Sensorized T-Shirt with Intarsia-Knitted Conductive Textile Integrated Interconnections: Performance Assessment of Cardiac Measurements during Daily Living Activities.

The development of smart wearable solutions for monitoring daily life health status is increasingly popular, with chest straps and wristbands being predominant. This study introduces a novel sensorized T-shirt design with textile electrodes connected via a knitting technique to a Movesense device. We aimed to investigate the impact of stationary and movement actions on electrocardiography (ECG) and heart rate (HR) measurements using our sensorized T-shirt. Various activities of daily living (ADLs), including sitting, standing, walking, and mopping, were evaluated by comparing our T-shirt with a commercial chest strap. Our findings demonstrate measurement equivalence across ADLs, regardless of the sensing approach. By comparing ECG and HR measurements, we gained valuable insights into the influence of physical activity on sensorized T-shirt development for monitoring. Notably, the ECG signals exhibited remarkable similarity between our sensorized T-shirt and the chest strap, with closely aligned HR distributions during both stationary and movement actions. The average mean absolute percentage error was below 3%, affirming the agreement between the two solutions. These findings underscore the robustness and accuracy of our sensorized T-shirt in monitoring ECG and HR during diverse ADLs, emphasizing the significance of considering physical activity in cardiovascular monitoring research and the development of personal health applications.

ClinApp: A Microservices-Based Platform for Efficient Medical Visit Scheduling.

Clin App is a platform streamlining medical appointment management and patient data collection using a conversational agent. Focused on healthcare professionals and patients, it offers appointment automation, questionnaire creation, and medical data management. This work showcases ClinApp's microservices-based architecture and its user-centered design.

Creative Art Therapy as an Efficient Way to Improve the Well-Being of People Living with Dementia.

Dementia is characterized by a decline in cognition and loss of functional abilities. It is becoming more and more prevalent which places a tremendous strain on the health and social care systems, in parallel the caregivers are under a lot of stress. Engaging in creative activities such as painting, drawing, dance, music, and drama can help reduce stress, anxiety, and depression, and promote a sense of can be beneficial for patients with dementia as it can help them maintain their cognitive abilities. Educating people with dementia, their relatives, and professionals to improve their quality of life through innovative creative arts therapies such as music, dance, and drama supported by digital tools is an invaluable resource for people and organizations seeking to improve their wellness. Furthermore, it is highlighted the value of involving family members and caregivers in the therapeutic process, recognizing their critical role in supporting the well-being of people with dementia.

Towards the Definition of an Intelligent System for Organizing Medical Visits and Collecting Medical Data.

Appointment Scheduling (AS), typically serves as the basis for the majority of non-urgent healthcare services and is a fundamental healthcare-related procedure which, if done correctly and effectively, can lead to significant benefits for the healthcare facility. The main objective of this work is to present ClinApp, an intelligent system able to schedule and manage medical appointments and collect medical data directly from patients.

Towards an Explainable AI-Based Tool to Predict Preterm Birth.

Preterm birth (PTB) is defined as delivery occurring before 37 weeks of gestation. In this paper, Artificial Intelligence (AI)-based predictive models are adapted to accurately estimate the probability of PTB. In doing so, pregnant women' objective results and variables extracted from the screening procedure in combination with demographics, medical history, social history, and other medical data are used. A dataset consisting of 375 pregnant women is used and a number of alternative Machine Learning (ML) algorithms are applied to predict PTB. The ensemble voting model produced the best results across all performance metrics with an area under the curve (ROC-AUC) of approximately 0.84 and a precision-recall curve (PR-AUC) of approximately 0.73. An attempt to provide clinicians with an explanation of the prediction is performed to increase trustworthiness.

A multinational investigation of healthcare needs, preferences, and expectations in supportive cancer care: co-creating the LifeChamps digital platform.

