The Use of a Virtual Reality Platform for the Assessment of the Memory Decline and the Hippocampal Neural Injury in Subjects with Mild Cognitive Impairment: The Validity of Smart Aging Serious Game (SASG)

An Ensemble Learning Algorithm for Cognitive Evaluation by an Immersive Virtual Reality Supermarket

Early screening for Mild Cognitive Impairment (MCI) is crucial in delaying cognitive deterioration and treating dementia. Conventional neuropsychological tests, commonly used for MCI detection, often lack ecological validity due to their simplistic and quiet testing environments. To address this gap, our study developed an immersive VR supermarket cognitive assessment program (IVRSCAP), simulating daily cognitive activities to enhance the ecological validity of MCI detection. This program involved elderly participants from Chengdu Second People's Hospital and various communities, comprising both MCI patients (N=301) and healthy elderly individuals (N=1027). They engaged in the VR supermarket cognitive test, generating complex datasets including User Behavior Data, Tested Cognitive Dimension Game Data, Trajectory Data, and Regional Data. To analyze this data, we introduced an adaptive ensemble learning method for imbalanced samples. Our study's primary contribution is demonstrating the superior performance of this algorithm in classifying MCI and healthy groups based on their performance in IVRSCAP. Comparative analysis confirmed its efficacy over traditional imbalanced sample processing methods and classic ensemble learning voting algorithms, significantly outperforming in metrics such as recall, F1-score, AUC, and G-mean. Our findings advocate the combined use of IVRSCAP and our algorithm as a technologically advanced, ecologically valid approach for enhancing early MCI detection strategies. This aligns with our broader aim of integrating realistic simulations with advanced computational techniques to improve diagnostic accuracy and treatment efficacy in cognitive health assessments.

Assessing and recovering Alzheimer's disease: a comparative analysis of standard neuropsychological approaches and virtual reality interventions with the use of digital storytelling

Background

Alzheimer's disease (AD), the most common form of dementia, is a progressive neurodegenerative disorder that predominantly affects the elderly population. Traditional assessment methods, including neuropsychological tests like the MMSE, have been the cornerstone of AD diagnosis for decades. These methods are grounded in a wealth of research and clinical experience, providing a robust framework for understanding the cognitive deficits of AD. The evolution of AD assessment and rehabilitation has recently been tackled with the introduction of Virtual Reality (VR) technologies.

Objectives

To evaluate the use of storytelling and reminiscence therapy in virtual reality programs as a complementary and enhancing modality alongside standard assessment and rehabilitation for Alzheimer's patients. To explore how regular interaction with VR narratives can slow cognitive decline or improve relevant features of cognitive functioning over the time. To propose a new assessment and rehabilitative tool based on the use of VR and digital storytelling.

Method

A comparative analysis of Standard Neuropsychological Approaches and Virtual Reality Interventions in patients with Alzheimer disorder was carried out. A literature overview on the empirical studies between 2019 and 2024 was conducted.

Results

We propose a new VR-based setup mediated by the use of storytelling for the assessment and recovery of AD.

Conclusion

The employment of storytelling within VR programs for the assessment and rehabilitation of Alzheimer's disease can positively impact both the cognitive and emotional realms of patients, with beneficial outcomes on caregivers' and families' burden. The successful implementation of this approach requires careful consideration of accessibility, data interpretation, and standard validation protocols.

Detection of Mild Cognitive Impairment Through Hand Motor Function Under Digital Cognitive Test: Mixed Methods Study

BACKGROUND

Early detection of cognitive impairment or dementia is essential to reduce the incidence of severe neurodegenerative diseases. However, currently available diagnostic tools for detecting mild cognitive impairment (MCI) or dementia are time-consuming, expensive, or not widely accessible. Hence, exploring more effective methods to assist clinicians in detecting MCI is necessary.

OBJECTIVE

In this study, we aimed to explore the feasibility and efficiency of assessing MCI through movement kinetics under tablet-based "drawing and dragging" tasks.

