0917 Designing a Wearable Technology-Based Sleep Intervention To Support Sleep Health Among Adolescents: Using a Participatory Design Approach
Bibliographic record
Abstract
Abstract Introduction Sleep problems during adolescence are increasingly common and have been associated with adverse physical and psychological health outcomes. Efforts to improve insufficient sleep among adolescents have resulted in increased sleep knowledge and temporary enhancements in sleep hygiene. Good sleep hygiene is established through the development of daily routines that support healthy sleep. Wearable technology offers a potential solution whereby adolescents can acquire and manage healthy sleep habits. In this study, we are co-designing with adolescents a prototype intervention using wearable technology to promote sustained improvements in their sleep hygiene. Methods Guided by participatory design approaches, the ongoing multi-phase mixed methods study is currently being conducted in a metropolitan area in western Canada. In phase 1, sleep data is being collected from a sample of 30 adolescent-parent dyads using wearable sensors (Actigraphy watches) and self-report sleep measures (questionnaires about sleep quality, hygiene, and beliefs and attitudes, as well as their general health) over a 10-day period. In phases 2 and 3, individual interviews and iterative user interface design sessions will be conducted with 25 adolescents. Results To date, thirteen adolescents-parent dyads (13-17 years, 9 females; 39-56 years, 11 females) have completed phase 1 of our study. Data analysis is currently being conducted to evaluate sleep onset/offset, total sleep time, wake after sleep onset, sleep efficiency, and sleep schedule differences between adolescents and their parents. Ten adolescents have completed individual interviews in phase 2 of the study. Preliminary qualitative data suggests that youth are aware of the importance of sleep to their overall health. However, they struggle with identifying credible information to act on from the various and sometimes conflicting sources (e.g. online, friends, family). Conclusion We anticipate that co-designing a wearable solution with adolescents will lead to a sleep intervention that is more relevant, persuasive, and useful in supporting their sleep health. Support This work is supported by the Sensor Technology in Monitoring Movement STiMM Program.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".