Department of Nutrition Science, Purdue University, West Lafayette, IN 47907, USA.
School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA.
Prev Med. 2021 Jul;148:106538. doi: 10.1016/j.ypmed.2021.106538. Epub 2021 Mar 30.
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.
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。