Published on 10.06.16 in Vol 4, No 2 (2016): Apr-Jun
Preprints (earlier versions) of this paper are available at http://preprints.jmir.org/preprint/5849, first published Apr 07, 2016.
Development and Validation of the User Version of the Mobile Application Rating Scale (uMARS)
Comment in: http://mhealth.jmir.org/2017/6/e16/
Background: The Mobile Application Rating Scale (MARS) provides a reliable method to assess the quality of mobile health (mHealth) apps. However, training and expertise in mHealth and the relevant health field is required to administer it.
Objective: This study describes the development and reliability testing of an end-user version of the MARS (uMARS).
Methods: The MARS was simplified and piloted with 13 young people to create the uMARS. The internal consistency and test-retest reliability of the uMARS was then examined in a second sample of 164 young people participating in a randomized controlled trial of a mHealth app. App ratings were collected using the uMARS at 1-, 3,- and 6-month follow up.
Results: The uMARS had excellent internal consistency (alpha = .90), with high individual alphas for all subscales. The total score and subscales had good test-retest reliability over both 1-2 months and 3 months.
Conclusions: The uMARS is a simple tool that can be reliably used by end-users to assess the quality of mHealth apps.
JMIR Mhealth Uhealth 2016;4(2):e72
- app evaluation;
- end user;
- app trial;
- mhealth trial;
- user testing;
- mobile application;
- app rating;
- mobile health;
- well being;
- mental health;
- emental health;
- cognitive behavioral therapy;
- anxiety disorders;
- depressive disorder;
- research translation;
- mHealth implementation;
- mHealth evaluation;
- randomized controlled trial;
Mobile health (mHealth) apps have the potential to provide around-the-clock access to evidence-based health information, education, and treatment to end users on a global scale. There are currently more than 165,000 mHealth apps (free and paid) publicly available , yet the accuracy of the health information contained in these apps is not scrutinized by regulatory bodies [ ], which could compromise user health and safety [ - ]. Concerns about the quality, efficacy, reliability, and security of mHealth apps are also often raised. While meta-analytic studies have demonstrated the efficacy of mHealth apps targeting physical activity and weight loss [ , ], the evidence base for other types of mHealth apps is poor at best [ - ].
In response to these issues, we developed the Mobile App Rating Scale (MARS) to provide researchers, professionals, and clinicians with a brief tool for classifying and assessing the quality of mHealth apps . The 23-item MARS contains 4 objective quality subscales—engagement, functionality, aesthetics, and information quality—and a subjective quality rating. The MARS has demonstrated high levels of interrater reliability for evaluating the quality of mHealth apps on well-being [ ] and mindfulness [ ]. However, training and expertise in mHealth and the relevant health field is required to administer it. This paper describes the development and pilot testing of a simpler, end user version of the MARS (uMARS) and provides preliminary evidence for its internal consistency and test-retest reliability.
Study 1: Development and Pilot Testing of the uMARS
The original MARS was simplified through the following process. The professional version was first reviewed by 2 researchers to remove complex terminology from its items and response scales. Three items requiring professional expertise, pertaining to evidence base, app goals, and accuracy of app description, were removed. Readability of the MARS and the draft uMARS was then determined using the Flesch Reading Ease test [, ], which has a score range of 0-100, with higher scores indicating easier readability. This measure also provides the estimated US school grade required for reading comprehension.
The draft uMARS was then pilot-tested with 13 young people, to ensure they understood the item content and response scales. The measure was embedded in prototype testing sessions of 2 mHealth apps: Ray’s Night Out  and Music eScape [ ]. Ray’s Night Out uses a harm-minimization approach to increase young people’s alcohol knowledge and awareness of their drinking limits; Music eScape teaches young people how to identify and manage affect using music. Both are available on the iOS Apple app store.
Eligible participants were Australian residents aged 16 to 25 years, who had access to an iPhone 4 or later model. The Ray’s Night Out group comprised 1 male and 8 females with a mean age of 20.7 years (SD 1.6). The Music eScape group comprised 3 males and 1 female, with a mean age of 21.5 years (SD 1.9). After testing the apps and rating them with the uMARS scale, participants were asked the question “Do you have any comments or suggestions about the uMARS rating scale?” to identify any unclear or difficult items.
