Is Binge Drinking Associated With Specific Types of Exercise and Free Time Sports? A Pooled Analysis With 718,147 Adults
Bibliographic record
Abstract
OBJECTIVE: To verify the association between exercise and free time sport types and binge drinking in a large sample of adults. METHODS: Data of 718,147 adults from the "Surveillance of Risk and Protection Factors for Chronic Diseases by Telephone Survey" were used. We described the demographic and behavioral variables, and negative binomial regression analyzed the association between exercise and free time sport types and binge drinking adjusted by demographics variables, body mass index status, and television time. RESULTS: Outdoor walking/running was the most common exercise reported (20.0%, 95% confidence interval [CI], 19.8%-20.2%), followed by team sports (8.1%; 95% CI, 8.0%-8.2%) and strengthening (8.0%; 95% CI, 7.9%-8.1%). The prevalence of binge drinking for each exercise and free time sport type ranged from 6.9% (water aerobics) to 31.9% (team sports). Participants engaging in strengthening (prevalence ratio = 1.12; 95% CI, 1.04-1.21, P = .002) and team sports (prevalence ratio = 1.11; 95% CI, 1.07-1.17, P < .001) were more likely to binge drink more frequently in the past 30 days than inactive participants. CONCLUSIONS: It appears that the participants' profile plays an important role in the underlying social context of this association. Participants with more frequent strengthening and less frequent team sports practice, who were primarily younger and single, were more likely to binge drink frequently.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".