The effectiveness of a virtual reality attention task to predict depression and anxiety in comparison with current clinical measures

Autor: Alexandra Voinescu, Daniel David, Radu-Adrian Lazarovicz, Ion Papavă, Liviu A. Fodor, Danae Stanton Fraser, Karin Petrini
Rok vydání: 2021
Předmět:
Zdroj: Voinescu, A, Petrini, K, Stanton Fraser, D, Lazarovicz, R-A, Papava, I, Fodor, L A & David, D 2023, ' The effectiveness of a virtual reality attention task to predict depression and anxiety in comparison with current clinical measures ', Virtual Reality, vol. 27, no. 1, pp. 119-140 . https://doi.org/10.1007/s10055-021-00520-7
ISSN: 1434-9957
1359-4338
Popis: Previous studies have revealed that attention and inhibition are impaired in individuals with elevated symptoms of depression and anxiety. Virtual reality (VR)-based neuropsychological assessment may be a valid instrument for assessing attention and inhibition given its higher ecological validity when compared to classical tests. However, it is still unclear as to whether a VR assessment can predict depression and anxiety with the same or higher level of effectiveness and adherence as classical neuropsychological measures. The current study examined the effectiveness of a new VR test, Nesplora Aquarium, by testing participants with low (N = 41) and elevated (N = 41) symptoms of depression and anxiety. Participants completed a continuous performance test where they had to respond to stimuli (species of fish) in a virtual aquarium, as well as paper-and-pencil and computerised tests. Participants’ performance in Nesplora Aquarium was positively associated with classic measures of attention and inhibition, and effectively predicted symptoms of depression and anxiety above and beyond traditional cognitive measures such as psychomotor speed and executive functioning, spatial working memory span. Hence, VR is a safe, enjoyable, effective and more ecological alternative for the assessment of attention and inhibition among individuals with elevated anxiety and depression symptoms.
Databáze: OpenAIRE