Long COVID as a risk factor for hypersomnolence and fatigue: insights from the 2nd International Covid Sleep Study Collaboration (ICOSS-2)
Bibliographic record
Abstract
BACKGROUND: Hypersomnolence, defined as excessive daytime sleepiness (EDS), excessive quantity of sleep (EQS), sleep inertia, and fatigue reduce quality of life. We assessed associations of the COVID-19 pandemic, infection without long-term sequalae (short COVID, SC), and long COVID (LC) on hypersomnolence and fatigue in a large population across different countries. METHODS: As part of an online questionnaire (ICOSS-2), we assessed EDS via the Epworth Sleepiness Scale (ESS), fatigue via Fatigue Severity Scale (FSS), and sleep duration at night and per 24 h. We also assessed the associations with EDS, sleep inertia, fatigue and napping by their frequencies, during the pandemic in COVID-negative, SC and LC participants. RESULTS: The final cohort comprised 13,656 participants (69.1 % women, 42.7 ± 16.6 years), with 12.4 % classified SC and 7.5 % LC. ESS scores were higher in LC (9.16, 95 % CI [8.78, 9.53]) compared to SC (7.26, [6.97, 7.55]) and COVID-negative (6.53, [6.43, 6.63]). LC also had higher odds of ESS>10 (OR 1.58, [1.18,2.09]). FSS scores were higher in LC (median 51, IQR 39-59) than SC (34, 25-44) and COVID-negative (35, 25-45), with LC having 2.22 higher odds of severe fatigue. LC cases also reported more EQS (≥10/24 h) than COVID-negative. Worsening of EDS, fatigue, sleep inertia, and napping was reported during pandemic to a greater extent in LC. CONCLUSIONS: LC was associated with higher levels of hypersomnolence and fatigue than in SC or COVID-negative participants, highlighting the need for interventions and future research focusing on sleep symptoms and their relation to long-term health outcomes.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".