The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
A Systematic Review of Quality Assessment Methods for Smartphone Health Apps
Abstract
Introduction: There are large numbers of health-related applications (apps) available in various app stores for many smartphone devices. Consequently, increasing numbers of articles are attempting to evaluate the content and the quality of health-related smartphone apps for specific health conditions. The aim of this article is to describe and summarize the methodologies used to determine the quality of health-related apps targeting health consumers and to propose a set of criteria for evaluating the quality of smartphone health-related apps. Materials and Methods: In 2013, literature searches were performed using Medline and CINAHL, and we included all articles that had the aim of assessing the quality of health-related smartphone apps. The assessment method used in these studies was summarized and scored using a set of quality criteria developed for this study. Results: From 606 articles generated by the search, only 10 met the inclusion criteria. Based on our quality criteria, the mean score was 5.05 out of 8 (range, 2–7). Eighty percent of the studies did not identify the app store country in which the apps were found. Forty percent of the studies did not clearly mention whether they only had assessed the app description or had downloaded the app content for evaluation. Sixty percent of the studies did not provide a list of the apps they had evaluated. Overall, we identified six evaluation methodologies used to assess the quality of health-related apps described in Results. Conclusions: This article provides a summary of currently used methods for assessing the quality of smartphone health-related apps and proposes a set of criteria to enable future studies to consistently review health-related app quality in a standardized manner.
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“popularity metrics may be biased by developers paying for downloads/positive reviews (BinDhim et al. 2015).”
From Evaluating the quality, safety, and functionality of commonly used smartphone apps for bipolar disorder mood and sleep self-management (2022) · cites this paper as BinDhim et al. 2015
Citations
Cites
Gagliardi, A. R. (2002) · BMJ · doi:10.1136/bmj.324.7337.569 · Cited by 378
iPhone Apps for Smoking Cessation
Abroms, L. C., Padmanabhan, N., Thaweethai, L., & Phillips, T. (2011) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2010.10.032 · Cited by 365
Smartphone applications for pain management
Rosser, B. A., & Eccleston, C. (2011) · Journal of Telemedicine and Telecare · doi:10.1258/jtt.2011.101102 · Cited by 364
Muessig, K., Pike, E. C., Legrand, S., & Hightow-Weidman, L. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2301 · Cited by 300
Apps for asthma self-management: a systematic assessment of content and tools
Huckvale, K., Car, M., Morrison, C. P., & Car, J. (2012) · BMC Medicine · doi:10.1186/1741-7015-10-144 · Cited by 252
Review Of Internet Health Information Quality Initiatives
Risk, A., & Dzenowagis, J. (2001) · Journal of Medical Internet Research · doi:10.2196/jmir.3.4.e28 · Cited by 237
Pandey, A., Hasan, S., Dubey, D., & Sarangi, S. (2013) · Journal of Cancer Education · doi:10.1007/s13187-012-0446-9 · Cited by 213
Colorectal smartphone apps: opportunities and risks
O’Neill, S., & Brady, R. R. W. (2012) · Colorectal Disease · doi:10.1111/j.1463-1318.2012.03088.x · Cited by 165
An evaluation of diabetes self-management applications for Android smartphones
Demidowich, A. P., Lu, K., Tamler, R., & Bloomgarden, Z. T. (2012) · Journal of Telemedicine and Telecare · doi:10.1258/jtt.2012.111002 · Cited by 154
Mobile personal health records: An evaluation of features and functionality
Kharrazi, H. H. K., Chisholm, R., VanNasdale, D. A., & Thompson, B. S. (2012) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2012.04.007 · Cited by 147
Research works printed but not linked: 1
- App review services. Available at www.appreviewservice.com/ (last accessed March8 2014).
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Items 101–101 of 101
Larsen, M., Nicholas, J., Han, J., Lemon, C., Okun, K., Torok, M., Wong, D., Wong, I., Wong, Q., & Huckvale, K. (2020) · BMJ Open · doi:10.1136/bmjopen-2020-039817 · Cited by 5
Metadata
- Authors
- BinDhim, N. F., Hawkey, A., & Trevena, L.
- Affiliations
- Saudi Electronic University (SA)
- Western Sydney University (AU)
- The University of Sydney (AU)
- Corresponding author
- BinDhim, Nasser F.
- Venue
- Telemedicine and e-Health
- Publisher
- SAGE Publications
- Published
- 2015-02
- Volume
- 21
- Issue
- 2
- Pages
- 97-104
- ISSN
- 1530-5627 (ISSN-L)
- 1556-3669
- OpenAlex
- W2133455974
- Cited by
- 142
- References
- 18
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 4 · 2023: 9 · 2022: 14 · 2021: 15 · 2020: 24 · 2019: 12 · 2018: 28 · 2017: 9 · 2016: 11 · 2015: 8
- FWCI
- 1.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Telemedicine Journal and e-Health (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- mobile health apps, app quality assessment, quality standards, evaluation methodology, App Store, health care consumers, mobile health
- MeSH terms
- Smartphone, Humans, Quality Assurance, Health Care, Telemedicine, Mobile Applications
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