The DOI matches its Crossref record; 59 of the 62 references it cites were found in a registry or an index.
Criteria for assessing the quality of mHealth apps: a systematic review
Open accessAbstract
Objective: Review the existing studies including an assessment tool/method to assess the quality of mHealth apps; extract their criteria; and provide a classification of the collected criteria. Methods: In accordance with the PRISMA statement, a literature search was conducted in MEDLINE, EMBase, ISI and Scopus for English language citations published from January 1, 2008 to December 22, 2016 for studies including tools or methods for quality assessment of mHealth apps. Two researchers screened the titles and abstracts of all retrieved citations against the inclusion and exclusion criteria. The full text of relevant papers was then individually examined by the same researchers. A senior researcher resolved eventual disagreements and confirmed the relevance of all included papers. The authors, date of publication, subject fields of target mHealth apps, development method, and assessment criteria were extracted from each paper. The extracted assessment criteria were then reviewed, compared, and classified by an expert panel of two medical informatics specialists and two health information management specialists. Results: Twenty-three papers were included in the review. Thirty-eight main classes of assessment criteria were identified. These were reorganized by expert panel into 7 main classes (Design, Information/Content, Usability, Functionality, Ethical Issues, Security and Privacy, and User-perceived value) with 37 sub-classes of criteria. Conclusions: There is a wide heterogeneity in assessment criteria for mHealth apps. It is necessary to define the exact meanings and degree of distinctness of each criterion. This will help to improve the existing tools and may lead to achieve a better comprehensive mHealth app assessment tool.
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“A recent systematic review identified 23 different published criteria for assessment of mHealth apps.”
From What is the clinical value of mHealth for patients? (2020) · cites this paper as [38]
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Metadata
- Authors
- Nouri, R., R Niakan Kalhori, S., Ghazisaeedi, M., Marchand, G., & Yasini, M.
- Affiliations
- Tehran University of Medical Sciences (IR)
- Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
- Research and Development Department, DMD Santé, Paris, France
- Corresponding author
- Ghazisaeedi, Marjan
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-05-16
- Volume
- 25
- Issue
- 8
- Pages
- 1089-1098
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC7646896
- OpenAlex
- W2803669305
- Cited by
- 231
- References
- 62
- Citations by year
- 2026: 14 · 2025: 25 · 2024: 37 · 2023: 35 · 2022: 37 · 2021: 37 · 2020: 30 · 2019: 14 · 2018: 2
- FWCI
- 3.75
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Deputy of Education of Tehran University of Medical Sciences
- 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 health apps, quality assessment, evaluation criteria, quality assessment tools, usability, privacy and security, ethical considerations, mobile application design, app functionality, content quality
- MeSH terms
- Evaluation Studies as Topic, Humans, Telemedicine, Mobile Applications
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 ›
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