Cluster analysis methods help to clarify the activity-BMI relationship of Chinese youth
Cluster analysis methods help to clarify the activity-BMI relationship of Chinese youth
复制标题
DOI：
10.1038/oby.2005.122
复制
发表时间：
2005-06-01
期刊：
影响因子：
--
通讯作者：
Popkin, BM
中科院分区：
文献类型：
--
作者：
Monda, KL;Popkin, BM
凯里·蒙达和巴里M。Popkin。聚类分析方法有助于阐明中国青年活动与BMI的关系。肥胖研究，2005，13：1042- 1051.目的：利用聚类分析建立中国青少年总体活动和不活动的模式，并评估其在预测超重状况中的应用.研究方法和步骤：研究人群来自1997年和2000年的中国健康与营养纵向调查，包括1997年和2000年横断面样本中的2702和2641名学童，以及纵向队列中的1175名儿童。聚类分析用于将儿童分组为不重叠的活动/不活动“聚类”，随后用于普遍和偶发超重模型。结果进行了比较，与传统的模式，活动和不活动单独编码，以评估是否进一步的洞察力，获得了与聚类分析methodology.Results：中度和高度活跃的青年被证明有显着降低超重的几率，在横截面和纵向分析使用聚类分析。在事件纵向模型中，青年在高活动/高不活动集群超重的几率最低[比值比= 0.12（0.03，0.44）];相反，传统模型的结果未能显示任何显着的超重和活动或inactivity.Discussion之间的关系：聚类分析方法，使研究人员能够同时捕获活动和不活动的新方法。在这项比较研究中，只有使用聚类方法，我们才发现活动对事件超重有显著影响，进一步提高了我们研究这种复杂关系的能力。很有趣。使用这两种方法都没有观察到任何影响或增加了不活动的水平，表明活动似乎是该人群超重的更重要决定因素。
MONDA, KERI L. AND BARRY M. POPKIN. Cluster analysis methods help to clarify the activity-BMI relationship of Chinese youth. Obes Res. 2005,13: 1042-1051.Objective: To use cluster analysis to create patterns of overall activity and inactivity in a diverse sample of Chinese youth and to evaluate their use in predicting overweight status.Research Methods and Procedures: The study populations were drawn from the 1997 and 2000 years of the longitudinal China Health and Nutrition Survey, comprised of 2702 and 2641 schoolchildren in the 1997 and 2000 cross-sectional samples, respectively, and 1175 children in the longitudinal cohort. Cluster analysis was used to group children into nonoverlapping activity/inactivity "clusters" that were Subsequently used in models of prevalent and incident overweight. Results were compared with traditional models, with activity and inactivity coded separately, to assess whether further insight was gained with the cluster analysis methodology.Results: Moderately and highly active youth were shown to have significantly decreased odds of overweight in both cross-sectional and longitudinal analyses using cluster analysis. In incident longitudinal models, youth in the high activity/high inactivity cluster had the lowest odds of overweight [odds ratio = 0.12 (0.03, 0.44)]; in contrast, results from traditional models failed to show any significant relationship between overweight and activity or inactivity.Discussion: Cluster analysis methods allow researchers to simultaneously capture activity and inactivity in new ways. In this comparative study, only with the clustering methodology did we find a significant effect of activity on incident overweight, furthering our ability to examine this complex relationship. Interestingly. no effect or increasing levels of inactivity was observed using either method, indicating that activity seems to be the more important determinant of overweight in this population.