Virtual reality technology in the detection of mild cognitive impairment: A systematic review and meta-analysis

Diagnostic performance of self-administered unsupervised computerized cognitive tests for the identification of mild cognitive impairment (MCI) and dementia: a systematic review and meta-analysis

OBJECTIVES

Self-administered, unsupervised computerized tools for community-based early cognitive screening are gradually being developed and applied. This study aims to evaluate the diagnostic performance and system usability of those tools in detecting MCI and dementia in older adults.

METHOD

Five electronic databases were systematically searched from inception to 17 August 2023. The included studies were reported using the PRISMA 2020 guideline, the risk of bias and applicability was assessed using QUADAS-2.

RESULTS

28 articles were included in the systematic review and 21 studies in the meta-analysis. The pooled sensitivity and specificity for unsupervised computerized tools were 0.84 (95% CI: 0.81 - 0.87) and 0.86 (95% CI: 0.82 - 0.89) respectively. Meta-regression showed that mean age, cognitive status, cognitive domains and administration time might be responsible for heterogeneity. Notably, tools taking less than 5 min had a pooled sensitivity of 0.85 (95% CI: 0.81-0.89). However, only 3 studies evaluated feasibility and system usability in the intended clinical settings.

CONCLUSION

Unsupervised computerized cognitive screening tools demonstrate good diagnostic performance for MCI/dementia, and tools taking less than 5 min appear particularly suitable for large-scale cognitive screening. Future research should combine various cognitive data to develop multimodal screening tools with better diagnostic performance, evaluate these tools' usability and improve age-friendly designs.

The Effects of Virtual Reality-Based Reminiscence Therapies for Older Adults With Cognitive Impairment: Systematic Review

BACKGROUND

Reminiscence therapy (RT) is a commonly used nonpharmaceutical treatment for cognitive impairment. Artifacts or conversations are used in RT to recall individuals' memories and past experiences. Virtual reality (VR) has increasingly been used as an assistive technology during RT. However, the effects of VR-based RT (VR-RT) methods remain unclear, and insights into the related benefits and challenges are urgently needed.

OBJECTIVE

The study aims to systematically review the effects of VR-RTs for older adults with cognitive impairment.

METHODS

Seven databases (MEDLINE, Academic Search Premier, CINAHL, Web of Science, PubMed, the Cochrane Central Register of Controlled Trials, and ScienceDirect) were searched to identify relevant articles published from inception to August 10, 2023. Peer-reviewed publications that assessed the effect of VR-RTs (ie, using virtual clues to evoke participants' memories or past experiences) on cognitive-related outcomes were included. Two independent researchers conducted the literature search, review, and data extraction processes. A narrative synthesis approach was used to analyze the extracted data.

RESULTS

Of the 537 identified articles, 22 were ultimately included in the data analysis. The results revealed that VR-RTs could maintain cognitive status (4/4, 100%) and reduce anxiety (2/2, 100%) in older adults with cognitive impairment. Nevertheless, one study found a cognitive improvement after VR-RTs, whereas cognitive degradation was observed at a 3- to 6-month follow-up measure. Around 88% (7/8) of the included studies indicated that VR-RTs improved memory; however, the evidence regarding the beneficial effects of VR-RTs was limited in improving quality of life (1/4, 25%) and reducing apathy (0/2, 0%) and depression (1/3, 33%). The results indicated that VR-RTs are safe, engaging, acceptable, and satisfying for older adults with cognitive impairment. In VR scenarios, personalized stimulus materials related to the users' youth experiences were more effective for treating cognitive impairment than other stimulus materials.

CONCLUSIONS

The results of this systematic review demonstrate the potential benefits of VR-RT for older adults with cognitive impairment, especially in improving emotion and memory and maintaining cognitive status. VR-RT is also safe and enjoyable for older adults. However, due to the trial heterogeneity of included studies, we can only provide qualitative results instead of performing meta-analysis to quantify the effect size of VR-RTs. Thus, more randomized controlled trials are required to examine the designs and effects of VR-RTs for groups of older adults with specific needs.

