Activities of daily living (ADLs) are fundamental routine tasks that the majority of physically and mentally healthy people can independently execute. In this paper, we present a semantic framework ...for detecting problems in ADLs execution, monitored through smart home sensors. In the context of this work, we conducted a pilot study, gathering raw data from various sensors and devices installed in a smart home environment. The proposed framework combines multiple Semantic Web technologies (i.e., ontology, RDF, triplestore) to handle and transform these raw data into meaningful representations, forming a knowledge graph. Subsequently, SPARQL queries are used to define and construct explicit rules to detect problematic behaviors in ADL execution, a procedure that leads to generating new implicit knowledge. Finally, all available results are visualized in a clinician dashboard. The proposed framework can monitor the deterioration of ADLs performance for people across the dementia spectrum by offering a comprehensive way for clinicians to describe problematic behaviors in the everyday life of an individual.
Assessing functional decline related to activities of daily living (ADLs) is deemed significant for the early diagnosis of dementia. As current assessment methods for ADLs often lack the ability to ...capture subtle changes, technology-based approaches are perceived as advantageous. Specifically, digital biomarkers are emerging, offering a promising avenue for research, as they allow unobtrusive and objective monitoring.
A study was conducted with the involvement of 36 participants assigned to three known groups (Healthy Controls, participants with Subjective Cognitive Decline and participants with Mild Cognitive Impairment). Participants visited the CERTH-IT Smart Home, an environment that simulates a fully functional residence, and were asked to follow a protocol describing different ADL Tasks (namely Task 1 - Meal, Task 2 - Beverage and Task 3 - Snack Preparation). By utilizing data from fixed in-home sensors installed in the Smart Home, the identification of the performed Tasks and their derived features was explored through the developed CARL platform. Furthermore, differences between groups were investigated. Finally, overall feasibility and study satisfaction were evaluated.
The composition of the ADLs was attainable, and differentiation among the HC group compared to the SCD and the MCI groups considering the feature "Activity Duration" in Task 1 - Meal Preparation was possible, while no difference could be noted between the SCD and the MCI groups.
This ecologically valid study was determined as feasible, with participants expressing positive feedback. The findings additionally reinforce the interest and need to include people in preclinical stages of dementia in research to further evolve and develop clinically relevant digital biomarkers.
Meditation imparts relaxation and constitutes an important non-pharmacological intervention for people with cognitive impairment. Moreover, EEG has been widely used as a tool for detecting brain ...changes even at the early stages of Alzheimer's Disease (AD). The current study investigates the effect of meditation practices on the human brain across the AD spectrum by using a novel portable EEG headband in a smart-home environment.
Forty (40) people (13 Healthy Controls-HC, 14 with Subjective Cognitive Decline-SCD and 13 with Mild Cognitive Impairment-MCI) participated practicing Mindfulness Based Stress Reduction (Session 2-MBSR) and a novel adaptation of the Kirtan Kriya meditation to the Greek culture setting (Session 3-KK), while a Resting State (RS) condition was undertaken at baseline and follow-up (Session 1-RS Baseline and Session 4-RS Follow-Up). The signals were recorded by using the Muse EEG device and brain waves were computed (alpha, theta, gamma, and beta).
Analysis was conducted on four-electrodes (AF7, AF8, TP9, and TP10). Statistical analysis included the Kruskal-Wallis (KW) nonparametric analysis of variance. The results revealed that both states of MBSR and KK lead to a marked difference in the brain's activation patterns across people at different cognitive states. Wilcoxon Signed-ranks test indicated for HC that theta waves at TP9, TP10 and AF7, AF8 in Session 3-KK were statistically significantly reduced compared to Session 1-RS
= -2.271,
= 0.023,
= -3.110,
= 0.002 and
= -2.341,
= 0.019,
= -2.132,
= 0.033, respectively.
The results showed the potential of the parameters used between the various groups (HC, SCD, and MCI) as well as between the two meditation sessions (MBSR and KK) in discriminating early cognitive decline and brain alterations in a smart-home environment without medical support.
The national e-prescription system in Greece is one of the most important achievements in the e-health sector. Healthcare professionals' feedback is essential to ensure the introduced system tends to ...their needs and reduces their everyday workload. The number of surveys collecting the users' views is limited, while the existing studies include only a small number of participants.
In this study, healthcare professionals' perceptions on e-prescription are explored. For this, a questionnaire was distributed online, containing closed- and open-ended questions aiming to address strengths and identify drawbacks in e-prescription. Answers were collected from primary health care physicians, specialized medical doctors and pharmacists.
