This study explores the influencing factors of residents’ outdoor health information communicant activeness under public health emergencies and analyzes the relationship between cognition, motivation, and dissemination behavior. Based on the theory of perceived risk and the Situational Theory of Problem-Solving (STOPS), this study builds a model demonstrating the factors that affect the health information communicant activeness of residents’ outdoor activities under public health emergencies and conducts empirical analysis through questionnaires and a structural equation model. Results showed that (1) perceived risk, problem recognition, and involvement recognition positively affected health information communicant activeness through situational motivation; (2) constraint recognition negatively affects health information communicant activeness through situational motivation; and (3) the referent criterion has a direct effect on communicant activeness. This study has great significance for understanding residents’ health information communicant activeness in outdoor activities and providing them with high-quality health information services.

Health chatbots have been growing into a necessary tool for dealing with risky and important contexts, such as medical and health information seeking. Meanwhile, trust towards chatbots influences people’s willingness to embrace technology and use it consistently. Thus, it is important to explore the mechanism of forming trust towards the health chatbots. The TAM has been introduced to explain the mechanism. This study extends the TAM framework by incorporating perceived risk and self-efficacy to develop an expanded model that explains the mechanisms underlying trust formation in health chatbots, applying a survey and investigating 480 Chinese chatbot users on the Credamo. The findings show that perceived risk reduces trust both directly and indirectly through perceived usefulness, perceived ease of use, and self-efficacy. Both parallel and serial mediation pathways were supported. These results offer a more complete insight into trust formation in high-risk AI contexts and provide practical guidance for chatbot design and governance in health communication.