Abstract
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.
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References
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.
Acknowledgements
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.
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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
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DOI: https://doi.org/10.1007/s10484-019-09445-z