Temporal dynamics in mental health symptoms and loneliness during the COVID-19 pandemic in a longitudinal probability sample: a network analysis.

Figuring out which symptoms are central for symptom escalation during the COVID-19 pandemic is important for targeting prevention and intervention. Previous studies have contributed to the understanding of the course of psychological distress during the pandemic, but less is known about key symptoms of psychological distress over time. Going beyond a pathogenetic pathway perspective, we applied the network approach to psychopathology to examine how psychological distress unfolds in a period of maximum stress (pre-pandemic to pandemic onset) and a period of repeated stress (pandemic peak to pandemic peak). We conducted secondary data analyses with the Understanding Society data (N = 17,761), a longitudinal probability study in the UK with data before (2019), at the onset of (April 2020), and during the COVID-19 pandemic (November 2020 & January 2021). Using the General Health Questionnaire and one loneliness item, we computed three temporal cross-lagged panel network models to analyze psychological distress over time. Specifically, we computed (1) a pre-COVID to first incidence peak network, (2) a first incidence peak to second incidence peak network, and (3) a second incidence peak to third incidence peak network. All networks were highly consistent over time. Loneliness and thinking of self as worthless displayed a high influence on other symptoms. Feeling depressed and not overcoming difficulties had many incoming connections, thus constituting an end-product of symptom cascades. Our findings highlight the importance of loneliness and self-worth for psychological distress during COVID-19, which may have important implications in therapy and prevention. Prevention and intervention measures are discussed, as single session interventions are available that specifically target loneliness and worthlessness to alleviate mental health problems.