Anxiety and Depression Among College Students Before and After the COVID-19 Pandemic Lockdown Lift: A Network Analysis Study Focus on the Transition Period

Abstract

Since the outbreak of the COVID-19 pandemic, college students experienced changed campus life during the evolving pandemic restrictions. Anxiety and depression have become increasingly prevalent, leading to the necessity for further examining their relationship and comorbidity. This study used the network analysis to investigate the interaction and causal relationship in the anxiety-depression network among Chinese college students during the pandemic. A longitudinal survey with two specific points among 705 college students were conducted from 12 December to 30 December 2022 (lockdown period, T1), and from 8 February to 13 March 2023 (lockdown lift period, T2). Contemporaneous network and cross-lagged panel network (CLPN) analysis were conducted to examine the issue from both cross-sectional and longitudinal perspectives. Both contemporaneous networks exhibited extensive links between anxiety and depression symptoms. The key central symptom was “STAI16: [Not] content” at T1, and was “STAI15: [Not] relaxed” at T2. CLPN analysis suggested that “STAI15: [Not] relaxed” had the highest in-prediction, while “STAI13: Jittery” had the highest out-prediction. The strongest transdiagnostic prediction was from “BDI6: Punishment” to “STAI9: Frightened”, and the bridge symptoms in both contemporaneous networks and CLPN included overlaps like “STAI11: [Not] self-confident” and “STAI14: Indecisive”, which served as important symptoms contributing to anxiety-depression comorbidity. These findings provide new insights into the causal relationships between depression and anxiety before and after lockdown lift, shed light on the comorbidity factors, and provide support for targeted interventions to address mental health challenges faced by college students in public crisis.

Keywords

anxiety / COVID-19 epidemic / cross-lagged panel network analysis / depression / network analysis

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