PURPOSE: This study is to evaluate healthcare needs, preferences, and expectations in supportive cancer care as perceived by cancer survivors, family caregivers, and healthcare professionals. METHODS: Key stakeholders consisted of cancer survivors diagnosed with breast cancer, prostate cancer, or melanoma; adult family caregivers; and healthcare professionals involved in oncology. Recruitment was via several routes, and data were collected via either online surveys or telephone interviews in Greece, Spain, Sweden, and the UK. Framework analysis was applied to the dataset. RESULTS: One hundred and fifty-five stakeholders participated: 70 cancer survivors, 23 family caregivers, and 62 healthcare professionals (13 clinical roles). Cancer survivors and family caregivers' needs included information and support on practical/daily living, as frustration was apparent with the lack of follow-up services. Healthcare professionals agreed on a multidisciplinary health service with a "focus on the patient" and availability closer to home. Most healthcare professionals acknowledged that patient-reported outcomes may provide "better individualised care". Cancer survivors and family caregivers generally felt that the digital platform would be useful for timely personalised support and aided communication. Healthcare professionals were supportive of the "proactive" functionality of the platform and the expected advantages. Anticipated challenges were integration obstacles such as workload/infrastructure and training/support in using the new technology. CONCLUSIONS: Obtaining key stakeholders' insights provided a foundation for action to further co-create the LifeChamps digital platform to meet needs and priorities and deliver enhanced supportive care to "older" cancer survivors. IMPLICATIONS FOR CANCER SURVIVORS: Co-creation provided insight into gaps where digital support may enhance health and well-being.

Risk Assessment of COVID-19 Cases in Emergency Departments and Clinics With the Use of Real-World Data and Artificial Intelligence: Observational Study.

BACKGROUND: The recent COVID-19 pandemic has highlighted the weaknesses of health care systems around the world. In the effort to improve the monitoring of cases admitted to emergency departments, it has become increasingly necessary to adopt new innovative technological solutions in clinical practice. Currently, the continuous monitoring of vital signs is only performed in patients admitted to the intensive care unit. OBJECTIVE: The study aimed to develop a smart system that will dynamically prioritize patients through the continuous monitoring of vital signs using a wearable biosensor device and recording of meaningful clinical records and estimate the likelihood of deterioration of each case using artificial intelligence models. METHODS: The data for the study were collected from the emergency department and COVID-19 inpatient unit of the Hippokration General Hospital of Thessaloniki. The study was carried out in the framework of the COVID-X H2020 project, which was funded by the European Union. For the training of the neural network, data collection was performed from COVID-19 cases hospitalized in the respective unit. A wearable biosensor device was placed on the wrist of each patient, which recorded the primary characteristics of the visual signal related to breathing assessment. RESULTS: A total of 157 adult patients diagnosed with COVID-19 were recruited. Lasso penalty function was used for selecting 18 out of 48 predictors and 2 random forest-based models were implemented for comparison. The high overall performance was maintained, if not improved, by feature selection, with random forest achieving accuracies of 80.9% and 82.1% when trained using all predictors and a subset of them, respectively. Preliminary results, although affected by pandemic limitations and restrictions, were promising regarding breathing pattern recognition. CONCLUSIONS: This study represents a novel approach that involves the use of machine learning methods and Edge artificial intelligence to assist the prioritization and continuous monitoring procedures of patients with COVID-19 in health departments. Although initial results appear to be promising, further studies are required to examine its actual effectiveness.

A Collective Intelligence Platform to Support Older Cancer Survivors: Towards the Definition of LifeChamps System and Big Data Reference Architecture.

Within the most recent years, most of the cancer patients are older age, which implies the necessity to a better understanding of aging and cancer connection. This work presents the LifeChamps solution built on top of cutting-edge Big Data architecture and HPC infrastructure concepts. An innovative architecture was envisioned supported by the Big Data Value Reference Model and answering the system requirements from high to low level and from logical to physical perspective, following the "4+1 architectural model".

Can Artificial Intelligence Enable the Transition to Electric Ambulances?

