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Background: The benefits of regular physical activity for health and quality of life are unarguable. New information, sensing and communication technologies have the potential to play a critical role in computerised decision support and coaching for physical activity., Objectives: We provide a literature review of recent research in the development of physical activity interventions employing computerised decision support, their feasibility and effectiveness in healthy and diseased individuals, and map out challenges and future research directions., Methods: We searched the bibliographic databases of PubMed and Scopus to identify physical activity interventions with computerised decision support utilised in a real-life context. Studies were synthesized according to the target user group, the technological format (e.g., web-based or mobile-based) and decision-support features of the intervention, the theoretical model for decision support in health behaviour change, the study design, the primary outcome, the number of participants and their engagement with the intervention, as well as the total follow-up duration., Results: From the 24 studies included in the review, the highest percentage (n = 7, 29%) targeted sedentary healthy individuals followed by patients with prediabetes/diabetes (n = 4, 17%) or overweight individuals (n = 4, 17%). Most randomized controlled trials reported significantly positive effects of the interventions, i.e., increase in physical activity (n = 7, 100%) for 7 studies assessing physical activity measures, weight loss (n = 3, 75%) for 4 studies assessing diet, and reductions in glycosylated hemoglobin (n = 2, 66%) for 3 studies assessing glycose concentration. Accelerometers/pedometers were used in almost half of the studies (n = 11, 46%). Most adopted decision support features included personalised goal-setting (n = 16, 67%) and motivational feedback sent to the users (n = 15, 63%). Fewer adopted features were integration with electronic health records (n = 3, 13%) and alerts sent to caregivers (n = 4, 17%). Theoretical models of decision support in health behaviour to drive the development of the intervention were not reported in most studies (n = 14, 58%)., Conclusions: Interventions employing computerised decision support have the potential to promote physical activity and result in health benefits for both diseased and healthy individuals, and help healthcare providers to monitor patients more closely. Objectively measured activity through sensing devices, integration with clinical systems used by healthcare providers and theoretical frameworks for health behaviour change need to be employed in a larger scale in future studies in order to realise the development of evidence-based computerised systems for physical activity monitoring and coaching., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Current healthcare systems are struggling with rising costs and unbalanced quality of care. Integrated care (IC) is a worldwide trend in healthcare reforms designed to tackle these problems. ACT@Scale is a partnership of leading European regions, industry and academia which aims to identify, transfer and scale-up existing integrated healthcare practices. In this context, participating programs are applying iterative process improvement cycles using collaborative methodologies. The vision of learning health systems (LHS) is similar to IC, but it focuses on IT means as a change enabler for rapid and continuous knowledge integration into better outcomes. In this paper, we present the ACT@Scale program as an example of an LHS that monitors integrated care performance and effects of process improvement cycles.

