Industry and workplace characteristics associated with the downloading of a COVID-19 contact tracing app in Japan: a nation-wide cross-sectional study View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2021-09-21

AUTHORS

Tomohiro Ishimaru, Koki Ibayashi, Masako Nagata, Ayako Hino, Seiichiro Tateishi, Mayumi Tsuji, Akira Ogami, Shinya Matsuda, Yoshihisa Fujino, Yoshihisa Fujino, Hajime Ando, Hisashi Eguchi, Kazunori Ikegami, Tomohiro Ishimaru, Arisa Harada, Ayako Hino, Kyoko Kitagawa, Kosuke Mafune, Shinya Matsuda, Ryutaro Matsugaki, Koji Mori, Keiji Muramatsu, Masako Nagata, Tomohisa Nagata, Ning Liu, Akira Ogami, Rie Tanaka, Seiishiro Tateishi, Kei Tokutsu, Mayumi Tsuji

ABSTRACT

BACKGROUND: To combat coronavirus disease 2019 (COVID-19), many countries have used contact tracing apps, including Japan's voluntary-use contact-confirming application (COCOA). The current study aimed to identify industry and workplace characteristics associated with the downloading of this COVID-19 contact tracing app. METHODS: This cross-sectional study of full-time workers used an online survey. Multiple logistic regression analysis was used to evaluate the associations of industry and workplace characteristics with contact tracing app use. RESULTS: Of the 27,036 participants, 25.1% had downloaded the COCOA. Workers in the public service (adjusted odds ratio [aOR] = 1.29, 95% confidence interval [CI] 1.14-1.45) and information technology (aOR = 1.38, 95% CI 1.20-1.58) industries were more likely to use the app than were those in the manufacturing industry. In contrast, app usage was less common among workers in the retail and wholesale (aOR = 0.87, 95% CI 0.76-0.99) and food/beverage (aOR = 0.81, 95% CI 0.70-0.94) industries, but further adjustment for company size attenuated these associations. Workers at larger companies were more likely to use the app. Compared with permanent employees, the odds of using the app were higher for managers and civil servants but lower for those who were self-employed. CONCLUSIONS: Downloading of COCOA among Japanese workers was insufficient; thus, the mitigating effect of COCOA on the COVID-19 pandemic is considered to be limited. One possible reason for the under-implementation of the contact tracing app in the retail and wholesale and food/beverage industries is small company size, as suggested by the fully adjusted model results. An awareness campaign should be conducted to promote the widespread use of the contact tracing app in these industries. More... »

PAGES

94

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12199-021-01016-1

DOI

http://dx.doi.org/10.1186/s12199-021-01016-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1141263012

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/34548033


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1117", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Public Health and Health Services", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Adult", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "COVID-19", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Contact Tracing", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Cross-Sectional Studies", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Female", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Industry", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Japan", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Male", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Middle Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Mobile Applications", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "SARS-CoV-2", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Smartphone", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Workplace", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Environmental Epidemiology, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan", 
          "id": "http://www.grid.ac/institutes/grid.271052.3", 
          "name": [
            "Department of Environmental Epidemiology, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ishimaru", 
        "givenName": "Tomohiro", 
        "id": "sg:person.01350506203.09", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01350506203.09"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Environmental Epidemiology, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan", 
          "id": "http://www.grid.ac/institutes/grid.271052.3", 
          "name": [
            "Department of Environmental Epidemiology, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ibayashi", 
        "givenName": "Koki", 
        "id": "sg:person.012133406043.50", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012133406043.50"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Occupational Health Practice and Management, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan", 
          "id": "http://www.grid.ac/institutes/grid.271052.3", 
          "name": [
            "Department of Occupational Health Practice and Management, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Nagata", 
        "givenName": "Masako", 
        "id": "sg:person.0705261763.49", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0705261763.49"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Mental Health, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan", 
