Predictors to Use Mobile Apps for Monitoring COVID-19 Symptoms and Contact Tracing: Survey Among Dutch Citizens

  1. Stephanie Jansen-Kosterink
  2. Marian Hurmuz
  3. Marjolein den Ouden
  4. Lex van Velsen

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Abstract

eHealth apps have been recognized as a valuable tool to reduce COVID-19’s effective reproduction number. The factors that determine the acceptance of COVID-19 apps remain unknown. The exception here is privacy.

Objective

The aim of this article was to identify antecedents of acceptance of (1) a mobile app for COVID-19 symptom recognition and monitoring and (2) a mobile app for contact tracing, both by means of an online survey among Dutch citizens.

Methods

Next to the demographics, the online survey contained questions focusing on perceived health, fear of COVID-19, and intention to use. We used snowball sampling via posts on social media and personal connections. To identify antecedents of the model for acceptance of the 2 mobile apps, we conducted multiple linear regression analyses.

Results

In total, 238 Dutch adults completed the survey; 59.2% (n=141) of the responders were female and the average age was 45.6 years (SD 17.4 years). For the symptom app, the final model included the predictors age, attitude toward technology, and fear of COVID-19. The model had an r2 of 0.141. The final model for the tracing app included the same predictors and had an r2 of 0.156. The main reason to use both mobile apps was to control the spread of the COVID-19 virus. Concerns about privacy was mentioned as the main reason to not use the mobile apps.

Conclusions

Age, attitude toward technology, and fear of COVID-19 are important predictors of the acceptance of COVID-19 mobile apps for symptom recognition and monitoring and for contact tracing. These predictors should be taken into account during the development and implementation of these mobile apps to secure acceptance.

Article activity feed

  1. Version published to 10.2196/28416
  2. Version published to 10.2196/preprints.28416
  3. ScreenIT

    SciScore for 10.1101/2020.06.02.20113423: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: At the beginning of the survey, participants were asked for consent to use their data for research purposes.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    2.3 Analyses: Data were analysed by using SPSS, version 19.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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:
    The following four limitations should be taken into account for this study. First, the selection bias of our sample. Given the results the education level of our sample is higher compared to the general population. In addition, most respondents had a moderate attitude towards technology. This could be explained by our way to distribute the survey by snowball sampling. Second, for our analysis the power of our sample was sufficient. However, a larger sample would improve the generalizability of our outcomes as mainly Dutch citizens from the eastern part of the Netherlands (87% of our sample) completed our survey. Third, in our survey the two mobile applications are introduced by means of a short description of their general aim. It is unclear if this description was sufficient for the responders to understand to purpose of both mobile applications. Fourth, the explained variance of both our models is relatively low. Normally, in studies such as these, this number is boosted by including the predictors ease of use and perceived usefulness. However, since including these factors leads to little practical results (concluding that the applications should be easy to use is a given and does not inform design), the identification COVID-19-related factors is an important extension of existing technology acceptance models.

    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.

    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.

  4. Version published to 10.1101/2020.06.02.20113423v1 on medRxiv