Use of Artificial Intelligence to understand adults' thoughts and behaviours relating to COVID-19.


Journal

Perspectives in public health
ISSN: 1757-9147
Titre abrégé: Perspect Public Health
Pays: United States
ID NLM: 101499631

Informations de publication

Date de publication:
May 2022
Historique:
pubmed: 22 1 2021
medline: 29 4 2022
entrez: 21 1 2021
Statut: ppublish

Résumé

The outbreak of severe acute respiratory syndrome coronavirus 2 (COVID-19) is a global pandemic that has had substantial impact across societies. An attempt to reduce infection and spread of the disease, for most nations, has led to a lockdown period, where people's movement has been restricted resulting in a consequential impact on employment, lifestyle behaviours and wellbeing. As such, this study aimed to explore adults' thoughts and behaviours in response to the outbreak and resulting lockdown measures. Using an online survey, 1126 adults responded to invitations to participate in the study. Participants, all aged 18 years or older, were recruited using social media, email distribution lists, website advertisement and word of mouth. Sentiment and personality features extracted from free-text responses using Artificial Intelligence methods were used to cluster participants. Findings demonstrated that there was varied knowledge of the symptoms of COVID-19 and high concern about infection, severe illness and death, spread to others, the impact on the health service and on the economy. Higher concerns about infection, illness and death were reported by people identified at high risk of severe illness from COVID-19. Behavioural clusters, identified using Artificial Intelligence methods, differed significantly in sentiment and personality traits, as well as concerns about COVID-19, actions, lifestyle behaviours and wellbeing during the COVID-19 lockdown. This time-sensitive study provides important insights into adults' perceptions and behaviours in response to the COVID-19 pandemic and associated lockdown. The use of Artificial Intelligence has identified that there are two behavioural clusters that can predict people's responses during the COVID-19 pandemic, which goes beyond simple demographic groupings. Considering these insights may improve the effectiveness of communication, actions to reduce the direct and indirect impact of the COVID-19 pandemic and to support community recovery.

Identifiants

pubmed: 33472547
doi: 10.1177/1757913920979332
pmc: PMC9047094
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

167-174

Références

Health Qual Life Outcomes. 2007 Nov 27;5:63
pubmed: 18042300
Psychol Sci. 2012 Jun;23(6):578-81
pubmed: 22547658
BMJ. 2020 May 22;369:m1985
pubmed: 32444460

Auteurs

S W Flint (SW)

School of Psychology, University of Leeds, Leeds LS2 9JT, UK.
Scaled Insights, Nexus, University of Leeds, Leeds, UK.

A Piotrkowicz (A)

Scaled Insights, Nexus, University of Leeds, Leeds, UK.
School of Computing, University of Leeds, Leeds, UK.

K Watts (K)

Department of Mathematical Sciences, United States Military Academy West Point, West Point, NY, USA.

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Classifications MeSH