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. 2024 Nov 22;12:1413. Originally published 2023 Oct 26. [Version 2] doi: 10.12688/f1000research.142038.2

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

Bhargav Bhat 1, Prajna Pramod Nayak 1,a, Ramprasad Vasthare 1, Deepak Kumar Singhal 1
PMCID: PMC12107232  PMID: 40433635

Version Changes

Revised. Amendments from Version 1

We have updated the tables and Discussion based on reviewer comments.

Abstract

Background

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 of this narrative review 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.

Methods

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.

Results

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.

Conclusions

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.

Keywords: smoking cessation, mobile health, mHealth, tobacco use cessation, mobile app, oral health

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. Most people making independent attempts fail to maintain their quit status for even one month. 2 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, there are 751.5 million internet users in India by the beginning of 2024, with internet penetration of 52.4%. In early 2024, India had 1.12 billion active cellular mobile connections, which accounted for 78% of the entire population, providing a sizable number of users to help 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 behaviour change potential. 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 This 23-item rating measure assesses the quality of mHealth apps on a 5-point scale (1=inadequate, 2=poor, 3=acceptable, 4=good, and 5=excellent). 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 analyze their features as regards to engagement, functionality, aesthetics, and information quality. Thereby recommending directions for the improvement of these apps.

Figure 1. Mobile Application Rating Scale (MARS) description.

Figure 1.

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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 1 to 5. Therefore, the total score of these sub-categories would be 1 to 5 (1=inadequate, 2=poor, 3=acceptable, 4=good, and 5=excellent) 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, are 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 (MARS training video: https://www.youtube.com/watch?v=25vBwJQIOcE). The study team also established specific instructions and guidelines for assessing smoking cessation apps so as to ensure consistency between the raters. Subsequently, they conducted an independent assessment of a standard application and discussed their scores. The quality scores were then calculated as an average of the ratings from the two reviewers.

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, 568 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).

Figure 2. Flowchart for identification of Apps through systematic screening of the available tobacco cessation apps.

Figure 2.

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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. 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).

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 10
2015-2018 5
2019-2022 5
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
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Table 2. General attributes of apps included in the study.

No. App name Rating Last update Cost Legitimate source
1 Smoke Free- Stop Smoking Now 4.6 06-11-2024 Paid Source identified - Individual
2 Quitsure 4.7 21-10-2024 Paid Source identified - Individual
3 Quit Now: Quit Smoking for Good 4.6 05-11-2024 Paid Source identified - University
4 Stop Tobacco Mobile Trainer. Quit Smoking App 4.5 22-06-2022 Paid Source identified - University
5 Easy Quit Stop Smoking 4.7 15-08-2024 Paid Source not identified
6 Quitzilla: Bad habit tracker 4.6 14-10-2024 Paid Source not identified
7 Smoking Log 4.4 25-09-2023 Free Source not identified
8 Quit Now: My Quitbuddy 4.7 03-10-2024 Free Source identified – Government funded
9 Craving To Quit! 4.4 11-09-2024 Paid Yes; source identified - University
10 Quit It- Stop Smoking Today 4.6 17-06-2019 Paid Source not identified
11 Quit Tracker: Stop Smoking 4.7 02-09-2024 Paid Source not identified
12 Smokefree: Quit smoking slowly 4.5 04-11-2023 Paid Source not identified
13 Kwit- Quit Smoking for Good 4 07-10-2024 Paid Source not identified
14 Quitstart 4.3 16-05-2023 Free Source identified – Government funded
15 Stop smoking - Stay sober 4.2 02-08-2024 Paid Source not identified
16 No Smoking 4.4 20-11-2023 Paid Source not identified
17 Qwit 4.1 03-10-2023 Free Source not identified
18 Quit Smoking cigarette - Smoxy 4.6 05-11-2024 Paid Source not identified
19 Quit Smoking- Get Healthy 4.7 25-08-2023 Free Source not identified
20 Quit Smoking 4.5 10 months ago Free Source not identified
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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).

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
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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).

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
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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) ( Table 5).

Table 5. Correlation between user ratings and MARS overall score.

User ratings MARS ratings
User ratings Pearson Correlation 1 .395 *
Sig. (2-tailed) .038
MARS rating Pearson Correlation .395 * 1
Sig. (2-tailed) .038
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*

Correlation is significant at the 0.05 level (2-tailed).

A Chi-square test was performed to analyse any association between app focus and app release dates. Older apps focused more on mere goal setting or substance use, as compared to behaviour change. Apps that have been recently developed are now focusing more on behaviour change and these results were statistically significant (P value = 0.000) ( Table 6).

