Abstract for: Exploring the Dynamics of Sleep and Depressive Symptoms in Young Adults

Rates of sleep disturbances and depressive symptoms have been steadily rising, with young adults showing particularly elevated risk. These issues are often interlinked, forming a feedback loop where poor sleep can exacerbate depressive symptoms, which in turn can disrupt sleep. System dynamics modeling is well-suited to capturing such complexity by providing a holistic view of how different biopsychosocial factors and their feedback loops contribute to this vicious cycle. We first develop a causal loop diagram outlining key interconnected variables influencing sleep and mental health in young adults. We then converted this diagram into an exploratory system dynamics model, allowing us to simulate the impacts of hypothetical interventions while accounting for the uncertainty of not yet having any empirical data available for parameter calibration. The model provides preliminary insight into potential leverage points for breaking the sleep-mental health cycle and highlights the types of data needed to calibrate the model and reduce uncertainty sufficiently to identify such leverage points with more precision. Future work will focus on developing a fully data-driven, calibrated SDM to optimize intervention strategies and advance a systems-based approach to tackling the rising burden of mental ill health in young adults. We used natural language processing and ChatGPT to analyze questionnaires