- Email: [email protected]
Influence of computer work under time pressure on cardiac activity
Computers in Biology and Medicine 58 (2015) 40–45
Contents lists available at ScienceDirect
Computers in Biology and Medicine journal homepage: www.elsevier.com/locate/cbm
Influence of computer work under time pressure on cardiac activity Ping Shi a,n, Sijung Hu b, Hongliu Yu a a b
Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai 200093, China School of Electronic, Electrical and Systems Engineering, Loughborough University, Ashby Road, Loughborough, Leicestershire LE11 3TU, United Kingdom
art ic l e i nf o
a b s t r a c t
Article history: Received 5 September 2014 Accepted 3 January 2015
Computer users are often under stress when required to complete computer work within a required time. Work stress has repeatedly been associated with an increased risk for cardiovascular disease. The present study examined the effects of time pressure workload during computer tasks on cardiac activity in 20 healthy subjects. Heart rate, time domain and frequency domain indices of heart rate variability (HRV) and Poincaré plot parameters were compared among five computer tasks and two rest periods. Faster heart rate and decreased standard deviation of R–R interval were noted in response to computer tasks under time pressure. The Poincaré plot parameters showed significant differences between different levels of time pressure workload during computer tasks, and between computer tasks and the rest periods. In contrast, no significant differences were identified for the frequency domain indices of HRV. The results suggest that the quantitative Poincaré plot analysis used in this study was able to reveal the intrinsic nonlinear nature of the autonomically regulated cardiac rhythm. Specifically, heightened vagal tone occurred during the relaxation computer tasks without time pressure. In contrast, the stressful computer tasks with added time pressure stimulated cardiac sympathetic activity. & 2015 Elsevier Ltd. All rights reserved.
Keywords: Stress Computer–mouse work Electrocardiogram (ECG) Heart rate variability (HRV) Spectral analysis Poincaré plot Cardiac activity
1. Introduction The use of computers is becoming an essential part of industrial, office and daily life. Computer work with clicking and moving a mouse generally involves a high demand of visual, motor and cognitive coordination. In many situations, the users are under stressful pressure to complete the computer work within a required time. The consequences of such mental stress include tension, irritability, inability to concentrate and a variety of physical symptoms usually reflected by a headache or an increased heart rate (HR). Porges described stress as an autonomic state that reflects a disruption of homeostasis due to depressed parasympathetic tone [1]. Workload-related stress often produces changes in behavioral (e.g., higher error rates) [2,3] and physiological (e.g., faster heartbeat) functions [4,5]. Thus, the degree of stress can be mirrored from behavior performance and can be quantified on a physiological level. HR and HR variability (HRV) allow the discrimination among levels of mental stress induced by workload [6–11]. Although HRV analysis has been a popular tool for assessing mental stress, the conventional spectral analysis in HRV has not yet reached a conclusion regarding the frequency components specific to the time pressure [7,9,10]. It still remains unclear how could HR change without an appropriate and
n
Corresponding author. E-mail address: [email protected] (P. Shi).
http://dx.doi.org/10.1016/j.compbiomed.2015.01.001 0010-4825/& 2015 Elsevier Ltd. All rights reserved.
assessable change in its autonomic modulation reflected on the spectral characteristics of HRV [7,9,10]. Heiden et al. [7] investigated the influences of time pressure and precision demands during computer-mouse work on muscle oxygenation and position sense in the upper extremity and found that HRV did not differ in the low and high frequency bands [7]. Garde et al. demonstrated that carrying out computer tasks under mental pressure did not result in any changes in the low and high frequency components in the normalized power spectra [10]. Similar results were reported by Wahlström and colleagues [9], where no significant difference in the ratio of low and high frequency components was noted between the control and stress conditions in the Stroop colour word test under time pressure and verbal provocation. Nevertheless, some studies have reported that the frequency components of HRV changed as a result of adding memory demands to a computer task [6,8,11,12]. One possibility for such an inconsistency is the nonlinear properties of HRV [13–15]. Consequently, the linear spectral analysis may not be able to uncover subtle but important alterations and anomalies in HR time series. Searchable literatures have reported the advantages of nonlinear methods, such as detrended fluctuation analysis (DFA), approximate entropy (ApEn) and Poincaré plot analysis, over the linear spectral analysis [16–19]. Nonlinear techniques are able to distinguish groups that have no differences in their spectra [17,19]. However, the physiological connection between the scaling factor of DFA or information entropy and the cardiac activities is indirect. Moreover, a larger data length was required for some nonlinear indices, such as DFA and ApEn [20]. Thus, an
P. Shi et al. / Computers in Biology and Medicine 58 (2015) 40–45
efficacious nonlinear measure is required for better physiological understanding of cardiac activities during lab experiments. As a simple yet powerful nonlinear measure, the quantifications of the Poincaré plot have recently been proposed to characterize the vagal modulation of R–R interval dynamics [18,19,21,22]. The Poincaré plot provides a useful visualisation of HRV by representing both shortand long-term variations in the R–R intervals, and its geometrical representations can reflect the cardiac behavior, e.g., a normal or reduced cardiac autonomic modulation [23]. Meanwhile, a physiological model for the Poincaré plot has been established and tested by Brennan and colleagues, in which the Poincaré plot is linked to the weighted combination of low and high frequency components in HRV [24]. The aim of this study was to investigate the cardiac activities during computer work under time pressure. Additionally, we attempted to analyze the reason for the failure of spectral measures in discriminating the levels of computer work stress under different time pressures.