Table 6. Association between app focus and app release dates.

Release year
Focus 2011-2014 (%) 2015-2018 (%) 2019-2022(%)
Behavior change 0 (0%) 1 (25%) 3 (75%)
Substance use 1 (50%) 1 (50%) 0 (0%)
Goal setting 9 (64.3%) 3 (21.4%) 2 (14.3%)
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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 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 utilized 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 many apps, perhaps making it difficult for users who aren't the most selective to find higher-quality apps. There were many apps with ratings above 4 stars, we included those with a rating of 4 stars or higher as well as more than 10,000 downloads in our inclusion criteria, since apps with high ratings but very few downloads could not be relied upon. This study reviewed 20 of these apps that 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, Quitsure app, Craving to quit, Kwit-quit smoking for good and Quit Now: Quit Smoking for Good apps, which focused on behaviour modification had higher user ratings and overall MARS scores with many features. In a study conducted by Thornton L et al (2017), My Quit buddy and Quitstart apps showed the highest MARS scores among 6 apps rated. 13 The scores of these apps were consistent with our findings. Another study conducted by Seo S et al (2023) showed similar results, where, Quitstart app showed moderately high score and My Quit buddy app obtained highest score. 14

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. Therapeutic approaches like cognitive behavioral therapy (CBT), acceptance and commitment therapy (ACT), and mindfulness practices need to be incorporated while developing such apps. 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 CBT is an effective approach, as it helps individuals identify and change the thoughts, behaviors, and triggers that contribute to their smoking habit. CBT helps individuals recognize the specific situations, feelings, or thoughts that trigger the urge to smoke and includes teaching coping skills to replace smoking with healthier behaviors. ACT is based on the idea that struggling against negative emotions, thoughts or experiences often leads to greater distress, avoidance and unhelpful behaviors. Instead of aiming to eliminate painful feelings, ACT encourages individuals to accept them while still acting toward meaningful life goals. 12 , 13

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. Inclusion of apps with over 10,000 downloads might have excluded newer but less popular apps which are equally virtuous. 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.

Funding Statement

The author(s) declared that no grants were involved in supporting this work.

[version 2; peer review: 2 approved

Data availability

Underlying data

Open Science Framework: Underlying data for ‘Mobile applications (apps) for tobacco cessation: Behaviour change potential and heuristic analysis using the Mobile Application Rating Scale (MARS)’, https://www.doi.org/10.17605/OSF.IO/JNBPF. 19

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0)

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F1000Res. 2025 May 26. doi: 10.5256/f1000research.174893.r380805

Reviewer response for version 2

Jinsong Chen 1

This study presents a heuristic evaluation of tobacco cessation mobile applications using the Mobile Application Rating Scale (MARS). A total of 670 apps were identified, from which 20 high-rated apps (≥4 stars and ≥10,000 downloads) were selected and analyzed across four quality domains: engagement, functionality, aesthetics, and information. The top-performing apps— QuitSure, Craving To Quit!, and Stop Tobacco Mobile Trainer—were found to excel in behaviour change potential. The authors categorized apps by focus (substance use, goal setting, and behaviour change), highlighted the increasing trend of behaviour-change-oriented apps, and used appropriate statistical tools to correlate app characteristics with MARS scores. The article also presents improvements from its initial version based on previous reviewer feedback.

Assessment & Comments

1.  Scientific Soundness:  Yes – The article is scientifically sound with clear objectives, a structured methodology, and relevant findings. The use of MARS is justified and properly implemented.

2.  Clarity and Structure:  Yes – The manuscript is clearly written, well-structured, and uses standard academic language.

Detailed Comments and Feedback

A.  Strengths

  • The use of the validated MARS tool offers a reliable framework for assessing app quality.

  • The classification of apps by behavioural focus is valuable and clearly operationalized.

  • Statistical correlation between user ratings/downloads and MARS scores adds analytical strength.

  • Tables are well-organized and present results in an accessible way.

B.  Addressed Concerns

  • The authors responded effectively to reviewer concerns from Version 1, such as:
    • Adding source credibility for apps in Table 2.
    • Clarifying MARS training and inter-rater reliability.
    • Including tables for correlation and chi-square analyses (now Table 5 and Table 6).
    • Rephrasing vague claims and avoiding repetition.