Study 2: Testing the uMARS Internal Consistency and Test-Retest Reliability
The uMARS () provides a 20-item measure that includes 4 objective quality subscales—engagement, functionality, aesthetics, and information quality—and 1 subjective quality subscale.
The reliability of the uMARS was evaluated as part of a randomized controlled trial (RCT), testing the efficacy and quality of Music eScape. The RCT sample comprised 164 Australians aged 19.8 years on average (SD 2.51); 34 males. The highest level of education completed by 59.8% of the sample was secondary school, and 24.4% had completed a bachelor’s degree or higher. Most participants (57.9%) were students and 35.4% had full-time, part-time, or casual employment.
Participants were randomly allocated via a Web-based research management tool developed at the Queensland University of Technology to receive immediate or 1-month delayed access to the Music eScape app. Young people were asked to use the app as much as they liked over a month, using their own iPhones. The current iOS version at the time of the trial was iOS8. Participants received weekly text messages reminding them to do so. App ratings were collected using the uMARS at 1-, 3-, and 6-month follow-ups in the immediate access group. In the delayed access group, uMARS ratings were collected at 2-, 3-, and 6-month follow-ups (ie, after 1, 2, and 5 months of app access). At each assessment point, participants were asked if they had used the app since the last assessment, and only those who reported some use were included in analyses.
The internal consistencies of the uMARS subscales and total score were calculated using Cronbach's alpha. For the purpose of analysis, the “N/A” answer option for items 13-16 of the information subscale was recoded as “system missing,” as this option represents a qualitatively different response.
Test-retest reliabilities were calculated for the subscales and total scores of the uMARS after 1 month of app use and at 3 months post baseline (ie, a test-retest period of 1-2 months), and over 3 months (ie, between assessments at 3 and 6 months post baseline). Interclass correlation coefficients (ICCs) [- ] were used, as they provide weighted values of rater agreement and assess proximity rather than equality of ratings. To calculate the ICCs, a random-effects average measures model with absolute agreement was utilized [ ]. Data were analyzed with SPSS version 23 (SPSS Inc, Armonk, NY, USA).
Study 1: Readability
Results of the Flesch-Kincaid readability tests are in. Scores indicated that the uMARS was written in plain English and that its required reading level was approximately grade 8.
Pilot Participant Feedback
No suggestions for further scale improvement were made. Seven of the 13 participants who pilot-tested the scale left the comments or suggestions item blank, 4 wrote “no,” and 1 wrote “Well done. Good questions. Well explained.” Another wrote “I thought it was shorter/there is a brief or revised version of it?”
Study 2: uMARS Internal Consistency
A total of 152 of the 164 (92%) participants completed the survey after 1 month of app use. Of these, 19 indicated they never used the app, and were excluded from analyses. For the remaining 133 participants, the total uMARS score had excellent internal consistency (Cronbach alpha = .90). Internal consistencies of its subscales were also very high (engagement alpha = .80; functionality alpha = .70; aesthetics alpha = .71; information alpha = .78; and satisfaction alpha = .78).
uMARS Test-Retest Reliability
Test-retest reliabilities are presented in. A total of 113 participants completed the scale after 1 month of app use and at 3 months post baseline (ie, a test-retest period of 1-2 months), and 74 completed both the 3- and 6-month surveys (giving a 3-month test-retest period). All included participants had used the app at least once since the previous survey. The uMARS total score demonstrated good [ ] levels of ICC of .66 and .70 over 1- to 2-month and 3-month periods, respectively. Levels for all subscales scores were similarly high.
|MARSa version||Reading ease||Readability level||Grade level||Reading age|
|Original||47.2||Difficult||9.5||15-16 years old|
|uMARSa||58.0||Plain English – fairly difficult||7.9||12-13 years old|
a MARS: Mobile App Rating Scale; uMARS: user version of the MARS.
