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Psychosomatic Medicine:
Original Articles

Acute Stress Affects Heart Rate Variability During Sleep

Hall, Martica PhD; Vasko, Raymond PhD; Buysse, Daniel MD; Ombao, Hernando PhD; Chen, Qingxia MS; Cashmere, J. David BS; Kupfer, David MD, and; Thayer, Julian F. PhD

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Author Information

University of Pittsburgh Department of Psychiatry (M.H., R.V., D.B., J.D.C., D.K.), Pittsburgh, PA; University of Illinois at Urbana-Champaign Department of Statistics and Beckman Institute (H.O.), IL; University of North Carolina at Chapel Hill Department of Biostatistics (Q.C.), Chapel Hill, NC; and National Institute of Aging, Baltimore, MD (J.F.T.).

Address correspondence and reprint requests to Dr. Martica Hall, Western Psychiatric Institute and Clinic, 3811 O’Hara Street, E-1101, Pittsburgh, PA, 15213. E-mail: hallmh@msx.upmc.edu

Received for publication December 5, 2001; revision received September 12, 2003.

The authors thank Drs. Richard Jennings, John Sollers, and Peter Gianaros for their assistance with modeling HRV during sleep. We also thank Drs. Kristen Salomon and Douglas Moul for their comments on this manuscript. Research was supported in part by MH01554, MH24652, MH30915, MH52247, MH24652, MH30915, and RR00056.

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Abstract

Objective: Although stress can elicit profound and lasting effects on sleep, the pathways whereby stress affects sleep are not well understood. In this study, we used autoregressive spectral analysis of the electrocardiogram (EKG) interbeat interval sequence to characterize stress-related changes in heart rate variability during sleep in 59 healthy men and women.

Methods: Participants (N = 59) were randomly assigned to a control or stress condition, in which a standard speech task paradigm was used to elicit acute stress in the immediate presleep period. EKG was collected throughout the night. The high frequency component (0.15–0.4 Hz Eq) was used to index parasympathetic modulation, and the ratio of low to high frequency power (0.04–0.15 Hz Eq/0.15–0.4 Hz Eq) of heart rate variability was used to index sympathovagal balance.

Results: Acute psychophysiological stress was associated with decreased levels of parasympathetic modulation during nonrapid eye movement (NREM) and rapid eye movement sleep and increased levels of sympathovagal balance during NREM sleep. Parasympathetic modulation increased across successive NREM cycles in the control group; these increases were blunted in the stress group and remained essentially unchanged across successive NREM periods. Higher levels of sympathovagal balance during NREM sleep were associated with poorer sleep maintenance and lower delta activity.

Conclusions: Changes in heart rate variability associated with acute stress may represent one pathway to disturbed sleep. Stress-related changes in heart rate variability during sleep may also be important in association with chronic stressors, which are associated with significant morbidity and increased risk for mortality.

AR = autoregressive;, BMI = body mass index;, EEG = electroencephalogram;, EKG = electrocardiogram;, EMG = electromyogram;, EOG = electro-oculogram;, FFT = Fast Fourier transform;, GSI = Global Severity Index;, HF = high-frequency;, HRV = heart rate variability;, IBI = interbeat interval;, LH = low-frequency;, NREM = nonrapid eye movement;, PSG = polysomnography;, PSQI = Pittsburgh Sleep Quality Index;, PSWQ = Penn State Worry Questionnaire;, REM = rapid eye movement;, SWS = slow-wave sleep;, VAS = visual analogue scale.

Stress can elicit profound and lasting effects on sleep (1–3). It is intuitively appealing to hypothesize that worry and intrusive thoughts delay the onset of sleep, and studies have supported this hypothesis (4–6). Similarly, stress-related intrusive thoughts and negative affect may color one’s subjective perceptions about the quality of one’s sleep (2,7). However, the pathways whereby stress reaches into the night to produce frequent awakenings from sleep, to lighten nonrapid eye movement (NREM) sleep, or to affect quantitative and qualitative components of rapid eye movement (REM) sleep are less clear. Emerging evidence suggests that stress also affects more subtle aspects of sleep such as high-frequency power in the beta-range (8).

