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The asthma mobile health study, smartphone data collected using ResearchKit

Widespread adoption of smart mobile platforms coupled with a growing ecosystem of sensors including passive location tracking and the ability to leverage external data sources create an opportunity to generate an unprecedented depth of data on individuals. Mobile health technologies could be utilize...

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Autores principales: Chan, Yu-Feng Yvonne, Bot, Brian M., Zweig, Micol, Tignor, Nicole, Ma, Weiping, Suver, Christine, Cedeno, Rafhael, Scott, Erick R., Gregory Hershman, Steven, Schadt, Eric E., Wang, Pei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5963336/
https://www.ncbi.nlm.nih.gov/pubmed/29786695
http://dx.doi.org/10.1038/sdata.2018.96
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author Chan, Yu-Feng Yvonne
Bot, Brian M.
Zweig, Micol
Tignor, Nicole
Ma, Weiping
Suver, Christine
Cedeno, Rafhael
Scott, Erick R.
Gregory Hershman, Steven
Schadt, Eric E.
Wang, Pei
author_facet Chan, Yu-Feng Yvonne
Bot, Brian M.
Zweig, Micol
Tignor, Nicole
Ma, Weiping
Suver, Christine
Cedeno, Rafhael
Scott, Erick R.
Gregory Hershman, Steven
Schadt, Eric E.
Wang, Pei
author_sort Chan, Yu-Feng Yvonne
collection PubMed
description Widespread adoption of smart mobile platforms coupled with a growing ecosystem of sensors including passive location tracking and the ability to leverage external data sources create an opportunity to generate an unprecedented depth of data on individuals. Mobile health technologies could be utilized for chronic disease management as well as research to advance our understanding of common diseases, such as asthma. We conducted a prospective observational asthma study to assess the feasibility of this type of approach, clinical characteristics of cohorts recruited via a mobile platform, the validity of data collected, user retention patterns, and user data sharing preferences. We describe data and descriptive statistics from the Asthma Mobile Health Study, whereby participants engaged with an iPhone application built using Apple's ResearchKit framework. Data from 6346 U.S. participants, who agreed to share their data broadly, have been made available for further research. These resources have the potential to enable the research community to work collaboratively towards improving our understanding of asthma as well as mobile health research best practices.
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spelling pubmed-59633362018-05-30 The asthma mobile health study, smartphone data collected using ResearchKit Chan, Yu-Feng Yvonne Bot, Brian M. Zweig, Micol Tignor, Nicole Ma, Weiping Suver, Christine Cedeno, Rafhael Scott, Erick R. Gregory Hershman, Steven Schadt, Eric E. Wang, Pei Sci Data Data Descriptor Widespread adoption of smart mobile platforms coupled with a growing ecosystem of sensors including passive location tracking and the ability to leverage external data sources create an opportunity to generate an unprecedented depth of data on individuals. Mobile health technologies could be utilized for chronic disease management as well as research to advance our understanding of common diseases, such as asthma. We conducted a prospective observational asthma study to assess the feasibility of this type of approach, clinical characteristics of cohorts recruited via a mobile platform, the validity of data collected, user retention patterns, and user data sharing preferences. We describe data and descriptive statistics from the Asthma Mobile Health Study, whereby participants engaged with an iPhone application built using Apple's ResearchKit framework. Data from 6346 U.S. participants, who agreed to share their data broadly, have been made available for further research. These resources have the potential to enable the research community to work collaboratively towards improving our understanding of asthma as well as mobile health research best practices. Nature Publishing Group 2018-05-22 /pmc/articles/PMC5963336/ /pubmed/29786695 http://dx.doi.org/10.1038/sdata.2018.96 Text en Copyright © 2018, The Author(s) http://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article.
spellingShingle Data Descriptor
Chan, Yu-Feng Yvonne
Bot, Brian M.
Zweig, Micol
Tignor, Nicole
Ma, Weiping
Suver, Christine
Cedeno, Rafhael
Scott, Erick R.
Gregory Hershman, Steven
Schadt, Eric E.
Wang, Pei
The asthma mobile health study, smartphone data collected using ResearchKit
title The asthma mobile health study, smartphone data collected using ResearchKit
title_full The asthma mobile health study, smartphone data collected using ResearchKit
title_fullStr The asthma mobile health study, smartphone data collected using ResearchKit
title_full_unstemmed The asthma mobile health study, smartphone data collected using ResearchKit
title_short The asthma mobile health study, smartphone data collected using ResearchKit
title_sort asthma mobile health study, smartphone data collected using researchkit
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5963336/
https://www.ncbi.nlm.nih.gov/pubmed/29786695
http://dx.doi.org/10.1038/sdata.2018.96
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