Experiential avoidance and smartphone addiction in adolescents: a study using multi-model machine learning and mediation analysis.
- 1Department of Social Medicine and Health Management, School of Public Health, Jilin University, Changchun, Jilin, China.
- 2Infectious Disease Prevention and Control Department, Xi'an New District Center for Disease Control and Prevention, Xi'an, Shaanxi, China.
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 (R² = 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.
Related
- Smartphone addiction, physical activity engagement, and sleep quality in Chinese adolescents: a parallel and serial mediation model
- Distinctive Features of Phubbing Behavior and Being Phubbed in Adolescents: Empathy, Social Cognition, Interpersonal Competence, and Smartphone Addiction
- The effect of physical activity on smartphone addiction among Chinese college students: a chain mediation of self-control and health-promoting lifestyle
- Smartphone addiction and self-esteem among university students: a moderated mediation and latent profile analysis.
- Perceived artificial intelligence dependence and anxiety symptoms among university students: a three-wave study of experiential avoidance and digital literacy
BibTeX
@article{zhang2026experiential,
title = {{Experiential avoidance and smartphone addiction in adolescents: a study using multi-model machine learning and mediation analysis.}},
author = {Xinyao Zhang and Weixin Zhang},
journal = {Frontiers in psychology},
year = {2026},
month = oct,
doi = {10.3389/fpsyg.2026.1860144},
url = {https://europepmc.org/article/MED/42903337},
note = {CC BY},
}
RIS
TY - JOUR
TI - Experiential avoidance and smartphone addiction in adolescents: a study using multi-model machine learning and mediation analysis.
AU - Xinyao Zhang
AU - Weixin Zhang
PY - 2026
DA - 2026/10/06
JO - Frontiers in psychology
DO - 10.3389/fpsyg.2026.1860144
UR - https://europepmc.org/article/MED/42903337
AB - 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 (R² = 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.
SN - 1664-1078
ER -