The long-term goal of the proposed work is to develop a tool to help diagnose and monitor movement disorders. Due to increasing and aging world population, more people are living with these disorders. As there is already a shortage of neurologists, and more specifically, movement disorder specialists that have the training to adequately diagnose and manage these disorders, there is a large number of patients that are not receiving optimal treatment. We propose to use a smartphone-based platform to assess the severity of symptoms that are common across different movement disorders in order to achieve our long-term goal. To that end, the current study proposes to tackle 4 specific aims.
The first aim will be to develop a mobile application to quantify common symptoms of movement disorders. We will develop a new mobile application that will enable the quantification of symptom severity; namely rest tremor, postural tremor, intention tremor, kinetic tremor, upper-limb coordination, bradykinesia, balance, gait, and cognitive impairments. The data collected from the smartphone-embedded sensors will be transmitted to a secure server where data can be visualized and analyzed.
The second aim will be to collect data using the mobile application from healthy individuals as well as individuals with different movement disorders. We will collect data from 30 healthy controls, 30 individuals with Essential tremor (ET), 30 individuals with Parkinson's disease (PD), 30 individuals with Huntington's disease (HD), 30 individuals with primary focal dystonia (PFD) of the upper-limb, 30 individuals with spinocerebellar ataxia (SCA, and 30 individuals with functional movement disorder (FMD). This dataset will enable us to develop algorithms (Aim 3) that will be used to assess symptom severity and differentiate the movement disorders according to the smartphone data.
The third aim will be to develop algorithms to estimate symptom severity and distinguish the different movement disorders from one another. Using the data from the smartphone-embedded sensors, we will utilize machine learning approaches to estimate symptom severity (i.e. tremor, bradykinesia, gait impairment, etc.). Then, based on the symptom severity estimation as well as from features extracted from the sensor data, we will classify the subjects in groups according to their clinical diagnosis. This will enable us to differentiate the selected movement disorders. Finally, the fourth aim will be to assess the usability of the smartphone platform for long-term monitoring of patients. Subjects of patients recruited for Aim 2 will be asked to use the smartphone application at home for 8 weeks in order to determine compliance with its use and its stability over time. This study will provide a novel tool to assess motor and non-motor symptoms that could be used in other areas of research. It will provide a large database of movement, cognitive, demographic, and medical history data of individuals with different movement disorders. Most importantly, it will help in the differentiation and monitoring of movement disorders to improve the clinical management of individuals with these disorders.
The prevalence of movement disorders is on the rise due to the expanding and aging world population while there is a lack of trained specialists in, and especially outside, of developed urban centers to adequately diagnose and manage those individuals. We propose that the ubiquity of smartphones could be leveraged to help non- specialists in the diagnosis, monitoring, and management of movement disorders that often exhibit overlapping symptoms such as Essential tremor, Parkinson's disease, Huntington's disease, primary focal dystonia, spinocerebllar ataxia, and functional movement disorders. We will develop a smartphone application that will collect data during different tasks to help differentiate these disorders and monitor them over time.
