Motor activity patterns can distinguish between interepisode bipolar disorder patients and healthy controls

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F22%3A43920301" target="_blank" >RIV/00023752:_____/22:43920301 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21230/22:00342494

Result on the web

<a href="https://www.cambridge.org/core/journals/cns-spectrums/article/motor-activity-patterns-can-distinguish-between-interepisode-bipolar-disorder-patients-and-healthy-controls/DACA92C20D2D9E4CC7530D174798A6AE" target="_blank" >https://www.cambridge.org/core/journals/cns-spectrums/article/motor-activity-patterns-can-distinguish-between-interepisode-bipolar-disorder-patients-and-healthy-controls/DACA92C20D2D9E4CC7530D174798A6AE</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1017/S1092852920001777" target="_blank" >10.1017/S1092852920001777</a>

Result language

angličtina

Original language name

Motor activity patterns can distinguish between interepisode bipolar disorder patients and healthy controls

Original language description

OBJECTIVE: Bipolar disorder (BD) is linked to circadian rhythm disruptions resulting in aberrant motor activity patterns. We aimed to explore whether motor activity alone, as assessed by longitudinal actigraphy, can be used to classify accurately BD patients and healthy controls into their respective groups.METHODS: Ninety-day actigraphy records from 25 inter-episode BD patients (i.e. Montgomery-Asberg Depression Rating Scale (MADRS) and Young Mania Rating Scale (YMRS) &lt;15) and 25 sex- and age-matched healthy controls (HC), were used in order to identify latent actigraphic biomarkers capable of discriminating between BD patients and HC. Mean values and time-variations of a set of standard actigraphy features were analysed and further validated using the random forest classifier. RESULTS: Using all actigraphy features, this method correctly assigned 88% (sensitivity=85%, specificity=91%) of BD patients and HC to their respective group. The classification success may be confounded by differences in employment between BD patients and HC. When motor activity features resistant to the employment status were used (the strongest feature being time variation of intradaily variability, Cohen’s d=1.33), 79% of the subjects (sensitivity=76%, specificity=81%) were correctly classified.CONCLUSION: A machine learning actigraphy-based model was capable of distinguishing between inter-episode BD patients and HC solely on the basis of motor activity. The classification remained valid even when features influenced by employment status were omitted. The findings suggest that temporal variability of actigraphic parameters may provide discriminative power for differentiating between BD patients and HC while being less affected by employment status.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20601 - Medical engineering

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

CNS Spectrums

ISSN

1092-8529

e-ISSN

2165-6509

Volume of the periodical

27

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

11

Pages from-to

82-92

UT code for WoS article

000754571100012

EID of the result in the Scopus database

2-s2.0-85092059678