          "id": "http://www.grid.ac/institutes/grid.271052.3", 
          "name": [
            "Department of Mental Health, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hino", 
        "givenName": "Ayako", 
        "id": "sg:person.0660260605.35", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0660260605.35"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Occupational Medicine, School of Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan", 
          "id": "http://www.grid.ac/institutes/grid.271052.3", 
          "name": [
            "Department of Occupational Medicine, School of Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tateishi", 
        "givenName": "Seiichiro", 
        "id": "sg:person.01007131620.48", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01007131620.48"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Environmental Health, School of Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan", 
          "id": "http://www.grid.ac/institutes/grid.271052.3", 
          "name": [
            "Department of Environmental Health, School of Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tsuji", 
        "givenName": "Mayumi", 
        "id": "sg:person.01005253747.92", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01005253747.92"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Work Systems and Health, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan", 
          "id": "http://www.grid.ac/institutes/grid.271052.3", 
          "name": [
            "Department of Work Systems and Health, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ogami", 
        "givenName": "Akira", 
        "id": "sg:person.0756210151.39", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0756210151.39"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Preventive Medicine and Community Health, School of Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan", 
          "id": "http://www.grid.ac/institutes/grid.271052.3", 
          "name": [
            "Department of Preventive Medicine and Community Health, School of Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Matsuda", 
        "givenName": "Shinya", 
        "id": "sg:person.0755324561.79", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0755324561.79"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Environmental Epidemiology, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan", 
          "id": "http://www.grid.ac/institutes/grid.271052.3", 
          "name": [
            "Department of Environmental Epidemiology, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Fujino", 
        "givenName": "Yoshihisa", 
        "id": "sg:person.01171406332.78", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01171406332.78"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Fujino", 
        "givenName": "Yoshihisa", 
        "type": "Person"
      }, 
      {
        "familyName": "Ando", 
        "givenName": "Hajime", 
        "id": "sg:person.011720734134.03", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011720734134.03"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Eguchi", 
        "givenName": "Hisashi", 
        "type": "Person"
      }, 
      {
        "familyName": "Ikegami", 
        "givenName": "Kazunori", 
        "type": "Person"
      }, 
      {
        "familyName": "Ishimaru", 
        "givenName": "Tomohiro", 
        "type": "Person"
      }, 
      {
        "familyName": "Harada", 
        "givenName": "Arisa", 
        "type": "Person"
      }, 
      {
        "familyName": "Hino", 
        "givenName": "Ayako", 
        "type": "Person"
      }, 
      {
        "familyName": "Kitagawa", 
        "givenName": "Kyoko", 
        "type": "Person"
      }, 
      {
        "familyName": "Mafune", 
        "givenName": "Kosuke", 
        "type": "Person"
      }, 
      {
        "familyName": "Matsuda", 
        "givenName": "Shinya", 
        "type": "Person"
      }, 
      {
        "familyName": "Matsugaki", 
        "givenName": "Ryutaro", 
        "id": "sg:person.010476745733.75", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010476745733.75"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Mori", 
        "givenName": "Koji", 
        "type": "Person"
      }, 
      {
        "familyName": "Muramatsu", 
        "givenName": "Keiji", 
        "type": "Person"
      }, 
      {
        "familyName": "Nagata", 
        "givenName": "Masako", 
        "type": "Person"
      }, 
      {
        "familyName": "Nagata", 
        "givenName": "Tomohisa", 
        "type": "Person"
      }, 
      {
        "familyName": "Liu", 
        "givenName": "Ning", 
        "type": "Person"
      }, 
      {
        "familyName": "Ogami", 
        "givenName": "Akira", 
        "type": "Person"
      }, 
      {
        "familyName": "Tanaka", 
        "givenName": "Rie", 
        "type": "Person"
      }, 
      {
        "familyName": "Tateishi", 
        "givenName": "Seiishiro", 
        "id": "sg:person.016524263611.30", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016524263611.30"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Tokutsu", 
        "givenName": "Kei", 
        "id": "sg:person.014137475647.71", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014137475647.71"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Tsuji", 
