Examining the Effects of Cannabis Use on Sleep Using Daily Diary Data
Bibliographic record
Abstract
BACKGROUND: College students in the United States widely report using alcohol and cannabis as a sleep aid. Given the prevalence of sleep problems and insufficient sleep in this population, the high incidence in use and co-use of cannabis and alcohol is unsurprising. Current evidence does not support alcohol as an effective sleep aid and research on the relationship of cannabis to sleep is limited and inconsistent. Furthermore, the majority of current cannabis and sleep studies are limited to retrospective, person-level analyses even though there is a wide range of individual and day-level differences in reactivity to intoxication. PURPOSE: The aim of this study is to examine cannabis and alcohol use and their associations with sleep at both the between-person level (i.e., between-subjects comparison of chronic use behaviors) and within-person level (i.e., day-level comparison of use behaviors). METHOD: This study is a secondary analysis of longitudinal data obtained from a study characterizing the effects of simultaneous alcohol and cannabis use. Participants (n=341) completed surveys up to five times per day during two bursts of 4 weeks (54 days total) that occurred during two consecutive college semesters. Self-reported quantities of cannabis use (as number of uses) and alcohol use (as number of drinks), as well as bedtimes (night) and wake times (morning) were reported. Linear mixed models were conducted in SAS 9.4 to characterize between-person and within-person (person-mean centered) correlations of cannabis or alcohol use and sleep duration. RESULTS: Significant main effects of within-person cannabis (Estimate: 0.019, SE: 0.007, t=2.86, p=0.004) and alcohol (Estimate: -0.0402, SE: 0.0076, t=-5.28, p<0.001) use were found, as was a between-person main effect of average cannabis use (Estimate: 0.038, SE: 0.012, t=3.28, p=0.001) across the full study period. The between-person main effect of average alcohol use was not significant. CONCLUSIONS: The results suggested that generally heavier cannabis users sleep more than their non-using/generally light using counterparts and that they sleep more on nights following heavier use days. Interestingly, the relationship between alcohol and sleep differed between the between-person and within-person levels: alcohol use was dose-dependently associated with reduced sleep duration; however, in this sample, generally heavier alcohol users did not appear to differ in overall sleep duration compared to generally lighter alcohol users. Importantly, this sample included a wide range of substance users, none of whom were in treatment for a cannabis use disorder (CUD) or alcohol use disorder (AUD). Whether these patterns of dose-dependence would be observed over longer time periods or in individuals who meet criteria for CUD or AUD remains to be studied. Future studies will assess the effects of alcohol and cannabis co-use patterns as well as timing of consumption.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".