Relationships Among Mobile Internet Use, Social Support, and Depressive Symptoms: Prospective Cohort Study Among Community Residents

TL;DR

This study explores how mobile internet use affects mental health by improving social support, which in turn reduces depressive symptoms in rural Chinese residents.

Contribution

The study reveals that mobile internet use enhances social support, which mediates its effect on reducing depressive symptoms over time.

Findings

Increased mobile internet use at baseline predicts improved social support at follow-up.

Social support fully mediates the relationship between mobile internet use and reduced depressive symptoms.

Baseline depressive symptoms predict decreased social support at follow-up.

Abstract

In the digital era, mobile internet integration into daily routines presents a paradoxical relationship with mental health outcomes. While previous cross-sectional studies report inconsistent associations between mobile internet use (MIU) and depressive symptoms, the longitudinal mechanisms involving social support remain underexplored.

This 2-wave longitudinal study aimed to examine the temporal relationships between MIU, social support, and depressive symptoms among rural Chinese residents. Specifically, we hypothesize that (1) increased MIU will predict improved perceived social support over time, and (2) enhanced social support will mediate the relationship between MIU and reduced depressive symptoms. The findings are intended to inform digital health strategies that leverage internet-based interactions to improve mental well-being in underserved communities.

A 2-wave longitudinal cohort study (4 y interval) was conducted among rural residents in Taierzhuang District, China (n=489 retained, mean age 63.30, SD 13.35 y; 310/489, 63.39% female). Multidimensional assessments included (1) demographic characteristics and MIU patterns via a customized survey, (2) social support was assessed using the Perceived Social Support Scale, and (3) depressive symptom severity via Center for Epidemiological Studies-Depression Scale. Advanced analytical strategies were implemented: least absolute shrinkage and selection operator regression for high-dimensional variable selection, complemented by cross-lagged panel modeling to disentangle temporal dependencies.

Our analysis found that the increase in MIU at baseline indicated the improvement of social support at follow-up (ρ=0.097, 95% CI 0.001-0.193; P=.049). There is a bidirectional cross-lagged relationship between social support and depressive symptoms. An increase in baseline social support indicates a reduction in later depressive symptoms (ρ=−0.096, 95% CI −0.183 to −0.01; P=.03). An increase in baseline depressive symptoms indicates a decrease in later social support (ρ=−0.109, 95% CI −0.213 to −0.004; P=.04). Baseline social support has a significant impact on later depressive symptoms. Further analysis revealed that social support fully mediated the relationship between MIU and depressive symptoms. There was no direct effect between the 2 stages of MIU and depressive symptoms.

This study used longitudinal data and developed cross-lagged models to improve the reliability of findings and applied least absolute shrinkage and selection operator regression to improve the explanatory power and predictive accuracy of the models. The findings offer practical insights for designing digital mental health interventions, particularly in underserved rural areas. Specifically, our results support the development of mobile-based platforms that facilitate meaningful internet-based social interactions to bolster perceived social support and thereby reduce depressive symptoms. We recommend that public health initiatives incorporate digital literacy training and promote internet-based behaviors that strengthen real-life social connections. Furthermore, mental health professionals should consider individuals’ internet use patterns when designing personalized intervention strategies.

Figures