Using Machine Learning and Thematic Analysis Methods to Evaluate Mental Health Apps Based on User Reviews
Bibliographic record
Abstract
The proliferation of smartphones has led to an increase in mobile health (mHealth) apps over the years. Thus, it is imperative to evaluate these apps by identifying shortcomings or barriers hampering effective delivery of intended services. In this paper, we evaluate 104 mental health apps on Google Play and App Store by performing sentiment analysis of 88125 user reviews using machine learning (ML), and then conducting thematic analysis on the reviews. We implement and compare the performance of five classifiers using supervised ML algorithms that are widely used for solving classification problems. The best performing classifier, with F1-score of 89.42%, was then used in predicting the sentiment polarity of reviews. Next, we conduct a thematic analysis of positive and negative reviews to identify themes representing various factors affecting the effectiveness of mental health apps positively and negatively. Our results uncover 21 negative themes and 29 positive themes. The negative themes fall under the following categories: usability issues, content issues, ethical issues, customer support issues, and billing issues. Some of the positive themes include aesthetically pleasing interface, app stability, customizability, high-quality content, content variation/diversity, personalized content, privacy and security, and low-subscription cost. Finally, we offer design recommendations on how the identified negative factors can be tackled to improve the effectiveness of mental health apps.
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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 | 0.026 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".