The Feasibility of Using Virtual Reality and Eye Tracking in Research With Older Adults With and Without Alzheimer's Disease

Visual Attributes of Wayfinding in Care Facilities by Individuals With Mild Cognitive Impairment (MCI)

Navigating unfamiliar environments poses significant challenges, especially for individuals with cognitive impairments. These individuals often struggle with maintaining orientation, recalling routines, and traveling through new environments due to their limited cognitive capacity. The current state of research on visual environmental attributes of wayfinding reveals a gap, particularly regarding individuals with mild cognitive impairment (MCI), compared to healthy older adults. Few studies have objectively identified the visual cues and environmental factors that influence wayfinding in those with cognitive impairments. This study addresses this research gap by objectively identifying the visual environmental attributes influencing wayfinding among MCI subjects in a memory care facility compared to healthy older adults. Using eye-tracking technology, we investigated which visual environmental attributes attract eye fixation during wayfinding in care facilities. Forty-eight subjects, categorized into MCI and non-MCI groups, navigated through unfamiliar indoor environments while their eye movements were tracked. The results, in agreement with previous research, showed that MCI group exhibit significant reduced success rates and longer navigation times compared to non-MCI group. Findings of the eye-fixation rank-order analysis showed that MCI subjects prioritized directional signs and architectural features, while non-MCI subjects focused on identifying signs and informative elements. These findings emphasize the importance of tailored design practices for care facilities, highlighting specific visual cues crucial for guiding navigation in individuals with MCI. The study provides insight to guide architects and healthcare professionals in creating inclusive environments that support independence and well-being for older adults with cognitive impairments, advocating for interventions to enhance wayfinding in care facilities.

Effective engagement in computerized cognitive training for older adults

Computerized cognitive training (CCT) is a frontline therapy to prevent or slow age-related cognitive decline. A prerequisite for CCT research to provide clinically relevant improvements in cognition is to understand effective engagement, i.e., the pattern of energy investment that ensures CCT effectiveness. Even though previous studies have assessed whether particular variables (e.g., gamification) predict engagement and/or CCT effectiveness, the field lacks a systematic approach to understanding effective engagement. Here, by comprehensively reviewing and evaluating engagement and adjacent literature, we propose a standardized measurement and operational framework to promote effective engagement with CCT targeting cognitive decline in older adults. We suggest that promoting effective engagement with CCT has two key steps: 1) comprehensively measuring engagement with CCT and 2) identifying which aspects of engagement are essential to achieve the pre-specified outcome of clinically relevant improvements in cognition. The proposed measurement and operational framework of effective engagement will allow future research to maximize older adults' engagement with CCT to slow/prevent age-related cognitive decline.

User Experience in Virtual Reality (VR) Applications for Elderly People with Cognitive Impairment and Dementia: A Scoping Review

BACKGROUND

In recent years, Virtual Reality (VR) has emerged as a promising tool to improve the well-being and functional capabilities of older adults. Although VR applications have shown positive results, their impact on user experience and therapeutic outcomes still needs to be evaluated.

OBJECTIVE

This scoping review aims to analyze existing studies on VR use in older adults with neurodegenerative disorders, focusing on the factors that influence usability, satisfaction, and immersion, as well as the effects on emotional and cognitive well-being.

MATERIALS AND METHODS

Empirical studies in English were included on VR applications applied to older adults with cognitive impairment without study design restrictions. The search was conducted in IEEE Xplore, PubMed, Scopus, and Web of Science, identifying a total of 650 initial results. After screening, 14 studies met the inclusion criteria.

RESULTS

Immersive VR tends to generate a greater sense of presence, which contributes to improving emotional well-being and reducing neuropsychiatric symptoms, such as apathy and depression. However, its impact on cognitive functions, including memory and executive skills, varied depending on the level of immersion and participant characteristics. Despite these positive findings, significant heterogeneity was evident in study designs, measurement instruments, and user experience indicators.

CONCLUSION

Virtual environments have great potential as a therapeutic tool for older adults, but their success depends on the personalization of applications and the adaptation of technology to the specific needs of this population. Future research should focus on developing standardized protocols, incorporating adaptive technologies such as artificial intelligence, and evaluating the longterm effects of VR to maximize its benefits and minimize its risks. This review was registered in Open Science Framework (OSF).

REGISTRATION NUMBER

10.17605/OSF.IO/PNU36.

