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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Rheumatol. Author manuscript; available in PMC 2010 September 1.
Published in final edited form as:
PMCID: PMC2909463




Patients with fibromyalgia syndrome (FMS) complain of inadequate sleep, which could contribute to common symptoms including sleepiness, fatigue, or pain. However, measures that consistently and objectively distinguish FMS patients remain elusive.


Fifteen women with FMS and 15 age- and gender-matched controls underwent 3 nights of polysomnography; Multiple Sleep Latency Tests to assess sleepiness; testing of auditory arousal thresholds during non-REM stage 2 and stage 4 sleep; overnight assessment of urinary free-cortisol; and analysis of 24-hour heart rate variability.


On the second night of polysomnography, women with FMS in comparison to controls showed more stage shifts (p=0.04) but did not differ significantly on any other standard polysomnographic measure or on the Multiple Sleep Latency Tests. Alpha EEG power during deep non-REM sleep, alone or as a proportion of alpha power during remaining sleep stages, also failed to distinguish the groups, as did auditory arousal thresholds. Urinary free cortisol did not differ between FMS and control subjects in a consistent manner. However, decreased short-term heart rate variability and especially ratio-based HRV among FMS subjects suggested diminished parasympathetic and increased sympathetic activity, respectively. Other HRV measures suggested decreased complexity of HRV among the FMS subjects.


Standard measures of sleep, a gold-standard measure of sleepiness, quantified alpha-delta EEG power, auditory arousal thresholds, and urinary free cortisol largely failed to distinguish FMS and control subjects. However, HRV analyses showed more promise, as they suggested both increased sympathetic activity and decreased complexity of autonomic nervous system function in FMS.

Key Indexing Terms: fibromyalgia, sleep, electroencephalography, pain, heart rate variability, polysomnography


Some of the most prominent complaints of patients with fibromyalgia syndrome (FMS) concern sleep, excessive daytime sleepiness, and fatigue rather than pain itself. Affected individuals frequently report light, easily-disturbed sleep, and daytime tiredness, fatigue, or sleepiness. The first objective evidence of any physiological abnormality in FMS, reported nearly 3 decades ago, was the alpha-delta pattern recorded in the electroencephalogram (EEG) of FMS patients studied during sleep (1). The anomalous appearance of alpha EEG frequencies, which usually characterize wakefulness, overriding delta waves of deep non-rapid eye movement (non-REM) sleep suggested a potential explanation for daytime fatigue. Subsequent research showed that patients with other pain syndromes often show alpha-delta sleep. The finding is no longer considered specific for FMS, and may not be sensitive either (24). Nevertheless, this or other non-specific polysomnographic measures of sleep disruption often distinguish FMS patients from controls, and correlate with pain and subjective daytime sleepiness (5). In comparison to continuous alpha activity throughout the night or none at all, alpha-delta activity in the absence of alpha during other stages may predict sleep complaints and post-sleep worsening of pain particularly well (6).

Primary sleep disorders such as sleep-disordered breathing and restless legs syndrome also can occur among FMS patients (79), and could exacerbate excessive daytime sleepiness (10). However, most FMS patients do not have these disorders, and most patients with these disorders do not develop FMS (11).

Investigators have looked for other physiological distinguishing characteristics in FMS. Affected patients sometimes complain of faintness, unsteadiness, palpitations, and blurred vision that could suggest autonomic dysfunction. One study of heart rate variability (HRV) found evidence of increased 24-hour sympathetic activity, and documented that this increase came from the night, when a decrease should normally accompany sleep (12). However, another HRV study, published in abstract form, could not confirm abnormal regulation during sleep in FMS patients (13). Fibromyalgia may be associated with abnormalities of the hypothalamic-pituitary-adrenal system, the primary endocrine stress axis, but findings have not been consistent. Some but not all studies of FMS patients suggest hyperactivity of the hypothalamic-pituitary-adrenal axis as reflected by elevated cortisol levels (1416) and a diminished response to acute stressors (14,17,18). In FMS, cortisol levels on awakening and one hour later, but not at other circadian times, are associated with concurrent pain ratings (16).

In sum, however, no objective markers of sleep disturbance have been consistently specific for FMS. Although fatigue and sleepiness have been studied using subjective questionnaires, the gold-standard laboratory measure of excessive daytime sleepiness, the Multiple Sleep Latency Test, has not been used to assess whether the complaints have physiological correlates. Similarly, although experimental sleep disruption may reduce pain thresholds and induce fatigue (19), the tendency for FMS patients to arouse easily to external stimuli has not been assessed objectively. The aims of this pilot study, therefore, were to explore a range of potentially-novel physiologic differences, between FMS patients and age-matched controls that might help to explain patient complaints of disturbed sleep, daytime sleepiness, fatigue, or pain. Preliminary reports and an ancillary, retrospective analysis of these data have been presented elsewhere (2022).



