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.
N1 - Funding Information: We gratefully acknowledge Helena Jahncke for her major contributions in designing and performing the data collection. The study was supported by a grant from the Swedish Research Council for Health, Working Life and Welfare (Forte Dnr. 2009-1761). Publisher Copyright: © 2020, Nordic Association of Occupational Safety and Health. All rights reserved. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
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 Question-naire) 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. Conclusion 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 Question-naire) 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. Conclusion 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.
KW - Calibration
KW - Calibration model
KW - Compositional data analysis
KW - Occupational health
KW - Sedentary behavior
UR - http://www.scopus.com/inward/record.url?scp=85077401385&partnerID=8YFLogxK
U2 - https://doi.org/10.5271/sjweh.3827
DO - https://doi.org/10.5271/sjweh.3827
M3 - Article
C2 - 31012945
SN - 0355-3140
VL - 46
SP - 32
EP - 42
JO - Scandinavian Journal of Work, Environment and Health
JF - Scandinavian Journal of Work, Environment and Health
IS - 1
ER -