Predictive modelling links exercise dependence to associated psychological and behavioral risk factors

Exercise Dependence (ED) refers to uncontrollable, excessive exercise with harmful effects on life. This study used machine learning to identify behavioral and psychological factors contributing to ED risk. A multi-step procedure was implemented for model construction and validation, utilizing controlled feature selection and bootstrapping. Data were collected over three time points (GR2021-22-23), recruiting 1099 participants (707 males, 64.3%; 392 females, 35.7%) with an average age of 24.8 ± 7.8 years. Based on the Exercise Dependence Scale-Revised (EDS-R), 5.6% (n = 62) were classified as "At Risk" of ED, 50.9% (n = 559) as "Non-Dependent-Symptomatic," and 43.5% (n = 478) as "Non-Dependent-Asymptomatic." Interestingly, 24.0% (n = 264) reported supplement use, 52.5% (n = 577) used analgesics in the last 12 months, 48.1% (n = 528) reported nicotine use, and 88.2% (n = 969) consumed alcohol during the same period. The final model predicted the GR2023 dataset with MAE = 6.90, R² = 0.59, and RE = 9.08%. Predictive performance on the GR2022 dataset was MAE = 5.65, R² = 0.79, and RE = 6.73%, while performance on the GR2021 dataset achieved MAE = 7.60, R² = 0.58, and RE = 7.24%. This study establishes a foundation for developing quantitative risk profiles for ED by analyzing multidimensional constructs and their contributions through interpretable machine learning. The methodology offers insights into how personality, psychological, and behavioral dimensions shape risk attitudes and provides robust predictive tools for assessing ED risk in sports contexts.