Smart Health System to Detect Dementia Disorders Using Virtual Reality

Virtual Reality and Serious Videogame-Based Instruments for Assessing Spatial Navigation in Alzheimer's Disease: A Systematic Review of Psychometric Properties

Over the past decade, research using virtual reality and serious game-based instruments for assessing spatial navigation and spatial memory in at-risk and AD populations has risen. We systematically reviewed the literature since 2012 to identify and evaluate the methodological quality and risk of bias in the analyses of the psychometric properties of VRSG-based instruments. The search was conducted primarily in July-December 2022 and updated in November 2023 in eight major databases. The quality of instrument development and study design were analyzed in all studies. Measurement properties were defined and analyzed according to COSMIN guidelines. A total of 1078 unique records were screened, and following selection criteria, thirty-seven studies were analyzed. From these studies, 30 instruments were identified. Construct and criterion validity were the most reported measurement properties, while structural validity and internal consistency evidence were the least reported. Nineteen studies were deemed very good in construct validity, whereas 11 studies reporting diagnostic accuracy were deemed very good in quality. Limitations regarding theoretical framework and research design requirements were found in most of the studies. VRSG-based instruments are valuable additions to the current diagnostic toolkit for AD. Further research is required to establish the psychometric performance and clinical utility of VRSG-based instruments, particularly the instrument development, content validity, and diagnostic accuracy for preclinical AD screening scenarios. This review provides a straightforward synthesis of the state of the art of VRSG-based instruments and suggests future directions for research.

"VR is the future": perspectives of healthcare professionals on virtual reality as a diagnostic tool for dementia status in primary care

BACKGROUND

Healthcare professionals (HPs) hold critical perspectives on the barriers and facilitating factors for the implementation of virtual reality (VR) dementia diagnosis tools in the clinical setting. This study aims to explore HP perspectives regarding the clinical implementation of dementia diagnosis tools using VR platforms.

METHODS

An exploratory qualitative interview study was carried out between July and September 2022. In-depth semi-structured interviews were conducted with HPs (n = 7) with clinical expertise in dementia diagnoses drawn from medicine, nursing and allied health practices. A hermeneutic phenomenological approach was used to frame the interview data across the dementia diagnosis pathway and application of new technology.

RESULTS

HPs were on average 36.29 years old (SD = 11.56) with 11.85 years of experience (SD = 12.80, range:4-42). Analyses identified three main themes related to the contemporary methods of dementia diagnosis, dementia diagnosis and the medical landscape and HP perspectives on the usefulness and barriers of VR implementation. VR was considered an innovative prospect, with improved ecological validity compared to commonplace, current cognitive assessments. Concerns of time commitments, monetary costs and the validity of the new technology were identified as key barriers to implementation. Overall, implementation of a new diagnostic tool was considered a complex process.

CONCLUSIONS

Our insight into general practice and nursing clinics can be supported to embed and integrate virtual reality platforms in primary care settings. Primary healthcare organizations require more funding and time related resources to produce a context in which VR tools could be implemented in a beneficial manner.

Virtual Reality in Health Science Education: Professors’ Perceptions

Virtual reality (VR) is a simulated experience in a three-dimensional (3D) computer-simulated world. Recent advances in technology position VR as a multipurpose technology in the healthcare sector and as a critical component in achieving Health 4.0. In this article, descriptive and correlationally quantitative research is carried out on the assessments made by Latin American health sciences university professors on the didactic use of virtual reality technologies. The main objective was to analyze the differences in the perceptions expressed by the public or private tenure of the universities where the professors teach. In addition, gender and age gaps were identified in the assessments obtained from each of the types of universities. The results reveal that Latin American health science professors at private universities have a higher selfconcept of their digital skills for the use of virtual reality in the lectures. This greater selfconcept also leads to a reduction in the gender and age gaps in the participating private universities with respect to the public counterparts. It is advisable to increase both faculty training in the didactic use of virtual reality and funding for its use, mainly in public universities.