Background and Objectives: Atrial Fibrillation (AF) is the most common cardiac arrhythmia. The initiation and the perpetuation of AF is linked with phenomena of atrial remodeling, referring to the modification of the electrical and structural characteristics of the atrium. P-wave morphology analysis can reveal information regarding the propagation of the electrical activity on the atrial substrate. The purpose of this study is to investigate patterns on the P-wave morphology that may occur in patients with Paroxysmal AF (PAF) and which can be the basis for distinguishing between PAF and healthy subjects., Methods: Vectorcardiographic signals in the three orthogonal axes (X, Y and Z), of 3-5 min duration, were analyzed during SR. In total 29 PAF patients and 34 healthy volunteers were included in the analysis. These data were divided into two distinct datasets, one for the training and one for the testing of the proposed approach. The method is based on the identification of the dominant and the secondary P-wave morphology by combining adaptive k-means clustering of morphologies and a beat-to-beat cross correlation technique. The P-waves of the dominant morphology were further analyzed using wavelet transform whereas time domain characteristics were also extracted. Following a feature selection step, a SVM classifier was trained, for the discrimination of the PAF patients from the healthy subjects, while its accuracy was tested using the independent testing dataset., Results: In the cohort study, in both groups, the majority of the P-waves matched a main and a secondary morphology, while other morphologies were also present. The percentage of P-waves which simultaneously matched the main morphology in all three leads was lower in PAF patients (90.4 ± 7.8%) than in healthy subjects (95.5 ± 3.4%, p= 0.019). Three optimal scale bands were found and wavelet parameters were extracted which presented statistically significant differences between the two groups. Classification between the two groups was based on a feature selection process which highlighted 7 features, while an SVM classifier resulted a balanced accuracy equal to 93.75%. The results show the virtue of beat-to-beat analysis for PAF prediction., Conclusion: The difference in the percentage of the main P-wave-morphology and in the P-wave time-frequency characteristics suggests a higher electrical instability of the atrial substrate in patients with PAF and different conduction patterns in the atria., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Introduction: Exercise-based cardiac rehabilitation (CR) independently alters the clinical course of cardiovascular diseases resulting in a significant reduction in all-cause and cardiac mortality. However, only 15%-30% of all eligible patients participate in a phase 2 ambulatory programme. The uptake rate of community-based programmes following phase 2 CR and adherence to long-term exercise is extremely poor. Newer care models, involving telerehabilitation programmes that are delivered remotely, show considerable promise for increasing adherence. In this view, the PATHway (Physical Activity Towards Health) platform was developed and now needs to be evaluated in terms of its feasibility and clinical efficacy., Methods and Analysis: In a multicentre randomised controlled pilot trial, 120 participants (m/f, age 40-80 years) completing a phase 2 ambulatory CR programme will be randomised on a 1:1 basis to PATHway or usual care. PATHway involves a comprehensive, internet-enabled, sensor-based home CR platform and provides individualised heart rate monitored exercise programmes (exerclasses and exergames) as the basis on which to provide a personalised lifestyle intervention programme. The control group will receive usual care. Study outcomes will be assessed at baseline, 3 months and 6 months after completion of phase 2 of the CR programme. The primary outcome is the change in active energy expenditure. Secondary outcomes include cardiopulmonary endurance capacity, muscle strength, body composition, cardiovascular risk factors, peripheral endothelial vascular function, patient satisfaction, health-related quality of life (HRQoL), well-being, mediators of behaviour change and safety. HRQoL and healthcare costs will be taken into account in cost-effectiveness evaluation., Ethics and Dissemination: The study will be conducted in accordance with the Declaration of Helsinki. This protocol has been approved by the director and clinical director of the PATHway study and by the ethical committee of each participating site. Results will be disseminated via peer-reviewed scientific journals and presentations at congresses and events., Trial Registration Number: NCT02717806. This trial is currently in the pre-results stage., Competing Interests: Competing interests: None declared., (© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.)

Advancing Care Coordination and Telehealth Deployment (ACT) is a European Union (EU) project, completed last October, which has developed a framework for evaluating and improving pioneering health care programs regarding coordinating care and telehealth (CC & TH) across specific EU regions. In this paper we present the key design decisions of the project's data model and the challenges faced. We focus on the definition of the multi-dimensional indicators in order to overcome data incompleteness and heterogeneity issues. Finally, we also suggest a graph based approach that could facilitate development of such data models in similar projects.

Rehabilitation is important for patients with cardiovascular diseases (CVD) to improve health outcomes and quality of life. However, adherence to current exercise programmes in cardiac rehabilitation is limited. We present the design and development of a Decision Support System (DSS) for telerehabilitation, aiming to enhance exercise programmes for CVD patients through ensuring their safety, personalising the programme according to their needs and performance, and motivating them toward meeting their physical activity goals. The DSS processes data originated from a Microsoft Kinect camera, a blood pressure monitor, a heart rate sensor and questionnaires, in order to generate a highly individualised exercise programme and improve patient adherence. Initial results within the EU-funded PATHway project show the potential of our approach.

Aims: Hypertension is a major risk factor for atrial fibrillation (AF); however, reliable non-invasive tools to assess AF risk in hypertensive patients are lacking. We sought to evaluate the efficacy of P wave wavelet analysis in predicting AF risk recurrence in a hypertensive cohort., Methods: We studied 37 hypertensive patients who presented with an AF episode for the first time and 37 age- and sex-matched hypertensive controls without AF. P wave duration and energy variables were measured for each subject [i.e. mean and max P wave energy along horizontal (x), coronal (y) and sagittal (z) axes in low, intermediate and high frequency bands]. AF-free survival was assessed over a follow-up of 12.1±0.4months., Results: P wave duration (Pdurz) and mean P wave energy in the intermediate frequency band across sagittal axis (mean2z) were independently associated with baseline AF status (p=0.008 and p=0.001, respectively). Based on optimal cut-off points, four groups were formed: Pdurz<83.2ms/mean2z<6.2μV(2) (n=23), Pdurz<83.2ms/mean2z≥6.2μV(2) (n=10), Pdurz≥83.2ms/mean2z<6.2μV(2) (n=22) and Pdurz≥83.2ms/mean2z≥6.2μV(2) (n=19). AF-free survival decreased (Log Rank p<0.0001) from low risk (Pdurz<83.2ms/mean2z<6.2μV(2)) to high-risk group (Pdurz≥83.2ms/mean2z≥6.2μV(2)). Patients presenting with longer and higher energy P waves were at 18 times higher AF risk compared to those with neither (OR: 17.6, 95% CI: 3.7-84.3) even after adjustment for age, sex, hypertension duration, left atrial size, beta-blocker, ACEi/ARBs and statin therapy., Conclusions: P wave temporal and energy characteristics extracted using wavelet analysis can potentially serve as screening tool to identify hypertensive patients at risk of AF recurrence., (Copyright © 2015 Elsevier Inc. All rights reserved.)

