Ioannis Tarnanas

Ecological Validity of Virtual Reality Daily Living Activities Screening for Early Dementia: Longitudinal Study

Background Dementia is a multifaceted disorder that impairs cognitive functions, such as memory, language, and executive functions necessary to plan, organize, and prioritize tasks required for goal-directed behaviors. In most cases, individuals with dementia experience difficulties interacting with physical and social environments. The purpose of this study was to establish ecological validity and initial construct validity of a fire evacuation Virtual Reality Day-Out Task (VR-DOT) environment based on performance profiles as a screening tool for early dementia. Objective The objectives were (1) to examine the relationships among the performances of 3 groups of participants in the VR-DOT and traditional neuropsychological tests employed to assess executive functions, and (2) to compare the performance of participants with mild Alzheimer’s-type dementia (AD) to those with amnestic single-domain mild cognitive impairment (MCI) and healthy controls in the VR-DOT and traditional neuropsychological tests used to assess executive functions. We hypothesized that the 2 cognitively impaired groups would have distinct performance profiles and show significantly impaired independent functioning in ADL compared to the healthy controls. Methods The study population included 3 groups: 72 healthy control elderly participants, 65 amnestic MCI participants, and 68 mild AD participants. A natural user interface framework based on a fire evacuation VR-DOT environment was used for assessing physical and cognitive abilities of seniors over 3 years. VR-DOT focuses on the subtle errors and patterns in performing everyday activities and has the advantage of not depending on a subjective rating of an individual person. We further assessed functional capacity by both neuropsychological tests (including measures of attention, memory, working memory, executive functions, language, and depression). We also evaluated performance in finger tapping, grip strength, stride length, gait speed, and chair stands separately and while performing VR-DOTs in order to correlate performance in these measures with VR-DOTs because performance while navigating a virtual environment is a valid and reliable indicator of cognitive decline in elderly persons. Results The mild AD group was more impaired than the amnestic MCI group, and both were more impaired than healthy controls. The novel VR-DOT functional index correlated strongly with standard cognitive and functional measurements, such as mini-mental state examination (MMSE; rho=0.26, P=.01) and Bristol Activities of Daily Living (ADL) scale scores (rho=0.32, P=.001). Conclusions Functional impairment is a defining characteristic of predementia and is partly dependent on the degree of cognitive impairment. The novel virtual reality measures of functional ability seem more sensitive to functional impairment than qualitative measures in predementia, thus accurately differentiating from healthy controls. We conclude that VR-DOT is an effective tool for discriminating predementia and mild AD from controls by detecting differences in terms of errors, omissions, and perseverations while measuring ADL functional ability.

Can a novel computerized cognitive screening test provide additional information for early detection of Alzheimer's disease?

Virtual reality testing of everyday activities is a novel type of computerized assessment that measures cognitive, executive, and motor performance as a screening tool for early dementia. This study used a virtual reality day‐out task (VR‐DOT) environment to evaluate its predictive value in patients with mild cognitive impairment (MCI).

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The “health state” of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.

Can a Novel Web-Based Computer Test Predict Poor Simulated Driving Performance? A Pilot Study With Healthy and Cognitive-Impaired Participants

Background Driving a car is a complex instrumental activity of daily living and driving performance is very sensitive to cognitive impairment. The assessment of driving-relevant cognition in older drivers is challenging and requires reliable and valid tests with good sensitivity and specificity to predict safe driving. Driving simulators can be used to test fitness to drive. Several studies have found strong correlation between driving simulator performance and on-the-road driving. However, access to driving simulators is restricted to specialists and simulators are too expensive, large, and complex to allow easy access to older drivers or physicians advising them. An easily accessible, Web-based, cognitive screening test could offer a solution to this problem. The World Wide Web allows easy dissemination of the test software and implementation of the scoring algorithm on a central server, allowing generation of a dynamically growing database with normative values and ensures that all users have access to the same up-to-date normative values. Objective In this pilot study, we present the novel Web-based Bern Cognitive Screening Test (wBCST) and investigate whether it can predict poor simulated driving performance in healthy and cognitive-impaired participants. Methods The wBCST performance and simulated driving performance have been analyzed in 26 healthy younger and 44 healthy older participants as well as in 10 older participants with cognitive impairment. Correlations between the two tests were calculated. Also, simulated driving performance was used to group the participants into good performers (n=70) and poor performers (n=10). A receiver-operating characteristic analysis was calculated to determine sensitivity and specificity of the wBCST in predicting simulated driving performance. Results The mean wBCST score of the participants with poor simulated driving performance was reduced by 52%, compared to participants with good simulated driving performance (P<.001). The area under the receiver-operating characteristic curve was 0.80 with a 95% confidence interval 0.68-0.92. Conclusions When selecting a 75% test score as the cutoff, the novel test has 83% sensitivity, 70% specificity, and 81% efficiency, which are good values for a screening test. Overall, in this pilot study, the novel Web-based computer test appears to be a promising tool for supporting clinicians in fitness-to-drive assessments of older drivers. The Web-based distribution and scoring on a central computer will facilitate further evaluation of the novel test setup. We expect that in the near future, Web-based computer tests will become a valid and reliable tool for clinicians, for example, when assessing fitness to drive in older drivers.

SERIOUS GAMING ENHANCES COGNITIVE FUNCTION IN MCI DUE TO ALZHEIMER'S DISEASE

In this study we are assessing a novel ICT-based cognitive intervention, called Long Lasting Memories that combines physical and cognitive training, using a battery of neuropsychological assessments (like the California Verbal Learning Test (CVLT), the Trail Making Test A and B (TMT-A, TMT-B), the Digit Span test and other) and also resting-state electroencephalography in order to compare cognitive performance at the neuropsychological assessments with Default Mode Network (DMN) connectivity patterns, which are hypothesized to be impaired at patients with MCI due to Alzheimers disease [2].

