Sleep health association with asthma, allergic rhinitis, and atopic dermatitis: Systematic review of population-based studies
Bibliographic record
Abstract
Poor sleep health is frequent among people with three common diseases that may co-occur: asthma, allergic rhinitis (AR), and atopic dermatitis (AD). However, few studies have assessed sleep health in people with coexisting diseases. The aims of this review were to systematically summarise: the proportion of people with asthma, or AR or AD, who have sleep disorders; and the evidence on the association of sleep health with these diseases in general populations. We searched three databases (Medline, Web of Science and Google Scholar) for population-based studies regarding the association between sleep health, asthma, AR, or AD published by May 2023. After a systematic review of the studies, we summarised the evidence including the most prevalent sleep outcomes according to four groups of exposure: 1) asthma; 2) AR; 3) AD and 4) coexisting diseases. A total of 20 studies were identified of which one used coexisting diseases as main exposure. The majority of the selected studies were of fair quality. The most frequently assessed outcomes were nocturnal sleep-related dysfunctions (e.g. insomnia) and daytime sleep-related dysfunctions (e.g. daytime sleepiness). High proportions of sleep disorders were found among people with asthma, AR or AD. We found significant evidence that people with asthma, allergic rhinitis, or atopic dermatitis had impaired sleep health. This systematic review highlights the need for methodologically robust population-based studies focused on the assessment of sleep outcomes among people with three diseases that may co-occur.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".