Embodied conversational agents (ECAs) have been designed and implemented to provide support to humans, especially in the area of health. With the recent advancements in large language models (LLMs), ECAs can now be equipped with natural language capabilities, engaging in turn-taking dialogue with humans while exhibiting verbal rapport-building behavior. Our innovative study designed LLM-based ECAs that provide health coaching to people through immersive virtual reality (VR). Specifically, male and female avatars were integrated with ChatGPT, text-to-speech, and speech-to-text APIs on a VR platform. For our experiment, we manipulated human-ECA similarity via gender-matching. Participants were randomly assigned to either a gender-matched or unmatched embodied health coach and completed two interaction tasks (get-to-know-you and health consultation) in immersive VR. Our quantitative evaluations showed that those in the gender-unmatched conditions rated certain interaction metrics more favorably compared to those in the gender-matched condition. The qualitative evaluation showed that while the lack of nonverbals and other technology-related limitations could be improved, the LLM-based ECAs showed the potential to support people’s health-related decision-making.