Innovation potential of PRIORITY project The capacity to deliver a patient-centric, optimally dosed exercise program which supports patients in adopting an active lifestyle in their
home-environment, but also allows for real in-person contacts with their treating physiotherapist is a powerful and compelling approach to sustainable healthcare. This type of hybrid exercise therapy model combines the best of center-based supervised therapy and solely homebased physical activity programs. As such it offers a highly accessible and cheap solution, which is maximally tailored to the individual’s need but will also support behavioural change towards the adoption of a physically active lifestyle in the home-environment. Further, PRIORITY has as its core concept the large-scale collection of sensed physical activity data (and patient input) which can be matched with behavioural
and physiological data in line with the notion of the ‘internet of me’ for health care. This offers the innovative possibility of developing predictive models, using machine learning, that can identify patients, who are most likely to show (or not show) a sustained use of physical activity and increase in physical fitness and other health outcomes. This will enable more personalized implementation strategies of exercise therapy in future. In conclusion, the utilization of our hybrid exercise intervention will reduce the surging burden of these high risk conditions for symptomatic heart failure on our public health and healthcare. Scientific objectives: with this research project we aim
• To strengthen the scientific evidence with regard to health outcomes, quality of life, care efficiency gains and economic benefits from the use of exercise as a preventive instrument in the treatment of patients at high risk for end-stage heart failure.
• To increase the proportion of patients with HF stage A-C that show a clinically relevant improvement in exercise capacity, sustained over longer-term (i.e.two year follow-up).
• To increase the proportion of HF patients stage A-C meeting the minimal physical activity recommendations as set forth by WHO at twoyear follow-up
• To validate our hybrid exercise intervention as a cost-effective treatment for the prevention of end-stage HF.
• To facilitate the creation of more personalized behavioral interventions and better tailored exercise prescription to maximize its therapeutic effect by means of developing models using machinelearning techniques which can predict uptake of physical activity behavior and changes in peak oxygen uptake and CV health