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Physical activity is a vital part of cardiac rehabilitation (CR). However, heart-healthy physical activity levels in people with cardiovascular disease drop significantly after CR. This exploratory study employs qualitative and survey methods within a co-creation approach. The aim is to understand the mechanisms of healthy behavior and habit formation in order to create a novel evidence-based (post-)rehabilitation approach that employs digital means to sustain long-term physical activity levels in people with cardiovascular disease.

As citizens, we must all take responsibility for our own health to some extent, and recent developments in medical informatics have provided some valuable new ways to help us do that.This book presents the proceedings of the 2020 Special Topic Conference of the European Federation for Medical Informatics (EFMI STC 2020), held for the first time as a virtual conference on 26 & 27 November 2020, due to restrictions associated with the COVID-19 pandemic.Entitled Integrated citizen centered digital health and social care – Citizens as data producers and service co-creators, this conference focused on the citizen-centered aspects of health informatics.This topic provided the opportunity for contributors to present innovative solutions to allow citizens to take greater responsibility for their health with the help of information and communication technology, and the 52 presented papers published here cover a wide range of areas under the broad, invited subject headings of: tools and technologies to support citizen-centered digital services; capacity building to enhance the development and use of digital services; confidentiality, data integrity and data protection to guarantee trustworthy services; citizen safety in digital services; effectiveness and impact of citizen-digital and integrated health and social services; evaluation approaches and methods for digital services; usability, usefulness and user acceptance of digital services; and guidelines for the successful implementation of digital services for citizens.Offering a current overview of research and applications, the book will be of interest to all those health professionals working to increase citizen use of digital healthcare.

Population-level studies confirm the existence of significant rates of overdiagnosis and overtreatment in a number of conditions, particularly those for which the screening of asymptomatic individuals is routine. The implication is that the possibility of being overdiagnosed and/or overtreated must be mentioned as a possible harm in generating informed consent and participation from the individual invited to be screened. But how should the rates of such preference-insensitive population-level phenomena be introduced into preference-sensitive individual decision making? Three possible strategies are rejected, including the currently dominant one that involves presenting the rates relevant to overdiagnosis and overtreatment as discrete pieces of information about a single criterion (typically condition-specific mortality). Extensive quotation from a review of cancer decision aids confirms that processing this complex and isolated information is not a practical approach. However, the task is unnecessary, since an outcome-focused multicriteria decision support tool will incorporate the effects of overdiagnosis and overtreatment – along with the effects of any underdiagnosis and undertreatment.

Empirical measures of ‘decision aid quality’, like normative ones, are of a formative construct and therefore embody interest-conflicted preferences in their criteria selection and weighting. The preferences of the International Patient Decision Aid Standards consortium distinguish the quality of the decision-making process and the quality of the choice that is made ‘(i.e., decision quality)’. The Decision Conflict Scale features heavily in their profile measure of the former and Decision Quality Instruments (DQIs), have been developed by members of the consortium to measure the latter. We confirm that both of these, and other components, like the higher-level measures, are preference-sensitive and interest-conflicted. Non-financial interest-conflicted preferences are endemic in healthcare research, policy-making, and practice. That they are inevitable means the main problem lies in the denial of this and attitude to and behaviour towards alternatives, equally interest-conflicted.

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Early detection of deterioration at hospitals could be beneficial in terms of reducing mortality and morbidity rates and costs. In this paper, we present a model based on Long Short-Term Memory (LSTM) neural network used in deep learning to predict the illness severity of patients in advance. Hence, by predicting health severity, this model can be used to identify deteriorating patients. Our proposed model utilizes continuous monitored vital signs, including heart rate, respiratory rate, oxygen saturation, and blood pressure automatically collected from patients during hospitalization. In this study, a short-time prediction using a sliding window approach is applied. The performance of the proposed model was compared with the Multi-Layer Perceptron (MLP) neural network, a feedforward class of neural network, based on R2 score and Root Mean Square Error (RMSE) metrics. The results showed that the LSTM has a better performance and could predict the illness severity of patients more accurately.