Validation of the Computerized Cognitive Assessment Test: NNCT

Population aging brings with it cognitive impairment. One of the challenges of the coming years is the early and accessible detection of cognitive impairment. Therefore, this study aims to validate a neuropsychological screening test, self-administered and in software format, called NAIHA Neuro Cognitive Test (NNCT), designed for elderly people with and without cognitive impairment. This test aims to digitize cognitive assessments to add greater accessibility than classic tests, as well as to present results in real time and reduce costs. To this end, a comparison is made with tests such as MMSE, Clock Drawing Test (CDT) and CAMCOG. For this purpose, the following statistical analyses were performed: correlations, ROC curves, and three ANOVAs. The NNCT test evaluates seven cognitive areas and shows a significant and positive correlation with other tests, at total and subareas levels. Scores are established for the detection of both mild cognitive impairment and dementia, presenting optimal sensitivity and specificity. It is concluded that the NNCT test is a valid method of detection of cognitive impairment.

Older Adults Get Lost in Virtual Reality: Visuospatial Disorder Detection in Dementia Using a Voting Approach Based on Machine Learning Algorithms

As the age of an individual progresses, they are prone to more diseases; dementia is one of these age-related diseases. Regarding the detection of dementia, traditional cognitive testing is currently one of the most accurate tests. Nevertheless, it has many disadvantages, e.g., it does not measure the extent of the brain damage and does not take the patient’s intelligence into consideration. In addition, traditional assessment does not measure dementia under real-world conditions and in daily tasks. It is therefore advisable to investigate the newest, more powerful applications that combine cognitive techniques with computerized techniques. Virtual reality worlds are one example, and allow patients to immerse themselves in a controlled environment. This study created the Medical Visuospatial Dementia Test (referred to as the “MVD Test”) as a non-invasive, semi-immersive, and cognitive computerized test. It uses a 3D virtual environment platform based on medical tasks combined with AI algorithms. The objective is to evaluate two cognitive domains: visuospatial assessment and memory assessment. Using multiple machine learning algorithms (MLAs), based on different voting approaches, a 3D system classifies patients into three classes: patients with normal cognition, patients with mild cognitive impairment (MCI), and patients with severe cognitive impairment (dementia). The model with the highest performance was derived from voting approach named Ensemble Vote, where accuracy was 97.22%. Cross-validation accuracy of Extra Tree and Random Forest classifiers, which was greater than 99%, indicated a greater discriminate capacity than that of other classes.

An FGM decomposition-based fuzzy MCDM method for selecting smart technology applications to support mobile health care during and after the COVID-19 pandemic

In a fuzzy multicriteria decision-making (MCDM) problem, a decision maker may have differing viewpoints on the relative priorities of criteria. However, traditional methods merge these viewpoints into a single one, which leads to an unrepresentative decision-making result. Several recent methods identify the multiple viewpoints of a decision maker by decomposing the decision maker's fuzzy judgment matrix into several symmetric fuzzy subjudgment matrices, which is an inflexible strategy. To enhance flexibility, this study proposed a fuzzy geometric mean (FGM) decomposition-based fuzzy MCDM method in which FGM is applied to decompose a fuzzy judgment matrix into several fuzzy subjudgment matrices that can be asymmetric. These fuzzy subjudgment matrices are diverse and more consistent than the original fuzzy judgment matrix. The proposed methodology was applied to select the best choice from a group of smart technology applications for supporting mobile health care during and after the COVID-19 pandemic. According to the experimental results, the proposed methodology provided a novel approach to decomposing fuzzy judgment matrices and produced more diverse fuzzy subjudgment matrices. .

Recommending Suitable Smart Technology Applications to Support Mobile Healthcare after the COVID-19 Pandemic Using a Fuzzy Approach

The COVID-19 pandemic seems to be entering its final stage. However, to restore normal life, the applications of smart technologies are still necessary. Therefore, this research is dedicated to exploring the applications of smart technologies that can support mobile healthcare after the COVID-19 pandemic. To this end, this study compares smart technology applications to support mobile healthcare within the COVID-19 pandemic with those before the pandemic, so as to estimate possible developments in this field. In addition, to quantitatively assess and compare smart technology applications that may support mobile healthcare after the COVID-19 pandemic, the calibrated fuzzy geometric mean (CFGM)-fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) approach is applied. The proposed methodology has been applied to evaluate and compare nine potential smart technology applications for supporting mobile healthcare after the COVID-19 pandemic. According to the experimental results, "vaccine passport and related applications" and "smart watches" were the most suitable smart technology applications for supporting mobile healthcare after the COVID-19 pandemic.