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<?covid-19-tdm?>
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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">JMIR</journal-id>
      <journal-id journal-id-type="nlm-ta">J Med Internet Res</journal-id>
      <journal-title>Journal of Medical Internet Research</journal-title>
      <issn pub-type="epub">1438-8871</issn>
      <publisher>
        <publisher-name>JMIR Publications</publisher-name>
        <publisher-loc>Toronto, Canada</publisher-loc>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="publisher-id">v22i11e22205</article-id>
      <article-id pub-id-type="pmid">33048825</article-id>
      <article-id pub-id-type="doi">10.2196/22205</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Original Paper</subject>
        </subj-group>
        <subj-group subj-group-type="article-type">
          <subject>Original Paper</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Associations Between COVID-19 Misinformation Exposure and Belief With COVID-19 Knowledge and Preventive Behaviors: Cross-Sectional Online Study</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="editor">
          <name>
            <surname>Eysenbach</surname>
            <given-names>Gunther</given-names>
          </name>
        </contrib>
        <contrib contrib-type="editor">
          <name>
            <surname>Kukafka</surname>
            <given-names>Rita</given-names>
          </name>
        </contrib>
      </contrib-group>
      <contrib-group>
        <contrib contrib-type="reviewer">
          <name>
            <surname>Tulbure</surname>
            <given-names>Bogdan Tudor</given-names>
          </name>
        </contrib>
        <contrib contrib-type="reviewer">
          <name>
            <surname>Son</surname>
            <given-names>Changwon</given-names>
          </name>
        </contrib>
        <contrib contrib-type="reviewer">
          <name>
            <surname>Hu</surname>
            <given-names>Zhiwen</given-names>
          </name>
        </contrib>
      </contrib-group>
      <contrib-group>
        <contrib id="contrib1" contrib-type="author">
          <name name-style="western">
            <surname>Lee</surname>
            <given-names>Jung Jae</given-names>
          </name>
          <degrees>PhD</degrees>
          <xref rid="aff1" ref-type="aff">1</xref>
          <ext-link ext-link-type="orcid">https://orcid.org/0000-0001-9704-2116</ext-link>
        </contrib>
        <contrib id="contrib2" contrib-type="author">
          <name name-style="western">
            <surname>Kang</surname>
            <given-names>Kyung-Ah</given-names>
          </name>
          <degrees>PhD</degrees>
          <xref rid="aff2" ref-type="aff">2</xref>
          <ext-link ext-link-type="orcid">https://orcid.org/0000-0002-3799-9554</ext-link>
        </contrib>
        <contrib id="contrib3" contrib-type="author">
          <name name-style="western">
            <surname>Wang</surname>
            <given-names>Man Ping</given-names>
          </name>
          <degrees>PhD</degrees>
          <xref rid="aff1" ref-type="aff">1</xref>
          <ext-link ext-link-type="orcid">https://orcid.org/0000-0003-4000-2388</ext-link>
        </contrib>
        <contrib id="contrib4" contrib-type="author">
          <name name-style="western">
            <surname>Zhao</surname>
            <given-names>Sheng Zhi</given-names>
          </name>
          <degrees>MPH</degrees>
          <xref rid="aff1" ref-type="aff">1</xref>
          <ext-link ext-link-type="orcid">https://orcid.org/0000-0003-3024-0956</ext-link>
        </contrib>
        <contrib id="contrib5" contrib-type="author">
          <name name-style="western">
            <surname>Wong</surname>
            <given-names>Janet Yuen Ha</given-names>
          </name>
          <degrees>PhD</degrees>
          <xref rid="aff1" ref-type="aff">1</xref>
          <ext-link ext-link-type="orcid">https://orcid.org/0000-0002-3000-4577</ext-link>
        </contrib>
        <contrib id="contrib6" contrib-type="author">
          <name name-style="western">
            <surname>O'Connor</surname>
            <given-names>Siobhan</given-names>
          </name>
          <degrees>PhD</degrees>
          <xref rid="aff3" ref-type="aff">3</xref>
          <ext-link ext-link-type="orcid">https://orcid.org/0000-0001-8579-1718</ext-link>
        </contrib>
        <contrib id="contrib7" contrib-type="author">
          <name name-style="western">
            <surname>Yang</surname>
            <given-names>Sook Ching</given-names>
          </name>
          <degrees>MBChB</degrees>
          <xref rid="aff4" ref-type="aff">4</xref>
          <ext-link ext-link-type="orcid">https://orcid.org/0000-0001-9003-0518</ext-link>
        </contrib>
        <contrib id="contrib8" contrib-type="author" corresp="yes">
          <name name-style="western">
            <surname>Shin</surname>
            <given-names>Sunhwa</given-names>
          </name>
          <degrees>PhD</degrees>
          <xref rid="aff2" ref-type="aff">2</xref>
          <address>
            <institution>College of Nursing</institution>
            <institution>Sahmyook University</institution>
            <addr-line>815 Hwarang-ro, Nowon-gu</addr-line>
            <addr-line>Seoul, 01795</addr-line>
            <country>Republic of Korea</country>
            <phone>82 2 3399 1595</phone>
            <email>shinsh@syu.ac.kr</email>
          </address>
          <ext-link ext-link-type="orcid">https://orcid.org/0000-0003-4052-9542</ext-link>
        </contrib>
      </contrib-group>
      <aff id="aff1">
        <label>1</label>
        <institution>School of Nursing</institution>
        <institution>University of Hong Kong</institution>
        <addr-line>Hong Kong</addr-line>
        <country>China (Hong Kong)</country>
      </aff>
      <aff id="aff2">
        <label>2</label>
        <institution>College of Nursing</institution>
        <institution>Sahmyook University</institution>
        <addr-line>Seoul</addr-line>
        <country>Republic of Korea</country>
      </aff>
      <aff id="aff3">
        <label>3</label>
        <institution>School of Health in Social Science</institution>
        <institution>The University of Edinburgh</institution>
        <addr-line>Edinburgh</addr-line>
        <country>United Kingdom</country>
      </aff>
      <aff id="aff4">
        <label>4</label>
        <institution>School of Medicine, Medical Sciences and Nutrition</institution>
        <institution>University of Aberdeen</institution>
        <addr-line>Aberdeen</addr-line>
        <country>United Kingdom</country>
      </aff>
      <author-notes>
        <corresp>Corresponding Author: Sunhwa Shin <email>shinsh@syu.ac.kr</email></corresp>
      </author-notes>
      <pub-date pub-type="collection">
        <month>11</month>
        <year>2020</year>
      </pub-date>
      <pub-date pub-type="epub">
        <day>13</day>
        <month>11</month>
        <year>2020</year>
      </pub-date>
      <volume>22</volume>
      <issue>11</issue>
      <elocation-id>e22205</elocation-id>
      <history>
        <date date-type="received">
          <day>6</day>
          <month>7</month>
          <year>2020</year>
        </date>
        <date date-type="rev-request">
          <day>11</day>
          <month>8</month>
          <year>2020</year>
        </date>
        <date date-type="rev-recd">
          <day>12</day>
          <month>9</month>
          <year>2020</year>
        </date>
        <date date-type="accepted">
          <day>8</day>
          <month>10</month>
          <year>2020</year>
        </date>
      </history>
      <copyright-statement>©Jung Jae Lee, Kyung-Ah Kang, Man Ping Wang, Sheng Zhi Zhao, Janet Yuen Ha Wong, Siobhan O'Connor, Sook Ching Yang, Sunhwa Shin. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 13.11.2020.</copyright-statement>
      <copyright-year>2020</copyright-year>
      <license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/">
        <p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.</p>
      </license>
      <self-uri xlink:href="http://www.jmir.org/2020/11/e22205/" xlink:type="simple"/>
      <abstract>
        <sec sec-type="background">
          <title>Background</title>
          <p>Online misinformation proliferation during the COVID-19 pandemic has become a major public health concern.</p>
        </sec>
        <sec sec-type="objective">
          <title>Objective</title>
          <p>We aimed to assess the prevalence of COVID-19 misinformation exposure and beliefs, associated factors including psychological distress with misinformation exposure, and the associations between COVID-19 knowledge and number of preventive behaviors.</p>
        </sec>
        <sec sec-type="methods">
          <title>Methods</title>