41
(4) Low-level pressure session (LS): It was also similar to HS, but the sequence had to be input within 90% of the average time; (5) Relaxed session (RS): The subjects were given a longer time than in the SS, i.e., 150% of the average time, to input each sequence. There was a 5-min break interval between two consecutive sessions. The ECG signals were recorded in two 8-min sessions before and after the computer work when the subjects were in rest, i.e., REST1 and REST2, respectively. The digit correct rate and sequence correct rate for each subject were recorded automatically and simultaneously throughout the experiments as the behavior performances outcome. The digit correct rate was calculated as the ratio of the digits correctly input over the total digits displayed, and sequence correct rate was defined as the sequences correctly input over the total sequences presented. 2.3. HR and HRV
2. Methods 2.1. Subjects In total, 20 subjects (male/female: 10/10; age: 23.0 7 2.6 years; height: 168.4 75.0 cm; weight: 57.677.9 kg) participated in this study. No subjects had a history of cardiovascular abnormality or were taking medication. All subjects were right-handed experienced computer users without musculoskeletal problems. The subjects were requested to avoid strenuous exercise physical exercise for 24 h prior to the participation in the experiments and were not allowed to consume hot drinks or those containing caffeine or eat a substantial meal for 1 h before the test. All participants gave their informed consent to participate in the study which was approved by the local ethics advisory committee. 2.2. Experimental protocol Each subject was seated comfortably with arm supports and asked to perform a standardised digit input test in five 8-min sessions. Each session was comprised of serially inputting six-digit sequences using a 3 3 numeric keyboard specifically designed for the test on the computer display. The sequences presented on the computer display were randomly generated. The ECG signals were continuously recorded with electrodes (Mode: 915S50, Shengfeng Strongest, China) attached on the inner sides of the participants’ two forearms and the right ankle, according to the driven-right-leg system. The ECG signals were amplified (ML135 Dual Bio Amp, ADInstruments Pty Ltd., Sydney, Australia) before the A/D conversion using a recording system (PowerLab 4/26, ADInstruments Pty Ltd., Sydney, Australia) at a sampling rate of 1000 Hz. The five sessions are detailed below: (1) Non-stress session (NS): The subjects were asked to input each sequence at their normal speed under no time pressure. The next six-digit sequence did not start until the current one was completed. The average time (unit: seconds per sequence) was used as the reference in the following stressful sessions; (2) High-level pressure session (HS): The subjects were asked to input each sequence within 80% of the average time. A timing bar over the input area indicated the remaining time. The next sequence task started at the end of 80% of the average time regardless of the completion of the current one; (3) Standard session (SS): It was similar to HS. However, the subjects were required to complete the input within 100% of the average time;
HR was extracted from the ECG signal to directly reflect the cardiac response to the computer work under time pressure. As a measure of the fluctuation of beat-to-beat differences, HRV is a reliable, noninvasive marker of cardiac activity. HRV characterizes the variations in the intervals between consecutive heartbeats, that is, the variations in the duration of R–R intervals between consecutive QRS complexes, as measured by ECG. The standard deviation of R–R interval (SDRR), was used to quantify HRV [25]. Power spectral analysis of the beat-to-beat fluctuations in R–R intervals was performed via fast Fourier transformation (FFT). Two frequency bands were defined as follows [25]: a low frequency (LF) band from 0.04 to 0.15 Hz and a high frequency (HF) band from 0.16 to 0.4 Hz. Within each frequency band the spectral power was expressed in normalized units (n.u.), where LF (n.u.) and HF (n.u.) represent the relative power component to the total power of HRV. Finally, the low-to-high ratio (HF/LF) was calculated. HF power is almost entirely mediated by the parasympathetic activity to the sinus node, which is directly associated with respiratory activity, whereas LF power reflects the mixed modulation of parasympathetic and sympathetic activities. Changes in the ratio LF/HF were taken to be an indication of changes in sympathetic activity, although this ratio may also be an index of sympathovagal balance [25,26]. 2.4. Poincaré plot The Poincaré plot is the scattering outline of the current R–R intervals plotted against the preceding R–R intervals, which includes both the outline and the detailed beat-to-beat behavior of cardiac-physiologic information. Tulppo et al. [19] proposed to fit an ellipse to the Poincaré plot (Fig. 1). The dispersion of the points perpendicular to the line-of-identity measures the width of the Poincaré cloud, and reflects the level of short-term HRV (SD1), whereas the dispersion of points along the line-of-identity measures the length of the cloud and indicates the level of long-term HRV (SD2) [19,27]. SD1 is equivalent to the standard deviation of differences between adjacent R–R intervals (SDSD), and SD2 reflects the proportional weighted SDRR and SDSD as follows [27,28]: ½SD12 ¼ 12 SDSD2
ð1Þ
½SD22 ¼ 2SDRR 2 12 SDSD2
ð2Þ
SD1, SD2 and their ratio SD1/SD2 have previously been applied in the measurement of nonlinear cardiac activities [18,19,22,23,29]. For instance, lower SD1 and SD2 often imply the decrease of HRV.
42
P. Shi et al. / Computers in Biology and Medicine 58 (2015) 40–45
Fig. 2. Behavior performances. (a) Digital correct rate and (b) sequence correct rate. Data are expressed as mean 7 standard deviation. np o 0.01 vs. NS and RS, §p o 0.01 vs. NS, SS and RS.
Fig. 1. A typical Poincaré plot. An ellipse is fitted to the data points and the Poincaré plot indices are calculated by estimating the short diameter (SD1), the long diameter (SD2) and the ratio of the short and long diameters (SD1/SD2 ratio) of the fitted ellipse.SD1 is the standard deviation of the distances of points from y ¼ x axis. SD2 is the standard deviation of the distances of points from y ¼ x þ 2RR axis.
2.5. Statistics All data were presented as the mean 7standard deviation. The significance of difference between sessions was compared using one-way ANOVA with repeated-measures. A po0.05 was considered statistically significant. The statistical analyses were run in MATLAB software (MathWorks Inc., MA, USA).
3. Results 3.1. Behavior performances Fig. 2 shows the performance of the computer-mouse work in the aspects of digit correct rate and sequence correct rate. Both digit correct rate and sequence correct rate decreased significantly in the sessions with higher pressure (HS and LS) compared to the non-stressful and less stressful sessions (NS, SS and RS) (all p o0.01). The digit correct rate and sequence correct rate of the SS session were significantly lower than those of NS and RS (all p o0.01).
Table 1 Values of HR and SDRR for seven sessions. The paired t-test was performed to compare different sessions. Data are expressed as the mean 7 standard deviation. Sessions REST1 NS HS SS LS RS REST2
HR (bpm)
SDRR (ms) †♦
84.6 7 12.3 83.6 7 11.4**†♦ 86.7 7 11.7‡♦ 82.8 7 11.4**†♦ 81.9 7 11.1**†♦ 78.4 7 9.2** 77.4 7 9.8**
‡
37.8 76.1 35.6710.4† 33.8710.1‡♦▲ 38.6 712.5† 37.7 711.6*‡♦ 44.3 710.2**†▲ 57.6 714.6**♦▲
LF (n.u.)
HF (n.u.)
52.8 7 12.8 45.6 7 13.4 53.17 14.5 48.37 12.9 47.6 7 12.7 49.77 13.9 51.9 7 13.8
36.9 7 9.7 43.8 7 11.5 41.0 7 11.1 40.9 7 12.2 42.8 7 10.6 43.2 7 11.7 35.37 9.9
n
p o 0.05 vs. HS. p o 0.01 vs. HS. † p o 0.05 vs. REST2. ‡ p o 0.01 vs. REST2. ♦ po 0.05 vs. RS. ▲ p o 0.05 vs. LS. nn
3.3. Poincaré plots Fig. 3 shows the Poincaré plots in each session for one representative subject throughout the experiment. The shape of the plot concentrated evidently under time pressure (HS and LS) and expanded when the subjects were in the relaxation states (REST1, RS and REST2). Table 2 summarizes the average SD1 and SD2 of the Poincaré plots for all subjects in each session as well as their ratio, SD1/SD2. The SD1 in RS significantly increased compared to those in REST1, NS and HS (all po0.05). SD2 was significantly lower in the most stressful session (HS) than in LS (po0.05), RS (po0.05) and REST2 (po0.01). SD1/SD2 was lower in REST2 than in the time pressure sessions (HS and LS, all po0.05).
3.2. HR and HRV The HR and the HRV indices are listed in Table 1. During the five computer task sessions, the most stressful session (HS) resulted in a faster HR than the other computer tasks (all po0.01) and the computer work under the relaxation state (RS) resulted in a lower HR than the other tasks (all po0.05). HR significantly slowed down after the computer work (REST2) compared to other sessions (REST1, NS, SS LS, [all po0.05] and HS [po0.01]) except RS. SDRR in HS was significantly lower than that in LS (po0.05). SDRR in REST2 significantly increased compared to those in NS, SS, LS (po0.05) and HS (po0.01). There were no significant differences for the normalized LF and HF between sessions (p40.05).
4. Discussion The behavior performance confirmed that the designed protocol successfully created a pressure for the subjects (see Fig. 2). Work stress affected several physiological processes in the subjects. The results from the present study indicate that the effects of work stress are partly mediated through (1) the increased HR reactivity to the stressful computer work and (2) the lower SDRR implying the decreased HRV. These two characteristics of work stress are associated with increased risk of cardiovascular disease [30,31]. Meanwhile, the decreased HRV during the stressful
P. Shi et al. / Computers in Biology and Medicine 58 (2015) 40–45
43
Fig. 3. Representative Poincaré plots from one subject during (a) REST1, (b) NS, (c) HS, (d) SS, (e) LS, (f) RS and (g) REST2. The changes of SD1 and SD2 for the same subject are presented in (h). The narrowing induced by withdrawn sympathetic activities was observed during the computer work, as was increased scatter due to an increased parasympathetic activity during REST1, non-pressure work (RS) and REST2.
