Prevalence of the Risk of Exercise Addiction Based on a New Classification: A Cross-Sectional Study in 15 Countries
Bibliographic record
Abstract
Abstract Exercise addiction is widely studied, but an official clinical diagnosis does not exist for this behavioral addiction. Earlier research using various screening instruments examined the absolute scale values while investigating the disorder. The Exercise Addiction Inventory-3 (EAI-3) was recently developed with two subscales, one denoting health-relevant exercise and the other addictive tendencies. The latter has different cutoff values for leisure exercisers and elite athletes. Therefore, the present 15-country study ( n = 3,760) used the EAI-3 to classify the risk of exercise addiction (REA), but only if the participant reported having had a negative exercise-related experience. Based on this classification, the prevalence of REA was 9.5% in the sample. No sex differences, and few cross-national differences were found. However, collectivist countries reported greater REA in various exercise contexts than individualist countries. Moreover, the REA among athletes was (i) twice as high as leisure exercisers, (ii) higher in organized than self-planned exercises, irrespective of athletic status, and (iii) higher among those who exercised for skill/mastery reasons than for health and social reasons, again irrespective of athletic status. Eating disorders were more frequent among REA-affected individuals than in the rest of the sample. These results do not align with recent theoretical arguments claiming that exercise addiction is unlikely to be fostered in organized sports. The present study questions the current research framework for understanding exercise addiction and offers a new alternative to segregate self-harming exercise from passionate overindulgence in athletic life.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".