C.  Minor Remaining Points (Optional but recommended for polish)

  1. Sample Size Limitation

    While 20 apps were evaluated in-depth, this is a small proportion of the initial 670. The authors have acknowledged this, but a brief note on how future work could scale or diversify the dataset (e.g., inclusion of lower-download but innovative apps) would be beneficial.

  2. Clarify “Older” Apps Definition

    The classification of app age and focus could be more explicitly defined in the Methods section, rather than inferred from Table 6.

  3. Discussion on Behaviour Change Techniques

    The discussion now touches on CBT and ACT, but a slightly deeper explanation of why these approaches are more effective (e.g., reinforcement, coping skills, identity shift) would further strengthen the section.

  4. Limitations Section Expansion

    The authors rightly list the limitations, but could also acknowledge that ratings and downloads can be gamified or manipulated, which may skew the inclusion threshold.

Conclusion

This revised version of the article has adequately addressed the major concerns raised by earlier reviewers. It presents a methodologically sound, clearly written, and timely assessment of mobile apps for tobacco cessation. The authors demonstrate a thorough understanding of the MARS framework and provide practical insights for developers and public health practitioners alike.

Final Recommendation : 

Approved – No further major revisions required.

Optional refinements listed above may enhance impact but are not mandatory for scientific soundness.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

I cannot comment. A qualified statistician is required.

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Digital Health, Clinical Trial, Behaviour Change

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

F1000Res. 2024 Dec 31. doi: 10.5256/f1000research.174893.r342742

Reviewer response for version 2

Cristina Rey-Reñones 1

APPROVED.

Is the work clearly and accurately presented and does it cite the current literature?

Partly

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

mhealth, tobacco, nursing

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

F1000Res. 2024 Oct 18. doi: 10.5256/f1000research.155532.r327336

Reviewer response for version 1

Ranjani Harish 1, Nitika Sharma 1, Catherine Vincy 1

General Comments:  The aim of the study was 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.

Specific Comments:

Title:

  1. Please specify the type of review in the title to facilitate better understanding of the article. 

Abstract:

  1. MARS (Mobile Application Rating Scale) could have been incorporated into the keywords to enhance the specificity.

Introduction

  1. Kindly give reference for the following lines in the introduction section: “The majority of people making independent attempts fail to maintain their quit status for even one month.”

  2. The second para of this section provides statistics on tobacco cessation from India. However, the following paragraph discusses global statistics on smartphone users, which appears to be out of sync with the flow of information. To maintain coherence, it would be better to align the information properly.

  3. The rationale of the review was well articulated in the introduction although a little more background on MARS and its scoring system and how it helps in heuristically evaluating the mhealth applications could be provided to strengthen the foundation of the study.

Methodology:

  1. The introduction highlights that rankings in app stores are highly volatile and primarily influenced by app usability, meaning that an app's behaviour change potential cannot be fully assessed based on its ratings or popularity alone. However, the inclusion criteria in the methods section focus on selecting apps with a rating of 4 stars or higher and more than 10,000 downloads. Justify.

  2. The inclusion of only apps with a rating of 4 stars or higher and over 10,000 downloads might exclude newer or less popular apps that could still be effective. This introduces a bias towards more established apps. Kindly justify.

  3. The reviewers were trained using a 37-minute YouTube video. Please answer the following questions:

    a) It may not be sufficient for thorough, expert-level assessment using the MARS tool. Kindly comment.

    b) While a high interrater reliability (0.804) is reported, no additional details are provided on how discrepancies between reviewers were resolved. This could affect the validity of the scoring and analysis if disagreements were not consistently addressed. Justify.

    c) How reliable and authentic was the video? It is suggested to include the link for the video in the references. 

  4. There’s no mention of how frequently the apps were updated or if the study accounted for updates during the research period in the methods section. App features and functionalities can change significantly with updates, which could affect the evaluation. It is suggested to include details regarding the same.

  5. Less information is provided on the availability of the mhealth application, especially if is it limited to India (developed) or Indian context. This may be an important factor as some of the apps belonging to the western population could not be practical for lifestyle or behaviour change therapies among Asian Indians.

  6. Statistical analyses used in the review seem basic thus undermining the actual benefits of the application and how it could be practically applied. Although the review outlines the combined effects of all the 20 applications, additional specifications or features of at least the top 5 applications could be briefed out to demonstrate how pragmatic these apps are for everyday practise.

Results:

  1. It is recommended to provide a separate breakdown in Figure 2 for each reviewer, rather than presenting an overall summary. For example, indicate how many apps were identified by Reviewer 1 and how many were identified by Reviewer 2 using the search terms.

