Neurophysiological determinants of occupational stress and burnout
DOI:
https://doi.org/10.12775/JEHS.2023.21.01.004Keywords
occupational stress, burnout, marker, EEG, MRIAbstract
Introduction
Research 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.
References
Mikołajewska E., Prokopowicz P., Chow Y., Masiak J., Mikołajewski D., Wójcik G.M., Wallace B., Eugene A.R., Olajossy M. From Neuroimaging toComputational Modeling of Burnout: The Traditional versus the Fuzzy Approach - A Review. Appl. Sci. 2022; 12:11524. https://doi.org/10.3390/app122211524
Chow Y., Masiak J., Mikołajewska E., Mikołajewski D., Wójcik G.M., Wallace B., Eugene A., Olajossy M. Limbic brain structures and burnout - A systematic review. Adv. Med. Sci. 2018; 63:192–198.
Luijtelaar G.V., Verbraak M., Bunt M.V., Keijsers G., Arns M. EEG findings in burnout patients. J. Neuropsychiatry Clin. Neurosci. 2010; 22:208–217.
Arnsten, A.F.T., Shanafelt T. Physician distress and burnout: The neurobiological perspective. Mayo Clin. Proc. 2021; 96:763–769.
American Medical Association. International Classification of Diseases. In Clinical Modification(ICD-10-CM) 2022: The Complete Official Codebook with Guidelines, 10th ed.; American Medical Association: Chicago, IL, USA, 2021.
World Health Organization. International Classification of Diseases, 11th ed.; World Health Organization: Geneva, Switzerland, 2022.
American Psychiatric Association. The Diagnostic and Statistical Manual of Mental Disorders, 5th ed.; DSM-5; American Psychiatric Association: Washington, DC, USA, 2013.
Khammissa R.A.G., Nemutandani S., Feller G., Lemmer J., Feller L. Burnout phenomenon: neurophysiological factors, clinical features, and aspects of management. J Int Med Res. 2022; 50(9):3000605221106428. doi: 10.1177/03000605221106428.
Golonka K., Mojsa-Kaja J., Popiel K., Marek T., Gawlowska M. Neurophysiological Markers of Emotion Processing in Burnout Syndrome. Front Psychol. 2017; 8:2155. doi: 10.3389/fpsyg.2017.02155.
Golonka K., Gawlowska M., Mojsa-Kaja J., Marek T. Psychophysiological Characteristics of Burnout Syndrome: Resting-State EEG Analysis. Biomed Res Int. 2019; 2019:3764354. doi: 10.1155/2019/3764354.
Xia L, Mo L, Wang J, Zhang W, Zhang D. Trait Anxiety Attenuates Response Inhibition: Evidence From an ERP Study Using the Go/NoGo Task. Front Behav Neurosci., 2020; 14:28. doi: 10.3389/fnbeh.2020.00028.
Golonka K., Mojsa-Kaja J., Marek T., Gawlowska M. Stimulus, response and feedback processing in burnout–An EEG study. International Journal of Psychophysiology 2018; 134:86-94.
Prokopowicz P., Mikołajewski D., Tyburek K Computational gait analysis for post-stroke rehabilitation purposes using fuzzy numbers, fractal dimension and neural networks. Bulletin of the Polish Academy of Sciences: Technical Sciences 2020; 68(2):191-198.
Duch W., Nowak W., Meller J., Osiński G., Dobosz K., Mikołajewski D., Computational approach to understanding autism spectrum disorders. Computer Science 2012; 13(2):47-47.
Duch W., Mikołajewski D. Brain stem - from general view to computational model based on switchboard rules of operations. Bio-Algorithms and Med-Systems, 2020; 16(1):20190059.
Hu L., Chang T.W., Lee Y.S., Huang C.H. A Moderated Mediation Model of Emotional Engagement in the Development of Emotional Exhaustion: The Moderating Role of Emotional Resources. Front Psychol. 2022; 13:878415. doi: 10.3389/fpsyg.2022.878415.
Wojcik G.M., Masiak J., Kawiak A., Kwasniewicz L., Schneider P., Postepski F., Gajos-Balinska A. Analysis of Decision-Making Process Using Methods of Quantitative Electroencephalography and Machine Learning Tools. Front Neuroinform. 2019;13:73. doi: 10.3389/fninf.2019.00073.
