Excessive mental workload represent a critical risk factor for workplace accidents. Heart rate variability (HRV) is a non-invasive low cost electrophysiological autonomic biomarker related to emotional and cognitive regulation. Several studies report that mental overload impairs parasympathetic-mediated HRV indices (e.g. rMSSD). However, the influence of resting state HRV as a predictor of long-term mental workload impairments remains unknown. Thirty participants (22 males; 8 females) had their HRV measured (5-min period) before performing the number search task. After the task, the mental load was accessed by the NASA-TLX questionnaire. A simple linear regression model between HRV and NASA-TLX dimensions showed that resting state rMSSD is associated to physical demand (ND-2, R2 = 0.143, p = 0.03) and frustration level (ND-6, R2 = 0.175, p = 0.02) dimensions of mental workload. The comparison between 1 and 5-min epochs suggests that regression models remain reliable even using the ultra-short term HRV (< 1 min) recording values (R2 values from 0.11 to 0.15 for ND-2 and R2 values from 0.16 to 0.19 for ND-6). These results suggest that resting state HRV is associated to long-term effects of mental workload on physical and emotional demands. In addition, the ultra-short term HRV indices remains reliable to assess ND-2 and ND-6 dimensions of mental workload when compared to gold-standard time interval (> 5 min). The resting state cardiac autonomic tone assessment optimizes the physiological approach with a quick, non-invasive and low-cost assessment that can provide insights about mental load adjustments to prevent work-related accidents.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Billman, G. E., Huikuri, H. V., Sacha, J., & Trimmel, K. (2015). An introduction to heart rate variability: Methodological considerations and clinical applications. Frontiers in Physiology,6, 2013–2015. https://doi.org/10.3389/fphys.201400177.
Boksem, M. A. S., Meijman, T. F., & Lorist, M. M. (2005). Effects of mental fatigue on attention: An ERP study. Brain Research. Cognitive Brain Research,25, 107–116. https://doi.org/10.1016/j.cogbrainres.2005.04.011.
Borghini, G., Astolfi, L., Vecchiato, G., Mattia, D., & Babiloni, F. (2014). Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness. Neuroscience and Biobehavioral Reviews,44, 58–75. https://doi.org/10.1016/j.neubiorev.2012.10.003.
Chalmers, J. A., Quintana, D. S., Abbott, M. J.-A., & Kemp, A. H. (2014). Anxiety disorders are associated with reduced heart rate variability: A meta-analysis. Frontiers in Psychiatry,5, 1–11. https://doi.org/10.3389/fpsyt.2014.00080.
Esco, M. R., & Flatt, A. A. (2014). Ultra-short-term heart rate variability indexes at rest and post-exercise in athletes: Evaluating the agreement with accepted recommendations. Journal of Sports Science and Medicine,13(3), 535–541.
Faber, L. G., Maurits, N. M., & Lorist, M. M. (2012). Mental fatigue affects visual selective attention. PLoS ONE,7(10), 1–10. https://doi.org/10.1371/journal.pone.0048073.
Fallahi, M., Motamedzade, M., Heidarimoghadam, R., Soltanian, A. R., & Miyake, S. (2016). Effects of mental workload on physiological and subjective responses during traffic density monitoring: A field study. Applied Ergonomics,52, 95–103. https://doi.org/10.1016/j.apergo.2015.07.009.
Flatt, A. A., & Esco, M. R. (2013). Validity of the ithleteTM smart phone application for determining ultra-short-term heart rate variability. Journal of Human Kinetics,39(1), 85–92. https://doi.org/10.2478/hukin-2013-0071.
Galy, E., Paxion, J., & Berthelon, C. (2017). Measuring mental workload with the NASA-TLX needs to examine each dimension rather than relying on the global score: An example with driving. Ergonomics,0139, 1–27. https://doi.org/10.1080/00140139.2017.1369583.
Giles, D., Draper, N., & Neil, W. (2016). Validity of the Polar V800 heart rate monitor to measure RR intervals at rest. European Journal of Applied Physiology,116(3), 563–571. https://doi.org/10.1007/s00421-015-3303-9.
Hansen, A. L., Johnsen, B. H., & Thayer, J. F. (2003). Vagal influence on working memory and attention. International Journal of Psychophysiology,48(3), 263–274. https://doi.org/10.1016/S0167-8760(03)00073-4.
Hart, S. G. (2006). Nasa-Task Load Index (NASA-TLX); 20 Years Later. Proceedings of the Human Factors and Ergonomics Society Annual Meeting,50(9), 904–908. https://doi.org/10.1177/154193120605000909.
Haynes, J.-D., & Rees, G. (2006). Decoding mental states from brain activity in humans. Nature Reviews Neuroscience,7(7), 523–534. https://doi.org/10.1038/nrn1931.
Horrey, W. J., Lesch, M. F., & Garabet, A. (2009). Dissociation between driving performance and drivers’ subjective estimates of performance and workload in dual-task conditions. Journal of Safety Research,40(1), 7–12. https://doi.org/10.1016/j.jsr.2008.10.011.
