236 The association between recent cannabis use and nightly sleep duration in adults in the USA from 2005-2018
Bibliographic record
Abstract
Abstract Introduction Shifts in medicolegal attitudes towards cannabis, coupled with widespread legalization, have led to North America having the highest prevalence of cannabis use worldwide. Amongst other known physiologic effects, regular cannabis use can cause changes to sleep duration and quality. The purpose of this study was to examine the relationship between recent cannabis use and sleep duration using a nationally representative data set. Methods A cross-sectional analysis of adults was undertaken using the National Health and Nutrition Examination Survey (NHANES) data from 2005–2018. Respondents were dichotomized by whether or not they had used cannabis in the past 30 days. The primary outcome was inadequate nightly sleep duration, defined as self-reported sleep duration less than 6 hours per night. Secondary outcomes were related to self-reported issues with sleep. Multiple logistic regression was used to adjust for potential confounders and survey sample weights were considered in the model. Results Compared to those with no recent cannabis use (n=18,631), recent users (n=3,135) were more likely to report less than 6 hours of sleep per night (aOR 1.33 95% 1.13–1.57, p<0.001). Recent users were also more likely to report difficulty falling asleep, staying asleep, or sleeping too much in the past two weeks (aOR 1.21, 95% CI: 1.09–1.35, p<0.001), and having ever mentioned these issues to a physician (aOR 1.21, 95% CI: 1.07–1.37, p=0.003). Respondents using cannabis at least 20 of the past 30 days were characterized as heavy users, and were even more likely than moderate users to report insufficient sleep. These results did not significantly differ between years of survey administration. Conclusion Recent cannabis use was associated with inadequate nightly sleep duration in adults and demonstrates a dose-dependent relationship. Although this relationship is complex and our findings cannot suggest directionality, they highlight the need to further characterize the sleep health of regular cannabis users in the general population. This is especially prudent as cannabinoids are becoming widely accepted for recreational use and increasingly prescribed as medical therapy. Support (if any):
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".