Smartphone-recorded physical activity for estimating cardiorespiratory fitness

Abstract While cardiorespiratory fitness is strongly associated with mortality and diverse outcomes, routine measurement is limited. We used smartphone-derived physical activity data to estimate fitness among 50 older adults. We recruited iPhone owners undergoing cardiac stress testing and collected...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Micah T. Eades, Athanasios Tsanas, Stephen P. Juraschek, Daniel B. Kramer, Ernest Gervino, Kenneth J. Mukamal
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/522877caf1274ec7a38424da87d5217c
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:522877caf1274ec7a38424da87d5217c
record_format	dspace
spelling	oai:doaj.org-article:522877caf1274ec7a38424da87d5217c2021-12-02T17:03:50ZSmartphone-recorded physical activity for estimating cardiorespiratory fitness10.1038/s41598-021-94164-x2045-2322https://doaj.org/article/522877caf1274ec7a38424da87d5217c2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94164-xhttps://doaj.org/toc/2045-2322Abstract While cardiorespiratory fitness is strongly associated with mortality and diverse outcomes, routine measurement is limited. We used smartphone-derived physical activity data to estimate fitness among 50 older adults. We recruited iPhone owners undergoing cardiac stress testing and collected recent iPhone physical activity data. Cardiorespiratory fitness was measured as peak metabolic equivalents of task (METs) achieved on cardiac stress test. We then estimated peak METs using multivariable regression models incorporating iPhone physical activity data, and validated with bootstrapping. Individual smartphone variables most significantly correlated with peak METs (p-values both < 0.001) included daily peak gait speed averaged over the preceding 30 days (r = 0.63) and root mean square of the successive differences of daily distance averaged over 365 days (r = 0.57). The best-performing multivariable regression model included the latter variable, as well as age and body mass index. This model explained 68% of variability in observed METs (95% CI 46%, 81%), and estimated peak METs with a bootstrapped mean absolute error of 1.28 METs (95% CI 0.98, 1.60). Our model using smartphone physical activity estimated cardiorespiratory fitness with high performance. Our results suggest larger, independent samples might yield estimates accurate and precise for risk stratification and disease prognostication.Micah T. EadesAthanasios TsanasStephen P. JuraschekDaniel B. KramerErnest GervinoKenneth J. MukamalNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-6 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Micah T. Eades Athanasios Tsanas Stephen P. Juraschek Daniel B. Kramer Ernest Gervino Kenneth J. Mukamal Smartphone-recorded physical activity for estimating cardiorespiratory fitness
description	Abstract While cardiorespiratory fitness is strongly associated with mortality and diverse outcomes, routine measurement is limited. We used smartphone-derived physical activity data to estimate fitness among 50 older adults. We recruited iPhone owners undergoing cardiac stress testing and collected recent iPhone physical activity data. Cardiorespiratory fitness was measured as peak metabolic equivalents of task (METs) achieved on cardiac stress test. We then estimated peak METs using multivariable regression models incorporating iPhone physical activity data, and validated with bootstrapping. Individual smartphone variables most significantly correlated with peak METs (p-values both < 0.001) included daily peak gait speed averaged over the preceding 30 days (r = 0.63) and root mean square of the successive differences of daily distance averaged over 365 days (r = 0.57). The best-performing multivariable regression model included the latter variable, as well as age and body mass index. This model explained 68% of variability in observed METs (95% CI 46%, 81%), and estimated peak METs with a bootstrapped mean absolute error of 1.28 METs (95% CI 0.98, 1.60). Our model using smartphone physical activity estimated cardiorespiratory fitness with high performance. Our results suggest larger, independent samples might yield estimates accurate and precise for risk stratification and disease prognostication.
format	article
author	Micah T. Eades Athanasios Tsanas Stephen P. Juraschek Daniel B. Kramer Ernest Gervino Kenneth J. Mukamal
author_facet	Micah T. Eades Athanasios Tsanas Stephen P. Juraschek Daniel B. Kramer Ernest Gervino Kenneth J. Mukamal
author_sort	Micah T. Eades
title	Smartphone-recorded physical activity for estimating cardiorespiratory fitness
title_short	Smartphone-recorded physical activity for estimating cardiorespiratory fitness
title_full	Smartphone-recorded physical activity for estimating cardiorespiratory fitness
title_fullStr	Smartphone-recorded physical activity for estimating cardiorespiratory fitness
title_full_unstemmed	Smartphone-recorded physical activity for estimating cardiorespiratory fitness
title_sort	smartphone-recorded physical activity for estimating cardiorespiratory fitness
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/522877caf1274ec7a38424da87d5217c
work_keys_str_mv	AT micahteades smartphonerecordedphysicalactivityforestimatingcardiorespiratoryfitness AT athanasiostsanas smartphonerecordedphysicalactivityforestimatingcardiorespiratoryfitness AT stephenpjuraschek smartphonerecordedphysicalactivityforestimatingcardiorespiratoryfitness AT danielbkramer smartphonerecordedphysicalactivityforestimatingcardiorespiratoryfitness AT ernestgervino smartphonerecordedphysicalactivityforestimatingcardiorespiratoryfitness AT kennethjmukamal smartphonerecordedphysicalactivityforestimatingcardiorespiratoryfitness
_version_	1718381848242946048

Smartphone-recorded physical activity for estimating cardiorespiratory fitness

Ejemplares similares