Evidence map›Paper›PMID 35690770›Full record

ArticleBMC public health2022

Prevalence, correlates, and trajectory of screen viewing among Chinese children in Changsha: a birth cohort study.

Chao Li, Gang Cheng, Simin He, Xiaowei Xie, Gang Tian, Ni Jiang, Xianying Min, Yan Shi, Rui Li, Tong Zhou and 1 more

Open access · goldAbstract read
In one paragraph

Article in BMC public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
5.9field-weighted citation impact, top 4% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Review
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 1 institution in 1 country.

Chao LiDepartment of Epidemiology and Medical Statistics, Xiangya School of Public Health, Central South University, Changsha, 410000, Hunan, China.
Gang ChengDepartment of Epidemiology and Medical Statistics, Xiangya School of Public Health, Central South University, Changsha, 410000, Hunan, China.
Simin HeDepartment of Epidemiology and Medical Statistics, Xiangya School of Public Health, Central South University, Changsha, 410000, Hunan, China.
Xiaowei XieDepartment of Epidemiology and Medical Statistics, Xiangya School of Public Health, Central South University, Changsha, 410000, Hunan, China.
Gang TianDepartment of Epidemiology and Medical Statistics, Xiangya School of Public Health, Central South University, Changsha, 410000, Hunan, China.
Ni JiangDepartment of Epidemiology and Medical Statistics, Xiangya School of Public Health, Central South University, Changsha, 410000, Hunan, China.
Xianying MinDepartment of Epidemiology and Medical Statistics, Xiangya School of Public Health, Central South University, Changsha, 410000, Hunan, China.
Yan ShiDepartment of Epidemiology and Medical Statistics, Xiangya School of Public Health, Central South University, Changsha, 410000, Hunan, China.
Rui LiDepartment of Epidemiology and Medical Statistics, Xiangya School of Public Health, Central South University, Changsha, 410000, Hunan, China.
Tong ZhouDepartment of Epidemiology and Medical Statistics, Xiangya School of Public Health, Central South University, Changsha, 410000, Hunan, China.
Yan YanDepartment of Epidemiology and Medical Statistics, Xiangya School of Public Health, Central South University, Changsha, 410000, Hunan, China. yanyan@csu.edu.cn.
Central South University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHigh screen viewing time has detrimental effects on children's health, development, and behavior developing. Children are being exposed to more and more media devices at an earlier age. This study was aimed to determine the amount of daily screen time and its variation and to assess potential factors of screen time by identifying the trajectory of screen time among children aged 1 to 5 years.

methodThis study was based on a representative sample of Changsha young children from a cohort study during 2015-2020. The demographic information and children's screen viewing time were collected by parents or caregivers through face-to-face interviews. The Latent growth model was used to test the effects of outdoor play on screen viewing time at eight time points, meanwhile, unconditional and conditional models were examined sequentially.

resultAfter excluding respondents with missing key variables, we included 953 children in the final analysis. Children's outdoor play was slightly increased at 18 months and subsequently declined at 24-60 months, with a maximum duration of 2.96 h per day. Children's average screen time was increased at 18-36 months, and decreased at 42-54 months, with a slight increase at 60 months. The duration of media exposure peaked at 1.4 h/d at age of 36 months and 60 months. Standardized coefficients of the outdoor play at age of 12 months showed negative effects on the screen time in children, but with positive influence at age of 24, 36, and 42 months (P <  0.01).

conclusionHigh proportions of young Chinese children in Changsha had more screen time than the AAP recommended according to our analysis. Significant predictors of screen time included pregnancy computer use, paternal educational level, and outdoor play in this study, however, further understanding of risk factors is needed to promote great public health efforts to reduce children's screen exposure.

Indexed as

Birth CohortScreen TimeChildChild, PreschoolChinaCohort StudiesFemaleHumansInfantParentsPregnancyPrevalenceTelevisionChildrenCohort studyMedia useOutdoor playScreen time

Identifiers

PMID35690770
PMCPMC9188020
OpenAlexW4282918519

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.