Cited by 286Digital Mental Health InterventionsMobile Health and mHealth ApplicationsUsability and User Interface Design

Insights from user reviews to improve mental health apps

Open access
Alqahtani, Felwah, Orji, Rita·2020·Health Informatics Journal

Abstract

Mental health applications hold great promise as interventions for addressing common mental issues. Although many people with mental health issues use mobile app interventions, their adherence level remains low. Low engagement affects the effectiveness of mobile interventions. However, there is still a dearth of research to explain the reasons for low engagement. User experience and usability are two factors that determine the adoption and usage of apps. Analyzing user reviews of mobile apps for mental health issues reveals user experience and what features users liked and disliked in the apps and hence informs future app design and refinements. This research aims to analyze user reviews of publicly available mental health applications to uncover their strengths, weaknesses, and gaps, hence revealing why users are likely to cease using these applications. We mined reviews of 106 mental health apps retrieved from Apple’s App Store and Google Play and employed thematic analysis on 13,549 reviews. The review analysis shows that users placed more emphasis on the user interface and the user-friendliness of the app. Users also appreciated apps that present them with a variety of options, functionalities, and content that they can choose. Again, apps that offer adaptive functionalities that allow users to adapt some app features also received high ratings. In contrast, poor usability emerged as the most common reason for abandoning mental health apps. Other pitfalls include lack of a content variety, lack of personalization, lack of customer service and trust, and security and privacy issues.

Integrity signals

Registry

Crossref · 10.1177/1460458219896492

Cites

27 of 27 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Alqahtani, F., & Orji, R.
Affiliations
Dalhousie University (CA)
Corresponding author
Alqahtani, Felwah
Venue
Health Informatics Journal
Published
2020-01-10
Volume
26
Issue
3
Pages
2042-2066
ISSN
  • 1355-4743 (ISSN-L)
  • 1460-4582
  • 1741-2811
OpenAlex
W2999181723
Cited by
286
References
27
Citations by year
2026: 38 · 2025: 62 · 2024: 62 · 2023: 50 · 2022: 39 · 2021: 29 · 2020: 6
FWCI
27.99
Type
journal article
Language
en
Open access
Locations
  • Health Informatics Journal (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • natural sciences and engineering research council of canada
  • king khalid university
Topics
  • Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
Keywords
mental health apps, online reviews, app abandonment, user engagement, usability, user experience, mobile health intervention, mobile application design, adaptive traits, personalization, privacy and security, customer service
MeSH terms
Humans, Mental Health, Privacy, 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