Evaluating User Feedback for an Artificial Intelligence–Enabled, Cognitive Behavioral Therapy–Based Mental Health App (Wysa): Qualitative Thematic Analysis
Bibliographic record
Abstract
BACKGROUND: Digital mental health apps are rapidly becoming a common source of accessible support across the world, but their effectiveness is often influenced by limited helpfulness and engagement. OBJECTIVE: This study's primary objective was to analyze feedback content to understand users' experiences with engaging with a digital mental health app. As a secondary objective, an exploratory analysis captured the types of mental health app users. METHODS: This study utilized a user-led approach to understanding factors for engagement and helpfulness in digital mental health by analyzing feedback (n=7929) reported on Google Play Store about Wysa, a mental health app (1-year period). The analysis of keywords in the user feedback categorized and evaluated the reported user experience into the core domains of acceptability, usability, usefulness, and integration. The study also captured key deficits and strengths of the app and explored salient characteristics of the types of users who benefit from accessible digital mental health support. RESULTS: The analysis of user feedback found the app to be overwhelmingly positively reviewed (6700/7929, 84.50% 5-star rating). The themes of engaging exercises, interactive interface, and artificial intelligence (AI) conversational ability indicated the acceptability of the app, while the nonjudgmentality and ease of conversation highlighted its usability. The app's usefulness was portrayed by themes such as improvement in mental health, convenient access, and cognitive restructuring exercises. Themes of privacy and confidentiality underscored users' preference for the integrated aspects of the app. Further analysis revealed 4 predominant types of individuals who shared app feedback on the store. CONCLUSIONS: Users reported therapeutic elements of a comfortable, safe, and supportive environment through using the digital mental health app. Digital mental health apps may expand mental health access to those unable to access traditional forms of mental health support and treatments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
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.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".