Self-tracking devices have in one decade made it possible to track and manage your own health data. These devises are potential key players in the development of personalized and participatory health care and may help to provide health care information beneficial for research and clinical decision making. One of the hopes of the ‘Quantified Self’ communities is that crowd-sourced knowledge about health-related issues may contribute productively to towards this goal.
The Medical Informatics Platform (MIP) is one of the six Human Brain Project platforms aiming to advance our understanding of brain’s function and its disorders. It allows for interactive access to clinically relevant information about the healthy and diseased brain and is expected to bring advancements into diagnostics, treatments and personalized medicine for brain disorders. The MIP relies on datasets obtained from hospitals and clinics and it visualizes the type of data that are available to the users through a website.
The MIP does at the current state not encounter data obtained by the public, patients and research participants themselves. However, in order to open a discussion on which role self-tracking should play in the HBP stakeholders outside HBP were invited to a join a two hour webinar on Self-Tracking in the Human Brain Project. Everyone interested in the topic were encouraged to take part in the discussion including Quantified Self communities, patient organizations, hospitals, clinics and researchers.
Guest speakers included:
- Dr. Ferath Kherif, Vice-director Laboratoire de Recherche en Neuroimagerie, CHUV. Project leader Medical informatics Platform.
- Prof. Philippe Ryvlin, Professor in Neurology, Head of department of clinical neuroscience, CHUV, Researcher in mobile health and neurotech
- Simon Bentholm: specialist in the use of data in welfare innovations and Co-founder of the Quantified Self Copenhagen Meetup group
- Dr. Balder Onarheim, Associate Professor in creativity at the Technical University of Denmark
- Dr. Max Little, Associate Professor at Aston University, Research fellow at the Media Lab, MIT. Applied mathematician being involved in developing methods for detecting Parkinson’s disease from voice recordings.