Insights from user reviews to improve mental health apps
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
- Candidate categories
- Insufficient payload (model declined to judge)
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Not applicableConsensus signal: Not applicable
- Genre
- Candidate signal: CommentaryConsensus signal: Commentary
- Teacher disagreement score
- 0.325
- Threshold uncertainty score
- 0.996
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.005 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
- Teacher spread
- 0.334 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- Health Informatics Journal
- Topic
- Digital Mental Health Interventions
- Field
- Psychology
- Canadian institutions
- Dalhousie University
- Funders
- Natural Sciences and Engineering Research Council of CanadaKing Khalid University
- Keywords
- Mental healthUsabilityPersonalizationVariety (cybernetics)Internet privacyThematic analysisPsychological interventionmHealthComputer scienceWorld Wide WebApp storeUser experience designPsychologyHuman–computer interactionQualitative research
- Has abstract in OpenAlex
- yes