Recent advances in mobile health have demonstrated great potential to leverage sensor-based technologies for quantitative, remote monitoring of health and disease – particularly for diseases affecting motor function such as Parkinson’s disease. Such approaches have been rolled out using research-grade wearable sensors and, increasingly, through the use of smartphones and consumer wearables, such as smart watches and fitness trackers. These devices not only provide the ability to measure much more detailed disease phenotypes but also provide the ability to follow patients longitudinally with much higher frequency than is possible through clinical exams. However, the conversion of sensor-based data streams into digital biomarkers is complex and no methodological standards have yet evolved to guide this process.
Parkinson’s disease (PD) is a neurodegenerative disease that primarily affects the motor system but also exhibits other symptoms. Typical motor symptoms of the disease include tremors, slowness (bradykinesia), posture and walking perturbations, muscle rigidity and speech perturbations. In the clinic, symptoms are evaluated using physician observation and patient reports. Multiple approaches are under investigation for development of digital biomarkers in PD using accelerometer data from mobile sensor devices with the goal of improving monitoring of treatment efficacy and disease progression for use in clinical care and drug development.
The Parkinson’s Disease Digital Biomarker DREAM Challenge is a first of it’s kind challenge, designed to benchmark methods for the processing of sensor data for development of digital signatures reflective of Parkinson’s Disease. Participants will be provided with raw sensor (accelerometer, gyroscope, and magnetometer) time series data recorded during the performance of pre-specified motor tasks, and will be asked to extract data features which are predictive of PD pathology. In contrast to traditional DREAM challenges, this one will focus on feature extraction rather than predictive modeling, and submissions will be evaluated based on their ability to predict disease phenotype using an array of standard machine learning algorithms.
Related publication:
Sieberts, S.K., Schaff, J., Duda, M. et al. Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge. npj Digit. Med. 4, 53 (2021). https://doi.org/10.1038/s41746-021-00414-7