|Subscale/item||1- to 2-month period (N=113)||3-month period (N=74)|
|Engagement||.71 (.66-.76)||.73 (.67-.78)|
|1||Entertainment||.60 (.41-.72)||.75 (.61-.85)|
|2||Interest||.69 (.55-.79)||.67 (.48-.79)|
|3||Customization||.61 (.44-.73)||.53 (.25-.70)|
|4||Interactivity||.55 (.35-.69)||.69 (.51-.81)|
|5||Target group||.72 (.59-.80)||.73 (.57-.83)|
|Functionality||.62 (.54-.68)||.69 (.61-.76)|
|6||Performance||.54 (.34-.69)||.71 (.53-.81)|
|7||Ease of use||.65 (.49-.76)||.72 (.55-.82)|
|8||Navigation||.62 (.45-.74)||.67 (.48-.79)|
|9||Gestural design||.61 (.44-.73)||.65 (.44-.78)|
|Aesthetics||.58 (.48-.66)||.68 (.59-.76)|
|10||Layout||.39 (.11-.58)||.48 (.18-.67)|
|11||Graphics||.70 (.56-.79)||.77 (.63-.85)|
|12||Visual appeal||.63 (.46-.75)||.80 (.68-.87)|
|Information||.48 (.38-.57)||.52 (.40-.62)|
|13||Quality of information||.48 (.24-.64)||.44 (.11-.65)|
|14||Quantity of information||.48 (.24-.64)||.32 (.08 to .57)|
|15||Visual information||.42 (.16-.60)||.75 (.61-.84)|
|16||Credibility of source||.51 (.29-.66)||.63 (.41-.77)|
|Total uMARSa||.66 (.63-.68)||.70 (.67-.78)|
|Subjective items||.70 (.64-.75)||.71 (.64-.77)|
|17||Would you recommend||.84 (.76-.89)||.75 (.60-.84)|
|18||How many times||.44 (.18-.61)||.48 (.17-.67)|
|19||Would you pay||.81 (.73-.87)||.82 (.71-.89)|
|20||Overall (star) rating||.71 (.59-.80)||.77 (.63-.85)|
a uMARS: user version of the Mobile App Rating Scale.
This study developed and tested an app user version of the original MARS to assist app developers and researchers with evaluating the quality of mHealth apps. The uMARS () provides a 20-item measure that includes 4 objective quality subscales—engagement, functionality, aesthetics, and information quality—and 1 subjective quality subscale. One further subscale, consisting of 6 items is added to measure users’ perceived impact of the evaluated app. The study demonstrated that the uMARS had excellent internal consistency for the full scale and good levels for all subscales. It is reassuring that even after a 3-month delay between ratings, test-retest reliability of the total score was good, and test-retest reliabilities of its subscales were fair to good, with the engagement and subjective subscales being particularly robust.
These results indicate that the uMARS provides a reliable measure of app quality in target users. Replication of the current results with multiple types of mHealth apps is required to provide additional confidence in its performance. Tests of its sensitivity to improvements in app quality and an examination of its ability to predict outcomes of mHealth apps are also needed. As the uMARS may potentially have applications beyond mHealth, tests of its performance in other domains are also indicated.
Current indications are that the uMARS will offer an unprecedented ability to readily obtain rich information from users about mobile apps. The scale can be used to obtain user feedback on the quality of mobile apps during the development and testing process, which may result in overall improvements in their quality.
The eTools for Wellbeing project is a partnership between the Queensland University of Technology and the Young and Well Cooperative Research Centre (Young and Well CRC). The eTools for Wellbeing Research Team wishes to acknowledge the generous support and collaborative efforts of all supporting partners. We would especially like to acknowledge the important contributions from the young people who took the time to engage in the project.
Associate Professor Leanne Hides is supported by an Australian Research Council Future Fellowship.
Conflicts of Interest
Multimedia Appendix 1PDF File (Adobe PDF File), 53KB
- Aitken M, Lyle J. Patient Adoption of mHealth: Use, Evidence and Remaining Barriers to Mainstream Acceptance. Parsippany, NJ: IMS Institute for Healthcare Informatics; 2015. URL: http://www.imshealth.com/files/web/IMSH%20Institute/Reports/Patient%20Adoption%20of%20mHealth/IIHI_Patient_Adoption_of_mHealth.pdf [accessed 2016-05-30] [WebCite Cache]
- Pagoto S, Bennett GG. How behavioral science can advance digital health. Transl Behav Med 2013 Sep;3(3):271-276 [FREE Full text] [CrossRef] [Medline]
- Chan S, Torous J, Hinton L, Yellowlees P. Towards a Framework for Evaluating Mobile Mental Health Apps. Telemed J E Health 2015 Dec;21(12):1038-1041. [CrossRef] [Medline]
- Su W. A preliminary survey of knowledge discovery on smartphone applications (apps): Principles, techniques and research directions for e-health. In: arXiv preprint arXiv. 2014 Jun 26 Presented at: International Conference on Complex Medical Engineering; June 26-29, 2014; Taipei, Taiwan p. 14077314.