Table 2 Values of Poincaré plot indices (SD1, SD2, SD1/SD2) for seven sessions. The paired t-test was performed to compare different sessions. Data are expressed as the mean 7 standard deviation. Sessions REST1 NS HS SS LS RS REST2
SD1
SD2 †♦
17.9 7 4.5 19.17 7.5♦ 18.9 7 7.9♦ 20.2 7 9.3 21.0 7 8.6 23.7 7 7.2n 25.0 7 9.5
SD1/SD2 ‡
50.17 8.4 46.5 7 13.2† 43.7 7 12.6‡♦▲ 50.4 7 16.3† 48.97 14.4n‡♦ 58.0 7 12.9nn†▲ 77.17 20.5nn♦▲
0.36 70.09n♦▲ 0.41 70.08 0.43 70.11† 0.41 70.12 0.42 70.07† 0.40 70.05 0.3370.12n▲
n
po 0.05 vs. HS. p o0.01 vs. HS. † p o0.05 vs. REST2. ‡ p o0.01 vs. REST2. ♦ p o 0.05 vs. RS. ▲ po 0.05 vs. LS. nn
computer tasks suggested a higher cardiac sympathetic activity or a lower vagal tone [32]. However, no significant differences in either LF or HF components were identified between any two sessions (see Table 1). The inability of spectral analysis to distinguish groups has also been reported in the literature [13,33,34], where an evident increase in HR was not followed by changes in the spectral characteristics of HRV in mental stress performed with verbalisation. In the present study, the spectral results suggest that an alternative method is required to investigate computer work under time pressure. Over recent years, short time Fourier transform and wavelet transform have been used to examine the mechanisms controlling HRV. Although these advanced spectral techniques has been suggested to be good indicators for distinguishing patients from healthy subjects, the correlation between the control of the autonomic nervous system and these advanced spectral techniques has not been well established [35,36]. It has been demonstrated that the Poincaré plot can provide useful physiological information of R–R interval dynamics during exercise, which is not easily detected using linear measures [19]. Similarly, in the present study, the Poincaré plot analysis showed
its sensitivity in distinguishing subtle variations. The meaning of the Poincaré plot parameters was specified in the work from Tulppo and his co-workers [19]. As stated in their work, SD1 quantifies the vagal modulation of HR, whereas decreased SD2 and increased SD1/SD2 suggest sympathetic activation. In the present study, RS and REST2 with higher SD1 and SD2 are consistent with heightened vagal tone, perhaps as a result of sustained sympathetic activation or accentuated antagonism [37] after a series of relatively stressful work sessions. The time pressure tasks with lower SD1 and SD2 were associated with sympathetic activation and reduced vagal tone. Meanwhile, HS and LS with higher SD1/ SD2 and with lower SD1/SD2 were related to the excitement of the sympathetic nerves during the two sessions of pressure computer tasks and sustained vagal activity during the rest after the computer work. Why the Poincaré plot performs better than spectral analysis is an interesting question. The time series of heart beats exhibits the pattern of a nonlinear process [38]. Due to the nonlinear components, the R–R interval time series signal cannot be properly assessed using linear techniques such as spectral analysis [38]. In the search for improved methods for decoding hidden information in the R–R interval dynamics, the parameters arising from nonlinear methods were identified. A Poincaré plot of HRV, consisting of plotting each R–R interval as a function of the previous interval, is such a nonlinear method that can be used to evaluate changes in heart dynamics with its trends. Poincaré analysis is used to assess the qualitative (nonlinear interactions between regulatory mechanisms) rather than the quantitative (time- or frequency domains) properties of HR dynamics. In addition to its sensibility to the nonlinear phenomena of HRV genesis, the combinational and weighted contributions of LF and HF to the Poincaré index (SD1 or SD2) [24] are supposed to make the Poincaré analysis technique more insightful to the subtle differences that are difficult to detect using LF or HF separately. Nonlinear indexes, such as DFA and ApEn, are recently introduced methods for measuring HRV and have been shown to be powerful tools for the characterization of various complex systems. However, no systematic study has been conducted to investigate the use of these methods in large patient populations, and no physiological connection between these methods and cardiac activities has been well established. Specifically, an association between sympathetic activity and
44
P. Shi et al. / Computers in Biology and Medicine 58 (2015) 40–45
the shape of the Poincaré plot has been established. where the narrower the pattern is observed, the larger the sympathetic activity was [39–41]. The visual pattern presented in this study agrees with this association. Fig. 3 shows the visual changes of SD1 and SD2 from the Poincaré cloud shape of one subject during the whole performance. The narrowing scatter induced by the augmentation of sympathetic activities could be noticed in the stressful computer work (HS and LS), as well as the increased scatter due to the enhanced parasympathetic activity in the relaxation computer work (RS) and two relaxing states (REST1 and REST2). The patterns of the Poincaré plots can reflect a normal or reduced cardiac autonomic modulation. The comet patterns, as shown in Fig. 3 (a) and (g) for REST1 and REST2, respectively, express an augmenting variability with an increasing R–R interval. The torpedo patterns or a pattern tending to torpedo shape, as shown in Fig. 3 (c)–(e) for HS, SS and LS, respectively, indicate a low beat-to-beat variability with the same dispersion along the diagonal irrespective of the mean R–R interval. Because the Poincaré scattergram gives a visually distinguishable pattern for the sessions, it can be considered to be a useful tool for describing the autonomic activity effect over the R–R interval dynamics.
5. Conclusion Stress is induced by an imbalance between external demands and the individual’s ability to meet those demands. The evaluation of stress is difficult due to the interpretation of behavioral and physiological reactivity. The present study shows the effectiveness of the Poincaré plot method for assessing the HRV changes during computer-mouse work under time pressure. Heightened vagal tone occurred during relaxation work without time pressure, and the stressful computer tasks with added time pressure stimulated cardiac sympathetic activity. Poincaré analysis possesses the features of nonlinear measures, allowing it to perform better than spectral analysis. Moreover, compared to the numerical information from the time and frequency domains, the Poincaré plot provides a more global look and a visual contact for changing HRV, and it could be used to interpret the physiological phenomena in a straightforward manner.
Competing interests statement The authors declare that they have no competing interests.
Acknowledgements This work was supported by Innovation Program of Shanghai Municipal Education Commission (Grant no. 14YZ091). References [1] S.W. Porges, Cardiac vagal tone: a physiological index of stress, Neurosci. Biobehav. Rev. 19 (1995) 225–233. [2] G. Durantin, J.F. Gagnon, S. Tremblay, F. Dehais, Using near infrared spectroscopy and heart rate variability to detect mental overload, Behav. Brain Res. 259 (2014) 16–23. [3] U. Aasa, B.R. Jensen, J. Sandfeld, H. Richter, E. Lyskov, A.G. Crenshaw, The impact of object size and precision demands on fatigue during computer mouse use, Adv. Physiother. 13 (2011) 118–127. [4] S. Sako, H. Sugiura, H. Tanoue, M. Kojima, M. Kono, R. Inaba, The position of a standard optical computer mouse affects cardiorespiratory responses during the operation of a computer under time constraints, Int. J. Occup. Med. Environ. Health 27 (2014) 547–559. [5] Y. Wang, G.P.Y. Szeto, C.C.H. Chan, Effects of physical and mental task demands on cervical and upper limb muscle activity and physiological responses during computer tasks and recovery periods, Eur. J. Appl. Physiol. 111 (2011) 2791–2803. [6] T.D. Dzhebrailova, I.I. Korobeinikova, E.N. Dudnik, Dynamics of heart rhythm characteristics and efficiency of intellectual activity during computer work, Bull. Exp. Biol. Med. 156 (2014) 615–619.
