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Abstract
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BACKGROUND: Sleep problems are highly prevalent during COVID-19 pandemic. However, only very limited studies have examined the changing patterns of insomnia symptom before and during the COVID-19 pandemic, and most of these studies were limited to two-wave designs and the variable-centered approach.
METHODS: The data was taken from a large-scale health-related cohort among Chinese college students. This cohort was a five-wave design and 3834 participants who completed at least two waves were included in the present study. Growth mixture modeling (GMM) was used to estimate trajectory classes for insomnia symptoms, followed by binary logistic regression to explore the association between trajectory classes and subsequent mental health problems.
RESULTS: GMM analyses extracted four distinct trajectories of insomnia symptoms: stable-low pattern (n = 2897, 75.6 %), increasing pattern (n = 405, 10.6 %), decreasing pattern (n = 182, 4.7 %), and stable-high pattern (n = 350, 9.1 %). Additionally, we found that individuals in stable-high and increasing patterns were more likely to experience mental health problems after the COVID-19 pandemic even after adjusting significant covariates and outcomes at baseline.
CONCLUSIONS: Appreciable heterogeneity in insomnia symptoms among college students was revealed before and during the COVID-19 pandemic. About 20 % of college students were classified as high-risk patterns of insomnia symptoms. Psychological interventions should target such vulnerable groups to reduce the rates of mental health problems.
Language: en |