A simulation-based network analysis of intervention targets for adolescent depressive and anxiety symptoms

Abstract

Although previous research has well explored central and bridge symptoms of mental health problems, little examined whether these symptoms can serve as effective targets for intervention practices. Based on the Ising model, this study constructed a network structure of depressive and anxiety symptoms. The NodeIdentifyR algorithm (NIRA) was used to simulate interventions within this network, examining the effects of alleviating or aggravating specific symptoms on the network’s sum scores. In this study, a total of 15,569 participants were recruited from China (50.87 % females, M_age = 13.44; SD = 0.97). The Ising model demonstrated that “sad mood” had the highest expected influence, and “irritability” had the highest bridge expected influence. Alleviating interventions suggested that decreasing the symptom value of “nervousness” resulted in the greatest projected reduction in network symptom activation, which may be a potential target symptom for treatment. Aggravating interventions indicated that elevating the symptom value of “sad mood” had the most projected increase in network activation, which may be a potential target for prevention. Additionally, network structure indices (e.g., central or bridge symptoms) need to be interpreted with more caution as intervention targets, since they may not be exactly the same. These findings enriched the comprehension of the depressive and anxiety network in Chinese adolescents, offering valuable insights for designing effective interventions.