          <p>A cross-sectional online survey was conducted with 1049 South Korean adults in April 2020. Respondents were asked about receiving COVID-19 misinformation using 12 items identified by the World Health Organization. Logistic regression was used to compute adjusted odds ratios (aORs) for the association of receiving misinformation with sociodemographic characteristics, source of information, COVID-19 misinformation belief, and psychological distress, as well as the associations of COVID-19 misinformation belief with COVID-19 knowledge and the number of COVID-19 preventive behaviors among those who received the misinformation. All data were weighted according to the Korea census data in 2018.</p>
        </sec>
        <sec sec-type="results">
          <title>Results</title>
          <p>Overall, 67.78% (n=711) of respondents reported exposure to at least one COVID-19 misinformation item. Misinformation exposure was associated with younger age, higher education levels, and lower income. Sources of information associated with misinformation exposure were social networking services (aOR 1.67, 95% CI 1.20-2.32) and instant messaging (aOR 1.79, 1.27-2.51). Misinformation exposure was also associated with psychological distress including anxiety (aOR 1.80, 1.24-2.61), depressive (aOR 1.47, 1.09-2.00), and posttraumatic stress disorder symptoms (aOR 1.97, 1.42-2.73), as well as misinformation belief (aOR 7.33, 5.17-10.38). Misinformation belief was associated with poorer COVID-19 knowledge (high: aOR 0.62, 0.45-0.84) and fewer preventive behaviors (≥7 behaviors: aOR 0.54, 0.39-0.74).</p>
        </sec>
        <sec sec-type="conclusions">
          <title>Conclusions</title>
          <p>COVID-19 misinformation exposure was associated with misinformation belief, while misinformation belief was associated with fewer preventive behaviors. Given the potential of misinformation to undermine global efforts in COVID-19 disease control, up-to-date public health strategies are required to counter the proliferation of misinformation.</p>
        </sec>
      </abstract>
      <kwd-group>
        <kwd>COVID-19</kwd>
        <kwd>misinformation</kwd>
        <kwd>infodemic</kwd>
        <kwd>infodemiology</kwd>
        <kwd>anxiety</kwd>
        <kwd>depression</kwd>
        <kwd>PTSD</kwd>
        <kwd>knowledge</kwd>
        <kwd>preventive behaviors</kwd>
        <kwd>prevention</kwd>
        <kwd>behavior</kwd>
      </kwd-group>
    </article-meta>
  </front>
  <body>
    <sec sec-type="introduction">
      <title>Introduction</title>
      <sec>
        <title>Background</title>
        <p>COVID-19 has brought significant challenges to public health with its high infectivity and severity, particularly in vulnerable groups (eg, older adults, those with chronic diseases) [<xref ref-type="bibr" rid="ref1">1</xref>-<xref ref-type="bibr" rid="ref4">4</xref>], leading to a rapid rise in cases worldwide. On top of managing the response to the COVID-19 global health crisis, the World Health Organization (WHO) and governments also face the challenge of an “infodemic,” which causes people to experience difficulties in finding credible and trustworthy sources amid an excess of information [<xref ref-type="bibr" rid="ref5">5</xref>]. Although the concept of an infodemic is not a new one, the digital age we are currently living in has magnified its effects and added complexities to the challenge it poses. With the widespread use of social media such as internet websites, social networking services (SNSs), and instant messaging services, people all over the world are more connected than ever, allowing information to be shared easily and quickly [<xref ref-type="bibr" rid="ref6">6</xref>]. However, a recent study has found that in the midst of the global pandemic, COVID-19 misinformation is just as likely to spread and engage users on social media platforms as accurate information, which can pose an equal threat to the COVID-19 public health response by affecting public awareness and knowledge of the disease [<xref ref-type="bibr" rid="ref7">7</xref>].</p>
        <p>Misinformation can be defined as information that is false or inaccurate and not supported by scientific evidence [<xref ref-type="bibr" rid="ref8">8</xref>]. Misinformation in the context of COVID-19 can include inaccurate information regarding the virus and its transmission, conspiracy theories, and fabricated reports regarding methods of prevention and treatment [<xref ref-type="bibr" rid="ref9">9</xref>]. Some of its consequences include the panic-buying and hoarding of goods, taking ineffective and potentially harmful remedies, ignoring advice from health authorities, and engaging in behavior that increases the risk of virus transmission [<xref ref-type="bibr" rid="ref10">10</xref>]. Despite the many efforts by the WHO and public health organizations to battle the infodemic, such as conducting campaigns against COVID-19 misinformation, cooperating with social media platforms, and regularly providing evidence-based information to the public (eg, COVID-19 advice for the public: myth busters [<xref ref-type="bibr" rid="ref11">11</xref>]), the proliferation of misinformation worldwide has remained rampant [<xref ref-type="bibr" rid="ref9">9</xref>,<xref ref-type="bibr" rid="ref12">12</xref>,<xref ref-type="bibr" rid="ref13">13</xref>]. Although social media can be used effectively to provide essential health-related information to the global community, misinformation does not require professional verification or review, and thus has the potential to proliferate quicker and be disseminated farther on social media due to existing algorithms that highlight popular or desired content. This highlights the tall challenge health authorities face in delivering accurate information to the public in precedence to the proliferation of misinformation [<xref ref-type="bibr" rid="ref4">4</xref>,<xref ref-type="bibr" rid="ref5">5</xref>] and the need for new strategies to build preparedness [<xref ref-type="bibr" rid="ref9">9</xref>] against future infodemics.</p>
        <p>South Korea (hereafter Korea) has one of the most developed technological infrastructures in the world, and the use of the internet is well-integrated into Koreans’ everyday lives. As such, information and communication technology (ICT) has also been employed in Korea’s response to the COVID-19 pandemic, including the monitoring and tracking of COVID-19 cases, conveying of health information to professionals and the public, and for allocating and distributing resources, such as COVID-19 test kits and protective equipment [<xref ref-type="bibr" rid="ref14">14</xref>,<xref ref-type="bibr" rid="ref15">15</xref>]. With the deep integration of ICT such as social media into Koreans’ daily lives and activities [<xref ref-type="bibr" rid="ref6">6</xref>], it is expected that Koreans will be highly exposed to COVID-19 information and misinformation, through either active searches for information or passive receiving of information through messages, emails, or news feeds. Notwithstanding our awareness of misinformation and the risk it poses to public health, there remains little evidence on the prevalence of COVID-19 misinformation exposure and its effects on health beliefs and behaviors, including psychological well-being [<xref ref-type="bibr" rid="ref16">16</xref>].</p>
      </sec>
      <sec>
        <title>Objectives</title>
        <p>We aimed to investigate the following: (1) the prevalence of COVID-19 misinformation exposure and misinformation belief, (2) associated factors including psychological distress with misinformation exposure; and (3) the associations between misinformation exposure and COVID-19 knowledge as well as preventive behaviors in Korean adults.</p>
      </sec>
    </sec>
    <sec sec-type="methods">
      <title>Methods</title>
      <sec>
        <title>Study Design and Sampling</title>
        <p>A cross-sectional online survey was conducted using the largest online survey platform in Korea [<xref ref-type="bibr" rid="ref17">17</xref>]. This platform was chosen as it has 5 million survey panel members nationwide (as of 2020) and has been used to conduct more than 160,000 surveys for academic (such as those by Kim et al [<xref ref-type="bibr" rid="ref18">18</xref>] and Ra et al [<xref ref-type="bibr" rid="ref19">19</xref>]), government, and industry research. The inclusion criteria were the following: (1) aged ≥20 years (according to Korea’s civil law, those aged ≥20 years are regarded as adults), (2) a resident in the Seoul Metropolitan area (including the Seoul, Gyeonggi-do, and Incheon areas, in which 50.0% of the Korean population resides as of 2020), (3) has encountered COVID-19 information from any source, and (4) a Korean speaker.</p>
