Evidence map›Paper›PMID 34717596›Full record

ArticleBMC public health2021

Modeling the change trajectory of sleep duration and its associated factors: based on an 11-year longitudinal survey in China.

Junyan Fang, Zhonglin Wen, Jinying Ouyang, Huihui Wang

Abstract read
In one paragraph

Article in BMC public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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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

4 authors.

Junyan FangSchool of Psychology and Center for Studies of Psychological Application, South China Normal University, Tianhe District, Guangzhou City, Guangzhou province, 510631, China.
Zhonglin WenSchool of Psychology and Center for Studies of Psychological Application, South China Normal University, Tianhe District, Guangzhou City, Guangzhou province, 510631, China. wenzl@scnu.edu.cn.
Jinying OuyangSchool of Psychology and Center for Studies of Psychological Application, South China Normal University, Tianhe District, Guangzhou City, Guangzhou province, 510631, China.
Huihui WangSchool of Psychology and Center for Studies of Psychological Application, South China Normal University, Tianhe District, Guangzhou City, Guangzhou province, 510631, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSleep duration is a vital public health topic, yet most existing studies have been limited to cross-sectional surveys or inconsistent classifications of sleep duration categories, and few characterized its continuous development process. The current study aimed to depict its change trajectory in the general population and identify associated factors from a dynamic perspective.

methodsA total of 3788 subjects (45.4% male, mean age 46.72 ± 14.89 years) from the China Health and Nutrition Survey were recruited, and their daily sleep duration for five consecutive measurements from 2004 to 2015 was recorded. We adopted latent growth modelling to establish systematic relations between sleep duration and time. Participants' sociodemographic characteristics, lifestyle, and health factors were taken as covariates.

resultsThe change in sleep duration could be depicted by a linear decreasing trajectory with the mean yearly decrease at 2.5 min/day. The trajectory did not differ by residence, BMI category, chronic disease situation, smoking status, or drinking status. Moreover, there were sex and age differences in the trajectory, and females and those under 30 were prone to larger decrease rates.

conclusionThe quantified yearly change in sleep duration provided insights for the prediction and early warning of insufficient sleep. Public health interventions focusing on slowing down the decrease rates among females and young individuals are warranted.

Indexed as

SleepAdultChinaCross-Sectional StudiesFemaleHumansLongitudinal StudiesMaleMiddle AgedNutrition SurveysAssociated factorsLongitudinal studySleep durationTrajectory

Identifiers

PMID34717596
PMCPMC8557058

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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.