Javorka, M., Trunkvalterova, Z., Tonhajzerova, I., Javorkova, J., Javorka, K., & Baumert, M. (2008). Short-term heart rate complexity is reduced in patients with type 1 diabetes mellitus. Clinical Neurophysiology,119(5), 1071–1081. https://doi.org/10.1016/j.clinph.2007.12.017.
Kemp, A. H., Quintana, D. S., Gray, M. A., Felmingham, K. L., Brown, K., & Gatt, J. M. (2010). Impact of depression and antidepressant treatment on heart rate variability: A review and meta-analysis. Biological Psychiatry,67(11), 1067–1074. https://doi.org/10.1016/j.biopsych.2009.12.012.
Laborde, S., Mosley, E., & Thayer, J. F. (2017). Heart rate variability and cardiac vagal tone in psychophysiological research—Recommendations for experiment planning, data analysis, and data reporting. Frontiers in Psychology,08, 1–18. https://doi.org/10.3389/fpsyg.2017.00213.
Lehrer, P., Vaschillo, E., Lu, S.-E., Eckberg, D., Vaschillo, B., Scardella, A., et al. (2006). Heart rate variability biofeedback: Effects of age on heart rate variability, baroreflex gain, and asthma. Chest,129(2), 278–284. https://doi.org/10.1378/chest.129.2.278.
Lotufo, P. A., Valiengo, L., Benseñor, I. M., & Brunoni, A. R. (2012). A systematic review and meta-analysis of heart rate variability in epilepsy and antiepileptic drugs. Epilepsia,53(2), 272–282. https://doi.org/10.1111/j.1528-1167.2011.03361.x.
Matthews, G., Reinerman-Jones, L. E., Barber, D. J., & Abich, J. (2015). The psychometrics of mental workload. Human Factors: The Journal of the Human Factors and Ergonomics Society,57(1), 125–143. https://doi.org/10.1177/0018720814539505.
Melo, H. M., Martins, T. C., Nascimento, L. M., Hoeller, A. A., Walz, R., & Takase, E. (2018). Ultra-short heart rate variability recording reliability: The effect of controlled paced breathing. Annals of Noninvasive Electrocardiology,4(4), 1–9. https://doi.org/10.1111/anec.12565.
Melo, H. M., Nascimento, L. M., & Takase, E. (2017). Mental fatigue and heart rate variability (HRV): The time-on-task effect. Psychology & Neuroscience,10(4), 428–436. https://doi.org/10.1037/pne0000110.
Mujica-Parodi, L. R., Korgaonkar, M., Ravindranath, B., Greenberg, T., Tomasi, D., Wagshul, M., et al. (2009). Limbic dysregulation is associated with lowered heart rate variability and increased trait anxiety in healthy adults. Human Brain Mapping,30(1), 47–58. https://doi.org/10.1002/hbm.20483.
Müller, K.-R., Tangermann, M., Dornhege, G., Krauledat, M., Curio, G., & Blankertz, B. (2008). Machine learning for real-time single-trial EEG-analysis: From brain–computer interfacing to mental state monitoring. Journal of Neuroscience Methods,167(1), 82–90. https://doi.org/10.1016/j.jneumeth.2007.09.022.
Munoz, M. L., Van Roon, A., Riese, H., Thio, C., Oostenbroek, E., Westrik, I., et al. (2015). Validity of (ultra-)short recordings for heart rate variability measurements. PLoS ONE,10(9), 1–15. https://doi.org/10.1371/journal.pone.0138921.
Nakamura, F. Y., Flatt, A. A., Pereira, L. A., Ramirez-Campillo, R., Loturco, I., & Esco, M. R. (2015). Ultra-short-term heart rate variability is sensitive to training effects in team sports players. Journal of Sports Science & Medicine, 14(3), 602–5. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4541125&tool=pmcentrez&rendertype=abstract
Nussinovitch, U., Elishkevitz, K. P., Katz, K., Nussinovitch, M., Segev, S., Volovitz, B., et al. (2011). Reliability of ultra-short ECG indices for heart rate variability. Annals of Noninvasive Electrocardiology,16(2), 117–122. https://doi.org/10.1111/j.1542-474X.2011.00417.x.
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2008). Situation awareness, mental workload, and trust in automation: viable, empirically supported cognitive engineering constructs. Journal of Cognitive Engineering and Decision Making,2(2), 140–160. https://doi.org/10.1518/155534308X284417.
Paxion, J., Galy, E., & Berthelon, C. (2014). Mental workload and driving. Frontiers in Psychology,5, 1–11. https://doi.org/10.3389/fpsyg.2014.01344.
Sakaki, M., Yoo, H. J., Nga, L., Lee, T.-H., Thayer, J. F., & Mather, M. (2016). Heart rate variability is associated with amygdala functional connectivity with MPFC across younger and older adults. NeuroImage,139, 44–52. https://doi.org/10.1016/j.neuroimage.2016.05.076.
