Suppr超能文献

美国成年人的时间体力活动模式与肥胖有关。

Temporal physical activity patterns are associated with obesity in U.S. adults.

机构信息

Department of Nutrition Science, Purdue University, West Lafayette, IN 47907, USA.

School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA.

出版信息

Prev Med. 2021 Jul;148:106538. doi: 10.1016/j.ypmed.2021.106538. Epub 2021 Mar 30.

Abstract

Few attempts have been made to incorporate multiple aspects of physical activity (PA) to classify patterns linked with health. Temporal PA patterns integrating time and activity counts were created to determine their association with health status. Accelerometry data from the National Health and Nutrition Examination Survey 2003-2006 was used to pattern PA counts and time of activity from 1999 adults with one weekday of activity. Dynamic time warping and kernel k-means clustering partitioned 4 participant clusters representing temporal PA patterns. Multivariate regression models determined associations between clusters and health status indicators and obesity, type 2 diabetes, and metabolic syndrome. Cluster 1 with a temporal PA pattern of the lowest activity counts reaching 4.8e cph from 6:00-23:00 was associated with higher body mass index (BMI) (β = 2.5 ± 0.6 kg/m, 95% CI: 1.0, 4.1), higher waist circumference (WC) (β = 6.4 ± 1.3 cm, 95% CI: 2.8, 10.0), and higher odds of obesity (OR: 2.4; 95% CI: 1.3, 4.4) compared with Cluster 3 with activity counts reaching 9.6e-1.2e cph between 16:00-21:00. Cluster 1 was also associated with higher BMI (β = 1.5 ± 0.5 kg/m, 95% CI: 0.1, 2.8) and WC (β = 3.6 ± 1.3 cm, 95% CI: 0.1, 7.0) compared to Cluster 4 with activity counts reaching 9.6e cph between 8:00-11:00. A Temporal PA pattern with the lowest PA counts had significantly higher mean BMI and WC compared to temporal PA patterns of higher activity counts performed early (8:00-11:00) or late (16:00-21:00) throughout the day. Temporal PA patterns appear to meaningfully link to health status.

摘要

目前很少有研究尝试综合多个方面的身体活动(PA)来对与健康相关的模式进行分类。本研究旨在创建整合时间和活动计数的时间 PA 模式,以确定其与健康状况的关联。本研究使用了 2003-2006 年全国健康和营养调查的加速度计数据,对 1999 名成年人在一个工作日的活动数据进行 PA 计数和活动时间的模式分析。动态时间扭曲和核 k-均值聚类将 4 个代表时间 PA 模式的参与者聚类进行分区。多元回归模型确定了聚类与健康状况指标以及肥胖、2 型糖尿病和代谢综合征之间的关联。聚类 1 的活动计数最低,从 6:00 到 23:00 达到 4.8e cph,与更高的体重指数(BMI)(β=2.5±0.6 kg/m,95%CI:1.0,4.1)、更高的腰围(WC)(β=6.4±1.3 cm,95%CI:2.8,10.0)和更高的肥胖症发病风险(OR:2.4;95%CI:1.3,4.4)相关,与聚类 3 相比,聚类 3 的活动计数在 16:00 到 21:00 之间达到 9.6e-1.2e cph。与聚类 4 相比,聚类 1 还与更高的 BMI(β=1.5±0.5 kg/m,95%CI:0.1,2.8)和 WC(β=3.6±1.3 cm,95%CI:0.1,7.0)相关,聚类 4 的活动计数在 8:00 到 11:00 之间达到 9.6e cph。与活动计数较高的早(8:00-11:00)或晚(16:00-21:00)全天时间的 PA 模式相比,最低 PA 计数的时间 PA 模式的平均 BMI 和 WC 显著更高。时间 PA 模式似乎与健康状况有明显的关联。

相似文献

1
Temporal physical activity patterns are associated with obesity in U.S. adults.
Prev Med. 2021 Jul;148:106538. doi: 10.1016/j.ypmed.2021.106538. Epub 2021 Mar 30.
4
Temporal Dietary Pattern Cluster Membership Varies on Weekdays and Weekends but Both Link to Health.
J Nutr. 2024 Feb;154(2):722-733. doi: 10.1016/j.tjnut.2023.12.049. Epub 2023 Dec 30.
6
Temporal Dietary Patterns Are Associated with Obesity in US Adults.
J Nutr. 2020 Dec 10;150(12):3259-3268. doi: 10.1093/jn/nxaa287.
8
Changes in waist circumference relative to body mass index in Chinese adults, 1993-2009.
Int J Obes (Lond). 2014 Dec;38(12):1503-10. doi: 10.1038/ijo.2014.74. Epub 2014 May 12.

引用本文的文献

2
Examining physical activity clustering using machine learning revealed a diversity of 24-hour step-counting patterns.
J Act Sedentary Sleep Behav. 2024 Aug 12;3(1):19. doi: 10.1186/s44167-024-00059-3.
5
Deep learning of movement behavior profiles and their association with markers of cardiometabolic health.
BMC Med Inform Decis Mak. 2024 Mar 13;24(1):74. doi: 10.1186/s12911-024-02474-7.
6
Temporal Dietary Pattern Cluster Membership Varies on Weekdays and Weekends but Both Link to Health.
J Nutr. 2024 Feb;154(2):722-733. doi: 10.1016/j.tjnut.2023.12.049. Epub 2023 Dec 30.
8
Higher walkability associated with increased physical activity and reduced obesity among United States adults.
Obesity (Silver Spring). 2023 Feb;31(2):553-564. doi: 10.1002/oby.23634. Epub 2022 Dec 12.
10

本文引用的文献

1
Temporal Dietary Patterns Are Associated with Obesity in US Adults.
J Nutr. 2020 Dec 10;150(12):3259-3268. doi: 10.1093/jn/nxaa287.
3
Distance metrics optimized for clustering temporal dietary patterning among U.S. adults.
Appetite. 2020 Jan 1;144:104451. doi: 10.1016/j.appet.2019.104451. Epub 2019 Sep 12.
4
Intensity and temporal patterns of physical activity and cardiovascular disease risk in midlife.
Prev Med. 2019 Jul;124:33-41. doi: 10.1016/j.ypmed.2019.04.023. Epub 2019 Apr 30.
5
Trends in Sedentary Behavior Among the US Population, 2001-2016.
JAMA. 2019 Apr 23;321(16):1587-1597. doi: 10.1001/jama.2019.3636.
6
Associations Between Timing of Meals, Physical Activity, Light Exposure, and Sleep With Body Mass Index in Free-Living Adults.
J Phys Act Health. 2019 Mar 1;16(3):214-221. doi: 10.1123/jpah.2017-0389. Epub 2019 Feb 24.
7
The Effects of Exercise and Physical Activity on Weight Loss and Maintenance.
Prog Cardiovasc Dis. 2018 Jul-Aug;61(2):206-213. doi: 10.1016/j.pcad.2018.07.014. Epub 2018 Jul 9.
8
Objectively Measured Baseline Physical Activity Patterns in Women in the mPED Trial: Cluster Analysis.
JMIR Public Health Surveill. 2018 Feb 1;4(1):e10. doi: 10.2196/publichealth.9138.
9
Physical Activity Assessment with the ActiGraph GT3X and Doubly Labeled Water.
Med Sci Sports Exerc. 2017 Sep;49(9):1935-1944. doi: 10.1249/MSS.0000000000001299.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验