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
{"title":"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","authors":"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","doi":"10.1016/j.addbeh.2025.108493","DOIUrl":null,"url":null,"abstract":"<div><div>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, R<sup>2</sup> = 0.59, and RE = 9.08 %. Predictive performance on the GR2022 dataset was MAE = 5.65, R<sup>2</sup> = 0.79, and RE = 6.73 %, while performance on the GR2021 dataset achieved MAE = 7.60, R<sup>2</sup> = 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.</div></div>","PeriodicalId":7155,"journal":{"name":"Addictive behaviors","volume":"172 ","pages":"Article 108493"},"PeriodicalIF":3.6000,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predictive modelling links exercise dependence to associated psychological and behavioral risk factors\",\"authors\":\"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\",\"doi\":\"10.1016/j.addbeh.2025.108493\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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, R<sup>2</sup> = 0.59, and RE = 9.08 %. Predictive performance on the GR2022 dataset was MAE = 5.65, R<sup>2</sup> = 0.79, and RE = 6.73 %, while performance on the GR2021 dataset achieved MAE = 7.60, R<sup>2</sup> = 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.</div></div>\",\"PeriodicalId\":7155,\"journal\":{\"name\":\"Addictive behaviors\",\"volume\":\"172 \",\"pages\":\"Article 108493\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2025-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Addictive behaviors\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0306460325002540\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHOLOGY, CLINICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Addictive behaviors","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0306460325002540","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, CLINICAL","Score":null,"Total":0}
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 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.
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.