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BackgroundHeart 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. ObjectiveThe 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. MethodsThis 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. ResultsThe 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. ConclusionsThe 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 RegistrationClinicalTrials.gov NCT03877328; https://clinicaltrials.gov/ct2/show/NCT03877328 International Registered Report Identifier (IRRID)DERR1-10.2196/17714

The overwhelming volume of patients in emergency departments (EDs) is a significant problem that hinders the delivery of high quality healthcare. Despite their great value, triage protocols are challenging to put into practice. This paper examines the utility of prediction models as a tool for clinical decision support, with a focus on medium-severity patients as defined by the ESI algorithm. 689 cases of medium-risk patients were gathered from the AHEPA hospital, evaluated, and their data fed three classifiers: XGBoost (XGB), Random Forest (RF) and Logistic Regression (LR), with the prediction goal being the outcome of their visit, i.e. admission or discharge. Essential features for the prediction task were determined using feature importance and distribution analysis. Despite having many missing values or high sparsity datasets, several symptoms and metrics are recommended as crucial for outcome prediction. When fed the patients’ vital signs, XGB achieved an accuracy score of 91.30%. Several chief complaints were also proven beneficial. Prediction models can, in general, not only lessen the drawbacks of triage implementation, but also enhance its delivery. [ABSTRACT FROM AUTHOR]

With the rise in global life expectancy, ensuring healthier aging experiences for the older population becomes paramount. This scoping review delves into the technologies employed in the remote health monitoring of the elderly over the past 15 years. Exploring the concept of ”Healthy Ageing” as proposed by the World Health Organization, this paper attempts to highlight the techniques and technologies used in health monitoring of the elderly population. The integration of wearable sensors in health monitoring presents promising avenues for enhancing healthcare delivery to older adults. However, challenges such as limited digital literacy and privacy concerns persist, necessitating innovative solutions for unobtrusive monitoring. This paper discusses the potential of passive and ambient sensors to address these challenges, offering insights into enhancing the well-being of the older population while preserving their independence and privacy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]