In total, 430 answers were collected (129 from primary health care physicians, 164 responses from specialized medical doctors and 137 pharmacists). Analysis of the collected answers reveals that the views of the three groups of healthcare professionals mostly converge. The positive impact e-prescribing systems have on the overall prescribing procedure in preventing errors and providing automation is commented. Among gaps identified and proposed improvements, health care professionals note the need for access to information on adverse drug reactions, side effects, drug-to-drug interactions and allergies. Flexible interaction with Therapeutic Prescription Protocols is desired to ameliorate monitoring and decision-making, while drug dosing features, and simplified procedures for copying, repeating, canceling a prescription, are perceived as useful to incorporate.
Collecting healthcare professionals' feedback is important, as their views can be transcribed to system requirements, to further promote e-prescribing and improve the provided health care services by facilitating decision making through safer and more efficient e-prescription. Introduction of the identified improvements can simplify the everyday workflow of healthcare professionals. To the best of our knowledge, a survey with more than 400 answered questionnaires on the use of e-prescription systems by healthcare professionals has never been conducted in Greece before.
Background
Smart homes offer a unique potential not only for supporting various users’ needs but also for monitoring their activities. Herein the findings of the RADAR‐AD Smart Home study are ...presented. Using data from activity sensors installed in a smart home environment simulating real‐life conditions, we aim to detect deficits while performing Activities of Daily Living (ADLs) in three known groups of people in different stages of cognitive decline related to Alzheimer’s Disease (AD).
Method
In a fully equipped Smart Home (https://smarthome.iti.gr/), Fibaro smart plugs (to monitor power consumption), motion‐, door‐, flood‐sensors and panic buttons were installed. During their 24 hour visit, participants 13 Healthy Control (HC), 14 Preclinical AD (PreAD), 13 Prodromal AD (ProAD) (Table 1) followed a protocol listing a number of ADLs (e.g., meal/beverage preparation) (Table 1). Data collection, feature extraction (i.e., activity duration, number of repetitions) and visualization of ADLs were performed through the CARL data collection and analysis platform for assisted living (https://carl.iti.gr/). (Figure 1). Two‐Way ANOVA and Mann‐Whitney were used to test the statistical significance between the groups.
Result
Comparing Activity Duration for different ADLs and groups (Figure 2), HC executed the ADLs in less time compared to PreAD and ProAD, while PreAD showed no difference in the majority of the tasks compared to ProAD. Three activities were completed by 50% of the HC, 36% of the PreAD and only 17% of the ProAD. Two‐way ANOVA revealed a statistically significant interaction between the effects of cognitive decline and Activity Duration F(3,106) = 3.504, p = 0.034. Mann‐Whitney analysis for the complex task “Meal Preparation” showed decreased duration for HC compared to PreAD (U = 33.00, p = 0.042) and ProAD (U = 17.00, p = 0.052).
Conclusion
The RADAR‐AD Smart Home study provides a proof‐of‐concept for the use of home‐based sensors for investigating ADLs in patients with cognitive decline.
This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (grant No 806999). www.imi.europa.eu. This communication reflects the views of the RADAR‐AD consortium and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein.
Abstract
Background
Remote monitoring technologies (RMTs), such as smartphone apps and smartwatches, are changing the way functional and cognitive performance are measured in Alzheimer’s disease ...(AD). Due to their sensitivity, objectivity, and the option of long‐term and continuous measurement, RMTs have the potential to detect a subtle decline in the earliest stages of AD. Here, we present the results of the European RADAR‐AD project (Remote Assessment of Disease and Relapse – Alzheimer’s disease), which aims to test feasibility, acceptability and validity of RMT measures across all stages of AD, from cognitively normal to mild dementia.
Method
Four study groups (amyloid negative healthy controls, and amyloid positive preclinical AD, prodromal AD, mild‐to‐moderate AD) were included in this cross‐sectional study (N = 175). During 8 weeks, participants wore two activity trackers (Fitbit and Axivity) measuring physical activity, heart rate and sleep continuously, and used two interactive smartphone apps (Mezurio and Altoida’s research algorithm: DNS‐MCI) measuring cognition daily/weekly. At baseline, participants underwent extensive neuropsychological, physical examinations, and did two sensor‐based tests (banking app and walk test). Features were extracted for all RMTs (Figure 1) and compared across groups using ANCOVA, with adjustment for relevant confounders. This study is part of an ongoing investigation into high‐end multimodal analyses for real‐world functional performance of continuous RMT data streams.
