647 Adolescent and Young Adult Sleep and Sleep-Related Behaviour Change During the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract Introduction Sleep disturbance, poor sleep quality, and dissatisfaction with sleep are common among adolescents and young adults (AYAs; e.g., Becker et al., 2018; Hicks et al., 2002; Hysing et al., 2013). Environmental and behavioural factors (e.g., early school start times, evening technology use and social pressures) are barriers to healthy sleep among AYAs that contribute to a “perfect storm” of sleep disturbance during this period (Carskadon, 2011; Crowley et al., 2018). Notwithstanding, few AYAs have access to sleep treatments. The COVID-19 pandemic lockdowns decreased academic and scheduling demands, providing an opportunity to study unconstrained AYA sleep and potentially facilitating better access to sleep interventions (Simpson & Manber, 2020). This study evaluated differences in baseline sleep and sleep-related behaviour change (i.e., how AYAs use an evidence-based app for sleep disturbance) before vs. during the lockdown. Methods Participants between the ages of 15 and 24 (M=20.66, SD=2.38) completed a 4-week feasibility study evaluating a free, transdiagnostic sleep self-management app (DOZE) before the lockdown (“Pre-Lockdown”; n=51) or during the lockdown (“Lockdown”; n=29). After 2 weeks of completing baseline sleep diaries, participants could set goals based on feedback and access tips, followed by 2 more weeks of completing sleep diaries. Results Compared to Pre-Lockdown, Lockdown demonstrated less variability in their sleep schedules (ps≤.011), less napping (p=.002), but increased time in bed (TIB; p<.001) and total wake time (p=.007). Total sleep time, lingering in bed in the morning, and sleep efficiency did not differ between groups. Relative to Pre-Lockdown, Lockdown showed a greater tendency to set goals to reduce schedule variability (p=.010) and to restrict excessive TIB (p=.005). Rates of goal setting for lingering in bed in the morning, sleepiness, naps, and sleep-interfering substance use did not differ between groups. Rates of accessing tips did not differ between groups. Conclusion Effects of COVID-19 lockdown on AYA sleep included less variability in their schedule and a decreased need for naps, but negative effects on TWT and TIB. As a result, AYAs set different goals during the COVID-19 lockdowns, focusing more on restricting excessive TIB than on schedule variability. Support (if any) Canadian Institutes of Health Research eHealth Innovation Partnership Program (#143551).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".