Wojcik G.M., Masiak J., Kawiak A., Kwasniewicz L., Schneider P., Polak N., Gajos-Balinska A. Mapping the Human Brain in Frequency Band Analysis of Brain Cortex Electroencephalographic Activity for Selected Psychiatric Disorders. Front Neuroinform. 2018;12:73. doi: 10.3389/fninf.2018.00073.
Wojcik G.M., Masiak J., Kawiak A., Schneider P., Kwasniewicz L., Polak N., Gajos-Balinska A. New Protocol for Quantitative Analysis of Brain Cortex Electroencephalographic Activity in Patients With Psychiatric Disorders. Front Neuroinform. 2018;12:27. doi: 10.3389/fninf.2018.00027.
Dutra H.S., Guirardello E.B., Li Y., Cimiotti J.P. Nurse burnout revisited: A comparison of computational methods. J. Nurs. Meas. 2019; 27:E17–E33.
Lou S.S., Liu H., Warner B.C., Harford D., Lu C., Kannampallil T. Predicting physician burnout using clinical activity logs: Model performance and lessons learned. J. Biomed. Inform. 2022; 127:104015.
Montoro C.I., Winterholler C., Terrasa J.L., Montoya P. Somatosensory gating is modulated by anodal transcranial direct current stimulation. Front. Neurosci. 2021; 15:651253.
Rosenblum Y., Shiner T., Bregman N., Fahoum F., Giladi N., Maidan I., Mirelman A. Event-related oscillations differentia te between cognitive, motor and visual impairments. J. Neurol. 2022; 269:3529–3540.
Moher D., Liberati A., Tetzlaff J., Altman D.G. The PRISMA Group preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med., 2009; 6:e1000097.
Nishitani N., Nagamine T., Fujiwara N., Yazawa S., Shibasaki H. Cortical-hippocampal auditory processing identified by magnetoencephalography. J. Cogn. Neurosci., 1998; 10:231–247.
Tement S., Pahor A., Jaušovec N. EEG alpha frequency correlates of burnout and depression: The role of gender. Biol. Psychol. 2016; 114:1–12.
Laughey W.F., Atkinson J., Craig A.M., Douglas L., Brown M.E., Scott J.L., Alberti, H., Finn G.M. Empathy in medical education: Its nature and nurture—A qualitative study of the views of students and tutors. Med. Sci. Educ., 2021; 31:1941–1950.
Lu L., Ko Y.M., Chen H.Y., Chueh J.W., Chen P.Y., Cooper C.L. Patient safety and staff well-being: Organizational culture as a resource. Int. J. Environ. Res. Public Health, 2022; 19:3722.
Duma G.M., Di Bono M.G., Mento G. Grounding adaptive cognitive control in the intrinsic, functional brain organization: An HD-EEG resting state investigation. Brain Sci., 2021; 11:1513.
Krebs C., Akesson E.J., Weinberg J. Lippincott’s Illustrated Review of Neuroscience; Wolters Kluwer/Lippincott Williams & Wilkins Health: Amsterdam, The Netherlands, 2012.
Cannard C., Wahbeh H., Delorme A. Electroencephalography correlates of well-being using a low-cost wearable system. Front. Hum. Neurosci., 2021; 15:745135.
Millard S.K., Bokelmann K., Schalbroeck R., van der Wee N.J.A., van Loey N.E.E., van Laarhoven A.I.M. No indications for altered EEG oscillatory activity in patients with chronic post-burn itch compared to healthy controls. Sci. Rep., 2022; 12:5184.
Maksoud R., Eaton-Fitch N., Matula M., Cabanas H., Staines D., Marshall-Gradisnik S. Systematic review of sleep characteristics in myalgic encephalomyelitis/chronic fatigue syndrome. Healthcare, 2021; 9:568.
Hong Q.N., Coutu M.F., Berbiche D. Evaluating the validity of the Work Role Functioning Questionnaire (Canadian French version) using classical test theory and item response theory. Work, 2017; 57:501–515.
