A Novel Unsupervised Machine Learning Approach to Assess Postural Dynamics in Euthymic Bipolar Disorder.

ABSTRACT: Bipolar disorder (BD) is a mood disorder with different phases alternating between euthymia, manic or hypomanic episodes, and depressive episodes. While motor abnormalities are commonly seen during depressive or manic episodes, not much attention has been paid to postural abnormalities during periods of euthymia and their association with illness burden. We collected 24-hour posture data in 32 euthymic participants diagnosed with BD using a shirt-based wearable. We extracted a set of nine time-domain features, and performed unsupervised participant clustering. We investigated the association between posture variables and 12 clinical characteristics of illness burden. Based on their postural dynamics during the daytime, evening, or nighttime, participants clustered in three clusters. Higher

SUBMITTER: Halabi R 

PROVIDER: S-EPMC11303098 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature