Recent advances in mobile health have demonstrated great potential to leverage sensor-based technologies for remote monitoring of health and disease – particularly for diseases affecting motor function such as Parkinson’s disease (PD). However, converting sensor-based data streams into digital biomarkers is complex and no methodological standards have yet evolved to guide this process.
The Parkinson’s Disease Digital Biomarker (PDDB) DREAM Challenge was first-of-its-kind, designed to benchmark methods for processing sensor data for development of digital signatures reflective of PD. Over 440 data experts from six continents participated in the PDDB DREAM Challenge.
The challenge was divided into two sub-challenges:
- In the first sub-challenge, participants used accelerometry and gyroscope data from mPower – a large mobile health study where over 15,000 individuals with PD or controls used their iPhones to, among other things, perform short walk and balance tests – to extract features that were used to predict whether the user had PD.
- In the second sub-challenge, participants extracted features for three different Parkinson’s symptoms from a study funded by The Michael J. Fox Foundation where patients performed tasks while wearing three wearable sensors. These features were used to predict clinician-assessed disease severity for tremor, dyskinesia and bradykinesia.