(PS12-A6) Assessing Attitudes and Predictive Factors Toward Peer-supported Mental Health Interventions in the Metaverse

There is a need for accessible and clinically effective intervention options for anxiety and depressive disorders, which affect hundreds of millions of people annually.  To this end, digital treatments have been recently developed that allow participants to experience an intervention anytime and anywhere in a controlled and measurable environment. Research indicates that digital treatments can implement evidence-based therapies such as cognitive-behavioral therapy with similar efficacy to face-to-face treatments, which is further improved when paired with peer support. The metaverse – Internet-connected, virtual three-dimensional environments wherein participants may interact with each other and the environment as avatars – is a promising avenue for digital treatments, as it combines the benefits of a digital medium and easy integration of peer support in a uniquely immersive and anonymous environment. However, there are no studies as of yet that critically examine issues related to the implementation of peer-supported metaverse mental health interventions, including participants’ general attitudes toward and willingness to use these types of interventions. Expanding the body of knowledge in these areas is crucial in order to identify the populations most likely to benefit from the implementation and expansion of peer-supported metaverse mental health interventions as well as the barriers to seeking out and using such interventions.   

To address this gap in the literature, we designed a survey to assess attitudes and predictive factors of attitudes toward peer-supported mental health interventions in the metaverse. We aim to examine factors that have been traditionally assessed in relation to attitudes toward general, non-metaverse mental health interventions (e.g., sociodemographic variables, ethnic/gender matching preferences) as well as factors relevant to specific properties of the metaverse (e.g., computer use and preferences). We are currently recruiting a large and diverse sample of adults from the general population to complete the survey online. Our approach to data collection and analysis incorporates mixed methods. We plan to use predictive statistical models to identify the factors surveyed that are quantitatively associated with differential attitudes among the general population and within subpopulations (e.g., racial-ethnic minorities). Furthermore, a thematic analysis of qualitative data on participants’ explanations of their attitudes toward peer-supported mental health interventions in the metaverse will add to the current body of research in the field. Data analyses and a discussion of the study’s findings and limitations will be completed this summer and will be ready to be presented by the conference dates.