Objectively-Derived Biorhythm Measures for a Staging Model: Towards Research Exactness and Appropriate Treatments in Bipolar Disorder (TREAT-BD)

Increasing evidence supports bipolar disorders as a neuroprogessive disorder, from at-risk to severe and refractory presentations, suggesting that longer illness durations are accompanied by more severe neuropathology. Sleep and circadian rhythm disturbances are highly prevalent in bipolar disorders and are associated with adverse effects on symptom burden and suicidality, treatment outcomes, and overall functioning, suggesting that an internal dysregulation of biological rhythms may be driving the illness course. While advances have been made in understanding illness progression in bipolar disorders, including identification of certain brain and inflammatory markers, the neurobiological mechanisms of bipolar disorders remain unclear and difficult, if not impossible, to access in routine clinical care. The current project thus proposes longitudinal assessment of sleep variables that are both objectively derived and, most importantly, clinically accessible. Actigraphy and smart garments will be used as complimentary tools to characterize sleep architecture at three 6-month intervals over 1 year, concurrently with symptom and functioning assessments. Changes in clinical and treatment history will be assessed every 3 months. Given the persistence and evolution of dysregulated sleep-wake patterns in bipolar disorders, understanding the association between disrupted sleep and the clinical course of the illness will contribute toward developing predictive clinical staging models in bipolar disorders. This project will provide practical insight into potential prognostic factors that could inform treatment outcomes, effects on functioning/adaptation, frequency of relapses, treatment-complicating comorbidities, and suicidal risk. Further, this project will highlight innovative tools that can be readily used by clinicians to assess quantifiable and objective markers of sleep and ultimately help classify patients' illness stage on a continuum of disorder progression.