Resident-side health information for identifying community noise risks in digital public health governance

Digital public health governance needs to identify community-level health risks beyond data generated after residents enter medical-service settings. In this study, resident-side health information refers to resident-reported information on residential noise perception, sleep disturbance, and health behavior that can supplement medical-service data. Noise pollution is a frequently perceived environmental problem in everyday life, and China's source-based noise governance framework needs resident-side evidence on whether different noise sources correspond to different sleep-related risks.

This study used cross-sectional questionnaire data from urban residents in China. A total of 451 valid responses were retained. Noise exposure was measured through subjective noise annoyance and self-reported residential noise-environment proxy indicators. Sleep disturbance and health behavior were also measured through self-reported items. The analysis included descriptive statistics, reliability and validity tests, correlation analysis, regression models, mediation analysis, and exploratory comparisons across source-specific noise annoyance.

Subjective noise annoyance (β = 0.427, p < 0.001), self-reported residential noise-environment proxy indicators (β = 0.466, p < 0.001), and overall noise exposure (β = 0.668, p < 0.001) were positively associated with sleep disturbance. Sleep disturbance was negatively associated with health behavior (β = -0.443, p < 0.001). The indirect statistical pathway from noise exposure to health behavior through sleep disturbance was significant (effect = -0.296, bootstrap 95% CI [-0.375, -0.222]). Source-specific comparisons showed more stable associations for construction and renovation noise, traffic noise, and social-life noise, whereas industrial noise did not show a stable association in the present sample.

In this cross-sectional self-reported sample, community noise exposure was linked to health behavior through sleep disturbance as a statistical pathway rather than as causal evidence. The findings provide exploratory resident-side evidence for identifying community noise risks and suggest that source-specific noise governance should consider how different noise sources enter residents' rest and recovery contexts.