The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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].”
Review and evaluation of mindfulness-based iPhone apps
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
Cited by
Sorted by citations
Borghouts, J., Eikey, E., Mark, G., De Leon, C., Schueller, S. M., Schneider, M., Stadnick, N., Zheng, K., Mukamel, D., & Sorkin, D. H. (2021) · Journal of Medical Internet Research · doi:10.2196/24387 · Cited by 1,069
The mHealth App Usability Questionnaire (MAUQ): Development and Validation Study
Zhou, L., Bao, J., Setiawan, I. M. A., Saptono, A., & Parmanto, B. (2019) · JMIR mHealth and uHealth · doi:10.2196/11500 · Cited by 675
“Shortly thereafter, a simpler user version of MARS (uMARS) was created for the general population for the same purpose [37].”
The Handbook of Behavior Change
Hagger, M. S., Cameron, L. D., Hamilton, K., Hankonen, N. E., & Lintunen, T. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318 · Cited by 446
Consumer Mobile Health Apps: Current State, Barriers, and Future Directions
Kao, C., & Liebovitz, D. M. (2017) · PM&R · doi:10.1016/j.pmrj.2017.02.018 · Cited by 350
Changing Behavior Using the Theory of Planned Behavior
Ajzen, I., & Schmidt, P. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.002 · Cited by 346
Validation of the Mobile Application Rating Scale (MARS)
Terhorst, Y., Philippi, P., Sander, L., Schultchen, D., Paganini, S., Bardus, M., Santo, K., Knitza, J., Machado, G. C., Schöeppe, S., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0241480 · Cited by 291
“Also using a multi-method approaches, for example by integrating alternative assessments like the user version of the MARS [60] or the ENLIGHT [28] could lead to a more comprehensive assessment of the quality of MHA.”
Bakker, D., & Rickard, N. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2017.11.016 · Cited by 231
Problems and Barriers Related to the Use of Digital Health Applications: Scoping Review
Giebel, G. D., Speckemeier, C., Abels, C., Plescher, F., Börchers, K., Wasem, J., Blase, N., & Neusser, S. (2023) · Journal of Medical Internet Research · doi:10.2196/43808 · Cited by 238
Criteria for assessing the quality of mHealth apps: a systematic review
Nouri, R., R Niakan Kalhori, S., Ghazisaeedi, M., Marchand, G., & Yasini, M. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy050 · Cited by 231
““Design” was found in 9 different articles(30,31,33,34,36,38,48,49,52) “Information/ Content” (using different terms) observed in 15 studies (38,41,42,44,45,47,49,51,52)“Usability” in 14 articles, 49,52 “Functionality” in 7 studies,(31,33,34,38,43,49,50) “Ethical Issues” in 1 study,(42) “Security and Privacy” in 8 art…”
Hajesmaeel‐Gohari, S., Khordastan, F., Fatehi, F., Samzadeh, H. R., & Bahaadinbeigy, K. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01764-2 · Cited by 181
The German Version of the Mobile App Rating Scale (MARS-G): Development and Validation Study
Messner, E.-M., Terhorst, Y., Barke, A., Baumeister, H., Stoyanov, S., Hides, L., Kavanagh, D., Pryss, R., Sander, L., & Probst, T. (2020) · JMIR mHealth and uHealth · doi:10.2196/14479 · Cited by 156
Self-citationThe role of mobile health technologies in allergy care: An EAACI position paper
Matricardi, P. M., Dramburg, S., Álvarez‐Perea, A., Antolín‐Amérigo, D., Apfelbacher, C., Atanasković‐Marković, M., Berger, U. E., Blaiss, M. S., Blank, S., Boni, E., et al. (2019) · Allergy · doi:10.1111/all.13953 · Cited by 160
Changing Behavior Using the Model of Action Phases
Keller, L., Gollwitzer, P. M., & Sheeran, P. (2020) · The Handbook of Behavior Change · doi:10.1017/9781108677318.006 · Cited by 147
Berry, K., Salter, A., Morris, R., James, S., & Bucci, S. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.8252 · Cited by 103
“For example, the user Mobile Application Rating Scale ([18]) assesses factors such as an app’s customization, interactivity, ease of use, and the quality and credibility of information.”