        "givenName": "Mayumi", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/s41467-020-20817-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1134855739", 
          "https://doi.org/10.1038/s41467-020-20817-6"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2021-09-21", 
    "datePublishedReg": "2021-09-21", 
    "description": "BACKGROUND: To combat coronavirus disease 2019 (COVID-19), many countries have used contact tracing apps, including Japan's voluntary-use contact-confirming application (COCOA). The current study aimed to identify industry and workplace characteristics associated with the downloading of this COVID-19 contact tracing app.\nMETHODS: This cross-sectional study of full-time workers used an online survey. Multiple logistic regression analysis was used to evaluate the associations of industry and workplace characteristics with contact tracing app use.\nRESULTS: Of the 27,036 participants, 25.1% had downloaded the COCOA. Workers in the public service (adjusted odds ratio [aOR] = 1.29, 95% confidence interval [CI] 1.14-1.45) and information technology (aOR = 1.38, 95% CI 1.20-1.58) industries were more likely to use the app than were those in the manufacturing industry. In contrast, app usage was less common among workers in the retail and wholesale (aOR = 0.87, 95% CI 0.76-0.99) and food/beverage (aOR = 0.81, 95% CI 0.70-0.94) industries, but further adjustment for company size attenuated these associations. Workers at larger companies were more likely to use the app. Compared with permanent employees, the odds of using the app were higher for managers and civil servants but lower for those who were self-employed.\nCONCLUSIONS: Downloading of COCOA among Japanese workers was insufficient; thus, the mitigating effect of COCOA on the COVID-19 pandemic is considered to be limited. One possible reason for the under-implementation of the contact tracing app in the retail and wholesale and food/beverage industries is small company size, as suggested by the fully adjusted model results. An awareness campaign should be conducted to promote the widespread use of the contact tracing app in these industries.", 
    "genre": "article", 
    "id": "sg:pub.10.1186/s12199-021-01016-1", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1115673", 
        "issn": [
          "1342-078X", 
          "1347-4715"
        ], 
        "name": "Environmental Health and Preventive Medicine", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "26"
      }
    ], 
    "keywords": [
      "workplace characteristics", 
      "company size", 
      "small company size", 
      "food/beverage industry", 
      "full-time workers", 
      "associations of industry", 
      "permanent employees", 
      "large companies", 
      "manufacturing industry", 
      "beverage industry", 
      "cross-sectional study", 
      "industry", 
      "public services", 
      "mitigating effect", 
      "online survey", 
      "civil servants", 
      "workers", 
      "Japanese workers", 
      "nation-wide cross-sectional study", 
      "multiple logistic regression analysis", 
      "app usage", 
      "coronavirus disease 2019", 
      "COVID-19 pandemic", 
      "logistic regression analysis", 
      "employees", 
      "managers", 
      "companies", 
      "COVID-19 contact", 
      "disease 2019", 
      "regression analysis", 
      "further adjustment", 
      "app use", 
      "awareness campaigns", 
      "servants", 
      "services", 
      "countries", 
      "survey", 
      "current study", 
      "association", 
      "campaign", 
      "apps", 
      "widespread use", 
      "study", 
      "characteristics", 
      "odds", 
      "Japan", 
      "possible reasons", 
      "pandemic", 
      "usage", 
      "reasons", 
      "participants", 
      "contact", 
      "use", 
      "model results", 
      "adjustment", 
      "size", 
      "contrast", 
      "analysis", 
      "effect", 
      "results", 
      "downloading", 
      "applications", 
      "Japan's voluntary-use contact-confirming application", 
      "'s voluntary-use contact-confirming application", 
      "COCOA"
    ], 
    "name": "Industry and workplace characteristics associated with the downloading of a COVID-19 contact tracing app in Japan: a nation-wide cross-sectional study", 
    "pagination": "94", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1141263012"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s12199-021-01016-1"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "34548033"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s12199-021-01016-1", 
      "https://app.dimensions.ai/details/publication/pub.1141263012"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-01-01T19:03", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/article/article_906.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1186/s12199-021-01016-1"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/s12199-021-01016-1'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s12199-021-01016-1'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s12199-021-01016-1'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s12199-021-01016-1'


 

This table displays all metadata directly associated to this object as RDF triples.