        <p>The company sent survey invitations containing general information about the survey such as its aim and participation incentive (KRW 1000 [US $1 is about KRW 1200]) via emails and a smartphone app to registered survey panel members who met the inclusion criteria on April 23, 2020. The survey closed on the same date (ie, recruitment was conducted for one day due to the cost involved). Details of the survey and consent statement were provided on the first page of the online survey. Respondents provided consent by clicking “Agree to participate in this survey” on the same page, before moving on to answer the survey questions. The survey took approximately 15 minutes to complete. The survey questionnaire is attached as <xref ref-type="supplementary-material" rid="app1">Multimedia Appendix 1</xref>. Ethical approval was obtained from an institutional review board at Sahmyook University in Seoul (Ref: 2-1040781-A-N-012020021HR). Meanwhile, as of April 23, 2020, Korea had 10,708 confirmed cases of COVID-19 since the first case was reported on January 24, 2020. The daily new cases of COVID-19 peaked on March 3, 2020 (803 cases), after which there was a downward trend until April 23, 2020 (14 new cases).</p>
      </sec>
      <sec>
        <title>Measurements</title>
        <p>Sociodemographic characteristics including sex, age, education level, household arrangement, and monthly personal income were collected.</p>
        <p>COVID-19 misinformation items used in this study were extracted from COVID-19 misinformation reports by the WHO [<xref ref-type="bibr" rid="ref11">11</xref>,<xref ref-type="bibr" rid="ref20">20</xref>], the main coordinator of the global COVID-19 pandemic response. In total, 12 misinformation items about COVID-19 transmission, infectivity, prevention, and treatment were included (see Question 2 in <xref ref-type="supplementary-material" rid="app1">Multimedia Appendix 1</xref>). Respondents were allocated to one of two groups: misinformation exposure (defined as having seen one or more items of misinformation, through active searching or passive receiving means) or misinformation nonexposure. Respondents were then asked if they believed any of the 12 misinformation items to be correct (hereafter misinformation belief) or incorrect.</p>
        <p>Measures for psychological distress included anxiety and depressive symptoms, using the Patient Health Questionnaire-4 (PHQ-4; four items), which consists of two subscales: the Generalized Anxiety Disorder-2 (GAD-2; two items) and Patient Health Questionnaire-2 (PHQ-2; two items) [<xref ref-type="bibr" rid="ref21">21</xref>]. The score of each subscale ranges from 0 to 6, and a score of ≥3 indicates a high risk of anxiety (GAD-2) and depression (PHQ-2), respectively [<xref ref-type="bibr" rid="ref21">21</xref>]. PHQ-4 was validated in Korean (Cronbach α=.79; acceptable convergent validity) [<xref ref-type="bibr" rid="ref22">22</xref>]. An additional Primary Care Post-Traumatic Stress Disorder Screen for DSM-5 (PC-PTSD-5; five items) that was validated in Korean (Cronbach α=.87; acceptable concurrent validity) [<xref ref-type="bibr" rid="ref23">23</xref>,<xref ref-type="bibr" rid="ref24">24</xref>] was also adopted to screen respondents for posttraumatic stress disorder (PTSD) risk. The scores range from 0 to 5 and the cutoff score for high risk of PTSD is 3.</p>
        <p>COVID-19 knowledge was assessed using five COVID-19 knowledge questions (definition, transmission modes, main symptoms, prevention, and treatment) that were extracted from a questionnaire developed by the WHO [<xref ref-type="bibr" rid="ref25">25</xref>] (see Questions 4-8 in <xref ref-type="supplementary-material" rid="app1">Multimedia Appendix 1</xref>). Higher scores indicate a higher COVID-19 knowledge level.</p>
        <p>The number of COVID-19 preventive behaviors that the respondents performed during the past three months was assessed with 10 answer options that were extracted from the COVID-19 preventive methods recommended by the WHO [<xref ref-type="bibr" rid="ref25">25</xref>] and Korea Centers for Disease Control and Prevention [<xref ref-type="bibr" rid="ref26">26</xref>], such as washing hands regularly, covering one’s mouth and nose when coughing, and social distancing (see Question 9 in <xref ref-type="supplementary-material" rid="app1">Multimedia Appendix 1</xref>). Higher scores indicated a higher engagement in COVID-19 preventive behaviors.</p>
      </sec>
      <sec>
        <title>Statistical Analysis</title>
        <p>All data were weighted by age and sex distributions in the Seoul Metropolitan area, according to the Korea census data in 2018 [<xref ref-type="bibr" rid="ref27">27</xref>]. Descriptive statistics were reported in numbers, proportions, means, and standard deviations (SD), as appropriate. The differences between the two groups (COVID-19 misinformation exposure group versus nonexposure group), including the respondents’ sociodemographic characteristics, source of information, COVID-19 misinformation belief, psychological well-being (ie, anxiety, depressive, and PTSD symptoms), and COVID-19 knowledge and preventive behaviors were analyzed by chi-square test or <italic>t</italic> test, as appropriate. The responses on the knowledge and preventive behavior questions were categorized into binary groups according to the mean scores for the chi-square analysis. Logistic regression was used to compute odds ratios (ORs) and adjusted ORs (aORs) to identify the association of misinformation exposure (a binary variable) with the sociodemographic characteristics, source of information, and COVID-19 misinformation belief. The associations of COVID-19 misinformation belief with COVID-19 knowledge and COVID-19 preventive behaviors among the respondents who encountered misinformation were also investigated. As subgroup analyses, we included the interaction term to test if demographic characteristics modify the associations of COVID-19 knowledge, preventive behaviors, misinformation belief, and psychosocial distress following misinformation exposure. Sex, age, highest education level, household arrangement, and monthly personal income were adjusted for the adjusted regression models. STATA 15 (StataCorp LLC) was used to conduct all analyses.</p>
      </sec>
    </sec>
    <sec sec-type="results">
      <title>Results</title>
      <p>Of 1054 people who initially responded to the survey, five were excluded from the study as they reported that they had not encountered any COVID-19 information and therefore could not complete the survey. Among the 1049 respondents, 50.04% (n=505) were male, the mean age was 40.60 years (SD 12.87), 74.94% (n=786) had tertiary education or higher, 88.50% (n=929) lived with others, and 55.95% (n=587) had a monthly personal income &#60;KRW 3,000,000 (US $2500; the average monthly income among those employed was KRW 2,970,000 [US $2475] in 2018; <xref ref-type="table" rid="table1">Table 1</xref>).</p>
      <table-wrap position="float" id="table1">
        <label>Table 1</label>
        <caption>
          <p>Characteristics of COVID-19 misinformation exposure by respondents’ demographics, and sociobehavioral and psychological symptoms (N=1049).</p>
        </caption>
        <table width="1000" cellpadding="5" cellspacing="0" border="1" rules="groups" frame="hsides">
          <col width="30"/>
          <col width="310"/>
          <col width="150"/>
          <col width="180"/>
          <col width="130"/>
          <col width="120"/>
          <col width="80"/>
          <thead>
            <tr valign="top">
              <td colspan="2">Variables</td>
              <td>Participants, n (%)<sup>a</sup></td>
              <td>Weighted values<sup>b</sup>, n (%)<sup>a</sup></td>
              <td colspan="2">Misinformation</td>
              <td><italic>P</italic> value<sup>c,d</sup></td>
            </tr>
            <tr valign="top">
              <td colspan="2">
                <break/>
              </td>
              <td>N=1049</td>
              <td>N=1049</td>
              <td>Not exposed (n=338)</td>
              <td>Exposed (n=711)</td>
              <td>
                <break/>
              </td>
            </tr>
          </thead>
          <tbody>
            <tr valign="top">
              <td colspan="7">
                <bold>Sex</bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Male</td>
              <td>446 (42.52)</td>
              <td>525 (50.04)</td>
              <td>179 (53.08)</td>
              <td>346 (48.60)</td>
              <td>.15<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Female</td>
              <td>603 (57.48)</td>
              <td>524 (49.96)</td>
              <td>158 (46.92)</td>
              <td>366 (51.40)</td>
              <td>.15<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td colspan="2">
                <bold>Age (years), mean (SD)</bold>
              </td>
              <td>39.51 (10.47)</td>
              <td>43.60 (12.87)</td>
              <td>45.86 (13.01)</td>
              <td>42.53 (12.67)</td>
              <td>.02<sup>d</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>20-29</td>
              <td>198 (18.88)</td>
              <td>196 (18.69)</td>
              <td>48 (14.09)</td>
              <td>149 (20.87)</td>
