Short-term associations between air pollution and nocturnal cough frequency.

BACKGROUND: Air pollution remains a leading environmental risk factor for health, yet its short-term associations with early respiratory symptoms are not well characterized because population-scale symptom surveillance data are scarce. We aimed to quantify the association between short-term exposure to fine particulate matter (PM2.5) and nocturnal cough frequency using large-scale mobile sensing data collected across multiple countries. METHODS: We analyzed nocturnal audio recordings from users of a widely used mobile sleep application (Sleep Cycle). An artificial intelligence-based cough detection model was applied to estimate nightly cough frequency. Data were aggregated at the city-day level across 32 cities in 12 countries over approximately 500 consecutive days. We used city-specific time-series models combined through random-effects meta-analysis and multi-city pooled models adjusting for meteorological conditions and influenza activity. In addition, we conducted an event-based analysis during a major wildfire episode to evaluate the acute respiratory impacts of extreme air pollution. RESULTS: Higher daily PM2.5 concentrations were consistently associated with increased nocturnal cough frequency across all analytical approaches. In the city-specific meta-analysis, each 10 μg/m3 increase in PM2.5 was associated with a 2.4% increase in nocturnal cough frequency (relative risk 1.024, 95% CI: 1.013-1.035). Pooled analyses showed a nonlinear exposure-response relationship, with measurable increases in cough frequency observed even at relatively low PM2.5 concentrations. Elevated cough frequency was also observed during the wildfire episode, coinciding with substantial increases in ambient PM2.5. CONCLUSIONS: Short-term exposure to PM2.5 is associated with increased nocturnal cough frequency at the population level, suggesting that respiratory symptoms may respond to air pollution at lower exposure levels than previously recognized. Large-scale mobile sensing of respiratory symptoms provides a feasible digital biomarker for real-time environmental health surveillance and could complement traditional public health monitoring systems.