Principal Investigator: Dr Vladimir Morozov
Shire Pharmaceuticals LLC, Lexington, Massachusetts, USATags: 41746, Accelerometer, biomarkers, chronic fatigue syndrome, Machine Learning, myalgic encephalomyelitis
Chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) is a complex disease that can be debilitating. The exact cause or causes of CFS/ME are unknown. Symptoms affect several body systems and may include severe fatigue or exhaustion, unrefreshing sleep, weakness, muscle and joint pain, impaired memory or concentration. ME/CFS symptoms may get worse after any activity, whether it’s physical or cognitive. There are no approved therapies indicated to treat CFS/ME. Development of new CFS/ME treatments is complicated by a lack of understanding of how the disease develops and its lengthy diagnostic path. Indeed, because of multiple symptoms, ME/CFS diagnosis is challenging. In order to better understand the impact of the disease on activity and in particular the post -exertional fatigue that is related to activity, we would like to develop a computer algorithm that translates accelerometer data into measure of CFS/ME severity. We call it digital biomarker. Such a digital biomarker would allow diagnosis and monitoring of CFS/ME patients remotely, cheaply and non-invasively. We are going to test the accelerometer biomarker along with patient-reported outcomes in a small study this year. If it is proven to be accurate, we will use this biomarker in bigger clinical trials testing new treatments for CFS/ME.