METHODS

We iteratively designed "drawing and dragging" tasks by conducting symposiums, programming, and interviews with stakeholders (neurologists, nurses, engineers, patients with MCI, healthy older adults, and caregivers). Subsequently, stroke patterns and movement kinetics were evaluated in healthy control and MCI groups by comparing 5 categories of features related to hand motor function (ie, time, stroke, frequency, score, and sequence). Finally, user experience with the overall cognitive screening system was investigated using structured questionnaires and unstructured interviews, and their suggestions were recorded.

RESULTS

The "drawing and dragging" tasks can detect MCI effectively, with an average accuracy of 85% (SD 2%). Using statistical comparison of movement kinetics, we discovered that the time- and score-based features are the most effective among all the features. Specifically, compared with the healthy control group, the MCI group showed a significant increase in the time they took for the hand to switch from one stroke to the next, with longer drawing times, slow dragging, and lower scores. In addition, patients with MCI had poorer decision-making strategies and visual perception of drawing sequence features, as evidenced by adding auxiliary information and losing more local details in the drawing. Feedback from user experience indicates that our system is user-friendly and facilitates screening for deficits in self-perception.

CONCLUSIONS

The tablet-based MCI detection system quantitatively assesses hand motor function in older adults and further elucidates the cognitive and behavioral decline phenomenon in patients with MCI. This innovative approach serves to identify and measure digital biomarkers associated with MCI or Alzheimer dementia, enabling the monitoring of changes in patients' executive function and visual perceptual abilities as the disease advances.

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.

A thematic review on therapeutic toys and games for the elderly with Alzheimer's disease

PURPOSE

Alzheimer's disease (AD) is a common and devastating neurological ailment that affects millions of the elderly worldwide. Therapeutic toys and games have emerged as potential non-pharmacological interventions for AD. However, despite a growing number of documents on the subject, research on the future direction of therapeutic toys and games for AD remains scarce. To address this gap, this study aims to (1) map the future trends of therapeutic toys and games for AD and (2) identify the categories and design characteristics.

MATERIALS AND METHODS

Using a thematic review framework, a systematic literature search was conducted in two electronic databases (Scopus and WoS) using established criteria. Thematic analysis was done using ATLAS.ti 23 to identify prominent themes, patterns and trends.

RESULTS

A total of 180 documents were found. Twenty-five articles met the inclusion criteria. A thematic review of these 25 articles identified 13 initial codes, which were been clustered into four themes: detection and evaluation; intervention; toy/game category; and design characteristics. The word "Cognitive" appears most frequently in documents according to word cloud.

CONCLUSIONS

Therapeutic toys and games are used to detect and as an intervention for AD. Most of the current studies focused on specific cognitive functions. More research is needed about play therapy for neuropsychiatric symptoms. This thematic review also proposed a conceptual framework for designing toys and games tailored to the needs of the elderly with AD, offering valuable insights to future researchers focusing on this domain.

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

BACKGROUND

In recent years, virtual reality technology has developed the potential to help in the early detection of mild cognitive impairment (MCI). However, integrative evidence of its detection performance for mild cognitive impairment is lacking, and meta-analysis or systematic reviews are required to further determine the effectiveness of virtual reality technology in screening for MCI.

METHODS

Literature searches were performed for MCI screening tests in the Cochrane Library, Web of Science, PsycINFO, PubMed, EMBASE, CINAHL, and Scopus. The primary outcome was the performance of VR tests for MCI detection. A protocol for this systematic review was registered in PROSPERO (Registration number: CRD42022302139).

RESULTS

A total of 14 studies in 13 reports were eventually included. The combined data with the bivariate random-effects model gave a summary point of 0.89 sensitivity (95 % confidence interval [CI]: 0.82-0.94) and 0.91 specificity (95 % CI: 0.82-0.96). The SROC curve was plotted, the DOR was 79.25 (95 % CI: 22.59-277.99), and the AUC was 0.95 (95 % CI: 0.93-0.97).

