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Psychophysiological Characteristics of Burnout Syndrome: Resting-State EEG Analysis
INTRODUCTION: The consequences of chronic work-related stress are related to various emotional, cognitive, and behavioral symptoms. Occupational burnout as a complex syndrome is characterized by exhaustion, cynicism, and lower professional efficacy. Moreover, the growing amount of research on the ne...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6701350/ https://www.ncbi.nlm.nih.gov/pubmed/31467886 http://dx.doi.org/10.1155/2019/3764354 |
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author | Golonka, Krystyna Gawlowska, Magda Mojsa-Kaja, Justyna Marek, Tadeusz |
author_facet | Golonka, Krystyna Gawlowska, Magda Mojsa-Kaja, Justyna Marek, Tadeusz |
author_sort | Golonka, Krystyna |
collection | PubMed |
description | INTRODUCTION: The consequences of chronic work-related stress are related to various emotional, cognitive, and behavioral symptoms. Occupational burnout as a complex syndrome is characterized by exhaustion, cynicism, and lower professional efficacy. Moreover, the growing amount of research on the neural correlates of burnout broadens the existing knowledge on the mechanisms underlying this syndrome. AIM OF THE STUDY: The aim of the study is to explore possible differences in brain activity between burnout and nonburnout employees. Frequency-specific EEG power analyses in a resting-state condition in burnout subjects and controls are presented. MATERIALS AND METHODS: Burnout employees (N=46; 19 men) were matched with the control group (N=49; 19 men; mean age: 36.14 years, SD=7.89). The Maslach Burnout Inventory–General Survey (MBI-GS) and the Areas of Worklife Survey (AWS) scale were used to measure burnout symptoms and work conditions, respectively. A 256-channel EEG (EGI System 300) was used to collect psychophysiological data. A repeated measures ANOVA was performed with condition (eyes-open vs. eyes-closed) and region (6 levels: extracted scalp regions) factors; burnout (2 levels: burnout vs. no burnout) was the grouping factor. RESULTS: A significant difference was observed only in the alpha frequency band: the burnout group revealed significantly lower alpha power in the eyes-open condition compared to the controls (p<0.05). The correlation analysis revealed that gender may significantly change the pattern of relations between EEG spectral characteristics and burnout symptoms. CONCLUSIONS: Reduced alpha power in burnout individuals suggests cortical hyperactivity and may be related to greater mental effort and the possible development of compensatory mechanisms by burnout subjects. |
format | Online Article Text |
id | pubmed-6701350 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-67013502019-08-29 Psychophysiological Characteristics of Burnout Syndrome: Resting-State EEG Analysis Golonka, Krystyna Gawlowska, Magda Mojsa-Kaja, Justyna Marek, Tadeusz Biomed Res Int Research Article INTRODUCTION: The consequences of chronic work-related stress are related to various emotional, cognitive, and behavioral symptoms. Occupational burnout as a complex syndrome is characterized by exhaustion, cynicism, and lower professional efficacy. Moreover, the growing amount of research on the neural correlates of burnout broadens the existing knowledge on the mechanisms underlying this syndrome. AIM OF THE STUDY: The aim of the study is to explore possible differences in brain activity between burnout and nonburnout employees. Frequency-specific EEG power analyses in a resting-state condition in burnout subjects and controls are presented. MATERIALS AND METHODS: Burnout employees (N=46; 19 men) were matched with the control group (N=49; 19 men; mean age: 36.14 years, SD=7.89). The Maslach Burnout Inventory–General Survey (MBI-GS) and the Areas of Worklife Survey (AWS) scale were used to measure burnout symptoms and work conditions, respectively. A 256-channel EEG (EGI System 300) was used to collect psychophysiological data. A repeated measures ANOVA was performed with condition (eyes-open vs. eyes-closed) and region (6 levels: extracted scalp regions) factors; burnout (2 levels: burnout vs. no burnout) was the grouping factor. RESULTS: A significant difference was observed only in the alpha frequency band: the burnout group revealed significantly lower alpha power in the eyes-open condition compared to the controls (p<0.05). The correlation analysis revealed that gender may significantly change the pattern of relations between EEG spectral characteristics and burnout symptoms. CONCLUSIONS: Reduced alpha power in burnout individuals suggests cortical hyperactivity and may be related to greater mental effort and the possible development of compensatory mechanisms by burnout subjects. Hindawi 2019-07-29 /pmc/articles/PMC6701350/ /pubmed/31467886 http://dx.doi.org/10.1155/2019/3764354 Text en Copyright © 2019 Krystyna Golonka et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Golonka, Krystyna Gawlowska, Magda Mojsa-Kaja, Justyna Marek, Tadeusz Psychophysiological Characteristics of Burnout Syndrome: Resting-State EEG Analysis |
title | Psychophysiological Characteristics of Burnout Syndrome: Resting-State EEG Analysis |
title_full | Psychophysiological Characteristics of Burnout Syndrome: Resting-State EEG Analysis |
title_fullStr | Psychophysiological Characteristics of Burnout Syndrome: Resting-State EEG Analysis |
title_full_unstemmed | Psychophysiological Characteristics of Burnout Syndrome: Resting-State EEG Analysis |
title_short | Psychophysiological Characteristics of Burnout Syndrome: Resting-State EEG Analysis |
title_sort | psychophysiological characteristics of burnout syndrome: resting-state eeg analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6701350/ https://www.ncbi.nlm.nih.gov/pubmed/31467886 http://dx.doi.org/10.1155/2019/3764354 |
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