Recent cannabis use and nightly sleep duration in adults: a population analysis of the NHANES from 2005 to 2018
Bibliographic record
Abstract
BACKGROUND: While popularly consumed for its perceived benefits as a sleeping aid, the impact of cannabis on sleep-wake regulation in clinical studies is inconclusive. The purpose of this study was to determine the relationship between cannabis use and nightly sleep duration in a nationally representative dataset. METHODS: A cross-sectional analysis of adults was undertaken using the National Health and Nutrition Examination Survey data from 2005 to 2018. Respondents were dichotomized as recent users or non-users if they had used or not used cannabis in the past 30 days, respectively. The primary outcome was nightly sleep duration, categorized as short (<6 hours), optimal (6-9 hours), and long (>9 hours). Multinomial logistic regression was used to adjust for sociodemographic and health-related covariates, and survey sample weights were used in modeling. RESULTS: From a sample representing approximately 146 million adults in the USA, 14.5% reported recent cannabis use. In an adjusted analysis, recent users were more likely than non-users to report both short sleep (OR 1.34, 95% CI 1.12 to 1.59, p<0.001) and long sleep (OR 1.56, 95% CI 1.25 to 1.96, p<0.001). Heavy users (≥20 of the past 30 days) were even more likely to be at the extremes of nightly sleep duration. DISCUSSION: Recent cannabis use was associated with the extremes of nightly sleep duration in a nationally representative sample of adults, with suggestions of a dose-response relationship. Our findings highlight the need to further characterize the sleep health of regular cannabis users in the population.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".