CONCLUSIONS

Virtual reality-based tests have shown considerable detection performance in detecting MCI, and therefore, virtual reality-based tests can serve as recommended screening methods. Future studies can consider longitudinal assessment and follow-up programs to identify progressive changes.

Virtual reality technology in neuropsychological testing: A systematic review

Neuropsychological testing aims to measure individuals' cognitive abilities (e.g. memory, attention), analysing their performance on specific behavioural tasks. Most neuropsychological tests are administered in the so-called 'paper-and-pencil' modality or via computerised protocols. The adequacy of these procedures has been recently questioned, with more specific concerns about their ecological validity, i.e. the relation between test scores observed in the laboratory setting and the actual everyday cognitive functioning. In developing more ecological tasks, researchers started to implement virtual reality (VR) technology as an administration technique focused on exposing individuals to simulated but realistic stimuli and environments, maintaining at the same time a controlled laboratory setting and collecting advanced measures of cognitive functioning. This systematic review aims to present how VR procedures for neuropsychological testing have been implemented in the last years. We initially explain the rationale for supporting VR as an advanced assessment tool, but we also discuss the challenges and risks that can limit the widespread implementation of this technology. Then, we systematised the large body of studies adopting VR for neuropsychological testing, describing the VR tools' distribution amongst different cognitive functions through a PRISMA-guided systematic review. The systematic review highlighted that only very few instruments are ready for clinical use, reporting psychometric proprieties (e.g. validity) and providing normative data. Most of the tools still need to be standardised on large cohorts of participants, having published only limited data on small samples up to now. Finally, we discussed the possible future directions of the VR neuropsychological test development linked to technological advances.

Digital Cognitive Biomarker for Mild Cognitive Impairments and Dementia: A Systematic Review

The dementia population is increasing as the world's population is growing older. The current systematic review aims to identify digital cognitive biomarkers from computerized tests for detecting dementia and its risk state of mild cognitive impairment (MCI), and to evaluate the diagnostic performance of digital cognitive biomarkers. A literature search was performed in three databases, and supplemented by a Google search for names of previously identified computerized tests. Computerized tests were categorized into five types, including memory tests, test batteries, other single/multiple cognitive tests, handwriting/drawing tests, and daily living tasks and serious games. Results showed that 78 studies were eligible. Around 90% of the included studies were rated as high quality based on the Newcastle-Ottawa Scale (NOS). Most of the digital cognitive biomarkers achieved comparable or even better diagnostic performance than traditional paper-and-pencil tests. Moderate to large group differences were consistently observed in cognitive outcomes related to memory and executive functions, as well as some novel outcomes measured by handwriting/drawing tests, daily living tasks, and serious games. These outcomes have the potential to be sensitive digital cognitive biomarkers for MCI and dementia. Therefore, digital cognitive biomarkers can be a sensitive and promising clinical tool for detecting MCI and dementia.

Predictive Accuracy of Digital Biomarker Technologies for Detection of Mild Cognitive Impairment and Pre-Frailty Amongst Older Adults: A Systematic Review and Meta-Analysis

Digital biomarker technologies coupled with predictive models are increasingly applied for early detection of age-related potentially reversible conditions including mild cognitive impairment (MCI) and pre-frailty (PF). We aimed to determine the predictive accuracy of digital biomarker technologies to detect MCI and PF with systematic review and meta-analysis. A computer-assisted search on major academic research databases including IEEE-Xplore was conducted. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines were adopted reporting in this study. Summary receiver operating characteristic curve based on random-effect bivariate model was used to evaluate overall sensitivity and specificity for detection of the respective age-related conditions. A total of 43 studies were selected for final systematic review and meta-analysis. 26 studies reported on detection of MCI with sensitivity and specificity of 0.48-1.00 and 0.55-1.00, respectively. On the other hand, there were 17 studies that reported on the detection of PF with reported sensitivity of 0.53-1.00 and specificity of 0.61-1.00. Meta-analysis further revealed pooled sensitivities of 0.84 (95% CI: 0.79-0.88) and 0.82 (95% CI: 0.74-0.88) for in-home detection of MCI and PF, respectively, while pooled specificities were 0.85 (95% CI: 0.80-0.89) and 0.82 (95% CI: 0.75-0.88), respectively. Besides MCI, and PF, in this work during systematic review, we also found one study which reported a sensitivity of 0.93 and a specificity of 0.57 for detection of cognitive frailty (CF). The meta-analytic result, for the first time, quantifies the predictive efficacy of digital biomarker technologies for detection of MCI and PF. Additionally, we found the number of studies for detection of CF to be notably lower, indicating possible research gaps to explore predictive models on digital biomarker technology for detection of CF.

