JMIR Publications

ABSTRACT

Background:

Early childhood obesity is becoming a public health concern due to its increasing prevalence all over the world. Establishing healthy eating habits and lifestyles in early childhood may help children to gain appropriate weight and further improve their health outcomes later in life.

Objective:

This study aims to classify clusters of young children according to their eating habits and identify the features of each cluster as they relate to childhood obesity.

Methods:

A total of 1,280 children were selected from the Panel Study on Korean Children. In addition to data on their eating habits (eating speed, meal time regularity, food amount consistency, and balanced eating), we also obtained data on sleep hours per day, outdoor activity hours per day, and body mass index at 5 years old. A cluster analysis was performed based on the children’s eating habits using unsupervised machine learning methods. Analysis of variance and chi-square analyses were conducted to identify differences in the children’s body mass index at 6 years old and the characteristics of their parents and family by cluster.

Results:

Four clusters were identified based on the children’s eating habits. Cluster 1 was characterized by a fast eating speed (fast eaters), cluster 2 was characterized by a slow eating speed (slow eaters), cluster 3 was characterized by irregular eating habits (poor eaters), and cluster 4 was characterized by a balanced diet and regular meal times and consistent food amounts (healthy eaters). When the clusters were compared, body mass index (P<.001), sleep duration (P=.012), and mother’s education level (P=.027) differed significantly. Fast eaters tended to have the highest body mass index at both ages, while slow eaters tended to have the lowest body mass index. Fast eaters tended to sleep longer than slow eaters.

Conclusions:

Efforts to establish healthy eating habits in early childhood may potentially contribute to the prevention of obesity in children. Future research is needed to take multidimensional lifestyles into account, such as children’s sleep patterns, activity levels, and their mother’s education level, to better understand childhood obesity.