Abstract
Original language | English |
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Article number | 3111 |
Journal | International Journal of Environmental Research and Public Health |
Volume | 16 |
Issue number | 17 |
DOIs | |
Publication status | Published - 2019 |
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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 journal › Article › Academic › peer-review
TY - JOUR
T1 - Calibration of self-reported time spent sitting, standing and walking among office workers: A compositional data analysis
AU - Hallman, David M.
AU - Mathiassen, Svend Erik
AU - van der Beek, Allard J.
AU - Jackson, Jennie A.
AU - Coenen, Pieter
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85071631368&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/31461868
U2 - 10.3390/ijerph16173111
DO - 10.3390/ijerph16173111
M3 - Article
VL - 16
JO - International Journal of Environmental Research and Public Health
JF - International Journal of Environmental Research and Public Health
SN - 1660-4601
IS - 17
M1 - 3111
ER -