Evidence map›Paper›PMID 37400802›Full record

ArticleBMC public health2023

Unhealthy lifestyles and clusters status among 3637 adolescents aged 11-23 years: a school-based cross-sectional study in China.

Yalin Song, Jingru Liu, Yize Zhao, Lu Gong, Qiuyuan Chen, Xili Jiang, Jiangtao Zhang, Yudan Hao, Huijun Zhou, Xiaomin Lou and 1 more

Abstract read
In one paragraph

Article in BMC public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 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

11 authors.

Yalin SongCollege of Public Health, Zhengzhou University, No.100 Science Avenue, Zhengzhou, 450001, Henan, People's Republic of China.
Jingru LiuCollege of Public Health, Zhengzhou University, No.100 Science Avenue, Zhengzhou, 450001, Henan, People's Republic of China.
Yize ZhaoCollege of Public Health, Zhengzhou University, No.100 Science Avenue, Zhengzhou, 450001, Henan, People's Republic of China.
Lu GongCollege of Public Health, Zhengzhou University, No.100 Science Avenue, Zhengzhou, 450001, Henan, People's Republic of China.
Qiuyuan ChenCollege of Public Health, Zhengzhou University, No.100 Science Avenue, Zhengzhou, 450001, Henan, People's Republic of China.
Xili JiangCollege of Public Health, Zhengzhou University, No.100 Science Avenue, Zhengzhou, 450001, Henan, People's Republic of China.
Jiangtao ZhangCollege of Public Health, Zhengzhou University, No.100 Science Avenue, Zhengzhou, 450001, Henan, People's Republic of China.
Yudan HaoCollege of Public Health, Zhengzhou University, No.100 Science Avenue, Zhengzhou, 450001, Henan, People's Republic of China.
Huijun ZhouCollege of Public Health, Zhengzhou University, No.100 Science Avenue, Zhengzhou, 450001, Henan, People's Republic of China.
Xiaomin LouCollege of Public Health, Zhengzhou University, No.100 Science Avenue, Zhengzhou, 450001, Henan, People's Republic of China.
Xian WangCollege of Public Health, Zhengzhou University, No.100 Science Avenue, Zhengzhou, 450001, Henan, People's Republic of China. wangxian@zzu.edu.cn.ORCID 0000-0001-7644-3893

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUnhealthy lifestyles are risk factors for non-communicable diseases (NCDs) and tend to be clustered, with a trajectory that extends from adolescence to adulthood. This study investigated the association of diets, tobacco, alcohol, physical activity (PA), screen time (ST) and sleep duration (SD) in a total of six lifestyles, separately and as cumulative lifestyle scores, with sociodemographic characteristics among school-aged adolescents in the Chinese city of Zhengzhou.

methodsIn the aggregate, 3,637 adolescents aged 11-23 years were included in the study. The questionnaire collected data on socio-demographic characteristics and lifestyles. Healthy and unhealthy lifestyles were identified and scored, depending on the individual score (0 and 1 for healthy and unhealthy lifestyles respectively), with a total score between 0 and 6. Based on the sum of the dichotomous scores, the number of unhealthy lifestyles was calculated and divided into three clusters (0-1, 2-3, 4-6). Chi-square test was used to analyze the group difference of lifestyles and demographic characteristics, and multivariate logistic regression was used to explore the associations between demographic characteristics and the clustering status of unhealthy lifestyles.

resultsAmong all participants, the prevalence of unhealthy lifestyles was: 86.4% for diet, 14.5% for alcohol, 6.0% for tobacco, 72.2% for PA, 42.3% for ST and 63.9% for SD. Students who were in university, female, lived in country (OR = 1.725, 95% CI: 1.241-2.398), had low number of close friends (1-2: OR = 2.110, 95% CI: 1.428-3.117; 3-5: OR = 1.601, 95% CI: 1.168-2.195), and had moderate family income (OR = 1.771, 95% CI: 1.208-2.596) were more likely to develop unhealthy lifestyles. In total, unhealthy lifestyles remain highly prevalent among Chinese adolescents.

conclusionIn the future, the establishment of an effective public health policy may improve the lifestyle profile of adolescents. Based on the lifestyle characteristics of different populations reported in our findings, lifestyle optimization can be more efficiently integrated into the daily lives of adolescents. Moreover, it is essential to conduct well-designed prospective studies on adolescents.

Indexed as

DietLife StyleNoncommunicable DiseasesSedentary BehaviorAdolescentChildChinaExerciseFemaleHumansMalePrevalenceRisk FactorsScreen TimeYoung AdultAdolescentClusterLife style

Identifiers

PMID37400802
PMCPMC10318770

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.