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Intending to enable a broader collaboration with the scientific community while maintaining privacy of the data stored and generated in Living Labs, this paper presents the Shareable Data Publishing and Access Service for Living Labs, implemented within the framework of the H2020 VITALISE project. Building upon previous work, significant enhancements and improvements are presented in the architecture enabling Living Labs to securely publish collected data in an internal and isolated node for external use. External researchers can access a portal to discover and download shareable data versions (anonymised or synthetic data) derived from the data stored across different Living Labs that they can use to develop, test, and debug their processing scripts locally, adhering to legal and ethical data handling practices. Subsequently, they may request remote execution of the same algorithms against the real internal data in Living Lab nodes, comparing the outcomes with those obtained using shareable data. The paper details the architecture, data flows, technical details and validation of the service with real-world usage examples, demonstrating its efficacy in promoting data-driven research in digital health while preserving privacy. The presented service can be used as an intermediary between Living Labs and external researchers for secure data exchange and to accelerate research on data analytics paradigms in digital health, ensuring compliance with data protection laws. [ABSTRACT FROM AUTHOR]

Introduction: 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. Methods: 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. Results: 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. Discussion: 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. [ABSTRACT FROM AUTHOR]

Diabetic foot ulcers (DFUs) pose a significant challenge in diabetes care, demanding advanced approaches for effective prevention and management. Smart insoles using sensor technology have emerged as promising tools to address the challenges associated with DFU and neuropathy. By recognizing the pivotal role of smart insoles in successful prevention and healthcare management, this scoping review aims to present a comprehensive overview of the existing evidence regarding DFU studies related to smart insoles, offloading sensors, and actuator technologies. This systematic review identified and critically evaluated 11 key studies exploring both sensor technologies and offloading devices in the context of DFU care through searches in CINAHL, MEDLINE, and ScienceDirect databases. Predominantly, smart insoles, mobile applications, and wearable technologies were frequently utilized for interventions and patient monitoring in diabetic foot care. Patients emphasized the importance of these technologies in facilitating care management. The pivotal role of offloading devices is underscored by the majority of the studies exhibiting increased efficient monitoring, prevention, prognosis, healing rate, and patient adherence. The findings indicate that, overall, smart insoles and digital technologies are perceived as acceptable, feasible, and beneficial in meeting the specific needs of DFU patients. By acknowledging the promising outcomes, the present scoping review suggests smart technologies can potentially redefine DFU management by emphasizing accessibility, efficacy, and patient centricity. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Decentralized clinical trials (DCTs) are becoming increasingly popular. Digital clinical trial platforms are software environments where users complete designated clinical trial tasks, providing investigators and trial participants with efficient tools to support trial activities and streamline trial processes. In particular, digital platforms with a modular architecture lend themselves to DCTs, where individual trial activities can correspond to specific platform modules. While design features can allow users to customize their platform experience, the real strengths of digital platforms for DCTs are enabling centralized data capture and remote monitoring of trial participants and in using digital technologies to streamline workflows and improve trial management. When selecting a platform for use in a DCT, sponsors and investigators must consider the specific trial requirements. All digital platforms are limited in their functionality and technical capabilities. Integrating additional functional modules into a central platform may solve these challenges, but few commercial platforms are open to integrating third-party components. The lack of common data standardization protocols for clinical trials will likely limit the development of one-size-fits-all digital platforms for DCTs. This viewpoint summarizes the current role of digital platforms in supporting decentralized trial activities, including a discussion of the potential benefits and challenges of digital platforms for investigators and participants. We will highlight the role of the digital platform in the development of DCTs and emphasize where existing technology is functionally limiting. Finally, we will discuss the concept of the ideal fully integrated and unified DCT and the obstacles developers must address before it can be realized. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Objectives: Meditation imparts relaxation and constitutes an important nonpharmacological 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. Methods: 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). Results: 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 Z = -2.271, p = 0.023, Z = -3.110, p = 0.002 and Z = -2.341, p = 0.019, Z = -2.132, p = 0.033, respectively. Conclusion: 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. [ABSTRACT FROM AUTHOR]