This paper presents the computational framework that is employed for the analysis of health related key drivers and indicators within ACT, a project aiming to improve the deployment of Care Coordination and Telehealth services/programmes across Europe, through an iterative evidence collection-evaluation-refinement process. An open-source solution is proposed, combining a series of established software technologies. The paper focuses on technical aspects of the framework and presents a worked example of a usage scenario.

Atrial Fibrillation (AF) is a condition in which heart rhythm is not associated with normal sinoatrial (SA) node pacemaker but it derives from different areas on the atrium, often from the area of Pulmonary veins (PVs) A way to eliminate the influence of PVs in the inducement of AF is the PVs isolation surgery. In this study, an effort is made towards investigating the morphology and dynamics of P-waves, when the potentially arrhythmogenic tissue in PVs is involved or isolated via ablation. For this reason, 20 patients who were subjected to PVs isolation were studied, via vectrorcardiography recordings obtained before and after the ablation. Wavelet energies for five frequency bands were analyzed, using a two dimensional representation. The proposed technique was applied for the analysis of wavelet energies in consecutive beats, and their correlation with the RR interval. Features for the evaluation of those plots were extracted, such as the axes of a fitted to the plot ellipse and the center of the mass. The statistical analysis demonstrated significant differences between the groups, which imply the modification of the atrial substrate concerning electrical conduction toward to a more stable condition.

Atrial fibrillation (AF) is one of the most common cardiac arrhythmia. Electrical properties of the atrial myocardium may be related to the appearance of this type of arrhythmia. However ectopic beats, occurring normally in healthy people, disturb cardiac rhythm. Those beats arise from fiber outside the region of SA node. With this work we aim at highlighting differences in the atrial activity between healthy subjects, healthy subjects presenting many ectopic events and patients with AF. The X-Y-Z leads of vectorcardiography recordings are considered. Wavelet-based parameters are extracted from a window which represents atrial activity originated from SA node and compared between signals of the three groups. Results show differences potentially related to the conduction system of the atrium between healthy people and people with AF, as well as between healthy people and people with ectopic events. No difference was found from the analysis of SA node beats between people with AF and healthy with ectopic events.

Atrial fibrillation (AF) is a complex phenomenon, related with a multitude of factors, including the electrical properties of the atrial substrate. The purpose of this work is to present a method that highlights electrocardiographic differences between normal subjects and patients with paroxysmal AF episodes (PAF), potentially related with substrate differences. Vectorcardiography recordings are considered and, for each lead (X-Y-Z), on a beat by beat basis, a steady window before QRS, corresponding to the atrial activity, is analysed via continuous wavelet transform. Wavelet-based parameters are calculated and compared between the normal and AF group, with the beat to beat variation of wavelet energy as the most important feature showing a significantly higher variability in the AF group.

Alterations in magnesium (Mg) homeostasis during and after orthotopic liver transplantation are common. The purpose of this study is to compare total Mg (TMg), calculated ionized Mg (cMg++) and measured ionized Mg (mMg++) during and immediately following liver transplantation. The newly developed first generation ion selective electrode analyzer, AVL 988-4, was used to measure mMg++ in 63 serum samples from 3 transplant recipients and 48 serum samples from 48 healthy volunteers. Analysis was divided into intraoperative (stages 1 to 3) and postoperative periods. Decreased TMg, cMg++ and mMg++ levels were observed intraoperatively and > 2 weeks postoperatively. The cMg++ levels were consistently higher than mMg++, presumably owing to the fact that the equation used for the calculation does not take complex-Mg++ into account. A better correlation was observed between mMg++ and cMg++ in the transplant group (r = 0.87 to 0.99) compared to controls (r = 0.74). The usefulness of direct measurement of Mg++ in liver transplantation remains to be determined.