Result
Compliance was high, but decreased with cognitive impairment (feasibility). User experience did not differ between groups but was lower for smartwatches compared to interactive smartphone apps (Table 1) (acceptability). Various individual sensors discriminated symptomatic AD participants from asymptomatic participants (p<0.05), for example the two active apps, but did not discriminate preclinical AD from healthy controls (Table 1) (validity).
Conclusion
The RADAR‐AD study provides unique insights in the feasibility, acceptability, and validity of remote monitoring of functional abilities in AD and their potential to differentiate between syndromic stages.
This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (grant No 806999) and their associated partners. www.imi.europa.eu. This communication reflects the views of the RADAR‐AD consortium and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein
.
Background
RADAR‐AD is a European project in the context of the Innovative Medicine Initiative (IMI) focusing on the earlier identification of patients at risk for developing Alzheimer’s Disease (AD) ...via a panel of remote monitoring technologies (RMTs), including smartphone apps and wearable devices.
Method
We examined the ability of 6 RMTs (Altoida, Axivity, Banking app, Fitbit, Physilog, and Mezurio) to distinguish between healthy controls (HC) and disease stages of preclinical (PreAD), prodromal (ProAD), and mild to moderate Alzheimer’s disease (MildAD) based on 175 patients (interim analysis). We trained three machine learning classifiers (Logistic Regression, Random Forest, and XGBoost) in a pairwise setting (HC vs. PreAD, HC vs. ProAD, HC vs. MildAD, PreAD vs. ProAD, and ProAD vs. MildAD). Since the interim dataset is still limited, we performed repeated, stratified nested cross‐validation to get a robust performance estimate. Each classifier was trained with the features of the different devices and a set of baseline variables. The latter include a patient’s gender, age, years of education, and body mass index (BMI) when physical conditions might play a role (Axivitiy, Fitbit, Physilog). In addition, we checked whether specific patterns of the study groups allowed discrimination of the different study groups based on the baseline variables alone. Therefore, we trained one Logistic Regression model with these variables and compared the performance of the other three models with this baseline. The models trained with the baseline and questionnaire‐based data served as the reference value in our benchmark that represents how well the discrimination of the different groups works with clinical tests.
Result
Our preliminary data show that RMTs can identify patients already in a prodromal disease stage (AUC ∼69%, Figure 1). Furthermore, the pairwise combination of data from a banking app and an app monitoring functional cognitive abilities via an augmented reality game slightly increased our model’s discriminative ability (Altoida ‐ Banking, Figure 2). The overall best performance was achieved when combining RMTs with the Amsterdam I‐ADL questionnaire.
Conclusion
Our results demonstrate the potential of RMTs and the Amsterdam I‐ADL questionnaire for identifying patients in prodromal stage in primary care settings.
Background
Augmented reality apps merge real world with virtual experiences and can be used to remotely assess complex instrumental activities of daily living (iADL) that are affected early in ...Alzheimer’s disease (AD). Our aim was to compare standard clinical measures with an augmented reality app to assess iADL that are related to memory and spatial navigation in early AD and its feasibility in the home‐setting.
Method
We administered an augmented reality app (Altoida Inc., Washington DC, USA) in an on‐going cross‐sectional study (RADAR‐AD: Remote Assessment of Disease and Relapse – Alzheimer’s Disease) in three groups: 1) amyloid negative healthy controls (HC, N = 49); and amyloid positive 2) preclinical AD (PreAD, N = 17); and 3) prodromal AD (ProAD, N = 29) (Table 1). Altoida’s research algorithm DNS‐MCI (Digital Neuro Signature) produces the outcome of a machine learning model trained to identify cognitively normal individuals from those with cognitive impairment). DNS‐MCI reflects performance in app‐based tasks assessing memory and visuo‐spatial function (placing and finding virtual objects, fire drill simulation) further including attention and motor performance (reaction time, finger tapping, navigational trajectory). At baseline, app‐based tasks were performed in the clinic together with a standard neuropsychological assessment and iADL questionnaires (Figure 1). Participants were furthermore given the option of using Altoida in the home environment.
Result
The DNS‐MCI score could significantly distinguish HC and PreAD participants from the ProAD group and was correlated with all neuropsychological tests and iADL questionnaires (Figures 1 and 2). Participants used the app on average 3‐4 times at home (Table 1). Baseline in‐clinic assessments were strongly correlated with at‐home assessments (r = 0.53, p<.001).