A design and evaluation framework for digital health interventions
Kowatsch, T., Otto, L., Harperink, S., Cotti, A., & Schlieter, H. (2019) · it - Information Technology · doi:10.1515/itit-2019-0019 · Cited by 129
“[45, 79, 81] 41, 12.4%”
InfluentialMobile health app usability and quality rating scales: a systematic review
Azad‐Khaneghah, P., Neubauer, N., Cruz, A. M., & Liu, L. (2020) · Disability and Rehabilitation Assistive Technology · doi:10.1080/17483107.2019.1701103 · Cited by 119
“…Health Care Apps Evaluation Tool [73], Royal College of Physicians ’ Health Informatics Unit checklist [74], Organisation for the Review of Care and Health Applications-24 Question Assessment (ORCHA-24) [75], and Mobile Application Rating Scale (MARS) [76 – 92] and its user ’ s version U-MARS [93].”
Domin, A., Spruijt‐Metz, D., Theisen, D., Ouzzahra, Y., & Vögele, C. (2021) · JMIR mhealth and uhealth · doi:10.2196/24308 · Cited by 126
Changing Behavior Using the Health Action Process Approach
Schwarzer, R., & Hamilton, K. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.007 · Cited by 114
Torous, J., Firth, J., Huckvale, K., Larsen, M. E., Cosco, T. D., Carney, R., Chan, S., Pratap, A., Yellowlees, P., Wykes, T., et al. (2018) · Journal of Nervous & Mental Disease · doi:10.1097/nmd.0000000000000864 · Cited by 101
Kim, B. Y., Sharafoddini, A., Tran, N., Wen, E. Y., & Lee, J. (2018) · JMIR mhealth and uhealth · doi:10.2196/mhealth.8613 · Cited by 115
“The popularity of MARS led to the further development of an Italian version [36] and an end-user version for nonresearchers [37].”
Hunt, M. G., Miguez, S. M., Dukas, B., Onwude, O., & White, S. J. (2021) · JMIR mhealth and uhealth · doi:10.2196/26152 · Cited by 99
Mental health apps in a college setting: openness, usage, and attitudes.
Kern, A., Hong, V., Song, J., Lipson, S., & Eisenberg, D. (2018) · mHealth · doi:10.21037/mhealth.2018.06.01 · Cited by 96
“Further research and collaboration are needed to evaluate these tools and optimize their integration into the broader field of behavioral health care (9-11).”
Computer-Assisted Cognitive-Behavior Therapy and Mobile Apps for Depression and Anxiety
Wright, J. H., Mishkind, M., Eells, T. D., & Chan, S. R. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1031-2 · Cited by 93
Review of Use and Integration of Mobile Apps Into Psychiatric Treatments
Chan, S. R., Godwin, H. T., González, Á. D. S., Yellowlees, P., & Hilty, D. M. (2017) · Current Psychiatry Reports · doi:10.1007/s11920-017-0848-9 · Cited by 91
Mobile health tools for the management of chronic respiratory diseases
Sleurs, K., Seys, S., Bousquet, J. J., Fokkens, W. J., Gorris, S., Pugin, B., & Hellings, P. W. (2019) · Allergy · doi:10.1111/all.13720 · Cited by 91
“A scoring system, so‐called the Mobile Application Rating Scale (MARS), has however been developed(39) and recently validated but targets the end user.(40) This scoring system is insufficient to determine the overall quality, including clinical proof of efficacy.”
Muro-Culebras, A., Escriche-Escuder, A., Martín‐Martín, J., Roldán‐Jiménez, C., De-Torres, I., Ruiz‐Muñoz, M., González‐Sánchez, M., Mayoral, F., Biró, A., Tang, W. J., et al. (2020) · JMIR mhealth and uhealth · doi:10.2196/15433 · Cited by 92
“The MARS [29] uses uMARS as the user version [31].”
