Evidence map›Paper›PMID 41039351›Full record

Observational studyBMC pediatrics2025

Percentile curve of balance development and network analysis with body shape and physical fitness in preschool children.

Deqiang Zhao, Xiaoxiao Chen, Aoyu Zhang, Yibei Wang, Yibo Gao, Jin He, Lupei Jiang, Haixia Hu, Yanfeng Zhang

Abstract readObservational Study
In one paragraph

Observational study in BMC pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Analysis of the Association Between Weight Status and Myopia in Children and Adolescents and Development of a Screening Model Based on Body Fat Percentage.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026
    Article
  2. 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

9 authors.

Deqiang ZhaoChina Institute of Sport Science, 100061, No.11 Tiyu Guan Road, Dongcheng District, Beijing, 100061, China.
Xiaoxiao ChenChina Institute of Sport Science, 100061, No.11 Tiyu Guan Road, Dongcheng District, Beijing, 100061, China. 77055025@qq.com.
Aoyu ZhangChina Institute of Sport Science, 100061, No.11 Tiyu Guan Road, Dongcheng District, Beijing, 100061, China.
Yibei WangChina Institute of Sport Science, 100061, No.11 Tiyu Guan Road, Dongcheng District, Beijing, 100061, China.
Yibo GaoChina Institute of Sport Science, 100061, No.11 Tiyu Guan Road, Dongcheng District, Beijing, 100061, China.
Jin HeChina Institute of Sport Science, 100061, No.11 Tiyu Guan Road, Dongcheng District, Beijing, 100061, China.
Lupei JiangChina Institute of Sport Science, 100061, No.11 Tiyu Guan Road, Dongcheng District, Beijing, 100061, China.
Haixia HuAiyoudong Children and Youth Sports Health Research Institute, Shandong, 261200, China.
Yanfeng ZhangChina Institute of Sport Science, 100061, No.11 Tiyu Guan Road, Dongcheng District, Beijing, 100061, China. zhangyanfeng0310@126.com.

Funding

Project 24-61 Supported by the Fundamental Research Funds for the China Institute of Sport Science 24-61Special Project on "Scientific and Technological Countermeasures for Positive Health and Population Aging" under the National Key Research and Development Program of China (2022YFC3600204)
6 · The paper itself

Abstract

objectiveThis study aimed to develop age- and sex-specific percentile reference curves and evaluation criteria for balance ability in preschool children using the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) model. It also sought to analyze the influencing factors of balance ability through network analysis, providing evidence to support strategies for improving balance development in early childhood.

methodsA cross-sectional study was conducted from April to July 2023, involving 5,559 preschool children aged 3 to 6 years from 12 districts (cities and counties) in Weifang City, Shandong Province, China. Participants were selected using a stratified, randomized, whole-cluster sampling method. Physical fitness tests and questionnaires on physical activity participation were administered. The GAMLSS model was used to generate balance ability percentile curves. Analysis of variance (ANOVA) and other statistical methods were employed to examine differences by age, sex, and urban-rural residence. Network analysis was conducted to identify key factors influencing balance ability.

resultsSignificant age-related differences in balance ability were observed (p < 0.05). Among 5-year-olds, girls demonstrated significantly better balance performance than boys (p < 0.01). Urban children outperformed rural peers in all age groups (p < 0.01). Balance beam completion time was significantly negatively correlated with height, weight, grip strength, and standing long jump performance (p < 0.01), while it was positively correlated with 10-meter shuttle run, 15-meter obstacle run, and two-legged continuous jump (p < 0.01).

conclusionsGirls exhibited earlier development in balance ability compared to boys, and urban children demonstrated superior balance performance relative to rural children. Physical morphology was closely related to balance ability, and various physical fitness components showed compensatory interactions, collectively influencing balance development in preschool children.

Indexed as

AlgorithmsChild DevelopmentModels, StatisticalPhysical FitnessPostural BalanceAge FactorsAnalysis of VarianceChildChild, PreschoolChinaCross-Sectional StudiesExercise TestFemaleHumansMaleNeural Networks, ComputerBalance2GAMLSS model1Network analysis5Percentile curve3Physical fitness test4

Identifiers

PMID41039351
PMCPMC12492546

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.