Clustering of Obesity-Related Risk Behaviors in Children and Their Mothers
Introduction
Sedentary behavior (e.g., television viewing, time spent sitting), poor diet, and a lack of physical activity have been shown to be independently associated with increased obesity in children, adolescents, and adults 1, 2, 3, 4, 5, 6, 7. Despite the fact that these behaviors operate through different mechanisms and have different determinants, their distributions are not random throughout the population, with clustering of health behaviors within individuals frequently observed 8, 9, 10, 11.
Simple correlations between health-related behaviors often are weak, as demonstrated (for example) by a Dutch study of almost 3000 adults in which correlation coefficients between fruit consumption and physical activity and smoking were 0.07 and 0.08, respectively (12). The low correlations between health behaviors can be partly explained by complex clustering patterns, which mean that behaviors are only correlated among certain subgroups of the population (12). Treating those exhibiting an individual health behavior as a homogenous group can obscure the true relationships between health behaviors and reduces the potential for a deeper understanding of behaviors and their outcomes.
Numerous authors 8, 9, 10, 11, 12, 13, 14, 15 have examined the patterns of health behaviors within populations of adults and adolescents and have included those behaviors relevant to obesity. Few, however, have focused solely on obesity-related behaviors, and fewer still have included the comprehensive range of obesity-related behaviors included in this study (16). We are not aware of any evidence regarding the clustering of obesity-related behaviors among children.
The purpose of this study was to assess, with the inclusion of behaviors related to both diet (fruit/vegetable consumption and energy dense food/drink consumption) and activity (physical activity and sedentary behavior), the clustering of obesity-related behaviors in children and their mothers. It might be expected that clustering patterns would be similar in children and their parents given the strong parental influence on childhood environments, and the importance of parental modeling on the obesity-related behaviors of children 13, 17, 18. Therefore an additional aim was to test the concordance of clustering patterns between mother-child pairs.
Section snippets
Sampling and Participants
The baseline data collection of the Resilience for Eating and Activity Despite Inequality (READI) study was conducted during 2007−2008 among 4349 women from 40 urban and 40 rural randomly selected socioeconomically disadvantaged suburbs within 200km of Melbourne, Australia. Details of sample selection have previously been published 19, 20. In brief, disadvantaged areas, defined as those in the bottom tertile of the Socio-Economic Index for Areas Score, were randomly selected. Using the
Results
The demographic and obesity-related behavioral characteristics of the mothers and children in the sample are reported in Table 1, Table 2.
Discussion
This study has demonstrated the complex relations between obesity-related behaviors both within children and mothers and within mother-child pairs. We have shown that both the mothers and their children in this study could be effectively classified into subgroups defined by particular behaviors. Concordance of clusters in children and their mothers, and particularly those based on sedentary behavior and consumption of energy dense food/drink, demonstrate the important influence of the home
Conclusions
In conclusion, we have demonstrated that complex patterns of obesity-related behaviors exist in both children and their mothers, and such patterns of behaviors should be considered in the creation of obesity prevention interventions. Studies that can test the effect of either isolated or multi-faceted interventions on distinct clusters of individuals may be warranted, with such studies being able to inform the targeting of obesity prevention activities. Our findings in relation to the familial
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The READI study was funded by an Australian National Health and Medical Research Council (NHMRC) Strategic Award, ID 374241. A.C. is supported by a capacity building grant from the NHMRC (grant 425845). J.S. and S.A.M. are supported by the National Heart Foundation of Australia (J.S., Career Development Fellowship; S.A.M., Research Fellowship). K.B. is supported by a NHMRC Senior Research Fellowship (grant 479513). D.C. and K.C. are supported by fellowships from the Victorian Health Promotion Foundation (D.C., Senior Research Fellowship; KC, Public Health Research Fellowship).