[7] M. Heiden, E. Lyskov, M. Djupsjobacka, F. Hellstrom, A.G. Crenshaw, Effects of time pressure and precision demands during computer mouse work on muscle oxygenation and position sense, Eur. J. Appl. Physiol. 94 (2005) 97–106. [8] N. Hjortskov, D. Rissen, A.K. Blangsted, N. Fallentin, U. Lundberg, K. Sogaard, The effect of mental stress on heart rate variability and blood pressure during computer work, Eur. J. Appl. Physiol. 92 (2004) 84–89. [9] J. Wahlström, M. Hagberg, P.W. Johnson, J. Svensson, D. Rempel, Influence of time pressure and verbal provocation on physiological and psychological reactions during work with a computer mouse, Eur. J. Appl. Physiol. 87 (2002) 257–263. [10] A.H. Garde, B. Laursen, A.H. Jorgensen, B.R. Jensen, Effects of mental and physical demands on heart rate variability during computer work, Eur. J. Appl. Physiol. 87 (2002) 456–461. [11] L. Finsen, K. Sogaard, C. Jensen, V. Borg, H. Christensen, Muscle activity and cardiovascular response during computer-mouse work with and without memory demands, Ergonomics 44 (2001) 1312–1329. [12] J. Taelman, S. Vandeput, I. Gligorijevic, A. Spaepen, S. Van Huffel, Time– frequency heart rate variability characteristics of young adults during physical, mental and combined stress in laboratory environment, Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011 (2011) 1973–1976. [13] R.K. Sunkaria, Recent trends in nonlinear methods of HRV analysis: a review, World Acad. Sci. Eng. Technol. 75 (2011) 566–571. [14] A.J.E. Seely, P.T. Macklem, Complex systems and the technology of variability analysis, Crit. Care 8 (2004) R367–384. [15] A.L. Goldberger, D.R. Rigney, B.J. West, Chaos and fractals in human physiology, Sci. Am. 262 (1990) 42–49. [16] O. Faust, V.R. Prasad, G. Swapna, S. Chattopadhyay, T.C. Lim, Comprehensive analysis of normal and diabetic heart rate signals: a review, J. Mech. Med. Biol. 12 (2012). [17] D. Jiang, M. He, Y. Qiu, Y. Zhu, S. Tong, Long-range correlations in heart rate variability during computer–mouse work under time pressure, Physica A 388 (2008) 1527–1534. [18] M.P. Tulppo, T.H. Makikallio, T. Seppanen, R.T. Laukkanen, H.V. Huikuri, Vagal modulation of heart rate during exercise: effects of age and physical fitness, Am. J. Physiol. 274 (1998) H424–429. [19] M.P. Tulppo, T.H. Makikallio, T.E. Takala, T. Seppanen, H.V. Huikuri, Quantitative beat-to-beat analysis of heart rate dynamics during exercise, Am. J. Physiol. 271 (1996) H244–252. [20] A. Voss, S. Schulz, R. Schroeder, M. Baumert, P. Caminal, Methods derived from nonlinear dynamics for analysing heart rate variability, Philos. Trans. A Math. Phys. Eng. Sci. 367 (2009) 277–296. [21] M.A. Woo, W.G. Stevenson, D.K. Moser, R.B. Trelease, R.M. Harper, Patterns of beat-to-beat heart rate variability in advanced heart failure, Am. Heart J. 123 (1992) 704–710. [22] P. Shi, Y. Zhu, J. Allen, S. Hu, Analysis of pulse rate variability derived from photoplethysmography with the combination of lagged Poincare plots and spectral characteristics, Med. Eng. Phys. 31 (2009) 866–871. [23] A. Gladuli, N.S. Moïse, S.A. Hemsley, N.F. Otani, Poincaré plots and tachograms reveal beat patterning in sick sinus syndrome with supraventricular tachycardia and varying AV nodal block, J. Vet. Cardiol. 13 (2011) 63–70. [24] M. Brennan, M. Palaniswami, P. Kamen, Poincare plot interpretation using a physiological model of HRV based on a network of oscillators, Am. J. Physiol. Heart Circ. Physiol. 283 (2002) H1873–1886. [25] Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, Heart rate variability. Standards of measurement, physiological interpretation, and clinical use, Eur. Heart J. 17 (1996) 354–381. [26] B. Xhyheri, O. Manfrini, M. Mazzolini, C. Pizzi, R. Bugiardini, Heart rate variability today, Prog. Cardiovasc. Dis. 55 (2012) 321–331. [27] M. Brennan, M. Palaniswami, P. Kamen, Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability? IEEE Trans. Biomed. Eng. 48 (2001) 1342–1347. [28] P.W. Kamen, A.M. Tonkin, Application of the Poincare plot to heart rate variability: a new measure of functional status in heart failure, Aust. N. Z. J. Med. 25 (1995) 18–26. [29] L. Mourot, M. Bouhaddi, S. Perrey, J.D. Rouillon, J. Regnard, Quantitative Poincare plot analysis of heart rate variability: effect of endurance training, Eur. J. Appl. Physiol. 91 (2004) 79–87. [30] L. Tonello, F.B. Rodrigues, J.W. Souza, C.S. Campbell, A.S. Leicht, D.A. Boullosa, The role of physical activity and heart rate variability for the control of work related stress, Front. Physiol. 5 (2014) 67. [31] F. Togo, M. Takahashi, Heart rate variability in occupational health —a systematic review, Ind. Health 47 (2009) 589–602. [32] M.J. Reed, C.E. Robertson, P.S. Addison, Heart rate variability measurements and the prediction of ventricular arrhythmias, QJM 98 (2005) 87–95. [33] L. Bernardi, J. Wdowczyk-Szulc, C. Valenti, S. Castoldi, C. Passino, G. Spadacini, P. Sleight, Effects of controlled breathing, mental activity and mental stress with or without verbalization on heart rate variability, J. Am. Coll. Cardiol. 35 (2000) 1462–1469. [34] R.P. Sloan, J.B. Korten, M.M. Myers, Components of heart rate reactivity during mental arithmetic with and without speaking, Physiol. Behav. 50 (1991) 1039–1045. [35] C.H. Peters, R. Vullings, M.J. Rooijakkers, J.W. Bergmans, S.G. Oei, P.F. Wijn, A continuous wavelet transform-based method for time–frequency analysis of artefact-corrected heart rate variability data, Physiol. Meas. 32 (2011) 1517–1527.
P. Shi et al. / Computers in Biology and Medicine 58 (2015) 40–45
[36] P.S. Addison, Wavelet transforms and the ECG: a review, Physiol. Meas. 26 (2005) R155–199. [37] M.N. Levy, Sympathetic–parasympathetic interactions in the heart, Circ. Res. 29 (1971) 437–445. [38] C. Braun, P. Kowallik, A. Freking, D. Hadeler, K.D. Kniffki, M. Meesmann, Demonstration of nonlinear components in heart rate variability of healthy persons, Am. J. Physiol. 275 (1998) H1577–1584. [39] P. Shi, Y. Chen, M.M. Guo, H.L. Yu, Acute effects of alcohol on heart rate variability: time-related changes and gender difference, Biomed. Eng. Appl. Basis Commun. 26 (2014).
45
[40] S. Carrasco, M.J. Gaitan, R. Gonzalez, O. Yanez, Correlation among Poincare plot indexes and time and frequency domain measures of heart rate variability, J. Med. Eng. Technol. 25 (2001) 240–248. [41] M.A. Woo, W.G. Stevenson, D.K. Moser, H.R. Middlekauff, Complex heart rate variability and serum norepinephrine levels in patients with advanced heart failure, J. Am. Coll. Cardiol. 23 (1994) 565–569.