364 TRIPLES      22 PREDICATES      106 URIs      97 LITERALS      21 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s12199-021-01016-1 schema:about N0d9f41643ede4f5e9548265129da76a9
2 N140fccbe6b1648c59639ca212746f41d
3 N242b24008c7242668cca2f5121bf1680
4 N24703fba4776456aac5e15010c0cfad3
5 N42ecbd787dfc45e08640f77d4d126917
6 N53c7c9b906944b40a1ba621f14cfb091
7 N78020e63ae1b4293bea56be9c8cfa761
8 N8d76089b78b54c6ba0a0d96b66072d62
9 N98baa6ab80ba43558338a84eff9f4308
10 Nb440df454e3f4cf995296a19b1d141f9
11 Nb4c5af8e54cc4734a8730f41c87dc30d
12 Ncc93ebdd5ada4c67b7cb1b6df7b934d2
13 Nd24264d64d3445328682f4e3551b47b9
14 Ne7869005d71f478aad6dfc9122847caf
15 anzsrc-for:11
16 anzsrc-for:1117
17 schema:author Nf7231ddf8b10496ba588c8458b72c89f
18 schema:citation sg:pub.10.1038/s41467-020-20817-6
19 schema:datePublished 2021-09-21
20 schema:datePublishedReg 2021-09-21
21 schema:description BACKGROUND: To combat coronavirus disease 2019 (COVID-19), many countries have used contact tracing apps, including Japan's voluntary-use contact-confirming application (COCOA). The current study aimed to identify industry and workplace characteristics associated with the downloading of this COVID-19 contact tracing app. METHODS: This cross-sectional study of full-time workers used an online survey. Multiple logistic regression analysis was used to evaluate the associations of industry and workplace characteristics with contact tracing app use. RESULTS: Of the 27,036 participants, 25.1% had downloaded the COCOA. Workers in the public service (adjusted odds ratio [aOR] = 1.29, 95% confidence interval [CI] 1.14-1.45) and information technology (aOR = 1.38, 95% CI 1.20-1.58) industries were more likely to use the app than were those in the manufacturing industry. In contrast, app usage was less common among workers in the retail and wholesale (aOR = 0.87, 95% CI 0.76-0.99) and food/beverage (aOR = 0.81, 95% CI 0.70-0.94) industries, but further adjustment for company size attenuated these associations. Workers at larger companies were more likely to use the app. Compared with permanent employees, the odds of using the app were higher for managers and civil servants but lower for those who were self-employed. CONCLUSIONS: Downloading of COCOA among Japanese workers was insufficient; thus, the mitigating effect of COCOA on the COVID-19 pandemic is considered to be limited. One possible reason for the under-implementation of the contact tracing app in the retail and wholesale and food/beverage industries is small company size, as suggested by the fully adjusted model results. An awareness campaign should be conducted to promote the widespread use of the contact tracing app in these industries.
22 schema:genre article
23 schema:inLanguage en
24 schema:isAccessibleForFree true
25 schema:isPartOf N40c903791f404ca1b543682ebb34e4a6
26 Nd0c86257e5e8482e90384bb042a5e02e
27 sg:journal.1115673
28 schema:keywords 's voluntary-use contact-confirming application
29 COCOA
30 COVID-19 contact
31 COVID-19 pandemic
32 Japan
33 Japan's voluntary-use contact-confirming application
34 Japanese workers
35 adjustment
36 analysis
37 app usage
38 app use
39 applications
40 apps
41 association
42 associations of industry
43 awareness campaigns
44 beverage industry
45 campaign
46 characteristics
47 civil servants
48 companies
49 company size
50 contact
51 contrast
52 coronavirus disease 2019
53 countries
54 cross-sectional study
55 current study
56 disease 2019
57 downloading
58 effect
59 employees
60 food/beverage industry
61 full-time workers
62 further adjustment
63 industry
64 large companies
65 logistic regression analysis
66 managers
67 manufacturing industry
68 mitigating effect
