Peer-reviewed article
Experiential avoidance and smartphone addiction in adolescents: a study using multi-model machine learning and mediation analysis
Xinyao Zhang, Weixin Zhang ·
Summary
Written by AI, not yet AICS's view · claude-sonnet-5-5This cross-sectional survey of 3,405 adolescents in China looked at which factors are linked to smartphone addiction. The authors compared eight machine learning models and used an explanation method (SHAP) to rank the factors. Experiential avoidance, meaning a tendency to avoid unpleasant thoughts and feelings, was the strongest predictor. Social support was linked to lower smartphone addiction, and most of this link worked through experiential avoidance. Depressive symptoms, BMI category, age and anxiety symptoms were also associated with smartphone addiction.
Key points
- The sample was 3,405 adolescents in China (54.4% boys, mean age 14.50 years).
- Linear models performed best (R2 = 0.478, RMSE = 8.66).
- Experiential avoidance was 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).
- Experiential avoidance mediated most of the negative association between social support and smartphone addiction (total effect = -0.231).
- The final model explained 49.4% of the variance in smartphone addiction.
Limitations
The cross-sectional design cannot show cause and effect or the direction of the relationships, so the mediation results describe associations only. The sample is from China and may not apply to adolescents in other settings.
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 Linear models demonstrated optimal performance (R 2 = 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.