Mindcraft, a Mobile Mental Health Monitoring Platform for Children and Young People: Development and Acceptability Pilot Study
Bibliographic record
Abstract
BACKGROUND: Children and young people's mental health is a growing public health concern, which is further exacerbated by the COVID-19 pandemic. Mobile health apps, particularly those using passive smartphone sensor data, present an opportunity to address this issue and support mental well-being. OBJECTIVE: This study aimed to develop and evaluate a mobile mental health platform for children and young people, Mindcraft, which integrates passive sensor data monitoring with active self-reported updates through an engaging user interface to monitor their well-being. METHODS: A user-centered design approach was used to develop Mindcraft, incorporating feedback from potential users. User acceptance testing was conducted with a group of 8 young people aged 15-17 years, followed by a pilot test with 39 secondary school students aged 14-18 years, which was conducted for a 2-week period. RESULTS: Mindcraft showed encouraging user engagement and retention. Users reported that they found the app to be a friendly tool helping them to increase their emotional awareness and gain a better understanding of themselves. Over 90% of users (36/39, 92.5%) answered all active data questions on the days they used the app. Passive data collection facilitated the gathering of a broader range of well-being metrics over time, with minimal user intervention. CONCLUSIONS: The Mindcraft app has shown promising results in monitoring mental health symptoms and promoting user engagement among children and young people during its development and initial testing. The app's user-centered design, the focus on privacy and transparency, and a combination of active and passive data collection strategies have all contributed to its efficacy and receptiveness among the target demographic. By continuing to refine and expand the app, the Mindcraft platform has the potential to contribute meaningfully to the field of mental health care for young people.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".