Digital Stress and Mental Health in Generation Z: A Longitudinal Network Analysis of Problematic Smartphone Use and...

Whether problematic smartphone use (PSU) positively or negatively links to depression, and whether developmental pathways differ across high-/low-risk groups, remain theoretically contested. This study advances the digital mental health discourse by resolving theoretical ambiguities in the Depression-PSU relationship through a novel symptom-network lens. We propose a dual-process framework across two risk groups, bridging gaps in existing models. Following prior work, participants scoring > 51 on the Mobile Phone Addiction Index were designated high-risk (PSU+) users (N = 778), and the remainder as low-risk (PSU-) users (N = 645). Cross-sectional and longitudinal symptom-to-symptom associations and symptom centrality were analysed via R. The key results are as follows: (1) Core Circuit: Both groups exhibited a self-reinforcing loop between escapism symptom of depression ('work initiation difficulty') and addictive symptoms of PSU ('excessive/uncontrolled use'). (2) Distinct pathways emerged in the PSU + Group: Revealed paradoxical disengagement effects where addictive symptoms of PSU inversely predicted affective symptoms of depression, suggesting behavioural addiction as emotion regulation failure; escapism symptoms of PSU negatively predicted affect symptoms of depression, and addictive symptoms of PSU did not positively predict negative consequence of PSU ('productive loss'). (3) Network Centrality: Negative consequence of PSU ('productivity loss') and one affect symptom of depression ('hopelessness') had the strongest in-prediction in the PSU + group, while addictive use and escapism symptom of depression had the strongest in-prediction in the PSU- group. The results highlight that excessive smartphone use can indicate both digital stress (PSU - group) and digital relaxation (PSU + group), challenging the traditional view of an unidirectional link between PSU and depression. The identified network signatures enable precision interventions targeting specific symptom pathways, informing next-generation therapeutics for tech-related mental health challenges.