Development and validity of computerized neuropsychological assessment devices for screening mild cognitive impairment: Ensemble of models with feature space heterogeneity and retrieval practice effect

OBJECTIVE

This study aimed to develop and validate computerized neuropsychological assessment devices for screening patients with mild cognitive impairment (MCI).

METHODS

We conducted this study in three phases. Phase I involved the development of a conceptual framework of Memory Guard (MG) based on the principles of the cognitive design system (CDS). Phase II involved three steps of feature engineering: item development, filter, and wrapper. Based on the initial items, the number of items in each dimension was determined through analytic hierarchy process. We constructed an initial set with a total of 198 items with three levels of difficulty. Next, we performed feature selection through comprehensive reliability and validity tests, which resulted in the best item bank of 38 test items. The features for modeling were obtained from the best item bank (option scores, reading time scores and total time scores), demographic variables and their MoCA groups. Regarding the heterogeneity of the feature space, we combined the AdaBoost with the Naive Bayes classification algorithm as the decision model of MG. For the screening tool to be used repeatedly, the retrieval practice effect was considered in the design. Phase III involved the validation of measuring instruments. The features incorporated into the modeling process were optimized based on the classification accuracy and area under curve. We also verified the classification effect of the other three classification models with MG.

RESULTS

After three steps of feature engineering, a total of 6 dimensions of cognitive areas were included in MG: orientation, memory, attention, calculation, recall, and language & executive function. 38 features were included in the model (17 features of option score, 20 features of time score, and 1 demographic feature). A total of 333 individuals from two communities in Shanghai and Henan province were included in the measuring instrument verification process. Women accounted for 68.2% of the sample. The median age was 63. 15.3% of the participants had bachelor's degrees or above and 111 participants lived in urban areas (33.3%). The results showed that MG had an accuracy of 93.75% and AUC of 0.923, with a sensitivity of 91.67% and a specificity of 95.45%. Compared to the other three classification models, MG that combined the AdaBoost with the Naive Bayes classification algorithm was the most accurate classifier.

CONCLUSIONS

MG was proved to be reliable and valid in early screening for patients with MCI. MG that integrated heterogeneous features such as demography, option scores, and time scores had a better predictive performance for screening MCI.

Diagnostic performance of digital cognitive tests for the identification of MCI and dementia: A systematic review

BACKGROUND

The use of digital cognitive tests is getting common nowadays. Older adults or their family members may use online tests for self-screening of dementia. However, the diagnostic performance across different digital tests is still to clarify. The objective of this study was to evaluate the diagnostic performance of digital cognitive tests for MCI and dementia in older adults.

METHODS

Literature searches were systematically performed in the OVID databases. Validation studies that reported the diagnostic performance of a digital cognitive test for MCI or dementia were included. The main outcome was the diagnostic performance of the digital test for the detection of MCI or dementia.

RESULTS

A total of 56 studies with 46 digital cognitive tests were included in this study. Most of the digital cognitive tests were shown to have comparable diagnostic performances with the paper-and-pencil tests. Twenty-two digital cognitive tests showed a good diagnostic performance for dementia, with a sensitivity and a specificity over 0.80, such as the Computerized Visuo-Spatial Memory test and Self-Administered Tasks Uncovering Risk of Neurodegeneration. Eleven digital cognitive tests showed a good diagnostic performance for MCI such as the Brain Health Assessment. However, all the digital tests only had a few validation studies to verify their performance.

