Association Between Household Dust Exposure and Sleep Duration: Findings from NHANES 2005–2006
Bibliographic record
Abstract
Objective: Indoor pollutants, such as household dust, are increasingly recognized as potential contributors to health problems, including sleep disturbances. This study investigates the association between household dust exposure and sleep quality among American adults, utilizing data from the National Health and Nutrition Examination Survey (NHANES) between 2005 and 2006. Methods: Data from NHANES were used in a cross-sectional design. Total dust weight (mg) was the primary exposure variable, and sleep outcomes included self-reported sleep duration, sleep latency, and physician-diagnosed sleep disorders. Data analysis was conducted using univariate and regression models in STATA (version 18.5), with adjustments for confounders. Results: 5,582 adults aged ≥18 years had available data on sleep duration, and 4,893 on sleep latency. In an adjusted model controlling for age and emotional support, dust weight was significantly associated with a slight decrease in sleep duration (adjusted β = -0.0001, p = 0.047). In multivariate logistic regression, dust weight showed a significant negative association with sleep duration (adjusted OR = 0.999, 95% CI: 0.999 to 0.999, p = 0.030), with age and emotional support demonstrating positive associations. Sleep latency showed no significant relationship with dust weight in linear regression analysis, even when controlled for emphysema and PHQ-9 score (adjusted β = -0.0002, 95% CI: -0.001 to 0.0009, p = 0.712). Multivariate logistic regression analysis also confirmed no significant association between dust weight and sleep latency. Conclusions: This study suggests that household dust exposure modestly impacts sleep duration, highlighting the value of improving indoor air quality to enhance sleep health.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".