Recommendations for mobile apps for mental health treatment: Qualitative interviews with psychiatrists
Bibliographic record
Abstract
Background: The number of mobile apps tailored for people living with mental health conditions has increased tremendously. However, the majority of the existing apps are not evidence-based and are being developed by teams without mental health expertise. Objective: We aimed to explore psychiatrists' perceptions of what they and their patients need in a mental health app and eventually inform the design of future mobile apps in this area. Methods: = 18) from three European countries: Austria, the Czech Republic, and Slovakia. Content analysis using inductive and deductive coding was used to analyze the interviews. Results: Four major themes were deductively identified: current system, gaps in the current system, recommendations for a mobile app, and promoting app use. Psychiatrists provided a comprehensive list of app features they suggested would be helpful. Of particular importance seemed to be enabling patients to self-monitor various aspects of their lives and including an emergency plan. Participants also emphasized that the app should be positive and motivating for patients to use, with some suggesting that users be able to communicate with other users for support. Within the theme of "current system," a common topic was the current shortage of psychiatrists and the feelings of time pressure amongst existing psychiatrists. Conclusions: The results of this study can be used by software developers to inform future designs of mental health mobile apps, which will hopefully translate to a greater availability of evidence-based apps that address clinical needs.
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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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".