The electrification of the transportation sector is seen as a main pathway to reduce CO2 emissions and mitigate the earth's climate change. Currently, Electric Vehicles (EVs) are entering the market fast. Although EVs have not been used as ambulances yet, the transition to the new type of vehicle is a matter of time. Thus, in this paper we discuss a number of research questions related to the efficient deployment of electric ambulances, focusing on the Artificial Intelligence (AI) point of view and we propose a framework for developing online algorithms that schedule the charging of electric ambulances and their assignment to patients.

The Greek translation and validation of the electronic version of the Meno-D rating scale for post-menopausal depression.

Menopausal transition and post-menopause constitute windows of increased vulnerability to depression. Recently, the Meno-D was introduced, a novel 12-item, with five distinct subscales. The aim of our study was to translate and validate the electronic version of the Meno-D among Greek post-menopausal women. Translation and back-translation were performed by an expert group, while face validity was assessed by five experts. Along with the Beck Depression Inventory-II, the Meno-D scale was distributed online to 502 post-menopausal women. A confirmatory factor analysis was performed to investigate construct validity and both convergent and discriminant validity were evaluated. The data analysis was performed using Statistical Package for Social Sciences and AMOS. The 5-factor model of Meno-D achieved adequate levels of goodness-of-fit indices, scoring lower values in discriminant validity examined with heterotrait-monotrait ratio and composite reliability. The significant correlation with the Beck Depression Inventory-ΙΙ revealed for all subscales was indicative of good convergent validity. An exploratory factor analysis was additionally performed, suggesting a 12-item tool comprising two subscales: (i) psychological and (ii) biological and achieving good levels of fit. Our data confirmed that the electronic version of Meno-D is a valid tool that can be used for screening and evaluation of depression in Greek post-menopausal women.

Digital Biomarkers for Supporting Transitional Care Decisions: Protocol for a Transnational Feasibility Study.

BACKGROUND: Virtual Health and Wellbeing Living Lab Infrastructure is a Horizon 2020 project that aims to harmonize Living Lab procedures and facilitate access to European health and well-being research infrastructures. In this context, this study presents a joint research activity that will be conducted within Virtual Health and Wellbeing Living Lab Infrastructure in the transitional care domain to test and validate the harmonized Living Lab procedures and infrastructures. The collection of data from various sources (information and communications technology and clinical and patient-reported outcome measures) demonstrated the capacity to assess risk and support decisions during care transitions, but there is no harmonized way of combining this information. OBJECTIVE: This study primarily aims to evaluate the feasibility and benefit of collecting multichannel data across Living Labs on the topic of transitional care and to harmonize data processes and collection. In addition, the authors aim to investigate the collection and use of digital biomarkers and explore initial patterns in the data that demonstrate the potential to predict transition outcomes, such as readmissions and adverse events. METHODS: The current research protocol presents a multicenter, prospective, observational cohort study that will consist of three phases, running consecutively in multiple sites: a cocreation phase, a testing and simulation phase, and a transnational pilot phase. The cocreation phase aims to build a common understanding among different sites, investigate the differences in hospitalization discharge management among countries, and the willingness of different stakeholders to use technological solutions in the transitional care process. The testing and simulation phase aims to explore ways of integrating observation of a patient's clinical condition, patient involvement, and discharge education in transitional care. The objective of the simulation phase is to evaluate the feasibility and the barriers faced by health care professionals in assessing transition readiness. RESULTS: The cocreation phase will be completed by April 2022. The testing and simulation phase will begin in September 2022 and will partially overlap with the deployment of the transnational pilot phase that will start in the same month. The data collection of the transnational pilots will be finalized by the end of June 2023. Data processing is expected to be completed by March 2024. The results will consist of guidelines and implementation pathways for large-scale studies and an analysis for identifying initial patterns in the acquired data. CONCLUSIONS: The knowledge acquired through this research will lead to harmonized procedures and data collection for Living Labs that support transitions in care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/34573.

Analytical exploratory tool for healthcare professionals to monitor cancer patients' progress.

