Context-Aware Digital Phenotyping of Youth Mental Health Using Mobile Ecological Prospective Assessments of Smartphone Use
Bibliographic record
Abstract
Abstract Background Youth mental disorders affect 12-14% of adolescents globally and remain underdiagnosed and undertreated. Digital phenotyping offers a scalable approach to real-time behavioural monitoring via smartphones, yet most studies rely solely on passive measures such as screen time, overlooking contextual factors. Methods This cross-sectional study was part of the Smart Platform, a digital citizen science initiative that engaged youth aged 13-21 years. Participants completed a baseline survey on sociodemographic characteristics and mental health (depression, anxiety, and suicidal ideation). Over the next seven days, context-aware digital phenotyping was conducted, defined as the collection of ecologically valid, time-stamped behavioural data from personal devices. This was implemented through mobile ecological prospective assessments (mEPAs) to capture self-reported smartphone use context, including activity type, location, and social setting. Multivariable logistic regression assessed associations between smartphone use context and mental health, adjusting for sociodemographic covariates. Results Eighty-four youth completed the baseline survey and at least one mEPA. A higher proportion of smartphone use at home was associated with lower odds of depression (OR=0.105, 95% CI: 0.028-0.276) and anxiety (OR=0.150, 0.053-0.345). A greater proportion of smartphone use while alone was associated with higher odds of depression (OR=3.802, 1.622-11.241), as was a greater proportion of time spent internet surfing (OR=2.663, 1.238-6.843). Longer duration of smartphone use outside the home was associated with higher odds of depression (OR=4.289, 1.443-16.579). Conclusion Context-aware smartphone metrics may offer more informative digital phenotyping indicators of youth mental health than duration alone, supporting integration of multi-context measures into early detection and precision prevention frameworks.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".