Alzheimer's early detection in post-acute COVID-19 syndrome: a systematic review and expert consensus on preclinical assessments

Introduction

The risk of developing Alzheimer's disease (AD) in older adults increasingly is being discussed in the literature on Post-Acute COVID-19 Syndrome (PACS). Remote digital Assessments for Preclinical AD (RAPAs) are becoming more important in screening for early AD, and should always be available for PACS patients, especially for patients at risk of AD. This systematic review examines the potential for using RAPA to identify impairments in PACS patients, scrutinizes the supporting evidence, and describes the recommendations of experts regarding their use.

Methods

We conducted a thorough search using the PubMed and Embase databases. Systematic reviews (with or without meta-analysis), narrative reviews, and observational studies that assessed patients with PACS on specific RAPAs were included. The RAPAs that were identified looked for impairments in olfactory, eye-tracking, graphical, speech and language, central auditory, or spatial navigation abilities. The recommendations' final grades were determined by evaluating the strength of the evidence and by having a consensus discussion about the results of the Delphi rounds among an international Delphi consensus panel called IMPACT, sponsored by the French National Research Agency. The consensus panel included 11 international experts from France, Switzerland, and Canada.

Results

Based on the available evidence, olfaction is the most long-lasting impairment found in PACS patients. However, while olfaction is the most prevalent impairment, expert consensus statements recommend that AD olfactory screening should not be used on patients with a history of PACS at this point in time. Experts recommend that olfactory screenings can only be recommended once those under study have reported full recovery. This is particularly important for the deployment of the olfactory identification subdimension. The expert assessment that more long-term studies are needed after a period of full recovery, suggests that this consensus statement requires an update in a few years.

Conclusion

Based on available evidence, olfaction could be long-lasting in PACS patients. However, according to expert consensus statements, AD olfactory screening is not recommended for patients with a history of PACS until complete recovery has been confirmed in the literature, particularly for the identification sub-dimension. This consensus statement may require an update in a few years.

Clear Aligners and Smart Eye Tracking Technology as a New Communication Strategy between Ethical and Legal Issues

Smart eye-tracking technology (SEET) that determines visual attention using smartphones can be used to determine the aesthetic perception of different types of clear aligners. Its value as a communication and comprehension tool, in addition to the ethical and legal concerns which it entails, can be assessed. One hundred subjects (50 F, 50 M; age range 15-70) were equally distributed in non-orthodontic (A) and orthodontic (B) groups. A smartphone-based SEET app assessed their knowledge of and opinions on aligners. Subjects evaluated images of smiles not wearing aligners, with/without attachments and with straight/scalloped gingival margins, as a guided calibration step which formed the image control group. Subsequently, the subjects rated the same smiles, this time wearing aligners (experimental images group). Questionnaire data and average values for each group of patients, and images relating to fixation times and overall star scores, were analyzed using these tests: chi-square, t-test, Mann-Whitney U, Spearman's rho, and Wilcoxon (p < 0.05). One-way ANOVA and related post-hoc tests were also applied. Orthodontic patients were found to be better informed than non-orthodontic patients. Aesthetic perception could be swayed by several factors. Attachments scored lower in aesthetic evaluation. Lips distracted attention from attachments and improved evaluations. Attachment-free aligners were better rated overall. A more thorough understanding as to the opinions, expectations and aesthetic perception of aligners can improve communication with patients. Mobile SEET is remarkably promising, although it does require a careful medicolegal risk-benefit assessments for responsible and professional use.

Virtual reality for neurorehabilitation: A bibliometric analysis of knowledge structure and theme trends

Background

As an emerging technology, virtual reality (VR) has been broadly applied in the medical field, especially in neurorehabilitation. The growing application of VR therapy promotes an increasing amount of clinical studies. In this paper, we present a bibliometric analysis of the existing studies to reveal the current research hotspots and guide future research directions.

Methods

Articles and reviews on the related topic were retrieved from the Science Citation Index Expanded of Web of Science Core Collection database. VOSviewer and Citespace software were applied to systematically analyze information about publications, countries, institutions, authors, journals, citations, and keywords from the included studies.

Results

A total of 1,556 papers published between 1995 and 2021 were identified. The annual number of papers increased gradually over the past three decades, with a peak publication year in 2021 (n = 276). Countries and institutions from North America and Western European were playing leading roles in publications and total citations. Current hotspots were focused on the effectiveness of VR therapy in cognitive and upper limb motor rehabilitation. The clusters of keywords contained the four targeted neurological diseases of VR, while the burst keywords represented that the latest studies were directed toward more defined types of VR therapy and greater study design.

