深圳市小学一年级学生膳食模式与体质量指数的关联
Chinese Journal of School Health(2018)
Abstract
目的 分析深圳市小学一年级学生的膳食模式及其与体质量指数(BMI)的关联,为指导学生科学膳食行为提供参考.方法 按照随机整群抽样原则,于2016年7-10月在深圳市抽取33所小学,以抽中学校的全部一年级学生共6 089名为调查对象.采用家长自填问卷的方式进行膳食调查,共调查14种食物的摄入频率,分析学生的膳食行为模式.采用有序Logistic回归模型分析不同膳食行为与儿童BMI的关联.结果 小学一年级学生中超重率为9.9%(605名),肥胖率为12.2%(742名);其中男生超重率为10.8%(360名),肥胖率为15.1%(489名);女生超重率为8.9%(345名),肥胖率为9.1%(253名).男生超重、肥胖率均高于女生(x2值分别为6.587,43.747,P值均<0.01).共提取了高糖高脂饮食、相对均衡饮食、高蛋白饮食、蔬菜水果饮食、补品饮食5种膳食模式,方差贡献率分别为0.245,0.170,0.076,0.070,0.069,累计方差贡献率为0.630.多因素Logistic回归分析显示,高糖高脂饮食会增加超重肥胖的风险(OR=1.069),相对均衡饮食则是超重肥胖的保护因素(OR=0.912).结论 深圳市不同性别小学一年级学生有不同的饮食模式和特点,高糖高脂饮食是主要的饮食行为,且与肥胖相关,应采取积极措施进行早期干预.
MoreTranslated text
Key words
Diet surveys,Body mass index,Health education,Students
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined