Stony Brook researcher receives $1.5 million NIH grant to evaluate daily activity patterns and heart rate of those who suffer from this debilitating illness
June 23, 2016 – By better understanding daily activity levels and heart rate patterns of those who suffer from Chronic Fatigue Syndrome (CFS), scientists hope to discover more about this complex illness condition. Fred Friedberg, PhD, Associate Professor of Psychiatry at Stony Brook University School of Medicine, has received a four-year $1.5 million grant from the National Institutes of Health to take this research approach to determine if heart rate fluctuations in combination with certain daily activity patterns can be used to predict or prevent relapse in people with CFS.
According to Dr. Friedberg, also the President of the International Association for Chronic Fatigue Syndrome/Myalgic Encephalomyelitis, CFS affects some one million people in the United State and millions worldwide. This condition is characterized by a state of chronic fatigue and other debilitating symptoms, such as post-exertional collapse and cognitive difficulties. These symptoms and related impairments persist for more than six months and have no clearly identified cause.
This study will involve patients self-reporting their symptoms and activities on a weekly online diary over a period of six months. Data will also be recorded from mobile heart devices and activity monitors that the patients wear at home. Over the six month study period, patients will regularly send this objective data back to the Stony Brook laboratory where the information will be downloaded and analyzed for patterns related to CFS symptoms, activities, and impairments. The participants will then be interviewed by a psychiatric nurse via phone about other potentially important illness factors including major life events they have experienced over the study period, their physical and social functioning, and changes in their illness status – i.e., improved or worsened.
“What is promising is that we have proposed an illness model to potentially identify the factors that lead to relapse or improvement,” said Dr. Friedberg. “If a predictor of relapse is discovered, such as heart rate variability in conjunction with certain activity patterns, we may be able to prevent or reduce relapse by adjusting such activity patterns in advance. This could potentially be the first biomarker of illness worsening or improvement in this illness.”
Dr. Friedberg expects that the data collected from the study will be used to generate a new, potentially more effective self-management program that ultimately helps patients avoid relapses and feel and function better.