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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68407700:21230/22:00342494

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

CNS Spectrums

ISSN

1092-8529

e-ISSN

2165-6509

Svazek periodika

27

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

82-92

Kód UT WoS článku

000754571100012

EID výsledku v databázi Scopus

2-s2.0-85092059678