Tremor is the rhythmic shaking of a body part and is a common and disabling problem seen in diseases such as Parkinson’s disease and Essential Tremor. At present there is no single diagnostic test to determine the cause of a person’s tremor. This represents a key barrier to progress in developing new treatments since the success of clinical trials of new drugs is highly dependent on accurately recruiting patients with the disease the treatment is for (and not one of its mimics). Trials also require accurate monitoring of the effects of treatment, preferably with the help of tools that can detect subtle, early improvements (biomarkers) so that promising treatments can be identified more quickly. Neurophysiological techniques, using sensors to measure the frequency and other properties of tremor as well as the activity of the muscles that are producing the tremor, represent a safe and non-invasive method of accurately measuring tremor.
This project aims to combine the use of sensors that measure tremor (accelerometry) and muscle activity (surface electromyography) with clinical assessment to develop new tests for reliably diagnosing and monitoring tremor. We will use a data-driven approach, involving carefully studying the clinical and neurophysiological features of patients with tremor and then performing a cluster analysis to see if we can identify subgroups of patients who behave in a similar way. We hope that we can identify subgroups that more accurately bring together patients with the same disease process than current diagnostic criteria which may lump together patients with different disease processes under the same diagnostic label (e.g. Essential Tremor). These diagnostic subgroups would hopefully then provide a better framework for future research and clinical trials.