Physical activity and sedentary behavior patterns using accelerometry from a national sample of United States adults
Physical activity and sedentary behavior patterns using accelerometry from a national sample of United States adults
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DOI：
10.1186/s12966-015-0183-7
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发表时间：
2015-02-15
影响因子：
8.7
通讯作者：
Herring, Amy H.
中科院分区：
文献类型：
--
作者：
Evenson, Kelly R.;Wen, Fang;Herring, Amy H.
背景：本研究描述了加速度计测定的成年人身体活动和久坐行为的模式，使用了一个具有全国代表性的美国样本。方法：利用2003-2006年全国健康与营养调查（NHANES）的数据，7931名18岁以上的成年人佩戴ActiGraph加速度计一周，至少佩戴3天，每天佩戴>= 8小时。临界值定义为中度至剧烈运动（MVPA; >= 2020和>= 760计数/分钟）、剧烈运动（> = 5999计数/分钟）和久坐行为（< 100计数/分钟）。使用潜类分析（LCA）来估计身体活动和久坐行为的模式。所有的估计都经过加权以反映美国人口。结果：对于MVPA占总穿着时间的加权百分比，从最小到最活跃的有5个类别：65.3%（加权平均9.3分钟/天），24.9%（32.1分钟/天），3.2%在工作日较低，但周末高得多（52.0分钟/天），5.9%（59.9分钟/天），最高类别0.7%（113.6分钟/天）。使用较低的MVPA阈值，出现了6个类别，每个类别的人口范围为1.2%至43.6%。由于患病率低，无法得出剧烈运动类别。对于久坐行为占总穿着时间的加权百分比，从久坐最多到最少确定了5个类别：6.3%（加权平均660.2分钟/天）、25.1%（546.8分钟/天）、37.7%（453.9分钟/天）、24.0%（354.8分钟/天）和7.0%（256.3分钟/天）。其中四个班级在一周的每一天都显示出大致相似的结果，不同班级的绝对百分比不同。相比之下，最少久坐的班级在周末（加权平均336.7-346.5分钟/天）的久坐时间比平日（加权平均255.2-292.4分钟/天）明显增加。结论：LCA模型提供了一个数据简化过程，以识别使用每分钟加速度计数据的模式，以探索有意义的对比。这些模型支持5或6种不同的MVPA和久坐行为模式。这些身体活动和久坐行为模式可以作为干预目标，也可以作为未来相关因素、决定因素或结果研究的独立或因变量。
Background: This study described the patterns of accelerometer-determined physical activity and sedentary behavior among adults using a nationally representative sample from the United States.Methods: Using 2003-2006 National Health and Nutrition Examination Survey (NHANES) data, 7931 adults at least 18 years old wore an ActiGraph accelerometer for one week, providing at least 3 days of wear for >= 8 hours/day. Cutpoints defined moderate to vigorous physical activity (MVPA; >= 2020 and >= 760 counts/minute), vigorous physical activity (> = 5999 counts/minute), and sedentary behavior (< 100 counts/minute). Latent class analysis (LCA) was used to estimate patterns of physical activity and sedentary behavior. All estimates were weighted to reflect the United States population.Results: For weighted percent of MVPA out of total wearing time, 5 classes were identified from least to most active: 65.3% of population (weighted mean 9.3 minutes/day), 24.9% (32.1 minutes/day), 3.2% that was low on the weekdays but much higher on the weekends (52.0 minutes/day), 5.9% (59.9 minutes/day), and 0.7% in the highest class (113.6 minutes/day). Using the lower MVPA threshold, 6 classes emerged with each class ranging in population from 1.2% to 43.6%. A vigorous activity class could not be derived due to low prevalence. For weighted percent of sedentary behavior out of total wearing time, 5 classes were identified from most to least sedentary: 6.3% of population (weighted mean 660.2 minutes/day), 25.1% (546.8 minutes/day), 37.7% (453.9 minutes/day), 24.0% (354.8 minutes/day), and 7.0% (256.3 minutes/day). Four of the classes showed generally similar results across every day of the week, with the absolute percents differing across classes. In contrast, the least sedentary class showing a marked rise in percent of time spent in sedentary behavior on the weekend (weighted mean 336.7-346.5 minutes/day) compared to weekdays (weighted mean 255.2-292.4 minutes/day).Conclusion: The LCA models provided a data reduction process to identify patterns using minute-by-minute accelerometry data in order to explore meaningful contrasts. The models supported 5 or 6 distinct patterns for MVPA and sedentary behavior. These physical activity and sedentary behavior patterns can be used as intervention targets and as independent or dependent variables in future studies of correlates, determinants, or outcomes.