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运用潜在类别分析对加速度计数据进行分析,研究全国青少年的久坐行为和日常身体活动模式。

National youth sedentary behavior and physical activity daily patterns using latent class analysis applied to accelerometry.

作者信息

Evenson Kelly R, Wen Fang, Hales Derek, Herring Amy H

机构信息

Department of Epidemiology at the Gillings School of Global Public Health, Center for Health Promotion and Disease Prevention, University of North Carolina, 137 East Franklin Street, Suite 306, Chapel Hill, NC, 27514, USA.

Department of Epidemiology at the Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.

出版信息

Int J Behav Nutr Phys Act. 2016 May 3;13:55. doi: 10.1186/s12966-016-0382-x.

Abstract

BACKGROUND

Applying latent class analysis (LCA) to accelerometry can help elucidated underlying patterns. This study described the patterns of accelerometer-determined sedentary behavior and physical activity among youth by applying LCA to a nationally representative United States (US) sample.

METHODS

Using 2003-2006 National Health and Nutrition Examination Survey data, 3998 youths 6-17 years wore an ActiGraph 7164 accelerometer for one week, providing > =3 days of wear for > =8 h/day from 6:00 am-midnight. Cutpoints defined sedentary behavior (<100 counts/minute), light activity (100-2295 counts/minute), moderate to vigorous physical activity (MVPA; > = 2296 counts/minute), and vigorous activity (> = 4012 counts/minute). To account for wear time differences, outcomes were expressed as percent of day in a given intensity. LCA was used to classify daily (Monday through Sunday) patterns of average counts/minute, sedentary behavior, light activity, MVPA, and vigorous activity separately. The latent classes were explored overall and by age (6-11, 12-14, 15-17 years), gender, and whether or not youth attended school during measurement. Estimates were weighted to account for the sampling frame.

RESULTS

For average counts/minute/day, four classes emerged from least to most active: 40.9% of population (mean 323.5 counts/minute/day), 40.3% (559.6 counts/minute/day), 16.5% (810.0 counts/minute/day), and 2.3% (1132.9 counts/minute/day). For percent of sedentary behavior, four classes emerged: 13.5% of population (mean 544.6 min/day), 30.1% (455.1 min/day), 38.5% (357.7 min/day), and 18.0% (259.2 min/day). For percent of light activity, four classes emerged: 12.3% of population (mean 222.6 min/day), 29.3% (301.7 min/day), 41.8% (384.0 min/day), and 16.6% (455.5 min/day). For percent of MVPA, four classes emerged: 59.9% of population (mean 25.0 min/day), 33.3% (60.9 min/day), 3.1% (89.0 min/day), and 3.6% (109.3 min/day). For percent of vigorous activity, three classes emerged: 76.8% of population (mean 7.1 min/day), 18.5% (23.9 min/day), and 4.7% (47.4 min/day). Classes were developed by age, gender, and school attendance since some patterns differed when stratifying by these factors.

CONCLUSION

The models supported patterns for average intensity, sedentary behavior, light activity, MVPA, and vigorous activity. These latent class derived patterns can be used in other youth studies to explore correlates or outcomes and to target sedentary behavior or physical activity interventions.

摘要

背景

将潜在类别分析(LCA)应用于加速度计有助于阐明潜在模式。本研究通过对具有全国代表性的美国样本应用LCA,描述了青少年中加速度计测定的久坐行为和身体活动模式。

方法

利用2003 - 2006年国家健康与营养检查调查数据,3998名6 - 17岁的青少年佩戴ActiGraph 7164加速度计一周,从上午6点至午夜每天佩戴≥8小时,且佩戴天数≥3天。切点定义了久坐行为(<100次/分钟)、轻度活动(100 - 2295次/分钟)、中度至剧烈身体活动(MVPA;≥2296次/分钟)和剧烈活动(≥4012次/分钟)。为了考虑佩戴时间差异,结果以给定强度下一天中的百分比表示。LCA分别用于对每日(周一至周日)平均计数/分钟、久坐行为、轻度活动、MVPA和剧烈活动的模式进行分类。总体以及按年龄(6 - 11岁、12 - 14岁、15 - 17岁)、性别和测量期间青少年是否上学对潜在类别进行了探索。估计值进行了加权以考虑抽样框架。

结果

对于平均计数/分钟/天,出现了从最不活跃到最活跃的四个类别:占人口的40.9%(平均323.5次/分钟/天)、40.3%(559.6次/分钟/天)、16.5%(810.0次/分钟/天)和2.3%(1132.9次/分钟/天)。对于久坐行为的百分比,出现了四个类别:占人口的13.5%(平均544.6分钟/天)、30.1%(455.1分钟/天)、38.5%(357.7分钟/天)和18.0%(259.2分钟/天)。对于轻度活动的百分比,出现了四个类别:占人口的12.3%(平均222.6分钟/天)、29.3%(301.7分钟/天)、41.8%(384.0分钟/天)和16.6%(455.5分钟/天)。对于MVPA的百分比,出现了四个类别:占人口的59.9%(平均25.0分钟/天)、33.3%(60.9分钟/天)、3.1%(89.0分钟/天)和3.6%(109.3分钟/天)。对于剧烈活动的百分比,出现了三个类别:占人口的76.8%(平均7.1分钟/天)、18.5%(23.9分钟/天)和4.7%(47.4分钟/天)。由于按这些因素分层时一些模式有所不同,因此按年龄、性别和是否上学对类别进行了划分。

结论

这些模型支持了平均强度、久坐行为、轻度活动、MVPA和剧烈活动的模式。这些从潜在类别得出的模式可用于其他青少年研究,以探索相关性或结果,并针对久坐行为或身体活动干预措施。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6576/4855777/3da57ce8cda2/12966_2016_382_Fig1_HTML.jpg

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