Wearable-derived heart rate variability and sleep monitoring as predictors of mood episodes in bipolar disorder: a case report.

BACKGROUND: Bipolar disorder is a chronic psychiatric condition characterized by alternating episodes of mania and depression, and the prediction and management of mood episodes remain significant clinical challenges. Traditional assessments of mood states have largely relied on subjective methods, such as clinical interviews and self-report questionnaires, which present limitations in terms of early detection and timely intervention. Recently, physiological and behavioral data obtained from wearable devices-particularly heart rate variability (HRV) and sleep parameters-have been proposed as potential digital biomarkers, offering novel opportunities for objective clinical evaluation.

CASE PRESENTATION: We conducted a single-case study involving a man in his 40s diagnosed with bipolar disorder, who continuously recorded HRV and sleep parameters using a wearable device over approximately eight months. These data were analyzed in relation to self-reported mood scores. The findings revealed that reductions in nocturnal RMSSD preceded the onset of depressive symptoms, while decreases in time spent in bed were significantly associated with the exacerbation of manic symptoms. In contrast, no clear associations were observed between daytime HRV or activity measures and mood scores.

CONCLUSION: This case study suggests that continuous monitoring of objective physiological measures, such as HRV and sleep parameters, may serve as useful digital biomarkers for predicting mood episodes and preventing relapse in bipolar disorder. Future research involving larger samples and the development of predictive models will be essential to advance the clinical application of these novel assessment approaches.