Behavioural changes during the COVID-19 pandemic: Results of a nationwide survey in Singapore

  1. Victoria JE Long
  2. Jean CJ Liu

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Abstract

Introduction: As part of infection control measures for COVID-19, individuals have been encouraged to adopt both preventive (such as handwashing) and avoidant behavioural changes (e.g. avoiding crowds). In this study, we examined whether demographics predicted the likelihood that a person would adopt these behaviours in Singapore. Methods: A total of 1,145 participants responded to an online survey conducted between 7 March and 21 April 2020. We collected demographic information and asked participants to report which of 17 behaviour changes they had undertaken because of the COVID-19 outbreak. Regression analyses were performed to predict the number of behavioural changes (preventive, avoidant, and total) as a function of demographics. Finally, we sought to identify predictors of persons who declared that they had not undertaken any of these measures following the outbreak. Results: Most participants (97%) reported at least one behavioural change on account of the pandemic, with changes increasing with the number of local COVID-19 cases (P<0.001). Additionally, women and those who were younger adopted more preventive behaviours (gender: P<0.001; age: P=0.001). Women were more likely to increase handwashing frequency, and younger individuals were more likely to wear face masks prior to legislation. Finally, women and those who were married adopted more avoidant behaviours (gender: P<0.001; marital status: P<0.001), with both groups avoiding crowded areas and staying home more than usual. Women also voluntarily reduced physical contact, whereas those who were married preferentially chose outdoor venues and relied on online shopping. Conclusion: Our characterisation of behavioural changes provides a baseline for public health advisories. Moving forward, health authorities can focus their efforts on encouraging segments of the population who do not readily adopt infection control measures against COVID-19.

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  1. Version published to 10.47102/annals-acadmedsg.2020391
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    SciScore for 10.1101/2020.08.06.20169870: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The study was approved by the Yale-NUS College Ethics Review Committee (#2020-CERC-001), and participants gave written consent in accordance with the Declaration of Helsinki.
    Consent: The study was approved by the Yale-NUS College Ethics Review Committee (#2020-CERC-001), and participants gave written consent in accordance with the Declaration of Helsinki.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All statistical analyses were conducted using R (Version 4.0) and STATA (Version 12.0).
    STATA
    suggested: (Stata, RRID:SCR_012763)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Limitations: In making these recommendations, we note that our study has several limitations. First, we relied on participants’ self-reports, which may be vulnerable to recollection biases. Future research will need to explore whether our findings translate to actual behavioural changes during the pandemic. Second, although our survey methodology captured behavioural changes at one particular time-point, the recommendation of infection control measures is a moving target. In the case of mask-wearing, for example, official advisories changed from masks not being needed, to being encouraged, to finally being mandated (as of 14 April 2020)28. Correspondingly, further research is needed to examine whether our findings continue to hold even as official advisories change. Conclusions: In conclusion, we conducted the first Singapore-based study of behavioural changes during the COVID-19 pandemic. Although the scale of this crisis has been unprecedented and many uncertainties remain, many of our findings reinforce longstanding patterns of how demographic characteristics can pre-dispose an individual to disease - in this case, via the uptake of measures that can minimize COVID-19 infection. Moving forward, our findings provide a template by which official messaging can be tailored for health promotion.

    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: 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.

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  3. Version published to 10.1101/2020.08.06.20169870v1 on medRxiv