69 model results
70 multiple logistic regression analysis
71 nation-wide cross-sectional study
72 odds
73 online survey
74 pandemic
75 participants
76 permanent employees
77 possible reasons
78 public services
79 reasons
80 regression analysis
81 results
82 servants
83 services
84 size
85 small company size
86 study
87 survey
88 usage
89 use
90 widespread use
91 workers
92 workplace characteristics
93 schema:name Industry and workplace characteristics associated with the downloading of a COVID-19 contact tracing app in Japan: a nation-wide cross-sectional study
94 schema:pagination 94
95 schema:productId N590b316493cd4a32ba4ace058fe63d95
96 N84d69f75eb7c4bf2b9c5cd6b8e79b13f
97 Nd0155607d59e4a7badc68cef6309a0c7
98 schema:sameAs https://app.dimensions.ai/details/publication/pub.1141263012
99 https://doi.org/10.1186/s12199-021-01016-1
100 schema:sdDatePublished 2022-01-01T19:03
101 schema:sdLicense https://scigraph.springernature.com/explorer/license/
102 schema:sdPublisher N3f34be910f4a480a81355d39e0fe99fb
103 schema:url https://doi.org/10.1186/s12199-021-01016-1
104 sgo:license sg:explorer/license/
105 sgo:sdDataset articles
106 rdf:type schema:ScholarlyArticle
107 N0bbbe34af3e444a487d606db84c46ba0 rdf:first N62682ea542ea4df4a833991b3b2cb7e6
108 rdf:rest Na7b96b8e9f4a4620942a27d8bc8b56ae
109 N0d9f41643ede4f5e9548265129da76a9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
110 schema:name Cross-Sectional Studies
111 rdf:type schema:DefinedTerm
112 N1136b7018b03426e9b03ac27e05afa21 schema:familyName Nagata
113 schema:givenName Masako
114 rdf:type schema:Person
115 N1162a92888c04e2b9b522eb4d62eb618 rdf:first N1136b7018b03426e9b03ac27e05afa21
116 rdf:rest N0bbbe34af3e444a487d606db84c46ba0
117 N133465e1c57144e795afe0dafd01bb45 rdf:first sg:person.0755324561.79
118 rdf:rest Ncbd80f065c1a4cf08ecd88c0dda5e32f
119 N140fccbe6b1648c59639ca212746f41d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
120 schema:name Industry
121 rdf:type schema:DefinedTerm
122 N17026b55da034867ae32f5b2bad83ca4 rdf:first N23f75424e5a24eefb6f8fe58827d2d34
123 rdf:rest N4eb4d93892b54d248142f4dd8428f102
124 N1956f9dfa649411e864d89af945f01a7 schema:familyName Fujino
125 schema:givenName Yoshihisa
126 rdf:type schema:Person
127 N1dec4fa98a3046ef9b4ed7f7c95a43f4 rdf:first sg:person.01007131620.48
128 rdf:rest Neee1a188c3c34c59b7648e9ac11c3a89
129 N1f254c72ac8e41a799d42db567ace3f4 rdf:first N38ae955d6f9a4b1ab91c7c44b55b821e
130 rdf:rest N4044ef1e442b4649ab15cfc40a35b5c8
131 N237951b85bb64a018d43eb8b73be4458 rdf:first sg:person.016524263611.30
132 rdf:rest Nd29dea0e1cf940b0be69502b0c6acfc3
133 N23f75424e5a24eefb6f8fe58827d2d34 schema:familyName Ogami
134 schema:givenName Akira
135 rdf:type schema:Person
136 N242b24008c7242668cca2f5121bf1680 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
137 schema:name Workplace
138 rdf:type schema:DefinedTerm
139 N24703fba4776456aac5e15010c0cfad3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
140 schema:name Smartphone
141 rdf:type schema:DefinedTerm
142 N29aa90a3ff76403581079dc852ee8a53 rdf:first N946811c5097947c5a73fe199156001e0
143 rdf:rest Ndde54f498d0840169c11345a294c9e3c
144 N2ea41538fcbd46bdb15f8ec8966f6ffa rdf:first Nde6a81db812d4463a01a94c50f832a86
145 rdf:rest Na0f0820d860245c7a9341b05da294e26
146 N38ae955d6f9a4b1ab91c7c44b55b821e schema:familyName Eguchi
147 schema:givenName Hisashi
148 rdf:type schema:Person
149 N3f34be910f4a480a81355d39e0fe99fb schema:name Springer Nature - SN SciGraph project
150 rdf:type schema:Organization
151 N4044ef1e442b4649ab15cfc40a35b5c8 rdf:first Nc1dc142e00fa4ab68ef33631c3e4e25e
152 rdf:rest N29aa90a3ff76403581079dc852ee8a53
153 N40c903791f404ca1b543682ebb34e4a6 schema:volumeNumber 26
