Cross-cultural validation of an expanded Exercise Addiction Inventory: A preliminary protocol report
Bibliographic record
Abstract
Background and aims. In the last thirty years, the continuously increasing number of studies investigating Exercise Addiction (EA) stimulated interest in developing instruments assessing the risk of exercise addiction (REA). One widely used tool is the Exercise Addiction Inventory (EAI) because it is a brief, easy-to-use, time-saving, and psychometrically validated tool. However, its items based on the Components Model of Addiction still lack some reoccurring symptoms associated with exercise addiction. This protocol report outlines the methods of developing and validating an expanded version of the EAI (EAI-3) in a large international sample.Methods. The EAI-3 will be administered to over 5000 regular exercisers in 15 languages through an online survey. The survey will also include questions from the Exercise Dependence Scale-Revised, the Obsessive-Compulsive Inventory-Revised, the SCOFF questionnaire, and the Ten Item Personality Inventory. We will investigate the factorial structure of the EAI-3 through confirmatory factor analysis. Moreover, we will test EAI-3’s measurement invariance across languages and gender. Finally, we aim to find a standard cutoff point for at-risk exercisers. Expected results. We expect to obtain a good fit of the EAI-3 structure and general measurement invariance. In addition, we expect associations with another EA measure and the other measures of mental health assessed in the study.Discussion and conclusions. We expect that the results will support an assessment tool useful in measuring the REA with greater accuracy and exhibiting reliability across gender and language (culture).
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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 | 0.049 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.010 |
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".