Experiential avoidance and smartphone addiction in adolescents: a study using multi-model machine learning and mediation analysis
Year: 2026
Type: article
Study design: Observational Study
Abstract: Background: Smartphone addiction is closely associated with adolescents' 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. Methods: A cross-sectional survey was conducted among 3,405 adolescents in China (1,853 boys, 54.4%; 1,552 girls, 45.6%; mean age = 14.50 years, SD = 1.18). 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's method was used for mediation effect analysis. Results: = 0.478, RMSE = 8.66). SHAP analysis identified experiential avoidance as the strongest predictor (SHAP value = 5.472), followed by depressive symptoms (1.723), BMI category (1.438), age (1.394), and anxiety symptoms (1.337). Mediation analysis revealed that social support's negative association with smartphone addiction (total effect = -0.231) was almost indirect (-0.252), with experiential avoidance mediating the majority of the total effect. The final model explained 49.4% of variance in smartphone addiction. Conclusion: 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 "reality deficit-virtual compensation" theoretical framework. Additionally, depressive symptoms, BMI category, age, and anxiety symptoms were also independently associated with smartphone addiction.
Source: Frontiers in Psychology
Authors: Xinyao Zhang, Weixin Zhang
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7220707909
