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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.
Afiliación
  • Bot BM; Sage Bionetworks, Seattle, Washington 98109, USA.
  • Suver C; Sage Bionetworks, Seattle, Washington 98109, USA.
  • Neto EC; Sage Bionetworks, Seattle, Washington 98109, USA.
  • Kellen M; Sage Bionetworks, Seattle, Washington 98109, USA.
  • Klein A; Sage Bionetworks, Seattle, Washington 98109, USA.
  • Bare C; Sage Bionetworks, Seattle, Washington 98109, USA.
  • Doerr M; Sage Bionetworks, Seattle, Washington 98109, USA.
  • Pratap A; Sage Bionetworks, Seattle, Washington 98109, USA.
  • Wilbanks J; Sage Bionetworks, Seattle, Washington 98109, USA.
  • Dorsey ER; Center for Human Experimental Therapeutics, University of Rochester Medical Center, Rochester, New York 14642, USA.
  • Friend SH; Sage Bionetworks, Seattle, Washington 98109, USA.
  • Trister AD; Sage Bionetworks, Seattle, Washington 98109, USA.
Sci Data ; 3: 160011, 2016 Mar 03.
Article en En | MEDLINE | ID: mdl-26938265
ABSTRACT
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.
Asunto(s)

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Enfermedad de Parkinson / Recolección de Datos / Conjuntos de Datos como Asunto Tipo de estudio: Observational_studies Idioma: En Revista: Sci Data Año: 2016 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Enfermedad de Parkinson / Recolección de Datos / Conjuntos de Datos como Asunto Tipo de estudio: Observational_studies Idioma: En Revista: Sci Data Año: 2016 Tipo del documento: Article