Kamen, P., & Tonkin, A. (1995). Application of the Poincare plot to heart rate variability: A new measure of functional status in heart failure. Australian and New ...
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An Examination of the Relationship Between Resting Heart Rate Variability and Heart Rate Reactivity to a Mental Arithmetic Stressor Christopher F. Sharpley,1,4 Peter Kamen,2 Maria Galatsis,3 Rod Heppel,3 Charles Veivers,3 and Kim Claus3
Resting heart rate variability can be an index of sympathetic or parasympathetic dominance, according to the frequency of the variability studied. Sympathetic dominance of this system has been linked to increased risk of cardiovascular disease (CVD). Similarly, rapid and dramatic increases in heart rate reactivity to a stressor task have also been suggested as indicating increased risk of CVD via atherogenesis. Although both of these variables have been related to the development of cardiovascular disease, and both may be related to increased sympathetic activity or parasympathetic withdrawal, most research studies have tended to focus on either variable independently of the other. In order to investigate whether these two indices of stressor reactivity were related in relatively young and healthy subjects, resting heart rate variability data were collected from 80 volunteers for 20 minutes. In addition, heart rate reactivity data were collected during a 2-minute mental arithmetic stressor, which has been previously shown to induce significant increases in heart rate. After classifying subjects according to whether their heart rate variability data were above or below the mean for their gender, heart rate reactivity data were examined via MANOVA to detect significant differences between subject groups. Females showed significant effects, and males showed nonsignificant trends, but these two sets of data were in different directions, suggesting that gender may be a confounding factor in the relationship between heart rate reactivity and heart rate variability. KEY WORDS: cardiovascular disease; heart rate reactivity; heart rate variability.
INTRODUCTION Despite large reductions in incidence during the last two decades, cardiovascular disease (CVD) remains a major cause of death in the western world. Although traditional risk factors such as essential hypertension, smoking, serum cholesterol, and obesity contribute 1 Institute
for Health Sciences, Bond University, Gold Coast, Qld 4225, Queensland. Dynamics, Box Hill, Victoria. 3 Monash University, Victoria. 4 Address all correspondence to Christopher F. Sharpley, Institute for Health Sciences, Bond University, Gold Coast, Qld 4225, Australia. 2 Cardiac
143 C 2000 Plenum Publishing Corporation 1090-0586/00/0900-0143$18.00/0 °
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to the CVD risk status of a particular individual, these factors do not account for more than 50% of new cases (Krantz, Contrada, Hill, & Freidler, 1988). Thus, a research focus upon psychosocial factors such as the Type A Behaviour Pattern, Hostility, Depression, Coping Style, and Cognitive Hardiness has attempted to develop models of these as contributing to CVD. However, the data on each of these factors have been inconsistent. An alternative research focus has been upon the underlying psychophysiological links between certain “psychological” states and those physiological mechanisms (Porges, 1994), which have been shown to contribute directly to the development of CVD. One of these physiological mechanisms that has been shown to significantly predict sudden cardiac death is low levels of heart rate variability (HRV) (e.g., Kleiger, Miller, Bigger, Moss, of Multicenter Post-Infarction Research Group, 1987; Kleiger, Miller, Bigger, & Moss, 1984; Martin et al., 1987). HRV refers to the amount of variance in the time period between successive R-R spikes in the cardiac cycle, and several techniques have been developed to analyze ECG data to determine the status of this variability. One such technique, which is of particular significance in gathering easily-interpretable data, is the Poincare plot of the R-R interval against the preceding R-R interval, a procedure that is resistant to the influence of ectopic beats and other arrhythmias (Kamen & Tonkin, 1995). The Poincare plot method uses a statistical measure of the standard deviation of successive R-R intervals to quantify HRV data, and can act as a reflection of the level of sympathetic-versus-parasympathetic dominance over heart rate. In a laboratory examination of the relationship between stressor tasks and HRV and heart rate, Langewitz and Ruddel (1989) submitted subjects to two mentally demanding tasks—reaction-time and mental arithmetic plus noise. Subjects were required to say their answers to the mental arithmetic problems aloud, and heart rate reactivity and HRV data were collected during the stressor periods. Data collected from 135 healthy men indicated a significant increase in heart rate and a decrease in HRV during the reaction-time task, and an increase in heart rate but no significant change in HRV during the mental arithmetic task. Langewitz and Ruddel (1989) suggested that these discrepant results were because speaking their answers to the mental arithmetic problems aloud interfered with subjects’ respiration patterns and made the collection of HRV data unreliable, thus leaving open the