Predicting public adherence to covid-19 preventive measures: A cross-sectional study in Hong Kong

Elsie Yan*, Daniel W. L. Lai, Vincent W. P. Lee, Haze K. L. Ng

*Corresponding author for this work

    Research output: Contribution to journalJournal articlepeer-review

    7 Citations (Scopus)


    Objectives: To effectively control the spread of COVID-19, the public’s adherence to relevant disease preventive measures (DPM) is critical. This study examined individuals’ adherence to various DPM and identified facilitators and barriers to adherence in a community sample in Hong Kong.

    Methods: In this cross-sectional study, telephone surveys were conducted over December 2020 and January 2021. Participants provided responses on their adherence to DPM as well as other psychosocial and cognitive factors via the phone.

    Results: Of the sample of 1255 Chinese adults (aged >18 years, 53% women), 94.4% wore face masks in public areas; 88.4% avoided touching their eyes, nose, and mouth; 82.1% performed hand hygiene practices; 81.5% used alcohol-based hand rubs; 74.6% abided by social distancing; and 39.7% tested for COVID-19 on a voluntary basis. Perceived benefits, perceived barriers, self-efficacy, cues to action, perceived acceptability, and disruptions to daily life related to COVID-19 were associated with individuals’ adherence to DPM.

    Conclusions: Adherence to DPM was strong in Hong Kong, and the adherence level could be predicted by various factors. It is vital to consider these factors in order to improve the public’s adherence.

    Original languageEnglish
    Article number12403
    JournalInternational Journal of Environmental Research and Public Health
    Issue number23
    Early online date25 Nov 2021
    Publication statusPublished - 1 Dec 2021

    Scopus Subject Areas

    • Pollution
    • Public Health, Environmental and Occupational Health
    • Health, Toxicology and Mutagenesis

    User-Defined Keywords

    • Adherence
    • COVID-19
    • Health belief
    • Pandemics
    • Preventive measures


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