Factors associated with acceptance of a digital contact tracing application for COVID-19 in the Japanese working-age population

  1. Tomohiro Ishimaru
  2. Koki Ibayashi
  3. Masako Nagata
  4. Seiichiro Tateishi
  5. Ayako Hino
  6. Mayumi Tsuji
  7. Hajime Ando
  8. Keiji Muramatsu
  9. Yoshihisa Fujino
  10. for the CORoNaWork Project

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Abstract

Objective

This study aimed to determine factors associated with acceptance of a Digital Contact Tracing (DCT) app for Coronavirus Disease 2019 (COVID-19) in the Japanese working-age population.

Methods

A cross-sectional study was performed for 27,036 full-time workers registered with an internet survey company during December 2020 in Japan.

Results

The rate of downloading the DCT app was 25.1%. The DCT app was more likely to be accepted by people with married status, university graduation or above, higher income, and occupations involving desk work. Fear of COVID-19 transmission, wearing a mask, using hand disinfection, willingness to be vaccinated against COVID-19, and presence of an acquaintance infected with COVID-19 were also associated with a greater likelihood of adopting the app.

Conclusions

The present findings have important implications for widespread adoption of DCT apps in working-age populations in Japan and elsewhere.

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  1. ScreenIT

    SciScore for 10.1101/2021.10.28.21265601: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsConsent: Of these, 33,302 participants who met the inclusion criteria provided informed consent and completed the baseline survey in an online format.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    6 Dependent variable: The dependent variable in the study was the following question about adoption of the COCOA: “Have you downloaded the Contact-Confirming Application (COCOA)?
    COCOA
    suggested: (TropGENE DB, RRID:SCR_005716)
    All statistical analyses were performed using Stata/SE 16.1 software (StataCorp, College Station, TX, USA).
    StataCorp
    suggested: (Stata, RRID:SCR_012763)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    There are several limitations to this study. First, we recruited participants who were registered with an internet research company. Therefore, we may have selected people who actively used the internet, smartphones, and apps. However, we consider this potential selection bias to be negligible because the COCOA downloading rate announced by the Ministry of Health, Labour and Welfare in December 2020 was 20.8%,23 and thus close to the rate in the present study (25.1%). Second, the present study used cross-sectional data for a single period of time. Therefore, the findings can only suggest associations between factors for this given period of time, and cannot capture changes over time within individuals. Third, in the multivariate logistic regression analyses, there was a possibility of unmeasured confounding. Unmeasured personal habits and behavioral patterns may have influenced the adoption of the DCT app.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2021.10.28.21265601v1 on medRxiv