Cannabis Use and Sport: A Systematic Review
Bibliographic record
Abstract
CONTEXT: Cannabis use has increased, in large part due to decriminalization. Despite this increase in usage, it remains unclear what proportion of athletes use cannabis and what effect it has on athletic performance and recovery. OBJECTIVE: To systematically review cannabis use among athletes, including epidemiology, effect on performance and recovery, and regulations for use in sport. DATA SOURCES: PubMed, MEDLINE, and EMBASE databases were queried from database inception through November 15, 2018. A hand search of policies, official documents, and media reports was performed for relevant information. STUDY SELECTION: All studies related to cannabis use in athletes, including impact on athletic performance or recovery, were included. STUDY DESIGN: Systematic review. LEVEL OF EVIDENCE: Level 4. DATA EXTRACTION: Demographic and descriptive data of included studies relating to epidemiology of cannabis use in athletes were extracted and presented in weighted means or percentages where applicable. RESULTS: Overall, 37 studies were included, of which the majority were cross-sectional studies of elite and university athletes. Among 11 studies reporting use among athletes (n = 46,202), approximately 23.4% of respondents reported using cannabis in the past 12 months. Two studies found a negative impact on performance, while another 2 studies found no impact. There was no literature on the influence of cannabis on athletic recovery. Across athletic organizations and leagues, there is considerable variability in acceptable thresholds for urine tetrahydrocannabinol levels (>15 to 150 ng/mL) and penalties for athletes found to be above these accepted thresholds. CONCLUSION: Overall, these results suggest that approximately 1 in 4 athletes report using cannabis within the past year. Based on the available evidence, cannabis does not appear to positively affect performance, but the literature surrounding this is generally poor. Given the variability in regulation across different sport types and competition levels, as well as the growing number of states legalizing recreational cannabis use, there is a need to improve our understanding of the effects of cannabis use on the athlete and perhaps adopt a clearer and overarching policy for the use of cannabis by athletes in all sports and at all levels.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".