Crowdsourcing Digital Health Measures to Predict Parkinson's Disease Severity: The Parkinson's Disease Digital Biomarker Dream Challenge
| dc.contributor.author | Sieberts, Solveig K. | |
| dc.contributor.author | Schaff, Jennifer | |
| dc.contributor.author | Duda, Marlena | |
| dc.contributor.author | Pataki, Balint Armin | |
| dc.contributor.author | Sun, Ming | |
| dc.contributor.author | Snyder, Phil | |
| dc.contributor.author | Omberg, Larsson | |
| dc.date.accessioned | 2025-09-25T10:43:20Z | |
| dc.date.available | 2025-09-25T10:43:20Z | |
| dc.date.issued | 2021 | |
| dc.description | Gormez, Yasin/0000-0001-8276-2030; Vergara-Diaz, Gloria/0000-0003-1072-1322; Guan, Yuanfang/0000-0001-8275-2852; Elo, Laura/0000-0001-5648-4532; Duda, Marlena/0000-0003-2369-2225; Raghava, Gajendra/0000-0002-8902-2876; Rainaldi, Erin/0000-0003-1082-7055; Sapienza, Stefano/0000-0002-0917-6454; Mohammadian Rad, Nastaran/0000-0003-3068-4127; Jonnagaddala, Jitendra/0000-0002-9912-2344; Klen, Riku/0000-0002-0982-8360; Kurz, Christoph/0000-0001-9498-8002; Han, Zhi/0000-0002-5603-8433; Glaab, Enrico/0000-0003-3977-7469; Jaakkola, Maria K/0000-0001-7199-0062; | en_US |
| dc.description.abstract | Consumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95). | en_US |
| dc.description.sponsorship | Robert Wood Johnson Foundation; Michael J. Fox Foundation; NIH NIGMS Bioinformatics Training Grant [5T32GM070449-12]; Canadian Institutes of Health Research; European Research Council ERC [677943]; European Union's Horizon 2020 research and innovation programme [675395]; Academy of Finland [296801, 304995, 310561, 314443]; Sigrid Juselius Foundation; Fonds Nationale de la Recherche (FNR) Luxembourg, through the National Centre of Excellence in Research (NCER) on Parkinson's disease [I1R-BIC-PFN-15NCER]; project PD-Strat [INTER/11651464]; Alfred Kordelin Foundation; UNSW Sydney Electronic Practice Based Research Network (ePBRN) program; UNSW Translational Cancer Research Network (TCRN) program; University of Rochester CTSA award from the National Center for Advancing Translational Sciences of the National Institutes of Health [UL1 TR002001]; Swiss National Science Foundation (SNSF) within the National Research Program (NRP) 75 "Big Data" [167302]; NIH [R35GM133346]; NSF [1452656]; Michael J. Fox Foundation [17373]; American Parkinson Disease Association [AWD007950]; Cohen Veterans Bioscience; Elder Research, an AI and Data Science consulting agency; National Institute of General Medical Sciences [R35GM133346] Funding Source: NIH RePORTER | en_US |
| dc.description.sponsorship | The Parkinson's Disease Digital Biomarker Challenge was funded by the Robert Wood Johnson Foundation and the Michael J. Fox Foundation. Data were contributed by users of the Parkinson mPower mobile application as part of the mPower study developed by Sage Bionetworks and described in Synapse [https://doi.org/10.7303/syn4993293].Resources and support for J.S. were provided by Elder Research, an AI and Data Science consulting agency. M.D. was supported by NIH NIGMS Bioinformatics Training Grant (5T32GM070449-12). J.F.D. was supported by a postdoctoral fellowship from the Canadian Institutes of Health Research. L.L.E. reports grants from the European Research Council ERC (677943), European Union's Horizon 2020 research and innovation programme (675395), Academy of Finland (296801, 304995, 310561 and 314443), and Sigrid Juselius Foundation, during the conduct of the study. EG1 acknowledges the funding support by the Fonds Nationale de la Recherche (FNR) Luxembourg, through the National Centre of Excellence in Research (NCER) on Parkinson's disease (I1R-BIC-PFN-15NCER), and as part of the grant project PD-Strat (INTER/11651464). M.K.J. was supported by Alfred Kordelin Foundation. J.J. is supported by UNSW Sydney Electronic Practice Based Research Network (ePBRN) and Translational Cancer Research Network (TCRN) programs. D.L. is supported in part by the University of Rochester CTSA award number UL1 TR002001 from the National Center for Advancing Translational Sciences of the National Institutes of Health. PS2 is supported by the Swiss National Science Foundation (SNSF) project No. 167302 within the National Research Program (NRP) 75 "Big Data". P.S. is an affiliated PhD fellow at the Max Planck ETH Center for Learning Systems. Y.G. is supported by NIH R35GM133346, NSF#1452656, Michael J. Fox Foundation #17373, American Parkinson Disease Association AWD007950. Cohen Veterans Bioscience contributed financial support to Early Signal Foundation's costs (U.R., C.E., and D.B.). | en_US |
| dc.identifier.doi | 10.1038/s41746-021-00414-7 | |
