Understanding exercise addiction, psychiatric characteristics and use of anabolic androgenic steroids among recreational athletes – An online survey study
Bibliographic record
Abstract
Background: The purpose of this paper was to explore maladaptive behaviors among physically active individuals, including exercise dependence and use of anabolic steroids. Both exercise addiction (EA) and use of anabolic androgenic steroids (AAS) correlate to high amounts of exercise and EA have been linked to eating disorders and other mental health problems. Methods: An internet survey was spread through fitness-related social media. Inclusion criteria were age ≥ 15 years and exercise frequency ≥ thrice weekly. Exercise addiction inventory identified those at-risk of EA (rEA). Characteristics of rEA were compared to those not at risk. In a separate analysis, AAS users were compared to AAS-naïve individuals. Results: In total, 3,029 participants completed the questionnaire. Of these, 11% screened positive for being rEA, and 23% for ED. Factors associated with EA included daily exercise, social phobia, eating disorders and OCD. Risk consumption of alcohol was a negative predictor. Thirty seven participants had taken AAS the last year. These were mainly men, bodybuilders/powerlifters and more often used amphetamines and opioids. Discussion: This exploratory study supports EA being strongly associated with eating disorders. Identified associations between EA and compulsive or anxiety disorders warrant further research to clarify if these associations arise prior to, together with, or secondary to EA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".