When assessing the health of older adults, oral interviews and written questionnaires are commonly used. However, these methods are time-consuming in terms of both execution and data aggregation. To address this issue, systems utilizing generative AI for health information collection through conversation have been developed and implemented. Despite these advancements, the motivation of older adults to consistently engage with such systems in their daily lives has not been thoroughly explored. In this study, we developed a smart-speaker extension that uses generative AI to monitor health status through casual conversations with older adult users. The system was tested in a two-week home trial with older adult participants. We conducted post-trial questionnaires and interviews, and we analyzed conversation log data. The results revealed that older adult users enjoy interacting with such systems and can integrate their use into their daily routines. Customized notifications through text messages encouraged system use, and the system’s ability to refer to previous conversations and address users by name was identified as a key factor motivating continued use.