Mobile Health Applications for Depression in China: A Systematic Review
Bibliographic record
Abstract
Mobile health (mHealth) applications (apps) have the potential to increase access to mental health care. In China, there is growing interest in mHealth apps for depression. Our objective was to systematically review research on mHealth for depression in China to identify benefits and challenges. A systematic literature search was conducted using Chinese and English databases in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Randomized and nonrandomized clinical studies on mHealth apps and depression in China were included. Study quality was assessed using the Cochrane Risk of Bias tool. Seven studies met the inclusion criteria with three randomized trials, two quasi-randomized trials, one clinical trial with an uncertain grouping method, and one study with a single-group design. All studies used the WeChat platform and included activities such as psychoeducation, self-management, supervised group chats, and/or remote contact with a healthcare team, in comparison to usual care. All studies reported significant and large benefits for outcomes, but the risk of bias was high. There are few rigorous evaluations of mHealth apps for depression in China, with all included studies involving WeChat programs and most using WeChat to extend nursing discharge care for inpatients with depression. While these studies showed significant improvement in health outcomes as compared to usual care, the results remain inconclusive because of the high risk of bias. mHealth holds promise for increasing access to mental health care in China, but issues such as efficacy, scalability, patient and clinician acceptability, and data privacy must be addressed.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".