Monda Keri L, Popkin Barry M
Carolina Population Center, University of North Carolina, 123 W. Franklin Street, Chapel Hill, NC 27516-3997, USA.
Obes Res. 2005 Jun;13(6):1042-51. doi: 10.1038/oby.2005.122.
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.
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.
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.
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 of increasing levels of inactivity was observed using either method, indicating that activity seems to be the more important determinant of overweight in this population.
运用聚类分析方法，在中国青少年的多样化样本中创建总体活动和不活动模式，并评估这些模式在预测超重状况方面的作用。
研究人群来自1997年和2000年的中国健康与营养纵向调查，1997年横断面样本中有2702名学童，2000年横断面样本中有2641名学童，纵向队列中有1175名儿童。聚类分析用于将儿童分组到不重叠的活动/不活动“簇”中，随后将这些簇用于现患和新发超重的模型中。将结果与传统模型（分别对活动和不活动进行编码）进行比较，以评估聚类分析方法是否能提供更多见解。
在使用聚类分析的横断面和纵向分析中，中度和高度活跃的青少年超重几率显著降低。在新发纵向模型中，高活动/高不活动簇中的青少年超重几率最低[比值比=0.12（0.03，0.44）]；相比之下，传统模型的结果未显示超重与活动或不活动之间有任何显著关系。
聚类分析方法使研究人员能够以新的方式同时捕捉活动和不活动情况。在这项比较研究中，只有采用聚类方法，我们才发现活动对新发超重有显著影响，这进一步增强了我们研究这种复杂关系的能力。有趣的是，两种方法均未观察到不活动水平增加的影响，这表明在该人群中，活动似乎是超重的更重要决定因素。
