Sociodemographic characteristics of missing data in digital phenotyping

Abstract The ubiquity of smartphones, with their increasingly sophisticated array of sensors, presents an unprecedented opportunity for researchers to collect longitudinal, diverse, temporally-dense data about human behavior while minimizing participant burden. Researchers increasingly make use of s...

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Autores principales:	Mathew V. Kiang, Jarvis T. Chen, Nancy Krieger, Caroline O. Buckee, Monica J. Alexander, Justin T. Baker, Randy L. Buckner, Garth Coombs, Janet W. Rich-Edwards, Kenzie W. Carlson, Jukka-Pekka Onnela
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/31c4a3e4abb443f8b6f11f1e61f5c8a4
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spelling	oai:doaj.org-article:31c4a3e4abb443f8b6f11f1e61f5c8a42021-12-02T18:46:55ZSociodemographic characteristics of missing data in digital phenotyping10.1038/s41598-021-94516-72045-2322https://doaj.org/article/31c4a3e4abb443f8b6f11f1e61f5c8a42021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94516-7https://doaj.org/toc/2045-2322Abstract The ubiquity of smartphones, with their increasingly sophisticated array of sensors, presents an unprecedented opportunity for researchers to collect longitudinal, diverse, temporally-dense data about human behavior while minimizing participant burden. Researchers increasingly make use of smartphones for “digital phenotyping,” the collection and analysis of raw phone sensor and log data to study the lived experiences of subjects in their natural environments using their own devices. While digital phenotyping has shown promise in fields such as psychiatry and neuroscience, there are fundamental gaps in our knowledge about data collection and non-collection (i.e., missing data) in smartphone-based digital phenotyping. In this meta-study using individual-level data from six different studies, we examined accelerometer and GPS sensor data of 211 participants, amounting to 29,500 person-days of observation, using Bayesian hierarchical negative binomial regression with study- and user-level random intercepts. Sensitivity analyses including alternative model specification and stratified models were conducted. We found that iOS users had lower GPS non-collection than Android users. For GPS data, rates of non-collection did not differ by race/ethnicity, education, age, or gender. For accelerometer data, Black participants had higher rates of non-collection, but rates did not differ by sex, education, or age. For both sensors, non-collection increased by 0.5% to 0.9% per week. These results demonstrate the feasibility of using smartphone-based digital phenotyping across diverse populations, for extended periods of time, and within diverse cohorts. As smartphones become increasingly embedded in everyday life, the insights of this study will help guide the design, planning, and analysis of digital phenotyping studies.Mathew V. KiangJarvis T. ChenNancy KriegerCaroline O. BuckeeMonica J. AlexanderJustin T. BakerRandy L. BucknerGarth CoombsJanet W. Rich-EdwardsKenzie W. CarlsonJukka-Pekka OnnelaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mathew V. Kiang Jarvis T. Chen Nancy Krieger Caroline O. Buckee Monica J. Alexander Justin T. Baker Randy L. Buckner Garth Coombs Janet W. Rich-Edwards Kenzie W. Carlson Jukka-Pekka Onnela Sociodemographic characteristics of missing data in digital phenotyping
description	Abstract The ubiquity of smartphones, with their increasingly sophisticated array of sensors, presents an unprecedented opportunity for researchers to collect longitudinal, diverse, temporally-dense data about human behavior while minimizing participant burden. Researchers increasingly make use of smartphones for “digital phenotyping,” the collection and analysis of raw phone sensor and log data to study the lived experiences of subjects in their natural environments using their own devices. While digital phenotyping has shown promise in fields such as psychiatry and neuroscience, there are fundamental gaps in our knowledge about data collection and non-collection (i.e., missing data) in smartphone-based digital phenotyping. In this meta-study using individual-level data from six different studies, we examined accelerometer and GPS sensor data of 211 participants, amounting to 29,500 person-days of observation, using Bayesian hierarchical negative binomial regression with study- and user-level random intercepts. Sensitivity analyses including alternative model specification and stratified models were conducted. We found that iOS users had lower GPS non-collection than Android users. For GPS data, rates of non-collection did not differ by race/ethnicity, education, age, or gender. For accelerometer data, Black participants had higher rates of non-collection, but rates did not differ by sex, education, or age. For both sensors, non-collection increased by 0.5% to 0.9% per week. These results demonstrate the feasibility of using smartphone-based digital phenotyping across diverse populations, for extended periods of time, and within diverse cohorts. As smartphones become increasingly embedded in everyday life, the insights of this study will help guide the design, planning, and analysis of digital phenotyping studies.
format	article
author	Mathew V. Kiang Jarvis T. Chen Nancy Krieger Caroline O. Buckee Monica J. Alexander Justin T. Baker Randy L. Buckner Garth Coombs Janet W. Rich-Edwards Kenzie W. Carlson Jukka-Pekka Onnela
author_facet	Mathew V. Kiang Jarvis T. Chen Nancy Krieger Caroline O. Buckee Monica J. Alexander Justin T. Baker Randy L. Buckner Garth Coombs Janet W. Rich-Edwards Kenzie W. Carlson Jukka-Pekka Onnela
author_sort	Mathew V. Kiang
title	Sociodemographic characteristics of missing data in digital phenotyping
title_short	Sociodemographic characteristics of missing data in digital phenotyping
title_full	Sociodemographic characteristics of missing data in digital phenotyping
title_fullStr	Sociodemographic characteristics of missing data in digital phenotyping
title_full_unstemmed	Sociodemographic characteristics of missing data in digital phenotyping
title_sort	sociodemographic characteristics of missing data in digital phenotyping
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/31c4a3e4abb443f8b6f11f1e61f5c8a4
work_keys_str_mv	AT mathewvkiang sociodemographiccharacteristicsofmissingdataindigitalphenotyping AT jarvistchen sociodemographiccharacteristicsofmissingdataindigitalphenotyping AT nancykrieger sociodemographiccharacteristicsofmissingdataindigitalphenotyping AT carolineobuckee sociodemographiccharacteristicsofmissingdataindigitalphenotyping AT monicajalexander sociodemographiccharacteristicsofmissingdataindigitalphenotyping AT justintbaker sociodemographiccharacteristicsofmissingdataindigitalphenotyping AT randylbuckner sociodemographiccharacteristicsofmissingdataindigitalphenotyping AT garthcoombs sociodemographiccharacteristicsofmissingdataindigitalphenotyping AT janetwrichedwards sociodemographiccharacteristicsofmissingdataindigitalphenotyping AT kenziewcarlson sociodemographiccharacteristicsofmissingdataindigitalphenotyping AT jukkapekkaonnela sociodemographiccharacteristicsofmissingdataindigitalphenotyping
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Sociodemographic characteristics of missing data in digital phenotyping

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