Sustained autonomic nervous system arousal may represent one pathway whereby stress affects sleep. Spectral analysis of heart rate variability provides a noninvasive technique for indirectly measuring sympathetic and parasympathetic modulation during sleep. Fast Fourier transform (FFT) and autoregressive techniques have identified specific heart rate variability components that are indirect indices of sympathetic and parasympathetic modulation (9,10). Both FFT and autroregressive techniques transform heart rate signals from the time domain (beat-to-beat intervals) to the frequency domain (power in bands that provide indices of sympathetic and parasympathetic modulation). The high-frequency component (HF, 0.15–0.4 Hz) represents the respiratory cycle and is mediated by the parasympathetic nervous system. Because the low-frequency component (LF, 0.04–0.15 Hz) reflects inputs from both branches of the autonomic nervous system, sympathetic modulation is generally imputed from the ratio of the LH to HF components (11). Studies of acute sympathetic withdrawal during ganglionic blockade and muscle sympathetic nerve activity as measured by microneurography indicate that the LF:HF ratio is an acceptable measure of sympathetic modulation (12,13).

These frequency domain techniques reveal reliable patterns of autonomic modulation across different sleep/wake states. Previous studies have compared levels of sympathetic and parasympathetic modulation during 3-minute to 5-minute epochs of discrete stages of sleep and wakefulness (eg, wake, NREM sleep, REM sleep). In general, NREM sleep is characterized by parasympathetic predominance, whereas REM sleep and wakefulness show increased levels of sympathetic modulation (14–16). These relationships are seen during discrete epochs of sleep at the beginning, middle, and end of the night (16). Gradations in autonomic modulation are also seen within NREM sleep (16,17). Slow-wave sleep (SWS) is associated with increased power in the HF (parasympathetic) component, and concomitant increases in sympathovagal modulation are seen during stages 1 and 2 compared with SWS. Levels of sympathovagal balance during stages 1, REM, and wakefulness are often indistinguishable.

Time series analysis of heart rate variability (HRV) has revealed a close temporal coupling between autonomic modulation and electroencephalogram (EEG)-assessed sleep that suggests common control mechanisms. For example, Bonnet and Arand (16) demonstrated that shifts in heart rate variability precede arousals from stage 2 sleep and the onset/offset of REM sleep by 10 to 20 beats. Analyses of interbeat autocorrelation coefficients also reveal increases in sympathovagal modulation 1 to 2 minutes before shifts to lighter stages of sleep, as defined by EEG activity (15). Time series analysis of SWS has revealed that oscillations in delta wave activity mirror changes in sympathovagal modulation (18). During individual NREM sleep cycles, increases in delta activity are associated with decreases in sympathetic modulation. Conversely, decreasing levels of delta activity during NREM and REM sleep are accompanied by increasing levels of sympathovagal balance.

Although previous research has established a link between autonomic modulation and sleep, this technique has not been applied to acute stress paradigms. We sought to evaluate the impact of stress on heart rate variability during sleep. A standard speech task was used as the stressor, given evidence that experimental speech tasks are reliably associated with increases in subjective stress and cardiovascular, endocrine, and immune responses (19,20). Stress was defined by experimental group (speech task vs. control condition), and heart rate variability was calculated using autoregressive spectral analysis of the electrocardiogram (EKG) interbeat interval sequence, which was recorded throughout sleep. It was hypothesized that stress would be associated with a decrease in parasympathetic modulation and an increase in sympathovagal balance throughout NREM and REM sleep. It was also hypothesized that stress-related changes in HRV would be associated with disrupted sleep.

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METHODS

Subjects

Participants were 59 healthy, nonsmoking undergraduates (mean age, 19.6 years; SD, 1.8 years) who were recruited through flyers and announcements in Introduction to Psychology classes at the University of Pittsburgh. The sample was almost equally divided between men (51%) and women (49%), and most were white (88%). Baseline levels of distress and worry were comparable with nonpatient collegiate samples. The majority of the sample had no history of insomnia by self-report (97%), although sleep quality ratings revealed mild to moderate habitual sleep disturbances in half of the sample at the time of sleep studies (21). Experimental groups did not differ on habitual sleep schedules or level of speech anxiety, which might affect study outcomes. All were fluent English speakers. Written, informed consent was obtained before initiating the study protocol.

