Evidence map›Paper›PMID 36011560›Full record

ArticleInternational journal of environmental research and public health2022

Impact of COVID-19 on 1000 m Running and Pull-Up Performance among College Men Living in China.

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

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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3 · Its place in the literature

Who cites it

9 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

6 authors.

Xiaolu FengDepartment of Physical Education, Tsinghua University, Beijing 100084, China.ORCID 0000-0003-3947-2817
Jun QiuInstitute of Sports and Health, Huzhou University, Huzhou 313000, China.
Yangyang WangDepartment of Physical Education, Tsinghua University, Beijing 100084, China.
Xinyi WenDepartment of Physical Education, Tsinghua University, Beijing 100084, China.ORCID 0000-0003-1218-3150
Lili BaiSchool of Sports Science, Tianjin Normal University, Tianjin 300387, China.ORCID 0000-0002-2216-8723
Hongjun YuDepartment of Physical Education, Tsinghua University, Beijing 100084, China.ORCID 0000-0003-1687-934X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study aimed to estimate the impact of the COVID-19 lockdown on fitness performance among Chinese college men during the pandemic period and to explore how fitness changed with a different college grade.

methodsWe conducted repeated measures of 1000 m running and pull-up testing on students from one university in China before and after the lockdown. A total of 7107 (age 19.21 ± 1.17 yr.) male students who completed the same 1000 m running and pull-up testing in 2019 and 2020 were included in the analysis.

resultsThe paired t-test result indicates a reduction in 1000 m running and pull-up performance by 10.91% (95% CI = 0.89, 0.95) and 23.89% (95% CI = -0.36, -0.31), respectively. Interestingly, college men in the 2017 grade (the third-year college men) had more decreases than in the 2019 grade (the first-year college men). The 1000 m running performance was decreased by 14.43% and 6.48% in the third- and the first-year college men, respectively. The pull-up performance was decreased by 39.11 % in the third-year college men while increased by 10.98% in the first-year college men.

conclusionsThe COVID-19 lockdown reduced 1000 m running and pull-up performances among Chinese college men. The reduction varies by grade and it seems to be particularly seriously decreased for the third-year college men while being modest for the first-year college men. Public policy was urgently needed to improve Chinese college men's fitness performance after the lockdown.

Indexed as

COVID-19RunningAdolescentAdultChinaCommunicable Disease ControlHumansMaleUniversitiesYoung Adult1000 m runningcollege studentsCOVID-19fitness testingpull-up

Identifiers

PMID36011560
PMCPMC9408354

What OpenQuestion holds

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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.