Objective: The high energy intake from energy-dense foods among children in developed countries is undesirable. Improving food parenting practices has the potential to lower snack intakes among children. To inform the development of interventions, we aimed to predict food parenting practice patterns around snacking (i.e. 'high covert control and rewarding', 'low covert control and non-rewarding', 'high involvement and supportive' and 'low involvement and indulgent').
Design: A cross-sectional survey was conducted. To predict the patterns of food parenting practices, multinomial logistic regression analyses were run with 888 parents. Predictors included predisposing factors (i.e. parents' and children's demographics and BMI, parents' personality, general parenting, and parenting practices used by their own parents) and parents' cognitions (i.e. perceived behaviour of other parents, subjective norms, attitudes, self-efficacy and outcome expectations).
Setting: The Netherlands (October-November 2014).
Subjects: Dutch parents of children aged 4-12 years old.
Results: After backward elimination, nineteen factors had a statistically significant contribution to the model (Nagelkerke R 2=0·63). Overall, self-efficacy and outcome expectations were among the strongest explanatory factors. Considering the predisposing factors only, the general parenting factor nurturance most strongly predicted the food parenting clusters. Nurturance particularly distinguished highly involved parents from parents employing a pattern of low involvement.
Conclusions: Parental cognitions and nurturance are important factors to explain the use of food parenting practices around snacking. The results suggest that intervention developers should attempt to increase self-efficacy and educate parents about what constitute effective and ineffective parenting practices. Promoting nurturance might be a prerequisite to achieve prolonged change.
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http://dx.doi.org/10.1017/S1368980017001112 | DOI Listing |
BMC Pediatr
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Research Product Department, R&D Center, Glac Biotech Co., Ltd, Tainan City, Taiwan.
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December 2024
Department of Anthropology, University of South Florida, 4202 E. Fowler Ave. SOC107, Tampa, FL, 33620, USA.
Milk anti-inflammatory compounds are ubiquitous in milk but vary greatly within and between populations. The causes of this variation and how this variation impacts infant phenotype is not well-characterized. The goal of this study was to explain how maternal characteristics across two disparate populations impact the levels of TGF-β2 and IL-1ra in human milk.
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Guizhou Engineering Research Center for Characteristic Flavor Perception and Quality Control of Drug-Food Homologous Resources, Guiyang University, Guiyang, 550005, People's Republic of China.
Natural compounds' derivatives as lead structures could effectively solve plant disease problems. In this article, amide compounds and amide ester compounds were synthetized through ferulic acid as the parent nucleus structure, and their biological activities in vitro and in vivo were evaluated. Compound 1q was screened out as the one with the best activity performance toward Xanthomonas axonopodis pv.
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Key Laboratory of Germplasm Enhancement, Physiology and Ecology of Food Crops in Cold Region, Ministry of Education, Northeast Agricultural University, Harbin, 150030, China.
Integrated genome-wide association study and linkage mapping revealed genetic basis of alkalinity tolerance during rice germination. The key gene OsWRKY49 was further verified in transgenic plants. With the widespread use of the rice direct seeding cultivation model, improving the tolerance of rice varieties to salinity-alkalinity at the germination stage has become increasingly important.
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December 2024
Department of Statistics, College of Science, Bahir Dar University, Bahir Dar, Ethiopia.
Introduction: Dynamic Bayesian networks improve the modeling of complex systems by incorporating continuous probabilistic relationships between covariates that change over time. This study aimed to analyze the complex causal links contributing to child undernutrition using dynamic Bayesian network modeling, examining both the best- and worst-case scenarios. The Young Cohort of the Ethiopian Young Lives dataset from 2002-2016 was used to analyze the complex relationships among various covariates influencing child undernutrition.
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