©2015 Elsevier
Contents lists available at ScienceDirect
Computers in Biology and Medicine journal homepage: www.elsevier.com/locate/cbm
Influence of computer work under time pressure on cardiac activity Ping Shi a,n, Sijung Hu b, Hongliu Yu a a b
Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai 200093, China School of Electronic, Electrical and Systems Engineering, Loughborough University, Ashby Road, Loughborough, Leicestershire LE11 3TU, United Kingdom
art ic l e i nf o
a b s t r a c t
Article history: Received 5 September 2014 Accepted 3 January 2015
Computer users are often under stress when required to complete computer work within a required time. Work stress has repeatedly been associated with an increased risk for cardiovascular disease. The present study examined the effects of time pressure workload during computer tasks on cardiac activity in 20 healthy subjects. Heart rate, time domain and frequency domain indices of heart rate variability (HRV) and Poincaré plot parameters were compared among five computer tasks and two rest periods. Faster heart rate and decreased standard deviation of R–R interval were noted in response to computer tasks under time pressure. The Poincaré plot parameters showed significant differences between different levels of time pressure workload during computer tasks, and between computer tasks and the rest periods. In contrast, no significant differences were identified for the frequency domain indices of HRV. The results suggest that the quantitative Poincaré plot analysis used in this study was able to reveal the intrinsic nonlinear nature of the autonomically regulated cardiac rhythm. Specifically, heightened vagal tone occurred during the relaxation computer tasks without time pressure. In contrast, the stressful computer tasks with added time pressure stimulated cardiac sympathetic activity. & 2015 Elsevier Ltd. All rights reserved.
Keywords: Stress Computer–mouse work Electrocardiogram (ECG) Heart rate variability (HRV) Spectral analysis Poincaré plot Cardiac activity
1. Introduction The use of computers is becoming an essential part of industrial, office and daily life. Computer work with clicking and moving a mouse generally involves a high demand of visual, motor and cognitive coordination. In many situations, the users are under stressful pressure to complete the computer work within a required time. The consequences of such mental stress include tension, irritability, inability to concentrate and a variety of physical symptoms usually reflected by a headache or an increased heart rate (HR). Porges described stress as an autonomic state that reflects a disruption of homeostasis due to depressed parasympathetic tone [1]. Workload-related stress often produces changes in behavioral (e.g., higher error rates) [2,3] and physiological (e.g., faster heartbeat) functions [4,5]. Thus, the degree of stress can be mirrored from behavior performance and can be quantified on a physiological level. HR and HR variability (HRV) allow the discrimination among levels of mental stress induced by workload [6–11]. Although HRV analysis has been a popular tool for assessing mental stress, the conventional spectral analysis in HRV has not yet reached a conclusion regarding the frequency components specific to the time pressure [7,9,10]. It still remains unclear how could HR change without an appropriate and
n
Corresponding author. E-mail address: [email protected] (P. Shi).
http://dx.doi.org/10.1016/j.compbiomed.2015.01.001 0010-4825/& 2015 Elsevier Ltd. All rights reserved.
assessable change in its autonomic modulation reflected on the spectral characteristics of HRV [7,9,10]. Heiden et al. [7] investigated the influences of time pressure and precision demands during computer-mouse work on muscle oxygenation and position sense in the upper extremity and found that HRV did not differ in the low and high frequency bands [7]. Garde et al. demonstrated that carrying out computer tasks under mental pressure did not result in any changes in the low and high frequency components in the normalized power spectra [10]. Similar results were reported by Wahlström and colleagues [9], where no significant difference in the ratio of low and high frequency components was noted between the control and stress conditions in the Stroop colour word test under time pressure and verbal provocation. Nevertheless, some studies have reported that the frequency components of HRV changed as a result of adding memory demands to a computer task [6,8,11,12]. One possibility for such an inconsistency is the nonlinear properties of HRV [13–15]. Consequently, the linear spectral analysis may not be able to uncover subtle but important alterations and anomalies in HR time series. Searchable literatures have reported the advantages of nonlinear methods, such as detrended fluctuation analysis (DFA), approximate entropy (ApEn) and Poincaré plot analysis, over the linear spectral analysis [16–19]. Nonlinear techniques are able to distinguish groups that have no differences in their spectra [17,19]. However, the physiological connection between the scaling factor of DFA or information entropy and the cardiac activities is indirect. Moreover, a larger data length was required for some nonlinear indices, such as DFA and ApEn [20]. Thus, an
P. Shi et al. / Computers in Biology and Medicine 58 (2015) 40–45
efficacious nonlinear measure is required for better physiological understanding of cardiac activities during lab experiments. As a simple yet powerful nonlinear measure, the quantifications of the Poincaré plot have recently been proposed to characterize the vagal modulation of R–R interval dynamics [18,19,21,22]. The Poincaré plot provides a useful visualisation of HRV by representing both shortand long-term variations in the R–R intervals, and its geometrical representations can reflect the cardiac behavior, e.g., a normal or reduced cardiac autonomic modulation [23]. Meanwhile, a physiological model for the Poincaré plot has been established and tested by Brennan and colleagues, in which the Poincaré plot is linked to the weighted combination of low and high frequency components in HRV [24]. The aim of this study was to investigate the cardiac activities during computer work under time pressure. Additionally, we attempted to analyze the reason for the failure of spectral measures in discriminating the levels of computer work stress under different time pressures.
41
(4) Low-level pressure session (LS): It was also similar to HS, but the sequence had to be input within 90% of the average time; (5) Relaxed session (RS): The subjects were given a longer time than in the SS, i.e., 150% of the average time, to input each sequence. There was a 5-min break interval between two consecutive sessions. The ECG signals were recorded in two 8-min sessions before and after the computer work when the subjects were in rest, i.e., REST1 and REST2, respectively. The digit correct rate and sequence correct rate for each subject were recorded automatically and simultaneously throughout the experiments as the behavior performances outcome. The digit correct rate was calculated as the ratio of the digits correctly input over the total digits displayed, and sequence correct rate was defined as the sequences correctly input over the total sequences presented. 2.3. HR and HRV
