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Characterizing the clinical relevance of digital phenotyping data quality with applications to a cohort with schizophrenia

Digital phenotyping, or the moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices and smartphones, in particular, holds great potential for behavioral monitoring of patients. However, realizing the potential of digital phenotyping re...

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Autores principales: Torous, John, Staples, Patrick, Barnett, Ian, Sandoval, Luis R., Keshavan, Matcheri, Onnela, Jukka-Pekka
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6550248/
https://www.ncbi.nlm.nih.gov/pubmed/31304300
http://dx.doi.org/10.1038/s41746-018-0022-8
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author Torous, John
Staples, Patrick
Barnett, Ian
Sandoval, Luis R.
Keshavan, Matcheri
Onnela, Jukka-Pekka
author_facet Torous, John
Staples, Patrick
Barnett, Ian
Sandoval, Luis R.
Keshavan, Matcheri
Onnela, Jukka-Pekka
author_sort Torous, John
collection PubMed
description Digital phenotyping, or the moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices and smartphones, in particular, holds great potential for behavioral monitoring of patients. However, realizing the potential of digital phenotyping requires understanding of the smartphone as a scientific data collection tool. In this pilot study, we detail a procedure for estimating data quality for phone sensor samples and model the relationship between data quality and future symptom-related survey responses in a cohort with schizophrenia. We find that measures of empirical coverage of collected accelerometer and GPS data, as well as survey timing and survey completion metrics, are significantly associated with future survey scores for a variety of symptom domains. We also find evidence that specific measures of data quality are indicative of domain-specific future survey outcomes. These results suggest that for smartphone-based digital phenotyping, metadata is not independent of patient-reported survey scores, and is therefore potentially useful in predicting future clinical outcomes. This work raises important questions and considerations for future studies; we explore and discuss some of these implications.
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spelling pubmed-65502482019-07-12 Characterizing the clinical relevance of digital phenotyping data quality with applications to a cohort with schizophrenia Torous, John Staples, Patrick Barnett, Ian Sandoval, Luis R. Keshavan, Matcheri Onnela, Jukka-Pekka NPJ Digit Med Article Digital phenotyping, or the moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices and smartphones, in particular, holds great potential for behavioral monitoring of patients. However, realizing the potential of digital phenotyping requires understanding of the smartphone as a scientific data collection tool. In this pilot study, we detail a procedure for estimating data quality for phone sensor samples and model the relationship between data quality and future symptom-related survey responses in a cohort with schizophrenia. We find that measures of empirical coverage of collected accelerometer and GPS data, as well as survey timing and survey completion metrics, are significantly associated with future survey scores for a variety of symptom domains. We also find evidence that specific measures of data quality are indicative of domain-specific future survey outcomes. These results suggest that for smartphone-based digital phenotyping, metadata is not independent of patient-reported survey scores, and is therefore potentially useful in predicting future clinical outcomes. This work raises important questions and considerations for future studies; we explore and discuss some of these implications. Nature Publishing Group UK 2018-04-06 /pmc/articles/PMC6550248/ /pubmed/31304300 http://dx.doi.org/10.1038/s41746-018-0022-8 Text en © The Author(s) 2018 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/.
spellingShingle Article
Torous, John
Staples, Patrick
Barnett, Ian
Sandoval, Luis R.
Keshavan, Matcheri
Onnela, Jukka-Pekka
Characterizing the clinical relevance of digital phenotyping data quality with applications to a cohort with schizophrenia
title Characterizing the clinical relevance of digital phenotyping data quality with applications to a cohort with schizophrenia
title_full Characterizing the clinical relevance of digital phenotyping data quality with applications to a cohort with schizophrenia
title_fullStr Characterizing the clinical relevance of digital phenotyping data quality with applications to a cohort with schizophrenia
title_full_unstemmed Characterizing the clinical relevance of digital phenotyping data quality with applications to a cohort with schizophrenia
title_short Characterizing the clinical relevance of digital phenotyping data quality with applications to a cohort with schizophrenia
title_sort characterizing the clinical relevance of digital phenotyping data quality with applications to a cohort with schizophrenia
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6550248/
https://www.ncbi.nlm.nih.gov/pubmed/31304300
http://dx.doi.org/10.1038/s41746-018-0022-8
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