Bidirectional relationship between sleep problems and long COVID: a longitudinal analysis of data from the COVIDENCE UK study
Bibliographic record
Abstract
Summary Background Studies into the bidirectional relationship between sleep and long COVID have been limited by retrospective pre-infection sleep data and infrequent post-infection follow-up. We therefore used prospectively collected monthly data to evaluate how pre-infection sleep characteristics affect risk of long COVID, and to track changes in sleep duration during the year after SARS-CoV-2 infection. Methods COVIDENCE UK is a prospective, population-based UK study of COVID-19 in adults. We included non-hospitalised participants with evidence of SARS-CoV-2 infection, and estimated odds ratios (ORs) for the association between pre-infection sleep characteristics and long COVID using logistic regression, adjusting for potential confounders. We assessed changes in sleep duration after infection using multilevel mixed models. We defined long COVID as unresolved symptoms at least 12 weeks after infection. We defined sleep quality according to age-dependent combinations of sleep duration and efficiency. COVIDENCE UK is registered with ClinicalTrials.gov, NCT04330599 . Findings We included 3994 participants in our long COVID risk analysis, of whom 327 (8.2%) reported long COVID. We found an inverse relationship between pre-infection sleep quality and risk of long COVID (medium vs good quality: OR 1.37 [95% CI 1.04–1.81]; medium–low vs good: 1.55 [1.12–2.16]; low vs good: 1.94 [1.11–3.38]). Greater variability in pre-infection sleep efficiency was also associated with long COVID (OR per percentage-point increase 1.06 [1.01–1.11]). We assessed post-infection sleep duration in 6860 participants, observing a 0.11 h (95% CI 0.08–0.13) increase in the first month after infection compared with pre-infection, with larger increases for more severe infections. After 1 month, sleep duration largely returned to pre-infection levels, although fluctuations in duration lasted up to 6 months after infection among people reporting long COVID. Interpretation Our findings highlight the bidirectional relationship between sleep and long COVID. While poor-quality sleep before SARS-CoV-2 infection associates with increased risk of long COVID thereafter, changes in sleep duration after infection in these non-hospitalised cases were modest and generally quick to resolve. Funding Barts Charity.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".