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

IF 3.6 2区医学 Q1 PSYCHOLOGY, CLINICAL

Addictive behaviors Pub Date : 2025-09-13 DOI:10.1016/j.addbeh.2025.108493

Thomas Zandonai , Giulio Bertamini , Juan José Lozano , Luca Mallia , Alessandra De Maria , Federica Galli , Pablo Monteagudo , Fabio Lucidi , Paola Venuti , Cesare Furlanello , Ana María Peirò

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引用次数: 0

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.

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预测模型将运动依赖与相关的心理和行为风险因素联系起来

运动依赖（Exercise Dependence， ED）是指不可控的、过度的、对生活有害的运动。这项研究使用机器学习来识别导致ED风险的行为和心理因素。利用可控特征选择和自举，实现了模型构建和验证的多步骤过程。在不同背景下的三个时间点（GR2021-22-23）收集数据，招募1099名参与者（男性707人，占64.3%；女性392人，占35.7%），平均年龄24.8±7.8岁。根据运动依赖量表修订版（EDS-R）， 5.6% （n = 62）被归类为ED“有风险”，50.9% （n = 559）被归类为“非依赖性症状”，43.5% （n = 478）被归类为“非依赖性无症状”。最终模型预测GR2023数据集的MAE = 6.90, R2 = 0.59, RE = 9.08%。GR2022数据集的预测性能MAE = 5.65, R2 = 0.79, RE = 6.73%，而GR2021数据集的预测性能MAE = 7.60, R2 = 0.58, RE = 7.24%。完美主义一直是最重要的预测因素，其次是追求苗条、追求肌肉发达和运动特征。通过互补的全数据分析证实了结果的泛化。本研究通过分析多维结构及其可解释性机器学习的贡献，为开发ED的定量风险概况奠定了基础。该方法提供了个性、心理和行为维度如何塑造风险态度的见解，并为评估运动环境下ED风险提供了强大的预测工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Addictive behaviors 医学-药物滥用

CiteScore

8.40

自引率

4.50%

发文量

283

审稿时长

46 days

期刊介绍： Addictive Behaviors is an international peer-reviewed journal publishing high quality human research on addictive behaviors and disorders since 1975. The journal accepts submissions of full-length papers and short communications on substance-related addictions such as the abuse of alcohol, drugs and nicotine, and behavioral addictions involving gambling and technology. We primarily publish behavioral and psychosocial research but our articles span the fields of psychology, sociology, psychiatry, epidemiology, social policy, medicine, pharmacology and neuroscience. While theoretical orientations are diverse, the emphasis of the journal is primarily empirical. That is, sound experimental design combined with valid, reliable assessment and evaluation procedures are a requisite for acceptance. However, innovative and empirically oriented case studies that might encourage new lines of inquiry are accepted as well. Studies that clearly contribute to current knowledge of etiology, prevention, social policy or treatment are given priority. Scholarly commentaries on topical issues, systematic reviews, and mini reviews are encouraged. We especially welcome multimedia papers that incorporate video or audio components to better display methodology or findings. Studies can also be submitted to Addictive Behaviors? companion title, the open access journal Addictive Behaviors Reports, which has a particular interest in ''non-traditional'', innovative and empirically-oriented research such as negative/null data papers, replication studies, case reports on novel treatments, and cross-cultural research.