Ernsting, C., Stühmann, L. M., Dombrowski, S. U., Voigt‐Antons, J., Kuhlmey, A., & Gellert, P. R. (2019) · JMIR mhealth and uhealth · doi:10.2196/12179 · Cited by 91
ACCU3RATE: A mobile health application rating scale based on user reviews
Biswas, M., Tania, M. H., Kaiser, M. S., Kabir, R., Mahmud, M., & Kemal, A. A. (2021) · PLoS ONE · doi:10.1371/journal.pone.0258050 · Cited by 82
Mustafa, N., Safii, N. S., Jaffar, A., Sani, N. S., Mohamad, M. I., Abd Rahman, A. H., & Mohd Sidik, S. (2021) · JMIR mHealth and uHealth · doi:10.2196/24457 · Cited by 69
“The mobile app rating scale and the user version of the mobile app rating scale include some usability components to assess the quality of mobile apps [16,17].”
Evaluating Plant Disease Detection Mobile Applications: Quality and Limitations
Siddiqua, A., Kabir, M. A., Ferdous, T., Ali, I. B., & Weston, L. A. (2022) · Agronomy · doi:10.3390/agronomy12081869 · Cited by 76
Woods, L. S., Duff, J. S., Roehrer, E., Walker, K., & Cummings, E. A. (2019) · JMIR Human Factors · doi:10.2196/13009 · Cited by 75
“Since data collection, the authors are aware of a user version of the MARS called uMARS [28], which would have suited this participant sample more specifically as health care consumers.”
Hensher, M., Cooper, P., Dona, S. W. A., Angeles, M. R., Nguyen, D., Heynsbergh, N., Chatterton, M. L., & Peeters, A. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab041 · Cited by 80
“Future frameworks can be validated and improved by undertaking pilot testing with randomly selected apps and statistical analyzes such as interrater reliability or internal consistency.(44) Other limitations of the currently available frameworks also show the need for improvement and the potential for development of a…”
Parenting apps review: in search of good quality apps
Virani, A., Duffett‐Leger, L., & Letourneau, N. L. (2019) · mHealth · doi:10.21037/mhealth.2019.08.10 · Cited by 74
“Health care professionals can encourage parents to look for the privacy policy of apps to ensure that personal information is not shared with the third party; to check for the source of information or Health On the Net (HON) seal, to verify the quality of health information; and to appraise apps using app evaluation t…”
Argent, R., Slevin, P., Bevilacqua, A., Neligan, M. C., Daly, A., & Caulfield, B. (2019) · Sensors · doi:10.3390/s19020432 · Cited by 75
Design and testing of a mobile health application rating tool
Levine, D. M., Co, Z., Newmark, L. P., Groisser, A. R., Holmgren, A. J., Haas, J. S., & Bates, D. W. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0268-9 · Cited by 71
Lu, D. J., Girgis, M., David, J. M., Chung, E. M., Atkins, K. M., & Kamrava, M. (2021) · Advances in Radiation Oncology · doi:10.1016/j.adro.2020.09.016 · Cited by 72
Gumley, A. I., Bradstreet, S., Ainsworth, J. D., Allan, S., Álvarez‐Jiménez, M., Aucott, L. S., Birchwood, M. J., Briggs, A. H., Bucci, S., Cotton, S., et al. (2022) · The Lancet Psychiatry · doi:10.1016/s2215-0366(22)00103-1 · Cited by 76
Loeckx, M., Rabinovich, R., Demeyer, H., Louvaris, Z., Tanner, R., Rubio, N., Frei, A. L., de Jong, C., Gimeno‐Santos, E., Rodrigues, F. M. M., et al. (2018) · JMIR mhealth and uhealth · doi:10.2196/mhealth.9774 · Cited by 74
Hoffman, L., Benedetto, E., Huang, H. Y., Grossman, E., Kaluma, D., Mann, Z., & Torous, J. B. (2019) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2019.00094 · Cited by 69
“Drawing on feedback from our Patient Partner Lead, staff member expertise, papers on app evaluation (24), and elements of the user version of the Mobile Apps Rating System (U-MARS) (25), the workgroup next narrowed the list to nine apps and drafted a process for introducing the apps through the Primary Care Behavioral…”
Gardner, B., Rebar, A. L., & Lally, P. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.041 · Cited by 73
LeBeau, K., Huey, L. G., & Hart, M. W. (2019) · JMIR mhealth and uhealth · doi:10.2196/13019 · Cited by 63
Bakker, D., Kazantzis, N., Rickwood, D., & Rickard, N. S. (2018) · Cognitive and Behavioral Practice · doi:10.1016/j.cbpra.2018.07.002 · Cited by 69
Kuhn, E. R., Miller, K. E., Puran, D., Wielgosz, J., YorkWilliams, S. L., Owen, J. E., Jaworski, B. K., Hallenbeck, H. W., McCaslin, S. E., & Taylor, K. L. (2021) · Behavior Therapy · doi:10.1016/j.beth.2021.11.003 · Cited by 64
Samad, S., Ahmed, F., Naher, S., Kabir, M. A., Das, A., Amin, S., & Islam, S. M. S. (2022) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2022.200103 · Cited by 65
“, 2015), uMARS – end- user version of MARS (Stoyanov et al., 2016), FinMARS – MARS for financial apps (Huebner et al.”
