Regulatory considerations for developing remote measurement technologies for Alzheimer's disease research.

The Remote Assessment of Disease and Relapse – Alzheimer’s Disease (RADAR-AD) consortium evaluated remote measurement technologies (RMTs) for assessing functional status in AD. The consortium engaged with the European Medicines Agency (EMA) to obtain feedback on identification of meaningful functional domains, selection of RMTs and clinical study design to assess the feasibility of using RMTs in AD clinical studies. We summarized the feedback and the lessons learned to guide future projects.

The Digital Platform and Its Emerging Role in Decentralized Clinical Trials.

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.

The PrescIT platform: An interoperable Clinical Decision Support System for ePrescription to Prevent Adverse Drug Reactions and Drug-Drug Interactions.

Introduction: Preventable medication errors have been proven to cause significant public health burden, and ePrescription is a key part of the process where medication errors and adverse effects could be prevented. Information systems and "intelligent" computational approaches could provide a valuable tool to prevent such errors with profound impact in clinical practice.

Objectives: The PrescIT platform is a Clinical Decision Support System (CDSS) that aims to facilitate the prevention of adverse drug reactions (ADRs) and drug-drug interactions (DDIs) in the phase of ePrescription in Greece.

Cancer Patients' Perspectives and Requirements of Digital Health Technologies: A Scoping Literature Review.

Digital health technologies have the potential to alleviate the increasing cancer burden. Incorporating patients' perspectives on digital health tools has been identified as a critical determinant for their successful uptake in cancer care. The main objective of this scoping review was to provide an overview of the existing evidence on cancer patients' perspectives and requirements for patient-facing digital health technologies.

Electronic prescription systems in Greece: a large-scale survey of healthcare professionals' perceptions.

Background: 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.

Evaluation of an electronic prescription platform: Clinicians' feedback on three distinct services aiming to facilitate clinical decision and safer e-prescription.

Background: Health Care Professionals (HCPs) are the main end-users of digital clinical tools such as electronic prescription systems. For this reason, it is of high importance to include HCPs throughout the design, development and evaluation of a newly introduced system to ensure its usefulness, as well as confirm that it tends to their needs and can be integrated in their everyday clinical practice.

Methods: In the context of the PrescIT project, an electronic prescription platform with three services was developed (i.

Assessing the cognitive decline of people in the spectrum of AD by monitoring their activities of daily living in an IoT-enabled smart home environment: a cross-sectional pilot study.

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.

A Semantic Framework to Detect Problems in Activities of Daily Living Monitored through Smart Home Sensors.

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.

NeuMa - the absolute Neuromarketing dataset en route to an holistic understanding of consumer behaviour.

Neuromarketing is a continuously evolving field that utilises neuroimaging technologies to explore consumers' behavioural responses to specific marketing-related stimulation, and furthermore introduces novel marketing tools that could complement the traditional ones like questionnaires. In this context, the present paper introduces a multimodal Neuromarketing dataset that encompasses the data from 42 individuals who participated in an advertising brochure-browsing scenario. In more detail, participants were exposed to a series of supermarket brochures (containing various products) and instructed to select the products they intended to buy.

Eliciting brain waves of people with cognitive impairment during meditation exercises using portable electroencephalography in a smart-home environment: a pilot study.

Objectives: 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.

Use of Real-World Data to Support Adverse Drug Reactions Prevention During ePrescription.

Adverse Drug Reactions (ADRs) are a crucial public health issue due to the significant health and monetary burden that they can impose. Real-World Data (RWD), e.g.

The PrescIT Knowledge Graph: Supporting ePrescription to Prevent Adverse Drug Reactions.

Adverse Drug Reactions (ADRs) are an important public health issue as they can impose significant health and monetary burdens. This paper presents the engineering and use case of a Knowledge Graph, supporting the prevention of ADRs as part of a Clinical Decision Support System (CDSS) developed in the context of the PrescIT project. The presented PrescIT Knowledge Graph is built upon Semantic Web technologies namely the Resource Description Framework (RDF), and integrates widely relevant data sources and ontologies, i.

A Sparse Representation Classification Scheme for the Recognition of Affective and Cognitive Brain Processes in Neuromarketing.

In this work, we propose a novel framework to recognize the cognitive and affective processes of the brain during neuromarketing-based stimuli using EEG signals. The most crucial component of our approach is the proposed classification algorithm that is based on a sparse representation classification scheme. The basic assumption of our approach is that EEG features from a cognitive or affective process lie on a linear subspace.

An integrated simulation framework for the prevention and mitigation of pandemics caused by airborne pathogens.

In this work, we developed an integrated simulation framework for pandemic prevention and mitigation of pandemics caused by airborne pathogens, incorporating three sub-models, namely the spatial model, the mobility model, and the propagation model, to create a realistic simulation environment for the evaluation of the effectiveness of different countermeasures on the epidemic dynamics. The spatial model converts images of real cities obtained from Google Maps into undirected weighted graphs that capture the spatial arrangement of the streets utilized next for the mobility of individuals. The mobility model implements a stochastic agent-based approach, developed to assign specific routes to individuals moving in the city, through the use of stochastic processes, utilizing the weights of the underlying graph to deploy shortest path algorithms.

NeuMark: A Riemannian EEG Analysis Framework for Neuromarketing.

