Sleep among Brazilian adolescents before and after the COVID-19 pandemic: repeated cross-sectional and longitudinal analyses
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic significantly disrupted adolescents' routines, including their sleep patterns, due to school closures, social isolation, and increased screen time. As routines normalized post-pandemic, understanding whether these changes persisted or reverted to pre-pandemic levels is essential. OBJECTIVE: To compare sleep variables between the periods before and after the COVID-19 pandemic in samples of Brazilian adolescents. METHODS: A repeated cross-sectional study with a nested cohort targeting high-school students from Southern Brazil was used. Different sleep variables were obtained from wrist-worn accelerometers and validated questionnaires. Generalized linear mixed models with Gaussian distribution and identity link function were used to compare sleep variables between the survey years. RESULTS: In 2019, 674 adolescents participated (51.8% female, mean age = 16.3, SD = 1.1), and in 2022, 625 participated (56.3% female, mean age = 16.5, SD = 1.2). In the longitudinal sample, 242 out of 333 eligible participants provided complete data in 2019, and 138 out of 286 agreed to participate in 2022. Cross-sectional data indicate significant differences for social jet lag (β: -0.28, p < 0.001) and self-reported sleep duration (β: -0.14, p = 0.03) between 2019 and 2022. Prospective data indicate significant changes for sleep regularity (β: -4.27, p < 0.001), daytime sleepiness (β: 1.05, p = 0.01), catch-up sleep (β: -0.35, p = 0.04) and self-reported sleep duration (β: -0.42, p < 0.001). However, effect sizes were all small. CONCLUSION: Our findings suggest that adolescent sleep characteristics in Brazil post-COVID-19 are similar to pre-pandemic levels, indicating that the initial impact of the pandemic on sleep did not persist after routines normalized.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".