              <td>.06<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>30-39</td>
              <td>351 (33.46)</td>
              <td>219 (20.85)</td>
              <td>65 (19.25)</td>
              <td>154 (21.61)</td>
              <td>.06<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>40-49</td>
              <td>311 (29.65)</td>
              <td>243 (23.17)</td>
              <td>78 (23.14)</td>
              <td>165 (23.18)</td>
              <td>.06<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>50-59</td>
              <td>148 (14.12)</td>
              <td>236 (22.54)</td>
              <td>74 (22.11)</td>
              <td>162 (22.74)</td>
              <td>.06<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>60-69</td>
              <td>41 (3.91)</td>
              <td>154 (14.75)</td>
              <td>72 (21.41)</td>
              <td>82 (11.59)</td>
              <td>.06<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td colspan="7">
                <bold>Education</bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>High school or below</td>
              <td>233 (22.21)</td>
              <td>263 (25.06)</td>
              <td>92 (27.13)</td>
              <td>171 (24.08)</td>
              <td>.83<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Tertiary or above</td>
              <td>816 (77.79)</td>
              <td>786 (74.94)</td>
              <td>246 (72.87)</td>
              <td>540 (75.92)</td>
              <td>.83<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td colspan="7">
                <bold>Household arrangement</bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Living alone</td>
              <td>142 (13.54)</td>
              <td>121 (11.50)</td>
              <td>32 (9.55)</td>
              <td>88 (12.43)</td>
              <td>.10<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Living with others</td>
              <td>907 (86.46)</td>
              <td>929 (88.50)</td>
              <td>306 (90.45)</td>
              <td>623 (87.57)</td>
              <td>.10<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td colspan="7">
                <bold>Monthly personal income, KRW</bold>
                <bold>(US $)</bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>&#60;3,000,000 (&#60;2500)<sup>e</sup></td>
              <td>622 (59.29)</td>
              <td>587 (55.95)</td>
              <td>165 (48.78)</td>
              <td>422 (59.35)</td>
              <td>.03<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>3,000,000-4,990,000 (2500-4158)</td>
              <td>264 (25.17)</td>
              <td>261 (24.92)</td>
              <td>99 (29.24)</td>
              <td>163 (22.86)</td>
              <td>.03<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>≥5,000,000 (&#62;4158)</td>
              <td>163 (15.54)</td>
              <td>201 (19.14)</td>
              <td>74 (21.98)</td>
              <td>127 (17.79)</td>
              <td>.03<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td colspan="7">
                <bold>COVID-19 information source<sup>f</sup></bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Television, radio, or newspaper (offline)</td>
              <td>999 (95.23)</td>
              <td>998 (95.07)</td>
              <td>316 (93.53)</td>
              <td>682 (95.80)</td>
              <td>.40<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Television, radio, or newspaper (online)</td>
              <td>1036 (98.76)</td>
              <td>1030 (98.21)</td>
              <td>328 (97.06)</td>
              <td>703 (98.76)</td>
              <td>.54<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Other internet websites</td>
              <td>864 (82.36)</td>
              <td>842 (80.26)</td>
              <td>262 (77.55)</td>
              <td>580 (81.55)</td>
              <td>.44<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Social network services</td>
              <td>753 (71.78)</td>
              <td>754 (71.82)</td>
              <td>220 (65.10)</td>
              <td>534 (75.01)</td>
              <td>.03<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Instant messaging</td>
              <td>873 (83.22)</td>
              <td>868 (82.71)</td>
              <td>262 (77.49)</td>
              <td>606 (85.19)</td>
              <td>.011<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td colspan="7">
                <bold>COVID-19 misinformation belief</bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>No</td>
              <td>604 (57.58)</td>
              <td>618 (58.86)</td>
              <td>291 (86.21)</td>
              <td>326 (45.87)</td>
              <td>&#60;.001<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Yes</td>
              <td>445 (42.42)</td>
              <td>432 (41.14)</td>
              <td>47 (13.79)</td>
              <td>385 (54.13)</td>
              <td>&#60;.001<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td colspan="2">
                <bold>Anxiety symptom, mean (SD)<sup>g</sup></bold>
              </td>
              <td>1.49 (1.60)</td>
              <td>1.51 (1.65)</td>
              <td>3.18 (1.58)</td>
              <td>3.66 (1.66)</td>
              <td>&#60;.001<sup>d</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>No</td>
              <td>853 (81.32)</td>
              <td>854 (81.38)</td>
              <td>290 (85.94)</td>
              <td>564 (79.22)</td>
              <td>.002<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Yes</td>
              <td>196 (18.68)</td>
              <td>195 (18.62)</td>
              <td>48 (14.06)</td>
              <td>148 (20.78)</td>
              <td>.002<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td colspan="2">
                <bold>Depressive symptom, mean (SD)<sup>h</sup></bold>
              </td>
              <td>2.02 (1.73)</td>
              <td>2.04 (1.78)</td>
              <td>3.76 (1.78)</td>
              <td>4.17 (1.77)</td>
              <td>.001<sup>d</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>No</td>
              <td>738 (70.35)</td>
              <td>739 (70.45)</td>
              <td>253 (74.85)</td>
              <td>486 (68.36)</td>
              <td>.01<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Yes</td>
              <td>311 (29.65)</td>
              <td>310 (29.55)</td>
              <td>85 (25.15)</td>
              <td>225 (31.64)</td>
              <td>.01<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td colspan="2">
                <bold>Posttraumatic stress disorder symptom, mean (SD)<sup>i</sup></bold>
              </td>
              <td>1.43 (1.09)</td>
              <td>1.43 (1.14)</td>
              <td>1.12 (1.04)</td>
              <td>1.57 (1.15)</td>
              <td>&#60;.001<sup>d</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>No</td>
              <td>902 (85.99)</td>
              <td>903 (86.05)</td>
              <td>306 (90.52)</td>
              <td>597 (83.92)</td>
              <td>.002<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Yes</td>
              <td>147 (14.01)</td>
              <td>146 (13.95)</td>
              <td>32 (9.48)</td>
              <td>114 (16.08)</td>
              <td>.002<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td colspan="2">
                <bold>COVID-19 knowledge, mean (SD)</bold>
              </td>
              <td>24.69 (2.56)</td>
              <td>24.65 (2.69)</td>
              <td>24.79 (2.24)</td>
              <td>24.58 (2.88)</td>
              <td>.38<sup>d</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Low (0-24)</td>
              <td>444 (42.33)</td>
              <td>446 (42.55)</td>
              <td>142 (41.97)</td>
              <td>305 (42.82)</td>
              <td>.99<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>High (25-35)</td>
              <td>605 (57.67)</td>
              <td>603 (57.45)</td>
              <td>196 (58.03)</td>
              <td>407 (57.18)</td>
              <td>.99<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td colspan="2">
                <bold>COVID-19 preventive behaviors, mean (SD)</bold>
              </td>
              <td>6.94 (2.42)</td>
              <td>7.01 (2.47)</td>
              <td>7.01 (2.46)</td>
              <td>7.02 (2.48)</td>
              <td>.72<sup>d</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>0-6 behaviors</td>
              <td>396 (37.75)</td>
              <td>388 (36.99)</td>
              <td>129 (38.15)</td>
              <td>259 (36.44)</td>
              <td>.70<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>≥7 behaviors</td>
              <td>653 (62.25)</td>
              <td>661 (63.01)</td>
              <td>209 (61.85)</td>
              <td>452 (63.57)</td>
              <td>.70<sup>c</sup></td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn id="table1fn1">
            <p><sup>a</sup>Calculated percentages were rounded off to one decimal place; accordingly, combined percentages can exceed 100%.</p>