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Procedures

The 1-night experimental protocol was performed in the Sleep and Chronobiology Center at Western Psychiatric Institute and Clinic. Participants were asked to refrain from consuming alcoholic beverages for 24 hours before their experimental session. They were also asked to refrain from exercising or drinking caffeinated beverages after noon on the day of their experimental session. Participants reported to the sleep laboratory at 8:00 pm to complete consent procedures, complete baseline questionnaires, and be prepared for sleep studies. After these procedures were completed, participants were free to study, watch television, or read until their habitual bedtime. Laboratory sleep times were scheduled to correspond with habitual sleep times to ensure the validity of sleep measures (22).

The experimental manipulation took place in the participants’ rooms immediately before their bedtime and included two parts: baseline recording and stressor manipulation. During baseline recording of mean arterial pressure, participants were seated in a chair immediately adjacent to their beds, and a Dinamap ambulatory monitor was connected to their nondominant arm. Participants were asked to sit quietly with their eyes closed while heart rate and blood pressure were recorded. After a 5-minute rest period, three recordings were taken at 2-minute intervals. Participants were then instructed to open their eyes, and the experimenter (M.H.) opened their group assignment card. After participants were notified of their group assignment, a tape recording with task instructions was played for them. Participants in the control group were told that they would be awakened in the morning and asked to sit quietly and read a popular magazine (eg, Rolling Stone, Time) for approximately 6 minutes. They were told that their heart rate and blood pressure would be monitored during this time, but that they would not be asked anything about the articles they chose to read. Participants in the stress group were told that they would be asked to give an oral speech on awakening in the morning. They were told that they would have 2 minutes to prepare the speech once their topic was given to them, and that the 15-minute speech would be recorded and evaluated for content and quality. Participants in the stress group were also told that their heart rate and blood pressure would be monitored throughout the speech preparation and presentation period. Immediately before lights out, participants in the stress group were reminded to get the best sleep they possibly could to perform well on their speech the following morning. A series of 10-cm visual analogue scales (VASs) was administered to all participants immediately after the experimental manipulation to quantify subjective stress associated with the manipulation.

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Electroencephalograph Sleep Studies

The polysomnography (PSG) montage included a single channel of EEG (C3 or C4 referenced to A1-A2), bilateral electro-oculograms (EOGs) referenced to A1-A2, bipolar submental electromyogram (EMG), and EKG. Descriptive measures are those that are most frequently related to stress and sleep. HF and LF filter settings were 100 Hz and 0.3 Hz for the EEG and EOG signals and 90 Hz and 10 Hz for EMG. The 60-Hz notch filter was activated. The amplified signals (Grass model 7P511) were low-pass filtered with antialiasing filters (70 Hz; 24 dB/octave) and then digitized at a sampling rate of 256 Hz with 12-bit resolution. The digitized data were band-limited to 50 Hz by a digital finite impulse response filter before being decimated from 256 to 128 Hz. Details regarding procedures for recording, processing, and storing data have been previously described (23,24). The digitized data were scored in 60-second epochs pursuant to modified criteria by Rechtschaffen and Kales (25). Quantitative analyses of sleep data were conducted using period-amplitude analysis for EEG delta (zero-crossing method) and REM activity (23). Respiratory measures were not recorded because of the low incidence of significant apnea in young, healthy, nonobese men and women (average sample male body mass index [BMI], 23.8, SD, 4.1; average sample female BMI, 23.2, SD, 3.3) and their potential impact on sleep measures of primary interest to the study.

