TY - JOUR
T1 - Identifying typologies of diurnal patterns in desk-based workers’ sedentary time
AU - Kurosawa, Sayaka
AU - Shibata, Ai
AU - Ishii, Kaori
AU - Koohsari, MohammadJavad
AU - Oka, Koichiro
N1 - Funding Information:
SK was supported by 34th (2017) Research-Aid from Meiji Yasuda Life Foundation of Health and Welfare (https://www.my-zaidan.or.jp/ josei/about/). AS was supported by a Grant-in-Aid for Scientific Research (No. 18K10986) from the Japan Society for the Promotion of Science (https://kaken.nii.ac.jp/ja/grant/KAKENHIPROJECT-18K10986/). KO was supported by a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (No. 20H04113) (https://kaken.nii.ac.jp/ja/grant/ KAKENHI-PROJECT-20H04113/), and a MEXTSupported Program for the Strategic Research Foundation at Private Universities (No. S1511017) (http://www.mext.go.jp/en/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
© 2021 Kurosawa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2021/4
Y1 - 2021/4
N2 - The purpose of this study was to identify typologies of diurnal sedentary behavior patterns and sociodemographic characteristics of desk-based workers. The sedentary time of 229 desk-based workers was measured using accelerometer devices. The within individual diurnal variations in sedentary time was calculated for both workdays and non-workdays. Diurnal variations in sedentary time during each time period (morning, afternoon, and evening) was calculated as the percentage of sedentary time during each time period divided by the percentage of the total sedentary time. A hierarchical cluster analysis (Ward’s method) was used to identify the optimal number of clusters. To refine the initial clusters, a non-hierarchical cluster analysis (k-means method) was performed. Four clusters were identified: stable sedentary cluster (46.7%), off-morning break cluster (26.6%), off-afternoon break cluster (8.3%), and evening sedentary cluster (18.3%). The stable sedentary cluster had the lowest variations in sedentary time throughout the day and the highest amount of total sedentary time. Participants in the off-morning and off-afternoon break clusters had nearly the same sedentary patterns but took short-term breaks during non-workday mornings or afternoons. The evening sedentary cluster had a completely different pattern, with a longer sedentary time during the evening both on workdays and non-workdays. Sociodemographic attributes such as sex, household income, educational attainment, employment status, sleep duration, and residential area, differed significantly between groups. Initiatives to address desk-based workers’ sedentary behavior need to focus not only on the workplace but also on the appropriate timing for reducing excessive sedentary time in non-work contexts depending on the characteristics and diurnal patterns of target subgroups.
AB - The purpose of this study was to identify typologies of diurnal sedentary behavior patterns and sociodemographic characteristics of desk-based workers. The sedentary time of 229 desk-based workers was measured using accelerometer devices. The within individual diurnal variations in sedentary time was calculated for both workdays and non-workdays. Diurnal variations in sedentary time during each time period (morning, afternoon, and evening) was calculated as the percentage of sedentary time during each time period divided by the percentage of the total sedentary time. A hierarchical cluster analysis (Ward’s method) was used to identify the optimal number of clusters. To refine the initial clusters, a non-hierarchical cluster analysis (k-means method) was performed. Four clusters were identified: stable sedentary cluster (46.7%), off-morning break cluster (26.6%), off-afternoon break cluster (8.3%), and evening sedentary cluster (18.3%). The stable sedentary cluster had the lowest variations in sedentary time throughout the day and the highest amount of total sedentary time. Participants in the off-morning and off-afternoon break clusters had nearly the same sedentary patterns but took short-term breaks during non-workday mornings or afternoons. The evening sedentary cluster had a completely different pattern, with a longer sedentary time during the evening both on workdays and non-workdays. Sociodemographic attributes such as sex, household income, educational attainment, employment status, sleep duration, and residential area, differed significantly between groups. Initiatives to address desk-based workers’ sedentary behavior need to focus not only on the workplace but also on the appropriate timing for reducing excessive sedentary time in non-work contexts depending on the characteristics and diurnal patterns of target subgroups.
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U2 - 10.1371/journal.pone.0248304
DO - 10.1371/journal.pone.0248304
M3 - Article
C2 - 33836010
AN - SCOPUS:85104161193
SN - 1932-6203
VL - 16
JO - PloS one
JF - PloS one
IS - 4 April 2021
M1 - e0248304
ER -