Positive Affect

The purpose of this study is to compare the effects of Virtual Reality-Reward Training (VR-RT) with an active control condition, Virtual Reality-Memory Training (VR-MT), on positive affect and other clinical symptoms. VR-Reward Training is a novel intervention aimed at enhancing savoring of positive experiences among individuals with depression and low positive affect through guided imaginal recounting following immersion in positive VR experiences. The current study tests an enhanced version of this training using improved virtual reality technology. Target enrollment is 80 participants with low positive affect, depression, and impaired functioning, who are at least 18 years old, who will be randomly assigned to 7 weeks of either Virtual Reality-Reward Training (VR-RT) or Virtual Reality-Memory Training (VR-MT). Participants will complete in-person VR sessions, laboratory assessments, self-report questionnaires as part of the study. A subset of 8 participants randomly assigned to VR-RT will complete fMRI scans and EMA surveys. The total length of participation is around 3 months.

FOREST is a positive emotion skills program designed to target mental health and coping needs for frontline violence prevention workers at UCAN. Ten skills are taught over a period of nine months during existing meetings and wellness activities, as well as in online modules in UCAN's Learning Management System (LMS). Through infusing the FOREST skills throughout UCAN, we hope to inspire organizational culture change that will emphasize the importance of wellbeing and enhance resilience, therefore reducing burnout and turnover.

This observational study will employ a multi-modal observation methodology, integrating data from wearable devices and smartphones to establish comprehensive digital biomarkers for identifying symptoms of mental health conditions in university students. The study aims to identify the digital behavioural markers associated with mental health conditions, develop predictive algorithms for mental health states from digital markers, and identify university students at risk for mental health conditions.