Calibration of self-reported time spent sitting, standing and walking among office workers: A compositional data analysis

David M. Hallman, Svend Erik Mathiassen, Allard J. van der Beek, Jennie A. Jackson, Pieter Coenen

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

We developed and evaluated calibration models predicting objectively measured sitting, standing and walking time from self-reported data using a compositional data analysis (CoDA) approach. A total of 98 office workers (48 women) at the Swedish Transport Administration participated. At baseline and three-months follow-up, time spent sitting, standing and walking at work was assessed for five working days using a thigh-worn accelerometer (Actigraph), as well as by self-report (IPAQ). Individual compositions of time spent in the three behaviors were expressed by isometric log-ratios (ILR). Calibration models predicting objectively measured ILRs from self-reported ILRs were constructed using baseline data, and then validated using follow-up data. Un-calibrated self-reports were inaccurate; root-mean-square (RMS) errors of ILRs for sitting, standing and walking were 1.21, 1.24 and 1.03, respectively. Calibration reduced these errors to 36% (sitting), 40% (standing), and 24% (walking) of those prior to calibration. Calibration models remained effective for follow-up data, reducing RMS errors to 33% (sitting), 51% (standing), and 31% (walking). Thus, compositional calibration models were effective in reducing errors in self-reported physical behaviors during office work. Calibration of self-reports may present a cost-effective method for obtaining physical behavior data with satisfying accuracy in large-scale cohort and intervention studies.
Original languageEnglish
Article number3111
JournalInternational Journal of Environmental Research and Public Health
Volume16
Issue number17
DOIs
Publication statusPublished - 2019

Cite this

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title = "Calibration of self-reported time spent sitting, standing and walking among office workers: A compositional data analysis",
abstract = "We developed and evaluated calibration models predicting objectively measured sitting, standing and walking time from self-reported data using a compositional data analysis (CoDA) approach. A total of 98 office workers (48 women) at the Swedish Transport Administration participated. At baseline and three-months follow-up, time spent sitting, standing and walking at work was assessed for five working days using a thigh-worn accelerometer (Actigraph), as well as by self-report (IPAQ). Individual compositions of time spent in the three behaviors were expressed by isometric log-ratios (ILR). Calibration models predicting objectively measured ILRs from self-reported ILRs were constructed using baseline data, and then validated using follow-up data. Un-calibrated self-reports were inaccurate; root-mean-square (RMS) errors of ILRs for sitting, standing and walking were 1.21, 1.24 and 1.03, respectively. Calibration reduced these errors to 36{\%} (sitting), 40{\%} (standing), and 24{\%} (walking) of those prior to calibration. Calibration models remained effective for follow-up data, reducing RMS errors to 33{\%} (sitting), 51{\%} (standing), and 31{\%} (walking). Thus, compositional calibration models were effective in reducing errors in self-reported physical behaviors during office work. Calibration of self-reports may present a cost-effective method for obtaining physical behavior data with satisfying accuracy in large-scale cohort and intervention studies.",
author = "Hallman, {David M.} and Mathiassen, {Svend Erik} and {van der Beek}, {Allard J.} and Jackson, {Jennie A.} and Pieter Coenen",
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Calibration of self-reported time spent sitting, standing and walking among office workers: A compositional data analysis. / Hallman, David M.; Mathiassen, Svend Erik; van der Beek, Allard J.; Jackson, Jennie A.; Coenen, Pieter.

In: International Journal of Environmental Research and Public Health, Vol. 16, No. 17, 3111, 2019.

Research output: Contribution to journalArticleAcademicpeer-review

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