Digital technologies for Parkinson’s disease
- Submission status
- Closed
- Submission deadline
While healthcare for persons with Parkinson’s disease continues to move rapidly into a digital age, there remain important challenges. Emerging digital technologies offer significant potential in many areas of clinical care and research that include the delivery of telemedicine, assessments of symptoms and remote patient monitoring, and expanded understanding of population health; however, much of this potential has yet to be realized, and clinical uptake remains limited in many of these areas. It is clear that innovations in mobile applications, wearable devices, electronic health records, and artificial intelligence provide unprecedented volumes and types of data that could be used to improve the clinical care and scientific understanding of Parkinson’s disease. It is less clear how this expansion of data will be used to improve the lives of patients.
On this basis, the editors at npj Parkinson’s Disease and npj Digital Medicine invite submissions of primary research studies that leverage digital technologies for improving the abilities to treat, assess, and understand Parkinson’s disease. The goal of this collection is to move toward realization of this significant potential for digital technologies in the research and clinical care of Parkinson’s disease.
We are particularly interested in manuscripts that address the following with relevance to Parkinson’s disease:
- Innovative digital approaches to the delivery of telemedicine
- Direct clinical applications of digital technologies and solutions for facilitating clinical uptake
- Use of wearable devices for monitoring of motor (e.g., physical activity) and non-motor (e.g., sleep) features
- Applications of artificial intelligence and machine learning in prediction of disease progression and subtyping
- Use of big data approaches to population health data, electronic health records, medical imaging, and other clinical data
- Novel techniques for measurement of symptoms
- Use of digital technologies to expand access of care or address social determinants of health
- Digitally-enabled insights into pathophysiology
- Data-driven personalization of care
Editors
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Ryan Roemmich, PhD
Kennedy Krieger Institute and Johns Hopkins University School of Medicine MD, USA
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Elena Moro, MD, PhD, FEAN, FAAN
Division of Neurology, Grenoble Alpes University Hospital, Grenoble, France
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Peter Shull, PhD
Department of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
Dr. Ryan Roemmich is a human movement scientist interested in motor function and rehabilitation of persons with neurologic damage or disorders. His background spans engineering, kinesiology, and neuroscience. His research focuses on combining these disciplines to understand how the nervous system controls movement and how we can improve movement abilities in persons with motor deficits.
Dr. Elena Moro is a Professor of Neurology at the Grenoble Alpes University and Director of the Movement Disorders Center at the Centre Hospitalier Universitaire (CHU) of Grenoble, France. She is also the Head of the Department of Neurology, Psychiatry, Neurological Rehabilitation and Forensic Medicine at the CHUGA. Her major research interest is neuromodulation for treating movement disorders. In particular, over the years she has been focused in better understanding the mechanisms of action of deep brain stimulation (DBS), DBS indication in Parkinson's disease and dystonia and exploring new DBS targets.
Dr. Peter Shull is a Professor at Shanghai Jiao Tong University in the mechanical engineering department, where he leads the Wearable Systems Lab. His focus is on developing wearable systems to explore principles of human movement and movement modification by combining biomechanics and haptics principles to create unique sensors, real-time models, sensor fusion and artificial intelligence algorithms, and novel feedback paradigms. He has performed pioneering research involving wearable systems, human computer interaction, hand gesture recognition, and real-time movement sensing and feedback to improve human health and performance across an array of medical and sports applications.
