The use of wearable/portable digital sensors in Huntington’s disease: a systematic review

In chronic neurological conditions, wearable/portable devices have potential as innovative tools to detect subtle early disease manifestations and disease fluctuations for the purpose of clinical diagnosis, care and therapeutic development. Huntington’s disease (HD) has a unique combination of motor and non-motor features which, combined with recent and anticipated therapeutic progress, gives great potential for such devices to prove useful. The present work aims to provide a comprehensive account of the use of wearable/portable devices in HD and of what they have contributed so far. We conducted a systematic review searching MEDLINE, Embase, and IEEE Xplore. Thirty references were identified. Our results revealed large variability in the types of sensors used, study design, and the measured outcomes. Digital technologies show considerable promise for therapeutic research and clinical management of HD. However, more studies with standardized devices and harmonized protocols are needed to optimize the potential applicability of wearable/portable devices in HD

Quantifying Parkinson\u27s Disease Symptoms Using Mobile Devices

Current assessments for evaluating the progression of Parkinson’s Disease are largely qualitative and based on small sets of data obtained from occasional doctor-patient interactions. There is a clinical need to improve the techniques used for mitigating common Parkinson’s Disease symptoms. Available data sets for researching the disease are minimal, hindering advancement toward understanding the underlying causes and effectiveness of treatment and therapies. Mobile devices present an opportunity to continuously monitor Parkinson’s Disease patients and collect important information regarding the severity of symptoms. The evolution of digital technology has opened doors for clinical research to extend beyond the clinic by incorporating complex sensors in commonly used devices. Leveraging these sensors to quantify characteristic Parkinson’s Disease symptoms may drastically improve patient care and the reliability of symptom assessment. The goal of this project is to design and develop a system for measuring and analyzing the cardinal symptoms of Parkinson’s using mobile devices. An application for the iPhone and Apple Watch is developed, utilizing the sensors on the devices to collect data during the performance of motor tasks. Assessments for tremor, bradykinesia, and postural instability are implemented to mimic UPDRS evaluations normally performed by a neurologist. The application connects to a cloud-based server to transfer the collected data for remote access and analysis. Example MatLab analysis demonstrates potential approaches for extracting meaningful data to be used for monitoring the progression of Parkinson’s Disease and the effectiveness of treatment and therapies. High-level verification testing is performed to show general efficacy of the assessment tasks. The system design successfully lays the groundwork for a mobile device-based assessment tool to objectively measure Parkinson’s Disease symptom

Wearable technologies to measure clinical outcomes in multiple sclerosis: A scoping review

Wearable technology refers to any sensor worn on the person, making continuous and remote monitoring available to many people with chronic disease, including multiple sclerosis (MS). Daily monitoring seems an ideal solution either as an outcome measure or as an adjunct to support rater-based monitoring in both clinical and research settings. There has been an increase in solutions that are available, yet there is little consensus on the most appropriate solution to use in either MS research or clinical practice. We completed a scoping review (using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines) to summarise the wearable solutions available in MS, to identify those approaches that could potentially be utilised in clinical trials, by evaluating the following: scalability, cost, patient adaptability and accuracy. We identified 35 unique products that measure gait, cognition, upper limb function, activity, mood and fatigue, with most of these solutions being phone applications