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Development and Validation of the User Version of the Mobile Application Rating Scale (uMARS)
Open accessAbstract
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.
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“The internal consistency and test–retest reliability of the uMARS total score and subscales were shown as excellent in the RCT (Cronbach’s alpha = 0.90) (46).”
From Improving mental well-being in psychocardiology—a feasibility trial for a non-blended web application as a brief metacognitive-based intervention in cardiovascular disease patients (2023) · cites this paper as [46]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Overall, the uMARS demonstrates good internal reliability (α = 0.90), and the subscales demonstrate moderate reliability (α = 0.71 and 0.80) (Stoyanov et al., 2016).”
From Pilot study of a well-being app to support New Zealand young people during the COVID-19 pandemic (2021) · cites this paper as Stoyanov et al., 2016
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“The usability and acceptability of the technology were assessed using qualitative methods and standard measures, such as the survey based on the Unified Theory of Acceptance and Use of Technology [27], the Mobile Application Rating Scale [28], and the System Usability and After-Scenario Questionnaire [29].”
From Barriers to and facilitators of user engagement with digital mental health interventions: Systematic review (2021) · cites this paper as [28]
In the citing paper
“Both MARS and uMARS included some usability components such as engagement, functionality, and information quality; however, they were not specifically designed to study usability with end users.”
From The mHealth App Usability Questionnaire (MAUQ): Development and Validation Study (2019) · cites this paper as [37]
In the citing paper
“Shortly thereafter, a simpler user version of MARS (uMARS) was created for the general population for the same purpose [37].”
From The mHealth App Usability Questionnaire (MAUQ): Development and Validation Study (2019) · cites this paper as [37]
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Citations
Cites
Intraclass correlations: Uses in assessing rater reliability.
Shrout, P. E., & Fleiss, J. L. (1979) · Psychological Bulletin · doi:10.1037/0033-2909.86.2.420 · Cited by 24,532 · cited 1×
“Interclass correlation coefficients (ICCs) [16-18] were used, as they provide weighted values of rater agreement and assess proximity rather than equality of ratings.”
Flesch, R. (1948) · Journal of Applied Psychology · doi:10.1037/h0057532 · Cited by 5,391 · cited 1×
“Readability of the MARS and the draft uMARS was then determined using the Flesch Reading Ease test [12,13], which has a score range of 0-100, with higher scores indicating easier readability.”
QUANTIFYING TEST-RETEST RELIABILITY USING THE INTRACLASS CORRELATION COEFFICIENT AND THE SEM
Weir, J. P. (2005) · The Journal of Strength and Conditioning Research · doi:10.1519/15184.1 · Cited by 4,864 · cited 1×
“Interclass correlation coefficients (ICCs) [16-18] were used, as they provide weighted values of rater agreement and assess proximity rather than equality of ratings.”
Computing Inter-Rater Reliability for Observational Data: An Overview and Tutorial
Hallgren, K. A. (2012) · Tutorials in Quantitative Methods for Psychology · doi:10.20982/tqmp.08.1.p023 · Cited by 3,884 · cited 3×
“The uMARS total score demonstrated good [16] levels of ICC of .”
Kincaid, J. P., Fishburne, Jr., Robert P., R., Richard L., C., & Brad, S. (1975) · doi:10.21236/ada006655 · Cited by 3,497
Mobile app rating scale: a new tool for assessing the quality of health mobile apps
Stoyanov, S. R., Hides, L., Kavanagh, D. J., Zelenko, O., Tjondronegoro, D., & Mani, M. (2015) · JMIR mHealth and uHealth · doi:10.2196/mhealth.3422 · Cited by 2,233 · cited 2×
“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 [11].”
Self-citationFree, C., Phillips, G., Watson, L., Galli, L., Felix, L., Edwards, P., Patel, V., & Haines, A. (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001363 · Cited by 1,187
Andersson, G., Cuijpers, P., Carlbring, P., Riper, H., & Hedman, E. (2014) · World Psychiatry · doi:10.1002/wps.20151 · Cited by 1,077 · cited 1×
“There are currently more than 165,000 mHealth apps (free and paid) publicly available [1], yet the accuracy of the health information contained in these apps is not scrutinized by regulatory bodies [2], which could compromise user health and safety [3-5].”
