The Effect of Medication on P-wave Beat-to-Beat Variability in Atrial Fibrillation during Sinus Rhythm.

Atrial Fibrillation (AF) is the most common cardiac arrhythmia, and its progressive nature is associated with gradual atrial remodeling. The P-wave in the surface Electrocardiogram (ECG) reflects the atrial activation, while the modification of the atrial pathophysiological properties leads to P-wave morphology (PWM) alternations. In paroxysmal AF (pAF), the modifications of the PWM may have a spontaneous rather than permanent presence in the ECG signal. The analysis of the P-waves, during sinus rhythm, on a beat-to-beat basis, has revealed the existence of at least two PWM. In addition, the wavelet characteristics of the P-wave matching the main morphology can accurately distinguish the patients with pAF from healthy volunteers. In this work, we examine the hypothesis that there is an effect of the anti-arrhythmic medication on beat-to-beat PWM alternations of pAF patients. ECG signals of high frequency (1000Hz), in the three orthogonal leads, were collected for 81 pAF patients of minimal and mild AF burden, 47 of which receiving antiarrhythmic medication treatment, and from 56 healthy volunteers. Kruskal-Wallis test was performed, and the preliminary results denote the existence of statistically significant differences between the groups. A 3-class Random Forest classifier was trained, using the forward wrapper approach, resulting in a high overall classification performance (AUC = 85.75%). This analysis is a step towards improving understanding of medication effect on the variability of P-wave.Clinical Relevance- The methodology presented in this paper can be used to perform a non-invasive characterization of low burden pAF patients using the ECG recording.

Exploring Associations Between Children's Obesogenic Behaviors and the Local Environment Using Big Data: Development and Evaluation of the Obesity Prevention Dashboard.

BACKGROUND: Obesity is a major public health problem globally and in Europe. The prevalence of childhood obesity is also soaring. Several parameters of the living environment are contributing to this increase, such as the density of fast food retailers, and thus, preventive health policies against childhood obesity must focus on the environment to which children are exposed. Currently, there are no systems in place to objectively measure the effect of living environment parameters on obesogenic behaviors and obesity. The H2020 project "BigO: Big Data Against Childhood Obesity" aims to tackle childhood obesity by creating new sources of evidence based on big data. OBJECTIVE: This paper introduces the Obesity Prevention dashboard (OPdashboard), implemented in the context of BigO, which offers an interactive data platform for the exploration of objective obesity-related behaviors and local environments based on the data recorded using the BigO mHealth (mobile health) app. METHODS: The OPdashboard, which can be accessed on the web, allows for (1) the real-time monitoring of children's obesogenic behaviors in a city area, (2) the extraction of associations between these behaviors and the local environment, and (3) the evaluation of interventions over time. More than 3700 children from 33 schools and 2 clinics in 5 European cities have been monitored using a custom-made mobile app created to extract behavioral patterns by capturing accelerometer and geolocation data. Online databases were assessed in order to obtain a description of the environment. The dashboard's functionality was evaluated during a focus group discussion with public health experts. RESULTS: The preliminary association outcomes in 2 European cities, namely Thessaloniki, Greece, and Stockholm, Sweden, indicated a correlation between children's eating and physical activity behaviors and the availability of food-related places or sports facilities close to schools. In addition, the OPdashboard was used to assess changes to children's physical activity levels as a result of the health policies implemented to decelerate the COVID-19 outbreak. The preliminary outcomes of the analysis revealed that in urban areas the decrease in physical activity was statistically significant, while a slight increase was observed in the suburbs. These findings indicate the importance of the availability of open spaces for behavioral change in children. Discussions with public health experts outlined the dashboard's potential to aid in a better understanding of the interplay between children's obesogenic behaviors and the environment, and improvements were suggested. CONCLUSIONS: Our analyses serve as an initial investigation using the OPdashboard. Additional factors must be incorporated in order to optimize its use and obtain a clearer understanding of the results. The unique big data that are available through the OPdashboard can lead to the implementation of models that are able to predict population behavior. The OPdashboard can be considered as a tool that will increase our understanding of the underlying factors in childhood obesity and inform the design of regional interventions both for prevention and treatment.

An open access database for the evaluation of respiratory sound classification algorithms.