154 rdf:type schema:PublicationVolume
155 N42ecbd787dfc45e08640f77d4d126917 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
156 schema:name SARS-CoV-2
157 rdf:type schema:DefinedTerm
158 N434bce14ac42411ab8c660dc70657b9f rdf:first N4771db1d5f9f4b5fb0f99cab29e4ad9d
159 rdf:rest rdf:nil
160 N4771db1d5f9f4b5fb0f99cab29e4ad9d schema:familyName Tsuji
161 schema:givenName Mayumi
162 rdf:type schema:Person
163 N4eb4d93892b54d248142f4dd8428f102 rdf:first Nf97cfcd387324312bcaf9f93b5a49bc4
164 rdf:rest N237951b85bb64a018d43eb8b73be4458
165 N53c7c9b906944b40a1ba621f14cfb091 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
166 schema:name Male
167 rdf:type schema:DefinedTerm
168 N590b316493cd4a32ba4ace058fe63d95 schema:name pubmed_id
169 schema:value 34548033
170 rdf:type schema:PropertyValue
171 N62682ea542ea4df4a833991b3b2cb7e6 schema:familyName Nagata
172 schema:givenName Tomohisa
173 rdf:type schema:Person
174 N67b4e13b866d4a43800f6db4690b59f6 rdf:first sg:person.0660260605.35
175 rdf:rest N1dec4fa98a3046ef9b4ed7f7c95a43f4
176 N742da82cc95c4e88b6d5a1a821c78f34 schema:familyName Mafune
177 schema:givenName Kosuke
178 rdf:type schema:Person
179 N76c406cae57345c2af9d7a91e90aeaa7 rdf:first Nc7c0708d04e14b42957e2d467c93e48e
180 rdf:rest N2ea41538fcbd46bdb15f8ec8966f6ffa
181 N78020e63ae1b4293bea56be9c8cfa761 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
182 schema:name Female
183 rdf:type schema:DefinedTerm
184 N84d69f75eb7c4bf2b9c5cd6b8e79b13f schema:name doi
185 schema:value 10.1186/s12199-021-01016-1
186 rdf:type schema:PropertyValue
187 N8d76089b78b54c6ba0a0d96b66072d62 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
188 schema:name COVID-19
189 rdf:type schema:DefinedTerm
190 N8f14570cc5464484add544734a0af379 schema:familyName Harada
191 schema:givenName Arisa
192 rdf:type schema:Person
193 N946811c5097947c5a73fe199156001e0 schema:familyName Ishimaru
194 schema:givenName Tomohiro
195 rdf:type schema:Person
196 N96e841cd771545a090e461830a61ca21 schema:familyName Matsuda
197 schema:givenName Shinya
198 rdf:type schema:Person
199 N98baa6ab80ba43558338a84eff9f4308 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
200 schema:name Mobile Applications
201 rdf:type schema:DefinedTerm
202 N9c26e22b5eec421ea670747595e57c30 rdf:first Nefd16464922a470580174ba3af774a71
203 rdf:rest N1162a92888c04e2b9b522eb4d62eb618
204 N9d0c3ce7e951440baa524addfcf432da rdf:first sg:person.010476745733.75
205 rdf:rest Nef15212b656f40e2a9926515043f6220
206 Na0f0820d860245c7a9341b05da294e26 rdf:first N742da82cc95c4e88b6d5a1a821c78f34
207 rdf:rest Nb08e38e2c89c4961b1deedd6f592b329
208 Na7b96b8e9f4a4620942a27d8bc8b56ae rdf:first Ncc54d6e4b52a41caab241964d7a2510f
209 rdf:rest N17026b55da034867ae32f5b2bad83ca4
210 Nb08e38e2c89c4961b1deedd6f592b329 rdf:first N96e841cd771545a090e461830a61ca21
211 rdf:rest N9d0c3ce7e951440baa524addfcf432da
212 Nb440df454e3f4cf995296a19b1d141f9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
213 schema:name Middle Aged
214 rdf:type schema:DefinedTerm
215 Nb4c5af8e54cc4734a8730f41c87dc30d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
216 schema:name Adult
217 rdf:type schema:DefinedTerm
218 Nc171a9fb187f408dad432750c01bffae rdf:first sg:person.0756210151.39
219 rdf:rest N133465e1c57144e795afe0dafd01bb45
220 Nc1dc142e00fa4ab68ef33631c3e4e25e schema:familyName Ikegami
221 schema:givenName Kazunori
222 rdf:type schema:Person
223 Nc7c0708d04e14b42957e2d467c93e48e schema:familyName Hino
224 schema:givenName Ayako
225 rdf:type schema:Person
226 Ncaf078a896094637906957a5d051b1f2 rdf:first sg:person.012133406043.50
227 rdf:rest Ne31be404ec714a82b460baf46a0c588b