Mapping mHealth research: a decade of evolution
Fiordelli, M., Diviani, N., & Schulz, P. J. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2430 · Cited by 570 · cited 1×
“While meta-analytic studies have demonstrated the efficacy of mHealth apps targeting physical activity and weight loss [6,7], the evidence base for other types of mHealth apps is poor at best [8-10].”
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Mani, M., Kavanagh, D. J., Hides, L., & Stoyanov, S. R. (2015) · JMIR mHealth and uHealth · doi:10.2196/mhealth.4328 · Cited by 495
Research works printed but not linked: 1
- SuWA preliminary survey of knowledge discovery on smartphone applications (apps): Principles, techniques and research directions for e-healtharXiv preprint arXiv20140626International Conference on Complex Medical EngineeringJune 26-29, 2014Taipei, Taiwan14077314
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Wohlgemut, J. M., Pisirir, E., Kyrimi, E., Stoner, R. S., Marsh, W., Perkins, Z., & Tai, N. R. M. (2023) · JAMIA Open · doi:10.1093/jamiaopen/ooad051 · Cited by 19
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Serlachius, A., Schache, K., Boggiss, A., Lim, D., Wallace-Boyd, K., Brenton-Peters, J., Buttenshaw, E., Chadd, S., Cavadino, A., Cao, N., et al. (2020) · JMIR Research Protocols · doi:10.2196/23716 · Cited by 17
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“49 As yet, there is no German translation of this user version.”
SELLES, W. D. L., Santos, E. C., Romero, B. D., & Lunardi, A. C. (2024) · Disability and Rehabilitation · doi:10.1080/09638288.2024.2323614 · Cited by 12
DeGroot, L. G., Gillette, R. A., Villalobos, J. P., Harger, G., Doyle, D. T., Bull, S. S., Bekelman, D. B., Boxer, R. S., Kutner, J. S., & Portz, J. D. (2024) · BMC Palliative Care · doi:10.1186/s12904-024-01561-w · Cited by 12
Park, G. E., Park, Y., Kim, K. G., Park, J. Y., Hwang, M., & Lee, S. (2024) · Healthcare Informatics Research · doi:10.4258/hir.2024.30.4.344 · Cited by 12
“It has demonstrated high internal consistency, with a Cronbach’s alpha of 0.90 [23].”
InfluentialPaglialonga, A., Pinciroli, F., & Tognola, G. (2017) · American Journal of Audiology · doi:10.1044/2017_aja-16-0132 · Cited by 12
Soomro, N., Chhaya, M. M. A., Soomro, M., Asif, N., Saurman, E., Lyle, D. M., & Sanders, R. H. (2019) · JMIR mhealth and uhealth · doi:10.2196/10978 · Cited by 12
“We used a modified user version of the Mobile App Rating Scale (uMARS) to critically appraise the app [34]. uMARS is derived from MARS, which is a validated mobile app evaluation tool and has been used extensively to rate the quality of medical-based apps [35-37].”
