Evidence map›Paper›PMID 40447706›Full record

ArticleScientific reports2025

A cross-sectional survey of videoconferencing fatigue and its influencing factors during COVID-19 in the Chinese population.

Yundi Ma, Jingjing Ren, Yan Qiu, Jiahong Zhu

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Yundi MaDepartment of General Practice, The First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Hangzhou, 310003, Zhejiang, China.
Jingjing RenDepartment of General Practice, The First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Hangzhou, 310003, Zhejiang, China. 3204092@zju.edu.cn.
Yan QiuDepartment of General Practice, The First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Hangzhou, 310003, Zhejiang, China.
Jiahong ZhuDepartment of General Practice, The First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Hangzhou, 310003, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study investigated videoconferencing fatigue and its influencing factors among Chinese people to inform interventions for reducing fatigue and enhancing well-being during COVID-19 and post-pandemic times. Data were collected via an online questionnaire from February 7 to 13, 2023, using convenience sampling. The questionnaire covered general profiles, health status, videoconferencing usage and intensity, and five nonverbal mechanisms, along with the Zoom Exhaustion and Fatigue scale, which assessed the main indicators. Univariate and step-wise multiple linear regression analyses were used to identify influencing factors. Of the 293 participants, 291 completed the survey (99.32%). The mean Zoom Exhaustion and Fatigue score was 2.18 out of 5. Factors significantly associated with increased fatigue included greater technological complexity, worse health status, more negative attitudes, higher levels of mirror anxiety and sense of feeling physically trapped, and higher cognitive load associated with producing nonverbal cues. Given the widespread use of videoconferencing tools, it is essential to understand the variables that influence videoconferencing fatigue thoroughly and to take the necessary steps to reduce it. According to the results of the study, recommendations to reduce fatigue include familiarizing with software, occasionally turning off cameras and microphones, changing posture, maintaining positive attitudes, reducing unnecessary meetings, and increasing break duration and frequency.

Indexed as

COVID-19FatigueVideoconferencingAdultAgedChinaCross-Sectional StudiesEast Asian PeopleFemaleHumansMaleMiddle AgedSurveys and QuestionnairesYoung AdultChinese populationCOVID-19Cross-sectional studyPredictorsVideoconferencing fatigue

Identifiers

PMID40447706
PMCPMC12125244

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.