Evidence map›Paper›PMID 37063104›Full record

ArticleFrontiers in human neuroscience2023

A dual-task-embedded virtual reality system for intelligent quantitative assessment of cognitive processing speed.

Yuzhao Zhou, Yixuan Zhao, Zirui Xiang, Zhixin Yan, Lin Shu, Xiangmin Xu, Lulu Zhang, Xiang Tian

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in human neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07019207 (The Effect of Virtual Reality-Based Dual-Task Training on Cognition, Cortical Activation, and Functional Level in Patients With Spinal Cord Injury), which is not on this map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT07019207 nanot yet recruitingnot on this mapstarted 2025, after this paper: background citation

The Effect of Virtual Reality-Based Dual-Task Training on Cognition, Cortical Activation, and Functional Level in Patients With Spinal Cord Injury

TypeinterventionalSponsorIstinye UniversityRan2025 to 2026Enrolled45ConditionsSpinal Cord InjuryArmsVR-Based Dual-Task Training with Motor Imagery, VR-Based Dual-Task Training without Motor Imagery, Proprioceptive Neuromuscular Facilitation
3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Yuzhao ZhouSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou, China.
Yixuan ZhaoSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou, China.
Zirui XiangSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou, China.
Zhixin YanSchool of Future Technology, South China University of Technology, Guangzhou, China.
Lin ShuSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou, China.
Xiangmin XuSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou, China.
Lulu ZhangDepartment of Psychiatry, Guangzhou First People's Hospital, The Second Affiliated Hospital, South China University of Technology, Guangzhou, China.
Xiang TianSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Processing speed is defined as the ability to quickly process information, which is generally considered as one of the affected cognitive functions of multiple sclerosis and schizophrenia. Paper-pencil type tests are traditionally used in the assessment of processing speed. However, these tests generally need to be conducted under the guidance of clinicians in a specific environment, which limits their application in cognitive assessment or training in daily life. Therefore, this paper proposed an intelligent evaluation method of processing speed to assist clinicians in diagnosis. Methods: We created an immersive virtual street embedded with Stroop task (VR-Street). The behavior and performance information was obtained by performing the dual-task of street-crossing and Stroop, and a 50-participant dataset was established with the label of standard scale. Utilizing Pearson correlation coefficient to find the relationship between the dual-task features and the cognitive test results, and an intelligent evaluation model was developed using machine learning. Results: Statistical analysis showed that all Stroop task features were correlated with cognitive test results, and some behavior features also showed correlation. The estimated results showed that the proposed method can estimate the processing speed score with an adequate accuracy (mean absolute error of 0.800, relative accuracy of 0.916 and correlation coefficient of 0.804). The combination of Stroop features and behavior features showed better performance than single task features. Discussion: The results of this work indicates that the dual-task design in this study better mobilizes participants' attention and cognitive resources, and more fully reflects participants' cognitive processing speed. The proposed method provides a new opportunity for accurate quantitative evaluation of cognitive function through virtual reality.

Indexed as

behavior datacognitive processing speeddual-taskevaluationmachine learningvirtual reality

Identifiers

PMID37063104
PMCPMC10097903

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