Digital Stress and Mental Health in Generation Z: A Longitudinal Network Analysis of Problematic Smartphone Use and Depression in High and Low Risk Profile

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.