2. Methods 2.1. Subjects In total, 20 subjects (male/female: 10/10; age: 23.0 7 2.6 years; height: 168.4 75.0 cm; weight: 57.677.9 kg) participated in this study. No subjects had a history of cardiovascular abnormality or were taking medication. All subjects were right-handed experienced computer users without musculoskeletal problems. The subjects were requested to avoid strenuous exercise physical exercise for 24 h prior to the participation in the experiments and were not allowed to consume hot drinks or those containing caffeine or eat a substantial meal for 1 h before the test. All participants gave their informed consent to participate in the study which was approved by the local ethics advisory committee. 2.2. Experimental protocol Each subject was seated comfortably with arm supports and asked to perform a standardised digit input test in five 8-min sessions. Each session was comprised of serially inputting six-digit sequences using a 3 3 numeric keyboard specifically designed for the test on the computer display. The sequences presented on the computer display were randomly generated. The ECG signals were continuously recorded with electrodes (Mode: 915S50, Shengfeng Strongest, China) attached on the inner sides of the participants’ two forearms and the right ankle, according to the driven-right-leg system. The ECG signals were amplified (ML135 Dual Bio Amp, ADInstruments Pty Ltd., Sydney, Australia) before the A/D conversion using a recording system (PowerLab 4/26, ADInstruments Pty Ltd., Sydney, Australia) at a sampling rate of 1000 Hz. The five sessions are detailed below: (1) Non-stress session (NS): The subjects were asked to input each sequence at their normal speed under no time pressure. The next six-digit sequence did not start until the current one was completed. The average time (unit: seconds per sequence) was used as the reference in the following stressful sessions; (2) High-level pressure session (HS): The subjects were asked to input each sequence within 80% of the average time. A timing bar over the input area indicated the remaining time. The next sequence task started at the end of 80% of the average time regardless of the completion of the current one; (3) Standard session (SS): It was similar to HS. However, the subjects were required to complete the input within 100% of the average time;
HR was extracted from the ECG signal to directly reflect the cardiac response to the computer work under time pressure. As a measure of the fluctuation of beat-to-beat differences, HRV is a reliable, noninvasive marker of cardiac activity. HRV characterizes the variations in the intervals between consecutive heartbeats, that is, the variations in the duration of R–R intervals between consecutive QRS complexes, as measured by ECG. The standard deviation of R–R interval (SDRR), was used to quantify HRV [25]. Power spectral analysis of the beat-to-beat fluctuations in R–R intervals was performed via fast Fourier transformation (FFT). Two frequency bands were defined as follows [25]: a low frequency (LF) band from 0.04 to 0.15 Hz and a high frequency (HF) band from 0.16 to 0.4 Hz. Within each frequency band the spectral power was expressed in normalized units (n.u.), where LF (n.u.) and HF (n.u.) represent the relative power component to the total power of HRV. Finally, the low-to-high ratio (HF/LF) was calculated. HF power is almost entirely mediated by the parasympathetic activity to the sinus node, which is directly associated with respiratory activity, whereas LF power reflects the mixed modulation of parasympathetic and sympathetic activities. Changes in the ratio LF/HF were taken to be an indication of changes in sympathetic activity, although this ratio may also be an index of sympathovagal balance [25,26]. 2.4. Poincaré plot The Poincaré plot is the scattering outline of the current R–R intervals plotted against the preceding R–R intervals, which includes both the outline and the detailed beat-to-beat behavior of cardiac-physiologic information. Tulppo et al. [19] proposed to fit an ellipse to the Poincaré plot (Fig. 1). The dispersion of the points perpendicular to the line-of-identity measures the width of the Poincaré cloud, and reflects the level of short-term HRV (SD1), whereas the dispersion of points along the line-of-identity measures the length of the cloud and indicates the level of long-term HRV (SD2) [19,27]. SD1 is equivalent to the standard deviation of differences between adjacent R–R intervals (SDSD), and SD2 reflects the proportional weighted SDRR and SDSD as follows [27,28]: ½SD12 ¼ 12 SDSD2
ð1Þ
½SD22 ¼ 2SDRR 2 12 SDSD2
ð2Þ
SD1, SD2 and their ratio SD1/SD2 have previously been applied in the measurement of nonlinear cardiac activities [18,19,22,23,29]. For instance, lower SD1 and SD2 often imply the decrease of HRV.
42
P. Shi et al. / Computers in Biology and Medicine 58 (2015) 40–45
Fig. 2. Behavior performances. (a) Digital correct rate and (b) sequence correct rate. Data are expressed as mean 7 standard deviation. np o 0.01 vs. NS and RS, §p o 0.01 vs. NS, SS and RS.
Fig. 1. A typical Poincaré plot. An ellipse is fitted to the data points and the Poincaré plot indices are calculated by estimating the short diameter (SD1), the long diameter (SD2) and the ratio of the short and long diameters (SD1/SD2 ratio) of the fitted ellipse.SD1 is the standard deviation of the distances of points from y ¼ x axis. SD2 is the standard deviation of the distances of points from y ¼ x þ 2RR axis.
2.5. Statistics All data were presented as the mean 7standard deviation. The significance of difference between sessions was compared using one-way ANOVA with repeated-measures. A po0.05 was considered statistically significant. The statistical analyses were run in MATLAB software (MathWorks Inc., MA, USA).
3. Results 3.1. Behavior performances Fig. 2 shows the performance of the computer-mouse work in the aspects of digit correct rate and sequence correct rate. Both digit correct rate and sequence correct rate decreased significantly in the sessions with higher pressure (HS and LS) compared to the non-stressful and less stressful sessions (NS, SS and RS) (all p o0.01). The digit correct rate and sequence correct rate of the SS session were significantly lower than those of NS and RS (all p o0.01).
Table 1 Values of HR and SDRR for seven sessions. The paired t-test was performed to compare different sessions. Data are expressed as the mean 7 standard deviation. Sessions REST1 NS HS SS LS RS REST2
HR (bpm)
SDRR (ms) †♦
84.6 7 12.3 83.6 7 11.4**†♦ 86.7 7 11.7‡♦ 82.8 7 11.4**†♦ 81.9 7 11.1**†♦ 78.4 7 9.2** 77.4 7 9.8**
‡
37.8 76.1 35.6710.4† 33.8710.1‡♦▲ 38.6 712.5† 37.7 711.6*‡♦ 44.3 710.2**†▲ 57.6 714.6**♦▲
LF (n.u.)
HF (n.u.)
52.8 7 12.8 45.6 7 13.4 53.17 14.5 48.37 12.9 47.6 7 12.7 49.77 13.9 51.9 7 13.8
36.9 7 9.7 43.8 7 11.5 41.0 7 11.1 40.9 7 12.2 42.8 7 10.6 43.2 7 11.7 35.37 9.9
n
p o 0.05 vs. HS. p o 0.01 vs. HS. † p o 0.05 vs. REST2. ‡ p o 0.01 vs. REST2. ♦ po 0.05 vs. RS. ▲ p o 0.05 vs. LS. nn
3.3. Poincaré plots Fig. 3 shows the Poincaré plots in each session for one representative subject throughout the experiment. The shape of the plot concentrated evidently under time pressure (HS and LS) and expanded when the subjects were in the relaxation states (REST1, RS and REST2). Table 2 summarizes the average SD1 and SD2 of the Poincaré plots for all subjects in each session as well as their ratio, SD1/SD2. The SD1 in RS significantly increased compared to those in REST1, NS and HS (all po0.05). SD2 was significantly lower in the most stressful session (HS) than in LS (po0.05), RS (po0.05) and REST2 (po0.01). SD1/SD2 was lower in REST2 than in the time pressure sessions (HS and LS, all po0.05).
3.2. HR and HRV The HR and the HRV indices are listed in Table 1. During the five computer task sessions, the most stressful session (HS) resulted in a faster HR than the other computer tasks (all po0.01) and the computer work under the relaxation state (RS) resulted in a lower HR than the other tasks (all po0.05). HR significantly slowed down after the computer work (REST2) compared to other sessions (REST1, NS, SS LS, [all po0.05] and HS [po0.01]) except RS. SDRR in HS was significantly lower than that in LS (po0.05). SDRR in REST2 significantly increased compared to those in NS, SS, LS (po0.05) and HS (po0.01). There were no significant differences for the normalized LF and HF between sessions (p40.05).
4. Discussion The behavior performance confirmed that the designed protocol successfully created a pressure for the subjects (see Fig. 2). Work stress affected several physiological processes in the subjects. The results from the present study indicate that the effects of work stress are partly mediated through (1) the increased HR reactivity to the stressful computer work and (2) the lower SDRR implying the decreased HRV. These two characteristics of work stress are associated with increased risk of cardiovascular disease [30,31]. Meanwhile, the decreased HRV during the stressful
P. Shi et al. / Computers in Biology and Medicine 58 (2015) 40–45
43
Fig. 3. Representative Poincaré plots from one subject during (a) REST1, (b) NS, (c) HS, (d) SS, (e) LS, (f) RS and (g) REST2. The changes of SD1 and SD2 for the same subject are presented in (h). The narrowing induced by withdrawn sympathetic activities was observed during the computer work, as was increased scatter due to an increased parasympathetic activity during REST1, non-pressure work (RS) and REST2.
