Evidence map›Paper›PMID 32678109›Full record

ArticleScientific reports2020

Five-year change in body mass index category of childhood and the establishment of an obesity prediction model.

Yuelin Sun, Yufang Xing, Junfeng Liu, Xiaoxia Zhang, Jingyu Liu, Zhaoxia Wang, Jingyang Bi, Xianghe Ping, Qiqiang Shen, Zhouqiao Zhao and 1 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.4field-weighted citation impact, top 34% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
  3. Effects ofGut microbes
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 2 institutions in 1 country.

Yuelin Sun *Yantai Center for Disease Control and Prevention, 17 Fuhou Road, Laishan District, YanTai, 264003, Shandong, China.
Yufang Xing *Yantai Center for Disease Control and Prevention, 17 Fuhou Road, Laishan District, YanTai, 264003, Shandong, China.
Junfeng LiuSchool of Mathematics and Information Sciences, Yantai University, YanTai, 264005, China.
Xiaoxia ZhangSchool of Mathematics and Information Sciences, Yantai University, YanTai, 264005, China.
Jingyu LiuYantai Center for Disease Control and Prevention, 17 Fuhou Road, Laishan District, YanTai, 264003, Shandong, China.
Zhaoxia WangYantai Center for Disease Control and Prevention, 17 Fuhou Road, Laishan District, YanTai, 264003, Shandong, China.
Jingyang BiSchool of Mathematics and Information Sciences, Yantai University, YanTai, 264005, China.
Xianghe PingSchool of Mathematics and Information Sciences, Yantai University, YanTai, 264005, China.
Qiqiang ShenSchool of Mathematics and Information Sciences, Yantai University, YanTai, 264005, China.
Zhouqiao ZhaoDepartment of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China.
Jinjie XuYantai Center for Disease Control and Prevention, 17 Fuhou Road, Laishan District, YanTai, 264003, Shandong, China. xjj-011309@163.com.
Yantai University · CNPeking University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The prevalence of childhood obesity in China has recently become increasingly severe, and intervention measures are needed to stop its growth. Currently, there is a lack of assessment and prediction methods for childhood obesity. We develop a predictive model that uses currently measured predictors [gender, age, urban/rural, height and body mass index (BMI)] to quantify children's probabilities of belonging to one of four BMI category 5 years later and identify the high-risk group for possible intervention. A total of 88,980 students underwent a routine standard physical examination and were reexamined 5 years later to complete the study. The full model shows that boys, urban residence and height have positive effects and that age has a negative effect on transition to the overweight or obese category along with significant BMI effects. Our model correctly predicts BMI categories 5 years later for 70% of the students. From 2018 to 2023, the prevalence of obesity in rural boys and girls is expected to increase by 4% and 2%, respectively, while that in urban boys and girls is expected to remain unchanged. Predictive models help us assess the severity of childhood obesity and take targeted interventions and treatments to prevent it.

Indexed as

Body-Weight TrajectoryAge FactorsBody Mass IndexChildFemaleHumansMaleModels, StatisticalPediatric ObesityRural PopulationSex FactorsUrban Population

Identifiers

PMID32678109
PMCPMC7367261
OpenAlexW3042780680

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.