A Novel Virtual Reality Assessment of Functional Cognition: Validation Study

Abstract

Background Cognitive deficits are present in several neuropsychiatric disorders, including Alzheimer disease, schizophrenia, and depression. Assessments used to measure cognition in these disorders are time-consuming, burdensome, and have low ecological validity. To address these limitations, we developed a novel virtual reality shopping task—VStore. Objective This study aims to establish the construct validity of VStore in relation to the established computerized cognitive battery, Cogstate, and explore its sensitivity to age-related cognitive decline. Methods A total of 142 healthy volunteers aged 20-79 years participated in the study. The main VStore outcomes included verbal recall of 12 grocery items, time to collect items, time to select items on a self-checkout machine, time to make the payment, time to order coffee, and total completion time. Construct validity was examined through a series of backward elimination regression models to establish which Cogstate tasks, measuring attention, processing speed, verbal and visual learning, working memory, executive function, and paired associate learning, in addition to age and technological familiarity, best predicted VStore performance. In addition, 2 ridge regression and 2 logistic regression models supplemented with receiver operating characteristic curves were built, with VStore outcomes in the first model and Cogstate outcomes in the second model entered as predictors of age and age cohorts, respectively. Results Overall VStore performance, as indexed by the total time spent completing the task, was best explained by Cogstate tasks measuring attention, working memory, paired associate learning, and age and technological familiarity, accounting for 47% of the variance. In addition, with λ=5.16, the ridge regression model selected 5 parameters for VStore when predicting age (mean squared error 185.80, SE 19.34), and with λ=9.49 for Cogstate, the model selected all 8 tasks (mean squared error 226.80, SE 23.48). Finally, VStore was found to be highly sensitive (87%) and specific (91.7%) to age cohorts, with 94.6% of the area under the receiver operating characteristic curve. Conclusions Our findings suggest that VStore is a promising assessment that engages standard cognitive domains and is sensitive to age-related cognitive decline.

Alpha3/alpha2 power ratios relate to performance on a virtual reality shopping task in ageing adults

“…3389/fnagi.2022.876832 FIGURE 4 Correlations between VStore Total Time and α3/α2 ratios stratified by tertiles. results confirm our previous findings (Porffy et al, 2022a), adding to a body of evidence that VR assessments may have an increased sensitivity in ageing healthy adults (Negut et al, 2016b). Indeed, the older cohort were slower to complete all VStore tasks, except making the payment.…”

“…Indeed, the older cohort were slower to complete all VStore tasks, except making the payment. The VStore Pay outcome -engaging working memory, executive functions, verbal learning, and requiring fast processing speed -was amongst the weakest predictors of age as a continuous outcome in the previous study (Porffy et al, 2022a). This outcome, therefore, is likely to be less sensitive in ARCD.…”

“…Alternatively, immediate recall may not be as sensitive to cognitive decline as delayed recall (Gomar et al, 2011). Taken together, these findings suggest that assessments embedded in VR are sensitive to cognitive decline associated with ageing (Negut et al, 2016b;Oliveira et al, 2018;Porffy et al, 2022a), and also potentially provide valid concurrent measurement of everyday functioning (Romero-Ayuso et al, 2021); therefore, utilising this technology may enhance the early detection of subtle changes in cognition and related functional decline.…”

“…Participants were excluded if they had (1) a diagnosis of any Axis I disorder (DSM-5) (American Psychiatric Association, 2013), (2) alcohol and/or substance use disorder, (3) clinically significant motion sickness, (4) a neurological illness, (5) mobility issues, or (6) were pregnant. The sample size was determined based on the difference in overall VStore performance in our previous study (Porffy et al, 2022a) -see Supplementary Material 1. Hence, we decided to recruit 20 participant per age group.…”

“…For example, age was the strongest predictor of performance on a recently developed VR test, ECO-VR, as opposed to education level or vocabulary skills (Oliveira et al, 2018). We recently reported that a novel VR shopping task, VStore, engages cognitive domains implicated in ARCD and AD, and is highly sensitive to predicting chronological age, both as a continuous and dichotomous outcome in healthy adults (Porffy et al, 2022a).…”