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Classification of occupational activity categories using accelerometry: NHANES 2003–2004

BACKGROUND: An individual’s occupational activity (OA) may contribute significantly to daily physical activity (PA) and sedentary behavior (SB). However, there is little consensus about which occupational categories involve high OA or low OA, and the majority of categories are unclassifiable with cu...

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Autores principales: Steeves, Jeremy A., Tudor-Locke, Catrine, Murphy, Rachel A., King, George A., Fitzhugh, Eugene C., Harris, Tamara B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4499449/
https://www.ncbi.nlm.nih.gov/pubmed/26122724
http://dx.doi.org/10.1186/s12966-015-0235-z
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author Steeves, Jeremy A.
Tudor-Locke, Catrine
Murphy, Rachel A.
King, George A.
Fitzhugh, Eugene C.
Harris, Tamara B.
author_facet Steeves, Jeremy A.
Tudor-Locke, Catrine
Murphy, Rachel A.
King, George A.
Fitzhugh, Eugene C.
Harris, Tamara B.
author_sort Steeves, Jeremy A.
collection PubMed
description BACKGROUND: An individual’s occupational activity (OA) may contribute significantly to daily physical activity (PA) and sedentary behavior (SB). However, there is little consensus about which occupational categories involve high OA or low OA, and the majority of categories are unclassifiable with current methods. The purpose of this study was to present population estimates of accelerometer-derived PA and SB variables for adults (n = 1112, 20–60 years) working the 40 occupational categories collected during the 2003–2004 National Health and Nutrition Examination Survey (NHANES). METHODS: ActiGraph accelerometer-derived total activity counts/day (TAC), activity counts/minute, and proportion of wear time spent in moderate-to-vigorous PA [MVPA], lifestyle, and light PA organized by occupational category were ranked in ascending order and SB was ranked in descending order. Summing the ranks of the six accelerometer-derived variables generated a summary score for each occupational category, which was re-ranked in ascending order. Higher rankings indicated higher levels of OA, lower rankings indicated lower levels of OA. Tertiles of the summary score were used to establish three mutually exclusive accelerometer-determined OA groupings: high OA, intermediate OA, and low OA. RESULTS: According to their summary score, ‘farm and nursery workers’ were classified as high OA and ‘secretaries, stenographers, and typists’ were classified as low OA. Consistent with previous research, some low OA occupational categories (e.g., ‘engineers, architects, and scientists’, ‘technicians and related support occupations’, ‘management related occupations’, ‘executives, administrators, and managers’, ‘protective services’, and ‘writers, artists, entertainers, and athletes’) associated with higher education and income had relatively greater amounts of MVPA compared to other low OA occupational categories, likely due to the greater percentage of men in those occupations and/or the influence of higher levels of leisure time PA. Men had more TAC, activity counts/minute and time in MVPA, but similar proportions of SB compared to women in all three OA groupings. CONCLUSIONS: Objectively measured PA allowed for a more precise estimate of the amount of PA and SB associated with different occupations and facilitated systematic classification of the 40 different occupational categories into three distinct OA groupings. This information provides new opportunities to explore the relationship between OA and health outcomes.
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spelling pubmed-44994492015-07-13 Classification of occupational activity categories using accelerometry: NHANES 2003–2004 Steeves, Jeremy A. Tudor-Locke, Catrine Murphy, Rachel A. King, George A. Fitzhugh, Eugene C. Harris, Tamara B. Int J Behav Nutr Phys Act Research BACKGROUND: An individual’s occupational activity (OA) may contribute significantly to daily physical activity (PA) and sedentary behavior (SB). However, there is little consensus about which occupational categories involve high OA or low OA, and the majority of categories are unclassifiable with current methods. The purpose of this study was to present population estimates of accelerometer-derived PA and SB variables for adults (n = 1112, 20–60 years) working the 40 occupational categories collected during the 2003–2004 National Health and Nutrition Examination Survey (NHANES). METHODS: ActiGraph accelerometer-derived total activity counts/day (TAC), activity counts/minute, and proportion of wear time spent in moderate-to-vigorous PA [MVPA], lifestyle, and light PA organized by occupational category were ranked in ascending order and SB was ranked in descending order. Summing the ranks of the six accelerometer-derived variables generated a summary score for each occupational category, which was re-ranked in ascending order. Higher rankings indicated higher levels of OA, lower rankings indicated lower levels of OA. Tertiles of the summary score were used to establish three mutually exclusive accelerometer-determined OA groupings: high OA, intermediate OA, and low OA. RESULTS: According to their summary score, ‘farm and nursery workers’ were classified as high OA and ‘secretaries, stenographers, and typists’ were classified as low OA. Consistent with previous research, some low OA occupational categories (e.g., ‘engineers, architects, and scientists’, ‘technicians and related support occupations’, ‘management related occupations’, ‘executives, administrators, and managers’, ‘protective services’, and ‘writers, artists, entertainers, and athletes’) associated with higher education and income had relatively greater amounts of MVPA compared to other low OA occupational categories, likely due to the greater percentage of men in those occupations and/or the influence of higher levels of leisure time PA. Men had more TAC, activity counts/minute and time in MVPA, but similar proportions of SB compared to women in all three OA groupings. CONCLUSIONS: Objectively measured PA allowed for a more precise estimate of the amount of PA and SB associated with different occupations and facilitated systematic classification of the 40 different occupational categories into three distinct OA groupings. This information provides new opportunities to explore the relationship between OA and health outcomes. BioMed Central 2015-06-30 /pmc/articles/PMC4499449/ /pubmed/26122724 http://dx.doi.org/10.1186/s12966-015-0235-z Text en © Steeves et al. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Steeves, Jeremy A.
Tudor-Locke, Catrine
Murphy, Rachel A.
King, George A.
Fitzhugh, Eugene C.
Harris, Tamara B.
Classification of occupational activity categories using accelerometry: NHANES 2003–2004
title Classification of occupational activity categories using accelerometry: NHANES 2003–2004
title_full Classification of occupational activity categories using accelerometry: NHANES 2003–2004
title_fullStr Classification of occupational activity categories using accelerometry: NHANES 2003–2004
title_full_unstemmed Classification of occupational activity categories using accelerometry: NHANES 2003–2004
title_short Classification of occupational activity categories using accelerometry: NHANES 2003–2004
title_sort classification of occupational activity categories using accelerometry: nhanes 2003–2004
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4499449/
https://www.ncbi.nlm.nih.gov/pubmed/26122724
http://dx.doi.org/10.1186/s12966-015-0235-z
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