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Background <p>The prevalence of depressive and anxiety symptoms in children is increasing, often presenting as co-occurring symptoms, yet screening for such co-occurrence remains inadequate. This study investigates repetitive negative thinking (RNT) as a transdiagnostic factor in the co-occurrence of depression and anxiety symptoms in children, aiming to develop novel early screening strategies.</p> Methods <p>Two cross-sectional surveys collected demographic information and self-reported measures of depression, anxiety, and RNT from primary school students in China. Structural equation modeling and network analysis were used to examine relationships among variables. Additionally, four machine learning algorithms (random forest, support vector machine, decision tree, and extreme gradient boosting) were applied to predict the co-occurrence of depression and anxiety symptoms.</p> Results <p>RNT and its factors were significantly positively correlated with depressive and anxiety symptoms (r = 0.56–0.68, <i>p</i> &lt; 0.001) and mediated 12.94% and 12.73% of their bidirectional relationship (with depression and anxiety as independent variables, respectively), with a same 95% CI: 8.78%–18.43% (<i>p</i> &lt; 0.001). Network analysis revealed that RNT’s core features exhibited the highest bridge betweenness and bridge expected influence, indicating a critical mediating role in the co-occurrence of symptoms. The random forest model showed optimal predictive performance (AUC = 0.90, recall = 0.95), supporting its applicability for early screening.</p> Conclusion <p>RNT, particularly its core features, may play an important transdiagnostic role in the co-occurrence of depression and anxiety symptoms in children. This study provides an effective method for early screening in resource-limited settings, particularly in educational settings. Future research should validate the utility of RNT-targeted interventions, such as mindfulness-based therapies.</p>

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Exploring the role of repetitive negative thinking in the transdiagnostic context of depression and anxiety in children

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月12日

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  • Kuiliang Li,
  • Lei Ren,
  • Xiao Li,
  • Chang Liu,
  • Xuejiao Tan,
  • Ming Ji,
  • Xi Luo

摘要

Background

The prevalence of depressive and anxiety symptoms in children is increasing, often presenting as co-occurring symptoms, yet screening for such co-occurrence remains inadequate. This study investigates repetitive negative thinking (RNT) as a transdiagnostic factor in the co-occurrence of depression and anxiety symptoms in children, aiming to develop novel early screening strategies.

Methods

Two cross-sectional surveys collected demographic information and self-reported measures of depression, anxiety, and RNT from primary school students in China. Structural equation modeling and network analysis were used to examine relationships among variables. Additionally, four machine learning algorithms (random forest, support vector machine, decision tree, and extreme gradient boosting) were applied to predict the co-occurrence of depression and anxiety symptoms.

Results

RNT and its factors were significantly positively correlated with depressive and anxiety symptoms (r = 0.56–0.68, p < 0.001) and mediated 12.94% and 12.73% of their bidirectional relationship (with depression and anxiety as independent variables, respectively), with a same 95% CI: 8.78%–18.43% (p < 0.001). Network analysis revealed that RNT’s core features exhibited the highest bridge betweenness and bridge expected influence, indicating a critical mediating role in the co-occurrence of symptoms. The random forest model showed optimal predictive performance (AUC = 0.90, recall = 0.95), supporting its applicability for early screening.

Conclusion

RNT, particularly its core features, may play an important transdiagnostic role in the co-occurrence of depression and anxiety symptoms in children. This study provides an effective method for early screening in resource-limited settings, particularly in educational settings. Future research should validate the utility of RNT-targeted interventions, such as mindfulness-based therapies.