Smoke, Sip, Sleep, Repeat: Investigating Daily-Level Bidirectional Relationships Between Separate And Simultaneous Alcohol-Cannabis Use And Sleep
Bibliographic record
Abstract
Sleep problems are common among young adults, and alcohol and cannabis are known to impact sleep. Given the high prevalence of simultaneous alcohol-cannabis use in this population, there is a need to clarify the mixed findings in existing research regarding the combined effects of alcohol and cannabis use on sleep. This study used daily diary methodology to examine daily relationships between simultaneous use (versus cannabis-only, alcohol-only, and no use) and key sleep indices (i.e., subjective sleep quality, sleep duration, and bedtime), exploring the moderating role of substance use problem severity. Young adults (N=151; 64% female; Mage = 22.07) completed daily morning surveys in a smartphone app assessing prior-day alcohol and cannabis use, bedtime, wake time, and subjective sleep quality. Participants also completed measures of alcohol and cannabis problem severity at baseline. Multilevel models (with days nested within participants) indicated that participants reported worse sleep on alcohol-only use days relative to simultaneous use and no-use days, while cannabis-only use was associated with better sleep relative to no use. Participants reported similar subjective sleep quality and sleep duration on cannabis-only and simultaneous use days. Further, alcohol problem severity moderated associations between substance use and sleep. Specifically, individuals with greater alcohol problem severity experienced poorer sleep on alcohol-only days relative to simultaneous use days, whereas those with lower alcohol problem severity reported poorer sleep on simultaneous use days compared to cannabis-only days. Reciprocal models examining the impacts of sleep variables on next-day likelihood of simultaneous or single substance use did not reveal any significant main effects. These findings provide insight into the daily-level relationships between alcohol and cannabis co-use and sleep health, highlighting the need for tailored sleep interventions based on substance use patterns and problem severity. Keywords: cannabis; alcohol; simultaneous use; co-use; sleep; ecological momentary assessment
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".