The network analysis of learning burnout and sleep quality among medical students during online learning in China

Previous studies had identified the significant issue of burnout and sleep quality in medical students. However, no studies have explored the interactions between learning burnout and sleep quality on a symptom level. This study used network analysis to explore the interaction and construct the network structure of learning burnout and sleep quality among medical students in China. We recruited 553 medical students to participate in our study. Learning Burnout of Undergraduates and Scale (LBUS) and the Pittsburgh Sleep Quality Index (PSQI) were used to measure learning burnout and sleep quality. Expected influence and bridge expected influence were used to identify the central and bridge symptoms. Results showed 'B9' (Tired of learning) and 'B17' (I want to learn but feel bored with it) had the highest expected influence. 'B12' (I often fall asleep while studying) and 'P_DD' (Daytime dysfunction) had the highest bridge expected influence. Our findings revealed the characteristics of learning burnout and sleep quality in online learning and provided information to further understand the difference in the influence of mental health between online and offline learning.