Virtual reality in functional neurological disorder: a theoretical framework and research agenda for use in the real world

Functional neurological disorder (FND) is a common and disabling condition at the intersection of neurology and psychiatry. Despite remarkable progress over recent decades, the mechanisms of FND are still poorly understood and there are limited diagnostic tools and effective treatments. One potentially promising treatment modality for FND is virtual reality (VR), which has been increasingly applied to a broad range of conditions, including neuropsychiatric disorders. FND has unique features, many of which suggest the particular relevance for, and potential efficacy of, VR in both better understanding and managing the disorder. In this review, we describe how VR might be leveraged in the treatment and diagnosis of FND (with a primary focus on motor FND and persistent perceptual-postural dizziness given their prominence in the literature), as well as the elucidation of neurocognitive mechanisms and symptom phenomenology. First, we review what has been published to date on the applications of VR in FND and related neuropsychiatric disorders. We then discuss the hypothesised mechanism(s) underlying FND, focusing on the features that are most relevant to VR applications. Finally, we discuss the potential of VR in (1) advancing mechanistic understanding, focusing specifically on sense of agency, attention and suggestibility, (2) overcoming diagnostic challenges and (3) developing novel treatment modalities. This review aims to develop a theoretical foundation and research agenda for the use of VR in FND that might be applicable or adaptable to other related disorders.

The effectiveness of brief reminiscence-based psychosocial interventions for cancer patients: A systematic review and meta-analysis

AIM

To determine the effectiveness of brief reminiscence-based psychosocial interventions in alleviating psychological distress in cancer patients.

BACKGROUND

Cancer patients suffer tremendous psycho-spiritual pain, which affects their quality of life. Brief reminiscence-based psychosocial interventions have demonstrated positive effects on the mental health of cancer patients; however, the efficacy of these interventions has been inconsistent.

DESIGN

A systematic review and meta-analysis.

METHODS

This review was conducted and reported in accordance with the PRISMA 2020 checklist provided by the EQUATOR network. The Cochrane Library, Web of Science, PsycINFO, PubMed, Embase, CINAHL and Scopus databases were systematically searched from inception to 27 November 2022 to identify randomised controlled trials (RCTs) published in English.

RESULTS

Twenty studies involving 1744 cancer participants were included. The meta-analysis showed statistically significant effects of brief reminiscence-based psychosocial interventions on hope, anxiety and depression at post-intervention. A separate analysis revealed that brief reminiscence-based psychosocial interventions had a sustainable effect on hope, spiritual well-being, anxiety and depression at 1 month after the intervention. However, no statistically significant effect on quality of life was found in our study either immediately after the intervention or at 1 month.

CONCLUSIONS

Brief reminiscence-based psychosocial interventions can significantly reduce anxiety and depressive symptoms and improve hope and spiritual well-being in cancer patients.

RELEVANCE TO CLINICAL PRACTICE

This study further supports that brief reminiscence-based psychosocial interventions should be incorporated into the routine care of cancer patients to address their psychosocial distress.

PATIENT OR PUBLIC CONTRIBUTION

All authors of this article contributed to the study conception and design. All authors of the included studies provided original data for this paper.

The Effects of Virtual Reality Training on Cognition in Older Adults: A Systematic Review, Meta-Analysis, and Meta-Regression of Randomized Controlled Trials

The aim of this systematic review, meta-analysis, and meta-regression was to examine the effects of virtual reality-based training on global cognition and executive function compared with conventional training or information-based treatment in older adults, regardless of cognitive level. A systematic literature search was conducted using four databases. A total of 31 randomized controlled trials were identified. Pooled effect sizes were calculated, the risk of bias was assessed, and evidence was graded. The primary analyses showed a small but statistically significant effect of virtual reality-based training compared with control on global cognition (Hedges' g 0.42, 95% confidence interval [0.17, 0.68], I2 = 70.1%, n = 876, 20 randomized controlled trials, low evidence) and executive function (Hedges' g 0.35, 95% confidence interval [0.06, 0.65], I2 = 68.4%, n = 810, 16 randomized controlled trials, very low evidence). Meta-regression yielded inconclusive results. Virtual reality-based training may be more effective than control in improving cognition in older adults; however, more high-quality studies are needed.

