Using wearable and mobile technology to measure and promote healthy sleep behaviors in adolescents: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this scoping review is to map the evidence related to how consumer-targeted wearable and mobile technology is being used to measure and/or promote sleep among adolescents. INTRODUCTION: Sleep is a key component of physical and mental health and is required for healthy development in adolescence. Efforts to improve insufficient and poor-quality sleep among adolescents have resulted in limited and temporary enhancements in sleep habits. Since good sleep hygiene is established through the development of daily routines, wearable technology offers a potential solution by providing real-time feedback, allowing adolescents to monitor and manage their sleep habits. INCLUSION CRITERIA: Studies that focus on adolescents between 13 and 24 years who use mobile or wearable technology to measure and/or promote sleep health will be considered for inclusion. METHODS: Using a scoping methodology, the authors will conduct a review of studies on the use of commercially available, wearable technology or mobile devices designed to measure and/or improve sleep among adolescents. Literature searched will include published primary studies, reviews, and dissertations from database inception to present. Databases searched will include MEDLINE, Embase, PsycINFO, CINAHL, CENTRAL, SPORTDiscus, JBI Evidence Synthesis, Cochrane Database of Systematic Reviews, Scopus, and ProQuest Dissertations and Theses. The search will be conducted using identified keywords and indexed terms, and studies will be limited to the English language. Data extracted will include study population, methods, description of sleep technology reported, sleep outcomes, and strategies used to promote healthy sleep behaviors. Quality assessment of included studies will be conducted to facilitate data mapping and synthesis.
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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 | 0.079 | 0.068 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.060 | 0.015 |
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".