Complex temporal dynamics of mental health indicators: A longitudinal network approach perspective.

Abstract

Background: Although mental disorders have long been considered complex dynamic systems, our understanding of the mutual interactions and temporal patterns of their symptoms remains limited.

Methods: In this longitudinal study, we examined the structure and dynamics of four key mental health indicators - depression, anxiety, post-traumatic stress disorder, and insomnia - in a representative sample of the Slovak population (effective N = 3,874) over 10 waves spanning 3.5 years. For each construct, a longitudinal panel network model was estimated.

Results: The temporal relationships between symptoms were mostly weak, with the autoregressive effects typically being stronger. In depression, anxiety, and insomnia, some causal chains and feedback loops were identified. In all constructs, both contemporaneous and between-person networks showed dense connections.

Conclusions: The findings provide critical insights into the complexity of mental health development, offering potential targets for intervention and prevention strategies.