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.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.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.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).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.
Abstract Background The ‘Remote Assessment of Disease and Relapse – Alzheimer’s Disease’ (RADAR‐AD) study is assessing functional decline in Alzheimer’s disease (AD) using remote monitoring techniques (RMT’s). Compared to traditional pen‐and‐paper clinical assessments, RMT’s can continuously and objectively monitor function during activities of daily living (ADL), which are arguably more sensitive to the earliest stages of AD. The aim of this abstract is to compare the results of the augmented reality task ‘Altoida’, that recreates an ADL requiring spatial navigation and memory, implemented as a tablet application, between 1) healthy controls, preclinical AD and prodromal AD, and with 2) standard clinical tests for cognitive and functional decline. Method We included amyloid negative cognitively normal (healthy controls, n=10), amyloid positive cognitively normal (preclinical AD, n=7) and amyloid positive mild cognitive impaired (prodromal AD, n=4) participants (Table 1) from the RADAR‐AD study. The outcome of the Altoida test, consisting of a motor task and two tasks in which participants have to hide‐and‐seek virtual objects, is the validated Neuromotor Index (NMI), with higher scores reflecting normative scoring, according to age, sex and education. Cognition was measured using a word‐list‐learning test, digit symbol substitution test (DSST), Rey complex figure, verbal fluency and Boston naming test. Functional decline was assessed using the Amsterdam Instrumented Activities of Daily Living (AIADL) questionnaire. Result In our preliminary sub‐sample, healthy controls showed higher NMI scores compared to the preclinical AD and prodromal AD participants (p=0.02) (Figure 1). The NMI was related to the DSST only (Figure 2). Conclusion NMI scores differed between cognitively normal healthy controls and cognitively normal preclinical AD participants, while no differences could be found in cognitive and functional tests between these groups. The sample size will increase in the coming months, but despite the currently small sample, the preliminary results are promising in evidencing that digital biomarkers are potentially more sensitive than standard clinical tests in detecting the early stages of AD, which could be helpful in developing new endpoints in clinical trials. This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking RADAR‐AD (grant No 806999) and their associated partners.
Abstract Background Digital Measures (DMs) derived from mobile devices and smartphone applications have received a strongly increasing attention during the last years, because they could allow for an accurate, quantitative monitoring of disease symptoms, even outside clinics. In addition, DMs may help to diagnose Alzheimer’s Disease (AD) in a pre‐symptomatic stage and thus increase the success chances of therapeutic interventions. However, before any use in clinical routine, DMs have to be evaluated carefully by assessing their relationship to established clinical scores and understanding their diagnostic benefit. In this regard the IMI project RADAR‐AD ( www.radar‐ad.org ) has the ambition to evaluate a broad panel of digital technologies with respect to their potential for early disease diagnosis while focusing on functional activities of daily living. Method An example of a panel of digital technology RADAR‐AD uses is a smartphone based virtual reality game resulting into an assessment of cognitive impairment. In our work we analyzed connections between digital readouts and cognitive features like MMSE (Mini Mental State Examination) via one of our recently developed Artificial Intelligence (AI) approaches called Variational Autoencoder Modular Bayesian Networks (VAMBN). Going one step further we also tested the possibility to accurately predict MMSE scores from DMs and vice versa via machine learning. Based on this finding we then simulated DMs within the ADNI cohort and re‐ran VAMBN. Result Application of VAMBN on the data from virtual reality game resulted into a network comprising DMs, MMSE sub‐item scores and demographic features (Figure 1). It thus allowed to disentangle and quantify the relationship between DMs and established clinical scores. The simulation of DM’s and application of VAMBN in the ADNI cohort allowed us to further predict connections of DMs with FAQ (Functional Activity Questionnaire) and even molecular mechanisms. Conclusion Our results indicate that there is a significant dependency between digital readouts and clinical scores such as MMSE and FAQ. Therefore, DM’s may have the potential to act as a vital measure in the diagnosis of AD in a pre‐symptomatic stage. Our next steps will focus on evaluating the diagnostic benefit of DMs compared to the questionnaire‐based FAQ.