  2. While the study title emphasizes behaviour change, "Mobile applications (apps) for tobacco cessation: Behaviour change potential and heuristic analysis using the Mobile Application Rating Scale (MARS)," the majority of the apps reviewed (14) primarily focused on goal-setting, with only 4 targeting behaviour change directly. Justify.

  3. The final number of apps subjected to analysis (20 out of 670) is very small (only 3 apps from iOS). The sample size may be too small to yield reliable or meaningful statistical significance. This limits the generalizability of the findings

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

    What is the definition of older apps? According to table 1, 50% apps were release between years 2019-2023 and an equal number were updated in 2023. This contradicts the results where the authors have mentioned that there were only 4 apps targeting behaviour change.

  5. Table showing correlation and chi square test is missing.

  6. It is suggested to remove pie chart as this is not adding any value to the manuscript.

  7. Further explanation of statistical tests, confidence intervals, and potential confounding factors would enhance the clarity and rigor of the analysis. Additionally, providing more detail on the interpretation of correlations and the significance of app focus trends would strengthen the discussion of results.

Discussion:

  1. This is the line from discussion section: “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.”

    a) Since, authors are talking a lot about these apps. In the results section as well It is suggested to include the app names based on this categorisation in the results section for clearer reference.

    b) Also, while the study notes that most apps focussed on goal setting. There is little exploration of the implications of having a majority of apps focused on goal setting rather than behaviour change.

  2. Some points mentioned in other sections of the study are repeated, such as the focus on the MARS scale and its dimensions. Kindly avoid such repetitions.

  3. "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."

    For the above statement, What criteria were used to determine low quality of an app?

  4. The discussion mentions similarities with previous studies but does not provide a detailed comparison or analysis of how the current findings add to or differ from existing literature.

  5. The recommendation for developing more behavior change-focused apps lacks specificity and concrete suggestions on features.

  6. Kindly review the calculation for the apps excluded. The number mentioned in results (558) and the number mentioned in fig 2 (54) gives a difference of 10 which is missed out in the review. Recheck and update the table or the count.

  7. While the discussion does a great job of highlighting the importance of behavior change apps, it could benefit from a deeper exploration of why behavior change techniques (e.g., cognitive-behavioral therapy, acceptance and commitment therapy) are more effective in driving smoking cessation. A more detailed explanation of the mechanisms behind these techniques, backed by literature, would strengthen the argument and provide additional value to readers who may be unfamiliar with these approaches.

Final Comments: 

The study aimed to evaluate tobacco cessation apps on Apple and Android app stores, analyzing their features related to engagement, functionality, aesthetics, and information quality. However, it faces issues with justifying its inclusion criteria, addressing the small sample size, and providing specific recommendations for improving behaviour change-focused apps.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Partly

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Partly

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

NA

We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above.

F1000Res. 2024 Nov 15.
Prajna Nayak 1

General Comments: The aim of the study was to identify tobacco cessation apps on Apple and Android app stores and to categorize and analyze their features as regards to engagement, functionality, aesthetics, and information quality. Thereby recommending directions for the improvement of these apps.

Specific Comments:

Title: Please specify the type of review in the title to facilitate better understanding of the article.

Author Response : We thank the reviewer for their suggestion, the ‘abstract’ has been edited to make it clear. We could not add the type of review in the title, as it can lengthen the title further (19 words).

Abstract:

MARS (Mobile Application Rating Scale) could have been incorporated into the keywords to enhance the specificity.

Author Response : We have now added it ma’am.

Introduction

  1. Kindly give reference for the following lines in the introduction section: “The majority of people making independent attempts fail to maintain their quit status for even one month.”

  2. The second para of this section provides statistics on tobacco cessation from India. However, the following paragraph discusses global statistics on smartphone users, which appears to be out of sync with the flow of information. To maintain coherence, it would be better to align the information properly.

  3. The rationale of the review was well articulated in the introduction although a little more background on MARS and its scoring system and how it helps in heuristically evaluating the mhealth applications could be provided to strengthen the foundation of the study.

Author Response :

  1. We have now added the reference, ma’am.

  2. .We have now provided India statistics to synchronize with the tobacco cessation statistics.

  3. We have now provided details on the MARS scale. More details are available in the methodology section ma’am.

Methodology:

  1. The introduction highlights that rankings in app stores are highly volatile and primarily influenced by app usability, meaning that an app's behavior change potential cannot be fully assessed based on its ratings or popularity alone. However, the inclusion criteria in the methods section focus on selecting apps with a rating of 4 stars or higher and more than 10,000 downloads. Justify.