Introduction: Cancer is a primary public concern in the European continent. Due to the large case numbers and survival rates, a significant population is living with cancer needs. Consequently, health professionals must deal with complex treatment decision-making processes. In this context, a large quantity of data is collected during cancer care delivery. Once collected, these data are complex for health professionals to access to support clinical decision-making and performance review. There is a need for innovative tools that make clinical data more accessible to support cancer health professionals in these activities. Methods: Following a co-creation, an interactive approach thanks to the Interactive Process Mining paradigm, and data from a tertiary hospital, we developed an exploratory tool to present cancer patients' progress over time. Results: This work aims to collect and report the process of developing an exploratory analytical Interactive Process Mining tool with clinical relevance for healthcare professionals for monitoring cancer patients' care processes in the context of the LifeChamps project together with a graphical and navigable Process Indicator in the context of prostate cancer patients. Discussion: The tool presented includes Process Mining techniques to infer actual processes and present understandable results visually and navigable, looking for different types of patients, trajectories, and behaviors.

Physical Training In-Game Metrics for Cognitive Assessment: Evidence from Extended Trials with the Fitforall Exergaming Platform.

Conventional clinical cognitive assessment has its limitations, as evidenced by the environmental shortcomings of various neuropsychological tests conducted away from an older person's everyday environment. Recent research activities have focused on transferring screening tests to computerized forms, as well as on developing short screening tests for screening large populations for cognitive impairment. The purpose of this study was to present an exergaming platform, which was widely trialed (116 participants) to collect in-game metrics (built-in game performance measures). The potential correlation between in-game metrics and cognition was investigated in-depth by scrutinizing different in-game metrics. The predictive value of high-resolution monitoring games was assessed by correlating it with classical neuropsychological tests; the area under the curve (AUC) in the receiver operating characteristic (ROC) analysis was calculated to determine the sensitivity and specificity of the method for detecting mild cognitive impairment (MCI). Classification accuracy was calculated to be 73.53% when distinguishing between MCI and normal subjects, and 70.69% when subjects with mild dementia were also involved. The results revealed evidence that careful design of serious games, with respect to in-game metrics, could potentially contribute to the early and unobtrusive detection of cognitive decline.

Semantic Representation of Patient Triage Data Collected in Emergency Departments.

Emergency Department (ED) overcrowding is a major issue for the efficient management of patients. To this end, triage algorithms have been developed to support the task of patient prioritization. In this paper an ontology was designed to represent the knowledge about patient triage procedure in EDs.

Design and evaluation of mobile scenario based learning in the self-management of chronic pain.

Chronic pain is a lifelong issue, being one of the main causes of disability, affecting a great number of people worldwide, many of which often avoid seeking medical advice from pain experts and/or demonstrate poor adherence to their therapeutic plan. One of the most important steps in achieving a manageable course of disease, is the ability of self-management. We aimed at applying a method of systematic patient education and self-management through the use of Virtual Patients (VPs), a well-established method for educating medical doctors and students but never before targeting patients. Two VPs scenarios were designed, tested and evaluated by patients with rheumatic disorders, achieving a SUS score of 88/100 "Best Imaginable", alongside with positive reviews from the participants. The positive feedback from the patients supports the potential of VP educational paradigm to educate these patients and equip them with disease coping skills and strategies.

Trends in ablation procedures in Greece over the 2008-2018 period: Results from the Hellenic Cardiology Society Ablation Registry.