Background: The on‐going 'Remote Assessment of Disease and Relapse – Alzheimer's Disease' (RADAR‐AD, https://www.radar‐ad.org/) international study uses remote monitoring technologies (RMT's) to continuously and objectively monitor functional decline in Alzheimer's Disease (AD). Managing finances is an Instrumental Activity of Daily Living (IADL), usually measured via traditional pen‐paper methods and interviews. To simulate this IADL, we present the 'Banking App' and preliminary results from the RADAR‐AD study. Method: In the app, users enter a PIN, an amount to withdraw and confirm all inputs, using a numpad on a tablet's touchscreen that resembles an ATM (Figure 1) provided in eight translations. Metrics collected include the duration and correctness of each step (i.e. PIN, Amount and confirmation) and attempt as a whole. The 36 participants to date (Figure 2), from four European sites, performed neuropsychological tests (e.g., MMSE, Rey) and include 20 amyloid negative cognitively normal healthy controls (HC) and 16 amyloid positive, of which 6 cognitively normal preclinical AD (PreAD), 6 cognitively impaired prodromal AD (ProAD) and 4 Mild to Moderate AD (MildAD). Clinicians provided a PIN and an amount to enter from a bill in a controlled lab setting. Statistical analysis included One Way ANOVA, t‐test and Mann‐Whitney between groups in pairs and Pearson correlation. Result: ANOVA showed significance between groups mainly in amount and confirmation durations (p=0.010, p=0.002), leading to t‐test in group pairs (Figures 3 in numbers, visualized in Figure 4), showing increasing durations for more cognitively impaired groups and significant difference between amount and confirmation durations between HC and MildAD (p=0.011, p=0.002). Pearson correlations and heat map (Figure 5) relate app metrics and neuropsychological tests (e.g., MMSE, ADCS with correct amount, p=0.005, and confirmation duration, p=0.001). Conclusion: Preliminary findings highlight that managing finances via an app relates to cognitive assessment, further supporting the potential of this IADL in early AD using technology. This promising early data including near‐significant trends (e.g., duration of tasks between PreAD and ProAD) will be investigated deeper as the study and data collection are on‐going. This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking RADAR‐AD (grant No 806999) and their associated partners. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate the potential of a knowledge-driven framework to improve the efficiency and effectiveness of care through remote and intelligent assessment. More specifically, we present a rule-based approach to detect health related problems from wearable lifestyle sensor data that add clinical value to take informed decisions on follow-up and intervention. We use OWL 2 ontologies as the underlying knowledge representation formalism for modelling contextual information and high-level concepts and relations among them. The conceptual model of our framework is defined on top of existing modelling standards, such as SOSA and WADM, promoting the creation of interoperable knowledge graphs. On top of the symbolic knowledge graphs, we define a rule-based framework for infusing expert knowledge in the form of SHACL constraints and rules to recognise patterns, anomalies and situations of interest based on the predefined and stored rules and conditions. A dashboard visualizes both sensor data and detected events to facilitate clinical supervision and decision making. Preliminary results on the performance and scalability are presented, while a focus group of clinicians involved in an exploratory research study revealed their preferences and perspectives to shape future clinical research using the framework. [ABSTRACT FROM AUTHOR]

The increasing ageing global population is causing an upsurge in ailments related to old age, primarily dementia and Alzheimer's disease, frailty, Parkinson's, and cardiovascular disease, but also a general need for general eldercare as well as active and healthy ageing. In turn, there is a need for constant monitoring and assistance, intervention, and support, causing a considerable financial and human burden on individuals and their caregivers. Interconnected sensing technology, such as IoT wearables and devices, present a promising solution for objective, reliable, and remote monitoring, assessment, and support through ambient assisted living. This paper presents a review of such solutions including both earlier review studies and individual case studies, rapidly evolving in the last decade. In doing so, it examines and categorizes them according to common aspects of interest such as health focus, from specific ailments to general eldercare; IoT technologies, from wearables to smart home sensors; aims, from assessment to fall detection and indoor positioning to intervention; and experimental evaluation participants duration and outcome measures, from acceptability to accuracy. Statistics drawn from this categorization aim to outline the current state-of-the-art, as well as trends and effective practices for the future of effective, accessible, and acceptable eldercare with technology. [ABSTRACT FROM AUTHOR]

Background: Mobile Health (mHealth) apps can delay the cognitive decline of people with dementia (PwD), by providing both objective assessment and cognitive enhancement., Objective: This patient involvement survey aims to explore human factors, needs and requirements of PwD, their caregivers, and Healthcare Professionals (HCPs) with respect to supportive and interactive mHealth apps, such as brain games, medication reminders, and geolocation trackers through a constructive questionnaire., Methods: Following the principles of user-centered design to involve end-users in design we constructed a questionnaire, containing both open-ended and closed-ended questions as well as multiple choice and Likert scale, in order to investigate the specific requirements and preferences for mHealth apps. We recruited 48 participants including people with cognitive impairment ( n  = 15), caregivers ( n  = 16), and HCPs ( n  = 17) and administered the questionnaire., Results: All participants are likely to use mHealth apps, with the primary desired features being the improvement of memory and cognition, assistance on medication treatment, and perceived ease to use. HCPs, caregivers, and PwD consider brain games as an important technology-based, non-pharmaceutical intervention. Both caregivers and patients are willing to use a medication reminder app frequently. Finally, caregivers are worried about the patient wandering. Therefore, global positioning system tracking would be particularly important to them. On the other hand, patients are concerned about their privacy, but are still willing to use a geolocation app for cases of emergency., Conclusion: This research contributes to mHealth app design and potential adoption. All three groups agree that mHealth services could facilitate care and ameliorate behavioral and cognitive disturbances of patients., Competing Interests: The authors have no conflict of interest to report., (© 2021 – The authors. Published by IOS Press.)