Wojcik G.M., Masiak J., Kawiak A., Kwaśniewicz L., Schneider P., Postępski F., Gajos-Balinska A. Analysis of Decision-Making Process Using Methods of Quantitative Electroencephalography and Machine Learning Tools. Front Neuroinform. 2019;13:73. doi: 10.3389/fninf.2019.00073.
Golonka K., Gawlowska M., Mojsa-Kaja J., Marek T. Psychophysiological Characteristics of Burnout Syndrome: Resting-State EEG Analysis. Biomed Res Int., 2019; 2019:3764354. doi: 10.1155/2019/3764354.
Høyland AL, Øgrim G, Lydersen S, Hope S, Engstrøm M, Torske T, Nærland T, Andreassen OA. Event-Related Potentials in a Cued Go-NoGo Task Associated with Executive Functions in Adolescents with Autism Spectrum Disorder; A Case-Control Study. Front Neurosci., 2017; 11:393. doi: 10.3389/fnins.2017.00393.
Falkenstein M., Hoormann J., Hohnsbein J. ERP components in Go/Nogo tasks and their relation to inhibition. Acta Psychol (Amst). 1999; 101(2-3):267-91. doi: 10.1016/s0001-6918(99)00008-6.
Jovanovic, H.; Perski, A.; Berglund, H.; Savic, I. Chronic stress is linked to 5-HT1A receptor changes and functional disintegration of the limbic networks. NeuroImage, 2011; 55:1178–1188.
Durning S.J., Costanzo M., Artino A.R., Dyrbye L.N., Beckman T.J., Schuwirth L., Holmboe E., Roy M.J., Wittich C.M., Lipner R.S. Functional neuroimaging correlates of burnout among internal medicine residents and faculty members. Front. Psychiatry, 2013; 4:131.
Savic I. Structural changes of the brain in relation to occupational stress. Cereb Cortex, 2013; 25:1554–1564.
Blix E., Perski A., Berglund H., Savic I. Long-term occupational stress is associated with regional reductions in brain tissue volumes. PLoS ONE, 2013; 8:e64065.
Tei S., Becker C., Kawada R., Fujino J., Jankowski K.F., Sugihara G., Murai T., Takahashi H. Can we predict burnout severityfrom empathy-related brain activity? Transl. Psychiatry, 2014; 4:e393.
Gavelin H.M., Neely A.S., Andersson M., Eskilsson, T., Järvholm L.S., Boraxbekk C.J. Neural activation in stress-related exhaustion: Cross-sectional observations and interventional effects. Psychiatry Res. Neuroimaging, 2017; 269:17–25.
Savic I., Perski A., Osika W. MRI shows that exhaustion syndrome due to chronic occupational stress is associated with partially reversible cerebral changes. Cereb Cortex, 2018; 28:894–906.
Roberts, B.L.; Karatsoreos, I.N. Brain-body responses to chronic stress: A brief review. Fac. Rev., 2021; 10:83.
Gavelin H.M., Neely A.S., Dunås T., Eskilsson T., Järvholm L.S., Boraxbekk C.J. Mental fatigue in stress-related exhaustion disorder: Structural brain correlates, clinical characteristics and relations with cognitive functioning. Neuroimage Clin., 2020; 27:102337.
Prokopowicz P., Mikołajewski D. Fuzzy-based computational simulations of brain functions—Preliminary concept. Bio-Algorithms Med. Syst., 2016; 12:99–104.
Mercer Lindsay N., Chen C., Gilam G., Mackey S., Scherrer G. Brain circuits for pain and its treatment. Sci. Transl. Med., 2021; 13:eabj7360.
De Hert S. Burnout in healthcare workers: Prevalence, impact and preventative strategies. Local Reg. Anesthesia, 2020; 13:171–183.
Weisbaum E., Chadi N. Applied mindfulness for physician wellbeing: A prospective qualitative study protocol. Front. Public Health, 2022; 10:807792.
Laughey W.F., Atkinson J., Craig A.M., Douglas L., Brown M.E., Scott J.L., Alberti H., Finn G.M. Empathy in medical education: Its nature and nurture—A qualitative study of the views of students and tutors. Med. Sci. Educ., 2021; 31:1941–1950.
Paiva-Salisbury M.L., Schwanz K.A. Building compassion fatigue resilience: Awareness, prevention, and intervention for pre-professionals and current practitioners. J. Health Serv. Psychol., 2022; 3:39–46.
