Spatiotemporal droplet dispersion measurements demonstrate face masks reduce risks from singing

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Abstract

COVID-19 has restricted singing in communal worship. We sought to understand variations in droplet transmission and the impact of wearing face masks. Using rapid laser planar imaging, we measured droplets while participants exhaled, said ‘hello’ or ‘snake’, sang a note or ‘Happy Birthday’, with and without surgical face masks. We measured mean velocity magnitude (MVM), time averaged droplet number (TADN) and maximum droplet number (MDN). Multilevel regression models were used. In 20 participants, sound intensity was 71 dB for speaking and 85 dB for singing (p < 0.001). MVM was similar for all tasks with no clear hierarchy between vocal tasks or people and > 85% reduction wearing face masks. Droplet transmission varied widely, particularly for singing. Masks decreased TADN by 99% (p < 0.001) and MDN by 98% (p < 0.001) for singing and 86–97% for other tasks. Masks reduced variance by up to 48%. When wearing a mask, neither singing task transmitted more droplets than exhaling. In conclusion, wide variation exists for droplet production. This significantly reduced when wearing face masks. Singing during religious worship wearing a face mask appears as safe as exhaling or talking. This has implications for UK public health guidance during the COVID-19 pandemic.

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  1. SciScore for 10.1101/2021.07.09.21260247: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Ethical Approval: Ethical approval for the study was obtained from University College London Ethics Committee (Approval: 14223/002).
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We employed Particle Tracking Velocimetry (PTV) to track individual droplets using the open-source package TracTrac for MATLAB.
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)

    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:
    Limitations: Our study has several limitations. Firstly, it was not possible to determine the exact lower limit of particle diameter that can be detected by our apparatus. However, our validation test using a medical nebuliser demonstrated that our method could detect droplets which are on average 3 µm in size. The range from 3-5 µm is the key droplet size for disease transmission and we detect this. Our study utilised only surgical type IIR face masks, and hence would not be applicable to all face coverings. In particular, some homemade masks are less effective in reducing droplet transmission compared to surgical face masks.32,37,38 Congregants in places of worship may therefore also need to be provided with additional guidance regarding the type of face coverings to wear. Finally, we only assessed individuals singing in a controlled laboratory. There are additional considerations in a real world setting such as in a place of worship which need to be considered, including ventilation and spacing of congregants.

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).


    Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on page 24. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.


    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.