Chronic cannabis use and sleep architecture: a cross-sectional analysis of polysomnography outcomes in a sleep-clinic cohort
Bibliographic record
Abstract
STUDY OBJECTIVES: Cannabis is widely self-administered as a sleep aid, yet objective evidence from large polysomnography cohorts remains scarce. We assessed whether long-term daily cannabis use is associated with alterations in overnight sleep architecture at a Canadian sleep clinic. METHODS: We retrospectively analyzed overnight polysomnography studies from 1449 adult sleep clinic patients. Exposure was chronic cannabis use, defined as ≥daily consumption for ≥1 year (n = 151). Never-users (n = 1298) served as the reference group. Nine polysomnography outcomes-total sleep time, sleep onset latency, wake after sleep onset, sleep efficiency, rapid eye movement (REM) latency, and N1, N2, N3, and REM sleep (presence and duration)-were modeled with outcome-appropriate regressions adjusted for 28 demographic, lifestyle, comorbidity, medication, and sleep-related covariates. RESULTS: Chronic cannabis use was associated with higher wake after sleep onset (β = 21%; 95% CI 6.7% to 37.2%), lower sleep efficiency (β = -3.8%; 95% CI -6.6% to 1.0%), and elevated N1 (β = 2.8 percentage points [pp]; 95% CI 0.3 to 5.6 pp). Nominally, total sleeping time was lower among cannabis users (β = -3.3%; 95% CI -6.3% to 0.3%). Effect directions and magnitudes persisted across sensitivity analyses. CONCLUSIONS: Among sleep-clinic patients, long-term daily cannabis use was associated with greater nocturnal wakefulness. Given that most participants had moderate-to-severe sleep apnea, findings should be interpreted with caution. Studies detailing dose, timing, and cannabinoid composition are needed to clarify causality and clinical relevance. Statement of Significance Cannabis is frequently used to manage sleep problems, yet its long-term effects on sleep architecture remain uncertain. This study provides the largest clinic-based assessment to date, linking chronic daily use to objectively measured increases in nocturnal wakefulness among sleep clinic patients primarily referred for sleep apnea, suggesting that habitual cannabis use may fragment sleep. These findings raise important questions for clinicians and researchers, given the widespread use of cannabis as a sleep aid. Longitudinal and experimental studies are needed to clarify how dose, timing, and cannabinoid profile influence sleep and to explore downstream consequences for cognition, mood, and long-term health. Clarifying these pathways will guide patient counseling, therapeutic decisions, and public policy as legalization and social acceptance continue to expand.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".