BACKGROUND: Bipolar disorder (BD) is associated with premature death and ischemic heart disease is the main cause of excess mortality. Heart rate variability (HRV) predicts mortality in patients with or without cardiovascular disease. While several studies have analyzed the association between HRV and BD, none has analyzed the association of HRV with illness burden in BD. METHODS: 53 participants with BD I and II used a wearable device to assess the association between HRV and factors characterizing illness burden, including illness duration, number and type of previous episode(s), duration of the most severe episode, history of suicide attempts or psychotic symptoms during episodes, and co-morbid psychiatric disorders. We ran unadjusted models and models controlling statistically for age, sex, pharmacotherapy, baseline functional cardiovascular capacity, BMI, years of education, and marital status. We also explored the association between HRV and an overall illness burden index (IBI) integrating all these factors using a weighted geometric mean. RESULTS: Adjusted and unadjusted models had similar results. Longer illness duration, higher number of depressive episodes, longer duration of most severe manic/hypomanic episode, co-morbid anxiety disorders, and family history of suicide were associated with reduced HRV, as was bipolar depression severity in the participants experiencing a depressive episode. Finally, a higher IBI score was associated with lower HRV. CONCLUSIONS: High illness burden is associated with reduced HRV in BD. While the IBI needs to be validated in a larger sample, it may provide an overall measure that captures illness burden in BD.

Centre for Addiction and Mental Health Toronto ON Canada

Department of Psychiatry Dalhousie University Halifax NS Canada; National Institute of Mental Health Klecany Czech Republic

Department of Psychiatry University of Toronto Toronto ON Canada; Centre for Addiction and Mental Health Toronto ON Canada

Institute for Mental Health Research The Royal Ottawa Hospital Ottawa ON Canada

ORCA Quantum Computing Toronto ON Canada

References provided by Crossref.org

Identifying patient-specific behaviors to understand illness trajectories and predict relapses in bipolar disorder using passive sensing and deep anomaly detection: protocol for a contactless cohort study