The application scenarios study on the intervention of cognitive decline in elderly population using metaverse technology_Journal of Biomedical Engineering

Authors：

ZHOU Defu ¹ , JIN Yi ¹ ,  CHEN Ying ²

1. Suzhou Vocational University Jiangsu Province Support Software Engineering R&D Center for Modern Information Technology Application in Enterprise, Suzhou, Jiangsu 215104, P. R. China;
2. Wuxi Maternity and Child Health Care Hospital, Women’s Hospital of Jiangnan University, Wuxi, Jiangsu 214002, P. R. China;

Corresponding author：

Keywords：

Cognitive decline; Mental health; Artificial intelligence; Metaverse in medicine

DOI：

10.7507/1001-5515.202208092

Video：

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China is facing the peak of an ageing population, and there is an increase in demand for intelligent healthcare services for the elderly. The metaverse, as a new internet social communication space, has shown infinite potential for application. This paper focuses on the application of the metaverse in medicine in the intervention of cognitive decline in the elderly population. The problems in assessment and intervention of cognitive decline in the elderly group were analyzed. The basic data required to construct the metaverse in medicine was introduced. Moreover, it is demonstrated that the elderly users can conduct self-monitoring, experience immersive self-healing and health-care through the metaverse in medicine technology. Furthermore, we proposed that it is feasible that the metaverse in medicine has obvious advantages in prediction and diagnosis, prevention and rehabilitation, as well as assisting patients with cognitive decline. Risks for its application were pointed out as well. The metaverse in medicine technology solves the problem of non-face-to-face social communication for elderly users, which may help to reconstruct the social medical system and service mode for the elderly population.

Citation： ZHOU Defu, JIN Yi, CHEN Ying. The application scenarios study on the intervention of cognitive decline in elderly population using metaverse technology. Journal of Biomedical Engineering, 2023, 40(3): 573-581. doi: 10.7507/1001-5515.202208092 Copy

