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Trajectories of 24-Hour Physical Activity Distribution and Relationship with Dyslipidemia

The association between physical activity (PA) and dyslipidemia is well known, but the relationship between a temporal pattern of PA and dyslipidemia remain unknown. Here, we aimed to identify the intensity and temporal patterns of PA clustered by the trajectory model and their relationship with dys...

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Autores principales: Wang, Xiaojing, Wang, Yongjun, Xu, Zechao, Guo, Xiang, Mao, Hongmei, Liu, Tingting, Gong, Weiyi, Gong, Zhaolong, Zhuo, Qin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9860816/
https://www.ncbi.nlm.nih.gov/pubmed/36678199
http://dx.doi.org/10.3390/nu15020328
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author Wang, Xiaojing
Wang, Yongjun
Xu, Zechao
Guo, Xiang
Mao, Hongmei
Liu, Tingting
Gong, Weiyi
Gong, Zhaolong
Zhuo, Qin
author_facet Wang, Xiaojing
Wang, Yongjun
Xu, Zechao
Guo, Xiang
Mao, Hongmei
Liu, Tingting
Gong, Weiyi
Gong, Zhaolong
Zhuo, Qin
author_sort Wang, Xiaojing
collection PubMed
description The association between physical activity (PA) and dyslipidemia is well known, but the relationship between a temporal pattern of PA and dyslipidemia remain unknown. Here, we aimed to identify the intensity and temporal patterns of PA clustered by the trajectory model and their relationship with dyslipidemia. The participants were 701 adults (305 males) aged 18–60 years undergoing continuous measurement of PA with Actigraph GT3X+ accelerometers for at least 3 days. A trajectory analysis was applied based on moderate-to-vigorous intensity physical activity (MVPA) accumulated values over every period per day. The association between PA and dyslipidemia was estimated using a logistic regression model. Four distinct PA trajectory groups in the population were identified (continued low, stable and moderate, late increasing, and early increasing). Specifically, the “moderate and stable group” was associated with a decreased rate of high TG (p < 0.05) and the “moderate and stable group” and “late increasing group” were associated with decreased rates of low HDL-C (p < 0.05). In conclusion, there were four activity trajectory groups in this population and the continued low PA trajectory was associated with a high prevalent rate of an abnormal lipid profile, and continued and moderate activity or late afternoon increasing activity might have lower HDL-C distribution.
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spelling pubmed-98608162023-01-22 Trajectories of 24-Hour Physical Activity Distribution and Relationship with Dyslipidemia Wang, Xiaojing Wang, Yongjun Xu, Zechao Guo, Xiang Mao, Hongmei Liu, Tingting Gong, Weiyi Gong, Zhaolong Zhuo, Qin Nutrients Article The association between physical activity (PA) and dyslipidemia is well known, but the relationship between a temporal pattern of PA and dyslipidemia remain unknown. Here, we aimed to identify the intensity and temporal patterns of PA clustered by the trajectory model and their relationship with dyslipidemia. The participants were 701 adults (305 males) aged 18–60 years undergoing continuous measurement of PA with Actigraph GT3X+ accelerometers for at least 3 days. A trajectory analysis was applied based on moderate-to-vigorous intensity physical activity (MVPA) accumulated values over every period per day. The association between PA and dyslipidemia was estimated using a logistic regression model. Four distinct PA trajectory groups in the population were identified (continued low, stable and moderate, late increasing, and early increasing). Specifically, the “moderate and stable group” was associated with a decreased rate of high TG (p < 0.05) and the “moderate and stable group” and “late increasing group” were associated with decreased rates of low HDL-C (p < 0.05). In conclusion, there were four activity trajectory groups in this population and the continued low PA trajectory was associated with a high prevalent rate of an abnormal lipid profile, and continued and moderate activity or late afternoon increasing activity might have lower HDL-C distribution. MDPI 2023-01-09 /pmc/articles/PMC9860816/ /pubmed/36678199 http://dx.doi.org/10.3390/nu15020328 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Xiaojing
Wang, Yongjun
Xu, Zechao
Guo, Xiang
Mao, Hongmei
Liu, Tingting
Gong, Weiyi
Gong, Zhaolong
Zhuo, Qin
Trajectories of 24-Hour Physical Activity Distribution and Relationship with Dyslipidemia
title Trajectories of 24-Hour Physical Activity Distribution and Relationship with Dyslipidemia
title_full Trajectories of 24-Hour Physical Activity Distribution and Relationship with Dyslipidemia
title_fullStr Trajectories of 24-Hour Physical Activity Distribution and Relationship with Dyslipidemia
title_full_unstemmed Trajectories of 24-Hour Physical Activity Distribution and Relationship with Dyslipidemia
title_short Trajectories of 24-Hour Physical Activity Distribution and Relationship with Dyslipidemia
title_sort trajectories of 24-hour physical activity distribution and relationship with dyslipidemia
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9860816/
https://www.ncbi.nlm.nih.gov/pubmed/36678199
http://dx.doi.org/10.3390/nu15020328
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