An AI-Generated Integrated Exercise Addiction Screening Scale (EASS-10): A Methodological Demonstration

Artificial intelligence (AI) is increasingly used in scientific research, including psychiatry. Exercise addiction (EA) research relies on self-report instruments, most of which lack a functional impairment criterion and cannot account for obsessive passion, potentially contributing to inflated prevalence estimates and limited clinical specificity. This study examined whether Claude AI (Sonnet 4.6) could assist in synthesizing a brief, theoretically grounded screening tool for EA by integrating validated constructs from the Passion Scale (PS), the Exercise Dependence Scale (EDS), and the Exercise Addiction Inventory (EAI). Using structured prompting and instrument upload, Claude AI created a 10-item scale by systematically mapping source-scale content onto established EA frameworks, including the components model and DSM-5 criteria. The instrument included nine Likert-type items and one binary gate for functional impairment to enhance clinical specificity. Although no empirical validation data were collected, a Monte Carlo simulation (N = 500) was used solely to verify that the prespecified unidimensional simulation model produced internally coherent response patterns. Because the simulated data were generated from and analyzed under the same unidimensional model, these results constitute a computational plausibility check rather than empirical structural validation. AI-assisted theoretical synthesis may offer a novel methodological approach to instrument development, though this remains a working hypothesis requiring empirical corroboration. The proposed Exercise Addiction Screening Scale (EASS-10) is presented as a proof-of-concept tool that now requires empirical validation in clinical and exercise populations.