Austin, S. F., Frøsig, A. J., Buus, N., Lincoln, T. M., von Malachowski, A., Schlier, B., Frost, M. M., & Simonsen, E. H. (2021) · Qualitative Health Research · doi:10.1177/1049732320986556 · Cited by 13
Evaluation of Oral Hygiene-Related Mobile Apps for Children in Sub-Saharan Africa
Kanouté, A., Carrouel, F., Gare, J. V., Dieng, S. N., Dieng, A., Diop, M., Faye, D., Fraticelli, L., & Bourgeois, D. M. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph191912565 · Cited by 12
Frej, A., Cano, M., Ruiz-Postigo, J. A., Macharia, P. M., Phillips, R. O., Amoako, Y. A., & Carrión, C. (2022) · JMIR Research Protocols · doi:10.2196/39393 · Cited by 12
Quality Assessment Criteria for Mobile Health Apps: A Systematic Review
HONGSANUN, W., & Insuk, S. (2020) · Walailak Journal of Science and Technology (WJST) · doi:10.48048/wjst.2020.6482 · Cited by 14
“Non App-development [44] Health care providers/Patients iOS, Android, Blackberry Adherence apps Non App-development [45] Adult bipolar disorder patients Android Self-management of bipolar disorder App development [46] GDM patients Android Gestational diabetes mellitus management Non App-development [47] Health care pr…”
Critical and Qualitative Approaches to Behavior Change
Chamberlain, K., & Lyons, A. C. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.030 · Cited by 13
Sensing human activity to improve sedentary lifestyle
Boerema, S. (2018) · doi:10.3990/1.9789036546041 · Cited by 12
Maximizing User Engagement with Behavior Change Interventions
Yardley, L., Morrison, L. G., Müller, I., & Bradbury, K. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.025 · Cited by 11
Evaluation of Patient-Facing Mobile Apps to Support Physiotherapy Care: Systematic Review
Merolli, M., Francis, J., Vallance, P., Bennell, K. L., Malliaras, P., & Hinman, R. S. (2024) · JMIR mhealth and uhealth · doi:10.2196/55003 · Cited by 11
“Should this have occurred, a different version of the MARS, the user-MARS, would have been required [61].”
A systematic search, heuristic evaluation and analysis of dental trauma mobile applications
Walia, T., Muthu, M., Saikia, A., Anthonappa, R. P., & Satyanarayana, M. S. (2024) · Dental Traumatology · doi:10.1111/edt.12964 · Cited by 11
Oudbier, S. J., Smets, E. M. A., Nieuwkerk, P. T., Neal, D. P., Nurmohamed, A. S., Meij, H. J., & Peute, L. (2024) · JMIR Medical Informatics · doi:10.2196/63703 · Cited by 13
Kim, H. H., Ko, J. W., Kim, D., Yoon, N., & Lee, J. (2025) · BMC Public Health · doi:10.1186/s12889-025-21974-3 · Cited by 11
“Furthermore, the quality of the content was assessed by researchers based on four dimensions: quality of information, quantity of information, visual information, and reliability of source [22].”
Chen, T. (2025) · Gender Technology and Development · doi:10.1080/09718524.2025.2503561 · Cited by 11
“…app Rating scale (MaRs) which evaluate engagement, functionality, esthetics, and information quality of apps according to researchers’ ratings (stoyanov et al., 2016), since ratings on MaRs and other similar tools are still influenced by individual perspectives, such as screen size or vision…”
Schaub, M. P., Berman, A. H., López‐Pelayo, H., Boumparis, N., Khadjesari, Z., Blankers, M., Gual, A., Riper, H. M., & Pas, L. (2020) · Journal of Medical Internet Research · doi:10.2196/20368 · Cited by 11
Smartphone Apps for Child Sexual Abuse Education: Gaps and Design Considerations
Kabir, M. A., Tasnim, R., Pritha, S. T., Amin, S., & Das, A. (2022) · International Journal of Mobile Learning and Organisation · doi:10.1504/ijmlo.2023.10049097 · Cited by 19
“For this, we took insight from the existing rating scales such as the mobile app rating scale (MARS) [25], end-user version of the MARS (uMARS) [26], and app rating scale for foot measurement apps (FootMARS) [24], and modified these for suiting the required evaluation of CSA education apps.”
Lares-Michel, M., Housni, F. E., Reyes-Castillo, Z., Huertas, J. R., Cervantes, V. G. A., & Nava, R. M. M. (2023) · British Journal Of Nutrition · doi:10.1017/s0007114523000843 · Cited by 10
Serre, F., Moriceau, S., Donnadieu, L., Forcier, C., Garnier, H., Alexandre, J., Dupuy, L., Philip, P., Levavasseur, Y., de Sevin, É., et al. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1143167 · Cited by 14
Carvalho, L. R., Haas, L. M., Zeni, G. D., Victor, M. M., Techele, S. P., Castanho, J. M., Coimbra, I. M., de Sousa, A. D. F., Ceretta, N., Guirado, A. G., et al. (2023) · European Psychiatry · doi:10.1192/j.eurpsy.2023.2422 · Cited by 12
Fraterman, I., Wollersheim, B. M., Tibollo, V., Glaser, S. L. C., Medlock, S. K., Ronald Cornet, A., Gabetta, M., Gisko, V., Barkan, E., Flora, N. D., et al. (2023) · JMIR Research Protocols · doi:10.2196/49252 · Cited by 10
Lützow, L., Teckenburg, I., Koch, V., Marxreiter, F., Jukic, J., Stallforth, S., Regensburger, M., Winkler, J., Klucken, J., & Gaßner, H. (2023) · Journal of Patient-Reported Outcomes · doi:10.1186/s41687-023-00631-6 · Cited by 11
“Feasibility and usability of the app was assessed using the System Usability Scale (SUS) and the User Version of the Mobile Application Rating Scale (uMARS) at the end of the study [51–53].”
