Correction of bias in self-reported sitting time among office workers - a study based on compositional data analysis

Pieter Coenen, SvendErik Mathiassen, Allard J. van der Beek, David M. Hallman

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Objective Emerging evidence suggests that excessive sitting has negative health effects. However, this evidence largely relies on research using self-reported sitting time, which is known to be biased. To correct this bias, we aimed at developing a calibration model estimating "true" sitting from self-reported sitting. Methods Occupational sitting time was estimated by self-reports (the International Physical Activity Questionnaire) and objective measurements (thigh-worn accelerometer) among 99 Swedish office workers at a governmental agency, at baseline and 3 and 12 months afterwards. Following compositional data analysis procedures, both sitting estimates were transformed into isometric log-ratios (ILR). This effectively addresses that times spent in various activities are inherently dependent and can be presented as values of only 0-100%. Linear regression was used to develop a simple calibration model estimating objectively measured "true" sitting ILR (dependent variable) from self-reported sitting ILR (independent variable). Additional self-reported variables were then added to construct a full calibration model. Performance of the models was assessed by root-mean-square (RMS) differences between estimated and objectively measured values. Models developed on baseline data were validated using the follow-up datasets. Results Uncalibrated self-reported sitting ILR showed an RMS error of 0.767. Simple and full calibration models (incorporating body mass index, office type, and gender) reduced this error to 0.422 (55%) and 0.398 (52%), respectively. In the validations, model performance decreased to 57%/62% (simple models) and 57%/62% (full models) for the two follow-up data sets, respectively. Conclusions Calibration adjusting for errors in self-reported sitting led to substantially more correct estimates of "true" sitting than uncalibrated self-reports. Validation indicated that model performance would change somewhat in new datasets and that full models perform no better than simple models, but calibration remained effective.
Original languageEnglish
Pages (from-to)32-42
JournalScandinavian Journal of Work, Environment and Health
Volume46
Issue number1
DOIs
Publication statusPublished - 2020

Cite this

@article{d87be6467ede469d8f8f929e0fdea36c,
title = "Correction of bias in self-reported sitting time among office workers - a study based on compositional data analysis",
abstract = "Objective Emerging evidence suggests that excessive sitting has negative health effects. However, this evidence largely relies on research using self-reported sitting time, which is known to be biased. To correct this bias, we aimed at developing a calibration model estimating {"}true{"} sitting from self-reported sitting. Methods Occupational sitting time was estimated by self-reports (the International Physical Activity Questionnaire) and objective measurements (thigh-worn accelerometer) among 99 Swedish office workers at a governmental agency, at baseline and 3 and 12 months afterwards. Following compositional data analysis procedures, both sitting estimates were transformed into isometric log-ratios (ILR). This effectively addresses that times spent in various activities are inherently dependent and can be presented as values of only 0-100{\%}. Linear regression was used to develop a simple calibration model estimating objectively measured {"}true{"} sitting ILR (dependent variable) from self-reported sitting ILR (independent variable). Additional self-reported variables were then added to construct a full calibration model. Performance of the models was assessed by root-mean-square (RMS) differences between estimated and objectively measured values. Models developed on baseline data were validated using the follow-up datasets. Results Uncalibrated self-reported sitting ILR showed an RMS error of 0.767. Simple and full calibration models (incorporating body mass index, office type, and gender) reduced this error to 0.422 (55{\%}) and 0.398 (52{\%}), respectively. In the validations, model performance decreased to 57{\%}/62{\%} (simple models) and 57{\%}/62{\%} (full models) for the two follow-up data sets, respectively. Conclusions Calibration adjusting for errors in self-reported sitting led to substantially more correct estimates of {"}true{"} sitting than uncalibrated self-reports. Validation indicated that model performance would change somewhat in new datasets and that full models perform no better than simple models, but calibration remained effective.",
author = "Pieter Coenen and SvendErik Mathiassen and {van der Beek}, {Allard J.} and Hallman, {David M.}",
year = "2020",
doi = "10.5271/sjweh.3827",
language = "English",
volume = "46",
pages = "32--42",
journal = "Scandinavian Journal of Work, Environment and Health",
issn = "0355-3140",
publisher = "Finnish Institute of Occupational Health",
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Correction of bias in self-reported sitting time among office workers - a study based on compositional data analysis. / Coenen, Pieter; Mathiassen, SvendErik; van der Beek, Allard J.; Hallman, David M.

