Magnetic Resonance Imaging (MRI) examinations are frequently associated with significant anxiety and phobias in patients, negatively impacting imaging quality and patient compliance. In this study, we explore the use of Virtual Reality Exposure Therapy (VRET) to reduce MRI-related anxiety by examining physiological and subjective responses across three virtual scenarios: a 2D video, a 360° video, and a fully immersive VR environment. The study aimed to determine how different levels of immersion and the order in which scenarios are experienced impact anxiety. Thirteen participants engaged in all three scenarios, with heart rate (HR), skin temperature (SKT), and electrodermal activity (EDA) monitored, and self-reported anxiety assessments collected before, during, and after the study. Results showed no significant differences in average or maximum heart rates between the three scenarios. However, the fully immersive VR environment generally elicited higher HR peaks, higher EDA, and lower SKT, suggesting stronger physiological responses in some participants. Self-reported anxiety decreased after the VR experience, particularly for participants with moderate to high anxiety levels prior to the sessions, independent of the scenario order. These findings suggest that individual responses to VRET vary, emphasizing the need for personalized approaches rather than a one-size-fits-all solution. While larger studies are necessary to validate these outcomes, the results suggest that incorporating real-time biofeedback monitoring in VRET could allow for dynamic adjustments to exposure levels based on participants’ physiological responses, creating a more adaptive and therapeutic environment.

We present an avatar and task-oriented dialog agent for monitoring user discomfort during a virtual reality (VR) cognitive exercise and providing personalized information and advice on its relief. The goal of this approach is to provide instantaneous assistance to users for a more comfortable VR experience, thereby enabling them to spend more time on cognitive tasks. We developed an avatar in a VR environment with which users may communicate verbally, and a dialog agent in a machine-learning based conversational AI platform. We performed a technical evaluation of the natural language understanding (NLU) component by comparing 2 models (BERT and StarSpace) using a train-test split, showing a significant benefit of BERT with smaller data sets. We validated the turn prediction using a train-test split and using randomly generated conversations. Both validations showed acceptable conversation-level accuracy. We undertook a usability study at two sites, showing effectiveness at both and good acceptability at one of the two. The framework outlined can be used to develop other virtual agents for cognitive self-care. Suggested improvements include validating the avatar with integrated BERT and reducing reliance on data augmentation, offline voice interaction modules, improved UX design, clinically validating the effect of the dialog agent on user discomfort and on cognitive performance, and increasing the ubiquity of the avatar within the VR cognitive care environment.