LockdownV1, Main, Exploration, bibRecord, 000E50

Subjective Well-Being of Chinese Sina Weibo Users in Residential Lockdown During the COVID-19 Pandemic: Machine Learning Analysis.

Identifieur interne : 000E50 ( Main/Exploration ); précédent : 000E49; suivant : 000E51

Subjective Well-Being of Chinese Sina Weibo Users in Residential Lockdown During the COVID-19 Pandemic: Machine Learning Analysis.

Auteurs : Yilin Wang [République populaire de Chine] ; Peijing Wu [République populaire de Chine] ; Xiaoqian Liu [République populaire de Chine] ; Sijia Li [République populaire de Chine] ; Tingshao Zhu [République populaire de Chine] ; Nan Zhao [République populaire de Chine]

Source :

Journal of medical Internet research [ 1438-8871 ] ; 2020.

RBID : pubmed:33290247

Descripteurs français

KwdFr :
- Adulte (MeSH), Adulte d'âge moyen (MeSH), Algorithmes (MeSH), Apprentissage machine (MeSH), Chine (épidémiologie), Femelle (MeSH), Humains (MeSH), Isolement social (psychologie), Jeune adulte (MeSH), Mâle (MeSH), Médias sociaux (MeSH), Pandémies (MeSH), Population d'origine asiatique (MeSH), Quarantaine (psychologie), Sujet âgé (MeSH), Sujet âgé de 80 ans ou plus (MeSH), Surveillance de la santé publique (méthodes).
MESH :
- méthodes : Surveillance de la santé publique.
- psychologie : Isolement social, Quarantaine.
- épidémiologie : Chine.
- Adulte, Adulte d'âge moyen, Algorithmes, Apprentissage machine, Femelle, Humains, Jeune adulte, Mâle, Médias sociaux, Pandémies, Population d'origine asiatique, Sujet âgé, Sujet âgé de 80 ans ou plus.
Wicri :
- geographic : République populaire de Chine.

English descriptors

KwdEn :
- Adult (MeSH), Aged (MeSH), Aged, 80 and over (MeSH), Algorithms (MeSH), Asian Continental Ancestry Group (MeSH), COVID-19 (epidemiology), China (epidemiology), Female (MeSH), Humans (MeSH), Machine Learning (MeSH), Male (MeSH), Middle Aged (MeSH), Pandemics (MeSH), Public Health Surveillance (methods), Quarantine (psychology), Social Isolation (psychology), Social Media (MeSH), Young Adult (MeSH).
MESH :
- geographic , epidemiology : China.
- epidemiology : COVID-19.
- methods : Public Health Surveillance.
- psychology : Quarantine, Social Isolation.
- Adult, Aged, Aged, 80 and over, Algorithms, Asian Continental Ancestry Group, Female, Humans, Machine Learning, Male, Middle Aged, Pandemics, Social Media, Young Adult.

Abstract

BACKGROUND

During the COVID-19 pandemic, residential lockdowns were implemented in numerous cities in China to contain the rapid spread of the disease. Although these stringent regulations effectively slowed the spread of COVID-19, they may have posed challenges to the well-being of residents.

OBJECTIVE

This study aims to explore the effects of residential lockdown on the subjective well-being (SWB) of individuals in China during the COVID-19 pandemic.

METHODS

The sample consisted of 1790 Sina Weibo users who were residents of cities that imposed residential lockdowns, of which 1310 users (73.18%) were female, and 3580 users who were residents of cities that were not locked down (gender-matched with the 1790 lockdown residents). In both the lockdown and nonlockdown groups, we calculated SWB indicators during the 2 weeks before and after the enforcement date of the residential lockdown using individuals' original posts on Sina Weibo. SWB was calculated via online ecological recognition, which is based on established machine learning predictive models.

RESULTS

The interactions of time (before the residential lockdown or after the residential lockdown) × area (lockdown or nonlockdown) in the integral analysis (N=5370) showed that after the residential lockdown, compared with the nonlockdown group, the lockdown group scored lower in some negative SWB indicators, including somatization (F

CONCLUSIONS

These findings increase our understanding of the psychological impact and cost of residential lockdown during an epidemic. The more negative changes in the SWB of residents in developed areas imply a greater need for psychological intervention under residential lockdown in such areas.

