Cited by 10Mobile Health and mHealth ApplicationsUsability and User Interface DesignDiabetes Management and Education

Content Analysis: First-Time Patient User Challenges with Top-Rated Commercial Diabetes Apps

Open access
Fu, Helen N.C., Jin, Diana, Adam, Terrence J.·2021·Telemedicine and e-Health

Abstract

Background/Introduction: Using a mobile application (app) may improve diabetes self-management. However, the use of diabetes apps is low, possibly due to design and usability issues. The purpose of this study was to identify barriers to app use among adult patients with diabetes who were testing diabetes apps for the first time. Materials and Methods: We conducted a content analysis of observation notes and patient comments collected during the testing of two top commercially available diabetes apps as part of a crossover randomized trial. Participants were adult patients with type 1 or type 2 diabetes on insulin therapy. We analyzed field notes and transcriptions of audio recordings. Open coding derived categories of usability issues, which then were grouped into themes and subthemes on usability problem types. Results: A total of 92 adult Android smartphone users were recruited online (e.g., Facebook) and in-person postings. Three major themes described problems with data input, app report display and presentation, and self-learning options. Data entry modes were problematic because of overcrowded app screens, complicated “save data” steps, and a lack of data entry confirmation. The app icons, wording, entry headings, and analysis reports were not intuitive to understand. Participants wanted self-learning options (e.g., pop-up messages) during app use. Conclusions: Patient testing of top commercially available diabetes apps revealed key usability design issues in data entry, app report, and self-help learning options. Good app training for patients is necessary for both initial use and long-term use of diabetes apps to support self-management.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1089/tmj.2020.0128

Cites

6 of 6 references verified0 citation errors · 0 not checkable

Statistics

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.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Authors
Fu, H. N. C., Jin, D., & Adam, T. J.
Affiliations
University of Minnesota (US)
Corresponding author
Fu, Helen N.C.
Venue
Telemedicine and e-Health
Published
2021-06
Volume
27
Issue
6
Pages
663-669
ISSN
  • 1530-5627 (ISSN-L)
  • 1556-3669
OpenAlex
W3049397091
Cited by
10
References
6
Citations by year
2026: 1 · 2025: 2 · 2024: 2 · 2023: 3 · 2022: 2
FWCI
1.30
Type
journal article
Language
en
Open access
Locations
  • Telemedicine Journal and e-Health (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Institutes of Health (UL1TR002494)
  • National Center for Advancing Translational Sciences
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
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
Keywords
diabetes apps, mobile health, usability, barriers to app use, type 1 diabetes, type 2 diabetes, insulin therapy, diabetes self-management, data entry, patient training, mobile application
MeSH terms
Smartphone, Self-Management, Adult, Diabetes Mellitus, Type 2, 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 ›

Are you an author, or do you see a problem with this page? Request a review