Abstract
BackgroundDespite the growing interest in the relation between adiposity in children and different lifestyle clusters, few studies used a longitudinal design to examine a large range of behaviors in various contexts, in particular eating- and sleep-related routines, and few studies have examined these factors in young children. The objectives of this study were to identify clusters of boys and girls based on diet, sleep and activity-related behaviors and their family environment at 2 and 5 years of age, and to assess whether the clusters identified varied across maternal education levels and were associated with body fat at age 5.MethodsAt 2 and 5 years, respectively, 1436 and 1195 parents from the EDEN mother-child cohort completed a questionnaire including behavioral data. A latent class analysis aimed to uncover gender-specific behavioral clusters. Body fat percentage was estimated by anthropometric and bioelectrical impedance measurements. Association between cluster membership and body fat was assessed with mutivariable linear regression models.ResultsAt 2 years, two clusters emerged that were essentially characterized by opposite eating habits. At 5 years, TV exposure was the most distinguishing feature, but the numbers and types of clusters differed by gender. An association between cluster membership and body fat was found only in girls at 5 years of age, with girls in the cluster defined by very high TV exposure and unfavorable mealtime habits (despite high outdoor playing and walking time) having the highest body fat. Girls whose mother had low educational attainment were more likely to be in this high-risk cluster. Girls who were on a cluster evolution path corresponding to the highest TV viewing time and the least favorable mealtime habits from 2 to 5 years of age had higher body fat at 5 years.ConclusionsEfforts to decrease TV time and improve mealtime routines may hold promise for preventing overweight in young children, especially girls growing up in disadvantaged families. These preventive efforts should start as early in life as possible, ideally before the age of two, and should be sustained over the preschool years.
Highlights
Despite the growing interest in the relation between adiposity in children and different lifestyle clusters, few studies used a longitudinal design to examine a large range of behaviors in various contexts, in particular eating- and sleep-related routines, and few studies have examined these factors in young children
Using latent class analysis, we identified distinct clusters of children based on patterns of diet, physical activity (PA), TV use, and sleep that differed at 2 and 5 years, and were genderdifferentiated by the age of 2
We found an association between cluster membership and body fat percentage only in girls at 5 years of age, this percentage was highest among those in the ‘very high TV–high outdoor PA’ cluster
Summary
Despite the growing interest in the relation between adiposity in children and different lifestyle clusters, few studies used a longitudinal design to examine a large range of behaviors in various contexts, in particular eating- and sleep-related routines, and few studies have examined these factors in young children. Behavioral factors that directly or indirectly affect the energy balance, such as consumption of energy-dense foods and sodas, high television (including DVD) use, low levels of physical activity (PA) and short sleep time, are considered major drivers of the OW-OB epidemic [3] These potentially modifiable obesogenic factors tend to co-occur in some individuals and may interact in multiple ways that modify the risk of OW-OB, with potentially synergistic effects [4, 5]. Evermore studies have examined patterns of diet, PA and sedentary behaviors in children and adolescents with exploratory data-driven methods [6,7,8] These include cluster analysis, based on either geometric (e.g., k-means) or probabilistic (e.g., mixture models) methods, they are aimed at grouping observations into homogeneous clusters [9]. This approach is attractive in part because of its potential practical value, understanding which obesogenic behaviors need to be targeted together and in whom can provide us with useful information for tailoring interventions to the needs of specific groups at higher risk [6, 10], and thereby increasing the effectiveness of existing pediatric obesity prevention strategies [11, 12]
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