DOI: 10.2196/24775
PubMed: 33290247
PubMed Central: PMC7747794

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Main, to step Corpus: 000878
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Le document en format XML

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<term>Apprentissage machine (MeSH)</term>
<term>Chine (épidémiologie)</term>
<term>Femelle (MeSH)</term>
<term>Humains (MeSH)</term>
<term>Isolement social (psychologie)</term>
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<term>Mâle (MeSH)</term>
<term>Médias sociaux (MeSH)</term>
<term>Pandémies (MeSH)</term>
<term>Population d'origine asiatique (MeSH)</term>
<term>Quarantaine (psychologie)</term>
<term>Sujet âgé (MeSH)</term>
<term>Sujet âgé de 80 ans ou plus (MeSH)</term>
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<term>Humans</term>
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<term>Adulte d'âge moyen</term>
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<term>Apprentissage machine</term>
<term>Femelle</term>
<term>Humains</term>
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<term>Mâle</term>
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<front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b>
</p>
<p>During the COVID-19 pandemic, residential lockdowns were implemented in numerous cities in China to contain the rapid spread of the disease. Although these stringent regulations effectively slowed the spread of COVID-19, they may have posed challenges to the well-being of residents.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>OBJECTIVE</b>
</p>
<p>This study aims to explore the effects of residential lockdown on the subjective well-being (SWB) of individuals in China during the COVID-19 pandemic.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>METHODS</b>
</p>
<p>The sample consisted of 1790 Sina Weibo users who were residents of cities that imposed residential lockdowns, of which 1310 users (73.18%) were female, and 3580 users who were residents of cities that were not locked down (gender-matched with the 1790 lockdown residents). In both the lockdown and nonlockdown groups, we calculated SWB indicators during the 2 weeks before and after the enforcement date of the residential lockdown using individuals' original posts on Sina Weibo. SWB was calculated via online ecological recognition, which is based on established machine learning predictive models.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>RESULTS</b>
</p>
<p>The interactions of time (before the residential lockdown or after the residential lockdown) × area (lockdown or nonlockdown) in the integral analysis (N=5370) showed that after the residential lockdown, compared with the nonlockdown group, the lockdown group scored lower in some negative SWB indicators, including somatization (F</p>
</div>
<div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b>
</p>
<p>These findings increase our understanding of the psychological impact and cost of residential lockdown during an epidemic. The more negative changes in the SWB of residents in developed areas imply a greater need for psychological intervention under residential lockdown in such areas.</p>
</div>
</front>
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<Title>Journal of medical Internet research</Title>
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<ArticleTitle>Subjective Well-Being of Chinese Sina Weibo Users in Residential Lockdown During the COVID-19 Pandemic: Machine Learning Analysis.</ArticleTitle>
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<Abstract><AbstractText Label="BACKGROUND">During the COVID-19 pandemic, residential lockdowns were implemented in numerous cities in China to contain the rapid spread of the disease. Although these stringent regulations effectively slowed the spread of COVID-19, they may have posed challenges to the well-being of residents.</AbstractText>
<AbstractText Label="OBJECTIVE">This study aims to explore the effects of residential lockdown on the subjective well-being (SWB) of individuals in China during the COVID-19 pandemic.</AbstractText>
