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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...

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
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.
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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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