Keri L Monda1, Barry M Popkin. 1. Carolina Population Center, University of North Carolina, 123 W. Franklin Street, Chapel Hill, NC 27516-3997, USA.
Abstract
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 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.
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 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.
Authors: Tracy Dearth-Wesley; Penny Gordon-Larsen; Linda S Adair; Bing Zhang; Barry M Popkin Journal: Int J Behav Nutr Phys Act Date: 2012-04-03 Impact factor: 6.457