Description

• Title:
  Neurophysiological determinants of occupational stress and burnout
• Creator:
  Mikołajewski, Dariusz;
  Masiak, Jolanta;
  Mikołajewska, Emilia
• Description:
  IntroductionResearch results show that one of the greatest health challenges of the 21st century, especially in developed countries, is becoming the fight against the effects of living too fast, including the fight against occupational stress and burnout. Aim of the study The purpose of this article is to elucidate the neurophysiological determinants of occupational stress and burnout, including ocupational, including through the path of research review and the development of computational models based on artificial intelligence. Materials and methods A literature search was conducted in six bibliographic databases: PubMed, EBSCO, PEDro, Web of Science, Scopus and Google Scholar. Articles were searched in English using the following keywords: occupational stress, burnout, marker, electroencephalography, EEG, magnetic resonance imaging, MRI, fMRI, computed tomography, CT, positron emission tomography, PET, computational model, machine learning, artificial intelligence, virtual patient, digital twin and similar. Neurophysiological determinants of occupational stress and burnout as far as computational models of occupational stress and burnout were analysed and discussed. Results The best currently observed neurophysiological markers of occupational stress and burnout may currently be a combination of EEG analysis (alpha power (IAF, PAF), P300, ERP (VPP and EPN)), diagnostic PET imaging (ACC, insular cortex and hippocampus) and monitoring changes in cortisol, prolactin, adrenocorticotropic hormone (ACTH), corticotropin-releasing hormone (CRH) and thyroid hormones, as well as plasma BDNF levels. In addition, ERPs (LPPs) are a marker significantly differentiating burnout from depression. Conclusions The combination of traditional clinimetric tests, the aforementioned neurophysiological tests and AI-based big data analysis will provide new classifiers, highly accurate results and new diagnostic methods.  
• Object availability:
  free access
• Rights: https://creativecommons.org/licenses/by-nc-sa/4.0 ;
  Prawa autorskie (c) 2023 Dariusz Mikołajewski, Jolanta Masiak, Emilia Mikołajewska
• Date:
  2023.04.19
• Type:
  info:eu-repo/semantics/article;
  info:eu-repo/semantics/publishedVersion
• Language:
  English
• Source:
  Journal of Education, Health and Sport; Vol. 21 No. 1 (2023); 33-46;
  Journal of Education, Health and Sport; Tom 21 Nr 1 (2023); 33-46;
  2391-8306
• Format:
  application/pdf
• Publisher:
  Nicolaus Copernicus University, Toruń, Poland
• Relation: https://apcz.umk.pl/JEHS/article/view/43534/35443
• Subject:
  occupational stress;
  burnout;
  marker;
  EEG;
  MRI
• Identifier: https://apcz.umk.pl/JEHS/article/view/43534
• Data provider:
  Akademicka Platforma Czasopism
• Can I use it?:
  seek permission
• Type:
  text