Objective: To evaluate the effectiveness of a multicomponent intervention (MCI) on children's dietary diversity and its impact pathway components of children's food knowledge and healthy food preferences.
Design: A 6-week cluster randomised controlled trial with a MCI consisting of child nutrition education plus family engagement through parental nutrition education, meal preparation and tasting was compared with two groups: single component intervention (SCI) of child nutrition education, and control, conducted during February to July 2018. Preschool centres were randomly assigned to one of the three arms. Children's food knowledge, healthy food preferences and dietary diversity scores were collected. Intervention effects were analysed using a pre-post analysis and a difference-in-difference model.
Setting: Fourteen preschool centres in an urban area of Kurunegala, Sri Lanka.
Participants: Child-parent dyads of children aged 4-6 years. Final analyses included 306 (for food knowledge and preferences) and 258 (for dietary diversity) dyads.
Results: MCI significantly influenced the impact pathways to children's dietary diversity by increasing children's food knowledge and healthy food preferences scores by 3·76 and 2·79 (P < 0·001), respectively, but not the dietary diversity score (P = 0·603), compared with the control arm. Relative to SCI, MCI significantly improved children's food knowledge score by 1·10 (P < 0·001), but no significant effects were noted for other outcome variables.
Conclusions: Improved food knowledge and preferences require a positive food environment and time to develop into healthy eating behaviours. Research into dietary diversity should broaden to incorporate the contextual roles of the home and general food environments to more completely understand food choices of children.
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http://dx.doi.org/10.1017/S1368980021001877 | DOI Listing |
Environ Microbiome
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Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
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Department of Maternal, Child & Adolescent Health, School of Public Health, Anhui Medical University, 81th Meishan Road, Hefei, 230032, Anhui Province, China.
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Migrant Health Research Group, School of Health, Leeds Beckett University, Leeds, UK.
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Tea Research Institute of Shandong Academy of Agricultural Sciences, Jinan 250100, China; College of Mechanical and Electronic Engineering, Shihezi University, Shihezi 832000, China. Electronic address:
Tea may be mixed with impurities during picking and processing, which can lower their quality. At present, the sorting of impurities in premium green tea mainly relies on manual labor, which is inefficient. In response to the technical challenges in this industry, this article uses deep learning technology to detect impurities in premium green tea.
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