Login / Signup

Cross-Sectional Associations Between Mothers and Children's Breakfast Routine-The Feel4Diabetes-Study.

Natalia Giménez LegarreAlba María Santaliestra-PasíasGreet CardonRurik ImreVioleta IotovaJemina KiveläStavros LiatisKonstantinos MakrilakisChristina MavrogianniTatjana MilenkovicAnna NánásiTsvetalina TankovaPatrick TimpelRuben WillemsYannis ManiosLuis Alberto Morenonull On Behalf Of The Feel Diabetes-Study Group
Published in: Nutrients (2021)
Positive influences of family members have been associated with a high probability of children's daily breakfast consumption. Therefore, the aim of this study was to scrutinize the association of breakfast routines between mothers and their children. The baseline data of the Feel4Diabetes-study was obtained in 9760 children (49.05% boys)-mother pairs in six European countries. A parental self-reported questionnaire gauging the frequency of breakfast consumption and of breakfast´ foods and beverages consumption was used. Agreement in routines of mothers and their children's breakfast consumption was analyzed in sex-specific crosstabs. The relationship of breakfast routine and food groups' consumption between mothers and their children was assessed with analysis of covariance. The highest proportion of children who always consumed breakfast were those whose mothers always consumed it. Children consuming breakfast regularly had a higher intake of milk or unsweetened dairy products and all kind of cereal products (low fiber and whole-grain) than occasional breakfast consumers (p < 0.05). The strong similarity between mothers and children suggests a transfer of breakfast routine from mothers to their children, as a high proportion of children who usually consume breakfast were from mothers also consuming breakfast. All breakfast foods and beverages consumption frequencies were similar between children and their mothers.
Keyphrases
  • young adults
  • cross sectional
  • risk assessment
  • skeletal muscle
  • clinical practice
  • machine learning
  • weight loss
  • big data
  • insulin resistance
  • artificial intelligence
  • human health
  • data analysis