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Impacts of parental breakfast consumption literacy on children's home breakfast consumption.

Yanming Lu
Published in: Nutrition and health (2023)
Aim: This study aimed to investigate the relationship between parental breakfast consumption literacy and their children's home breakfast consumption. Methods: This study, employing a cross-sectional design, conducted in September 2021, consisted of a total of 275 children aged 6-7 years and 275 parents. One-way analysis of variance and independent-sample t -test were applied to compare children's home breakfast consumption amongst sub-groups. The relationship between parental breakfast consumption literacy and children's home breakfast consumption was assessed utilising multiple linear regression models controlling for socio-demographic factors. All data were analysed by the software of R Commander. Results : Parents with inadequate breakfast consumption literacy had less children's home breakfast consumption ( p  = 0.006), such relationship ( p  = 0.002, 95% CI: 4.76-7.65) had significance controlling for socio-demographic factors. Living in urban areas ( p  = 0.006, 95% CI: 6.43-9.92), parental unemployment ( p  = 0.004, 95% CI: 5.47-9.43), and low parental educational level ( p  = 0.005, 95% CI: 2.34-4.76) were significantly associated with less children's home breakfast consumption. Conclusion: Adequate parental breakfast consumption literacy was associated with more children's home breakfast consumption. Parental-based health education interventions show promise in promoting home breakfast consumption in the family setting. Residential status, parental level of education, and parental employment were associated with children's home breakfast consumption. Future research should understand more about the experiences and contexts of children's home breakfast consumption within the family environments, with a focus on employing qualitative approaches.
Keyphrases
  • healthcare
  • young adults
  • public health
  • mental health
  • health information
  • risk assessment
  • deep learning
  • current status