OBJECTIVE: In 2008, the radiofrequency ablation (RFA) procedures registry of the Hellenic Society of Cardiology was created. This online database allowed electrophysiologists around the country to input data for all performed ablation procedures. The aim of this study is to provide a thorough report and interpretation of the data submitted to the registry between 2008 and 2018. METHODS: In 2008, a total of 27 centers/medical teams in 24 hospitals were licensed to perform RFA in Greece. By 2018, the number had risen to 31. Each center was tasked with inserting their own data into the registry, which included patient demographics (anonymized), type of procedure and technique, complications, and outcomes. RESULTS: A total of 18587 procedures in 17900 patients were recorded in the period of 2008-2018. By 2018, slightly more than 70% of procedures were performed in 7 high-volume centers (>100 cases/year). The most common procedure since 2014 was atrial fibrillation ablation, followed by atrioventricular nodal reentry tachycardia ablation. Complication rates were low, and success rates remained high, whereas the 6-month relapse rates declined steadily. CONCLUSION: This online RFA registry has proved that ablation procedures in Greece have reached a very high standard, with results and complication rates comparable to European and American standards. Ablation procedures for atrial fibrillation are increasing constantly, with it being the most common intervention over the last 6-year period, although the absolute number of procedures still remains low, compared to other European countries.

Development of Cognitive and Physical Exercise Systems, Clinical Recordings, Large-Scale Data Analytics, and Virtual Coaching for Heart Failure Patients: Protocol for the BioTechCOACH-ForALL Project.

BACKGROUND: Heart failure is a chronic disease affecting patient morbidity and mortality. Current guidelines for heart failure patient treatment are focused on improving their clinical status, functional capacity, and quality of life. However, these guidelines implement numerous instructions including medical treatment adherence, physical activity, and self-care management. The complexity of the therapeutic instructions makes them difficult to follow especially by older adults. OBJECTIVE: The challenge of this project is to (1) measure real-life adherence to a regular physical exercise program and (2) attempt to influence older adult patients with heart failure toward embracing a more physically active self-care lifestyle. METHODS: This research consists of two studies, including a lab experiment and a pragmatic evaluation of technology at patients' homes. The lab experiment aims at exploring in an objective way (measuring neurophysiological responses to stimuli) patient engagement with different characteristics of virtual agents, while the home study is a 3-phase prospective study where the developed technology platform is tested by heart failure patients in their own home environments. Patients undergo evaluation of their physical activity and cognitive status using standard evaluation methods (6-minute walk test, questionnaires) and receive wearable devices to accurately measure everyday life activity levels (home study phases 1-3). During home study phases 2 and 3, exergames (serious games for physical exercise) to provide a physical exercise plan as a joyful activity are delivered to patients' private households and e-coaching techniques are implemented in the final phase (home study phase 3) of the protocol, to influence patient attitudes toward a more healthy and recommended lifestyle. RESULTS: The trial is still ongoing. Recruitment is ongoing, and the project has progressed for some participants through phase 2 of the home study. The sample size for both studies is 28 participants; 10 have already been included in the study, and both baseline clinical and patient-reported outcome data are retrieved. Phases 2 and 3 of the home pilot study are expected to be completed within 6 months. CONCLUSIONS: The main challenge of the project is the change of attitude of older age heart failure patients through an e-coaching system. Given the adoption of a cocreation and living lab approach and the main objective for real-life evaluation, the project is ready to react to any collected feedback, even during the implementation of the research plan. Clinical assessment and objective evaluation are expected to provide all required information for reliable findings. TRIAL REGISTRATION: ClinicalTrials.gov NCT03877328; https://clinicaltrials.gov/ct2/show/NCT03877328. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/17714.

Towards the Definition of an Intelligent Triage and Continuous Monitoring System for Hospital Emergency Departments and Clinics.

Recent statistics have demonstrated that Emergency Departments (EDs) in Greece lack in organization and service. In most cases, patient prioritization is not automatically implemented. The main objective of this paper is to present IntelTriage, a smart triage system, that dynamically assigns priorities to patients in an ED and monitors their vital signs and location during their stay in the clinic through wearable biosensors. Initital scenarios and functional requirements are presented as preliminary results.

A New Approach for Ageing at Home: The CAPTAIN System.

Our work exhibits how previous projects on the Active and Healthy Ageing field have advanced to the conception of CAPTAIN, a radically new approach towards increased end-user acceptance. The goal is to create intuitive technology that does not require specific skills for interaction and blends in with real life. CAPTAIN will be co-designed by all types of stakeholders, including older adults, involved in all stages, from the initial design to delivery of the final system.