Although several studies have demonstrated the efficacy of virtual reality exposure therapy, current virtual reality systems require therapists to manually adjust anxiety stressors for the patient to elicit the required anxiety response during exposure. Introducing a feedback-loop mechanism to measure anxiety and adjust anxiety stressors automatically enables a more autonomously delivery of anxiety exposure. To study effectiveness of such an automatic regulation mechanism, an experiment was conducted to examine the system’s ability of keeping individuals’ anxiety on a fixed target level. A group of 24 healthy participants were exposed to both (1) a static condition, where anxiety stressors remained fixed during the session, and to (2) a dynamic condition, where anxiety stressors were automatically adjusted in correspondence with the individual’s self-reported anxiety and heart rate. Prior to virtual reality exposure, a personalised anxiety measure was automatically established for each participant. Here, imaginary exposure was used to measure anxiety response in a lower and a higher anxiety situation. The results revealed that a personalized anxiety measure can be established automatically. Furthermore, while anxiety dropped in the static condition, anxiety in the dynamic condition remained centered around the target anxiety level (-4.82%) over time. Our findings have first and foremost implications for the use of virtual reality programs that target social anxiety. Yet, we anticipate that the- proposed feedback loop mechanism can be beneficial when applying virtual reality to address emotions in general.
|Date made available||2019|
|Publisher||TU Delft - 4TU Centre for research data|
|Date of data production||2015 - 2018|
Hartanto, D. (Creator), Brinkman, W. P. (. (Creator), Kampmann, I. (Creator), Morina, N. (Creator), Emmelkamp, P. (Creator), Neerincx, M. A. (. (Creator) (2019). Computer-based Fear Regulation during Virtual Reality Exposure for Social Anxiety. TU Delft - 4TU Centre for research data. 10.4121/UUID:A1318B55-BB48-44B4-99D6-883520988980