Evidence map›Paper›PMID 30475874›Full record

ArticlePloS one2018

The longitudinal trajectory of body mass index in the Chinese population: A latent growth curve analysis.

Feifei Huang, Minqiang Zhang, Yan Li, Zhe Li, Junyan Fang, Kaiyin Guo

Abstract read
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Thirty-Year Urbanization Trajectories and Obesity in Modernizing China.International journal of environmental research and public health · 2022
    Article
  3. Article
  4. Article
  5. Article
4 · The record

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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

6 authors.

Feifei HuangSchool of Psychology, South China Normal University, Guangzhou, China.
Minqiang ZhangSchool of Psychology, South China Normal University, Guangzhou, China.ORCID 0000-0002-6855-8671
Yan LiSchool of Psychology, South China Normal University, Guangzhou, China.
Zhe LiSchool of Psychology, South China Normal University, Guangzhou, China.
Junyan FangSchool of Psychology, South China Normal University, Guangzhou, China.
Kaiyin GuoGuangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsThe objective of this study was to investigate the longitudinal trajectory of BMI in the Chinese population, and to assess whether the gender or other personal characteristics were related to BMI, and whether there was a change in trajectory over time.

methodsData were obtained from 3,574 Chinese (5 to 89 years of age) who participated in the 2000-2011 of the China Health and Nutrition Survey. Latent growth curve models were used to investigate the longitudinal trajectory of BMI, and to examine the effect of some personal characteristics on BMI trajectory.

resultsThe linear model resulted in a mean initial BMI value of 22.90 and a significant mean slope (Ms = 0.19, t = 10.73, p<0.001), suggesting a steady increase in BMI over time for the whole sample. For covariates, the educational level, alcohol and physical activity had differences on initial scores for BMI (β = 0.05, p<0.001; β = -0.12, p<0.05; β = -0.08, p<0.05; respectively.), and the age had differences on both the initial scores and slope for BMI (β = 0.01, p = <0.05; β = -0.03, p<0.01; respectively.). Baseline measures revealed gender-associated differences on initial scores for BMI, and the slope for male was significantly steeper than that for female (β = -0.11, p<0.05). The initial BMI status of Chinese living in the rural areas was significantly higher than that of Chinese living in the urban areas, and the slope for rural status was significantly steeper than that for urban status (β = 0.21, p<0.001).

conclusionsResults indicated a linear trajectory of BMI in the Chinese population over a 12-year period. The longitudinal trajectories differed by age, gender and urban-rural status, suggesting different interventions should be adopted for different groups.

Indexed as

Body Mass IndexAdolescentAdultAgedAged, 80 and overChildChild, PreschoolChinaExerciseFemaleHumansLongitudinal StudiesMaleMiddle AgedModels, StatisticalObesity

Identifiers

PMID30475874
PMCPMC6257919

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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.