European Journal of Applied Physiology

, Volume 111, Issue 7, pp 1497–1505 | Cite as

Instantaneous changes in heart rate regulation due to mental load in simulated office work

  • Joachim Taelman
  • Steven Vandeput
  • Elke Vlemincx
  • Arthur Spaepen
  • Sabine Van Huffel
Original Article

Abstract

The cardiac regulation effects of a mental task added to regular office work are described. More insight into the time evolution during the different tasks is created by using time–frequency analysis (TFA). Continuous wavelet transformation was applied to create time series of instantaneous power and frequency in specified frequency bands (LF 0.04–0.15 Hz; HF 0.15–0.4 Hz), in addition to the traditional linear heart rate variability (HRV) parameters. In a laboratory environment, 43 subjects underwent a protocol with three active conditions: a clicking task with low mental load and a clicking task with high mental load (mental arithmetic) performed twice, each followed by a rest condition. The heart rate and measures related to vagal modulation could differentiate the active conditions from the rest condition, meaning that HRV is sensitive to any change in mental or physical state. Differences between physical and mental stress were observed and a higher load in the combined task was observed. Mental stress decreased HF power and caused a shift toward a higher instantaneous frequency in the HF band. TFA revealed habituation to the mental load within the task (after 3 min) and between the two tasks with mental load. In conclusion, the use of TFA in this type of analysis is important as it reveals extra information. The addition of a mental load to a physical task elicited further effect on HRV parameters related to autonomic cardiac modulation.

Keywords

Heart rate variability (HRV) Mental load Physical load Time–frequency analysis Continuous wavelet transform 

Notes

Acknowledgments

Dr. Sabine Van Huffel is a full professor at the Katholieke Universiteit Leuven, Belgium. We thank the European Commission for funding part of this work under contract IST-027291 (ConText). The research was supported by the Research Council KUL [GOA Ambiorics, GOA MaNet, CoE EF/05/006 Optimization in Engineering (OPTEC), PFV/10/002 (OPTEC), IDO 05/010 EEG-fMRI, IDO 08/013 Autism, IOF-KP06/11 FunCopt, several PhD/postdoc & fellow grants], Flemish government [FWO: PhD/postdoctoral grants, projects: FWO G.0302.07 (SVM), G.0341.07 (Data fusion), G.0427.10N (Integrated EEG-fMRI) research communities (ICCoS, ANMMM); IWT: TBM070713-Accelero, TBM070706-IOTA3, TBM080658-MRI (EEG-fMRI), PhD Grants], Belgian Federal Science Policy Office [(IUAP P6/04 (DYSCO, `Dynamical systems, control and optimization’, 2007–2011);ESA PRODEX No 90348 (sleep homeostasis)] and EU [FAST (FP6-MC-RTN-035801), Neuromath (COST-BM0601)].

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Joachim Taelman
    • 1
  • Steven Vandeput
    • 1
  • Elke Vlemincx
    • 2
  • Arthur Spaepen
    • 3
  • Sabine Van Huffel
    • 1
  1. 1.Department of Electrical Engineering (ESAT), SCD:SISTA (BIOMED)Katholieke Universiteit LeuvenLeuven-HeverleeBelgium
  2. 2.Research Group on Health Psychology, Department of PsychologyKatholieke Universiteit LeuvenLeuvenBelgium
  3. 3.Department of Kinesiology and Rehabilitation Sciences (FaBeR)Katholieke Universiteit LeuvenLeuven-HeverleeBelgium

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