          </fn>
          <fn id="table1fn2">
            <p><sup>b</sup>Data were weighted by sex and age distribution of the general population in the Seoul metropolitan area.</p>
          </fn>
          <fn id="table1fn3">
            <p><sup>c</sup><italic>P</italic> for chi-square (computed using unweighted data).</p>
          </fn>
          <fn id="table1fn4">
            <p><sup>d</sup><italic>P</italic> for <italic>t</italic> test (computed using unweighted data).</p>
          </fn>
          <fn id="table1fn5">
            <p><sup>e</sup>Average monthly income among employees was KRW 2,970,000 in 2018.</p>
          </fn>
          <fn id="table1fn6">
            <p><sup>f</sup>Multiple responses allowed.</p>
          </fn>
          <fn id="table1fn7">
            <p><sup>g</sup>Generalized Anxiety Disorder Questionnaire-2 (GAD-2) score ≥3.</p>
          </fn>
          <fn id="table1fn8">
            <p><sup>h</sup>Patient Health Questionnaire-2 (PHQ-2) score ≥3.</p>
          </fn>
          <fn id="table1fn9">
            <p><sup>i</sup>Primary Care Post-Traumatic Stress Disorder Screen for DSM-5 (PC-PTSD-5) score ≥3.</p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
      <p>The majority of the respondents encountered COVID-19 information from diverse media including television, newspapers, internet websites, SNSs, and instant messaging. Overall, 57.45% (n=603) had high levels (score of 25-35) of COVID-19 knowledge and 63.01% (n=661) reported conducting ≥7 COVID-19 preventive behaviors. In total, 18.68% (n=196), 31.55% (n=331), and 13.95% (n=146) reported anxiety, depressive, and PTSD symptoms, respectively (<xref ref-type="table" rid="table1">Table 1</xref>). Overall, 41.14% (n=432) reported believing in one or more of the 12 COVID-19 misinformation items, while exposure to at least one COVID-19 misinformation item in the preceding three months was reported by 67.78% (n=711) of the respondents. In addition, 49.76% (n=354) and 48.14% (n=342) encountered misinformation about reusing masks (<xref ref-type="table" rid="table2">Table 2</xref>), which was the most common item of misinformation reported.</p>
      <table-wrap position="float" id="table2">
        <label>Table 2</label>
        <caption>
          <p>Respondents’ exposure to COVID-19 misinformation (N=1049).</p>
        </caption>
        <table width="1000" cellpadding="5" cellspacing="0" border="1" rules="groups" frame="hsides">
          <col width="30"/>
          <col width="590"/>
          <col width="180"/>
          <col width="200"/>
          <thead>
            <tr valign="top">
              <td colspan="2">Variables</td>
              <td>Participants, n (%)<sup>a</sup></td>
              <td>Weighted values<sup>b</sup>, n (%)<sup>a</sup></td>
            </tr>
          </thead>
          <tbody>
            <tr valign="top">
              <td colspan="4">
                <bold>Number of COVID-19 misinformation items</bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>0</td>
              <td>321 (30.60)</td>
              <td>338 (32.19)</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>1</td>
              <td>279 (26.60)</td>
              <td>260 (24.74)</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>2</td>
              <td>205 (19.54)</td>
              <td>206 (19.63)</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>≥3 items</td>
              <td>244 (23.26)</td>
              <td>246 (23.44)</td>
            </tr>
            <tr valign="top">
              <td colspan="2">
                <bold>COVID-19 misinformation items<sup>c</sup></bold>
              </td>
              <td>728 (69.40)</td>
              <td>711 (67.78)</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Masks can be sterilized and reused after steaming with hot water</td>
              <td>346 (47.53)</td>
              <td>354 (49.76)</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Masks can be reused after spraying alcohol on its surface</td>
              <td>345 (47.39)</td>
              <td>342 (48.14)</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Drinking tea can prevent infection</td>
              <td>231 (31.73)</td>
              <td>219 (30.78)</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Gargling can disinfect the respiratory tract to prevent infection</td>
              <td>150 (20.60)</td>
              <td>168 (23.55)</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Coronavirus is artificially developed</td>
              <td>172 (23.63)</td>
              <td>156 (21.88)</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Basking in the sun can prevent infection</td>
              <td>110 (15.11)</td>
              <td>109 (15.30)</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Gargling with salt can prevent infection</td>
              <td>104 (14.29)</td>
              <td>109 (15.29)</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Taking antibiotics can prevent or treat infection</td>
              <td>94 (12.91)</td>
              <td>102 (14.34)</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Flip the sides of a used mask to reuse it</td>
              <td>90 (12.36)</td>
              <td>90 (12.60)</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Drinking alcohol/smoking can prevent infection</td>
              <td>59 (8.10)</td>
              <td>70 (9.88)</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Only older adults can be infected</td>
              <td>25 (3.43)</td>
              <td>32 (4.46)</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>A vaccine is available now</td>
              <td>15 (2.06)</td>
              <td>14 (1.99)</td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn id="table2fn1">
            <p><sup>a</sup>Calculated percentages were rounded off to one decimal place; accordingly, combined percentages can exceed 100%.</p>
          </fn>
          <fn id="table2fn2">
            <p><sup>b</sup>Data were weighted by sex and age distribution of the general population in the Seoul metropolitan area in Korea.</p>
          </fn>
          <fn id="table2fn3">
            <p><sup>c</sup>The COVID-19 misinformation items were extracted from World Health Organization documents [<xref ref-type="bibr" rid="ref11">11</xref>,<xref ref-type="bibr" rid="ref20">20</xref>].</p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
      <p>COVID-19 misinformation exposure was negatively associated with being older (60-69 years age group: aOR 0.40, 95% CI 0.25-0.64 versus 20-29 years age group) and having a higher monthly personal income (KRW 3,000,000-4,990,000 [US $2500-$4158]: aOR 0.66, 95% CI 0.47-0.93 versus &#60;KRW 3,000,000 [US $2500]). COVID-19 misinformation exposure was positively associated with a tertiary education or above (aOR 1.42, 95% CI 1.02-1.96 versus high school or below). Of the information sources, misinformation exposure was associated with SNSs (aOR 1.75, 95% CI 1.31-2.35 versus other information sources) and instant messaging (aOR 1.79, 95% CI 1.27-2.51 versus other information sources), while offline and online television, radio, and newspapers and other websites were not statistically significant. Misinformation exposure was also significantly associated with misinformation belief (aOR 7.33, 95% CI 5.17-10.38 versus no misinformation belief) and with psychological distress, including anxiety (aOR 1.80, 95% CI 1.24-2.61 versus no anxiety symptoms), depressive (aOR 1.47, 95% CI 1.09-2.00 versus no depressive symptoms), and PTSD symptoms (aOR 1.97, 95% CI 1.42-2.73 versus no PTSD symptoms). However, misinformation exposure was not associated with COVID-19 knowledge and preventive behaviors (<xref ref-type="table" rid="table3">Table 3</xref>).</p>
      <table-wrap position="float" id="table3">
        <label>Table 3</label>
        <caption>
          <p>Associated factors with COVID-19 misinformation exposure (N=1049)<sup>a</sup>.</p>
        </caption>
        <table width="1000" cellpadding="5" cellspacing="0" border="1" rules="groups" frame="hsides">
          <col width="30"/>
          <col width="450"/>
          <col width="250"/>
          <col width="270"/>
          <thead>
            <tr valign="top">
              <td colspan="2">Variables</td>
              <td colspan="2">Misinformation exposure (yes/no)</td>
            </tr>
            <tr valign="bottom">
              <td colspan="2">
                <break/>
              </td>
              <td>Crude odds ratio (95% CI)</td>
              <td>Adjusted odds ratio (95% CI)<sup>b</sup></td>
            </tr>
          </thead>
          <tbody>
            <tr valign="top">
              <td colspan="4">
                <bold>Sex</bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Male</td>
              <td>Reference</td>
              <td>Reference</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Female</td>