- Andersson G, Cuijpers P, Carlbring P, Riper H, Hedman E. Guided Internet-based vs. face-to-face cognitive behavior therapy for psychiatric and somatic disorders: a systematic review and meta-analysis. World Psychiatry 2014 Oct;13(3):288-295 [FREE Full text] [CrossRef] [Medline]
- Fanning J, Mullen SP, McAuley E. Increasing physical activity with mobile devices: a meta-analysis. J Med Internet Res 2012 Nov;14(6):e161 [FREE Full text] [CrossRef] [Medline]
- Free C, Phillips G, Watson L, Galli L, Felix L, Edwards P, et al. The effectiveness of mobile-health technologies to improve health care service delivery processes: a systematic review and meta-analysis. PLoS Med 2013;10(1):e1001363 [FREE Full text] [CrossRef] [Medline]
- Fiordelli M, Diviani N, Schulz PJ. Mapping mHealth research: a decade of evolution. J Med Internet Res 2013 May;15(5):e95 [FREE Full text] [CrossRef] [Medline]
- Mani M, Kavanagh DJ, Hides L, Stoyanov SR. Review and Evaluation of Mindfulness-Based iPhone Apps. JMIR Mhealth Uhealth 2015;3(3):e82 [FREE Full text] [CrossRef] [Medline]
- Heffner JL, Vilardaga R, Mercer LD, Kientz JA, Bricker JB. Feature-level analysis of a novel smartphone application for smoking cessation. Am J Drug Alcohol Abuse 2015 Jan;41(1):68-73 [FREE Full text] [CrossRef] [Medline]
- Stoyanov SR, Hides L, Kavanagh DJ, Zelenko O, Tjondronegoro D, Mani M. Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR Mhealth Uhealth 2015 Mar;3(1):e27. [CrossRef] [Medline]
- Flesch R. A new readability yardstick. Journal of Applied Psychology 1948 Jun;32(3):221-233. [CrossRef] [Medline]
- Kincaid J, Fishburne R, Rogers R, Chissom B. Chief of Naval Technical Training: Naval Air Station Memphis. 1975. Derivation of new readability formulas (automated readability index, fog count, and Flesch reading ease formula) for Navy enlisted personnel URL: http://www.dtic.mil/cgi-bin/GetTRDoc?Location=U2&doc=GetTRDoc.pdf&AD=ADA006655
- Hides L, Stoyanov S, Zelenko O, Kavanagh D. Apple App Store. Brisbane, Australia: Young and Well CRC & Queensland University of Technology Ray's Night Out URL: https://itunes.apple.com/au/app/rays-night-out/id978589497?mt=8 [accessed 2016-04-28] [WebCite Cache]
- Hides L, Kavanagh D, Stoyanov S, Genevieve D, Zelenko O, Cockshaw W, et al. Apple App Store. Brisbane, Australia: Young and Well CRC & Queensland University of Technology; 2014. Music eScape URL: https://itunes.apple.com/au/app/music-escape/id971949389?mt=8 [accessed 2016-04-28] [WebCite Cache]
- Hallgren K. Computing Inter-Rater Reliability for Observational Data: An Overview and Tutorial. Tutor Quant Methods Psychol 2012;8(1):23-34 [FREE Full text] [Medline]
- Shrout PE, Fleiss JL. Intraclass correlations: uses in assessing rater reliability. Psychol Bull 1979 Mar;86(2):420-428. [CrossRef]
- Weir J. Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. J Strength Cond Res 2005 Feb;19(1):231-240. [CrossRef] [Medline]
|ICC: interclass correlation coefficient|
|MARS: Mobile App Rating Scale|
|mHealth: mobile health|
|RCT: randomized controlled trial|
Edited by G Eysenbach; submitted 07.04.16; peer-reviewed by P Yellowlees, S Chan; comments to author 27.04.16; revised version received 28.04.16; accepted 30.04.16; published 10.06.16
©Stoyan R Stoyanov, Leanne Hides, David J Kavanagh, Hollie Wilson. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 10.06.2016.
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