  2. The inclusion of only apps with a rating of 4 stars or higher and over 10,000 downloads might exclude newer or less popular apps that could still be effective. This introduces a bias towards more established apps. Kindly justify.

  3. The reviewers were trained using a 37-minute YouTube video. Please answer the following questions:

    a) It may not be sufficient for thorough, expert-level assessment using the MARS tool. Kindly comment.

    b) While a high interrater reliability (0.804) is reported, no additional details are provided on how discrepancies between reviewers were resolved. This could affect the validity of the scoring and analysis if disagreements were not consistently addressed. Justify.

    c) How reliable and authentic was the video? It is suggested to include the link for the video in the references. 

  4. There’s no mention of how frequently the apps were updated or if the study accounted for updates during the research period in the methods section. App features and functionalities can change significantly with updates, which could affect the evaluation. It is suggested to include details regarding the same.

  5. Less information is provided on the availability of the mhealth application, especially if is it limited to India (developed) or Indian context. This may be an important factor as some of the apps belonging to the western population could not be practical for lifestyle or behavior change therapies among Asian Indians.

  6. Statistical analyses used in the review seem basic thus undermining the actual benefits of the application and how it could be practically applied. Although the review outlines the combined effects of all the 20 applications, additional specifications or features of at least the top 5 applications could be briefed out to demonstrate how pragmatic these apps are for everyday practice.

Author Response : 

  1. We have now added the justification in the 1 st para of Discussion, ma’am.

  2. We have now added the justification in the 1 st para of Discussion and also mentioned in the limitations, ma’am.

  3. Since the video was uploaded by the developer of the scale himself, we used it as a reference for understanding the scale better. We have also added on how the discrepancies were resolved in the Methods section. (13) MARS training video - YouTube , we have now added the link to the video, ma’am.

  4. As the study was conducted in a relatively short period of time, we have not mentioned updates during study period.

  5. Since the Apple and Android App stores both show all the apps, irrespective of the country that developed the app, we could not isolate the Indian apps. Also, 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.

  6. As this could lengthen the manuscript, we could not brief on top 5 apps. Instead, we have tried to summarize the results of top 20 apps distinctly.

Results:

  1. It is recommended to provide a separate breakdown in Figure 2 for each reviewer, rather than presenting an overall summary. For example, indicate how many apps were identified by Reviewer 1 and how many were identified by Reviewer 2 using the search terms.

  2. While the study title emphasizes behavior change, "Mobile applications (apps) for tobacco cessation: Behavior change potential and heuristic analysis using the Mobile Application Rating Scale (MARS)," the majority of the apps reviewed (14) primarily focused on goal-setting, with only 4 targeting behavior change directly. Justify.

  3. The final number of apps subjected to analysis (20 out of 670) is very small (only 3 apps from iOS). The sample size may be too small to yield reliable or meaningful statistical significance. This limits the generalizability of the findings

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

    What is the definition of older apps? According to table 1, 50% apps were release between years 2019-2023 and an equal number were updated in 2023. This contradicts the results where the authors have mentioned that there were only 4 apps targeting behavior change.

  5. Table showing correlation and chi square test is missing.

  6. It is suggested to remove pie chart as this is not adding any value to the manuscript.

  7. Further explanation of statistical tests, confidence intervals, and potential confounding factors would enhance the clarity and rigor of the analysis. Additionally, providing more detail on the interpretation of correlations and the significance of app focus trends would strengthen the discussion of results.

Author Response : 

  1. Though both authors downloaded, selected and excluded apps together and then reviewed each app independently.

  2. Since we had to unbiasedly select the apps out of 54 apps, we included only the highly rated and most downloaded apps. Focus of the apps were checked later during the study.

Nevertheless, other 14 apps, though had primary focus as goal setting, also had behavior counselling in their content. Hence, we have included them.

  1. Though we initially got 670 apps, the actual tobacco cessation apps were only 54. (Figure 2 flowchart for app identification). Hence, 20 out of 54 were selected, ma’am.

  2. We have now added a table mentioning the app release dates and app primary focus. (Table 6).

  3. We have now added it ma’am (Table 5)

  4. We have removed the pie chart now.

  5. We have now mentioned.

Discussion:

  1. This is the line from discussion section: “According to the findings of our study, the apps that focus on behavior modification had higher user ratings and overall MARS scores with many features.”

    a) Since, authors are talking a lot about these apps. In the results section as well It is suggested to include the app names based on this categorization in the results section for clearer reference.

    b) Also, while the study notes that most apps focused on goal setting. There is little exploration of the implications of having a majority of apps focused on goal setting rather than behavior change.

