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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., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: YG serves as scientific advisor for Eli Lilly and Company and Merck & Co.; serves as scientific advisor and receives grants from Merck KGaA. YH has grant funding through BBJ from Sanofi S.A. and UCB for unrelated projects. BBJ has grant funding from Sanofi S.A. and UCB for unrelated projects. AJ is funded by MJ Fox Foundation for data curation. All other authors report no competing interests., (Copyright: © 2023 Sieberts et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)