Conclusions

Our study offers information regarding to the current hotspots and emerging trends in the VR for rehabilitation field. It could guide future research and application of VR therapy in neurorehabilitation.

Biosensors toward behavior detection in diagnosis of alzheimer’s disease

In recent years, a huge number of individuals all over the world, elderly people, in particular, have been suffering from Alzheimer’s disease (AD), which has had a significant negative impact on their quality of life. To intervene early in the progression of the disease, accurate, convenient, and low-cost detection technologies are gaining increased attention. As a result of their multiple merits in the detection and assessment of AD, biosensors are being frequently utilized in this field. Behavioral detection is a prospective way to diagnose AD at an early stage, which is a more objective and quantitative approach than conventional neuropsychological scales. Furthermore, it provides a safer and more comfortable environment than those invasive methods (such as blood and cerebrospinal fluid tests) and is more economical than neuroimaging tests. Behavior detection is gaining increasing attention in AD diagnosis. In this review, cutting-edge biosensor-based devices for AD diagnosis together with their measurement parameters and diagnostic effectiveness have been discussed in four application subtopics: body movement behavior detection, eye movement behavior detection, speech behavior detection, and multi-behavior detection. Finally, the characteristics of behavior detection sensors in various application scenarios are summarized and the prospects of their application in AD diagnostics are presented as well.

Leveraging technology to personalize cognitive enhancement methods in aging

As population aging advances at an increasing rate, efforts to help people maintain or improve cognitive function late in life are critical. Although some studies have shown promise, the question of whether cognitive training is an effective tool for improving general cognitive ability remains incompletely explored, and study results to date have been inconsistent. Most approaches to cognitive enhancement in older adults have taken a 'one size fits all' tack, as opposed to tailoring interventions to the specific needs of individuals. In this Perspective, we argue that modern technology has the potential to enable large-scale trials of public health interventions to enhance cognition in older adults in a personalized manner. Technology-based cognitive interventions that rely on closed-loop systems can be tailored to individuals in real time and have the potential for global testing, extending their reach to large and diverse populations of older adults. We propose that the future of cognitive enhancement in older adults will rely on harnessing new technologies in scientifically informed ways.

Virtual reality for the assessment and rehabilitation of neglect: where are we now? A 6-year review update

Unilateral spatial neglect (USN) is a frequent repercussion of a cerebrovascular accident, typically a stroke. USN patients fail to orient their attention to the contralesional side to detect auditory, visual, and somatosensory stimuli, as well as to collect and purposely use this information. Traditional methods for USN assessment and rehabilitation include paper-and-pencil procedures, which address cognitive functions as isolated from other aspects of patients' functioning within a real-life context. This might compromise the ecological validity of these procedures and limit their generalizability; moreover, USN evaluation and treatment currently lacks a gold standard. The field of technology has provided several promising tools that have been integrated within the clinical practice; over the years, a "first wave" has promoted computerized methods, which cannot provide an ecological and realistic environment and tasks. Thus, a "second wave" has fostered the implementation of virtual reality (VR) devices that, with different degrees of immersiveness, induce a sense of presence and allow patients to actively interact within the life-like setting. The present paper provides an updated, comprehensive picture of VR devices in the assessment and rehabilitation of USN, building on the review of Pedroli et al. (2015). The present paper analyzes the methodological and technological aspects of the studies selected, considering the issue of usability and ecological validity of virtual environments and tasks. Despite the technological advancement, the studies in this field lack methodological rigor as well as a proper evaluation of VR usability and should improve the ecological validity of VR-based assessment and rehabilitation of USN.

A Mixed Statistical and Machine Learning Approach for the Analysis of Multimodal Trail Making Test Data

Eye-tracking can offer a novel clinical practice and a non-invasive tool to detect neuropathological syndromes. In this paper, we show some analysis on data obtained from the visual sequential search test. Indeed, such a test can be used to evaluate the capacity of looking at objects in a specific order, and its successful execution requires the optimization of the perceptual resources of foveal and extrafoveal vision. The main objective of this work is to detect if some patterns can be found within the data, to discern among people with chronic pain, extrapyramidal patients and healthy controls. We employed statistical tests to evaluate differences among groups, considering three novel indicators: blinking rate, average blinking duration and maximum pupil size variation. Additionally, to divide the three patient groups based on scan-path images—which appear very noisy and all similar to each other—we applied deep learning techniques to embed them into a larger transformed space. We then applied a clustering approach to correctly detect and classify the three cohorts. Preliminary experiments show promising results.