Explainable Boosting Models of Attachment Insecurity and Digital Dependency in Adolescents - Brossard, Camille; Declerck, Thomas (Journal of Adolescent and Youth Psychological Studies (JAYPS), 2026)
Objective: The present study aimed to develop and interpret explainable machine learning models examining the nonlinear and interactive associations between attachment insecurity dimensions and digital dependency among adolescents.Methods and Materials: This cross-sectional quantitative study was conducted among 742 secondary school adolescents aged 13–18 years recruited through multistage cluster sampling from urban regions. Participants completed standardized self-report measures assessing attachment anxiety and attachment avoidance, digital dependency, and demographic variables including daily screen time. Data were analyzed using Explainable Boosting Machines (EBMs), a transparent generalized additive modeling approach enhanced with gradient boosting. The dataset was divided into training and testing subsets, and model performance was evaluated using R², RMSE, and MAE indices. Five-fold cross-validation was implemented to optimize hyperparameters. Global feature importance scores, partial dependence functions, and interaction analyses were generated to examine nonlinear thresholds and synergistic risk patterns.Findings: The EBM model explained a substantial proportion of variance in digital dependency scores (R² = 0.47) with satisfactory predictive accuracy. Attachment anxiety emerged as a strong psychological predictor, demonstrating a nonlinear threshold effect in which digital dependency increased sharply at moderate levels of anxiety and plateaued at very high levels. Attachment avoidance showed a weaker but significant positive association with digital dependency, characterized by a gradual linear trend. Daily screen time was the most influential predictor overall, and a significant interaction effect was identified between attachment anxiety and screen time, indicating amplified dependency risk among adolescents high on both variables.Conclusion: The findings underscore the central role of attachment insecurity—particularly attachment anxiety—in shaping adolescents’ vulnerability to digital dependency and demonstrate the utility of explainable machine learning approaches for identifying nonlinear developmental risk patterns.