              <td>1.20 (0.92-1.55)</td>
              <td>1.15 (0.87-1.52)</td>
            </tr>
            <tr valign="top">
              <td colspan="4">
                <bold>Age (years)</bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>20-29</td>
              <td>Reference</td>
              <td>Reference</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>30-39</td>
              <td>0.76 (0.49-1.17)</td>
              <td>0.74 (0.46-1.17)</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>40-49</td>
              <td>0.68 (0.44-1.03)</td>
              <td>0.70 (0.45-1.10)</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>50-59</td>
              <td>0.69 (0.45-1.06)</td>
              <td>0.76 (0.48-1.20)</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>60-69</td>
              <td>0.37 (0.23-0.58)<sup>c</sup></td>
              <td>0.40 (0.25-0.64)<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td colspan="4">
                <bold>Education</bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>High school or below</td>
              <td>Reference</td>
              <td>Reference</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Tertiary or above</td>
              <td>1.17 (0.87-1.58)</td>
              <td>1.42 (1.02-1.96)<sup>d</sup></td>
            </tr>
            <tr valign="top">
              <td colspan="4">
                <bold>Household arrangement</bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Living alone</td>
              <td>Reference</td>
              <td>Reference</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Living with others (including family)</td>
              <td>0.74 (0.49-1.14)</td>
              <td>0.83 (0.53-1.29)</td>
            </tr>
            <tr valign="top">
              <td colspan="4">
                <bold>Monthly personal income, KRW (US $)<sup>e</sup></bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>&#60;3,000,000 (&#60;2500)</td>
              <td>Reference</td>
              <td>Reference</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>3,000,000-4,990,000 (2500-4158)</td>
              <td>0.64 (0.47-0.87)<sup>f</sup></td>
              <td>0.66 (0.47-0.93)<sup>d</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>≥5,000,000 (≥4158)</td>
              <td>0.67 (0.47-0.93)<sup>d</sup></td>
              <td>0.74 (0.51-1.07)</td>
            </tr>
            <tr valign="top">
              <td colspan="4">
                <bold>COVID-19 information source<sup>g</sup></bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Television, radio, or newspaper (offline)</td>
              <td>1.58 (0.89-2.78)</td>
              <td>1.79 (0.99-3.22)</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Television, radio, or newspaper (online)</td>
              <td>2.41 (0.97-6.03)</td>
              <td>2.52 (0.98-6.52)</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Other internet websites</td>
              <td>1.28 (0.93-1.76)</td>
              <td>1.24 (0.89-1.72)</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Social network services</td>
              <td>1.61 (1.22-2.13)<sup>f</sup></td>
              <td>1.75 (1.31-2.35)<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Instant messaging</td>
              <td>1.67 (1.20-2.32)<sup>f</sup></td>
              <td>1.79 (1.27-2.51)<sup>f</sup></td>
            </tr>
            <tr valign="top">
              <td colspan="4">
                <bold>COVID-19 misinformation belief</bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>No</td>
              <td>Reference</td>
              <td>Reference</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Yes</td>
              <td>7.38 (5.24-10.39)<sup>c</sup></td>
              <td>7.33 (5.17-10.38)<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td colspan="4">
                <bold>COVID-19 knowledge</bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Low (0-24)</td>
              <td>Reference</td>
              <td>Reference</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>High (25-35)</td>
              <td>0.97 (0.74-1.26)</td>
              <td>0.97 (0.74-1.27)</td>
            </tr>
            <tr valign="top">
              <td colspan="4">
                <bold>COVID-19 preventive behaviors</bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>0-6 behaviors</td>
              <td>Reference</td>
              <td>Reference</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>≥7 behaviors</td>
              <td>1.08 (0.82-1.41)</td>
              <td>1.13 (0.86-1.49)</td>
            </tr>
            <tr valign="top">
              <td colspan="4">
                <bold>Anxiety symptom<sup>h</sup></bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>No</td>
              <td>Reference</td>
              <td>Reference</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Yes</td>
              <td>1.60 (1.12-2.29)<sup>f</sup></td>
              <td>1.80 (1.24-2.61)<sup>f</sup></td>
            </tr>
            <tr valign="top">
              <td colspan="4">
                <bold>Depressive symptom<sup>i</sup></bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>No</td>
              <td>Reference</td>
              <td>Reference</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Yes</td>
              <td>1.38 (1.03-1.84)<sup>d</sup></td>
              <td>1.47 (1.09-2.00)<sup>d</sup></td>
            </tr>
            <tr valign="top">
              <td colspan="4">
                <bold>Posttraumatic stress disorder symptom<sup>j</sup></bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>No</td>
              <td>Reference</td>
              <td>Reference</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Yes</td>
              <td>1.84 (1.34-2.53)<sup>c</sup></td>
              <td>1.97 (1.42-2.73)<sup>c</sup></td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn id="table3fn1">
            <p><sup>a</sup>All data were weighted by sex and age distribution of the general population in the Seoul metropolitan area in Korea.</p>
          </fn>
          <fn id="table3fn2">
            <p><sup>b</sup>Adjusted for sex, age, highest education level, household arrangement, and monthly personal income.</p>
          </fn>
          <fn id="table3fn3">
            <p><sup>c</sup><italic>P</italic>&#60;.001.</p>
          </fn>
          <fn id="table3fn4">
            <p><sup>d</sup><italic>P</italic>&#60;.05.</p>
          </fn>
          <fn id="table3fn5">
            <p><sup>e</sup>Average monthly income among employees was KRW 2,970,000 in 2018.</p>
          </fn>
          <fn id="table3fn6">
            <p><sup>f</sup><italic>P</italic>&#60;.01.</p>
          </fn>
          <fn id="table3fn7">
            <p><sup>g</sup>Multiple responses were allowed (the reference groups were those who responded “No”).</p>
          </fn>
          <fn id="table3fn8">
            <p><sup>h</sup>Generalized Anxiety Disorder Questionnaire-2 (GAD-2) score ≥3.</p>
          </fn>
          <fn id="table3fn9">
            <p><sup>i</sup>Patient Health Questionnaire-2 (PHQ-2) score ≥3.</p>
          </fn>
          <fn id="table3fn10">
            <p><sup>j</sup>Primary Care Post-Traumatic Stress Disorder (PTSD) Screen for DSM-5 (PC-PTSD-5) score ≥3.</p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
      <p>Subgroup analyses showed that the associations of misinformation exposure with misinformation belief, COVID-19 knowledge, preventive behaviors, and psychological distress differed according to the respondents’ demographics (sex, age, education, and monthly personal income; <xref ref-type="supplementary-material" rid="app2">Multimedia Appendix 2</xref>). Among the respondents who reported misinformation exposure, misinformation belief was associated with lower COVID-19 knowledge levels (high: aOR 0.62, 95% CI 0.45-0.84 versus low) and fewer COVID-19 preventive behaviors (≥7 behaviors: aOR 0.54, 95% CI 0.39-0.74 versus 0-6 behaviors; <xref ref-type="table" rid="table4">Table 4</xref>).</p>
      <table-wrap position="float" id="table4">
        <label>Table 4</label>
        <caption>
          <p>Associations of COVID-19 knowledge and number of COVID-19 preventive behaviors with COVID-19 misinformation belief among respondents who were exposed to misinformation (N=711)<sup>a</sup>.</p>
        </caption>
        <table width="1000" cellpadding="5" cellspacing="0" border="1" rules="groups" frame="hsides">
          <col width="30"/>
          <col width="220"/>
          <col width="220"/>
          <col width="250"/>
          <col width="280"/>
          <thead>
            <tr valign="top">
              <td colspan="2">Variables</td>
              <td>Participants, n (%)</td>
              <td colspan="2">Misinformation belief (yes/no)</td>
            </tr>
            <tr valign="bottom">
              <td colspan="2">
                <break/>
              </td>
              <td>
                <break/>