Psychometric Evaluation of a Tablet-Based Tool to Detect Mild Cognitive Impairment in Older Adults: Mixed Methods Study

BACKGROUND

With the rapid aging of the global population, the prevalence of mild cognitive impairment (MCI) and dementia is anticipated to surge worldwide. MCI serves as an intermediary stage between normal aging and dementia, necessitating more sensitive and effective screening tools for early identification and intervention. The BrainFx SCREEN is a novel digital tool designed to assess cognitive impairment. This study evaluated its efficacy as a screening tool for MCI in primary care settings, particularly in the context of an aging population and the growing integration of digital health solutions.

OBJECTIVE

The primary objective was to assess the validity, reliability, and applicability of the BrainFx SCREEN (hereafter, the SCREEN) for MCI screening in a primary care context. We conducted an exploratory study comparing the SCREEN with an established screening tool, the Quick Mild Cognitive Impairment (Qmci) screen.

METHODS

A concurrent mixed methods, prospective study using a quasi-experimental design was conducted with 147 participants from 5 primary care Family Health Teams (FHTs; characterized by multidisciplinary practice and capitated funding) across southwestern Ontario, Canada. Participants included health care practitioners, patients, and FHT administrative executives. Individuals aged ≥55 years with no history of MCI or diagnosis of dementia rostered in a participating FHT were eligible to participate. Participants were screened using both the SCREEN and Qmci. The study also incorporated the Geriatric Anxiety Scale-10 to assess general anxiety levels at each cognitive screening. The SCREEN's scoring was compared against that of the Qmci and the clinical judgment of health care professionals. Statistical analyses included sensitivity, specificity, internal consistency, and test-retest reliability assessments.

RESULTS

The study found that the SCREEN's longer administration time and complex scoring algorithm, which is proprietary and unavailable for independent analysis, presented challenges. Its internal consistency, indicated by a Cronbach α of 0.63, was below the acceptable threshold. The test-retest reliability also showed limitations, with moderate intraclass correlation coefficient (0.54) and inadequate κ (0.15) values. Sensitivity and specificity were consistent (63.25% and 74.07%, respectively) between cross-tabulation and discrepant analysis. In addition, the study faced limitations due to its demographic skew (96/147, 65.3% female, well-educated participants), the absence of a comprehensive gold standard for MCI diagnosis, and financial constraints limiting the inclusion of confirmatory neuropsychological testing.

CONCLUSIONS

The SCREEN, in its current form, does not meet the necessary criteria for an optimal MCI screening tool in primary care settings, primarily due to its longer administration time and lower reliability. As the number of digital health technologies increases and evolves, further testing and refinement of tools such as the SCREEN are essential to ensure their efficacy and reliability in real-world clinical settings. This study advocates for continued research in this rapidly advancing field to better serve the aging population.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/25520.

Integrating Biomarkers From Virtual Reality and Magnetic Resonance Imaging for the Early Detection of Mild Cognitive Impairment Using a Multimodal Learning Approach: Validation Study

BACKGROUND

Early detection of mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer disease, is crucial for preventing the progression of dementia. Virtual reality (VR) biomarkers have proven to be effective in capturing behaviors associated with subtle deficits in instrumental activities of daily living, such as challenges in using a food-ordering kiosk, for early detection of MCI. On the other hand, magnetic resonance imaging (MRI) biomarkers have demonstrated their efficacy in quantifying observable structural brain changes that can aid in early MCI detection. Nevertheless, the relationship between VR-derived and MRI biomarkers remains an open question. In this context, we explored the integration of VR-derived and MRI biomarkers to enhance early MCI detection through a multimodal learning approach.