question of whether mental arithmetic (a commonly used laboratory stressor) actually does decrease HRV when delivered without the need for subjects to say their answers aloud. Mental arithmetic is considered to be typical of day-to-day environmental stressor because it is cognitively-demanding, time-pressured, and high scores are rewarded. These are the three major characteristics of workplace stressors noted by Cinciripinni (1986), and it may be of value to examine the effects of mental arithmetic upon the HRV data of subjects who are not required to speak during problem-solving. While HRV data are often collapsed into a single reported factor, there are, in fact, two separate underlying aspects to HRV—a low frequency aspect that refers to sympathetic modulation of heart rate (commonly referred to as SDRR—the standard deviation of the time interval between R spikes), and a high frequency aspect—which refers to the vagal or parasympathetic modulation of heart rate (commonly referred to as SDDRR—the standard deviation of the total (delta) RR intervals). Reference to each of these aspects of HRV can be used as an indicator of an individual’s predominant sympathetic-parasympathetic dominant state. Another cardiovascular reactivity variable that is associated with responses to stressors and CVD is heart rate reactivity (HRR). HRR refers to the involuntary increase of a subject’s
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heart rate during a stressor task, and many studies have been conducted on the effects of both physical and psychological events upon heart rate reactivity (Sharpley & Gordon, 1999). As with HRV responses to stressor events, it is hypothesized that certain biobehavioral constructs such as social support, Type A Behavior Pattern, anger, and hostility are linked to the size of an individual’s HRR responses to stressor events via sympathetic dominance and/or parasympathetic withdrawal of heart rate (Matthews et al., 1986). Several commentaries have described causal pathways between HRR and atherosclerosis (e.g., Krantz & Manuck, 1984; Ross, 1986; Ross & Glomset, 1976), but one of the most elegant is by Texon (1980), who mapped the geographical location of atherosclerotic plaques in human cadavers. Texon found that these plaques occurred at specific sites, on the arterial tree, which were associated with hemodynamic effects of increased blood flow (such as those present during an incident of stressor-induced HRR) and that this localization was largely independent of dietary factors. Texon’s findings regarding the localization of atherosclerotic lesions were replicated by Halon, Sapoznikov, Lewis, and Gotsman (1983), who mapped the distribution of atherosclerosis in living men who had been referred for angiogram examination. Together, these data argue that the atherogenic process occurs in specific sites, which are determined by hemodynamic factors, thus linking HRR (and stressor hyperreactivity of the cardiovascular system) to atherosclerosis. Both HRR and HRV are associated with CVD, an association that may be mediated by psychosocial factors. Because both HRV and HRR are also linked to exaggerated sympathetic nervous system responses and/or withdrawal of parasympathetic activity during stressor events, examining the relationship between these two as a further step in elucidating the possible links between psychosocial factors and CVD was of interest. Relatively little attention has been paid to this question in the wider literature, although some studies have shown inconsistent results. For example, Lane, Adcock and Burnett (1992) instructed 32 healthy males to relax for 5 to 10 minutes for resting heart rate to be obtained, followed by serial subtraction as a stressor event. Data were collected on Respiratory Sinus Arrythmia (RSA) gathered during rest and HRR to the mental arithmetic event. Although no significant relationship was noted between RSA and HRR, this may have been because RSA has not been always consistently associated with HRV (see Bernston et al., 1997, for a review). Therefore, the present study examined the relationship between resting HRV values and later HRR to a mental arithmetic stressor. In particular, it was hypothesized that subjects with HRV indicative of sympathetic dominance/parasympathetic withdrawal during rest would be more likely to show significant increases in HRR during the mental arithmetic stressor. The stressor task to be used in the present study did not require subjects to vocalize their responses to it because such vocalization may be a possible limitation when assessing HRV responses, as noted by Langewitz and Ruddel (1989). In addition, data were collected on healthy subjects without any signs of CVD to avoid contamination of already established CVD with HRR or HRV data. If a model of causality between psychosocial factors and CVD-inducing responses is to be developed, it is important that it includes data from subjects who do not have advanced CVD so that causal, rather than simply correlational, links can be postulated. That is, finding a correlation between HRV and HRR in patients with established CVD pathology does not allow for reliable determination of whether the HRV/HRR responses developed prior to CVD (and therefore may be seen as contributing to that development) or whether they developed alongside CHD and are merely outcomes of other atherogenic processes.
