The MyHeart approach for solving the key challenges is based on the development of intelligent biomedical clothes for preventive care application tailored to specific user groups. In order to focus on the user motivation and the individual benefit, MyHeart has defined the main objectives along 5 different application areas. These application areas reflect the main risks for developing a CVD and address the user need for early diagnose to limit the severity of an acute event.
The five identified application areas are:
- CardioActive: Application cluster for improved physical activity
- CardioBalance: Application cluster for improved nutrition and dieting
- CardioSleep: Application cluster for improved sleep and relaxation phases
- CardioRelax: Application cluster for improved solutions to deal with stress
- CardioSafe: Applications for early diagnosis and prediction of acute events
Physical inactivity is a major risk factor for developing a cardiac disease and 57% of the European citizens follow a sedentary lifestyle. People must be made aware of this, and stimulated to be more active. For this group we will develop solutions for determining the activity levels including the assessment of the fitness condition of the user. The solutions will include the assessment of basic vital signs like heart rate, breathing rate and activity classification, the determination of speed, distance and height levels as well as determination of position to advise on directions. The system will automatically determine the specific activity and give feedback on the present status as well as on the achieved improvement of the physical status. The system will allow determining dynamically the posture and gesture of the user and enabling dedicated physical training in remote settings. These kinds of solutions will enable not only exciting gaming applications but also novel services for sport and fitness applications. Specific training plans and recommendations for training will be personalised on the individual condition and the ambition level of the user. Additionally, specific solutions for interactive gaming solutions for improved preventive outcome will be developed. Specific attention will be paid on the motivation to stay active by feedback on status, community building and virtual competition.
More than 20% of all European citizens suffer from obesity defined by a body mass index exceeding 30. For these citizens we will develop solutions to actively manage their dieting and nutrition by personalised dieting plans, continuous feedback, guided physical training plans. We will work on location dependant services to guide the user for healthier food, e.g. salad bars, special dieting restaurants or point of sales. Special attention will be paid to the motivation of the customer via community building and new methods of electronic peer pressure.
More than 25% of all European citizens suffer from sleep disorders, like sleep apnea and insomnia. These patients are at elevated risk to develop a cardio vascular disease. We will develop solutions for assessing the individual sleep quality and the diagnosing of sleep disorders at home. We will explore novel methods for improving sleep quality and the therapy of sleep disorder based on biofeedback and personalised relaxation exercises. Special attention will be paid for diagnosing sleep quality related diseases like depression, which is a frequent complication of post myocardial infarction patients.
Stress is a major behavioural risk factor for CVD and more than 40% of all European citizen suffer from stress. We will develop solutions not only for diagnosing acute stress events and a stress meter but we will also develop specific relaxation methods to deal with stress. Biofeedback tools will be used tailored to individual needs and enabled by Web and mobile services. The solution will limit the stress related risk for CVD and will improve the personal performance in the working environment.
For the early diagnose action line we will develop solutions to continuously analyse the vital signs of the user in order to determine acute events and predict acute events. We will develop diagnosis system for:
- Myocardial infarction: The objective is to detect myocardial infarctions and ischaemic events. In case an ischaemic event is diagnosed an immediate alarm is sent via mobile or fixed networks to an emergency service. The early diagnose of ischaemic events allows to limit the irreversible damage to the heart muscle (reducing time to treatment to less than 1 hour).
- Stroke Prevention: 15 % of all strokes are due to atrial fibrillation (AF). Our strategy is to diagnose and treat AF to reduce the overall incidence of stroke. For the detection of AF we will develop an automated diagnosis tool and derive individual self-therapy recommendations. We will explore fundamentally new ways of dealing with AF including the ‘pill-in-the-pocket’ approach.
- Pump Failure Prevention: The approach is to monitor high-risk congestive heart failure patients and detect early indicators for pump failure. Early diagnosis will allow to direct the user to institutional points of care for further treatment. By use of implant therapy (Cardiac Resynchronisation Therapy) or other treatment (for e.g. Ablation) the propagation of the disease can be stopped or slowed down.
- Sudden Cardiac Arrest Prevention: In the European Union 18% of all death in the age group 20-75 are due to sudden cardiac arrest claiming each year roughly 500.000 life’s in Europe. In half of all these cases sudden cardiac death is the first manifestation of a cardio-vascular disease. Currently the chances for survival are as low as 5%. The consortium will develop methods to detect VT episodes, elongation of the QT interval and other early indicators for sudden cardiac arrest. By this method we can effectively identify people at risk for sudden cardiac arrest. After diagnosis, implant therapy or ablation therapy can be used to prevent SCA in this risk group.
- Hypo-Hyperglycaemic shock: The continuous measurement of ECG, breathing rate and activity will be used to develop solutions to detect and potentially also predict hypoglycaemic events. The user will be effectively empowered to prevent an event by self-medication.