OBJECTIVE

We aimed to evaluate and compare the efficacy of VR-derived and MRI biomarkers in the classification of MCI while also examining the strengths and weaknesses of each approach. Furthermore, we focused on improving early MCI detection by leveraging multimodal learning to integrate VR-derived and MRI biomarkers.

METHODS

The study encompassed a total of 54 participants, comprising 22 (41%) healthy controls and 32 (59%) patients with MCI. Participants completed a virtual kiosk test to collect 4 VR-derived biomarkers (hand movement speed, scanpath length, time to completion, and the number of errors), and T1-weighted MRI scans were performed to collect 22 MRI biomarkers from both hemispheres. Analyses of covariance were used to compare these biomarkers between healthy controls and patients with MCI, with age considered as a covariate. Subsequently, the biomarkers that exhibited significant differences between the 2 groups were used to train and validate a multimodal learning model aimed at early screening for patients with MCI among healthy controls.

RESULTS

The support vector machine (SVM) using only VR-derived biomarkers achieved a sensitivity of 87.5% and specificity of 90%, whereas the MRI biomarkers showed a sensitivity of 90.9% and specificity of 71.4%. Moreover, a correlation analysis revealed a significant association between MRI-observed brain atrophy and impaired performance in instrumental activities of daily living in the VR environment. Notably, the integration of both VR-derived and MRI biomarkers into a multimodal SVM model yielded superior results compared to unimodal SVM models, achieving higher accuracy (94.4%), sensitivity (100%), specificity (90.9%), precision (87.5%), and F1-score (93.3%).

CONCLUSIONS

The results indicate that VR-derived biomarkers, characterized by their high specificity, can be valuable as a robust, early screening tool for MCI in a broader older adult population. On the other hand, MRI biomarkers, known for their high sensitivity, excel at confirming the presence of MCI. Moreover, the multimodal learning approach introduced in our study provides valuable insights into the improvement of early MCI detection by integrating a diverse set of biomarkers.

Efficacy of virtual reality technology interventions for cognitive and mental outcomes in older people with cognitive disorders: An umbrella review comprising meta-analyses of randomized controlled trials

We conducted an umbrella review of virtual reality (VR) technology interventions and cognitive improvement in older adults with cognitive disorders to establish a hierarchy of evidence. We systematically searched PubMed, Web of Science, Scopus, and PsycINFO databases from database creation to February 2023. We included meta-analyses relevant to our study objectives for the overall review. We assessed the methodological quality according to AMSTAR2, and we used the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) method to assess the credibility of the evidence. This overall review was registered with the International Prospective Register of Systematic Reviews (CRD42023423063). We identified six meta-analyses that included 12 cognitive outcomes, but only memory (Standardized Mean Difference(SMD) = 0.27, 95% confidence interval (CI): 0.04 to 0.49), depression (SMD = -1.26, 95% CI: -1.8 to -0.72), and global cognition (SMD = 0.42, 95% CI: 0.18 to 0.66) improved through the VR technology intervention. Using the 95% prediction interval (PI) results, we found that VR technology did not significantly affect the cognitive abilities of people with cognitive decline despite increasing the subject size. We conclude that the VR technology intervention improved only specific cognitive abilities.

Being in Virtual Reality and Its Influence on Brain Health-An Overview of Benefits, Limitations and Prospects

BACKGROUND

Dynamic technological development and its enormous impact on modern societies are posing new challenges for 21st-century neuroscience. A special place is occupied by technologies based on virtual reality (VR). VR tools have already played a significant role in both basic and clinical neuroscience due to their high accuracy, sensitivity and specificity and, above all, high ecological value.

