{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T05:53:48Z","timestamp":1791266028594,"version":"4.1.0"},"reference-count":52,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T00:00:00Z","timestamp":1791244800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Smartphone addiction is closely associated with adolescents\u2019 mental health and educational outcomes. Current research lacks comprehensive machine learning studies for predicting smartphone addiction factors, and studies examining relationships between various factors including mediation effects are also insufficient.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A cross-sectional survey was conducted among 3,405 adolescents in China (1,853 boys, 54.4%; 1,552 girls, 45.6%; mean age\u202f=\u202f14.50\u202fyears, SD\u202f=\u202f1.18). Eight machine learning algorithms (Linear Regression, Ridge Regression, Random Forest, XGBoost, LightGBM, CatBoost, Support Vector Regression, Bayesian Ridge Regression) were employed with SHAP analysis for interpretability. Predictive variables included demographic characteristics, social support, experiential avoidance, self-efficacy, and emotional symptoms. Baron and Kenny\u2019s method was used for mediation effect analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Linear models demonstrated optimal performance (R\n                      <jats:sup>2<\/jats:sup>\n                      \u202f=\u202f0.478, RMSE\u202f=\u202f8.66). SHAP analysis identified experiential avoidance as the strongest predictor (SHAP value\u202f=\u202f5.472), followed by depressive symptoms (1.723), BMI category (1.438), age (1.394), and anxiety symptoms (1.337). Mediation analysis revealed that social support\u2019s negative association with smartphone addiction (total effect\u202f=\u202f\u22120.231) was almost indirect (\u22120.252), with experiential avoidance mediating the majority of the total effect. The final model explained 49.4% of variance in smartphone addiction.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Experiential avoidance is a strong correlate of adolescent smartphone addiction and plays a mediating role in the association between social support and smartphone addiction. This finding is consistent with the \u201creality deficit-virtual compensation\u201d theoretical framework. Additionally, depressive symptoms, BMI category, age, and anxiety symptoms were also independently associated with smartphone addiction.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyg.2026.1860144","type":"journal-article","created":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T05:21:39Z","timestamp":1791264099000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Experiential avoidance and smartphone addiction in adolescents: a study using multi-model machine learning and mediation analysis"],"prefix":"10.3389","volume":"17","author":[{"given":"Xinyao","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Social Medicine and Health Management, School of Public Health, Jilin University","place":["Changchun, Jilin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weixin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Infectious Disease Prevention and Control Department, Xi'an New District Center for Disease Control and Prevention","place":["Xi'an, Shaanxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,10,6]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"e0175383","DOI":"10.1371\/journal.pone.0175383","article-title":"Use of a machine learning framework to predict substance use disorder treatment success","volume":"12","author":"Acion","year":"2017","journal-title":"PLoS One"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"700","DOI":"10.3390\/healthcare11050700","article-title":"The psychometric properties of the DASS-21 and its association with problematic internet use among Chinese college freshmen","volume":"11","author":"Cao","year":"2023","journal-title":"Health"},{"key":"ref3","first-page":"873","article-title":"Reliability and validity of the Chinese version of the acceptance and action questionnaire-second edition (AAQ-II) in college students","volume":"27","author":"Cao","year":"2013","journal-title":"Chin. 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