228 Ncb5c5af77a824f09bcba4b8da43ede13 rdf:first N1956f9dfa649411e864d89af945f01a7
229 rdf:rest Nfedd86b9db074899831fecad501af237
230 Ncbd80f065c1a4cf08ecd88c0dda5e32f rdf:first sg:person.01171406332.78
231 rdf:rest Ncb5c5af77a824f09bcba4b8da43ede13
232 Ncc54d6e4b52a41caab241964d7a2510f schema:familyName Liu
233 schema:givenName Ning
234 rdf:type schema:Person
235 Ncc93ebdd5ada4c67b7cb1b6df7b934d2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
236 schema:name Japan
237 rdf:type schema:DefinedTerm
238 Nd0155607d59e4a7badc68cef6309a0c7 schema:name dimensions_id
239 schema:value pub.1141263012
240 rdf:type schema:PropertyValue
241 Nd0c86257e5e8482e90384bb042a5e02e schema:issueNumber 1
242 rdf:type schema:PublicationIssue
243 Nd24264d64d3445328682f4e3551b47b9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
244 schema:name Contact Tracing
245 rdf:type schema:DefinedTerm
246 Nd29dea0e1cf940b0be69502b0c6acfc3 rdf:first sg:person.014137475647.71
247 rdf:rest N434bce14ac42411ab8c660dc70657b9f
248 Ndde54f498d0840169c11345a294c9e3c rdf:first N8f14570cc5464484add544734a0af379
249 rdf:rest N76c406cae57345c2af9d7a91e90aeaa7
250 Nde6a81db812d4463a01a94c50f832a86 schema:familyName Kitagawa
251 schema:givenName Kyoko
252 rdf:type schema:Person
253 Ne31be404ec714a82b460baf46a0c588b rdf:first sg:person.0705261763.49
254 rdf:rest N67b4e13b866d4a43800f6db4690b59f6
255 Ne7869005d71f478aad6dfc9122847caf schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
256 schema:name Humans
257 rdf:type schema:DefinedTerm
258 Neb10ea453e3342bb9c19272205fcae34 schema:familyName Mori
259 schema:givenName Koji
260 rdf:type schema:Person
261 Neee1a188c3c34c59b7648e9ac11c3a89 rdf:first sg:person.01005253747.92
262 rdf:rest Nc171a9fb187f408dad432750c01bffae
263 Nef15212b656f40e2a9926515043f6220 rdf:first Neb10ea453e3342bb9c19272205fcae34
264 rdf:rest N9c26e22b5eec421ea670747595e57c30
265 Nefd16464922a470580174ba3af774a71 schema:familyName Muramatsu
266 schema:givenName Keiji
267 rdf:type schema:Person
268 Nf7231ddf8b10496ba588c8458b72c89f rdf:first sg:person.01350506203.09
269 rdf:rest Ncaf078a896094637906957a5d051b1f2
270 Nf97cfcd387324312bcaf9f93b5a49bc4 schema:familyName Tanaka
271 schema:givenName Rie
272 rdf:type schema:Person
273 Nfedd86b9db074899831fecad501af237 rdf:first sg:person.011720734134.03
274 rdf:rest N1f254c72ac8e41a799d42db567ace3f4
275 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
276 schema:name Medical and Health Sciences
277 rdf:type schema:DefinedTerm
278 anzsrc-for:1117 schema:inDefinedTermSet anzsrc-for:
279 schema:name Public Health and Health Services
280 rdf:type schema:DefinedTerm
281 sg:journal.1115673 schema:issn 1342-078X
282 1347-4715
283 schema:name Environmental Health and Preventive Medicine
284 schema:publisher Springer Nature
285 rdf:type schema:Periodical
286 sg:person.01005253747.92 schema:affiliation grid-institutes:grid.271052.3
287 schema:familyName Tsuji
288 schema:givenName Mayumi
289 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01005253747.92
290 rdf:type schema:Person
291 sg:person.01007131620.48 schema:affiliation grid-institutes:grid.271052.3
292 schema:familyName Tateishi
293 schema:givenName Seiichiro
294 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01007131620.48
295 rdf:type schema:Person
296 sg:person.010476745733.75 schema:familyName Matsugaki
297 schema:givenName Ryutaro
298 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010476745733.75
299 rdf:type schema:Person
300 sg:person.01171406332.78 schema:affiliation grid-institutes:grid.271052.3
301 schema:familyName Fujino
302 schema:givenName Yoshihisa
303 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01171406332.78
304 rdf:type schema:Person