CONCLUSIONS

Digital cognitive tests showed good performances for MCI and dementia. The digital test can collect digital data that is far beyond the traditional ways of cognitive tests. Future research is suggested on these new forms of cognitive data for the early detection of MCI and dementia.

Diagnostic Validity of the Smart Aging Serious Game: An Innovative Tool for Digital Phenotyping of Mild Neurocognitive Disorder

BACKGROUND

The Smart Aging Serious Game (SASG) is an ecologically-based digital platform used in mild neurocognitive disorders. Considering the higher risk of developing dementia for mild cognitive impairment (MCI) and vascular cognitive impairment (VCI), their digital phenotyping is crucial. A new understanding of MCI and VCI aided by digital phenotyping with SASG will challenge current differential diagnosis and open the perspective of tailoring more personalized interventions.

OBJECTIVE

To confirm the validity of SASG in detecting MCI from healthy controls (HC) and to evaluate its diagnostic validity in differentiating between VCI and HC.

METHODS

161 subjects (74 HC: 37 males, 75.47±2.66 mean age; 60 MCI: 26 males, 74.20±5.02; 27 VCI: 13 males, 74.22±3.43) underwent a SASG session and a neuropsychological assessment (Montreal Cognitive Assessment (MoCA), Free and Cued Selective Reminding Test, Trail Making Test). A multi-modal statistical approach was used: receiver operating characteristic (ROC) curves comparison, random forest (RF), and logistic regression (LR) analysis.

RESULTS

SASG well captured the specific cognitive profiles of MCI and VCI, in line with the standard neuropsychological measures. ROC analyses revealed high diagnostic sensitivity and specificity of SASG and MoCA (AUCs > 0.800) in detecting VCI versus HC and MCI versus HC conditions. An acceptable to excellent classification accuracy was found for MCI and VCI (HC versus VCI; RF: 90%, LR: 91%. HC versus MCI; RF: 75%; LR: 87%).

CONCLUSION

SASG allows the early assessment of cognitive impairment through ecological tasks and potentially in a self-administered way. These features make this platform suitable for being considered a useful digital phenotyping tool, allowing a non-invasive and valid neuropsychological evaluation, with evident implications for future digital-health trails and rehabilitation.

The Smart Aging Platform for Assessing Early Phases of Cognitive Impairment in Patients With Neurodegenerative Diseases

Background: Smart Aging is a serious game (SG) platform that generates a 3D virtual reality environment in which users perform a set of screening tasks designed to allow evaluation of global cognition. Each task replicates activities of daily living performed in a familiar environment. The main goal of the present study was to ascertain whether Smart Aging could differentiate between different types and levels of cognitive impairment in patients with neurodegenerative disease.

Methods: Ninety-one subjects (mean age = 70.29 ± 7.70 years)—healthy older adults (HCs, n = 23), patients with single-domain amnesic mild cognitive impairment (aMCI, n = 23), patients with single-domain executive Parkinson's disease MCI (PD-MCI, n = 20), and patients with mild Alzheimer's disease (mild AD, n = 25)—were enrolled in the study. All participants underwent cognitive evaluations performed using both traditional neuropsychological assessment tools, including the Mini-Mental State Examination (MMSE), Montreal Overall Cognitive Assessment (MoCA), and the Smart Aging platform. We analyzed global scores on Smart Aging indices (i.e., accuracy, time, distance) as well as the Smart Aging total score, looking for differences between the four groups.

Results: The findings revealed significant between-group differences in all the Smart Aging indices: accuracy (p < 0.001), time (p < 0.001), distance (p < 0.001), and total Smart Aging score (p < 0.001). The HCs outperformed the mild AD, aMCI, and PD-MCI patients in terms of accuracy, time, distance, and Smart Aging total score. In addition, the mild AD group was outperformed both by the HCs and by the aMCI and PD-MCI patients on accuracy and distance. No significant differences were found between aMCI and PD-MCI patients. Finally, the Smart Aging scores significantly correlated with the results of the neuropsychological assessments used.

