Association between hypersomnolence and the COVID-19 pandemic: The International COVID-19 Sleep Study (ICOSS)
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic and related restriction measures have affected our daily life, sleep, and circadian rhythms worldwide. Their effects on hypersomnolence and fatigue remain unclear. METHODS: The International COVID-19 Sleep Study questionnaire which included items on hypersomnolence such as excessive daytime sleepiness (EDS), and excessive quantity of sleep (EQS), as well as sociodemographic factors, sleep patterns, psychological symptoms, and quality of life was distributed in 15 countries across the world from May to September in 2020. RESULTS: Altogether responses from 18,785 survey participants (65% women, median age 39 years) were available for analysis. Only 2.8% reported having had COVID-19. Compared to before the pandemic, the prevalence of EDS, EQS, and fatigue increased from 17.9% to 25.5%, 1.6%-4.9%, and 19.4%-28.3% amid the pandemic, respectively. In univariate logistic regression models, reports of having a COVID-19 were associated with EQS (OR 5.3; 95%-CI 3.6-8.0), EDS (2.6; 2.0-3.4), and fatigue (2.8; 2.1-3.6). In adjusted multivariate logistic regression, sleep duration shorter than desired (3.9; 3.2-4.7), depressive symptoms (3.1; 2.7-3.5), use of hypnotics (2.3; 1.9-2.8), and having reported COVID-19 (1.9; 1.3-2.6) remained strong predictors of EDS. Similar associations emerged for fatigue. In the multivariate model, depressive symptoms (4.1; 3.6-4.6) and reports of having COVID-19 (2.0; 1.4-2.8) remained associated with EQS. CONCLUSIONS: A large increase in EDS, EQS, and fatigue occurred due to the COVID-19 pandemic, and especially in self-reported cases of COVID-19. These findings warrant a thorough understanding of their pathophysiology to target prevention and treatment strategies for long COVID condition.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".