A Novel Virtual Reality Assessment of Functional Cognition (VStore): Validation Study

Lilla Porffy*, Mitul Mehta, Joel Patchitt, Celia Boussebaa, Jack Brett, Teresa D'Oliveira, Elias Mouchlianitis, Sukhi Shergill

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Background: Cognitive deficits are present in a number of neuropsychiatric disorders including, Alzheimer’s 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.
Objectives: 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: Hundred and forty-two healthy volunteers aged 20-79 took part in the study. Main VStore outcomes included: 1) verbal recall of 12 grocery items, 2) time to collect items, 3) time to select items on a self-checkout machine, 4) time to make the payment, 5) time to order coffee, and 6) 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. Also, two ridge regression and two logistic regression models supplemented with receiver operating characteristic curves were built; with VStore outcomes in the first, 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 lambda = 5.16, the ridge regression model selected five parameters for VStore when predicting age (MSE = 185.80, SE= 19.34). With lambda = 9.49 for Cogstate, the model selected all eight tasks (MSE = 226.80, SE = 23.48). Finally, VStore was found to be highly sensitive (86%) and specific (96%) to age cohorts with 95% 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 shows sensitivity to age-related cognitive decline.
Original languageEnglish
Pages (from-to)e27641
JournalJournal of International Medical Research
Issue number1
Publication statusAccepted/In press - 6 Oct 2021


  • Virtual reality
  • Virtual reality assessment
  • Cognition
  • Functional cognition
  • Functional capacity
  • Neuropsychological testing
  • VR


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