| dc.identifier.issn | 2398-6352 | |
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| dc.identifier.uri | https://doi.org/10.1038/s41746-021-00414-7 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12573/3554 | |
| dc.language.iso | en | en_US |
| dc.publisher | Nature Portfolio | en_US |
| dc.relation.ispartof | NPJ Digital Medicine | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.title | Crowdsourcing Digital Health Measures to Predict Parkinson's Disease Severity: The Parkinson's Disease Digital Biomarker Dream Challenge | en_US |
| dc.type | Article | en_US |
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| gdc.author.id | Gormez, Yasin/0000-0001-8276-2030 | |
| gdc.author.id | Vergara-Diaz, Gloria/0000-0003-1072-1322 | |
| gdc.author.id | Guan, Yuanfang/0000-0001-8275-2852 | |
| gdc.author.id | Elo, Laura/0000-0001-5648-4532 | |
| gdc.author.id | Duda, Marlena/0000-0003-2369-2225 | |
| gdc.author.id | Raghava, Gajendra/0000-0002-8902-2876 | |
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| gdc.author.wosid | Guan, Yuanfang/D-3141-2011 | |
| gdc.author.wosid | Elo, Laura/A-9449-2015 | |
| gdc.author.wosid | Perrin, Dimitri/H-8630-2013 | |
| gdc.author.wosid | Klén, Riku/G-4339-2016 | |
| gdc.author.wosid | Parisi, Federico/Aaf-8206-2019 | |
| gdc.author.wosid | Görmez, Yasin/Jef-8096-2023 | |
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| gdc.description.department | Abdullah Gül University | en_US |
| gdc.description.departmenttemp | [Sieberts, Solveig K.; Snyder, Phil; Chae, Yooree; Chaibub Neto, Elias; Perumal, Thanneer M.; Mangravite, Lara M.; Omberg, Larsson] Sage Bionetworks, Seattle, WA 98121 USA; [Schaff, Jennifer] Elder Res Inc, Charlottesville, VA USA; [Duda, Marlena; Guan, Yuanfang] Univ Michigan, Dept Computat Med & Bioinformat, Ann Arbor, MI USA; [Pataki, Balint Armin] Eotvos Lorand Univ, Dept Phys Complex Syst, Budapest, Hungary; [Sun, Ming] Google Inc, New York, NY USA; [Daneault, Jean-Francois; Parisi, Federico; Costante, Gianluca; Golabchi, Fatemeh Noushin; Sapienza, Stefano; Vergara-Diaz, Gloria; Bonato, Paolo] Harvard Med Sch, Dept PM&R, Spaulding Rehabil Hosp, Charlestown, MA USA; [Daneault, Jean-Francois] Rutgers State Univ, Dept Rehabil & Movement Sci, Newark, NJ USA; [Parisi, Federico; Costante, Gianluca; Bonato, Paolo] Harvard Univ, Wyss Inst, Boston, MA USA; [Rubin, Udi; Espino, Carlos; Shokhirev, Nikolai; Brunner, Daniela] Early Signal Fdn, 311 W 43rd St, New York, NY USA; [Banda, Peter; Glaab, Enrico] Univ Luxembourg, Luxembourg Ctr Syst Biomed, Esch Sur Alzette, Luxembourg; [Dorsey, E. Ray] Univ Rochester, Ctr Hlth Technol, Rochester, NY USA; [Aydin, Zafer; Gormez, Yasin] Abdullah Gul Univ, Dept Elect & Comp Engn, Kayseri, Turkey; [Chen, Aipeng] UNSW Sydney, Prince Wales Clin Sch, Sydney, NSW, Australia; [Elo, Laura L.; Jaakkola, Maria K.; Klen, Riku; Venalainen, Mikko S.] Univ Turku, Turku Biosci Ctr, Tykistokatu 6, Turku, Finland; [Elo, Laura L.; Jaakkola, Maria K.; Klen, Riku; Venalainen, Mikko S.] Abo Akad Univ, Tykistokatu 6, Turku, Finland; [Goan, Ethan] Queensland Univ Technol, Sch Elect Engn & Robot, Brisbane, Qld, Australia; [Jaakkola, Maria K.] Univ Turku, Dept Math & Stat, Turku, Finland; [Jonnagaddala, Jitendra] UNSW Sydney, Sch Publ Hlth & Community Med, Sydney, NSW, Australia; [Jonnagaddala, Jitendra] UNSW Sydney, WHO Collaborating Ctr eHlth, Sydney, NSW, Australia; [Li, Dongmei] Univ Rochester, Clin & Translat Sci Inst, Med Ctr, Rochester, NY USA; [McDaniel, Christian] Univ Georgia, Artificial Intelligence, Athens, GA USA; [McDaniel, Christian] Univ Georgia, Comp Sci, Athens, GA USA; [Perrin, Dimitri] Queensland Univ Technol, Sch Comp Sci, Brisbane, Qld, Australia; [Rad, Nastaran Mohammadian] Radboud Univ Nijmegen, Inst Comp & Informat Sci, Nijmegen, Netherlands; [Rad, Nastaran Mohammadian] Fdn Bruno Kessler FBK, Via Sommarive 18, Trento, Italy; [Rad, Nastaran Mohammadian] Univ Trento, Trento, Italy; [Rainaldi, Erin] Verily Life Sci, 269 East Grand Ave, San Francisco, CA USA; [Schwab, Patrick] Swiss Fed Inst Technol, Inst Robot & Intelligent Syst, Zurich, Switzerland; [Zhang, Yuqian] Shanghai Jiao Tong Univ, Sch Biomed Engn, Shanghai, Peoples R China; [Wang, Yuanjia] Columbia Univ, Dept Biostat, Mailman Sch Publ Hlth, New York, NY USA; [Brunner, Daniela] Columbia Univ, Dept Psychiat, New York, NY USA | en_US |
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| gdc.oaire.keywords | Cardiovascular and Metabolic Diseases | |
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| gdc.oaire.keywords | HYPOTHESIS TESTS | |
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