Identifying characteristics of adolescents with persistent loneliness during COVID-19: A multi-country eight-wave longitudinal study.

Journal: JCPP advances

Volume: 4

Issue: 1

Year of Publication: 

Affiliated Institutions:  Department of Psychology King's College London Institute of Psychiatry, Psychology & Neuroscience (IoPPN) London UK. Department of Psychology University of Allahabad Prayagraj India. Department of Psychology University of Bath Bath UK. Department of Psychology Banaras Hindu University Varanasi India. Manchester Institute of Education The University of Manchester School of Environment, Education and Development Manchester UK. Youth Resilience Unit Centre for Psychiatry and Mental Health Wolfson Institute of Population Health, Queen Mary University of London London UK.

Abstract summary 

Elevated loneliness experiences characterise young people. While loneliness at this developmental juncture may emerge from age-typical upheaval in social relationships, there is little data on the extent to which young people experience high and persistent levels of loneliness, and importantly, who is most vulnerable to these experiences. Using the widespread social restrictions associated with the COVID-19 pandemic, which precipitated loneliness in many, we aimed to examine adolescents' loneliness profiles across time and the demographic predictors (age, sex, and country) of more severe trajectories.Participants aged 12-18 years, recruited into a multi-wave study ( = 1039) across three sites (UK, Israel, and India) completed a 3-item loneliness measure fortnightly across 8 timepoints during the pandemic.Latent class growth analysis suggested 5 distinct trajectories: (1) low stable (33%), (2) low increasing (19%), (3) moderate decreasing (17%), (4) moderate stable (23%), and (5) high increasing (8%). Females and older adolescents were more likely to experience persistently high loneliness.These findings indicate a need for interventions to reduce loneliness in adolescents as we emerge from the pandemic, particularly for those groups identified as being at highest risk.

Authors & Co-authors:  Riddleston Shukla Lavi Saglio Fuhrmann Pandey Singh Qualter Lau

Study Outcome 

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Statistics
Citations :  Asparouhov, T. , & Muthén, B. O. (2014). Auxiliary variables in mixture modeling: Three‐step approaches using Mplus. Structural Equation Modeling: A Multidisciplinary Journal, 21(3), 329–341. 10.1080/10705511.2014.915181
Authors :  9
Identifiers
Doi : e12206
SSN : 2692-9384
Study Population
Females
Mesh Terms
Other Terms
COVID‐19 pandemic;adolescent mental health;perceived social isolation;social restrictions;youth loneliness
Study Design
Longitudinal Study,Longitudinal Study,Longitudinal Study,Longitudinal Study,Longitudinal Study,Longitudinal Study
Study Approach
Country of Study
Publication Country
United States