Taelman, J., Vandeput, S., Vlemincx, E., Spaepen, A., & Van Huffel, S. (2011). Instantaneous changes in heart rate regulation due to mental load in simulated office work. European Journal of Applied Physiology,111(7), 1497–1505. https://doi.org/10.1007/s00421-010-1776-0.
Tarvainen, M. P., Niskanen, J.-P., Lipponen, J. A., Ranta-aho, P. O., & Karjalainen, P. A. (2014). Kubios HRV—Heart rate variability analysis software. Computer Methods and Programs in Biomedicine,113(1), 210–220. https://doi.org/10.1016/j.cmpb.2013.07.024.
Task Force of The European Society of Cardiology and The North American Society of Pacing and Eletrophysiology. (1996). Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. European Heart Journal,17, 354–381.
Thayer, J. F., Ahs, F., Fredrikson, M., Sollers, J. J., & Wager, T. D. (2012). A meta-analysis of heart rate variability and neuroimaging studies: Implications for heart rate variability as a marker of stress and health. Neuroscience and Biobehavioral Reviews,36(2), 747–756. https://doi.org/10.1016/j.neubiorev.2011.11.009.
Thayer, J. F., Hansen, A. L., Saus-Rose, E., & Johnsen, B. H. (2009). Heart rate variability, prefrontal neural function, and cognitive performance: The neurovisceral integration perspective on self-regulation, adaptation, and health. Annals of Behavioral Medicine,37(2), 141–153. https://doi.org/10.1007/s12160-009-9101-z.
Thayer, J. F., & Lane, R. D. (2000). A model of neurovisceral integration in emotion regulation and dysregulation. Journal of Affective Disorders, 61(3), 201–16. http://www.ncbi.nlm.nih.gov/pubmed/11163422
Thayer, J. F., & Lane, R. D. (2009). Claude Bernard and the heart-brain connection: Further elaboration of a model of neurovisceral integration. Neuroscience and Biobehavioral Reviews,33(2), 81–88. https://doi.org/10.1016/j.neubiorev.2008.08.004.
Vesterinen, V., Häkkinen, K., Hynynen, E., Mikkola, J., Hokka, L., & Nummela, A. (2013). Heart rate variability in prediction of individual adaptation to endurance training in recreational endurance runners. Scandinavian Journal of Medicine and Science in Sports,23(2), 171–180. https://doi.org/10.1111/j.1600-0838.2011.01365.x.
Voss, A., Schroeder, R., Heitmann, A., Peters, A., & Perz, S. (2015). Short-term heart rate variability—Influence of gender and age in healthy subjects. PLoS ONE,10(3), 1–33. https://doi.org/10.1371/journal.pone.0118308.
Wascher, E., Rasch, B., Sänger, J., Hoffmann, S., Schneider, D., Rinkenauer, G., et al. (2014). Frontal theta activity reflects distinct aspects of mental fatigue. Biological Psychology,96(1), 57–65. https://doi.org/10.1016/j.biopsycho.2013.11.010.
Yan, S., Tran, C. C., Wei, Y., & Habiyaremye, J. L. (2017). Driver’s mental workload prediction model based on physiological indices. International Journal of Occupational Safety and Ergonomics 1–37. https://doi.org/10.1080/10803548.2017.1368951
Young, M. S., Brookhuis, K. A., Wickens, C. D., & Hancock, P. A. (2014). State of science: Mental workload in ergonomics. Ergonomics,58(1), 1–17. https://doi.org/10.1080/00140139.2014.956151.
Yu, R., Mobbs, D., Seymour, B., Rowe, J. B., & Calder, A. J. (2014). The neural signature of escalating frustration in humans. Cortex,54(1), 165–178. https://doi.org/10.1016/j.cortex.2014.02.013.
Zhao, C., Zhao, M., Liu, J., & Zheng, C. (2012). Electroencephalogram and electrocardiograph assessment of mental fatigue in a driving simulator. Accident Analysis and Prevention,45, 83–90. https://doi.org/10.1016/j.aap.2011.11.019.
This work was supported by PRONEX Program (Programa de Núcleos de Excelência—NENASC Project) of FAPESC-CNPq-MS, Santa Catarina Brazil (process number 56802/2010). RW is a Researcher Fellow from CNPq (Brazilian Council for Scientific and Technologic Development, Brazil), AAH is supported by scholarships from CAPES/PNPD and HMM is supported by CAPES/DS scholarship.
Conflict of interest
The authors have no conflict of interest, source of funding or financial ties to disclose and no current or past relationship with companies or manufacturers who could benefit from the results of the present study.
This research was approved by the local ethics committee (Comitê de Ética e Pesquisa com Seres Humanos da UFSC—CEPSH), which can be checked by CAAE: 44053615.4.0000.0121. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Melo, H.M., Hoeller, A.A., Walz, R. et al. Resting Cardiac Vagal Tone is Associated with Long-Term Frustration Level of Mental Workload: Ultra-short Term Recording Reliability. Appl Psychophysiol Biofeedback 45, 1–9 (2020). https://doi.org/10.1007/s10484-019-09445-z
- Heart rate variability
- Mental workload
- Frustration level
- Autonomic nervous system