Conclusion: These findings, although preliminary due to the small sample size, suggest the validity of Smart Aging as a screening tool for the detection of cognitive impairment in patients with neurodegenerative diseases.

Photo-Realistic Interactive Virtual Environments for Neurorehabilitation in Mild Cognitive Impairment (NeuroVRehab.PT): A Participatory Design and Proof-of-Concept Study

Mild cognitive impairment (MCI) is characterized by cognitive, psychological, and functional impairments. Digital interventions typically focus on cognitive deficits, neglecting the difficulties that patients experience in instrumental activities of daily living (IADL). The global conjecture created by COVID-19 has highlighted the seminal importance of digital interventions for the provision of healthcare services. Here, we investigated the feasibility and rehabilitation potential of a new design approach for creating highly realistic interactive virtual environments for MCI patients’ neurorehabilitation. Through a participatory design protocol, a neurorehabilitation digital platform was developed using images captured from a Portuguese supermarket (NeuroVRehab.PT). NeuroVRehab.PT’s main features (e.g., medium-sized supermarket, the use of shopping lists) were established according to a shopping behavior questionnaire filled in by 110 older adults. Seven health professionals used the platform and assessed its rehabilitation potential, clinical applicability, and user experience. Interviews were conducted using the think-aloud method and semi-structured scripts, and four main themes were derived from an inductive semantic thematic analysis. Our findings support NeuroVRehab.PT as an ecologically valid instrument with clinical applicability in MCI neurorehabilitation. Our design approach, together with a comprehensive analysis of the patients’ past experiences with IADL, is a promising technique to develop effective digital interventions to promote real-world functioning.

Virtual Reality in the Assessment, Understanding and Treatment of Mental Health Disorders

Computer scientists usually describe virtual reality (VR) as a set of fancy hardware and software technologies. However, psychology and neuroscience are starting to consider VR as the most advanced form of human-computer interaction allowing individuals to act, communicate and become present in a computer-generated environment. In this view, the feeling of "being there" experienced during a VR experience can become a powerful tool for personal change: it offers a dynamic and social world where individuals can live and share a specific experience. For this reason, the use of VR in mental health shows promise: different researches support its clinical efficacy for conditions including anxiety disorders, stress-related disorders, obesity and eating disorders, pain management, addiction and schizophrenia. However, more research is needed to transform the promises of VR in a real clinical tool for mental health. This Special Issue aims to present the most recent advances in the mental health applications of VR, as well as their implications for future patient care.

Implementing Remote Memory Clinics to Enhance Clinical Care During and After COVID-19

Social isolation is likely to be recommended for older adults due to COVID-19, with ongoing reduced clinical contact suggested for this population. This has increased the need for remote memory clinics, we therefore review the literature, current practices and guidelines on organizing such remote memory clinics, focusing on assessment of cognition, function and other relevant measurements, proposing a novel pathway based on three levels of complexity: simple telephone or video-based interviews and testing using available tests (Level 1), digitized and validated methods based on standard pen-and-paper tests and scales (Level 2), and finally fully digitized cognitive batteries and remote measurement technologies (RMTs, Level 3). Pros and cons of these strategies are discussed. Remotely collected data negates the need for frail patients or carers to commute to clinic and offers valuable insights into progression over time, as well as treatment responses to therapeutic interventions, providing a more realistic and contextualized environment for data-collection. Notwithstanding several challenges related to internet access, computer skills, limited evidence base and regulatory and data protection issues, digital biomarkers collected remotely have significant potential for diagnosis and symptom management in older adults and we propose a framework and pathway for how technologies can be implemented to support remote memory clinics. These platforms are also well-placed for administration of digital cognitive training and other interventions. The individual, societal and public/private costs of COVID-19 are high and will continue to rise for some time but the challenges the pandemic has placed on memory services also provides an opportunity to embrace novel approaches. Remote memory clinics' financial, logistical, clinical and practical benefits have been highlighted by COVID-19, supporting their use to not only be maintained when social distancing legislation is lifted but to be devoted extra resources and attention to fully potentiate this valuable arm of clinical assessment and care.