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Updated: May 4, 2026

Assessment of Child Anthropometry in a Large Epidemiologic Study
Published on: February 2, 2017
Clustering of children's obesity-related behaviours: associations with sociodemographic indicators
R M Leech1, S A McNaughton1, A Timperio1
1Centre for Physical Activity and Nutrition Research (C-PAN), School of Exercise and Nutrition Sciences, Deakin University, Burwood, Victoria, Australia.
Background/Objectives:
Research suggests obesity-related behaviours cluster together in children and adolescents, but how these cluster patterns differ by sociodemographic indicators remains unclear. Furthermore, few studies examining clustering of behaviours have included younger children or an objective measure of physical activity (PA) and sedentary behaviour. Therefore, the aim of this study was to examine clustering patterns of diet, PA and sedentary behaviour in 5- to 6- and 10- to 12-year-old children, and their cross-sectional associations with sociodemographic indicators.
Subjects/Methods:
In this cross-sectional study, data from the baseline wave (2002/2003) of the Health Eating and Play study (HEAPS) were used. Questionnaires were completed by parents of Australian children aged 5-6 (n=362) and 10-12 years (n=610). Children wore accelerometers for up to 7 days. K-medians cluster analysis identified groups of children with similar diet, PA and sedentary behaviours. Chi-square tests assessed cluster differences by gender, maternal education and marital status.
Results:
For each age group, three reliable and meaningful clusters were identified and labelled 'most healthy', 'energy-dense (ED) consumers who watch TV' and 'high sedentary behaviour/low moderate-to-vigorous PA (MVPA)'. Clusters varied by sociodemographic indicators. For example, a higher proportion of older girls comprised the 'high sedentary behaviour/low MVPA' cluster (χ(2)=22.4, P<0.001). Among both age groups, the 'ED consumers who watch TV' cluster comprised more children with lower educated mothers (younger children: χ(2)=34.9, P<0.001; older children: χ(2)=27.3, P<0.001).
Conclusions:
Identification of cluster patterns of obesity-related risk factors in children, and across sociodemographic groups may assist the targeting of public health initiatives, to those most in need.
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