Targeting childhood loneliness in china: in silico interventions and moderated network analysis.

Background: Childhood loneliness is a significant public health concern, particularly in China due to distinct sociocultural contexts. Prior research often overlooks symptom-level interactions, limiting the precision of targeted interventions. This study applied network-based methodologies to clarify the structure of loneliness, identify intervention targets, and examine the roles of psychological factors and family socioeconomic status (SES).

Methods: A total of 2,593 school-age children from Shantou, China, were assessed for loneliness, depressive symptoms, anxiety symptoms, ADHD symptoms, perceived social support, hope, and family SES. In silico interventions using the NodeIdentifyR algorithm (NIRA) within an Ising model identified effective targets for prevention and intervention. A Graphical Gaussian Model (GGM) mapped loneliness within a broader psychological context, and a Moderated Network Model (MNM) tested SES influences.

Results: Lack of friendship and peer acceptance emerged as key targets for prevention and intervention, respectively. Loneliness functioned as both a central and bridging symptom in the psychological network, closely connected to other psychological variables. Higher SES buffered its associations with depressive symptoms and hope.

Conclusions: Early, peer-focused, and context-sensitive strategies may more effectively support children's well-being. This study is the first to apply network analysis and in silico intervention methods, providing novel perspectives and strategies for the prevention and intervention of childhood loneliness among Chinese children.