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

Abstract

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 in diverse contexts (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." 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 %. Perfectionism consistently emerged as the most important predictors, followed by Drive for Thinness, Drive for Muscularity, and sport characteristics. Result generalization was confirmed by a complementary, whole-data analysis. 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.