Neuromarketing exploits neuroimaging techniques so as to reinforce the predictive power of conventional marketing tools, like questionnaires and focus groups. Electroencephalography (EEG) is the most commonly encountered neuroimaging technique due to its non-invasiveness, low-cost, and its very recent embedding in wearable devices. The transcription of brainwave patterns to consumer attitude is supported by various signal descriptors, while the quest for profitable novel ways is still an open research question.

Exploring the Connection of Brain Computer Interfaces and Multimedia Use With the Social Integration of People With Various Motor Disabilities: A Questionnaire-Based Usability Study.

We have designed a platform to aid people with motor disabilities to be part of digital environments, in order to create digitally and socially inclusive activities that promote their quality of life. To evaluate in depth the impact of the platform on social inclusion indicators across patients with various motor disabilities, we constructed a questionnaire in which the following indicators were assessed: (i) Well Being, (ii) Empowerment, (iii) Participation, (iv) Social Capital, (v) Education, and (vi) Employment. In total 30 participants (10 with Neuromuscular Disorders-NMD, 10 with Spinal Cord Injury-SCI, and 10 with Parkinson's Disease-PD) used the platform for ~1 month, and its impact on social inclusion indicators was measured before and after the usage.

Exploring Network Properties Across Preclinical Stages of Alzheimer's Disease Using a Visual Short-Term Memory and Attention Task with High-Density Electroencephalography: A Brain-Connectome Neurophysiological Study.

Background: Visual short-term memory (VSTMT) and visual attention (VAT) exhibit decline in the Alzheimer's disease (AD) continuum; however, network disruption in preclinical stages is scarcely explored.

Objective: To advance our knowledge about brain networks in AD and discover connectivity alterations during VSTMT and VAT.

Methods: Twelve participants with AD, 23 with mild cognitive impairment (MCI), 17 with subjective cognitive decline (SCD), and 21 healthy controls (HC) were examined using a neuropsychological battery at baseline and follow-up (three years).

Reconstructing EOG From EEG Timeseries: A Spatial Filtering Approach.

Unobtrusive mental state monitoring based on neurosphysiological signals has seen thriving developments over the past decade, with a wide area of applications, from rehabilitation to neuroergonomics and neuromarketing. Particularly, electroencephalography (EEG) and electrooculography (EOG) have been popular techniques to obtain cognitive-relevant biosignals. However, current wearable systems may still pose practical inconvenience, motivating further interest to integrate EOG+EEG recording into streamlined frontal-only sensor montages with sufficient signal fidelity.

Adaptation and Validation of the Memory Alteration Test (M@T) in Greek Middle-Aged, Older, and Older-Old Population with Subjective Cognitive Decline and Mild Cognitive Impairment.

Background: The Memory Alteration Test (M@T) is a verbal episodic and semantic memory screening test able to detect subjective cognitive decline (SCD) and Mild Cognitive Impairment (MCI).

Objective: To adapt M@T, creating a Greek version of the Memory Alteration Test (M@T-GR), and to validate M@T-GR compared to the Mini-Mental State Examination (MMSE), and Subjective Cognitive Decline- Questionnaire (SCD-Q) MyCog and TheirCog.

Methods: 232 people over 55 years old participated in the study and they were classified as healthy controls (HC, n = 65), SCD (n = 78), or MCI (n = 89).

Human Factors and Requirements of People with Cognitive Impairment, Their Caregivers, and Healthcare Professionals for mHealth Apps Including Reminders, Games, and Geolocation Tracking: A Survey-Questionnaire Study.

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.

Using Business Process Management Notation to Model Therapeutic Prescription Protocols: The PrescIT Approach.

Clinical Decision Support Systems (CDSS) could play a prominent role in preventing Adverse Drug Reactions (ADRs) especially when integrated in larger healthcare systems (e.g. Electronic Health Record - EHR systems, Hospital Management Systems - HMS, e-Prescription systems etc.

Unlocking the Subconscious Consumer Bias: A Survey on the Past, Present, and Future of Hybrid EEG Schemes in Neuromarketing.

Fueled by early success stories, the neuromarketing domain advanced rapidly during the last 10 years. As exciting new techniques were being adapted from medical research to the commercial domain, many neuroscientists and marketing practitioners have taken the chance to exploit them so as to uncover the answers of the most important marketing questions. Among the available neuroimaging technologies, electroencephalography (EEG) stands out as the less invasive and most affordable method.

Wearable Devices for Assessing Function in Alzheimer's Disease: A European Public Involvement Activity About the Features and Preferences of Patients and Caregivers.

Alzheimer's Disease (AD) impairs the ability to carry out daily activities, reduces independence and quality of life and increases caregiver burden. Our understanding of functional decline has traditionally relied on reports by family and caregivers, which are subjective and vulnerable to recall bias. The Internet of Things (IoT) and wearable sensor technologies promise to provide objective, affordable, and reliable means for monitoring and understanding function.

Remote monitoring technologies in Alzheimer's disease: design of the RADAR-AD study.

Background: Functional decline in Alzheimer's disease (AD) is typically measured using single-time point subjective rating scales, which rely on direct observation or (caregiver) recall. Remote monitoring technologies (RMTs), such as smartphone applications, wearables, and home-based sensors, can change these periodic subjective assessments to more frequent, or even continuous, objective monitoring. The aim of the RADAR-AD study is to assess the accuracy and validity of RMTs in measuring functional decline in a real-world environment across preclinical-to-moderate stages of AD compared to standard clinical rating scales.