<AbstractText Label="METHODS">The sample consisted of 1790 Sina Weibo users who were residents of cities that imposed residential lockdowns, of which 1310 users (73.18%) were female, and 3580 users who were residents of cities that were not locked down (gender-matched with the 1790 lockdown residents). In both the lockdown and nonlockdown groups, we calculated SWB indicators during the 2 weeks before and after the enforcement date of the residential lockdown using individuals' original posts on Sina Weibo. SWB was calculated via online ecological recognition, which is based on established machine learning predictive models.</AbstractText>
<AbstractText Label="RESULTS">The interactions of time (before the residential lockdown or after the residential lockdown) × area (lockdown or nonlockdown) in the integral analysis (N=5370) showed that after the residential lockdown, compared with the nonlockdown group, the lockdown group scored lower in some negative SWB indicators, including somatization (F<sub>1,5368</sub>
=13.593, P<.001) and paranoid ideation (F<sub>1,5368</sub>
=14.333, P<.001). The interactions of time (before the residential lockdown or after the residential lockdown) × area (developed or underdeveloped) in the comparison of residential lockdown areas with different levels of economic development (N=1790) indicated that the SWB of residents in underdeveloped areas showed no significant change after the residential lockdown (P>.05), while that of residents in developed areas changed.</AbstractText>
<AbstractText Label="CONCLUSIONS">These findings increase our understanding of the psychological impact and cost of residential lockdown during an epidemic. The more negative changes in the SWB of residents in developed areas imply a greater need for psychological intervention under residential lockdown in such areas.</AbstractText>
<CopyrightInformation>©Yilin Wang, Peijing Wu, Xiaoqian Liu, Sijia Li, Tingshao Zhu, Nan Zhao. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 17.12.2020.</CopyrightInformation>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Wang</LastName>
<ForeName>Yilin</ForeName>
<Initials>Y</Initials>
<Identifier Source="ORCID">0000-0003-3710-1123</Identifier>
<AffiliationInfo><Affiliation>Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Psychology, Nankai University, Tianjin, China.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Wu</LastName>
<ForeName>Peijing</ForeName>
<Initials>P</Initials>
<Identifier Source="ORCID">0000-0001-9108-5270</Identifier>
<AffiliationInfo><Affiliation>Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Beijing University of Posts and Telecommunications, Beijing, China.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Liu</LastName>
<ForeName>Xiaoqian</ForeName>
<Initials>X</Initials>
<Identifier Source="ORCID">0000-0003-2740-8420</Identifier>
<AffiliationInfo><Affiliation>Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Li</LastName>
<ForeName>Sijia</ForeName>
<Initials>S</Initials>
<Identifier Source="ORCID">0000-0003-4952-4123</Identifier>
<AffiliationInfo><Affiliation>Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Zhu</LastName>
<ForeName>Tingshao</ForeName>
<Initials>T</Initials>
<Identifier Source="ORCID">0000-0003-0020-3812</Identifier>
<AffiliationInfo><Affiliation>Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Zhao</LastName>
<ForeName>Nan</ForeName>
<Initials>N</Initials>
<Identifier Source="ORCID">0000-0002-8119-4577</Identifier>
<AffiliationInfo><Affiliation>Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.</Affiliation>
</AffiliationInfo>
</Author>
</AuthorList>
<Language>eng</Language>
<PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType>
<PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType>
</PublicationTypeList>
<ArticleDate DateType="Electronic"><Year>2020</Year>
<Month>12</Month>
<Day>17</Day>
</ArticleDate>
</Article>
<MedlineJournalInfo><Country>Canada</Country>
<MedlineTA>J Med Internet Res</MedlineTA>
<NlmUniqueID>100959882</NlmUniqueID>
<ISSNLinking>1438-8871</ISSNLinking>
</MedlineJournalInfo>
<CitationSubset>IM</CitationSubset>
<MeshHeadingList><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000369" MajorTopicYN="N">Aged, 80 and over</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000465" MajorTopicYN="N">Algorithms</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D044466" MajorTopicYN="N">Asian Continental Ancestry Group</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000086382" MajorTopicYN="N">COVID-19</DescriptorName>
<QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D002681" MajorTopicYN="N" Type="Geographic">China</DescriptorName>
<QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000069550" MajorTopicYN="Y">Machine Learning</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D058873" MajorTopicYN="N">Pandemics</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D062486" MajorTopicYN="N">Public Health Surveillance</DescriptorName>
<QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D011790" MajorTopicYN="N">Quarantine</DescriptorName>
<QualifierName UI="Q000523" MajorTopicYN="Y">psychology</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D012934" MajorTopicYN="N">Social Isolation</DescriptorName>
<QualifierName UI="Q000523" MajorTopicYN="Y">psychology</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D061108" MajorTopicYN="N">Social Media</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D055815" MajorTopicYN="N">Young Adult</DescriptorName>
</MeshHeading>
</MeshHeadingList>
<KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">COVID-19</Keyword>
<Keyword MajorTopicYN="Y">online ecological recognition</Keyword>
<Keyword MajorTopicYN="Y">residential lockdown</Keyword>
<Keyword MajorTopicYN="Y">subjective well-being</Keyword>
</KeywordList>
</MedlineCitation>
<PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year>
<Month>10</Month>
<Day>07</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="accepted"><Year>2020</Year>
<Month>12</Month>
<Day>05</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="revised"><Year>2020</Year>
<Month>11</Month>
<Day>19</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="pubmed"><Year>2020</Year>
<Month>12</Month>
<Day>9</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="medline"><Year>2021</Year>
<Month>1</Month>
<Day>5</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="entrez"><Year>2020</Year>
<Month>12</Month>
<Day>8</Day>
<Hour>17</Hour>
<Minute>16</Minute>
</PubMedPubDate>
</History>
<PublicationStatus>epublish</PublicationStatus>
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<affiliations><list><country><li>République populaire de Chine</li>
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<tree><country name="République populaire de Chine"><noRegion><name sortKey="Wang, Yilin" sort="Wang, Yilin" uniqKey="Wang Y" first="Yilin" last="Wang">Yilin Wang</name>
</noRegion>
<name sortKey="Li, Sijia" sort="Li, Sijia" uniqKey="Li S" first="Sijia" last="Li">Sijia Li</name>
<name sortKey="Li, Sijia" sort="Li, Sijia" uniqKey="Li S" first="Sijia" last="Li">Sijia Li</name>
<name sortKey="Liu, Xiaoqian" sort="Liu, Xiaoqian" uniqKey="Liu X" first="Xiaoqian" last="Liu">Xiaoqian Liu</name>
<name sortKey="Liu, Xiaoqian" sort="Liu, Xiaoqian" uniqKey="Liu X" first="Xiaoqian" last="Liu">Xiaoqian Liu</name>
<name sortKey="Wang, Yilin" sort="Wang, Yilin" uniqKey="Wang Y" first="Yilin" last="Wang">Yilin Wang</name>
<name sortKey="Wang, Yilin" sort="Wang, Yilin" uniqKey="Wang Y" first="Yilin" last="Wang">Yilin Wang</name>
<name sortKey="Wu, Peijing" sort="Wu, Peijing" uniqKey="Wu P" first="Peijing" last="Wu">Peijing Wu</name>
<name sortKey="Wu, Peijing" sort="Wu, Peijing" uniqKey="Wu P" first="Peijing" last="Wu">Peijing Wu</name>
<name sortKey="Wu, Peijing" sort="Wu, Peijing" uniqKey="Wu P" first="Peijing" last="Wu">Peijing Wu</name>
<name sortKey="Zhao, Nan" sort="Zhao, Nan" uniqKey="Zhao N" first="Nan" last="Zhao">Nan Zhao</name>
<name sortKey="Zhao, Nan" sort="Zhao, Nan" uniqKey="Zhao N" first="Nan" last="Zhao">Nan Zhao</name>
<name sortKey="Zhu, Tingshao" sort="Zhu, Tingshao" uniqKey="Zhu T" first="Tingshao" last="Zhu">Tingshao Zhu</name>
<name sortKey="Zhu, Tingshao" sort="Zhu, Tingshao" uniqKey="Zhu T" first="Tingshao" last="Zhu">Tingshao Zhu</name>
</country>
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Subjective Well-Being of Chinese Sina Weibo Users in Residential Lockdown During the COVID-19 Pandemic: Machine Learning Analysis.

Subjective Well-Being of Chinese Sina Weibo Users in Residential Lockdown During the COVID-19 Pandemic: Machine Learning Analysis.

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