TY - JOUR
T1 - A Novel Virtual Reality Assessment of Functional Cognition
T2 - Validation Study
AU - Porffy, Lilla Alexandra
AU - Mehta, Mitul A.
AU - Patchitt, Joel
AU - Boussebaa, Celia
AU - Brett, Jack
AU - D’Oliveira, Teresa
AU - Mouchlianitis, Elias
AU - Shergill, Sukhi S.
N1 - Funding Information:
The authors would like to thank Dr Dan W Joyce for his comments and suggestions on the data analysis. LAP is supported by the UK Medical Research Council (MR/N013700/1) and is a King’s College London member of the Medical Research Council Doctoral Training Partnership in Biomedical Sciences. SSS is supported by the National Institute for Health Research Maudsley Biomedical Research Centre.
Publisher Copyright:
© 2022 Journal of Medical Internet Research. All rights reserved.
PY - 2022/1
Y1 - 2022/1
N2 - 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.
AB - 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.
KW - Cognition
KW - Functional capacity
KW - Functional cognition
KW - Neuropsychological testing
KW - Virtual reality
KW - Virtual reality assessment
UR - http://www.scopus.com/inward/record.url?scp=85123878120&partnerID=8YFLogxK
U2 - 10.2196/27641
DO - 10.2196/27641
M3 - Article
C2 - 35080501
AN - SCOPUS:85123878120
SN - 1438-8871
VL - 24
JO - Journal of Medical Internet Research
JF - Journal of Medical Internet Research
IS - 1
M1 - e27641
ER -