Research has often treated depression as a unitary construct, relying on severity scores or diagnostic thresholds; however, recent studies emphasize that depression is a heterogeneous disorder characterized by dynamic symptom interactions. We aimed to identify unique relations among depressive symptoms when examined longitudinally. We used a 28-day daily diary design in young adults (N = 363). Three symptom networks, estimated from Bayesian structural equation modelling, identified key symptoms that (1) predicted other symptoms within individuals over time (within-subject temporal), (2) co-occurred within the same day (within-subject contemporaneous) and (3) clustered across individuals (between-subject). Results revealed that (1) at the within-subject level, higher levels of sleep disturbance, sad mood, and concentration difficulties predicted higher levels of multiple symptoms the following day, (2) at the within-subject level, sad mood, anhedonia, and fatigue tended to co-occur with many other symptoms and (3) at the between-subject level, individuals with higher levels of anhedonia, anxiety and concentration difficulties tended to experience a broader range of depressive symptoms. These findings underscore the complexity of depressive symptom interactions and highlight potential ways in which depression may manifest. Future research should explore the identified relations to clarify causal relations among symptoms as well as trait-level vulnerability to symptoms.
Quinn et al. (Fri,) studied this question.