A three-wave network analysis of COVID-19's impact on schizotypal traits, paranoia and mental health through loneliness

DOI

Background The 2019 coronavirus (COVID-19) pandemic has impacted people’s mental wellbeing. Studies to date have examined the prevalence of mental health symptoms (anxiety, depression, loneliness), yet fewer longitudinal studies have compared across background factors and other psychological variables to identify vulnerable sub-groups. This study tests to what extent higher levels of psychotic-like experiences – indexed by schizotypal traits and paranoia – are associated with various mental health variables 6- and 12-months since April 2020. Methods Over 2,300 adult volunteers (18-89 years, female=74.9%) with access to the study link online were recruited from the UK, USA, Greece, and Italy. Self-reported levels of schizotypy, paranoia, anxiety, depression, aggression, loneliness, and stress from three timepoints (17 April to 13 July 2020, N1 =1,599; 17 October to 31 January 2021, N2 =774; and 17 April to 31 July 2021, N3 =586) were mapped using network analysis and compared across time and background variables (sex, age, income, country). Results Schizotypal traits and paranoia were positively associated with poorer mental health through loneliness, with no effect of age, sex, income levels, countries, and timepoints. Loneliness was the most influential variable across all networks, despite overall reductions in levels of loneliness, schizotypy, paranoia, and aggression during the easing of lockdown. Individuals with higher levels of schizotypal traits/paranoia reported poorer mental health outcomes than individuals in the low-trait groups.Conclusion Schizotypal traits and paranoia are associated with poor mental health outcomes through self-perceived loneliness, suggesting that increasing social/community cohesion may improve individuals’ mental wellbeing in the long run. Keywords: Network Analysis; Schizotypy; Anxiety; Depression; Stress; Loneliness; Sleep; COVID-19; Longitudinal; Mental Health.

Identifier
DOI https://doi.org/10.5522/04/16592966.v1
Related Identifier https://ndownloader.figshare.com/files/30717479
Metadata Access https://api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:figshare.com:article/16592966
Provenance
Creator Wong, Keri Ka-Yee ORCID logo; Wang, Yi; Esposito, Gianluca ORCID logo; Raine, Adrian ORCID logo
Publisher University College London UCL
Contributor Figshare
Publication Year 2022
Rights https://creativecommons.org/licenses/by-nc-sa/4.0/
OpenAccess true
Contact researchdatarepository(at)ucl.ac.uk
Representation
Language English
Resource Type Dataset
Discipline Psychology; Social and Behavioural Sciences