Gagnon, J., Probst, S., Chartrand, J., & Lalonde, M. (2024) · JMIR Formative Research · doi:10.2196/50839 · Cited by 11
DNVF-Memorandum – Gesundheits- und Medizin-Apps (GuMAs)
Krämer, U., Borges, U., Fischer, F., Hoffmann, W. U., Pobiruchin, M., & Vollmar, H. C. (2019) · Das Gesundheitswesen · doi:10.1055/a-1038-9173 · Cited by 10
MyoBeatz: Using music and rhythm to improve prosthetic control in a mobile game for health
Prahm, C., Kayali, F., & Aszmann, O. C. (2019) · SeGAH ... IEEE ... International Conference on Serious Games and Applications for Health/IEEE International Conference on Serious Games and Applications for Health · doi:10.1109/segah.2019.8882432 · Cited by 11
Tanner, A. E., Dowshen, N., Philbin, M. M., Rulison, K. L., Camacho-González, A., Lee, S., Moore, S. J., Fortenberry, J. D., & Hussen, S. A. (2021) · JMIR Research Protocols · doi:10.2196/24565 · Cited by 12
Characteristics of Acute Childhood Illness Apps for Parents: Environmental Scan
Benoit, J. R. A., Hartling, L., Chan, M. C., & Scott, S. D. (2021) · Journal of Medical Internet Research · doi:10.2196/29441 · Cited by 12
“We based the app assessments on the MARS, a validated tool designed for health app assessment [40,43].”
Gelgoot, E. N., Kruglova, K., Chan, P. T. K., Lo, K. C., Rosberger, Z., Chown, P., Kazdan, J., O’Connell, S. B. L., & Zelkowitz, P. (2022) · JMIR Cancer · doi:10.2196/33594 · Cited by 10
A Transgender Health Information Resource: Participatory Design Study
Morse, B., Soares, A., Kwan, B. M., Allen, M., Lee, R. S., DeSanto, K., Holliman, B. D., Ytell, K., & Schilling, L. M. (2023) · JMIR Human Factors · doi:10.2196/42382 · Cited by 10
“All uMARS items are assessed on a 5-point scale (1=inadequate, 2=poor, 3=acceptable, 4=good, and 5=excellent) [87].”
Influential
Metadata
- Authors
- Stoyanov, S. R., Hides, L., Kavanagh, D. J., & Wilson, H.
- Affiliations
- Queensland University of Technology (AU)
- Corresponding author
- Hides, Leanne
- Venue
- JMIR mhealth and uhealth
- Publisher
- JMIR Publications Inc.
- Published
- 2016-06-10
- Volume
- 4
- Issue
- 2
- Pages
- e72
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC4920963
- OpenAlex
- W2415928823
- Cited by
- 872
- References
- 14
- Citations by year
- 2026: 95 · 2025: 135 · 2024: 138 · 2023: 108 · 2022: 107 · 2021: 89 · 2020: 103 · 2019: 51 · 2018: 28 · 2017: 14 · 2016: 3
- FWCI
- 59.52
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR mhealth and uhealth (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- QUT ePrints (Queensland University of Technology) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Faculty of Health; Institute of Health and Biomedical Innovation
- Funders
- Queensland University of Technology
- Young and Well Cooperative Research Centre
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
- Keywords
- Mobile App Rating Scale, Umar, mobile health apps, app quality assessment, youth, internal consistency, test-retest reliability, scale development, app ratings
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