In: Scandinavian Journal of Work, Environment and Health, Vol. 46, No. 1, 2020, p. 32-42.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Correction of bias in self-reported sitting time among office workers - a study based on compositional data analysis

AU - Coenen, Pieter

AU - Mathiassen, SvendErik

AU - van der Beek, Allard J.

AU - Hallman, David M.

PY - 2020

Y1 - 2020

N2 - Objective Emerging evidence suggests that excessive sitting has negative health effects. However, this evidence largely relies on research using self-reported sitting time, which is known to be biased. To correct this bias, we aimed at developing a calibration model estimating "true" sitting from self-reported sitting. Methods Occupational sitting time was estimated by self-reports (the International Physical Activity Questionnaire) and objective measurements (thigh-worn accelerometer) among 99 Swedish office workers at a governmental agency, at baseline and 3 and 12 months afterwards. Following compositional data analysis procedures, both sitting estimates were transformed into isometric log-ratios (ILR). This effectively addresses that times spent in various activities are inherently dependent and can be presented as values of only 0-100%. Linear regression was used to develop a simple calibration model estimating objectively measured "true" sitting ILR (dependent variable) from self-reported sitting ILR (independent variable). Additional self-reported variables were then added to construct a full calibration model. Performance of the models was assessed by root-mean-square (RMS) differences between estimated and objectively measured values. Models developed on baseline data were validated using the follow-up datasets. Results Uncalibrated self-reported sitting ILR showed an RMS error of 0.767. Simple and full calibration models (incorporating body mass index, office type, and gender) reduced this error to 0.422 (55%) and 0.398 (52%), respectively. In the validations, model performance decreased to 57%/62% (simple models) and 57%/62% (full models) for the two follow-up data sets, respectively. Conclusions Calibration adjusting for errors in self-reported sitting led to substantially more correct estimates of "true" sitting than uncalibrated self-reports. Validation indicated that model performance would change somewhat in new datasets and that full models perform no better than simple models, but calibration remained effective.

AB - Objective Emerging evidence suggests that excessive sitting has negative health effects. However, this evidence largely relies on research using self-reported sitting time, which is known to be biased. To correct this bias, we aimed at developing a calibration model estimating "true" sitting from self-reported sitting. Methods Occupational sitting time was estimated by self-reports (the International Physical Activity Questionnaire) and objective measurements (thigh-worn accelerometer) among 99 Swedish office workers at a governmental agency, at baseline and 3 and 12 months afterwards. Following compositional data analysis procedures, both sitting estimates were transformed into isometric log-ratios (ILR). This effectively addresses that times spent in various activities are inherently dependent and can be presented as values of only 0-100%. Linear regression was used to develop a simple calibration model estimating objectively measured "true" sitting ILR (dependent variable) from self-reported sitting ILR (independent variable). Additional self-reported variables were then added to construct a full calibration model. Performance of the models was assessed by root-mean-square (RMS) differences between estimated and objectively measured values. Models developed on baseline data were validated using the follow-up datasets. Results Uncalibrated self-reported sitting ILR showed an RMS error of 0.767. Simple and full calibration models (incorporating body mass index, office type, and gender) reduced this error to 0.422 (55%) and 0.398 (52%), respectively. In the validations, model performance decreased to 57%/62% (simple models) and 57%/62% (full models) for the two follow-up data sets, respectively. Conclusions Calibration adjusting for errors in self-reported sitting led to substantially more correct estimates of "true" sitting than uncalibrated self-reports. Validation indicated that model performance would change somewhat in new datasets and that full models perform no better than simple models, but calibration remained effective.

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UR - https://www.ncbi.nlm.nih.gov/pubmed/31012945

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