Usability of Mobile Health Apps for Postoperative Care: Systematic Review
Patel, B., & Thind, A. (2020) · JMIR Perioperative Medicine · doi:10.2196/19099 · Cited by 64
Fijačko, N., Gosak, L., Cilar, L., Novšak, A., Creber, R. M., Skok, P., & Štiglic, G. (2020) · JMIR mhealth and uhealth · doi:10.2196/16365 · Cited by 68
Changing Behavior Using Social Cognitive Theory
Łuszczyńska, A., & Schwarzer, R. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.003 · Cited by 81
Jupp, J. C. Y., Sultani, H., Cooper, C. A., Peterson, K. A., & Truong, T. H. (2018) · Pediatric Blood & Cancer · doi:10.1002/pbc.27278 · Cited by 60
Povey, J., Sweet, M., Nagel, T., Mills, P. P. J. R., Stassi, C. P., Puruntatameri, A. M. A., Lowell, A., Shand, F., & Dingwall, K. (2020) · Internet Interventions · doi:10.1016/j.invent.2020.100318 · Cited by 48
Asthma and Allergy Mobile Apps in 2018
Kagen, S., & Garland, A. (2019) · Current Allergy and Asthma Reports · doi:10.1007/s11882-019-0840-z · Cited by 58
Lambrecht, A., Vuillerme, N., Raab, C., Simón, D., Meßner, E.-M., Hagen, M., Bayat, S., Kleyer, A., Aubourg, T., Schett, G. A., et al. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.715345 · Cited by 58
Amor-García, M. Á., Collado-Borrell, R., Escudero-Vilaplana, V., Melgarejo-Ortuño, A., Herranz-Alonso, A., Arranz Arija, J. Á., & Sanjurjo-Sáez, M. (2020) · JMIR mHealth and uHealth · doi:10.2196/17609 · Cited by 49
“MARS is limited by its subjectivity [13,21].”
Mobile Apps for Dental Caries Prevention: Systematic Search and Quality Evaluation
Chen, R., Santo, K., Wong, G. K. M., Sohn, W., Spallek, H., Chow, C. K., & Irving, M. J. (2020) · JMIR mhealth and uhealth · doi:10.2196/19958 · Cited by 67
“This scale provides a standardized approach with 19 objective items and provides appraisal across 4 subscales: engagement, functionality, esthetics, and information quality [38,40].”
Ha, S., Ho, S. H., Bae, Y.-H., Lee, M., Kim, J. H., Kim, J. H., & Lee, J. (2023) · Journal of Medical Internet Research · doi:10.2196/50029 · Cited by 57
“In the research by Stoyanov et al [43], the reported Cronbach α value was .78; in this study, it was .743.”
