Digital Sleep Disruption: Unraveling the Network Structure of Technology Use and Sleep Problems Through Network Analysis.

BACKGROUND: Sleep problems have emerged as a critical health concern in the digital age, yet the complex mechanisms linking technology use to sleep disruption remain poorly understood. Previous research has typically examined isolated relationships between specific technological behaviors and sleep outcomes, overlooking the complex interplay among various digital factors. This study aims to address this gap by employing network analysis to investigate the interconnected relationships among multiple technology-related factors and their collective influence on sleep problems.

METHODS: Using network analysis, this study examined how different aspects of digital technology use collectively influence sleep problems in a large sample of Chinese adults (N = 9443). Participants were recruited through stratified random sampling based on age groups and geographical regions, with the sample size determined a priori using Monte Carlo simulations to ensure stable network estimation. Participants completed validated measures assessing screen time, before-bed electronic device use, electronic device dependency, social media anxiety, digital information overload, virtual social pressure, blue light exposure, circadian rhythm disruption, online gaming addiction, work-life digital integration, and sleep problems. Participants with diagnosed sleep disorders, those engaged in shift work, or those who had traveled across time zones in the past month were excluded to minimize confounding effects on natural sleep patterns.

RESULTS: Network analysis revealed complex interconnections among technology-related factors and sleep problems. Blue light exposure demonstrated the strongest direct edge weight with sleep problems (r = 0.31, p < 0.001), followed by circadian rhythm disturbance (r = 0.26, p < 0.001). The network structure indicated that screen time, bedtime device use, electronic device dependence, virtual social pressure, and work-life digital integration showed weaker direct associations with sleep problems but demonstrated substantial indirect pathways through intermediate variables. Online gaming addiction, digital information overload, social media anxiety, and circadian rhythm disturbance exhibited moderate centrality indices, suggesting their role as potential mediators within the network.

CONCLUSIONS: These findings advance our understanding of technology-induced sleep disruption and suggest potential targets for intervention. The network approach reveals that addressing sleep problems in the digital age requires consideration of both direct physiological impacts and indirect psychological pathways. The identification of specific factors with high centrality provides empirical guidance for developing targeted intervention strategies.