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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".