              </td>
              <td>Crude odds ratio (95% CI)</td>
              <td>Adjusted odds ratio (95% CI)<sup>b</sup></td>
            </tr>
          </thead>
          <tbody>
            <tr valign="top">
              <td colspan="5">
                <bold>Knowledge</bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Low (0-24)</td>
              <td>259 (36.44)</td>
              <td>Reference</td>
              <td>Reference</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>High (25-35)</td>
              <td>452 (63.56)</td>
              <td>0.59 (0.44-0.80)<sup>c</sup></td>
              <td>0.62 (0.45-0.84)<sup>c</sup></td>
            </tr>
            <tr valign="top">
              <td colspan="5">
                <bold>Preventive behaviors</bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>0-6 behaviors</td>
              <td>305 (42.82)</td>
              <td>Reference</td>
              <td>Reference</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>≥7 behaviors</td>
              <td>407 (57.18)</td>
              <td>0.51 (0.37-0.70)<sup>d</sup></td>
              <td>0.54 (0.39-0.74)<sup>d</sup></td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn id="table4fn1">
            <p><sup>a</sup>All data were weighted by sex and age distribution of the general population in the Seoul metropolitan area in Korea.</p>
          </fn>
          <fn id="table4fn2">
            <p><sup>b</sup>Adjusted for sex, age, highest education level, household arrangement, and monthly personal income.</p>
          </fn>
          <fn id="table4fn3">
            <p><sup>c</sup><italic>P</italic>&#60;.01.</p>
          </fn>
          <fn id="table4fn4">
            <p><sup>d</sup><italic>P</italic>&#60;.001.</p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
    </sec>
    <sec sec-type="discussion">
      <title>Discussion</title>
      <sec>
        <title>Principal Findings</title>
        <p>In this cross-sectional survey of Korean adults, more than two-thirds of the respondents reported COVID-19 misinformation exposure between the end of January 2020 and the end of April 2020, as COVID-19 evolved into a global pandemic. A previous study [<xref ref-type="bibr" rid="ref28">28</xref>] identified a similar prevalence, where 70% of respondents reported misinformation exposure during the 2018 Ebola virus epidemic, affirming a substantial exposure risk to inaccurate or false health-related information during serious infectious disease outbreaks.</p>
        <p>We identified that misinformation exposure was significantly associated with younger age, higher education levels, and lower incomes. Existing studies reported that younger people, including university students, preferred to obtain health information via online means and perceived themselves as having a high level of digital health literacy [<xref ref-type="bibr" rid="ref29">29</xref>-<xref ref-type="bibr" rid="ref31">31</xref>]. Such characteristics would expose young people to more COVID-19 misinformation and information. However, contrary to their perceptions, they lacked the skills to evaluate health resources and apply gathered information to health-related decisions [<xref ref-type="bibr" rid="ref29">29</xref>-<xref ref-type="bibr" rid="ref31">31</xref>]. This indicates that despite their proficiencies in using technology and the internet, effective interventions are required to improve young people’s digital health literacy, which is the ability to search for, understand, and critically evaluate health information through electronic sources, then apply gained knowledge to health issues [<xref ref-type="bibr" rid="ref32">32</xref>]. Meanwhile, to our best knowledge, the associations between misinformation exposure and demographic characteristics have been underinvestigated in the existing literature. We performed subgroup analyses that offer additional details of interactions between the respondents’ demographic characteristics and COVID-19 variables. Further studies that provide in-depth understanding on how and why these demographic characteristics are associated with misinformation exposure will be useful.</p>
        <p>Consistent with previous reports on the role of social media in health information dissemination and misinformation propagation [<xref ref-type="bibr" rid="ref7">7</xref>,<xref ref-type="bibr" rid="ref9">9</xref>,<xref ref-type="bibr" rid="ref33">33</xref>,<xref ref-type="bibr" rid="ref34">34</xref>], respondents in this study reported greater COVID-19 misinformation exposure through SNSs and instant messaging. This can also be attributed to the significance of social media like SNSs and instant messaging in Koreans’ daily lives, as those services also include product marketing, shopping, and payment services, contributing to Korea having the third highest social media usage penetration in the world [<xref ref-type="bibr" rid="ref35">35</xref>].</p>
        <p>Many recent studies have identified that the COVID-19 pandemic and fear of being infected have had negative effects on public mental health, reporting increased depression and anxiety [<xref ref-type="bibr" rid="ref36">36</xref>-<xref ref-type="bibr" rid="ref38">38</xref>]. The respondents in this study were similarly identified to be at high risk of psychological distress; compared to the nonexposure group, the misinformation exposure group notably had around 1.8 and 1.47 times higher anxiety and depressive symptoms, respectively. This demonstrates the alarming negative effect that misinformation can have on public mental health. Higher levels of social media use during the COVID-19 pandemic have been shown to result in higher levels of anxiety (OR 1.72) and a combination of depression and anxiety (OR 1.91) [<xref ref-type="bibr" rid="ref39">39</xref>]. It is purported that prolonged and frequent use of social media throughout the ongoing pandemic increases exposure to misinformation along with accurate information. The mixture of accurate and false information can deliver conflicting messages and amplify uncertainties regarding COVID-19 and its perceived health risks [<xref ref-type="bibr" rid="ref40">40</xref>], resulting in psychological distress [<xref ref-type="bibr" rid="ref41">41</xref>,<xref ref-type="bibr" rid="ref42">42</xref>]. A vicious cycle can be triggered, as evidence has shown that psychological distress itself can drive people to look for more information, which in turn causes further distress [<xref ref-type="bibr" rid="ref43">43</xref>]. Despite the ongoing COVID-19 situation, this study also identified that the respondents had symptoms of PTSD in relation to the pandemic, raising concern that the psychological impact can persist and lead to poor physical health outcomes [<xref ref-type="bibr" rid="ref44">44</xref>].</p>
        <p>In this study, no association between misinformation exposure and COVID-19 knowledge as well as preventive behaviors was found. However, we identified that COVID-19 misinformation belief was negatively associated with COVID-19 knowledge and preventive behaviors, while it was positively associated with misinformation exposure. Similar to our findings, Allington et al [<xref ref-type="bibr" rid="ref45">45</xref>] found that frequent use of SNSs, which propagate more misinformation than any other media [<xref ref-type="bibr" rid="ref7">7</xref>], for COVID-19 information was associated with having conspiracy beliefs. Conspiracy beliefs, in turn, showed a negative relationship with COVID-19 preventive behaviors [<xref ref-type="bibr" rid="ref45">45</xref>-<xref ref-type="bibr" rid="ref47">47</xref>]. Vinck et al [<xref ref-type="bibr" rid="ref28">28</xref>] also reported that the belief in Ebola virus misinformation resulted in a lower likelihood of adopting preventive behaviors.</p>
        <p>The Health Belief Model (HBM) is a theoretical framework widely used in public health to understand heath behaviors for disease prevention (see Champion and Skinner [<xref ref-type="bibr" rid="ref48">48</xref>]). It theorizes that people’s beliefs about their susceptibility to COVID-19 infection and its severity (collectively known as perceived threat), as well as perceptions about the benefits of and barriers to engaging in preventive behaviors will be predictive of their likelihood of engaging in those behaviors, while cues trigger engagement. Additionally, one’s understanding about COVID-19 can alter individual beliefs and thus indirectly influence behavior [<xref ref-type="bibr" rid="ref48">48</xref>]. Based on the HBM, our findings suggest that those who believed in misinformation that they were exposed to had lesser accurate knowledge of COVID-19, which could include inaccurate knowledge about preventive behaviors. A less accurate understanding of COVID-19 in turn altered the perceptions they had about COVID-19, such as reduced perceived COVID-19 threat, reduced perceived benefits from preventive behaviors, heightened perceived benefits from inappropriate preventive behaviors, or heightened perceived barriers to preventive behaviors. These altered perceptions and beliefs regarding COVID-19 thus resulted in reduced engagement in recommended preventive behavior, as our findings show, or potentially increased engagement in inappropriate preventive behaviors. The HBM thus offers insights into interventions that can improve engagement in recommended preventive behaviors by delivering information targeted at the core HBM beliefs of perceived susceptibility, severity, benefits, and barriers [<xref ref-type="bibr" rid="ref49">49</xref>]. Although our findings show no association between misinformation exposure and COVID-19 knowledge, increasing exposure to accurate or corrective COVID-19 information can be useful to denormalize misinformation beliefs, thus changing perceptions of COVID-19 to increase the likelihood of engaging in recommended preventive behaviors [<xref ref-type="bibr" rid="ref4">4</xref>].</p>