Conclusion
App‐based augmented reality tasks are applicable in the home setting and successful in capturing cognitive impairment in early AD. Future research should focus on fine graining algorithms to also detect possible subtle impairment in preAD.
This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (grant No 806999). www.imi.europa.eu. This communication reflects the views of the RADAR‐AD consortium and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein.
Background
Gait is a complex everyday activity that depends upon supraspinal activity and a host of cognitive functions such as attention and executive functions. As cognition declines in ...neurodegenerative diseases, the interaction and competition for neuronal resources during motor‐cognitive dual‐tasking (e.g., walking while talking) might be a sensitive measure of subtle functional impairments in early Alzheimer’s disease (AD). Here, we aim to identify gait deficits due to neuronal competition across the AD spectrum.
Method
This investigation is part of the ongoing Remote Assessment of Disease and Relapse – Alzheimer’s Disease (RADAR‐AD) study. We attached three inertial measurement units (accelerometer and gyroscope) to both feet and one hip to assess dual task effects (DTE) assessing gait performance with/without concurrent serial subtraction‐by‐1 task in four groups: 1) amyloid negative healthy controls (HC, N = 59); and 2) amyloid positive preclinical AD (PreAD, N = 30); 3) prodromal AD (ProAD, N = 51); and 4) mild‐to‐moderate AD dementia (MildAD, N = 44) (Table 1). We furthermore investigated associations of DTE with observer‐reported cognition.
Result
Group comparisons showed that dual‐tasking induced lower cadence and increased stance, which were significantly different between HC and ProAD. Several DTE measures of variability differed significantly between PreAD and MildAD, with variability in the path length separating best between PreAD and ProAD (Table 2, Figure 1). DTE measures were associated with observer‐rated divided attention only in the MildAD group.
Conclusion
Neuronal competition as assessed with motor‐cognitive dual‐tasking, specifically the DTE variability, might reflect functional deficits already in early AD, and could be a valuable additional measure to detect early impairments not captured by cognitive or motor tests alone. Future studies should implement an adaptive cognitive load to improve sensitivity/specificity in early AD stages and investigate the use of sensor technologies in predicting and monitoring changes in gait and fall prevention in later stages of the disease.
This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (grant No 806999). www.imi.europa.eu. This communication reflects the views of the RADAR‐AD consortium and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein.
Background
Remote monitoring technologies (RMTs), such as smartphone apps, smartwatches, and in‐home sensors, are rapidly changing the way functional and cognitive performance is measured in ...Alzheimer’s disease (AD) patients. Here, we present results from the European RADAR‐AD study on the use of multimodal data streams for the identification of functional deficits across all syndromic stages of AD.
Method
Four study groups (Healthy controls (HC), preclinical AD (pre. AD), prodromal AD (pro. AD), and mild AD) were included in this cross‐sectional study. The RMT features (gait measures from Timed up and Go (TUG), Dual Task Effect (DTE) using physilog, acoustic features from Speech task in Mezurio, neurocognitive function using Altoida, managing finances with Banking app) with in‐clinic neuropsychological (NP) tests, activities of daily living with Amsterdam IADL and demographics (age, gender, education years) were analyzed for different combinations of the multimodal digital biomarker of disease stage in AD across different pairwise comparisons. The analysis includes data from 175 participants (HC = 67, Pre.AD = 26, Pro.AD = 50, Mild AD = 32) collected for 8 weeks. An extreme gradient boosting (XGBoost) machine learning model was trained to obtain a multimodal biomarker of AD disease stage with repeated cross‐validation (5‐fold with 4 repeats) (Figure 1). This investigation is part of the ongoing RADAR‐AD study.
Result
The multimodal combination of RMTs achieved a mean AUC of > 0.60 in all pairwise comparisons with a mean AUC > 0.65 for HC vs (Pre.AD, Pro.AD and Mild) (Figures 2 and 3). The addition of NP tests increases the performance considerably across all pairwise comparisons except HC vs Pre.AD.
Conclusion
Our results highlight the advantage of combining RMTs to identify functional deficits in the early stage of AD. In particular, in prodromal and mild AD patients, a combined signal shows much more strength compared to individual tests.
This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (grant No 806999). www.imi.europa.eu. This communication reflects the views of the RADAR‐AD consortium and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein.