305 sg:person.011720734134.03 schema:familyName Ando
306 schema:givenName Hajime
307 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011720734134.03
308 rdf:type schema:Person
309 sg:person.012133406043.50 schema:affiliation grid-institutes:grid.271052.3
310 schema:familyName Ibayashi
311 schema:givenName Koki
312 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012133406043.50
313 rdf:type schema:Person
314 sg:person.01350506203.09 schema:affiliation grid-institutes:grid.271052.3
315 schema:familyName Ishimaru
316 schema:givenName Tomohiro
317 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01350506203.09
318 rdf:type schema:Person
319 sg:person.014137475647.71 schema:familyName Tokutsu
320 schema:givenName Kei
321 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014137475647.71
322 rdf:type schema:Person
323 sg:person.016524263611.30 schema:familyName Tateishi
324 schema:givenName Seiishiro
325 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016524263611.30
326 rdf:type schema:Person
327 sg:person.0660260605.35 schema:affiliation grid-institutes:grid.271052.3
328 schema:familyName Hino
329 schema:givenName Ayako
330 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0660260605.35
331 rdf:type schema:Person
332 sg:person.0705261763.49 schema:affiliation grid-institutes:grid.271052.3
333 schema:familyName Nagata
334 schema:givenName Masako
335 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0705261763.49
336 rdf:type schema:Person
337 sg:person.0755324561.79 schema:affiliation grid-institutes:grid.271052.3
338 schema:familyName Matsuda
339 schema:givenName Shinya
340 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0755324561.79
341 rdf:type schema:Person
342 sg:person.0756210151.39 schema:affiliation grid-institutes:grid.271052.3
343 schema:familyName Ogami
344 schema:givenName Akira
345 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0756210151.39
346 rdf:type schema:Person
347 sg:pub.10.1038/s41467-020-20817-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1134855739
348 https://doi.org/10.1038/s41467-020-20817-6
349 rdf:type schema:CreativeWork
350 grid-institutes:grid.271052.3 schema:alternateName Department of Environmental Epidemiology, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
351 Department of Environmental Health, School of Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
352 Department of Mental Health, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
353 Department of Occupational Health Practice and Management, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
354 Department of Occupational Medicine, School of Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
355 Department of Preventive Medicine and Community Health, School of Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
356 Department of Work Systems and Health, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
357 schema:name Department of Environmental Epidemiology, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
358 Department of Environmental Health, School of Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
359 Department of Mental Health, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
360 Department of Occupational Health Practice and Management, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
361 Department of Occupational Medicine, School of Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
362 Department of Preventive Medicine and Community Health, School of Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
363 Department of Work Systems and Health, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
364 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...