Evidence map›Paper›PMID 42440526›Full record

ArticleNeuroscience applied2026

Investigating behavioral inertia in passively sensed smartphone parameters to differentiate affective episodes in patients with bipolar disorder.

E M Langner, C Bittendorf, E Mühlbauer, W E Severus, M Bauer, A J Cleare, A Lasch, J Martini, A Pfennig, U W Ebner-Priemer and 1 more

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Article in Neuroscience applied, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

E M LangnerDepartment of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Faculty of Medicine, Dresden University of Technology, Dresden, Germany.
C BittendorfMental mHealth Lab, Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany.
E MühlbauerDepartment of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Faculty of Medicine, Dresden University of Technology, Dresden, Germany.
W E SeverusAsklepios Klinik Nord-Ochsenzoll, Hamburg, Germany.
M BauerDepartment of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Faculty of Medicine, Dresden University of Technology, Dresden, Germany.
A J CleareDepartment of Psychological Medicine, Centre for Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
A LaschDepartment of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Faculty of Medicine, Dresden University of Technology, Dresden, Germany.
J MartiniDepartment of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Faculty of Medicine, Dresden University of Technology, Dresden, Germany.
A PfennigDepartment of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Faculty of Medicine, Dresden University of Technology, Dresden, Germany.
U W Ebner-PriemerMental mHealth Lab, Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany.
V M LudwigDepartment of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Faculty of Medicine, Dresden University of Technology, Dresden, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bipolar disorders are severe, recurrent mental illnesses characterized by distinct mood states associated with different behavioral patterns. While (emotional) inertia, the tendency of a system to persist or change, has been linked to depressive episodes, research on its role in hypomanic or manic states remains sparse. Moreover, behavioral inertia, assessed via passive sensing using smartphone parameters is equally underexplored. This study investigated the temporal dynamics during (hypo-)manic and depressive episodes in passively sensed data in patients with bipolar disorder, complementing previous research on depression and using autocorrelation (AR) and moving averages (Mov.AVG) as dynamic measures. Data were drawn from the BipoSense study (N = 29), with 10 587 observed patient days including 20 (hypo)manic and 30 depressive episodes. Multilevel logit models were used to analyse AR and Mov.AVG across several parameters of activity, communication, sleep and phone use. During depressive episodes, AR was significantly increased in activity and communication domains, particularly in later depressive weeks, reflecting heightened behavioural inertia. In contrast, Mov.AVG showed consistent decreases across activity, communication and sleep domains. For (hypo-)manic episodes, AR patterns were more heterogenous (both, elevated and decreased values), suggesting inconsistent temporal dynamics. However, Mov.AVG revealed a coherent pattern of increased activity, communication and phone use and reduced sleep, aligning with the clinical picture of (hypo-)mania. Concomitant analyses revealed that AR and Mov.AVG seem to explain different variance in the dynamic of patients experiencing bipolar episodes. This study provides the first insight of behavioural inertia in patients with BD using passive sensing smartphone parameters. It underscores the importance of future research investigating behavioral inertia and especially the dynamics during (hypo-)manic episodes on larger cohorts and longer observation periods.

Indexed as

AutocorrelationBehavioral inertiaBipolar disorderDepressive episodesManic episodesPassive sensing

Identifiers

PMID42440526
PMCPMC13333381

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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.