Evidence map›Paper›PMID 41145890›Full record

ArticleInternational journal of bipolar disorders2025

Circadian rhythm parameters differentiate euthymic, manic and depressive mood states in bipolar disorders - an explorative pilot study.

J Clemens, E Mühlbauer, I Reinhard, M Bauer, A B Neubauer, P Ritter, V M Ludwig, W E Severus, U W Ebner-Priemer, S E Schmitz

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In one paragraph

Article in International journal of bipolar disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

J ClemensDepartment of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Carl Gustav Carus, Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany. Julia.Clemens@ukdd.de.
E MühlbauerDepartment of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Carl Gustav Carus, Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany.
I ReinhardDepartment of Biostatistics, Medical Faculty Mannheim, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany.
M BauerDepartment of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Carl Gustav Carus, Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany.
A B NeubauerInstitute of Psychology, RWTH Aachen University, Aachen, Germany.
P RitterDepartment of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Carl Gustav Carus, Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany.
V M LudwigDepartment of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Carl Gustav Carus, Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany.
W E SeverusDepartment of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Carl Gustav Carus, Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany.
U W Ebner-PriemerMental mHealth Lab, Institute of Sport and Sport Sciences, Karlsruhe Institute of Technology, Karlsruhe, Germany.
S E SchmitzMental mHealth Lab, Institute of Sport and Sport Sciences, Karlsruhe Institute of Technology, Karlsruhe, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBipolar disorders (BD) pose significant therapeutic health challenges due to recurrent and largely unpredictable depressive and (hypo)manic episodes. Traditional self-report methods for symptom monitoring are limited by their dependence on patient adherence which is frequently diminished during symptomatic phases. Circadian movement patterns, measured via actigraphy, have emerged as promising digital biomarkers for distinguishing mood states in BD. This study examined the utility of circadian rhythm parameters in differentiating euthymic, depressive, and (hypo)manic states.

methodsThis study analyzed data from 27 BD patients (mean age = 46 years, 16 female) monitored over 12 months as part of the BipoSense project. Wrist-worn accelerometers continuously recorded physical activity, while mood state was assessed using daily self-reports and biweekly expert evaluations. Circadian rhythm parameters included interdaily stability (IS), intradaily variability (IV), mean activity difference (MeanDiff), and circadian form difference (FormDiff). IS and IV reflect rhythm stability and fragmentation, while MeanDiff and FormDiff quantify overall activity and deviations in circadian rhythm form. Multilevel models were used to predict categorical mood states (depressive, (hypo)manic, euthymic) and dimensional symptom severity.

resultsPhysical activity data from 23 patients yielded 2,669 valid days for analysis. In multilevel logistic models, lower MeanDiff (B = -.02, P < .001), reflecting reduced overall activity, lower IS (B = -.80, P = .009), indicating less stable circadian rhythms, and higher FormDiff (B = .03, P < .001), denoting a more rigid circadian activity pattern, were significantly associated with increased odds of depressive days compared to euthymic days. Conversely, higher MeanDiff (B = .02, P = .007) was linked to higher odds of (hypo)manic days. Dimensional linear mixed models showed a similar pattern: lower MeanDiff (β = -.11, P < .001), IS (β = -.06, P = .001), and IV (β = -.06, P = .002), together with higher FormDiff (β = .10, P < .001), predicted increased depressive symptom levels. Conversely, higher MeanDiff (β = .10, P < .001), IS (β = .04, P = .024), IV (β = .07, P < .001), and lower FormDiff (β = -.07, P = .001) were associated with heightened (hypo)manic symptoms.

conclusionsCircadian rhythm parameters can effectively differentiate mood states in BD, highlighting their potential as clinical markers for episode transitions. Although the study was explorative by nature, the findings emphasize the potential value of integrating circadian biomarkers into digital phenotyping for mood state monitoring. Future studies should explore extended monitoring periods, larger samples, and real-time feedback systems to improve early intervention and personalized treatment strategies in BD.

Indexed as

ActigraphyBipolar disordersCircadian rhythmEarly warning signsEcological momentary assessmentPhysical activity

Identifiers

PMID41145890
PMCPMC12559485

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.