DEFENCE

Smoking tobacco and physical inactivity are key preventable risk factors of cardiovascular disease (CVD). Perfect Fit aims to prevent CVD, promote well-being, and reduce healthcare costs, particularly targeting disadvantaged populations where smoking and physical inactivity are prevalent. The Research The project develops tailored, evidence-based, near real-time computer coaching for quitting smoking and enhancing PA. For every individual, a personal model is designed which generates personalized recommendations based on high-quality existing and newly collected data, and adapts to changing circumstances/progress (similar to a TomTom navigation system), using machine learning techniques and incorporating domain-specific expert knowledge (e.g. health behaviour change strategies). Virtual coaches (VCs) communicate advice in a motivating way that fits individuals’ persuasive communication styles. Perfect Fit integrates big-data science, sensor technology, and personalized real-time feedback to support smoking cessation and promote adequate physical activity (in both gym settings and daily life). The key questions of this study are: Which adaptivity is needed to create a robust, safe, and effective interaction between individuals and machines? How can we develop advanced data science methods and embed this in current smoking cessation and PA coaching practice? How do measurement modalities, feedback and communication affect individuals’ smoking status and PA? Origin This project was funded within the Big Data & Health Program. The focus of this public-private research program is the use of big data for the early detection and prevention of cardiovascular diseases. The program has been developed by NWO, ZonMw, the Dutch Heart Foundation, the Top Sectors Life Sciences & Health (LSH), ICT and Creative Industry, the Ministry of Health, Welfare and Sport, and the Netherlands eScience Center. Within this research program, the ambitions of the Dutch Heart Foundation, the Ministry of Health, Welfare and Sport, and the Netherlands eScience Center were aligned with the ambitions of Commit2Data for the Top Sectors ICT, LSH, and Creative Industry, as described in the 2018-2019 Kennis- en Innovatiecontracts between NWO and the Top Sectors.

About 10% of the COVID-19 affected patients develop critical illness with a high mortality rate. This critical illness appears to be strongly linked with cardiovascular disease, as the prevalence of cardiovascular comorbidities and risk factors (such as diabetes and obesity) are often found among hospitalized COVID-19 patients. The consortium COVID@Heart believes that mitigating this cardiovascular burden of Covid-19 should start early, while patients are (still) outside the hospital. The Research COVID@Heart has three core activities: Develop a tool to identify high-risk cardiovascular patients with COVID-19 in a home environment, before the critical illness emerges. This tool will allow general practitioners to better notify high-risk patients, monitor them more closely (e.g. by using home saturation measurements), prescribe preventive cardiovascular medication earlier ('moon shot') and refer them to a hospital promptly when needed. Create a diagnostic tool to improve early differentiation between COVID-19 and a myocardial infarction, addressing the challenge of overlapping symptoms faced by general practitioners. Design a questionnaire supplemented by select biomarkers and blood tests to enhance the detection of cardiovascular disease in COVID-19 survivors experiencing prolonged symptoms of fatigue and shortness of breath, as these symptoms are potentially linked to accelerated subclinical cardiovascular disease. Origin Accurate information on how cardiovascular patients fared while still at home is lacking. This information is crucial to prevent hospital admissions. Therefore, COVID@HEART focuses on people who are not hospitalized but are at home and treated by their general practitioners. The Dutch Heart Foundation supports and funds this research into the best treatment for cardiovascular patients with a coronavirus infection.