Prevalence and factors associated with overweight or obesity among 2- to 6-year-old children in Hunan, China: a cross-sectional study

Rural-Urban Differences in Physical Fitness and Overweight Prevalence of Children and Adolescents from Central South China

PURPOSE

This present study aims to determine the rural-urban differences in physical fitness and overweight prevalence among children and adolescents from Central South China.

METHODS

All the original parameters of physical fitness indicators for 216,718 participants aged from 7 to 15 years old were obtained from the website of the Chinese National Student Physical Fitness Database and were analyzed by SPSS.

RESULTS

(1) Children and adolescents from rural areas were observed to have a more adverse physical fitness ratio, and the urban-rural differences were identified in each physical fitness indicator. (2) Rural areas had a higher overweight prevalence (p < 0.01). When compared to urban cities, overweight students from rural towns measured a significantly poorer cardiorespiratory and muscular fitness in primary school (p < 0.05), while the result in middle school was showed the opposite. (3) Rural-urban residence and sex were the moderately correlated factors for muscular fitness among overweight students.

CONCLUSIONS

Urban children and adolescents in central south China had an overall healthier profile than their rural peers, particularly in overweight groups. The government and related functional departments should take the factors of rural-urban residence and sex of students into consideration when building a state strategy and interventions to promote physical activity and health.

Breastfeeding and overweight/obesity among children and adolescents: a cross-sectional study

Background

Overweight and obesity are major public health crises among children and adolescents and contribute to a significant economic burden. We aimed to investigate the relationship between breastfeeding duration and overweight and obesity in children and adolescents in Qingdao, China in 2017.

Methods

This study conducted a survey with 10,753 students (5370 boys and 5383 girls) aged 6 to 16 years from the Shibei District of Qingdao, China in 2017. Anthropometric measurements were taken by well-trained personnel and self-completed questionnaires were used to collect data from students. A variety of statistical methods were used in this study, including univariate and multivariate analyses, as well as linear and nonlinear regression models.

Results

The prevalence of overweight and obesity was 15.45% and 19.76%, respectively. There was a significant negative correlation between breastfeeding duration and BMI in children and adolescents (β = -0.025, 95% CI: -0.033, -0.005, P < 0.01). Among boys, the BMI in children and adolescences of those who have been breastfed for more than 12 months was significantly lower than that of others whose breastfeeding duration was less than 12 months (β = -0.440, 95%CI -0.655, -0.224, P < 0.01). Breastfeeding has a particularly positive effect on the prevalence of obesity in boys aged 9 to 11 years (OR = 0.978, 95% CI: 0.958,0.999, P < 0.05).

Conclusion

Breastfeeding can significantly reduce the prevalence of overweight and obesity among children and adolescents aged 6 to 16 years. Those who were breastfed for more than 12 months had a lower risk of developing overweight and obesity, especially boys between the ages of 9 and 11.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12887-022-03394-z.

Predicting risk of overweight or obesity in Chinese preschool-aged children using artificial intelligence techniques

OBJECTIVES

We adopted the machine-learning algorithms and deep-learning sequential model to determine and optimize most important factors for overweight and obesity in Chinese preschool-aged children.

METHODS

This is a cross-sectional survey conducted in 2020 at Beijing and Tangshan. Using a stratified cluster random sampling strategy, children aged 3-6 years were enrolled. Data were analyzed using the PyCharm and Python.

RESULTS

A total of 9478 children were eligible for inclusion, including 1250 children with overweight or obesity. All children were randomly divided into the training group and testing group at a 6:4 ratio. After comparison, support vector machine (SVM) outperformed the other algorithms (accuracy: 0.9457), followed by gradient boosting machine (GBM) (accuracy: 0.9454). As reflected by other 4 performance indexes, GBM had the highest F1 score (0.7748), followed by SVM with F1 score at 0.7731. After importance ranking, the top 5 factors seemed sufficient to obtain descent performance under GBM algorithm, including age, eating speed, number of relatives with obesity, sweet drinking, and paternal education. The performance of the top 5 factors was reinforced by the deep-learning sequential model.

CONCLUSIONS

We have identified 5 important factors that can be fed to GBM algorithm to better differentiate children with overweight or obesity from the general children, with decent prediction performance.