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Mealtime Environment and Feeding Practices in Urban Family Child Care Homes in the United States.

Lucine FrancisNancy PerrinMaureen M BlackJerilyn K Allen
Published in: Childhood obesity (Print) (2021)
Background: Family Child Care Homes (FCCHs) are the second-largest childcare option in the US. Given that young children are increasingly becoming overweight and obese, it is vital to understand the FCCH mealtime environment. There is much interest in examining the impact of the Child and Adult Care Food Program (CACFP), a federal initiative to support healthy nutrition, by providing cash reimbursements to eligible childcare providers to purchase nutritious foods. This study examines the association among the FCCH provider characteristics, the mealtime environment, and the quality of foods offered to 2-5-year-old children in urban FCCHs and examines the quality of the mealtime environment and foods offered by CACFP participation. Methods: A cross-sectional design with a proportionate stratified random sample of urban FCCHs by the CACFP participation status was used. Data were collected by telephone using the Nutrition and Physical Activity Self-Assessment for Child Care survey. Results: A total of 91 licensed FCCHs (69 CACFP, 22 non-CACFP) participated. FCCH providers with formal nutrition training met significantly more of the quality standards for foods offered than providers without nutrition training (β = 0.22, p = 0.034). The mealtime environment was not related to any FCCH provider characteristics. CACFP-participating FCCH providers had a healthier mealtime environment (β = 0.326, p = 0.002) than non-CACFP FCCHs. Conclusions: Findings suggest that nutrition training and CACFP participation contribute to the quality of nutrition-related practices in the FCCH. We recommend more research on strengthening the quality of foods provided in FCCHs and the possible impact on childhood obesity.
Keyphrases
  • physical activity
  • quality improvement
  • healthcare
  • primary care
  • palliative care
  • mental health
  • sleep quality
  • cross sectional
  • climate change
  • machine learning
  • depressive symptoms
  • human health