Adam, A., Hellig, J. C., Perera, M., Bolton, D. M., & Lawrentschuk, N. (2017) · World Journal of Urology · doi:10.1007/s00345-017-2150-1 · Cited by 54
Serlachius, A. S., Schache, K., Kieser, A., Arroll, B., Petrie, K. J., & Dalbeth, N. (2019) · JMIR mhealth and uhealth · doi:10.2196/15021 · Cited by 53
Warner, L. M., & French, D. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.032 · Cited by 62
Escriche-Escuder, A., De-Torres, I., Roldán‐Jiménez, C., Martín‐Martín, J., Muro-Culebras, A., González‐Sánchez, M., Ruiz‐Muñoz, M., Mayoral, F., Biró, A., Tang, W. J., et al. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17249209 · Cited by 48
Designing and Implementing the MySusCof App—A Mobile App to Support Food Waste Reduction
Haas, R., Aşan, H., Doğan, O., Michalek, C. R., Karaca, O., & Bulut, Z. A. (2022) · Foods · doi:10.3390/foods11152222 · Cited by 53
Nutrition-Related Mobile Apps in the China App Store: Assessment of Functionality and Quality
Li, Y., Ding, J., Wang, Y., Tang, C., & Zhang, P. (2019) · JMIR mhealth and uhealth · doi:10.2196/13261 · Cited by 53
Changing Behavior Using Ecological Models
Salmon, J., Hesketh, K. D., Arundell, L., Downing, K. L., & Biddle, S. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.017 · Cited by 57
Understanding users’ perspectives on mobile apps for anxiety management
Balaskas, A., Schueller, S. M., Cox, A. L., & Doherty, G. J. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.854263 · Cited by 56
Efficacy and outcomes of a mobile app targeting alcohol use in young people
Hides, L. M., Quinn, C. A., Cockshaw, W. D., Stoyanov, S. R., Zelenko, O., Johnson, D. M., Tjondronegoro, D., Quek, L., & Kavanagh, D. J. (2017) · Addictive Behaviors · doi:10.1016/j.addbeh.2017.09.020 · Cited by 50
The Arabic Version of the Mobile App Rating Scale: Development and Validation Study
Bardus, M., Awada, N., Ghandour, L. A., Fares, E.-J., Gherbal, T., Al-Zanati, T., & Stoyanov, S. R. (2019) · JMIR mhealth and uhealth · doi:10.2196/16956 · Cited by 46
“A simplified version for end users (user version of the MARS, uMARS) has also been developed [39]; it includes the same domains of the MARS tool, using simplified language and omitting items that would require rater expertise, so that it can be used without training and by laypersons or end users…”
Digital Medication Adherence Support: Could Healthcare Providers Recommend Mobile Health Apps?
Backes, C., Moyano, C. E., Rimaud, C., Bienvenu, C., & Schneider, M. P. (2021) · Frontiers in Medical Technology · doi:10.3389/fmedt.2020.616242 · Cited by 48
“Through elimination of duplicates and terms with little relation to medication adherence, a total of 17 English and 65 French search terms have been identified for the search of medication adherence apps ( Appendix 6 ).”
InfluentialTan, R. H., Cvetkovski, B., Kritikos, V. S., O’Hehir, R. E., Lourenço, O. M., Bousquet, J., & Bosnic-Anticevich, S. Z. (2019) · Journal of Asthma · doi:10.1080/02770903.2019.1640728 · Cited by 45
Walsh, J. C., & Groarke, J. M. (2019) · European Psychologist · doi:10.1027/1016-9040/a000351 · Cited by 51
Changing Behavior Using Self-Determination Theory
Hagger, M. S., Hankonen, N. E., Chatzisarantis, N. L. D., & Ryan, R. M. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.008 · Cited by 67
Giebel, G. D., Abels, C., Plescher, F., Speckemeier, C., Schrader, N. F., Börchers, K., Wasem, J., Neusser, S., & Blase, N. (2024) · Journal of Medical Internet Research · doi:10.2196/49982 · Cited by 44
Bardus, M., Ali, A., Demachkieh, F., & Hamadeh, G. (2019) · JMIR mHealth and uHealth · doi:10.2196/mhealth.9836 · Cited by 44
“Unfortunately, the uMARS source study does not provide solutions in case of poor internal consistency or low agreement [43], and other studies employing uMARS did not report such issues [54,55].”
