Does depression drive technology overuse or vice-versa? a cross-lagged panel analysis of bidirectional relationships among Chinese university students - Zhan, Yuting; Ding, Xu (BMC Psychology, 2025)
The escalating prevalence of depression among university students coincides with unprecedented technology engagement, yet the directional relationship remains contested. While cross-sectional research suggests associations between technology use patterns and depressive symptoms, longitudinal evidence examining bidirectional influences remains scarce, particularly in non-Western populations. This study aimed to examine the bidirectional relationships between specific technology use patterns and depression severity among Chinese university students using a methodologically rigorous longitudinal design. This study conducted a four-wave longitudinal study with assessments at 3-month intervals among undergraduate students (N = 737) from three universities in eastern China. Participants completed validated measures of depression (Patient Health Questionnaire-9), anxiety (Generalized Anxiety Disorder-7), and technology use patterns (duration, timing, motivational contexts). Cross-lagged panel models with random intercepts were used to examine bidirectional relationships while controlling for between-person differences and covariates. Total technology use exhibited significant bidirectional relationships with depression, but specific patterns showed distinct relationships. Night-time use (β = 0.16, 95% CI [0.08-0.24], p < 0.001) and social-comparison-motivated use (β = 0.19, 95% CI [0.11-0.27], p < 0.001) predicted subsequent increases in depression, with stronger effects than the reverse pathway (depression to increased technology use). Conversely, depression predicted increased escapism-motivated technology use (β = 0.23, 95% CI [0.14-0.32], p < 0.001) more strongly than the reverse pathway. Body mass index significantly moderated these relationships, with stronger technology-to-depression effects among participants with overweight/obesity (β = 0.27, 95% CI [0.16-0.38], p < 0.001) compared to normal-weight participants (β = 0.11, 95% CI [0.03-0.19], p = 0.009). The observed relationships remained significant after adjusting for anxiety, sleep quality, and socioeconomic factors. These findings reveal complex, pattern-specific bidirectional relationships between technology use and depression, with important temporal precedence differences. The results suggest that certain technology use contexts may contribute more strongly to depression development, while depression may drive other specific usage patterns. These findings have implications for targeted intervention approaches addressing both depression and problematic technology use among university students.