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METHOD Subjects Eighty healthy volunteer participants (35 male), aged 18 to 27 years (M = 23.2 years), were selected from a larger pool who responded to a verbal advertisement to participate in “a study about your heart,” which was made at Monash University, Victoria. Selection was based upon the criteria of having no prior history of CVD or any current illness, nor any current medication except the contraceptive pill. About 75% of the sample were students and the rest full-time employees. Informed consent was obtained from all participants, and they were free to withdraw from the project at any stage. All procedures were approved by the Standing Committee on Ethics in Research on Humans at Monash University. Apparatus All heart rate, HRV and HRR data were collected via a BIOVIEW Series IV physiological recording system produced by Zencor, Melbourne. The BIOVIEW-IV collects data on heart rate, ECG and HRV, EMG, Skin Temperature and EDA via an interface with a pc, but only the ECG data were collected in this study. Subjects’ ECGs were analyzed via two data-collection programs that accompany the BIOVIEW-IV. One (BIOVIEW) allows for easy calculation of HRR in beats per minute as a measure of the differences between heart rate during a designated stressor event and during a designated rest event. Another software program (HRVIEW) produces HRV data under several headings, including a Poincare plot. HRV data are collected in milliseconds, and the Poincare plot allows for easy identification of ectopic beats and other artefacts. During the rest period, subjects reclined on a lounge chair so that they were supine, allowing for collection of resting HRV without any contamination of the reductions in HRV that may accompany tilting and sitting. ECG electrodes (Meditrace Ag/AgCl ECG conductive adhesive electrodes) were attached to subjects’ chests as described below. The mental arithmetic task has been described in detail elsewhere (e.g., Sharpley, 1989, 1994), and consisted of a series of 20 questions of the type “87 − 34 + 13 = ?”, which were presented in standardized format via audiotape so that each problem took 4 seconds to be read out and subjects were given 2 seconds to write their response onto a prepared and provided sheet of paper. This task has been shown to produce reliable changes in heart rate across a range of ages and both genders (Sharpley & Scuderi, 1994). Subjects received two sets of mental arithmetic questions, preceded by instructions and two practice questions to ensure that understood the task. The mental arithmetic questions were presented with the subjects seated in a small soundproof booth in the same room as the lounge on which they reclined during collection of resting heart rate and HRV data. A $10 note was placed about 1 meter in front of the subject and attached to the wall in the booth, and subjects were told that “the person who gets most mental arithmetic problems correct will earn this reward of $10.” Procedure All subjects were tested individually. Upon arrival at the research laboratory in the Centre for Stress Management and Research at Monash University, subjects were greeted
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and given a description of the research study. After informed consent was obtained from subjects, a health questionnaire was administered to determine that they were not suffering from CVD or any other illness and had been fasting from caffeine and alcohol for 12 hours. Subjects then had ECG electrodes attached to their right shoulder, the left anterior axillary line at the tenth intercostal space, and the right lower quadrant. Some preliminary ECG data were collected to ensure that these electrodes were functioning satisfactorily. Subjects were then instructed to lie down on the lounge for 5 minutes as an adaptation period (they had been in the research laboratory for about 10 minutes prior to this time), and then 20 minutes of resting ECG data were collected for HRV analysis. Subjects were then asked to move from the lounge and to sit in a chair in the small booth. Headphones were attached to subjects so that the mental arithmetic problems could be heard. The experimenter remained outside the booth and all monitoring equipment was also outside the booth. The mental arithmetic protocol consisted of the following steps, all delivered via standardized audiotape instructions. Adaptation-5 minutes: Subjects were given an excerpt