InfluentialGumley, A. I., Bradstreet, S., Ainsworth, J. D., Allan, S., Álvarez‐Jiménez, M., Beattie, L., Bell, I. H., Birchwood, M. J., Briggs, A. H., Bucci, S., et al. (2020) · JMIR Research Protocols · doi:10.2196/15058 · Cited by 41
Systematic evaluation of content and quality of English and German pain apps in European app stores
Terhorst, Y., Meßner, E.-M., Schultchen, D., Paganini, S., Portenhauser, A. A., Eder, A.-S., Bauer, M., Papenhoff, M. C., Baumeister, H., & Sander, L. (2021) · Internet Interventions · doi:10.1016/j.invent.2021.100376 · Cited by 42
Keller, J., Roitzheim, C., Radtke, T., Schenkel, K., & Schwarzer, R. (2021) · JMIR mhealth and uhealth · doi:10.2196/26397 · Cited by 45
Choi, S. K., Yelton, B., Ezeanya, V. K., Kannaley, K., & Friedman, D. B. (2018) · Journal of Applied Gerontology · doi:10.1177/0733464818790187 · Cited by 42
“MARS is a recently developed tool, used to evaluate the quality of health-related apps, and its psychometric properties have been reported as good (Stoyanov et al., 2016).”
Saliasi, I., Martinon, P., Darlington, E., Smentek, C., Tardivo, D., Bourgeois, D. M., Dussart, C., Carrouel, F., & Fraticelli, L. (2021) · JMIR mhealth and uhealth · doi:10.2196/30480 · Cited by 40
Manning, V., Piercy, H., Garfield, J. B. B., Clark, S. G., Andrabi, M. N., & Lubman, D. I. (2021) · JMIR mhealth and uhealth · doi:10.2196/31353 · Cited by 45
Eis, S., Solà-Morales, O., Duarte‐Díaz, A., Vidal‐Alaball, J., Perestelo‐Pérez, L., Robles, N., & Carrión, C. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19042186 · Cited by 43
Pilot study of a well-being app to support New Zealand young people during the COVID-19 pandemic
Serlachius, A., Boggiss, A., Lim, D., Schache, K., Wallace-Boyd, K., Brenton-Peters, J., Buttenshaw, E., Chadd, S., Cavadino, A., Cao, N., et al. (2021) · Internet Interventions · doi:10.1016/j.invent.2021.100464 · Cited by 35
“User engagement was assessed by the App Subjective Quality sub-scale and the Perceived Impact subscale of the end-user version of the uMARS measure (Stoyanov et al., 2016).”
InfluentialBell, I., Arnold, C., Gilbertson, T., D’Alfonso, S., Castagnini, E., Chen, N. A., Nicholas, J., O’Sullivan, S., Valentine, L., & Álvarez‐Jiménez, M. (2023) · Journal of Medical Internet Research · doi:10.2196/47860 · Cited by 40
“Acceptability was assessed with the subscales (range 1-5, with higher scores indicating more positive evaluations) of the Mobile Application Rating Scale–User version [52].”
InfluentialFerguson, M., Maidment, D. W., Gomez, R., Coulson, N., & Wharrad, H. J. (2020) · International Journal of Audiology · doi:10.1080/14992027.2020.1825839 · Cited by 41
“The Mobile Application Rating Scale: user version (uMARS) (Stoyanov et al. 2016) is a questionnaire that assesses the quality 4 M.”