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25.	Graham S, Depp C, Lee E E, et al. Artificial intelligence for mental health and mental illnesses: an overview. Curr Psychiatry Rep, 2019, 21(11): 116.
26.	Lee E E, Torous J, De Choudhury M, et al. Artificial intelligence for mental health care: clinical applications, barriers, facilitators, and artificial wisdom. Biol Psychiatry Cogn Neurosci Neuroimaging, 2021, 6(9): 856-864.
27.	Chi C L, Zeng W, Oh W, et al. Personalized long-term prediction of cognitive function: using sequential assessments to improve model performance. J Biomed Inform, 2017, 76: 78-86.
28.	Chun C T, Seward K, Patterson A, et al. Evaluation of available cognitive tools used to measure mild cognitive decline: a scoping review. Nutrients, 2021, 13(11): 3974.
29.	Eshuis L V, van Gelderen M J, van Zuiden M, et al. Efficacy of immersive PTSD treatments: a systematic review of virtual and augmented reality exposure therapy and a meta-analysis of virtual reality exposure therapy. J Psychiatr Res, 2021, 143: 516-527.
30.	Philippe T J, Sikder N, Jackson A, et al. Digital health interventions for delivery of mental health care: systematic and comprehensive meta-review. JMIR Ment Health, 2022, 9(5): e35159.
31.	Zhu S, Sui Y, Shen Y, et al. Effects of virtual reality intervention on cognition and motor function in older adults with mild cognitive impairment or dementia: a systematic review and meta-analysis. Front Aging Neurosci, 2021, 13: 586999.
32.	Amirthalingam J, Paidi G, Alshowaikh K, et al. Virtual reality intervention to help improve motor function in patients undergoing rehabilitation for cerebral palsy, Parkinson's disease, or stroke: a systematic review of randomized controlled trials. Cureus, 2021, 13(7): e16763.
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1. Shivkumar V, Subramanian T, Agarwal P, et al. Uptake of telehealth in Parkinson's disease clinical care and research during the COVID-19 pandemic. Parkinsonism Relat Disord, 2021, 86: 97-100.
2. Larson D N, Schneider R B, Simuni T. A new era: the growth of video-based visits for remote management of persons with Parkinson's disease. J Parkinsons Dis, 2021, 11(s1): S27-S34.
3. Jordà-Siquier T, Petrel M, Kouskoff V, et al. APP accumulates with presynaptic proteins around amyloid plaques: A role for presynaptic mechanisms in Alzheimer's disease?. Alzheimers Dement, 2022, 18(11): 2099-2116.
4. Tracy T E, Madero-Pérez J, Swaney D L, et al. Tau interactome maps synaptic and mitochondrial processes associated with neurodegeneration. Cell, 2022, 185(4): 712-728.
5. Qazi T J, Quan Z, Mir A, et al. Epigenetics in Alzheimer’s disease: perspective of DNA methylation. Mol Neurobiol, 2018, 55(2): 1026-1044.
6. Wesenhagen K E J, Teunissen C E, Visser P J, et al. Cerebrospinal fluid proteomics and biological heterogeneity in Alzheimer's disease: A literature review. Crit Rev Clin Lab Sci, 2020, 57(2): 86-98.
7. Gal J, Katsumata Y, Zhu H, et al. Apolipoprotein E proteinopathy is a major dementia-associated pathologic biomarker in individuals with or without the APOE epsilon 4 allele. Am J Pathol, 2022, 192(3): 564-578.
8. Mostafavi S, Gaiteri C, Sullivan S E, et al. A molecular network of the aging human brain provides insights into the pathology and cognitive decline of Alzheimer's disease. Nat Neurosci, 2018, 21(6): 811-819.
9. Filograna R, Mennuni M, Alsina D, et al. Mitochondrial DNA copy number in human disease: the more the better?. FEBS Lett, 2021, 595(8): 976-1002.
10. Mathys H, Davila-Velderrain J, Peng Z, et al. Single-cell transcriptomic analysis of Alzheimer's disease. Nature, 2019, 570(7761): 332-337.
11. 车伯琛, 阴慧娟, 吴金鹏, 等. 阿尔茨海默病实验室诊断技术的研究进展. 国际生物医学工程杂志, 2020, 43(5): 412-417, 424.
12. Varesi A, Carrara A, Pires V G, et al. Blood-based biomarkers for Alzheimer's disease diagnosis and progression: an overview. Cells, 2022, 11(8): 1367.
13. Teunissen C E, Verberk I M W, Thijssen E H, et al. Blood-based biomarkers for Alzheimer's disease: towards clinical implementation. Lancet Neurol, 2022, 21(1): 66-77.
14. Wang X, Huang W, Su L, et al. Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer's disease. Mol Neurodegener, 2020, 15(1): 55.
15. Graham S A, Lee E E, Jeste D V, et al. Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review. Psychiatry Res, 2020, 284: 112732.
16. Bai B, Vanderwall D, Li Y, et al. Proteomic landscape of Alzheimer's disease: novel insights into pathogenesis and biomarker discovery. Mol Neurodegener, 2021, 16(1): 55.
17. Toth L, Hoffmann I, Gosztolya G, et al. A speech recognition-based solution for the automatic detection of mild cognitive impairment from spontaneous speech. Curr Alzheimer Res, 2018, 15(2): 130-138.
18. Richardson D P, Foxe J J, Mazurek K A, et al. Neural markers of proactive and reactive cognitive control are altered during walking: A mobile brain-body imaging (MoBI) study. Neuroimage, 2022, 247: 118853.
19. Huh S. Application of computer-based testing in the Korean medical licensing examination, the emergence of the metaverse in medical education, journal metrics and statistics, and appreciation to reviewers and volunteers. J Educ Eval Health Prof, 2022, 19: 2.
20. Locurcio L L. Dental education in the metaverse. Br Dent J, 2022, 232(4): 191.
21. Werner H, Ribeiro G, Arcoverde V, et al. The use of metaverse in fetal medicine and gynecology. Eur J Radiol, 2022, 150: 110241.
22. Barrett-Young A, Ambler A, Cheyne K, et al. Associations between retinal nerve fiber layer and ganglion cell layer in middle age and cognition from childhood to adulthood. JAMA Ophthalmol, 2022, 140(3): 262-268.
23. Uddin M, Wang Y, Woodbury-Smith M. Artificial intelligence for precision medicine in neurodevelopmental disorders. NPJ Digit Med, 2019, 2: 112.
24. Chaddad A, Li J, Lu Q, et al. Can autism be diagnosed with artificial intelligence? a narrative review. Diagnostics (Basel), 2021, 11(11): 2032.
25. Graham S, Depp C, Lee E E, et al. Artificial intelligence for mental health and mental illnesses: an overview. Curr Psychiatry Rep, 2019, 21(11): 116.
26. Lee E E, Torous J, De Choudhury M, et al. Artificial intelligence for mental health care: clinical applications, barriers, facilitators, and artificial wisdom. Biol Psychiatry Cogn Neurosci Neuroimaging, 2021, 6(9): 856-864.
27. Chi C L, Zeng W, Oh W, et al. Personalized long-term prediction of cognitive function: using sequential assessments to improve model performance. J Biomed Inform, 2017, 76: 78-86.
28. Chun C T, Seward K, Patterson A, et al. Evaluation of available cognitive tools used to measure mild cognitive decline: a scoping review. Nutrients, 2021, 13(11): 3974.
29. Eshuis L V, van Gelderen M J, van Zuiden M, et al. Efficacy of immersive PTSD treatments: a systematic review of virtual and augmented reality exposure therapy and a meta-analysis of virtual reality exposure therapy. J Psychiatr Res, 2021, 143: 516-527.
30. Philippe T J, Sikder N, Jackson A, et al. Digital health interventions for delivery of mental health care: systematic and comprehensive meta-review. JMIR Ment Health, 2022, 9(5): e35159.
31. Zhu S, Sui Y, Shen Y, et al. Effects of virtual reality intervention on cognition and motor function in older adults with mild cognitive impairment or dementia: a systematic review and meta-analysis. Front Aging Neurosci, 2021, 13: 586999.
32. Amirthalingam J, Paidi G, Alshowaikh K, et al. Virtual reality intervention to help improve motor function in patients undergoing rehabilitation for cerebral palsy, Parkinson's disease, or stroke: a systematic review of randomized controlled trials. Cureus, 2021, 13(7): e16763.
33. Liu Z, Ren L, Xiao C, et al. Virtual reality aided therapy towards health 4. 0: a two-decade bibliometric analysis. Int J Environ Res Public Health, 2022, 19(3): 1525.
34. Sakaki K, Nouchi R, Matsuzaki Y, et al. Benefits of VR physical exercise on cognition in older adults with and without mild cognitive decline: a systematic review of randomized controlled trials. Healthcare (Basel), 2021, 9(7): 883.

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