Physical activity among senior primary school students on weekends in Beijing / 中国学校卫生

Objective@#To analyze the duration and influencing factors of moderate and vigorous physical activity(MVPA) on weekends for primary school students in grades 4 to 6 in Beijing, and to provide a reference for formulating health education and promotion measures.@*Methods@#Multi stage stratified cluster sampling method was used to randomly select 2 515 students from grades 4-6 in 14 primary schools in Beijing, and a self administered questionnaire was used to record MVPA on weekend, social demographic characteristics, other related health behaviors and knowledge. Multivariate Logistic regression analysis was used to explore the influencing factors of MVPA on weekends.@*Results@#The prevalence of insufficient MVPA on weekends in Beijing was 63.54 %, and the prevalence was higher among girls (69.92%) than boys (57.81%) ( χ 2=39.65, P <0.01). Multiple Logistic regression analysis revealed that girls ( OR =1.74), living in rural areas ( OR =1.41), participants attending general schools ( OR = 1.34 ), from divorced family ( OR =1.46), and short sleep duration ( OR =1.50) were more likely to fail to meet the MVPA recommendations( P <0.05).@*Conclusion@#It is quite common that no sufficient weekend MVPA among senior primary school students, among them, the outer suburbs and schools with relatively weak teaching resources are the key places that need attention, and girls are the key groups that need attention.

Prevalence and associated factors of electronic products usage among primary school students of grade 4-6 in Beijing / 中国学校卫生

Objective@#To explore the prevalence and associated factors of non learning-based screen time of 4-6 grade school students in Beijing, and to provide a basic data for further research on hazard control measures such as myopia, overweight and obesity among children and adolescents in Beijing.@*Methods@#Multistage stratified random cluster sampling method was adopted. A total of 2 515 primary school students were randomly selected in from schools in Beijing, self-developed questionnaire was used to investigate on the time for each electronic products usage for non-learning purpose, the total of electronic products usage every day and other information.@*Results@#The rates of screen time >15 minutes each time and ≥1 h daily were 48.43%, 22.90%, respectively. Multiple Logistic regression analysis showed that ordinary school, 6 th grade, male, single-parent family and other types of family, and lack of moderate-to-vigorous physical activity at weekend were associated with the students’ non learning-based screen time (OR=1.66, 2.28, 1.27, 1.44, 1.87, 2.20, P<0.05).@*Conclusion@#The situation of excessive screen time of primary school students was improved in Beijing, but still be prevelent, male students of grade 4 should be given more attention, and more importance should be attached to the offect of family in children.

Three-dimensional CT liver image segmentation based on hierarchical contextual active contour / 生物医学工程学杂志

In this paper, we propose a new active contour algorithm, i. e. hierarchical contextual active contour (HCAC), and apply it to automatic liver segmentation from three-dimensional CT (3D-CT) images. HCAC is a learning-based method and can be divided into two stages. At the first stage, i.e. the training stage, given a set of abdominal 3D-CT training images and the corresponding manual liver labels, we tried to establish a mapping between automatic segmentations (in each round) and manual reference segmentations via context features, and obtained a series of self-correcting classifiers. At the second stage, i.e. the segmentation stage, we firstly used the basic active contour to segment the image and subsequently used the contextual active contour (CAC) iteratively, which combines the image information and the current shape model, to improve the segmentation result. The current shape model is produced by the corresponding self-correcting classifier (the input is the previous automatic segmentation result). The proposed method was evaluated on the datasets of MICCAI 2007 liver segmentation challenge. The experimental results showed that we would get more and more accurate segmentation results by the iterative steps and the satisfied results would be obtained after about six rounds of iterations.