Spanish adaptation and validation of the User Version of the Mobile Application Rating Scale (uMARS)
Martín‐Payo, R., Carrasco-Santos, S., Cuesta, M., Stoyan, S., González-Méndez, X., & Fernández-Álvarez, M. D. M. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab216 · Cited by 40
Saeidnia, H. R., Kozak, M., Ausloos, M., Herţeliu, C., Mohammadzadeh, Z., Ghorbi, A., Karajizadeh, M., & Hassanzadeh, M. (2022) · JMIR Formative Research · doi:10.2196/39718 · Cited by 50
Digital transformation evaluation of telehealth using convergence, maturity, and adoption
Mishra, V., & Sharma, M. G. (2022) · Health Policy and Technology · doi:10.1016/j.hlpt.2022.100684 · Cited by 39
Planning and Implementation Intention Interventions
Rhodes, R. E., Grant, S. J., & de Bruijn, G. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.039 · Cited by 46
Attitudes and Persuasive Communication Interventions
Hamilton, K., & Johnson, B. T. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.031 · Cited by 44
Changing Behavior Using Habit Theory
Orbell, S., & Verplanken, B. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.013 · Cited by 50
Dambi, J. M., Norman, C., Doukani, A., Potgieter, S., Turner, J., Musesengwa, R., Verhey, R., & Chibanda, D. (2022) · JMIR Mental Health · doi:10.2196/37968 · Cited by 44
Ko, S., Lee, J., An, D., & Woo, H. (2022) · JMIR mhealth and uhealth · doi:10.2196/40921 · Cited by 38
“The uMARS was subsequently developed, based on the MARS, to allow consumers to evaluate the quality of mHealth apps. uMARS has excellent internal consistency [25].”
Changing Behavior: A Theory- and Evidence-Based Approach
Hagger, M. S., Cameron, L. D., Hamilton, K., Hankonen, N. E., & Lintunen, T. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.001 · Cited by 47
Ulrich, S., Gantenbein, A. R., Zuber, V., Von Wyl, A., Kowatsch, T., & Künzli, H. (2024) · Journal of Medical Internet Research · doi:10.2196/50132 · Cited by 36
“We assessed the acceptance of BalanceUP with a slightly modified and translated version of the Mobile App Rating Scale (uMARS) [103] assessing engagement (eg, entertainment, interest, customization, interactivity, and target group of the app); information (eg, quality of information, quantity of…”
Hauser-Ulrich, S., Lienhard, N., Künzli, H., & Kowatsch, T. (2024) · JMIR mhealth and uhealth · doi:10.2196/54945 · Cited by 50
“We evaluated the feasibility and acceptance of MISHA using the user version of the Mobile App Rating Scale (uMARS) [86] after the intervention.”
Gamifying Breastfeeding for Fathers: Process Evaluation of the Milk Man Mobile App
White, B. K., Giglia, R. C., White, J. A., Dhaliwal, S. S., Burns, S. K., & Scott, J. A. (2019) · JMIR Pediatrics and Parenting · doi:10.2196/12157 · Cited by 38
Tarrant, M., Haslam, C., Carter, M., Calitri, R., & Haslam, S. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.044 · Cited by 38
Paldán, K., Steinmetz, M., Simanovski, J., Rammos, C., Ullrich, G., Jánosi, R. A., Moebus, S., Rassaf, T., & Lortz, J. (2021) · JMIR mhealth and uhealth · doi:10.2196/24214 · Cited by 37
Gumley, A. I., Bradstreet, S., Ainsworth, J. D., Allan, S., Álvarez‐Jiménez, M., Birchwood, M. J., Briggs, A. H., Bucci, S., Cotton, S., Engel, L., et al. (2022) · Health Technology Assessment · doi:10.3310/hlze0479 · Cited by 41
Gasteiger, N., Dowding, D., Norman, G., McGarrigle, L., Eost‐Telling, C., Jones, D. J., Vercell, A., Ali, S. M., & O’Connor, S. (2023) · BMJ Open · doi:10.1136/bmjopen-2023-073283 · Cited by 37
“Nevertheless, Stoyanov et al 41 reported that the objective measures correlated well with the subjective measures.”
Faieta, J. M., Sheehan, J., & DiGiovine, C. P. (2020) · Assistive Technology · doi:10.1080/10400435.2020.1829174 · Cited by 36
Guo, Y., Yang, F., Hu, F., Li, W., Ruggiano, N., & Lee, H. Y. (2019) · JMIR Aging · doi:10.2196/15290 · Cited by 36
Povey, J., Sweet, M., Nagel, T., Lowell, A., Shand, F., Vigona, J., & Dingwall, K. M. (2022) · JMIR Formative Research · doi:10.2196/28342 · Cited by 26
Changing Behavior Using Integrated Theories
Hagger, M. S., & Hamilton, K. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.015 · Cited by 44
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
