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The mPower study, Parkinson disease mobile data collected using ResearchKit
Current measures of health and disease are often insensitive, episodic, and subjective. Further, these measures generally are not designed to provide meaningful feedback to individuals. The impact of high-resolution activity data collected from mobile phones is only beginning to be explored. Here we...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4776701/ https://www.ncbi.nlm.nih.gov/pubmed/26938265 http://dx.doi.org/10.1038/sdata.2016.11 |
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author | Bot, Brian M. Suver, Christine Neto, Elias Chaibub Kellen, Michael Klein, Arno Bare, Christopher Doerr, Megan Pratap, Abhishek Wilbanks, John Dorsey, E. Ray Friend, Stephen H. Trister, Andrew D. |
author_facet | Bot, Brian M. Suver, Christine Neto, Elias Chaibub Kellen, Michael Klein, Arno Bare, Christopher Doerr, Megan Pratap, Abhishek Wilbanks, John Dorsey, E. Ray Friend, Stephen H. Trister, Andrew D. |
author_sort | Bot, Brian M. |
collection | PubMed |
description | Current measures of health and disease are often insensitive, episodic, and subjective. Further, these measures generally are not designed to provide meaningful feedback to individuals. The impact of high-resolution activity data collected from mobile phones is only beginning to be explored. Here we present data from mPower, a clinical observational study about Parkinson disease conducted purely through an iPhone app interface. The study interrogated aspects of this movement disorder through surveys and frequent sensor-based recordings from participants with and without Parkinson disease. Benefitting from large enrollment and repeated measurements on many individuals, these data may help establish baseline variability of real-world activity measurement collected via mobile phones, and ultimately may lead to quantification of the ebbs-and-flows of Parkinson symptoms. App source code for these data collection modules are available through an open source license for use in studies of other conditions. We hope that releasing data contributed by engaged research participants will seed a new community of analysts working collaboratively on understanding mobile health data to advance human health. |
format | Online Article Text |
id | pubmed-4776701 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-47767012016-03-07 The mPower study, Parkinson disease mobile data collected using ResearchKit Bot, Brian M. Suver, Christine Neto, Elias Chaibub Kellen, Michael Klein, Arno Bare, Christopher Doerr, Megan Pratap, Abhishek Wilbanks, John Dorsey, E. Ray Friend, Stephen H. Trister, Andrew D. Sci Data Data Descriptor Current measures of health and disease are often insensitive, episodic, and subjective. Further, these measures generally are not designed to provide meaningful feedback to individuals. The impact of high-resolution activity data collected from mobile phones is only beginning to be explored. Here we present data from mPower, a clinical observational study about Parkinson disease conducted purely through an iPhone app interface. The study interrogated aspects of this movement disorder through surveys and frequent sensor-based recordings from participants with and without Parkinson disease. Benefitting from large enrollment and repeated measurements on many individuals, these data may help establish baseline variability of real-world activity measurement collected via mobile phones, and ultimately may lead to quantification of the ebbs-and-flows of Parkinson symptoms. App source code for these data collection modules are available through an open source license for use in studies of other conditions. We hope that releasing data contributed by engaged research participants will seed a new community of analysts working collaboratively on understanding mobile health data to advance human health. Nature Publishing Group 2016-03-03 /pmc/articles/PMC4776701/ /pubmed/26938265 http://dx.doi.org/10.1038/sdata.2016.11 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0 This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0 Metadata associated with this Data Descriptor is available at http://www.nature.com/sdata/ and is released under the CC0 waiver to maximize reuse. |
spellingShingle | Data Descriptor Bot, Brian M. Suver, Christine Neto, Elias Chaibub Kellen, Michael Klein, Arno Bare, Christopher Doerr, Megan Pratap, Abhishek Wilbanks, John Dorsey, E. Ray Friend, Stephen H. Trister, Andrew D. The mPower study, Parkinson disease mobile data collected using ResearchKit |
title | The mPower study, Parkinson disease mobile data collected using ResearchKit |
title_full | The mPower study, Parkinson disease mobile data collected using ResearchKit |
title_fullStr | The mPower study, Parkinson disease mobile data collected using ResearchKit |
title_full_unstemmed | The mPower study, Parkinson disease mobile data collected using ResearchKit |
title_short | The mPower study, Parkinson disease mobile data collected using ResearchKit |
title_sort | mpower study, parkinson disease mobile data collected using researchkit |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4776701/ https://www.ncbi.nlm.nih.gov/pubmed/26938265 http://dx.doi.org/10.1038/sdata.2016.11 |
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