from an encyclopedia on the topic “The Solar System” and asked to read this quietly. Subjects were told that this material was not germane to the experiment and was “just to help fill in the time.” This adaptation period was used to establish that subjects’ heart rates had returned to resting level after the transfer from the lounge in the larger room to the chair in the booth. First Rest Period-2 minutes: Subjects were instructed to “put down the reading material, sit back, close your eyes and relax.” Instruction period-2 minutes: Subjects received audiotaped information that explained that they were going to be asked some mental arithmetic problems, and two examples of these were given and worked via commentary from the audiotape. This activity served to determine that subjects understood the instructions and were ready to answer the mental arithmetic problems. The $10 note reward and conditions for winning it were pointed out during this period. First Mental Arithmetic (MA) period-2 minutes: Subjects were asked to write down their answers to the 20 MA problems as they were presented via the audiotape. Each problem was read out on the tape over a period of 4 seconds, and subjects were allowed 2 seconds to write their answers on the sheet of paper prepared and provided for this. Second Rest Period-2 minutes: Subjects were instructed (via the audiotape) to “stop writing, sit back, close your eyes, and relax as you did before.” Their MA answer sheets were collected by the experimenter. Second MA Period-2 minutes: This was the same as the first MA period, except that a different set of MA problems was presented on the audiotape. Subjects were then asked to return to the larger room and lie on the lounge again. After 5 minutes (to allow for heart rate to return to rest after the movement), subjects’ HRV were again collected for 10 minutes. This period was sufficient to allow for collection of enough data to perform reliable statistical analyses. The first period of HRV data collection was double this length of time to ensure that no equipment or subject artefacts were present in the HRV data. Data from this second rest period were not examined in this study. Data Reduction HRR values for each of the two MA periods were collected by subtracting the mean heart rate during the last 60 seconds of the Rest period from the mean heart rate of the first
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minute of the MA period. In this way, two HRR values were obtained, one (HRR1) from the first MA period, and another (HRR2) from the second MA period. Mental Arithmetic scores for each MA trial were calculated for each subject and called MA1 and MA2. HRV data, including standard deviation of R-R intervals (SDRR-a measure of sympathetic activity) and standard deviation of the delta R-R intervals (SDDRR-measure of parasympathetic activity) were calculated and subjects were classified according to their scores as being above versus below the mean for each of these variables. RESULTS HR and HRR data. Mean heart rate during the initial rest period (MHR1) was 69.30 bpm (SD = 10.36 bpm), and 65.63 bpm (SD = 8.91 bpm) during the final rest period (MHR2), indicating that subjects’ resting heart rates were within normal ranges. In order to ensure that HRR data were free from the confounding effects of initial values, an analysis of covariance on each of the HRR variables from the two MA trials with the respective pre-MA trial rest period as covariate was performed. Both sets of HRR data were significantly different to their respective rest period HR data. In addition, there was no significant correlation (r ) between either HRR scores and the relevant rest HR values. HRV data. Mean SDRR was 64.31 msec (SD = 25.59 msec) and mean SDDRR was 57.90 msec (SD = 33.56 msec). Raw data from males and females are shown in Table I, plus results of MANOVA analyses of these data. Because a significant main effect was noted between genders (F(6,73) = 5.483), p < .01), further data analysis was performed separately for males and females. The HRR values from the two MA periods for each subject were calculated and then used as dependent variables in MANOVAs, which used each gender’s HRV data as independent variables (i.e., SDRR, SDDRR). The results of each of these analyses is presented below for males and then for females. Using SDRR as the independent variable, with males classified according to their scores above or below the mean for this variable, a nonsignificant main effect was noted when the two HRR data sets were used as dependent variables (F(2,32) = .114, ns, B = .07). Because of the exploratory nature of this study and the lack of clear and established relationships between the two dependent HRR variables and the two HRV variables, Huberty and Morris’s (1989) recommendations regarding the value of univariate analyses in identifying the major relationships in multivariate studies such as this one were followed. Univariate tests revealed that those males who were below Table I. Means, Standard Deviations and Univariate Effects for Males and Females for HR, HRR and HRV Dataa Males