Patients were recruited from outpatient rheumatology referral clinics or through advertisements. Healthy control subjects were obtained thru advertisements. This study was reviewed by the University of Michigan Institutional Review Board and was conducted according to principals laid out in the Helsinki Declaration. Investigators interviewed and examined all subjects to determine that they met (or for controls, failed to meet) criteria for FMS as outlined by the American College of Rheumatology (23). Testing included dolorimeter exam, complete blood count with differential, complete metabolic profiles, creatine phosphokinase, erythrocyte sedimentation rate, thyroid stimulating hormone, urine pregnancy test (if necessary), and urine drug screen. Screening for psychiatric disorders employed the Mini-International Neuropsychiatric Interview, which identifies DSM-IV and ICD-10 psychiatric disorders (24).

Inclusion criteria were 1) age ≥ 18 and ≤ 65 years; 2) ability to discontinue psychotropic medications, hypnotics, analgesics, and herbal or over-the-counter supplements at least 2 weeks prior to the study (acetaminophen and diphenhydramine were allowed up to 3 days prior to the study); 3) American College of Rheumatology 1990 criteria for FMS (for patients); and 4) signed informed consent. Exclusion criteria included 1) presence of an ongoing medical condition associated with pain or fatigue; 2) caffeine, cigarette, or alcohol use in excess of 500 mg/day, ½ pack/day, or 5 drinks/week and unwillingness to discontinue this at least 3 days prior to the study; 3) recreational drug use confirmed on urine drug testing; 4) average time in bed of < 4 hours or regular bedtime later than 1:00 am; 5) exogenous corticosteroids in any form for 3 months prior to study, or regular use of corticosteroids in the last 6 months; 6) pregnancy; 7) evidence of concurrent psychiatric illness in patients, or at any time in the past for controls; 8) known primary sleep disorder.

Daily diaries were used for two weeks prior to study and included information regarding the time in bed, sleep quality, and pain. The McGill Pain Questionnaire (25) was used as a measure of clinical pain. We also used a numerical rating scale, the Gracely Box Scale (2629) to assess present pain intensity. The Center for Epidemiological Studies Depression Scale (CES-D) (30) was used to assess mood. Group mean values were calculated for each measure.

Nocturnal Polysomnograms and Multiple Sleep Latency Tests

Digital polysomnography (Telefactor DEEG/TWIN, W. Conshohocken PA) included six EEG channels (F3-A2, F4-A1, C3-A2, C4-A1, O1-A2, O2-A1, with sampling rates of 200 Hz), 2 electro-oculogram channels, chin and bilateral anterior tibialis surface EMG, 2 EKG leads, nasal and oral airflow (thermocouples), thoracic and abdominal excursion (piezoelectric strain gauges), and finger oximetry.

The Multiple Sleep Latency Test was conducted on the day after the second nocturnal polysomnogram and followed standard procedures (31). Five nap attempts were scheduled 2 hours apart, usually at about 8:00, 10:00, 12:00, 2:00, and 4:00 pm. Subjective sleepiness was assessed before each nap attempt using the Stanford Sleepiness Scale (32).

Sleep studies were scored by a single registered polysomnographic technologist masked to subject group (FMS vs. control). Borderline polysomnographic features were arbitrated with an investigator board-certified in sleep medicine. Sleep and arousal scoring followed standard criteria (33,34). An apnea was scored when nasal/oral airflow stopped for 10 seconds or more. An hypoponea was scored when airflow, chest excursion, or abdominal excursion diminished for at least 10 seconds, followed by an arousal, awakening, or oxygen desaturation ≥ 4%. Periodic leg movements were scored when they lasted 0.5 to 5.0 seconds, were separated by 5 to 90 seconds, and occurred in a series of at least 4 in a row. On the Multiple Sleep Latency Tests, the sleep latency of each nap was scored as the time between lights out and the first epoch of stage 1 sleep (31). The mean sleep latency on the five nap attempts was calculated.

Spectral Analysis

One channel (C3-A2) of EEG, from lights-on to lights-off on the second laboratory night, was converted to ASCII text using Telefactor’s built-in utility for spectral analysis. One-second segments (200 points) were used for Fourier power analysis implemented with MATLAB software. A Hanning window was applied to each segment before Fourier transform. An average of powers derived from each of 30 one-second segments was used to characterize power for each 30-second epoch. Power was calculated separately for delta (1–4 Hz) and alpha (8–12 Hz) EEG frequency bands. The natural log transform of alpha power in slow-wave sleep (stages 3 and 4) was computed to normalize the distribution. The ratio of alpha power during slow-wave sleep to that power in all remaining sleep stages was also calculated (6).

Auditory Arousal Threshold (AAT)

For the AAT evaluation (35), subjects were monitored for a third night. Real-time signal generation software was used for precision control of an ordinary computer sound card (SoundMAX Digital Audio v5.0), the output of which was fed to inexpensive "ear bud"-style headphones (RadioShack). The system was calibrated using a B&K model 4134 condenser microphone, and found capable of delivering up to 88 dB sound pressure level (SPL) to each ear. The signal generation software, DaqGen, is available as a free download from and was used to calibrate the sound card to obtain resolution of better than 0.1 dB at all levels.