OBJECTIVE: Over the last few decades, there has been significant interest in the automatic analysis of respiratory sounds. However, currently there are no publicly available large databases with which new algorithms can be evaluated and compared. Further developments in the field are dependent on the creation of such databases. APPROACH: This paper describes a public respiratory sound database, which was compiled for an international competition, the first scientific challenge of the IFMBE's International Conference on Biomedical and Health Informatics. The database includes 920 recordings acquired from 126 participants and two sets of annotations. One set contains 6898 annotated respiratory cycles, some including crackles, wheezes, or a combination of both, and some with no adventitious respiratory sounds. In the other set, precise locations of 10 775 events of crackles and wheezes were annotated. MAIN RESULTS: The best system that participated in the challenge achieved an average score of 52.5% with the respiratory cycle annotations and an average score of 91.2% with the event annotations. SIGNIFICANCE: The creation and public release of this database will be useful to the research community and could bring attention to the respiratory sound classification problem.

The development and codesign of the PATHway intervention: a theory-driven eHealth platform for the self-management of cardiovascular disease.

Cardiovascular diseases (CVDs) are a leading cause of premature death worldwide. International guidelines recommend routine delivery of all phases of cardiac rehabilitation (CR). Uptake of traditional CR remains suboptimal, as attendance at formal hospital-based CR programs is low, with community-based CR rates and individual long-term exercise maintenance even lower. Home-based CR programs have been shown to be equally effective in clinical and health-related quality of life outcomes and yet are not readily available. The aim of the current study was to develop the PATHway intervention (physical activity toward health) for the self-management of CVD. Increasing physical activity in individuals with CVD was the primary behavior. The PATHway intervention was theoretically informed by the behavior change wheel and social cognitive theory. All relevant intervention functions, behavior change techniques, and policy categories were identified and translated into intervention content. Furthermore, a person-centered approach was adopted involving an iterative codesign process and extensive user testing. Education, enablement, modeling, persuasion, training, and social restructuring were selected as appropriate intervention functions. Twenty-two behavior change techniques, linked to the six intervention functions and three policy categories, were identified for inclusion and translated into PATHway intervention content. This paper details the use of the behavior change wheel and social cognitive theory to develop an eHealth intervention for the self-management of CVD. The systematic and transparent development of the PATHway intervention will facilitate the evaluation of intervention effectiveness and future replication.

Computerized decision support for beneficial home-based exercise rehabilitation in patients with cardiovascular disease.

BACKGROUND: Exercise-based rehabilitation plays a key role in improving the health and quality of life of patients with Cardiovascular Disease (CVD). Home-based computer-assisted rehabilitation programs have the potential to facilitate and support physical activity interventions and improve health outcomes. OBJECTIVES: We present the development and evaluation of a computerized Decision Support System (DSS) for unsupervised exercise rehabilitation at home, aiming to show the feasibility and potential of such systems toward maximizing the benefits of rehabilitation programs. METHODS: The development of the DSS was based on rules encapsulating the logic according to which an exercise program can be executed beneficially according to international guidelines and expert knowledge. The DSS considered data from a prescribed exercise program, heart rate from a wristband device, and motion accuracy from a depth camera, and subsequently generated personalized, performance-driven adaptations to the exercise program. Communication interfaces in the form of RESTful web service operations were developed enabling interoperation with other computer systems. RESULTS: The DSS was deployed in a computer-assisted platform for exercise-based cardiac rehabilitation at home, and it was evaluated in simulation and real-world studies with CVD patients. The simulation study based on data provided from 10 CVD patients performing 45 exercise sessions in total, showed that patients can be trained within or above their beneficial HR zones for 67.1 ±â€¯22.1% of the exercise duration in the main phase, when they are guided with the DSS. The real-world study with 3 CVD patients performing 43 exercise sessions through the computer-assisted platform, showed that patients can be trained within or above their beneficial heart rate zones for 87.9 ±â€¯8.0% of the exercise duration in the main phase, with DSS guidance. CONCLUSIONS: Computerized decision support systems can guide patients to the beneficial execution of their exercise-based rehabilitation program, and they are feasible.

Learning Healthcare Systems: Scaling-Up Integrated Care Programs.

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.

Computerised decision support in physical activity interventions: A systematic literature review.

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.

PATHway I: design and rationale for the investigation of the feasibility, clinical effectiveness and cost-effectiveness of a technology-enabled cardiac rehabilitation platform.

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.

PATHway: Decision Support in Exercise Programmes for Cardiac Rehabilitation.

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.