Mental Health Apps in China: Analysis and Quality Assessment
Bibliographic record
Abstract
BACKGROUND: Mental disorders have been a great burden on health care systems, affecting the quality of life of millions of people worldwide. Developing countries, including China, suffer from the double burden of both the increasing mental health issues in population and the deficiency in mental health care resources. The use of mobile health technologies, especially for mobile phone apps, can be a possible solution. OBJECTIVE: This review aimed to describe the features and assess the quality of mental health apps in major mobile phone app markets in China and further discuss the priorities for mental health app development. METHODS: Keywords including psychology, psychological health, psychological hygiene, psychological health service(s), mental, mental health, mental hygiene, mental health service(s), depression, and anxiety were searched in Chinese in 3 Android app markets (Baidu Mobile Assistant, Tencent MyApp, and 360 Mobile Assistant) and iOS App Store independently. Mental health apps were then selected according to established criteria for in-depth analysis and quality assessment by the Mobile App Rating Scale. RESULTS: In total, 63 of 997 mental health apps were analyzed in depth, of which 78% (49/63) were developed by commercial entities for general population, 17% (11/63) were for patients or clients of specialized psychiatric hospitals or counseling agencies, 3% (2/63) were by government or local Centers for Disease Control and Prevention for general information, and 2% (1/63) for students of a university. Major built-in features of the apps included counseling services, mental health education, and self-assessment of mental health status by validated self-rating scales. The overall quality score of the MH apps was acceptable. CONCLUSIONS: Mental health apps are emerging in the area of mobile health in China. Popular mental health apps usually provide a synthetic platform organizing resources of information, knowledge, counseling services, self-tests, and management for the general population with mental health-related inquiries. The quality of the apps was rated as acceptable on average, suggesting some space for improvement. Official guidelines and regulations are urgently required for the field in the future.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".