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Heart Rate Variability

Heart rate variability was estimated using autoregressive spectra of interbeat intervals (IBIs) throughout sleep (9,26,27). This article focuses on two measures of HRV: the HF band, which is an index of parasympathetic modulation, and the ratio of LF to HF bands, which is an index of sympathovagal balance (26). The IBI sequence was extracted from the EKG signal using an automated IBI extraction algorithm (28). The IBI record was then examined for artifacts and edited manually to correct ectopic beats and arrhythmias (26). Corrections were made by interpolating preceding/successive beats (29). Importantly, the number of ectopic beats, arrhythmias, wakefulness, and movement artifacts in NREM and REM sleep did not differ between experimental groups (NREM t = 1.2(57) < 0.18 and REM t = −1.7(57) < 0.10). HRV was evaluated in 2-minute IBI epochs. The LF band power was computed as the sum of the powers corresponding to any peaks centered in the range from 0.04 Hz Eq to 0.15 Hz Eq, where the hertz equivalent frequency scale was obtained by dividing the cycles/beat frequencies by the mean IBI for the epoch (9). Similarly, the HF band was accumulated between 0.15 Hz Eq and 0.4 Hz Eq. Averages for the HF band and LF:HF ratio were calculated for each NREM and REM period and all-night NREM and REM. To reduce the numbers of variables, only normalized HF power and the LF:HF ratio were used in statistical analyses (12).

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Questionnaires

Self-report questionnaires were used to quantify general tendency to worry, current levels of psychosomatic distress and ambient stress, and habitual sleep quality. The Penn State Worry Questionnaire (PSWQ) (30,31) was used to measure general tendency to worry. Higher PSWQ scores are associated with a greater degree of worry. Symptoms of psychosomatic distress were quantified with the Symptom Checklist 90-R (32). The Global Severity Index (GSI) score reflects both symptom number and severity; higher values indicate higher levels of distress. Three VASs (33) were used to quantify levels of current ambient stress. Participants were asked to place a mark on a 10-cm line to indicate how “tense,” “uptight,” and “stressed” they felt at baseline. Ambient stress scores were calculated as the sum of the 3 VAS items, with higher numbers indicating higher levels of subjective stress (range, 0–30). As a manipulation check, the subjective stress VAS was also administered immediately after the experimental manipulation. The Pittsburgh Sleep Quality Index (PSQI) (21) was used to quantify habitual sleep quality. The PSQI includes seven domains (subjective sleep quality; sleep latency, duration, efficiency, and disturbances; use of sleep medication; and daytime dysfunction) that may be summed to yield an overall score of subjective sleep over the preceding month. Scores for overall sleep disturbance range from 0 to 21, with higher numbers indicating poorer quality of sleep.

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Statistical Analyses

During preliminary analyses, analyses of variance were used to determine group comparability, analyses of covariance were used to test the experimental manipulation. Covariates for the experimental manipulation checks were baseline levels of ambient stress and mean arterial pressure at baseline. Control variables for hypothesis testing were ambient stress at baseline and total number of minutes of wakefulness during NREM and REM sleep. Ambient stress was included as a covariate because of significant group differences at baseline. Wakefulness, which did not differ between groups, was included as a covariate because of its impact on HRV (14,16,34). Analyses of covariance and linear mixed-effects models were used to test the hypothesis that stress (experimental group) is a significant predictor of heart rate variability during NREM and REM sleep. For all analyses, significance levels were p <.05. Mixed effects models were chosen to evaluate heart rate variability profiles across successive sleep cycles, and although multiple models were tested, only the linear component was shown to be significant across time.

Four NREM sleep periods were analyzed for each participant. Because of variability in sleep lengths, fewer participants had a fourth REM period, so only three periods of REM sleep were analyzed for each participant. Finally, partial correlations were used to evaluate relationships among heart rate variability and sleep, controlling for ambient stress at baseline and wakefulness during sleep. Other potential sources of variance that did not differ between experimental groups (eg, Symptom Checklist 90-R, PSWQ) did not affect the strength or direction of significant group effects on study outcomes, and were not included in the statistical analyses reported.

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RESULTS

At baseline, experimental groups were comparable in age, sex, BMI, and worry. However, ambient stress at baseline was significantly higher in the control group compared with the stress group (F [1,57] = 13.38, p < .001). Manipulation checks revealed that the task notification was stressful. After controlling for baseline values of ambient stress and mean arterial pressure, subjective stress and mean arterial pressure on task notification were significantly higher in the stress group than in the control group (F [1,55] = 17.38, p < .001 and F [1,55] = 6.03, p < .05, respectively). Reactivity values, as defined by an increase greater than 1 SD around the mean (35), also differed between groups. Reactivity, as measured by subjective stress, was significantly greater in the stress group (29%) than in the control group (3.6%; Fisher Exact p <.01). There was also a trend for higher reactivity as measured by mean arterial pressure in the stress group (16.7%) compared with the control group (7.1%; Fisher Exact p <.42). Baseline characteristics of the sample are shown in Table 1.

