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Immersive virtual reality (VR) games are increasingly becoming part of everyday life. Several studies support immersive VR technology as a treatment method for mental health problems. There is however little insight into the prevalence of commercially available VR games for treatment of mental health problems on commercial platforms such as STEAM, and to what extent they can be used as tools for treatment or add-on treatment of mental health problems. The aim of this study was to take a first look at the prevalence and overview of content of commercially available games. The games were found using search words related to psychiatric diagnosis or care. We performed a search for keywords related to symptoms, diagnosis, and treatment strategies of mental health problems. The search was performed November 2020 on STEAM. A scheme was created for elimination and inclusion of games, eliminating those games which were irrelevant to mental health or had triggering elements such as violence, excessive movement which could trigger nausea, horror and pornographic imagery. In total, 735 hits were found, 565 unique games. After the games were reviewed for content via trailers, descriptions and screenshots, 32%, i.e. 182 games passed the inclusion criteria. Majority of the games which were excluded were either not connected to mental health, contained violence, adult content or were in other ways irrelevant or inappropriate. Commercial platforms are only at the beginning of the development towards therapeutic content. Currently the quality and usability for therapeutic use is scarce but holds great potential. [ABSTRACT FROM AUTHOR]

Background: The use of chatbots in mental health support has increased exponentially in recent years, with studies showing that they may be effective in treating mental health problems. More recently, the use of visual avatars called digital humans has been introduced. Digital humans have the capability to use facial expressions as another dimension in human-computer interactions. It is important to study the difference in emotional response and usability preferences between text-based chatbots and digital humans for interacting with mental health services., Objective: This study aims to explore to what extent a digital human interface and a text-only chatbot interface differed in usability when tested by healthy participants, using BETSY (Behavior, Emotion, Therapy System, and You) which uses 2 distinct interfaces: a digital human with anthropomorphic features and a text-only user interface. We also set out to explore how chatbot-generated conversations on mental health (specific to each interface) affected self-reported feelings and biometrics., Methods: We explored to what extent a digital human with anthropomorphic features differed from a traditional text-only chatbot regarding perception of usability through the System Usability Scale, emotional reactions through electroencephalography, and feelings of closeness. Healthy participants (n=45) were randomized to 2 groups that used a digital human with anthropomorphic features (n=25) or a text-only chatbot with no such features (n=20). The groups were compared by linear regression analysis and t tests., Results: No differences were observed between the text-only and digital human groups regarding demographic features. The mean System Usability Scale score was 75.34 (SD 10.01; range 57-90) for the text-only chatbot versus 64.80 (SD 14.14; range 40-90) for the digital human interface. Both groups scored their respective chatbot interfaces as average or above average in usability. Women were more likely to report feeling annoyed by BETSY., Conclusions: The text-only chatbot was perceived as significantly more user-friendly than the digital human, although there were no significant differences in electroencephalography measurements. Male participants exhibited lower levels of annoyance with both interfaces, contrary to previously reported findings., (©Almira Osmanovic Thunström, Hanne Krage Carlsen, Lilas Ali, Tomas Larson, Andreas Hellström, Steinn Steingrimsson. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 29.04.2024.)