eHealth4MS

Multiple Sclerosis (MS) affects two million people worldwide. It is an autoimmune disease that can occur at any age and at 30 years on average. It affects the central nervous system, causing vision problems, muscle weakness, difficulties in motor coordination and balance, as well as mental deficits and depression, having an impact on all areas of life. The combination of individualization of treatment, regular monitoring and the correct support and guidance of patients with MS provides a guarantee for the best possible outcome. Holistic and patient-centered solutions, as well as the role of the specialist doctor who develops the care plan, help maintain autonomy and improve health. However, continuous monitoring is often financially or practically impossible, and is often based on unreliable subjective observation.

New assisted living technologies combined with intelligent applications can deliver such reliable and remote monitoring, supporting both patients and physicians to provide personalized care to stop or delay disease progression through timely intervention. Despite the growing interest and prospects that are presented, the existing technology solutions for MS only facilitate communication between patient and doctor. In general, existing health applications, beyond MS, usually focus on a single aspect of life, such as physical activity or sleep, and fall short on analyzing and combining patient information, sensors, and clinical profiles to draw timely conclusions.

The eHealth4MS project aims to develop an eHealth platform, for intelligent collection and interpretation of data from devices, for the remote, reliable monitoring of people with MS in non-clinical environments (e.g. home), leading to more efficient, cost-effective and more accessible care, but also allowing self-management of their disease, increasing their quality of life.

The system relies on Internet of Things (IoT) devices to monitor quality features of all aspects of daily life, such as exercise, sleep quality and stress, through comfortable and friendly wearables and smartphones. These data, as well as the clinical knowledge of the disease and the profile of the patients are represented uniformly using the technologies of the semantic web (RDF / OWL2 ontologies). Then, through intelligent interpretation and combination of knowledge, clinical features such as daily activity and quality of sleep, symptoms, behavioral patterns and problematic situations, such as unsociability or introversion, anxiety, sleep disorders and deviation from the treatment plan emerge. The results are presented through customized web applications to patients, for self-management of the disease, and to physicians, for making more valid and effective decisions.

Through a pilot application of the system in a monitoring study, in real home environments of users in multiple stages of MS, the evaluation of its usability, acceptance, performance and clinical value is expected.

Consortium

Center for Research and Technology Hellas (CERTH) Greece – Information Technologies Institute (ITI)
3rd Neurological Clinic, Aristotle University of Thessaloniki (AUTH), General Hospital of Thessaloniki “Papanikolaou”