Females
Variable
Mean
SD
Mean
SD
F(1,78)
p
MHR1 MHR2 HRR1 HRR2 SDRR SDDRR
67.29 63.38 18.04 12.89 74.36 62.92
(10.86) bpm (9.26) bpm (12.86) bpm (7.74) bpm (25.12) msec (34.84) msec
70.87 67.38 16.62 12.26 56.48 53.99
(9.79) bpm (8.31) bpm (10.80) bpm (7.88) bpm (23.14) msec (32.38) msec
2.396 4.136 .288 .127 1.403 10.903
ns .045 ns ns ns .001
an
= 80 (35 males, 45 females).
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the mean for SDRR (i.e., who showed relatively low sympathetic activity during rest) had a mean HRR1 of 18.26 bpm compared to 17.27 bpm for those males whose SDRR was above the mean (F(1,33) = .115, ns, B = .05). Similarly, when the HRR from the second MA task was used as the dependent variable, low SDRR males had HRR2 of 13.51 bpm compared to males whose SDRR was above the mean (12.23 bpm: F(1,33) = .234, ns, B = .05). When the SDDRR scores (i.e., an index of parasympathetic activity) from pretask rest were used to classify males’ HRR1 responses, another nonsignificant main effect was noted (F(2,32) = .122, ns, B = .05). Those males whose SDDRR scores were below the mean showed a HRR to the first MA task of 19.02 bpm compared to those males whose SDDRR was above the mean (HRR = 16.86 bpm: F(1,33) = .238, ns, B = .05). A similar result was found for HRR to the second MA task, with below the mean SDDRR males showing a HRR2 of 13.25 bpm versus those males whose SDDRR was above the mean (HRR2 = 12.46 bpm: F(1,33) = .089, ns, B = .05). These results for males failed to indicate significant relationships between HRV data collected at rest and HRR data during the two MA tasks, although the lack of statistical power in some contrasts argues for at least some further consideration of the trends present in these data. Females’ data were analysed the same way. Taking SDRR subgroups first, there was no significant main effect (F(2,42) = 2.653, p = .082, B = .50), but significant univariate effects. First, using SDRR as the independent variable, females with above-average SDRR scores showed significantly greater HRR1 (20.78 bpm) than females with below average SDRR scores (13.56 bpm: F(1,43) = 5.394, p < .03, B = .61), and a trend toward significantly greater HRR2 scores (above-average SDRR group = 14.80 bpm, below average SDRR group = 10.40 bpm: F(1,43) = 3.627, p = .064, B = .45). Second, although there was no significant main effect for SDDRR (F(2,42) = 2.130, ns, B = .13), the univariate effect for HRR1 was significant (above-average SDDRR group HRR1 = 20.77 bpm, below-average SDDRR group HRR1 = 14.09 bpm: F(1,43) = 4.358, p < .05 : B = .53), but the univariate effect for HRR2 was not significant (above-average SDDRR group HRR2 = 14.59 bpm: below-average SDDRR group HRR2 = 10.59 : F(1,43) = 2.470, ns, B = .33). Again, the consideration of data trends is valuable given some limitations in statistical power. To more clearly see these trends, data have been presented graphically in Figure 1. These data should be treated with caution because of the lack of consistent significant effects and the risk of Type I error when multiple comparisons are being made. Of the eight contrasts performed here, only two were significant at traditional levels with another showing a trend. However, that is a rate of 25%, well above the expected error rate of 5%, which might be the result of chance, and those results that are significant should be taken as reliable. Other trends are also of interest in an exploratory study such as this. Figure 1 suggests that females with higher than average sympathetic activity (i.e., higher SDRR scores) during pre-task rest also showed significantly higher HRR1, which may be classified as a sympathetic response to the MA task. A similar, but nonsignificant, relationship was apparent in the HRR2 data for females who were classified according to their SDRR. (It may be conjectured that the second application of the MA task did not induce the same level of sympathetic responsivity or parasympathetic withdrawal because the stressor task was by now familiar to subjects.) Thus, there is some consistency between these two indices of sympathetic activity for females. Males’ data did not show this relationship, and the lack of statistical power in those contrasts performed on males’ data suggests that no firm conclusion can be drawn apart from noting the lack of consistency across genders’ HRR and SDRR responses.
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Fig. 1. HRR (bpm) data from Mental Arithmetic task for males and females classified according to high versus low sympathetic (SDRR) or parasympathetic (SDDRR) activity during rest.
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Concerning SDDRR data, there is an apparent contradiction between females’ HRR1 and HRR2 data. If, as suggested by several authors (e.g., Kamen & Tonkin, 1995), SDDRR represents parasympathetic activity, then the significant relationship between high SDDRR and high HRR1 may represent a decoupling of the sympathetic and parasympathetic nervous systems as they relate to the hearts of these females. Such decoupling has been suggested by others (e.g., Porges, 1994), and is a function of the multiple brain origin sites of the vagus system. Of interest again is the apparent lack of a similar reaction in the males studied here. DISCUSSION The most immediately apparent outcome of this study is the lack of consistent across gender significant relationships between two sets of cardiovascular indicators of sympathetic and parasympathetic activity—heart rate reactivity and heart rate variability. That the hypothesized significant relationship was shown for females in regard to a sympathetic activation index (i.e., SDRR) but not in regard to an index of parasympathetic activity (i.e., SDDRR) suggests decoupling of these two systems as reported by previous authors. In itself, this result is of interest because it adds to our understanding of the kinds of situations under which such decoupling can occur. Why it occurred for females and not for males is not so clear. In fact, if the visual trends are taken as a basis for conjecture, it may be that males showed distinctly different responses, although these are only small and without statistical power at this stage. Further investigation of these behaviors with males is likely to clarify this point. It may be of value to suggest some possible reasons for the lack of clear responses by males here. These subjects were relatively young and free from cardiovascular pathology, and the lack of clear relationships between males’ HRV and HRR data could be attributed to at least three possible causes. First, the development of sympathetic dominance in terms of cardiovascular responses may require a prolonged period of chronic reactivity before it is apparent. Although HRR data have been collected on quite young children (e.g., Murphy, Alpert, Willey, & Somes, 1988), and there is evidence that this reactivity maintains its consistency over time (Manuck & Garland, 1980), it may be that the extent of sympathetic dominance that is observed in studies of HRR is not sufficient to be noted in HRV data collected from young males who are healthy, as was the present sample. However, as noted in the opening sections of this paper, there are good reasons for investigating the relationship between HRV and HRR with healthy young subjects. If there had been a significant relationship between HRV and HRR with this sample, it would have been likely that the two variables were interacting and the effects of this accumulating from an early age. That they were not (at least for males), and that the HRR values were sufficiently high to suggest that subjects did, on average, show large sympathetic increase/parasympathetic withdrawal responses to the MA stressors, suggests that the development of consequential HRV (a risk factor for CVD) may well take several decades of chronic sympathetic activation to be detectable via present HRV technology. In cardiovascular health terms, although HRR may well be associated with the development of atherosclerosis in brief periods (at least in monkeys), it may take a much longer time for the “damage” to accumulate to be evident via HRV analysis. Most studies of HRV and its relationship with CHD have been (quite justifiably) on older persons or patients with evident cardiac pathology.
