Developing better digital health measures of Parkinson’s disease using free living data and a crowdsourced data analysis challenge

One of the promising opportunities of digital health is its potential to lead to more holistic understandings of diseases by interacting with the daily life of patients and through the collection of large amounts of real-world data. Validating and benchmarking indicators of disease severity in the home setting is difficult, however, given the large number of confounders present in the real world and the challenges in collecting ground truth data in the home. Here we leverage two datasets collected from patients with Parkinson’s disease, which couples continuous wrist-worn accelerometer data with frequent symptom reports in the home setting, to develop digital biomarkers of symptom severity. Using these data, we performed a public benchmarking challenge in which participants were asked to build measures of severity across 3 symptoms (on/off medication, dyskinesia, and tremor). 42 teams participated and performance was improved over baseline models for each subchallenge. Additional ensemble modeling across submissions further improved performance, and the top models validated in a subset of patients whose symptoms were observed and rated by trained clinicians.Author summary: Motion sensors available in consumer devices like smartphones, smartwatches and fitness trackers have enormous potential for use in tracking health and, in the case of movement disorders, understanding symptom severity. In this case, we use data collected from smartphones and smartwatches collected passively as patients go about their daily lives to measure symptom severity in Parkinson’s disease. We challenged analysts around the world to develop algorithms to interpret the sensor data from the smart-devices and scored their submissions to determine those that performed the best. 42 teams from around the world participated, and for all 3 symptoms we measured (on/off medication, dyskinesia and tremor) the models using the sensor data showed the ability to predict symptom severity. We also validated these models against symptom severity scores reported by trained doctors.