Evaluating predictive artificial intelligence approaches used in mobile health platforms to forecast mental health symptoms among youth: a systematic review
Bibliographic record
Abstract
• Multimodal approaches via wearables and smartphones enhance prediction. • Successful predictors are smartphone usage, sleep patterns, and physical activity. • Current challenges are small sample sizes, data privacy issues, & incomplete data. • Key prediction algorithms include Logistic Regression and Support Vector Machines. • Current predictive models lack human-centred artificial intelligence. The youth mental health crisis is exacerbated by limited access to care and resources. Mobile health (mHealth) platforms using predictive artificial intelligence (AI) can improve access and reduce barriers, enabling real-time responses and precision prevention. This systematic review evaluates predictive AI approaches in mHealth platforms for forecasting mental health symptoms among youth (13–25 years). We searched studies from Embase, PubMed, Web of Science, PsycInfo, and CENTRAL, to identify relevant studies. From 11 studies identified, three studies predicted multiple symptoms, with depression being the most common (63%). Most platforms used smartphones and 25% integrated wearables. Key predictors included smartphone usage (N=5), sleep metrics (N=6), and physical activity (N=5). Nuanced predictors like usage locations and sleep stages improved prediction. Logistic regression was most used (N=6), followed by Support Vector Machines (N=3) and ensemble methods (N=4). F-scores for anxiety and depression ranged from 0.73 to 0.84, and AUCs from 0.50 to 0.74. Stress models had AUCs of 0.68 to 0.83. Bayesian model selection and Shapley values enhanced robustness and interpretability. Barriers included small sample sizes, privacy concerns, missing data, and underrepresentation bias. Rigorous evaluation of predictive performance, generalizability, and user engagement is critical before mHealth platforms are integrated into psychiatric care.
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 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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".