      </sec>
      <sec>
        <title>Implications</title>
        <p>As misinformation exposure is associated with misinformation belief, it is essential to manage and stem the propagation of misinformation through popular mediums like social media and counteract misinformation exposure with evidence-based information exposure. A study identified that official authorities had produced only a few COVID-19 information videos through a popular video streaming website (YouTube, a website with around 2 billion monthly users), while videos containing misinformation were disproportionately increasing [<xref ref-type="bibr" rid="ref50">50</xref>]. Governments, health agencies, and researchers should take advantage of such social media outlets by producing and sharing evidence-based and corrective information through YouTube videos and simple but impactful infographics. Additionally, governments and health agencies can work closely with social media platforms to ensure that health-related information has increased visibility without involving unilateral censorship, develop misinformation alerts, and provide verification of information source quality, particularly in the event of global health crises [<xref ref-type="bibr" rid="ref51">51</xref>]. These can accordingly mitigate the development of misinformation belief.</p>
        <p>Increasing digital health literacy among the public, particularly young people, will also be essential, as misinformation exposure did not reflect improved COVID-19 knowledge or preventive behaviors but was associated with misinformation belief. Educational or training programs on digital health literacy should be developed and delivered to the public and can be introduced in schools to cultivate these skills from a young age.</p>
        <p>In-depth, follow-up, and longitudinal studies that explore misinformation selection and beliefs, as well as how misinformation beliefs transform health behaviors will be beneficial as foundations to digital health literacy program development and anti-misinformation strategies.</p>
      </sec>
      <sec>
        <title>Limitations</title>
        <p>This study has several limitations. First, the causal relationships between COVID-19 misinformation exposure and belief, COVID-19 knowledge, and psychological distress were uncertain due to the cross-sectional study design [<xref ref-type="bibr" rid="ref52">52</xref>]. Second, we used survey questions to measure respondents’ COVID-19 misinformation exposure, COVID-19 knowledge, and preventive behaviors, which are not validated. Third, we collected self-reported data from the respondents that would cause recall and social desirability biases. Fourth, although all data were weighted according to the South Korea census data, there were relatively fewer people aged ≥60 years who participated in this study. We recruited adult respondents only and did not recruit teenagers (ie, those aged &#60;20 years), who popularly use social media for information acquisition. The inclusion of younger people in future studies would provide additional evidence about their misinformation exposure and belief. Fifth, we conducted an online survey by recruiting panel members using a survey company and thus there is a possibility of sampling bias. For instance, those sampled were based in urban, not rural, areas and drawn from a high-income Asian country with prior experience of managing outbreaks of infectious diseases (eg, Middle East Respiratory Syndrome).</p>
      </sec>
      <sec>
        <title>Conclusion</title>
        <p>We investigated the prevalence of misinformation exposure and factors that were associated with misinformation exposure and belief, including psychological distress, COVID-19 knowledge, and preventive behaviors in an adult population in the Seoul Metropolitan area, Korea. COVID-19 misinformation exposure was associated with misinformation belief, while misinformation belief was associated with poorer knowledge and engagement in fewer preventive behaviors. Given the potential of such misinformation to undermine global efforts in the COVID-19 response, public health strategies should be kept up-to-date and involve collaborations with multiple stakeholders, including social media platforms, to counter the proliferation of misinformation and win the fight against the infodemic.</p>
      </sec>
    </sec>
  </body>
  <back>
    <app-group>
      <supplementary-material id="app1">
        <label>Multimedia Appendix 1</label>
        <p>Survey questionnaire.</p>
        <media xlink:href="jmir_v22i11e22205_app1.docx" xlink:title="DOCX File , 27 KB"/>
      </supplementary-material>
      <supplementary-material id="app2">
        <label>Multimedia Appendix 2</label>
        <p>Interaction effect of demographic characteristics with COVID-19 knowledge, preventive behaviours, misinformation belief, and psychological distress on misinformation exposure.</p>
        <media xlink:href="jmir_v22i11e22205_app2.docx" xlink:title="DOCX File , 32 KB"/>
      </supplementary-material>
    </app-group>
    <glossary>
      <title>Abbreviations</title>
      <def-list>
        <def-item>
          <term id="abb1">aOR</term>
          <def>
            <p>adjusted odds ratio</p>
          </def>
        </def-item>
        <def-item>
          <term id="abb2">GAD-2</term>
          <def>
            <p>Generalized Anxiety Disorder-2</p>
          </def>
        </def-item>
        <def-item>
          <term id="abb3">HBM</term>
          <def>
            <p>Health Belief Model</p>
          </def>
        </def-item>
        <def-item>
          <term id="abb4">ICT</term>
          <def>
            <p>information and communication technology</p>
          </def>
        </def-item>
        <def-item>
          <term id="abb5">OR</term>
          <def>
            <p>odds ratio</p>
          </def>
        </def-item>
        <def-item>
          <term id="abb6">PHQ-4</term>
          <def>
            <p>Patient Health Questionnaire-4</p>
          </def>
        </def-item>
        <def-item>
          <term id="abb7">PTSD</term>
          <def>
            <p>posttraumatic stress disorder</p>
          </def>
        </def-item>
        <def-item>
          <term id="abb8">SNS</term>
          <def>
            <p>social networking service</p>
          </def>
        </def-item>
        <def-item>
          <term id="abb9">WHO</term>
          <def>
            <p>World Health Organization</p>
          </def>
        </def-item>
      </def-list>
    </glossary>
    <ack>
      <p>We acknowledge the contributions of all respondents in this study.</p>
    </ack>
    <fn-group>
      <fn fn-type="conflict">
        <p>None declared.</p>
      </fn>
    </fn-group>
    <ref-list>
      <ref id="ref1">
        <label>1</label>
        <nlm-citation citation-type="web">
          <article-title>COVID-19 dashboard 2020</article-title>
          <source>Johns Hopkins University</source>
          <access-date>2020-06-12</access-date>
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        </nlm-citation>
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        <nlm-citation citation-type="journal">
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              <given-names>J</given-names>
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              <surname>Shaw</surname>
              <given-names>R</given-names>
            </name>
          </person-group>
          <article-title>Corona Virus (COVID-19) "Infodemic" and Emerging Issues through a Data Lens: The Case of China</article-title>
          <source>Int J Environ Res Public Health</source>
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          <day>30</day>
          <volume>17</volume>
          <issue>7</issue>
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          <comment>
            <ext-link ext-link-type="uri" xlink:type="simple" xlink:href="https://www.mdpi.com/resolver?pii=ijerph17072309"/>
          </comment>
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