Co-Designed Mental Health Screening App (Here for You) for University Students: Pilot Feasibility Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Mental health disorders are a growing public health concern among university students globally and in India, exacerbated by stigma and limited access to care. Mobile health (mHealth) apps offer a potential solution, but user engagement and cultural relevance remain key challenges. This pilot study evaluated Here for You, a mental health screening app co-designed with Indian university students to provide accessible, nonstigmatizing support. OBJECTIVE: This mixed methods study aimed to (1) describe the user-centered codevelopment and pilot testing process of the Here for You app; (2) evaluate the app's feasibility, user acceptability, and engagement; and (3) assess the concurrent validity of the app's screening tool, the Depression, Anxiety, and Stress Scale-21 (DASS-21) against established clinical measures (Hamilton Depression Rating Scale [HAM-D], Hamilton Anxiety Rating Scale [HAM-A], and Perceived Stress Scale [PSS]). METHODS: This study used a 4-phase user-centered design involving students with lived mental health experience, clinicians, and developers. A purposive sample of 30 university students (mean age 21, SD 1.8 years; n=15, 50% female) diagnosed with depression, anxiety, or stress participated. Participants completed the DASS-21 via the app and underwent clinical assessments using the HAM-D, HAM-A, and PSS scales. User experience was evaluated using the User Mobile App Rating Scale and qualitative feedback. Data analysis included Pearson correlation coefficients and thematic analysis. RESULTS: App-based DASS-21 scores showed strong correlations with clinician-administered scales: HAM-D (r=0.819; P<.001), HAM-A (r=0.887; P<.001), and PSS (r=0.972; P<.001), indicating high concurrent validity. However, wide CIs reflected the small sample size typical of pilot studies. The app received high usability ratings on a 5-point scale (User Mobile App Rating Scale mean score 4.4), exceeding published benchmarks for mental health apps in low-resource settings, particularly for functionality (mean 4.7, SD 0.3) and aesthetics (mean 4.5, SD 0.4). Qualitative feedback highlighted usability and enhanced privacy due to features such as quick exit, cultural resonance, and the desire for integrated support features. The co-design process directly addressed student concerns, implementing features such as simplified language and crisis support links. CONCLUSIONS: This pilot study provides preliminary evidence for the feasibility and user acceptability of the Here for You app, co-designed using a participatory approach with Indian university students. Strong correlations between app-based screening and clinical assessments (r=0.819, r=0.887, and r=0.972) suggest promising concurrent validity. These findings from a single-site pilot study require validation through multisite studies across diverse educational and cultural contexts before broader implementation recommendations. By integrating user experience, clinical rigor, and ethical safeguards, such as adherence to digital personal data protection guidelines, the app offers a culturally resonant and scalable model for digital mental health screening in low-resource settings. This approach underscores the value of the "nothing about us without us" principle in developing effective mHealth interventions.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".