  2. Some points mentioned in other sections of the study are repeated, such as the focus on the MARS scale and its dimensions. Kindly avoid such repetitions.

  3. "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."

    For the above statement, What criteria were used to determine low quality of an app?

  4. The discussion mentions similarities with previous studies but does not provide a detailed comparison or analysis of how the current findings add to or differ from existing literature.

  5. The recommendation for developing more behavior change-focused apps lacks specificity and concrete suggestions on features.

  6. Kindly review the calculation for the apps excluded. The number mentioned in results (558) and the number mentioned in fig 2 (54) gives a difference of 10 which is missed out in the review. Recheck and update the table or the count.

  7. While the discussion does a great job of highlighting the importance of behavior change apps, it could benefit from a deeper exploration of why behavior change techniques (e.g., cognitive-behavioral therapy, acceptance and commitment therapy) are more effective in driving smoking cessation. A more detailed explanation of the mechanisms behind these techniques, backed by literature, would strengthen the argument and provide additional value to readers who may be unfamiliar with these approaches.

Author Response : 

  1. We have now named those apps focusing on behavior change in the results section. Although the primary focus of majority of apps was goal setting, they also had behavior modification components in them.

  2. We have now removed the repetitions, ma’am.

  3. Most of the other apps had mere goal setting or basic details of health affects before and after quitting tobacco. Hence we had mentioned like so. We thank the reviewers for drawing attention on this, and we have now corrected the sentence.

  4. We have now added.

  5. We have now added ma’am.

  6. We have corrected it.

  7. We have now briefly explained important behavior change techniques for smoking cessation.

F1000Res. 2024 Jan 25. doi: 10.5256/f1000research.155532.r236779

Reviewer response for version 1

Cristina Rey-Reñones 1

Dear Author's,

I trust this message finds you well. I recently read your article titled "Mobile Applications (Apps) for Tobacco Cessation: Behavior Change Potential and Heuristic Analysis Using the Mobile Application Rating Scale (MARS) [Version 1; Peer Review: Awaiting Peer Review]." I commend you on addressing a pertinent contemporary need in your research.

The work is presented with clarity and precision. Notably, a total of 21 bibliographic citations are included, with 13 (61%) sourced from the last five years.

In light of the commendable effort, I would like to offer a couple of constructive suggestions for consideration. Firstly, it would be beneficial to specify which of the applications studied have scientific evidence supporting their efficacy, effectiveness, and efficiency. This additional detail would enhance the comprehensiveness of your analysis.

Additionally, I would like to suggest broadening the scope of your study by incorporating other forms of tobacco consumption, such as "vaping" or "e-cigarette," in addition to traditional tobacco use. This expansion would contribute to a more comprehensive understanding of the subject matter.

Once again, congratulations on your valuable contribution to the field. I appreciate your dedication to advancing research in this area.

Kind regards, Inline graphic

Is the work clearly and accurately presented and does it cite the current literature?

Partly

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

mhealth, tobacco, nursing

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

F1000Res. 2024 Nov 15.
Prajna Nayak 1

We thank the reviewer for their suggestions, we have now made the following corrections:

1. As mentioned by the reviewer to add scientific evidence available for the applications studied, we have now added a column in table 2 on the sources of information if available. This will enable readers on the legitimacy of the source.

2. We could not add the incorporate other forms of tobacco consumption, such as "vaping" or "e-cigarette," in addition to traditional tobacco use. Because, we had not used these terms in our ‘Search strategy’. Addition of these would have resulted in apps designed to assist in quitting e-cigarettes too. Hence, we are afraid we will not be able to incorporate them, now.

Associated Data

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

    Data Citations

    1. Nayak PP: Underlying data for ‘Mobile applications (apps) for tobacco cessation: Behaviour change potential and heuristic analysis using the Mobile Application Rating Scale (MARS)’.[Dataset]. Open Science Framework. 2023. 10.17605/OSF.IO/JNBPF [DOI]

    Data Availability Statement

    Underlying data

    Open Science Framework: Underlying data for ‘Mobile applications (apps) for tobacco cessation: Behaviour change potential and heuristic analysis using the Mobile Application Rating Scale (MARS)’, https://www.doi.org/10.17605/OSF.IO/JNBPF. 19

    Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0)

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