Implementation of a teen sleep app in <scp>Canadian</scp> high schools: Preliminary evidence of acceptability, engagement, and capacity for supporting healthy sleep habits
Bibliographic record
Abstract
Summary High school students suffer from mental health challenges and poorer academic performance resulting from sleep disturbances. Unfortunately, approaches to this problem sometimes focus on increasing sleep duration by going to bed early; a strategy with limited success because teens experience a phase delay in bedtimes. There is a need for approaches that leverage behavioural sleep science and are accessible, scalable, and easily disseminated to students. DOZE (Delivering Online Zzz's with Empirical Support) is a self‐management app that is grounded in sleep and circadian basic science. Although initial testing supports it as a feasible and acceptable app in a research context, it has not been tested as a strategy to use in schools. The present study tested DOZE in private high schools in Canada. Two‐hundred and twenty‐three students downloaded the app and completed daily sleep diaries over 4 weeks. Students reported a more regularised routine for bedtime, M diff = −0.43 h, p < 0.001, 95% CI [−0.65, −0.21], and rise time, M diff = −0.61 h, p < 0.001, 95% CI [−0.84, −0.38], in addition to a higher total sleep time, M diff = 0.18 h, p < 0.008, 95% CI [0.05, 0.31]. Students also rated DOZE to be highly acceptable. The evidence suggests that students find DOZE to be acceptable and engagement in this nonclinical population was reasonably high under minimal researcher supervision. This makes DOZE an attractive option and a step towards broad‐based sleep health services. High powered replications with control groups are needed to increase empirical rigour.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".