Ciobanu A.M., Damian A.C., Neagu C. Association between burnout and immunological and endocrine alterations. ROM J. Morphol. Embryol., 2021; 62:13–18.
Buselli R., Veltri A., Baldanzi S., Marino R., Bonotti A., Chiumiento M., Girardi M., Pellegrini L., Guglielmi G., Dell’Osso L. Plasma Brain-Derived Neurotrophic Factor (BDNF) and serum cortisol levels in a sample of workers exposed to occupational stress and suffering from adjustment disorders. Brain Behav., 2019; 9:e01298.
de Beer L.T. Is there utility in specifying professional efficacy as an outcome of burnout in the employee health impairment process. Int. J. Environ. Res. Public Health, 2021; 18:6255.
Hoogduin C.A.L., Schaap C.P.D.R., Methorst G.J. Burnout: Klinisch Beeld en Diagnostiek. In Behandelingsstrategieën in Bij Burnout, 2nd ed.; Hoogduin, C.A.L., Schaap, C.P.D.R., Kladler, A.J., Eds.; Houten/Diegem, Bohn Stafleu van Loghum: Houten, The Netherlands, 2001.
Mikołajewski D., Masiak J., Mikołajewska E., Wójcik G.M., Kopowski J. Work-related adverse health changes in the IT occupational group - a narrative review of the literature. Studia i Materiały Informatyki Stosowanej, 2021; 13(3):13-21.
Mikołajewski D., Masiak J., Mikołajewska E., Wójcik G.M. Symptoms of burnout as the basis of
a computational model. Studia i Materiały Informatyki Stosowanej, 2021; 13(3):22-28.
Mikołajewski D., Masiak J., Mikołajewska E., Wójcik G.M. Computational modelling of the prevalence of burnout symptoms in computer scientists and physiotherapists - preliminary results. Studia i Materiały Informatyki Stosowanej, 2021; 13(3):29-35.
Prokopowicz P., Mikołajewski D., Tyburek K., Kotlarz P. Fuzzy-based description.of computation - complexity of central nervous system. Journal of Telecommunications and Information Technology, 2020; 3: 5-66.
Mikołajewski D., Masiak J., Mikołajewska E., Wójcik G.M. Multi-criteria analysis of burnout scores. Studia i Materiały Informatyki Stosowanej, 2022; 14(3):32-37.
Rajkumar R.P. Harnessing the neurobiology of resilience to protect the mental well-being of healthcare workers during the COVID-19 pandemic. Front. Psychol., 2021; 12:621853.
Schaufeli W.B., Maslach C., Marek M. The future of burnout. In: Professional burnout. Routledge 2017, pp. 253-259.
Marek T. Professional Burnout: Recent Developments in Theory and Research: Recent Developments In Theory And Research. CRC Press 2018.
Golonka K., Mojsa-Kaja J., Blukacz M., Gawłowska M., Marek T. Occupational burnout and its overlapping effect with depression and anxiety. Int J Occup Med Environ Health 2019; 32(2):229-244. doi: 10.13075/ijomeh.1896.01323.
Mikołajewska, E; Mikołajewski, D Integrated IT environment for people with disabilities: a new concept. Open Medicine, 2014; 9 (1):177-182. doi.org/10.2478/s11536-013-0254-6.
Prokopowicz, P., Mikołajewski, D., Mikołajewska, E., Kotlarz, P. Fuzzy system as an assessment tool for analysis of the health-related quality of life for the people after stroke. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, 10245 LNAI, pp. 710–721.
Rojek I, Kozielski M, Dorożyński J, Mikołajewski D. AI-Based Prediction of Myocardial Infarction Risk as an Element of Preventive Medicine. Applied Sciences, 2022; 12(19):9596. https://doi.org/10.3390/app12199596.
Prokopowicz P., Mikołajewski D., Tyburek K., Mikołajewska E. Computational gait analysis for post-stroke rehabilitation purposes using fuzzy numbers, fractal dimension and neural networks. Bulletin of the Polish Academy of Sciences: Technical Sciences, 2020; 68(2):191-198. doi: 10.24425/bpasts.2020.131843.
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