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Second, the laboratory assessment of HRR may not necessarily generalize to HRV. Previous studies of the generalizability of laboratory-induced HRR have clearly shown that the data obtained from the application of standardized stressors under laboratory conditions do not generalize to the typical everyday working life of healthy subjects (Sharpley & McLean, 1991). As with HRR, it may be necessary to gather HRV data in real-life situations in order to test the relationship between typical responsiveness and the underlying or consequential effects of chronic sympathetic arousal. Third, although there is a sound theoretical rationale why HRR and HRV ought to be related, they may not be. Reasons why this could be the case must, of necessity, be conjectural, but could revolve around the neurological processes that instigate HRR and HRV, and which have been referred to above. Porges’ (1995) comments and suggestions on this point are valuable and may be a starting point for understanding the present data. Although HRV and HRR are both influenced by sympathetic and parasympathetic activity, they may be activated via parallel but discrete neural pathways in the brain, and further investigation is necessary in this area before an adequate understanding is possible. Although these data do not argue for the inclusion of HRR as a precursor in the development of HRV, prospective studies may show that this is the case. Neither HRR nor HRV has been under examination for long enough for the collection of databases that are sufficient to enable longitudinal studies to be performed. However, this process is in action at several sites and, like the decades of data collection on blood pressure, may eventually allow a more detailed assessment of the hypothetical link between reactivity (and the psychosocial factors which influence that reactivity) and the development of HRV as a major risk factor for CVD. Before CVD risk can be reliably reduced via interventions designed to modify psychosocial factors, this link needs to be firmly established, at least by prospective data if not in healthy subjects. In the meantime, a focus upon behavioral procedures designed to increase HRV may be of more immediate value. Although there are only a few studies of the application of behavioral medicine to the task of training patients to increase their HRV, and at least two of those have focused upon RSA as a dependent variable rather than HRV, there are some data that both healthy subjects (del Paso, Godoy, & Vila, 1992) and patients who had suffered a sudden cardiac arrest (Cowan, Kogan, Burr, Hendershot, & Buchanan, 1990) can significantly increase their HRV with biofeedback and respiration training. While CVD remains a major cause of death in the western world, these procedures deserve attention and replication so that wider applications of these clinical interventions may be developed and made available in both preventative and clinical environments. REFERENCES Berntson, G., Bigger, J., Eckberg, D., Grossman, P., Kaufman, P., Malik, M., Nagaraja, H., Porges, S., Saul, J., Stone, P., & Van Der Molen, M. (1997). Heart rate variability: Origins, methods, and interpretive caveats. Psychophysiology, 34, 623–648. Cincirippini, P. (1986). Cognitive stress and cardiovascular reactivity-I: Relationships to hypertension. American Heart Journal, 112, 1044–1050. Cowan, M., Kogan, H., Burr, R., Hendershot, S., & Buchanan, L. (1990). Power spectral analysis of heart rate variability after biofeedback training. Journal of Electrocardiology, 23 (suppl.), 85–94. del Paso, G., Godoy, J., & Vila, J. (1992). Self-regulation of respiratory sinus arrhythmia. Biofeedback and SelfRegulation, 17, 261–275. Halon, D., Sapoznikov, D., Lewis, B., & Gotsman, M. (1983). Localization of lesions in coronary circulation. American Journal of Cardiology, 52, 921–926.
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