Quantitative evaluation of stress in Japanese anesthesiology residents based on heart rate variability and psychological testing

  • Kohshi Hattori
  • Masaaki AsamotoEmail author
  • Mikiya Otsuji
  • Nobuko Ito
  • Satoshi Kasahara
  • Yoko Hashimoto
  • Yoshitsugu Yamada
Original Research


Clinical anesthesiologists, particularly residents, work in stressful environments. However, evidence-based physiological and psychological tests to evaluate stress are still lacking. In this single-center study of 33 residents, we investigated the relationship between heart rate variability (HRV), which had the potential to screen residents’ stress levels using Holter electrocardiography (ECG) and psychological mood as assessed by the Profile of Mood States (POMS) questionnaire. HRV analysis revealed 2 findings. Firstly, standard deviation of the average of 5-min normal-to-normal R–R intervals (SDANN) was significant lower than that of same-aged healthy volunteers (69.3 ± 27.9 vs. 137.0 ± 43.0 ms, P < 0.05), which indicated suppression of autonomic nervous system activity throughout their work. Secondly, at induction of anesthesia, significant higher low frequency/high frequency ratio (LF/HF ratio: 1.326 vs. 0.846; P < 0.05) and lower HF (3326 vs. 5967 ms2; P < 0.05) and lower standard deviation of normal-to-normal R–R intervals (SDNN: 50.5 vs. 79.4 ms; nervous system was suppressed at the induction of anesthesia: expected to be the most stressful period of their work. On the other hand, deviation scores of POMS questionnaire elucidated that all the residents were within normal range of psychological mood, and without any significant diurnal changes with respect to total mood disturbance deviation (TMD) scores (48 vs. 47; P = 0.368). HRV elucidated physiological stress among anesthesiology residents quantitatively by evaluating autonomic nervous activities, especially at induction of anesthesia. These changes in HRV could be observed regardless of psychological mood.


Heart rate variability Autonomic nervous system Psychological mood Perioperative stress monitoring Working environment of anesthesiology residents 





Autonomic nervous system










Fast Fourier transform




High frequency


Heart rate variability


Low frequency


Low frequency/high frequency


Parasympathetic nervous system


Profile of mood states


Standard deviation of the average of 5-min normal-to-normal R-R intervals


Standard deviation of the normal-to-normal R-R intervals


Sympathetic nervous system




Total mood disturbance


Total power





We are grateful to everyone who helped us successfully complete this research study, especially Kanji Uchida of the Department of Anesthesia and Pain Relief Center at The University of Tokyo Hospital, for providing logical and objective guidance for our study and statistical advice, and serving as the chairman of the resident educational working group in our department.

Author contributions

KH contributed to conceptualization, data collection, formal analysis, methodology and preparing the manuscript. MA contributed to conceptualization, data collection and editing the manuscript, MO contributed to statistical support. NI helped data collection and approved the final manuscript. SK attested the psychological mood evaluation. YH helped data collection. YY helped conduct the study as the supervisor and approved the final manuscript.


This study was funded by Grants-in-Aid for Scientific Research (KAKENHI) No. 26670244.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Institutional review board

This study was reviewed and approved by the ethics committee of The University of Tokyo, Faculty of Medicine (Tokyo, Japan, #10699). This study was also registered to University hospital Medical Information Network Clinical Trial Registry (Tokyo, Japan #UMIN000019247).


  1. 1.
    Yasuda N, Shingu C, Miyagawa H, Mori M, Kitano T, Noguchi T. Assessment of anesthesiologist’s stress of working overnight using profile of mood states. Jpn J Anesthesiol. 2008;57(6):764–7.Google Scholar
  2. 2.
    Adnet F, Racine SX, Borron SW, Clemessy JL, Fournier JL, Lapostolle F, et al. A survey of tracheal intubation difficulty in the operating room: a prospective observational study. Acta Anaesthesiol Scand. 2001;45:327–32.CrossRefGoogle Scholar
  3. 3.
    Asai T, Koga K, Vaughan RS. Respiratory complications associated with tracheal intubation and extubation. Br J Anaesth. 1998;80:767–75.CrossRefGoogle Scholar
  4. 4.
    O’Brien IA, O’Hare P, Corrall RJ. Heart rate variability in healthy subjects: effect of age and the derivation of normal ranges for tests of autonomic function. Br Heart J. 1986;55(4):348–54.CrossRefGoogle Scholar
  5. 5.
    Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task force of the European Society of cardiology and the north american society of pacing and electrophysiology. Circulation 93(5):1043–1065; 1996.Google Scholar
  6. 6.
    Yadav RL, Yadav PK, Yadav LK, Agrawal K, Sah SK, Islam MN. Association between obesity and heart rate variability indices: an intuition toward cardiac autonomic alteration: a risk of CVD. Diabetes Metab Syndr Obes. 2017;10:57–64.CrossRefGoogle Scholar
  7. 7.
    Castaldo R, Melillo P, Bracale U, Caserta M, Triassi M, Pecchia L. Acute mental stress assessment via short term HRV analysis in healthy adults: a systematic review with meta-analysis. Biomed Signal Process Control. 2015;18:370–7.CrossRefGoogle Scholar
  8. 8.
    Umetani K, Singer DH, McCraty R, Atkinson M. Twenty-four hour time domain heart rate variability and heart rate: relations to age and gender over nine decades. J Am Coll Cardiol. 1998;31(3):593–601.CrossRefGoogle Scholar
  9. 9.
    Morfeld M, Petersen C, Krüger-Bödeker A, von Mackensen S, Bullinger M. The assessment of mood at workplace: psychometric analyses of the revised profile of mood states (POMS) questionnaire. Psychosoc Med. 2007;4:6.Google Scholar
  10. 10.
    McNair DM, Lorr M, Droppleman LF. Manual for the profile of mood states. San Diego, CA: Educational and Industrial Testing Service; 1971.Google Scholar
  11. 11.
    Heuchert JP, McNair MM (2012) Profile of mood states Second Edition.Google Scholar
  12. 12.
    Kanda Y. Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics. Bone Marrow Transplant. 2013;48(3):452–8. Scholar
  13. 13.
    Hjortskov N, Rissén D, Blangsted AK, Fallentin N, Lundberg U, Søgaard K. The effect of mental stress on heart rate variability and blood pressure during computer work. Eur J Appl Physiol. 2004;92(1–2):84–9.CrossRefGoogle Scholar
  14. 14.
    The Standards for Educational and Psychological Testing. AERA (American Educational Research Association), APA (American Psychological Association), NCME (the National Council on Measurement in Education).; 2014.
  15. 15.
    Terakado A, Matsushima E. Work stress among nurses engaged in palliative care on general wards. Psychooncology. 2015;24(1):63–9. Scholar
  16. 16.
    Hellhammer DH, Wüst S, Kudielka BM. Salivary cortisol as a biomarker in stress research. Psychoneuroendocrinology. 2009;34(2):163–71.CrossRefGoogle Scholar
  17. 17.
    Petrakova L, Doering BK, Vits S, Engler H, Rief W, Schedlowski M, Grigoleit JS. Psychosocial stress increases salivary alpha-amylase activity independently from plasma noradrenaline levels. PLoS ONE. 2015;10(8):e0134561.CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Anesthesiology and Pain Relief CenterThe University of Tokyo HospitalTokyoJapan
  2. 2.Department of AnesthesiologyTokyo Teishin HospitalTokyoJapan
  3. 3.Department of NeuropsychiatryThe University of Tokyo HospitalTokyoJapan

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