Table 2 Values of Poincaré plot indices (SD1, SD2, SD1/SD2) for seven sessions. The paired t-test was performed to compare different sessions. Data are expressed as the mean 7 standard deviation. Sessions REST1 NS HS SS LS RS REST2
SD1
SD2 †♦
17.9 7 4.5 19.17 7.5♦ 18.9 7 7.9♦ 20.2 7 9.3 21.0 7 8.6 23.7 7 7.2n 25.0 7 9.5
SD1/SD2 ‡
50.17 8.4 46.5 7 13.2† 43.7 7 12.6‡♦▲ 50.4 7 16.3† 48.97 14.4n‡♦ 58.0 7 12.9nn†▲ 77.17 20.5nn♦▲
0.36 70.09n♦▲ 0.41 70.08 0.43 70.11† 0.41 70.12 0.42 70.07† 0.40 70.05 0.3370.12n▲
n
po 0.05 vs. HS. p o0.01 vs. HS. † p o0.05 vs. REST2. ‡ p o0.01 vs. REST2. ♦ p o 0.05 vs. RS. ▲ po 0.05 vs. LS. nn
computer tasks suggested a higher cardiac sympathetic activity or a lower vagal tone [32]. However, no significant differences in either LF or HF components were identified between any two sessions (see Table 1). The inability of spectral analysis to distinguish groups has also been reported in the literature [13,33,34], where an evident increase in HR was not followed by changes in the spectral characteristics of HRV in mental stress performed with verbalisation. In the present study, the spectral results suggest that an alternative method is required to investigate computer work under time pressure. Over recent years, short time Fourier transform and wavelet transform have been used to examine the mechanisms controlling HRV. Although these advanced spectral techniques has been suggested to be good indicators for distinguishing patients from healthy subjects, the correlation between the control of the autonomic nervous system and these advanced spectral techniques has not been well established [35,36]. It has been demonstrated that the Poincaré plot can provide useful physiological information of R–R interval dynamics during exercise, which is not easily detected using linear measures [19]. Similarly, in the present study, the Poincaré plot analysis showed
its sensitivity in distinguishing subtle variations. The meaning of the Poincaré plot parameters was specified in the work from Tulppo and his co-workers [19]. As stated in their work, SD1 quantifies the vagal modulation of HR, whereas decreased SD2 and increased SD1/SD2 suggest sympathetic activation. In the present study, RS and REST2 with higher SD1 and SD2 are consistent with heightened vagal tone, perhaps as a result of sustained sympathetic activation or accentuated antagonism [37] after a series of relatively stressful work sessions. The time pressure tasks with lower SD1 and SD2 were associated with sympathetic activation and reduced vagal tone. Meanwhile, HS and LS with higher SD1/ SD2 and with lower SD1/SD2 were related to the excitement of the sympathetic nerves during the two sessions of pressure computer tasks and sustained vagal activity during the rest after the computer work. Why the Poincaré plot performs better than spectral analysis is an interesting question. The time series of heart beats exhibits the pattern of a nonlinear process [38]. Due to the nonlinear components, the R–R interval time series signal cannot be properly assessed using linear techniques such as spectral analysis [38]. In the search for improved methods for decoding hidden information in the R–R interval dynamics, the parameters arising from nonlinear methods were identified. A Poincaré plot of HRV, consisting of plotting each R–R interval as a function of the previous interval, is such a nonlinear method that can be used to evaluate changes in heart dynamics with its trends. Poincaré analysis is used to assess the qualitative (nonlinear interactions between regulatory mechanisms) rather than the quantitative (time- or frequency domains) properties of HR dynamics. In addition to its sensibility to the nonlinear phenomena of HRV genesis, the combinational and weighted contributions of LF and HF to the Poincaré index (SD1 or SD2) [24] are supposed to make the Poincaré analysis technique more insightful to the subtle differences that are difficult to detect using LF or HF separately. Nonlinear indexes, such as DFA and ApEn, are recently introduced methods for measuring HRV and have been shown to be powerful tools for the characterization of various complex systems. However, no systematic study has been conducted to investigate the use of these methods in large patient populations, and no physiological connection between these methods and cardiac activities has been well established. Specifically, an association between sympathetic activity and
44
P. Shi et al. / Computers in Biology and Medicine 58 (2015) 40–45
the shape of the Poincaré plot has been established. where the narrower the pattern is observed, the larger the sympathetic activity was [39–41]. The visual pattern presented in this study agrees with this association. Fig. 3 shows the visual changes of SD1 and SD2 from the Poincaré cloud shape of one subject during the whole performance. The narrowing scatter induced by the augmentation of sympathetic activities could be noticed in the stressful computer work (HS and LS), as well as the increased scatter due to the enhanced parasympathetic activity in the relaxation computer work (RS) and two relaxing states (REST1 and REST2). The patterns of the Poincaré plots can reflect a normal or reduced cardiac autonomic modulation. The comet patterns, as shown in Fig. 3 (a) and (g) for REST1 and REST2, respectively, express an augmenting variability with an increasing R–R interval. The torpedo patterns or a pattern tending to torpedo shape, as shown in Fig. 3 (c)–(e) for HS, SS and LS, respectively, indicate a low beat-to-beat variability with the same dispersion along the diagonal irrespective of the mean R–R interval. Because the Poincaré scattergram gives a visually distinguishable pattern for the sessions, it can be considered to be a useful tool for describing the autonomic activity effect over the R–R interval dynamics.
5. Conclusion Stress is induced by an imbalance between external demands and the individual’s ability to meet those demands. The evaluation of stress is difficult due to the interpretation of behavioral and physiological reactivity. The present study shows the effectiveness of the Poincaré plot method for assessing the HRV changes during computer-mouse work under time pressure. Heightened vagal tone occurred during relaxation work without time pressure, and the stressful computer tasks with added time pressure stimulated cardiac sympathetic activity. Poincaré analysis possesses the features of nonlinear measures, allowing it to perform better than spectral analysis. Moreover, compared to the numerical information from the time and frequency domains, the Poincaré plot provides a more global look and a visual contact for changing HRV, and it could be used to interpret the physiological phenomena in a straightforward manner.
Competing interests statement The authors declare that they have no competing interests.
Acknowledgements This work was supported by Innovation Program of Shanghai Municipal Education Commission (Grant no. 14YZ091). References [1] S.W. Porges, Cardiac vagal tone: a physiological index of stress, Neurosci. Biobehav. Rev. 19 (1995) 225–233. [2] G. Durantin, J.F. Gagnon, S. Tremblay, F. Dehais, Using near infrared spectroscopy and heart rate variability to detect mental overload, Behav. Brain Res. 259 (2014) 16–23. [3] U. Aasa, B.R. Jensen, J. Sandfeld, H. Richter, E. Lyskov, A.G. Crenshaw, The impact of object size and precision demands on fatigue during computer mouse use, Adv. Physiother. 13 (2011) 118–127. [4] S. Sako, H. Sugiura, H. Tanoue, M. Kojima, M. Kono, R. Inaba, The position of a standard optical computer mouse affects cardiorespiratory responses during the operation of a computer under time constraints, Int. J. Occup. Med. Environ. Health 27 (2014) 547–559. [5] Y. Wang, G.P.Y. Szeto, C.C.H. Chan, Effects of physical and mental task demands on cervical and upper limb muscle activity and physiological responses during computer tasks and recovery periods, Eur. J. Appl. Physiol. 111 (2011) 2791–2803. [6] T.D. Dzhebrailova, I.I. Korobeinikova, E.N. Dudnik, Dynamics of heart rhythm characteristics and efficiency of intellectual activity during computer work, Bull. Exp. Biol. Med. 156 (2014) 615–619.
