Recent large language models (LLMs) offer the potential to support public health monitoring by facilitating health disclosure through open-ended conversations but rarely preserve the knowledge gained about individuals across repeated interactions. Augmenting LLMs with long-term memory (LTM) presents an opportunity to improve engagement and self-disclosure, but we lack an understanding of how LTM impacts people’s interaction with LLM-driven chatbots in public health interventions. We examine the case of CareCall—an LLM-driven voice chatbot with LTM—through the analysis of 1,252 call logs and interviews with nine users. We found that LTM enhanced health disclosure and fostered positive perceptions of the chatbot by offering familiarity. However, we also observed challenges in promoting self-disclosure through LTM, particularly around addressing chronic health conditions and privacy concerns. We discuss considerations for LTM integration in LLM-driven chatbots for public health monitoring, including carefully deciding what topics need to be remembered in light of public health goals.

The rapid increase in the number of young adults living alone gives rise to a demand for the resolution of social isolation problems. Social robot technologies play a substantial role for this purpose. However, existing technologies try to solve the problem only through one-to-one interaction with robots, which in turn fails to utilize the real-world social relationships. Privacy concern is an additional issue since most social robots rely on the visual information for the interactions. To this end, we propose 'Fribo', auditory information centered social robot that recognizes user's activity by analyzing occupants' living noise and shares the activity information with close friends. A four-week field study with the first prototype of Fribo confirms that activity sharing through the use of anonymized living noise promises a virtual cohabiting experience that triggers more frequent real-world social interactions with less feeling of privacy intrusion. Based on this finding and the further qualitative analysis, we suggest a design principle of sound-based social networking robots and its associated new interactions, then present the second prototype of Fribo inspired by the implications from the field study.