Offline and online health-seeking behaviors of community population with acute respiratory infection: a cross-sectional study

Objective Emerging diverse health-seeking behaviors, especially online behaviors, of acute respiratory infection (ARI) patients generate timely, continuous big data that offer a novel and comprehensive approach for optimizing respiratory infectious disease surveillance. This study aimed to analyze the frequency and sequence of health-seeking behaviors of community residents after ARI onset. Methods A cross-sectional survey was conducted in 6 districts and counties of Chengdu, China, in January 2025, with a total of 2340 recovered ARI patients with acute respiratory infection enrolled in the final analysis. Cox-proportional hazard regression models and Wilcoxon rank sum tests were employed to assess the associations between offline/online health-seeking behaviors and hospital visits, while chi-square tests were applied to compare differences in health-seeking behaviors between urban and rural ARI cases. Results Urban residents had a higher absence rate and were more likely to purchase medicines offline or online, while rural cases had higher rates of online or phone consultations and hospital visits ( p  < 0.05). Cases with hospital visits were significantly more likely to search health information through social media or internet, monitor health with wearable health devices and be absent from work earlier than cases without hospital visits (p < 0.05). ARI cases’ living area, age, number and duration of symptoms, absence from work, purchasing medicine from online channels, online or phone medical consultations and monitoring health with wearable health devices had significant effect on hospital visits. In all significant factors, only cases who purchase medicine from online channels had a lower risk of visiting the hospital compared with those who didn’t ( hazard ratios = 0.806, 95% confidence interval = 0.651–0.998). Conclusion Our findings reveal the potential of incorporating health-seeking behaviors other than hospital visits into infectious disease surveillance and early warning systems as multi-source data.