Evidence map›Paper›PMID 37467159›Full record

ArticleAnnals of medicine2023

Impact of the COVID-19 lockdown on physical fitness among college women living in China.

Xiaolu Feng, Xinyi Wen, Yangyang Wang, Lili Bai, Hongjun Yu

Abstract read
In one paragraph

Article in Annals of medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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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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

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Xiaolu FengDepartment of Sports Science, College of Education, Zhejiang University, Hangzhou, China.ORCID 0000-0003-3947-2817
Xinyi WenDepartment of Physical Education, Tsinghua University, Beijing, China.
Yangyang WangDepartment of Physical Education, Tsinghua University, Beijing, China.
Lili BaiSchool of Sports Science, Tianjin Normal University, Tianjin, China.ORCID 0000-0002-2216-8723
Hongjun YuDepartment of Physical Education, Tsinghua University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposesThe purpose of this study was to evaluate the effects of the COVID-19 lockdown on physical fitness among college women living in China and to explore how fitness changed with different physical conditions.

methodsWe performed repeated measures of BMI, 800 m running and sit-up performance assessment on college women from one university in China pre and post the COVID-19 lockdown. A total of 3658 (age 19.15 ± 1.08 yr.) college women who completed the same assessment pre and post the COVID-19 lockdown were included in the analysis. We analyzed the data using one way ANOVA and paired-samples

resultsDue to the COVID-19 lockdown, the result shows a significant increase in BMI by 2.91% (95% CI =0.33, 0.40) and a significant decline in 800 m running and sit-up by 7.97% (95% CI =0.69, 0.77) and 4.91% (95% CI = -0.27, -0.19), respectively. College women in the highest quartile level of physical condition (Quartile 4) had more decreases than college women in the lowest quartile level (Quartile 1). Their BMI level was increased by 3.69% and 0.98% in college women in Quartile 4 and Quartile 1, respectively. Their performance of 800 m running was decreased by 9.32% and 7.37% in college women in Quartile 4 and Quartile 1, respectively. Their performance of sit-up was decreased by 13.88% in college women in Quartile 4 while it increased by 10.91% in college women in Quartile 1, respectively.

conclusionsThe COVID-19 lockdown might increase the BMI level and decrease 800 m running and sit-up performance among college women living in China. The decrease for college women in higher quartile level of physical condition (Quartile 4) were more seriously while college women in lower quartile level of physical condition (Quartile 1) were modest.

Indexed as

COVID-19AdolescentAdultBody Mass IndexChinaCommunicable Disease ControlFemaleHumansPhysical FitnessUniversitiesYoung Adult800 m runningcollege womenCOVID-19physical fitnesssit-up

Identifiers

PMID37467159
PMCPMC10360984

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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.