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. 2025 Dec 15:391:119972.
doi: 10.1016/j.jad.2025.119972. Epub 2025 Jul 28.

Early warning signals of bipolar relapse: Investigating critical slowing down in smartphone data

Vera M Ludwig  1 , Carl A Bittendorf  2 , Iris Reinhard  3 , Andreas B Neubauer  4 , Eva Mennigen  5 , Esther Mühlbauer  5 , Wolfram E Severus  6 , Michael Bauer  5 , Ulrich W Ebner-Priemer  7
Affiliations
Free article

Early warning signals of bipolar relapse: Investigating critical slowing down in smartphone data

Vera M Ludwig et al. J Affect Disord. .
Free article

Abstract

Background: Early warning signals (EWS) based on dynamical systems theory, such as increased autocorrelation (AR) and variance, may indicate impending mood episodes in bipolar disorder (BD). This study examines whether smartphone-based digital phenotyping can detect these signals before depressive and (hypo)manic episodes.

Methods: We analyzed smartphone sensor and self-report data from 29 BD patients (16 female, mean age of 43,97 ± 11.9 years) over one year, totaling 10,587 patient days and including 30 depressive and 20 (hypo)manic episodes in 22 patients. 26 bi-weekly expert interviews per patient established daily disease status. AR, variance and moving averages were calculated from passively assessed activity, communication, and smartphone-usage, as well as self-reported sleep parameters. Multilevel logit models assessed whether these measures could predict pre-episode weeks of depression or mania compared to euthymic days. Receiver operating characteristics (ROC) curves evaluated clinical utility.

Results: Several parameters significantly predicted pre-episode weeks, but no single robust predictor emerged. Manic episodes were best predicted by altered AR and variance in activity-related measures, while sleep parameters predicted both manic and depressive transitions. Latent factors combining multiple parameters showed stronger predictive potential than individual variables. However, ROC analyses revealed that even the best predictors did not meet predefined clinical utility thresholds.

Conclusions: Smartphone-based digital phenotyping holds promise for early detection of BD mood episodes. However, predictive accuracy remains below clinically useful levels. Future research should refine parameters, explore machine-learning approaches, and optimize analytical frameworks to realize the full potential of relapse prediction in BD.

Keywords: Bipolar disorder; Critical slowing down; Digital phenotyping; Early warning signals; Passive sensing; Prediction.

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Conflict of interest statement

Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Michael Bauer reports financial support was provided by German Research Foundation. Michael Bauer reports a relationship with Alfred E Tiefenbacher GmbH und Co KG that includes: consulting or advisory. Michael Bauer reports a relationship with Compass Pathfinder Limited that includes: consulting or advisory. Michael Bauer reports a relationship with GH Research that includes: consulting or advisory. Michael Bauer reports a relationship with MedLink International Inc. that includes: consulting or advisory. Michael Bauer reports a relationship with Janssen Global Services LLC that includes: consulting or advisory. Michael Bauer reports a relationship with LivaNova USA, Inc. that includes: consulting or advisory. Michael Bauer reports a relationship with Novartis Pharma Switzerland that includes: consulting or advisory. Michael Bauer reports a relationship with Sunovion Respiratory Development Inc. that includes: consulting or advisory. Michael Bauer reports a relationship with MedTriX GmbH that includes: speaking and lecture fees. Michael Bauer reports a relationship with Streamedup GmbH that includes: speaking and lecture fees. Ulrich Ebner-Priemer reports a relationship with Boehringer Ingelheim GmbH that includes: consulting or advisory. Ulrich Ebner-Priemer reports a relationship with Angelini Pharma Germany GmbH that includes: speaking and lecture fees. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. MB has received competitive grant support by Deutsche Forschungsgemeinschaft (DFG), Bundesministerium für Bildung und Forschung (BMBF), European Commission, Sächsische Aufbaubank (SAB), and served as an advisor to Alfred E. Tiefenbacher GmbH Co. KG, COMPASS Pathfinder Ltd., GH Research, MedEd-Link Inc. Janssen Global Services, LLC, Livanova, Novartis Switzerland, Sunovion, and has received lecture fees from MedTrix GmbH, Streamedup GmbH. UE-P reports consultancy for Boehringer-Ingelheim and speaker honorarium from Angelini Pharma. The author asserts that the mentioned companies had no influence over the content of this article. VML, CAB, IR, ABN, EM, EM, and WES report no potential conflict of interest.