Virtual-reality-based attention assessment of ADHD: ClinicaVR: Classroom-CPT versus a traditional continuous performance test

Abstract

Virtual-reality-based assessment may be a good alternative to classical or computerized neuropsychological assessment due to increased ecological validity. ClinicaVR: Classroom-CPT (VC) is a neuropsychological test embedded in virtual reality that is designed to assess attention deficits in children with attention deficit hyperactivity disorder (ADHD) or other conditions associated with impaired attention. The present study aimed to (1) investigate the diagnostic validity of VC in comparison to a traditional continuous performance test (CPT), (2) explore the task difficulty of VC, (3) address the effect of distractors on the performance of ADHD participants and typically-developing (TD) controls, and (4) compare the two measures on cognitive absorption. A total of 33 children diagnosed with ADHD and 42 TD children, aged between 7 and 13 years, participated in the study and were tested with a traditional CPT or with VC, along with several cognitive measures and an adapted version of the Cognitive Absorption Scale. A mixed multivariate analysis of covariance (MANCOVA) revealed that the children with ADHD performed worse on correct responses had more commissions and omissions errors than the TD children, as well as slower target reaction times . The results showed significant differences between performance in the virtual environment and the traditional computerized one, with longer reaction times in virtual reality. The data analysis highlighted the negative influence of auditory distractors on attention performance in the case of the children with ADHD, but not for the TD children. Finally, the two measures did not differ on the cognitive absorption perceived by the children.

Virtual Reality-based Assessment and Rating Scales in ADHD Diagnosis

“…In this sense, as many researchers remark (Epstein et al 2011;Negut, Jurma, & David, 2017;Nolin et al, 2016;Parsey & Schmitter-Edgecombe, 2013) clinical application of Virtual Reality (VR) certainly provides new opportunities for assessment, such as customization for target populations, specific cognitive domains, and unique settings. VR scenarios allow for measurement of simulated everyday tasks in a safe and controlled environment and offer the opportunity to assess the influence of environmental stimuli (e.g., distractions, interruptions) on cognitive performance, which may provide a more ecologically valid assessment of everyday skills (Díaz-Orueta, 2017; Parsey & Schmitter-Edgecombe, 2013).…”

“…When it comes to VR applied to the Continuous Performance Test (CPT) (considered the most accurate tool to measure attentional processes) (Epstein et al, 2011;Negut et al, 2017;Negut, Matu, Sava, & David, 2017;Nolin et. al.…”

“…However, a recent meta-analysis conducted by Negut et al (2017) pointed out that VR measures are more complex and difficult because they replicate conditions similar to everyday life and, as a consequence, performance obtained on these tests is usually poorer than on classical measures. Thus, tasks embedded in VR may have an increased level of complexity and difficulty that requires additional cognitive resources.…”

Human Bond Communications: Architectures, Challenges, and Possibilities

“…The virtual classroom developed by Rizzo depicts a good measure for the diagnosis and characterization of ADHD symptoms (see D 17 in Table I.). Virtual Reality provides a more realistic and ecologically-valid assessment environment [11]. Ecological validity describes the degree to which a psychological test offers results similar to those found in real life [11].…”

Predictive Modelling of The Dynamic Patterns of Thinking in Attention-Deficit/Hyperactivity Disorder: Diagnostic Accuracy of Spatiotemporal Fractal Measures

“…This review included other existing methods proposed to diagnose ADHD, that were not covered in the former systematic review. To make the indirect comparison possible, this in-house scoping review included only diagnostic accuracy studies that reported the chosen metrics of diagnostic for methods based on; electroencephalography and eventrelated potentials (Marcano et al, 2017;Loo et al, 2016;Snyder et al, 2015;Mohammadi et al, 2016;Gloss et al, 2016;Biederman et al, 2017;Gamma and Kara, 2016;Marcano et al, 2018;Manouilenko et al, 2017), structural and functional neuroimaging (Iannaccone et al, 2015;Rangarajan et al, 2014;de Celis Alonso et al, 2017;Qureshi et al, 2017;Serrallach et al, 2016;Hasaneen et al, 2017;Tan et al, 2017b;Uddin et al, 2017), simulated virtual reality and computer games (Negut et al, 2017(Negut et al, , 2016Berger et al, 2017;Faraone et al, 2016), and peripheral biochemical markers (Faraone et al, 2014;Scassellati and Bonvicini, 2015;Scassellati et al, 2012;Thome et al, 2012). According with this complementary "scoping review", the method presented in this research, seems to outperforms all the diagnostic accuracy metrics reported in the trials scrutinised in the aforementioned review.…”