Parkinson's disease (PD) is the fastest growing neurodegeneration and has a prediagnostic phase with a lot of challenges to identify clinical and laboratory biomarkers for those in the earliest stages or those 'at risk'. Despite the current research effort, further progress in this field hinges on the more effective application of digital biomarker and artificial intelligence applications at the prediagnostic stages of PD. It is of the highest importance to stratify such prediagnostic subjects that seem to have the most neuroprotective benefit from drugs. However, current initiatives to identify individuals at risk or in the earliest stages that might be candidates for future clinical trials are still challenging due to the limited accuracy and explainability of existing prediagnostic detection and progression prediction solutions. In this brief paper, we report on a novel digital neuro signature (DNS) for prodromal-PD based on selected digital biomarkers previously discovered on preclinical Alzheimer's disease. (AD). Our preliminary results demonstrated a standard DNS signature for both preclinical AD and prodromal PD, containing a ranked selection of features. This novel DNS signature was rapidly repurposed out of 793 digital biomarker features and selected the top 20 digital biomarkers that are predictive and could detect both the biological signature of preclinical AD and the biological mechanism of a-synucleinopathy in prodromal PD. The resulting model can provide physicians with a pool of patients potentially eligible for therapy and comes along with information about the importance of the digital biomarkers that are predictive, based on SHapley Additive exPlanations (SHAP). Similar initiatives could clarify the stage before and around diagnosis, enabling the field to push into unchartered territory at the earliest stages of the disease.
That portion of the Internet known as the World Wide Web has been riding an exponential growth curve since 1994 (Network Wizards, 1999; Rutkowski, 1998), coinciding with the introduction of NCSA’s graphically-based software interface Mosaic for “browsing” the World Wide Web (Hoffman, Novak, & Chatterjee, 1995). Currently, over 43 million hosts are connected to the Internet worldwide (Network Wizards, 1999). In terms of individual users, somewhere between 40 to 80 million adults (eStats, 1999) inthe United States alone have access to around 800 million unique pages of content (Lawrence & Giles, 1999), globally distributed on arguably one of the most important communication innovations in history.
Research investigating treatments and interventions for cognitive decline fail due to difficulties in accurately recognizing behavioral signatures in the presymptomatic stages of the disease. For this validation study, we took our previously constructed digital biomarker-based prognostic models and focused on generalizability and robustness of the models.We validated prognostic models characterizing subjects using digital biomarkers in a longitudinal, multi-site, 40-month prospective study collecting data in memory clinics, general practitioner offices, and home environments.Our models were able to accurately discriminate between healthy subjects and individuals at risk to progress to dementia within 3 years. The model was also able to differentiate between people with or without amyloid neuropathology and classify fast and slow cognitive decliners with a very good diagnostic performance.Digital biomarker prognostic models can be a useful tool to assist large-scale population screening for the early detection of cognitive impairment and patient monitoring over time.
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.
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.
Biomarker progressions explain higher variability in cognitive decline than baseline values alone. This study examines progressions of established biomarkers along with a novel marker in a longitudinal cognitive decline.A total of 215 subjects were used with a diagnosis of normal, mild cognitive impairment (MCI) or Alzheimer's disease (AD) at baseline. We calculated standardized biomarker progression rates and used them as predictors of outcome within 5 years.Early cognitive declines were more strongly explained by fluorodeoxyglucose-positron emission tomography, precuneus and medial temporal cortical thickness, and the complex instrumental activities of daily living (iADL) marker progressions. Using Cox proportional hazards model, we found that these progressions were a significant risk factor for conversion from both MCI to AD (adjusted hazard ratio 1.45; 95% confidence interval 1.20-1.93; P = 1.23 × 10(-5)) and cognitively normal to MCI (adjusted hazard ratio 1.76; 95% confidence interval 1.32-2.34; P = 1.55 × 10(-5)).Compared with standard biological biomarkers, complex functional iADL markers could also provide predictive information for cognitive decline during the presymptomatic stage. This has important implications for clinical trials focusing on prevention in asymptomatic individuals.
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