track their progress towards their goals and maintain their motivation to stop using tobacco. However, it has been noted that the number of goal-setting apps has dropped over time.

Our study thus points to a potential market for developing behaviour change targeting apps that offer knowledge and expertise for controlling the quitting process. Like in previous studies, apps that utilize behaviour change and cognitive techniques, which are evidence based, such as the 5 A’s, acceptance and commitment therapy and hypnosis were having the highest MARS score among all the apps. ^ 5 ^ ^,^ ^ 13 ^ ^,^ ^ 14 ^

The aesthetic and information subscales had slightly lower mean scores than the engagement and functionality subscales, which shows that even though the apps had good graphics, a pleasing visual appearance, and accurate descriptions and information, it is still crucial that the target audience is well-engaged and is persuaded to use the apps. The results of previous studies using MARS for quality assessment of mobile apps for the management of tinnitus ^ 20 ^ and asthma management ^ 21 ^ showed the engagement and aesthetic scores were lower, which indicates that these factors are less important in the design of health management apps. These noticeable features could be improved and developed in the next version of the apps.

Generally, while creating an app, developers must take into account both intriguing and crucial features as well as high-quality, fact-based material. The typical mean scores for engagement and functionality point to prospective areas that could be improved. Further research like randomised controlled trials need to be done to determine whether these apps result in behaviour change, and not just improvement in knowledge.

This study contains some limitations. First, popularity is the basis for app store rankings. As a result, the top apps that each search displayed did not always represent the best apps. We did not have access to numerous international apps created exclusively for use within each nation as phone numbers were required for registration and app usage. Applications go through upgrades and modifications frequently. Several apps assessed may have been upgraded to newer versions since the MARS evaluation was conducted. The most recent version and these updates could change the outcome of this research. Also, given that the apps were only used once and the quality ratings were based on brief usage, it's probable that some features were overlooked by the reviewers while evaluating the apps. The strength of our study is that the raters paid for the upgraded version of a number of apps to access the full version so as to not miss any distinguishable features of those apps.

Conclusions

The content and functionality of behaviour change-focused applications were of higher quality than those of other app categories. These recently developed mHealth apps can effectively supersede the traditional smoking cessation methods. This study can be a reference for those who use and create smoking cessation apps. Users can select the best smoking cessation app for their needs by using the classification of the application.