Understanding Daily Multiple Sclerosis related Fatigue: a
Participatory Health Informatics Approach
MSF-PHIA Team
Researchers
- José Luis Sevillano Ramos
- Octavio Rivera Romero
- Enrique Dorronzoro Zubiete
- María Dolores Hernández Vázquez
- Lourdes Miró Amarante
Hired Personnel
- Javier Castillo Delgado
- Elena Escobar Linero
- Javier Civit Masot
MSF-PHIA Collaborators
Dr. Luis Fernández Luque
Dra. Elia Gabarrón
Dra. Anja Hochsprung
Dr. Guillermo Izquierdo
Dr. Jan Kool
Dr. Shabbir Syed
Ana Isabel Venegas
Abstract
Multiple Sclerosis (MS) is a chronic and progressive disease that is the most common disabling neurological condition in young adults. Because of its early onset (20-40 years old), MS has high socio-economic impact. Persons with MS must deal with the day-to-day effects that the disease on their lives. Among with all the symptoms that persons with MS experience, fatigue has been reported as one of the worst and is the most common work-related disability associated with MS. Fatigue also makes persons with MS less active and more sedentary, which has a strong impact in their quality of life. Different strategies have been attempted to effectively self-manage MS fatigue, however adherence to said strategies is commonly low. To improve adherence, personalization is required to adapt recommendations to the appropriate level of perceived fatigue. To be effective, personalization should be carried out using real time information collected in their real-life setting. Through a combination of research methods, this project aimed to identify and examine key factors in the daily self-management of MS fatigue.
This leaded to the development of an artificial intelligence-based model to estimate personalized levels of MS fatigue; and the definition of guidelines to support designers to develop further context-aware and personalised technological solutions for self-management of MS fatigue.