Sleep well, worry less: A co-design study for the development of the SMILE app
Bibliographic record
Abstract
Objective With the coronavirus disease 2019 pandemic exacerbating mental health concerns, the prevalence rates of anxiety and sleep problems have increased alarmingly among youth. Although 90% of patients with anxiety experience sleep problems, current interventions for anxiety often do not target sleep problems in youth. Given this lack, we designed the SMILE app, an intervention that addresses both anxiety and sleep problems simultaneously. Methods As users’ perspectives are essential to ensure app engagement and uptake, the features, designs, and functions of the SMILE app were evaluated using a participatory app design approach. Participants ( N = 17) were youth aged 15 to 25 who reported co-morbid anxiety and sleep issues above clinical thresholds. After completing an online screening survey assessing demographics, anxiety, and sleep problems, participants shared app feedback through group-based, semi-structured co-design sessions. Qualitative analyses were conducted to identify common themes from participants’ feedback. Results While participants expressed enthusiasm for the SMILE app's features, particularly the Visualization, Journaling, and Psychoeducation features, and their variety, they criticized the design aspects of the app, such as the font and text amount. Most participants stated they would use the SMILE app or recommend it to a friend. Conclusion By actively involving the target population in the design process, the SMILE app has the potential to notably improve the mental well-being of youth, though further research and development are required to realize this potential fully.
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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 | 0.026 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".