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Assessment of the Health IT Usability Evaluation Model (Health-ITUEM) for evaluating mobile health (mHealth) technology
Open accessAbstract
Background Over two decades of research has been conducted using mobile devices for health related behaviors yet many of these studies lack rigor. There are few evaluation frameworks for assessing the usability of mHealth, which is critical as the use of this technology proliferates. As the development of interventions using mobile technology increase, future work in this domain necessitates the use of a rigorous usability evaluation framework.Methods We used two exemplars to assess the appropriateness of the Health IT Usability Evaluation Model (Health-ITUEM) for evaluating the usability of mHealth technology. In the first exemplar, we conducted 6 focus group sessions to explore adolescents' use of mobile technology for meeting their health Information needs. In the second exemplar, we conducted 4 focus group sessions following an Ecological Momentary Assessment study in which 60 adolescents were given a smartphone with pre-installed health-related applications (apps).Data analysis We coded the focus group data using the 9 concepts of the Health-ITUEM: Error prevention, Completeness, Memorability, Information needs, Flexibility/Customizability, Learnability, Performance speed, Competency, Other outcomes. To develop a finer granularity of analysis, the nine concepts were broken into positive, negative, and neutral codes. A total of 27 codes were created. Two raters (R1 and R2) initially coded all text and a third rater (R3) reconciled coding discordance between raters R1 and R2.Results A total of 133 codes were applied to Exemplar 1. In Exemplar 2 there were a total of 286 codes applied to 195 excerpts. Performance speed, Other outcomes, and Information needs were among the most frequently occurring codes.Conclusion Our two exemplars demonstrated the appropriateness and usefulness of the Health-ITUEM in evaluating mobile health technology. Further assessment of this framework with other study populations should consider whether Memorability and Error prevention are necessary to include when evaluating mHealth technology.
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Metadata
- Authors
- Brown, W., Yen, P.-Y., Rojas, M., & Schnall, R.
- Affiliations
- New York State Psychiatric Institute (US)
- The Ohio State University (US)
- Columbia University (US)
- Corresponding author
- Schnall, Rebecca
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2013-12
- Volume
- 46
- Issue
- 6
- Pages
- 1080-1087
- ISSN
- 1532-0464 (ISSN-L)
- PMC
- PMC3844064
- OpenAlex
- W2010233908
- Cited by
- 299
- References
- 49
- Citations by year
- 2026: 15 · 2025: 21 · 2024: 21 · 2023: 23 · 2022: 22 · 2021: 25 · 2020: 32 · 2019: 36 · 2018: 38 · 2017: 21 · 2016: 25 · 2015: 12 · 2014: 8
- FWCI
- 27.03
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (KL2TR000081, TR000081)
- National Institute of Nursing Research (P30NR010677)
- U.S. National Library of Medicine (T15LM007079)
- National Center for Research Resources (RR 024157, KL2RR024157)
- National Center for Advancing Translational Sciences (TR000081)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- mobile health, mobile health technology, usability evaluation, evaluation framework, adolescents, mobile health apps, health information needs, learnability, memorability, error prevention, performance speed, ecological momentary assessment
- MeSH terms
- Evaluation Studies as Topic, Humans, Information Services, Focus Groups, Telemedicine
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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