The longitudinal mediating effect of rumination on the relationship between depressive symptoms and problematic smartphone use in Chinese university students: A three-wave cross-lagged panel analysis

Predictors of smartphone addiction in adolescents with depression: combing the machine learning and moderated mediation model approach

Smartphone addiction (SA) significantly impacts the physical and mental health of adolescents, and can further exacerbate existing mental health issues in those with depression. However, fewer studies have focused on the predictors of SA in adolescents with depression. This study employs machine learning methods to identify key risk factors for SA, using the interpretable SHapley Additive exPlanations (SHAP) method to enhance interpretability. Additionally, by constructing a mediation moderation model, the interactions between significant risk factors are analyzed. The study included 2203 adolescents with depression. Machine learning results from four models (Random Forest, Support Vector Machine, Logistic Regression, XGBoost) consistently identified emotion-focused coping, rumination, and school bullying as the strongest predictors of SA. Further mediation moderation analyses based on the Interaction of Person-Affect-Cognition-Execution (I-PACE) model revealed that rumination significantly mediated the relationship between school bullying and SA, and emotion-focused coping significantly moderated the relationships between school bullying and both rumination and SA. This is the first study to use machine learning to explore the predictors of SA in depressive adolescents and further analyze the interactions among these predictors. Future interventions for SA in adolescents with depression may benefit from psychotherapy that addresses emotion-focused coping and rumination.

Longitudinal Trajectories of Cognitive Function Among Chinese Middle-Aged and Older Adults: The Role of Sarcopenia and Depressive Symptoms

Objectives: The longitudinal relationship between sarcopenia, depression, and cognitive impairment has been insufficiently studied in China. This study aimed to characterize the association between sarcopenia and cognitive impairment and the mediating role of depression using nationally representative data. Methods: 7091 middle-aged and older adults were analyzed from the China Health and Retirement Longitudinal Study (CHARLS) across three waves (2011, 2013, and 2015). Cognitive trajectories were modeled using a group-based trajectory model (GBTM), while multivariable ordinal logistic regression was employed to evaluate the associations with cognitive trajectories. The mediating role of depressive symptoms was assessed through bootstrap mediation analysis and cross-lagged panel modeling (CLPM). Results: Trajectory analysis identified four distinct cognitive function patterns: "High and Stable" trajectory (n = 2563, 36.73%), "Middle and Stable" group (n = 2860, 38.76%), "Middle and Decline" group (n = 1280, 18.62%), and "Low and Decline" group (n = 388, 5.90%). Sarcopenia and depressive symptoms were associated with the "Low and Decline" trajectory of cognitive function [Overall: OR (95%CI) of 0.315 (0.259, 0.382) and 0.417 (0.380, 0.459)]. Mediation analysis indicated that depressive symptoms accounted for 11.78% of the relationship between sarcopenia and cognitive trajectories. The cross-lagged panel modeling demonstrated a significant mediation pathway of "T1 cognitive function → T2 depression → T3 sarcopenia", with T2 depression mediating 5.31% of the total effect. Conclusions: Our study identified four distinct cognitive trajectories, with sarcopenia and depressive symptoms significantly associated with worse cognitive trajectories over time. Depressive symptoms mediated the relationship between sarcopenia and cognitive function. This highlights the importance of integrating mental health and physical health interventions to address the interconnected risks associated with aging.