Smartphone Applications for Mental Health—A Rapid Review
Bibliographic record
Abstract
Objectives: The purpose of this article is to determine what evidence exists about the effectiveness of monitoring and managing mental health via smartphone application. This study aims to inform health care decision makers of available evidence as well as the necessary components and potential barriers to success. Methods: A rapid review was conducted which yielded eleven primary research studies evaluating nine unique smartphone applications. Results: The literature demonstrated many benefits of smartphone applications for mental health including: positive perceptions and experiences by patients and doctors as well as their willingness to use the applications to impact their mental health. Further benefits include the apps ability to provide valid and detailed symptom monitoring, to apply proven psychotherapy methods, to improve access to care and to positively impact clinical outcomes. A potential barrier to success addressed by the literature is ensuring patients’ long-term adherence to this treatment method. Key success factors to delivering effective mental health care via a smartphone app include individually tailored apps, adaptive learning, a feedback system, and clinical, peer and technical support. Conclusions: Preliminary outcomes in this area seem promising. However, there is a limited amount of high-quality research studies and more rigorous scientific investigation into this area is needed.
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".