OBJECTIVE

Being in a digital world affects the functioning of the body as a whole and its individual systems. The data obtained so far, both from experimental and modeling studies, as well as (clinical) observations, indicate their great and promising potential, but apart from the benefits, there are also losses and negative consequences for users.

METHODS

This review was conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework across electronic databases (such as Web of Science Core Collection; PubMed; and Scopus, Taylor & Francis Online and Wiley Online Library) to identify beneficial effects and applications, as well as adverse impacts, especially on brain health in human neuroscience.

RESULTS

More than half of these articles were published within the last five years and represent state-of-the-art approaches and results (e.g., 54.7% in Web of Sciences and 63.4% in PubMed), with review papers accounting for approximately 16%. The results show that in addition to proposed novel devices and systems, various methods or procedures for testing, validation and standardization are presented (about 1% of articles). Also included are virtual developers and experts, (bio)(neuro)informatics specialists, neuroscientists and medical professionals.

CONCLUSIONS

VR environments allow for expanding the field of research on perception and cognitive and motor imagery, both in healthy and patient populations. In this context, research on neuroplasticity phenomena, including mirror neuron networks and the effects of applied virtual (mirror) tasks and training, is of interest in virtual prevention and neurogeriatrics, especially in neurotherapy and neurorehabilitation in basic/clinical and digital neuroscience.

Digital Marker for Early Screening of Mild Cognitive Impairment Through Hand and Eye Movement Analysis in Virtual Reality Using Machine Learning: First Validation Study

BACKGROUND

With the global rise in Alzheimer disease (AD), early screening for mild cognitive impairment (MCI), which is a preclinical stage of AD, is of paramount importance. Although biomarkers such as cerebrospinal fluid amyloid level and magnetic resonance imaging have been studied, they have limitations, such as high cost and invasiveness. Digital markers to assess cognitive impairment by analyzing behavioral data collected from digital devices in daily life can be a new alternative. In this context, we developed a "virtual kiosk test" for early screening of MCI by analyzing behavioral data collected when using a kiosk in a virtual environment.

OBJECTIVE

We aimed to investigate key behavioral features collected from a virtual kiosk test that could distinguish patients with MCI from healthy controls with high statistical significance. Also, we focused on developing a machine learning model capable of early screening of MCI based on these behavioral features.

METHODS

A total of 51 participants comprising 20 healthy controls and 31 patients with MCI were recruited by 2 neurologists from a university hospital. The participants performed a virtual kiosk test-developed by our group-where we recorded various behavioral data such as hand and eye movements. Based on these time series data, we computed the following 4 behavioral features: hand movement speed, proportion of fixation duration, time to completion, and the number of errors. To compare these behavioral features between healthy controls and patients with MCI, independent-samples 2-tailed t tests were used. Additionally, we used these behavioral features to train and validate a machine learning model for early screening of patients with MCI from healthy controls.

RESULTS

In the virtual kiosk test, all 4 behavioral features showed statistically significant differences between patients with MCI and healthy controls. Compared with healthy controls, patients with MCI had slower hand movement speed (t49=3.45; P=.004), lower proportion of fixation duration (t49=2.69; P=.04), longer time to completion (t49=-3.44; P=.004), and a greater number of errors (t49=-3.77; P=.001). All 4 features were then used to train a support vector machine to distinguish between healthy controls and patients with MCI. Our machine learning model achieved 93.3% accuracy, 100% sensitivity, 83.3% specificity, 90% precision, and 94.7% F1-score.

CONCLUSIONS

Our research preliminarily suggests that analyzing hand and eye movements in the virtual kiosk test holds potential as a digital marker for early screening of MCI. In contrast to conventional biomarkers, this digital marker in virtual reality is advantageous as it can collect ecologically valid data at an affordable cost and in a short period (5-15 minutes), making it a suitable means for early screening of MCI. We call for further studies to confirm the reliability and validity of this approach.