Original article

Scand J Work Environ Health Online-first -article    pdf


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

by Coenen P, Mathiassen SE, van der Beek AJ, Hallman DM

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.

This article refers to the following texts of the Journal: 2018;44(2):163-170  2016;42(3):237-245  2011;37(1):6-29
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