Early warning signals observed in motor activity preceding mood state change in bipolar disorder.

Autor: Jakobsen P; Division of Psychiatry, Haukeland University Hospital, Bergen, Norway.; Department of Clinical Medicine, University of Bergen, Bergen, Norway., Côté-Allard U; Department of Technology Systems, University of Oslo, Oslo, Norway., Riegler MA; SimulaMet, Oslo, Norway., Stabell LA; Division of Psychiatry, Haukeland University Hospital, Bergen, Norway.; Department of Clinical Medicine, University of Bergen, Bergen, Norway., Stautland A; Division of Psychiatry, Haukeland University Hospital, Bergen, Norway.; Department of Clinical Medicine, University of Bergen, Bergen, Norway., Nordgreen T; Division of Psychiatry, Haukeland University Hospital, Bergen, Norway.; Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway., Torresen J; Department of Informatics, University of Oslo, Oslo, Norway., Fasmer OB; Department of Clinical Medicine, University of Bergen, Bergen, Norway., Oedegaard KJ; Division of Psychiatry, Haukeland University Hospital, Bergen, Norway.; Department of Clinical Medicine, University of Bergen, Bergen, Norway.
Jazyk: angličtina
Zdroj: Bipolar disorders [Bipolar Disord] 2024 Aug; Vol. 26 (5), pp. 468-478. Date of Electronic Publication: 2024 Apr 19.
DOI: 10.1111/bdi.13430
Abstrakt: Introduction: Alterations in motor activity are well-established symptoms of bipolar disorder, and time series of motor activity can be considered complex dynamical systems. In such systems, early warning signals (EWS) occur in a critical transition period preceding a sudden shift (tipping point) in the system. EWS are statistical observations occurring due to a system's declining ability to maintain homeostasis when approaching a tipping point. The aim was to identify critical transition periods preceding bipolar mood state changes.
Methods: Participants with a validated bipolar diagnosis were included to a one-year follow-up study, with repeated assessments of the participants' mood. Motor activity was recorded continuously by a wrist-worn actigraph. Participants assessed to have relapsed during follow-up were analyzed. Recognized EWS features were extracted from the motor activity data and analyzed by an unsupervised change point detection algorithm, capable of processing multi-dimensional data and developed to identify when the statistical property of a time series changes.
Results: Of 49 participants, four depressive and four hypomanic/manic relapses among six individuals occurred, recording actigraphy for 23.8 ± 0.2 h/day, for 39.8 ± 4.6 days. The algorithm detected change points in the time series and identified critical transition periods spanning 13.5 ± 7.2 days. For depressions 11.4 ± 1.8, and hypomania/mania 15.6 ± 10.2 days.
Conclusion: The change point detection algorithm seems capable of recognizing impending mood episodes in continuous flowing data streams. Hence, we present an innovative method for forecasting approaching relapses to improve the clinical management of bipolar disorder.
(© 2024 The Authors. Bipolar Disorders published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
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