Table 1
Table 1
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Heart rate variability during NREM and REM sleep was significantly different between groups. Acute stress was associated with decreases in parasympathetic modulation during NREM and REM sleep and increases in sympathovagal balance during NREM sleep. As shown in Figure 1, average all-night parasympathetic modulation was higher in the control group during NREM (F [1,52] = 5.94, p < .02) and REM sleep (F [1,52] = 10.08, p < 0.01). Conversely, average all-night sympathovagal balance was higher in the stress group during NREM sleep (F [1,55] = 4.09, p < .05). There were no significant group differences for sympathovagal balance during REM sleep.

Fig. 1
Fig. 1
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We also evaluated the relation between acute stress and HRV profiles across individual successive sleep cycles. Results of mixed effects analyses indicate that parasympathetic modulation increased across successive NREM periods in the control group (slope estimate = 343.63, t [57] = 2.79, p < .01) but not in the stress group (Figure 2). The group by time interaction for NREM sleep was significant (estimated slope difference = −344.75, t [110] = −2.02, p < .05). Results of REM sleep analyses mirrored full-night analysis of covariance results. Parasympathetic modulation was higher in the control group during each REM sleep period (estimated difference between groups = −1775.01, t[113] = −2.17, p < .05). There were no reliable time or group by time interactions for sympathovagal balance across NREM and REM sleep profiles.

Fig. 2
Fig. 2
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Partial correlations were used to evaluate relationships between heart rate variability and sleep. Sympathovagal balance during NREM sleep was significantly associated with sleep maintenance and average automated delta counts after controlling for ambient stress at baseline and experimental group. Higher levels of sympathovagal balance during NREM sleep were associated with lower sleep maintenance (r [for reverse log] = 0.43, p < .01) and lower average automated delta counts (r = −0.28, p < .01). Heart rate variability was not reliably correlated with other measures of sleep, including visually scored sleep stages (percent stage 1, 2, delta, REM sleep) and subjective sleep quality.

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DISCUSSION

Acute stress alters heart rate variability during sleep. During NREM sleep, acute stress was associated with decreased levels of parasympathetic modulation and increased levels of sympathovagal balance. Acute stress was also related to an altered profile of parasympathetic modulation across the night. Parasympathetic modulation increased across successive NREM cycles in the control group; these increases were blunted in the stress group and remained essentially unchanged across successive NREM periods. Heart rate variability during REM sleep was also affected. Acute stress was associated with decreased levels of parasympathetic modulation during the night as a whole and during each individual REM period. Stress-related changes in heart rate variability were not without consequence. Elevated levels of sympathovagal balance during NREM sleep were associated with modest but significant decreases in sleep maintenance, which is a reflection of the amount of wakefulness during sleep, and lower automated delta counts, which are an index of deep sleep.

These data are germane to the study of sleep in general and more specific investigations on stress and insomnia. From a broad perspective, these data indicate that exogenous factors, such as psychological stress, may act as modulators on the relation between heart rate variability and cortical EEG activation. Bonnet and Arand (16) have shown that autonomic influences on heart rate precede the initiation of EEG arousals and REM sleep by 10 to 20 beats. Similarly, Gronfier et al. (18) have reported that changes in HRV precede the initiation of EEG delta activity. Modeled relationships between sympathovagal modulation and EEG delta activity indicated reciprocal patterns between these two parameters. Within each NREM cycle, delta activity emerged as sympathovagal balance decreased, and peak delta activity followed the nadir in sympathetic modulation (18). Importantly, the sleep parameters that have been temporally linked to EEG activation in these studies were also correlated with sympathovagal balance in the present study. We found that stress-related increases in sympathovagal balance during NREM sleep were significantly associated with indices of wakefulness after sleep onset (sleep maintenance) and delta activity. If sympathovagal balance plays a causal role in EEG activation, as suggested by previous studies (16,18), stress-related changes in HRV may indicate a mechanism whereby acute stress disrupts sleep.