Functional impairment in Virtual-Reality-Daily-Living-Activities as a defining feature of amnestic MCI: Cognitive and psychomotor correlates

Background: Early definitions of mild cognitive impairment (MCI) excluded the presence of functional impairment; instead, preservation of a persons ability to perform activities of daily living (ADL) was a diagnostic criterion. However, recent studies have reported varying degrees of functional impairment associated with MCI. Hence, we aimed to assess the potential functional impairment associated with MCI and its predictors by means of virtual reality. Methods: We assessed 71 healthy elderly subjects, 65 amnestic single-domain MCI subjects (a-MCI), 42 amnestic multi-domain MCI subjects (md-MCI) and 45 mild dementia of Alzheimers type (mild-AD) subjects using Virtual Reality Activities of Daily Living (VR-ADL). VR-ADL focuses on the subtle errors and pattern in performing everyday activities and has the advantage of not depending on a subjective rating of an individual person. We further assessed functional capacity by both neuropsychological tests (including measures of attention, memory, working memory, executive functions, language, and depression) and also evaluated performance in finger-tapping, grip strength, stride length, gait speed and chair stands separately and while performing VR-ADLs in order to correlate performance in these measures with VR-ADLs. Usual gait speed is a valid and reliable indicator of physical performance, and predicts incident disability, hospitalization, institutionalization, falls, fractures and cognitive decline in elderly persons [1]. We hypothesize that the three cognitively impaired groups will have lower baseline cognitive, VR-ADL and upper-extremity function (UEF) and a greater reduction in performance in subsequent measurements than the cognitively healthy participants. Results: The md-MCI group was more impaired than the a-MCI group, and both were more impaired than healthy subjects in all VR-ADL measures. Also, the mild-AD was significantly more impaired than the MCI groups and healthy controls. Conclusions: Functional impairment is a defining characteristic of MCI and is partly dependent on the degree of cognitive impairment. Virtual Reality measures of functional ability seem more sensitive to functional impairment in MCI than qualitative measures. We conclude that VR-ADL is an effective tool for discriminating MCI and mild-AD from control and does so by detecting differences in terms of errors, omissions and perseverations while measuring ADL functional ability.

Combining qualitative and quantitative methods to analyze serious games outcomes: A pilot study for a new cognitive screening tool.

Computer games for a serious purpose - so called serious games can provide additional information for the screening and diagnosis of cognitive impairment. Moreover, they have the advantage of being an ecological tool by involving daily living tasks. However, there is a need for better comprehensive designs regarding the acceptance of this technology, as the target population is older adults that are not used to interact with novel technologies. Moreover given the complexity of the diagnosis and the need for precise assessment, an evaluation of the best approach to analyze the performance data is required. The present study examines the usability of a new screening tool and proposes several new outlines for data analysis.

PRE-CLINICAL (ALZHEIMER'S) DIAGNOSIS (PCD) BY TRACKING MICRO ERRORS DURING COMPLEX ACTIVITIES OF DAILY LIVING: A DIGITAL BIOMARKER

Background: Impairments of gait and balance often progress through the course of dementia, and are associated with increased risk of falls. Regular assessment of gait and balance could therefore be informative in tracking changes in functional status, and identifying individuals at a high risk of falling to allow for preventative measures. We have developed a technology, called AMBIENT, which enables the frequent, accurate, unobtrusive, and cost-effective measurement of gait and balance parameters. The objective of this study was to demonstrate the feasibility of using AMBIENT for frequent assessment of mobility in people with dementia in a residential facility. Methods: We conducted a pilot longitudinal study with 20 participants (age: 76.9 6 6.7 years, female: 50%) in the geriatric psychiatry unit at the Toronto Rehabilitation Institute, an eighteen-bed inpatient dementia care unit for older adults with behavioral symptoms. The AMBIENT setup included radio frequency identification to identify study participants and a Microsoft Kinect sensor to track body posture. The system automatically monitored participants’ gait as they walked within the view of the sensor during their daily routine and computed the spatiotemporal parameters of gait. Demographic and baseline descriptive measures were collected and falls events tracked. Results: On average, 97 walking sequences per person were collected over a length of stay of 466 37 days. There were 14 falls among study participants: 12 participants did not fall during their length of stay, 4 fell once, 2 fell twice, and 2 fell 3 times. Quantitative measures of gait were stride length (0.8 6 0.1 m), stride time (1.4 6 0.2 s), cadence (89.36 18.1 steps/min), velocity (0.66 0.1 m/s), step length asymmetry (1.2 6 0.6), and step time asymmetry (1.2 6 0.5). Conclusions:This pilot study demonstrates the feasibility of longitudinal tracking of gait over time in a residential dementia setting. Our long-term goal is to translate longitudinal gait parameters onto fall-risk measures. Machine learning techniques will be used to build a robust, multivariate predictive model capable of detecting changes in mobility and falls risk.

Adaptive prompt system using a ghost shadowing approach: A preliminary development

Errorless Learning (EL) is an effective learning method that can be applied for the rehabilitation of IADL in patients with Alzheimers disease (AD). Prompt fading is one of the key concepts in order to successfully apply the EL. However, issue of the prompt dependence has not been satisfactorily solved. The aim of the study is to propose an adaptive prompt system using a shadowing approach (Ghost shadowing) based on the model of “sense of agency” to deal with this problem. In this paper, we present a prototype system as well as the result of the assessment test for the quality of the sense of agency in order to validate the subjective aspect of the proposed approach as a preliminary study.