Academic burnout profiles and their correlates: a latent profile analysis of Chinese university students

Abstract

This study employed a person-centered approach to identify latent profiles of academic burnout among Chinese university students and to examine the associations between academic burnout profiles and smartphone addiction, sleep quality, and mindfulness. A sample of 2,948 Chinese university students was recruited to complete measures of academic burnout, smartphone addiction, sleep quality, and mindfulness. Latent profile analysis (LPA) was used to identify distinct burnout profiles, and multinomial logistic regression was used to analyze factors associated with profile membership. Three distinct profiles of academic burnout were identified: a Low Burnout profile (18.15%), a Medium Burnout profile (50.88%), and a High Burnout profile (30.97%). The profiles differed significantly on all correlates, with the high burnout group exhibiting the most severe smartphone addiction, the poorest sleep quality, and the lowest mindfulness. Regression analysis revealed that higher smartphone addiction and poorer sleep quality were significantly associated with membership in the Medium and High Burnout profiles relative to the Low Burnout profile, whereas higher mindfulness was significantly associated with lower likelihood of belonging to higher burnout profiles. Academic burnout among Chinese university students is a heterogeneous experience, with a majority falling into an at-risk or intermediate state. Smartphone addiction, poor sleep, and low mindfulness are associated with higher burnout risk. These findings highlight the need for universities to develop targeted, profile-based interventions to provide precise and effective mental health support. However, due to the cross-sectional design, causal relationships cannot be inferred.