[7] M. Heiden, E. Lyskov, M. Djupsjobacka, F. Hellstrom, A.G. Crenshaw, Effects of time pressure and precision demands during computer mouse work on muscle oxygenation and position sense, Eur. J. Appl. Physiol. 94 (2005) 97–106. [8] N. Hjortskov, D. Rissen, A.K. Blangsted, N. Fallentin, U. Lundberg, K. Sogaard, The effect of mental stress on heart rate variability and blood pressure during computer work, Eur. J. Appl. Physiol. 92 (2004) 84–89. [9] J. Wahlström, M. Hagberg, P.W. Johnson, J. Svensson, D. Rempel, Influence of time pressure and verbal provocation on physiological and psychological reactions during work with a computer mouse, Eur. J. Appl. Physiol. 87 (2002) 257–263. [10] A.H. Garde, B. Laursen, A.H. Jorgensen, B.R. Jensen, Effects of mental and physical demands on heart rate variability during computer work, Eur. J. Appl. Physiol. 87 (2002) 456–461. [11] L. Finsen, K. Sogaard, C. Jensen, V. Borg, H. Christensen, Muscle activity and cardiovascular response during computer-mouse work with and without memory demands, Ergonomics 44 (2001) 1312–1329. [12] J. Taelman, S. Vandeput, I. Gligorijevic, A. Spaepen, S. Van Huffel, Time– frequency heart rate variability characteristics of young adults during physical, mental and combined stress in laboratory environment, Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011 (2011) 1973–1976. [13] R.K. Sunkaria, Recent trends in nonlinear methods of HRV analysis: a review, World Acad. Sci. Eng. Technol. 75 (2011) 566–571. [14] A.J.E. Seely, P.T. Macklem, Complex systems and the technology of variability analysis, Crit. Care 8 (2004) R367–384. [15] A.L. Goldberger, D.R. Rigney, B.J. West, Chaos and fractals in human physiology, Sci. Am. 262 (1990) 42–49. [16] O. Faust, V.R. Prasad, G. Swapna, S. Chattopadhyay, T.C. Lim, Comprehensive analysis of normal and diabetic heart rate signals: a review, J. Mech. Med. Biol. 12 (2012). [17] D. Jiang, M. He, Y. Qiu, Y. Zhu, S. Tong, Long-range correlations in heart rate variability during computer–mouse work under time pressure, Physica A 388 (2008) 1527–1534. [18] M.P. Tulppo, T.H. Makikallio, T. Seppanen, R.T. Laukkanen, H.V. Huikuri, Vagal modulation of heart rate during exercise: effects of age and physical fitness, Am. J. Physiol. 274 (1998) H424–429. [19] M.P. Tulppo, T.H. Makikallio, T.E. Takala, T. Seppanen, H.V. Huikuri, Quantitative beat-to-beat analysis of heart rate dynamics during exercise, Am. J. Physiol. 271 (1996) H244–252. [20] A. Voss, S. Schulz, R. Schroeder, M. Baumert, P. Caminal, Methods derived from nonlinear dynamics for analysing heart rate variability, Philos. Trans. A Math. Phys. Eng. Sci. 367 (2009) 277–296. [21] M.A. Woo, W.G. Stevenson, D.K. Moser, R.B. Trelease, R.M. Harper, Patterns of beat-to-beat heart rate variability in advanced heart failure, Am. Heart J. 123 (1992) 704–710. [22] P. Shi, Y. Zhu, J. Allen, S. Hu, Analysis of pulse rate variability derived from photoplethysmography with the combination of lagged Poincare plots and spectral characteristics, Med. Eng. Phys. 31 (2009) 866–871. [23] A. Gladuli, N.S. Moïse, S.A. Hemsley, N.F. Otani, Poincaré plots and tachograms reveal beat patterning in sick sinus syndrome with supraventricular tachycardia and varying AV nodal block, J. Vet. Cardiol. 13 (2011) 63–70. [24] M. Brennan, M. Palaniswami, P. Kamen, Poincare plot interpretation using a physiological model of HRV based on a network of oscillators, Am. J. Physiol. Heart Circ. Physiol. 283 (2002) H1873–1886. [25] Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, Heart rate variability. Standards of measurement, physiological interpretation, and clinical use, Eur. Heart J. 17 (1996) 354–381. [26] B. Xhyheri, O. Manfrini, M. Mazzolini, C. Pizzi, R. Bugiardini, Heart rate variability today, Prog. Cardiovasc. Dis. 55 (2012) 321–331. [27] M. Brennan, M. Palaniswami, P. Kamen, Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability? IEEE Trans. Biomed. Eng. 48 (2001) 1342–1347. [28] P.W. Kamen, A.M. Tonkin, Application of the Poincare plot to heart rate variability: a new measure of functional status in heart failure, Aust. N. Z. J. Med. 25 (1995) 18–26. [29] L. Mourot, M. Bouhaddi, S. Perrey, J.D. Rouillon, J. Regnard, Quantitative Poincare plot analysis of heart rate variability: effect of endurance training, Eur. J. Appl. Physiol. 91 (2004) 79–87. [30] L. Tonello, F.B. Rodrigues, J.W. Souza, C.S. Campbell, A.S. Leicht, D.A. Boullosa, The role of physical activity and heart rate variability for the control of work related stress, Front. Physiol. 5 (2014) 67. [31] F. Togo, M. Takahashi, Heart rate variability in occupational health —a systematic review, Ind. Health 47 (2009) 589–602. [32] M.J. Reed, C.E. Robertson, P.S. Addison, Heart rate variability measurements and the prediction of ventricular arrhythmias, QJM 98 (2005) 87–95. [33] L. Bernardi, J. Wdowczyk-Szulc, C. Valenti, S. Castoldi, C. Passino, G. Spadacini, P. Sleight, Effects of controlled breathing, mental activity and mental stress with or without verbalization on heart rate variability, J. Am. Coll. Cardiol. 35 (2000) 1462–1469. [34] R.P. Sloan, J.B. Korten, M.M. Myers, Components of heart rate reactivity during mental arithmetic with and without speaking, Physiol. Behav. 50 (1991) 1039–1045. [35] C.H. Peters, R. Vullings, M.J. Rooijakkers, J.W. Bergmans, S.G. Oei, P.F. Wijn, A continuous wavelet transform-based method for time–frequency analysis of artefact-corrected heart rate variability data, Physiol. Meas. 32 (2011) 1517–1527.
P. Shi et al. / Computers in Biology and Medicine 58 (2015) 40–45
[36] P.S. Addison, Wavelet transforms and the ECG: a review, Physiol. Meas. 26 (2005) R155–199. [37] M.N. Levy, Sympathetic–parasympathetic interactions in the heart, Circ. Res. 29 (1971) 437–445. [38] C. Braun, P. Kowallik, A. Freking, D. Hadeler, K.D. Kniffki, M. Meesmann, Demonstration of nonlinear components in heart rate variability of healthy persons, Am. J. Physiol. 275 (1998) H1577–1584. [39] P. Shi, Y. Chen, M.M. Guo, H.L. Yu, Acute effects of alcohol on heart rate variability: time-related changes and gender difference, Biomed. Eng. Appl. Basis Commun. 26 (2014).
45
[40] S. Carrasco, M.J. Gaitan, R. Gonzalez, O. Yanez, Correlation among Poincare plot indexes and time and frequency domain measures of heart rate variability, J. Med. Eng. Technol. 25 (2001) 240–248. [41] M.A. Woo, W.G. Stevenson, D.K. Moser, H.R. Middlekauff, Complex heart rate variability and serum norepinephrine levels in patients with advanced heart failure, J. Am. Coll. Cardiol. 23 (1994) 565–569.
©2015 Elsevier