Role of exercise in the treatment of alcohol use disorders
Bibliographic record
Abstract
Excessive alcohol use can cause harmful effects on the human body, which are associated with serious health problems, and it can also lead to the development of alcohol use disorders (AUDs). There is certain evidence that physical exercise positively affects excessive alcohol use and the associated problems by leading to reduced alcohol intake. A literature search was conducted using the databases PubMed, Medline and Web of Science. The search terms used as keywords were: Addiction, abuse, alcohol use disorders, exercise training, β-endorphin, opioids, brain, ethanol and alcohol. The current study presents the studies that reported on the use of exercise in the treatment of AUDs between 1970 and 2015. The potential psychological and physiological mechanisms that contribute to the action of exercise were also reviewed, highlighting the role of β-endorphin and the hypothalamic-pituitary-adrenal axis in AUDs and the possible association among physical activity, the endogenous opioid system and the desire for alcohol. Only 11 studies were identified that refer to the effect of exercise on alcohol consumption and/or the associated outcomes. Six of those studies concluded that exercise may have a positive impact towards alcohol consumption, abstinence rates or the urge to drink. One of those studies also indicated that a bout of exercise affects the endogenous opioids, which may be associated with the urge to drink. Another 3 studies indicated that responses to acute exercise in individuals with AUDs are different compared to those in healthy ones. Generally, despite limited research data and often contradictory results, there is certain early promising evidence for the role of exercise as an adjunctive tool in the treatment of AUDs. Physiological and biochemical parameters that would confirm that exercise is safe for individuals with AUDs should be examined in future studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".