Differences in preschool-age children's dietary intake between meals consumed at childcare and at home.

Preschool children need optimal nutrition, including a variety of nutrient-dense foods, for growth and development. The purpose of this study was to determine differences in foods and nutrients consumed at childcare and home environments. Children ages 3-to-5 years (n = 90, 3.8 ± 0.7 years; 56% female) from 16 childcare centers participated in this cross-sectional study from 2011 to 2014. Lunches at childcare were observed for two days; three days of dinners at home were reported by caregivers. Nutrient-dense and energy-dense foods were counted and nutrient content of meals was determined using FoodWorks®. More servings of fruit (0.92 ± 0.82 vs. 0.15 ± 0.26; p ≤ 0.0001), vegetables (1.47 ± 1.43 vs. 0.62 ± 0.60; p ≤ 0.0001), and low-fat dairy (0.83 ± 0.32 vs. 0.07 ± 0.19; p ≤ 0.0001) were consumed at childcare than at home. More servings of high-fat, high-sugar foods (0.08 ± 0.18 vs. 0.43 ± 0.39, p ≤ 0.0001) and sugary drinks (0.22 ± 0.41 vs. 0.39 ± 0.35. p ≤ 0.001) were consumed at home than at childcare. There were no differences between environments in whole-grains, high-fat meats, or high-fat high-sugar condiments consumed. On average, children consumed 333.0 ± 180.3 kcal at childcare and 454.7 ± 175.3 at home (p ≤ 0.0001). There were no differences in macronutrient profiles or in iron, zinc, folate, or vitamin B6 intake. More calcium (86.2 ± 44.6 vs. 44.6 ± 22.2 mg/kcal, p ≤ 0.0001) and vitamin A/kcal (56.1 ± 36.9 vs. 26.5 ± 24.2 RAE/kcal, p ≤ 0.0001) were consumed at childcare than at home. Preschool children are consuming more nutrient-dense foods and a more servings of fruit and vegetables at childcare during lunch than at home during dinner. Childcare and parents should work together to provide early and consistent exposure to nutrient-rich foods to ensure optimal nutrition for developing children.

Association of childcare arrangement with overweight and obesity in preschool-aged children: a narrative review of literature.

The time children spend in childcare overlaps with daily meals and opportunities to be active. Thus these environments have the opportunity to promote-or hinder-healthy weight gain among children who attend them. The purpose of this narrative review was to compile findings from studies examining childcare type and weight outcomes among preschool-age children. A literature search was conducted using PubMed, PsychInfo and ERIC. Inclusion criteria were infant- to 5-year-old children exposed to any type of childcare with a cross-sectional or longitudinal weight outcome. Among 385 studies screened, 18 were included. For comparison across studies, type of childcare was categorized as: childcare center, Head Start, nanny/babysitter, non-relative care/family childcare home and relative care. Four studies found no association with childcare type and obesity, and 10 studies reported mixed results by type of care or subpopulation analyses. Two studies found an overall positive association, and two reported an inverse association. There were differences in direction of associations and findings by type of care arrangement. For Head Start, three of eight studies demonstrated a negative relationship with obesity; none demonstrated a positive association. No other childcare type demonstrated this inverse association. Informal types of care (relative and non-relative care in a home) were positively associated with child obesity in 3 of 10 studies. This association was less commonly reported among formal childcare centers (2 of 15 studies). The majority of studies, however, reported mixed findings or no association by childcare type. Results suggested no consistent evidence for a relationship between childcare and obesity risk, except Head Start. This review exposed the need for a consistent definition of childcare type and the exploration of unmeasured confounders, such as the nutrition and physical activity environment of childcare settings, to understand how they contribute to or protect against the development of overweight/obesity among children.

Adolescent BMI trajectories with clusters of physical activity and sedentary behaviour: an exploratory analysis.

OBJECTIVE: The purpose of this study is to identify distinct body mass index (BMI) trajectories associated with weight classification, and to examine demographic characteristics and clusters of obesogenic behaviours in adolescents with these trajectories. METHODS: Data were extracted from the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development (n = 1,006, Grades 5-8). The independent variables were physical activity (accelerometer and child report), sports participation, television/video watching time and recreational computer use. The dependent variable was raw BMI. Growth mixture modelling, mixture modelling and independent t-test analyses were used. RESULTS: Two distinct BMI trajectories were identified - one with the mean BMI within the Overweight-Obese classification (≥85th percentile) and the other within the healthy weight classification (5th- 84th percentile). Two clusters of physical and sedentary behaviours were identified in adolescents with the Overweight-Obese BMI trajectory. These clusters differed in the type of sedentary behaviour (computer vs. television/video). Three clusters were identified in adolescents with the Healthy Weight BMI trajectory. These clusters differed in levels of physical activity and types of sedentary behaviour. CONCLUSION: This study contributes to the understanding of multi-dimensional obesogenic behavioural patterns and highlights the importance of understanding types of sedentary behaviour in adolescents.

A structural equation model of environmental correlates of adolescent obesity for age and gender groups.

BACKGROUND: The relationships between environmental correlates of adolescent obesity are complex and not yet well defined by current research, especially when considering age and gender. OBJECTIVE: The purpose of this study was to test a model of proximal (home) and distal (neighbourhood) environmental correlates of obesity for adolescent age and gender groups. METHODS: This was a descriptive, cross-sectional study, using the 2007 National Survey of Children's Health of 39 542 children ages 11-17 years. RESULTS: The model fit the data well for early adolescents (ages 11-14 years) (root mean square standard error of approximation [RMSEA] 0.040, 90% confidence interval [CI]: 0.039-0.041; comparative fit index [CFI] 0.947; Tucker-Lewis index [TLI] 0.929) and middle adolescents (ages 15-17 years) (RMSEA 0.037, 90% CI: 0.036-0.038; CFI 0.052; TLI 0.937). The model also fit the data well for boy adolescents (RMSEA 0.038, 90% CI: 0.037-0.039; CFI 0.951; TLI 0.935) and girl adolescents (RMSEA 0.038, 90% CI: 0.037-0.040; CFI 0.949; TLI 0.932). CONCLUSIONS: All models provide loadings of the environmental correlates of adolescent obesity for specific age and gender groups that can be used for early identification of risks and targeted interventions.

International prevalence of physical activity in youth and adults.

Understanding population prevalences of physical activity is important to develop benchmarks for current baseline levels to monitor future changes, and for making country/regional comparisons. The purpose was to review the global prevalence of physical activity levels among youth and adults. Standardized literature searches for articles about national prevalences of physical activity were conducted in PubMed. A total of 34 studies in adults and 28 studies in youth were deemed contemporary (1996 - present) and met the inclusion criteria for this review. Wide variability was present between countries in the prevalence of physical activity (as measured and defined by individual studies). Studies with the highest reported prevalences of physical activity were for men in Sweden (77%), women in Denmark (81%), 12- to 15-year-old boys in Australia (74%) and <12-year-old girls (75%) in China. The countries with the lowest reported prevalences of physical activity were men in Brazil (4%), women in Saudi Arabia (2%) and Thailand (2%), and 17- to 18-year-old boys (0%) and 17- to 18-year-old girls (0%) in Russia. The ranges of prevalence of physical activity, mode of data collection, and determination of meeting the physical activity threshold vary greatly between countries. However, the aggregation of these data can be a useful resource to practitioners, interventionists and epidemiologists.