The evaluation of stress-related changes in HRV during sleep complements previous research on the physiological correlates of chronic stress. In these studies, overnight urinary neuroendocrine samples have been used to quantify stress-related arousal across the life span. Several studies have reported heightened levels of cortisol, catecholamines, and their metabolites in children and adults exposed to chronic environmental stress (2,36–39). HRV analysis provides better temporal resolution of the impact of stress on sleep than do overnight neuroendocrine samples, is noninvasive, and also allows evaluation of the relationship between sympathetic and parasympathetic modulation throughout the sleep period. The overall profile that emerged in the present study was one of vagal withdrawal in response to acute stress, as evidenced by group differences in HF power during both NREM and REM sleep. Increases in sympathovagal balance during NREM sleep were correlated with measures of sleep continuity and slow-wave activity, which are the indices of sleep that have been most closely linked to stress in previous research (1,5,8,40,41). Moreover, blunted parasympathetic modulation profiles in the stress group suggest that acute stress may interfere with the restorative function of sleep. Similar effects have been shown in patients with fibromyalgia, who also report disturbed sleep and fatigue (42).

The present findings also complement research on the psychophysiology of insomnia, which is characterized by heightened physiological arousal during sleep and wakefulness. Indicators of heightened arousal in insomnia include reduced overall sleep propensity and elevations in body and skin temperature, muscle tone, electrodermal and metabolic activity, and heart rate (43–47). More recently, Bonnet and Arand (48) evaluated HRV in a sample of patients with insomnia. HRV profiles were similar to those associated with acute stress in the present study. In comparison with a group of age-matched and sex-matched healthy sleepers, patients with insomnia exhibited lower power in the HF band and higher power in the ratio of LF to HF power, regardless of sleep stage (wakefulness, stage 1, stage 2, REM). We have previously shown that stress is a correlate of EEG activity in patients with insomnia (8). The present study suggests that stress-related changes in heart rate variability may also play a role in insomnia, although more research is needed on this topic (49).

This study represents the largest study conducted to date on HRV during sleep in healthy adults. The study sample also included an equal number of male and female participants, which is important in light of reported sex differences in HRV during sleep (50). Several limitations to this study are likely to have affected study outcomes and generalizability. For example, our analyses did not control for respiration rate or evaluate the impact of normalized measures of HRV on study outcomes. Although measurement of respiration rate may have provided more precise indices of relations between HRV and sleep in this sample, others have shown that uncorrected measures of HRV are at least as accurate as corrected measures (51,52). Similarly, the use of raw log-transformed data is unlikely to have affected the HF component, because it is highly correlated with normalized values, and the ratio of LF to HF is unchanged by normalization (26). More in-depth evaluation of several background measures may have helped clarify between-group differences and reduce variance in study outcomes. Moreover, relationships between acute stress and sleep may be more easily detected after study participants have habituated to the sleep laboratory environment for at least 1 night. Sample measures that should be included in future studies of acute stress and sleep include clinical assessment of participant sleep histories and sleep-disordered breathing, measures of habitual and acute substance use, and the use of multiple sleep study nights.

Future studies are needed to characterize more fully the relationships among HRV during sleep; EEG-assessed sleep; and subjective sleep quality in chronic stress, insomnia, and other vulnerable populations. For example, relationships among psychological stress, heart rate variability, and sleep may be especially important in sleep disorders associated with psychiatric illness or insomnia, in which stressors may both precipitate and perpetuate clinically significant sleep disruptions. As noted by Bonnet and Arand (48,53), chronic sympathetic activation during sleep may play an important role in health outcomes. Stress-related changes in heart rate variability during sleep may also be important in association with chronic stressors such as caregiver strain, which have been associated with significant morbidity and increased risk for mortality (54–57). Stress-related changes in HRV during sleep may also contribute to, or signal, increased risk of mortality in people with prolonged sleep latencies and reduced sleep efficiency (58).

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Keywords:

stress,; sleep,; sympathetic nervous system,; parasympathetic nervous system,; heart rate variability,; spectral analysis.

Copyright © 2004 by American Psychosomatic Society

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