Prior to AAT testing, each subject had waking auditory threshold determined in the room where the sleep study was conducted. The AAT testing was conducted four times during the first four hours of sleep, during stage 2, stage 4, stage 2, and stage 4 sequentially. These stages were chosen because stage 2 generally represents the preponderant sleep stage each night, and stage 4 represents slow wave sleep previously reported to show abnormality in FMS (1). After 5 continuous min of the targeted stage, 988 Hz sinusoidal tone bursts of 2 sec duration, at 10 sec intervals, were generated starting at the subjects’ awake auditory threshold and increasing in 10 dB steps until behavioral awakening occurred or a maximum of 80 dB was reached. The AAT was defined as the dB level that produced >5 sec of wakefulness, or 80 dB if the maximum was reached without a behavioral awakening. Net stage-specific AAT was calculated for each subject by subtracting the awake auditory threshold from the awakening threshold during sleep. Results from the two trials within the given stage were averaged.

Urinary Free Cortisol (UFC)

The first morning void was collected for measurement of free cortisol after each night.

Heart Rate Variability (HRV)

A Holter monitor (DMS Holter, Stateside, NV) was placed just prior to bedtime on the first night of study and was worn for the remainder of the study except during bathing. The sampling rate for the ECG signal was 128 Hz, which means that the absolute peak of the ECG signal was detected within ± 4 ms. Recordings were scanned using Cardioscan software (DMS Holter) at the Washington University School of Medicine Heart Rate Variability laboratory by experienced Holter technicians blinded to subjects’ fibromyalgia status. Each recording was overread by an investigator (PKS). Beat-stream files, representing the time and classification of each QRS complex, were transferred to a computer (Sun Microsystems, Mountain View, CA) for time domain, frequency domain and non-linear HRV analysis using standard methods (3642). Time domain indices of HRV are statistical calculations performed on the set of normal-normal (N-N) interbeat intervals. Frequency domain analysis partitioned the variance in the HR signal (actually heart period or N-N intervals) into its underlying frequency components using power spectral analysis. Non-linear HRV quantifies the structure of the HR time series.

The HRV indices were categorized according to the period over which they were assessed. Longer-term HRV indices quantify HRV cycles over periods of >5 min (SDANN, ultra low frequency power). These indices are predominantly influenced by circadian rhythms and by sustained periods of activity. Intermediate-term indices quantify HRV over periods ≤5 min averaged over the entire recording period (SDNNIDX, very low frequency power, low frequency power). These quantify a combination of sympathetic and parasympathetic influences on heart rate and may include thermoregulation and baroreceptor activity as well as the effect of daily activities. Short-term HRV indices describe respiration-mediated beat-by-beat changes in heart rate and reflect primarily parasympathetic influences (e.g., pNN50, rMSSD, high frequency power). Ratio indices, such as normalized low frequency power or the low frequency power / high frequency power ratio, may reflect sympathovagal balance: low frequency power reflects a mixture of sympathetic and parasympathetic modulation of heart rate whereas high frequency power reflects parasympathetic control only (12,43).

Detrended fluctuation analysis quantifies the fractal scaling properties of the short-term R-R interval time series (44,45). Normal values for the short-term detrended fractal scaling exponent (DFA1) are approximately 1.1. Higher values indicate less complexity and more periodicity in the HR time series, whereas lower values indicate more random fluctuations. The other non-linear measure used in this study was the Poincaré plot ratio (SD12) which is the ratio of the axis of an ellipse fitted to a scatterplot of each N-N interval vs. the next (44). A higher SD12 indicates a relative predominance of beat-to-beat changes in HRV. Definitions for these indices are found in the Legend for Table 4. At least 18 hours of usable 5-min segments were required for the 24-hour HRV data and 4.5 hours of usable segments were required for nighttime (00:00–00:06) HRV data reported here. Usable 5-min segments were defined as those in which at least 80% of intervals were scored as normal-to-normal intervals.

Table 4
Nighttime Heart Rate Variability (HRV) (00:00–06:00)

Sample Size

Sample size for this pilot study was estimated from anticipated Multiple Sleep Latency Test and AAT results. To have 90% power in a paired t-test with α = 0.05 to detect a 5-minute sleep latency difference between FMS and control subjects, with a standard deviation no larger than 5.5 minutes, both thought reasonable based on previous research [e.g., (46)], a sample size of 15 subjects per group was required. An expected mean AAT for normal individuals of 58 ± 10 dB suggested that 15 subjects per group would be needed to detect a 12 dB difference (s.d. ≤ 13.3 dB) between groups with 90% power.


The main explanatory variable was group (FMS vs. control). Data were summarized as mean ± standard deviation and compared using Student’s T-test or Wilcoxon’s rank sum test. Primary outcome variables were the natural log transform of alpha power during slow-wave sleep, mean sleep latency on the Multiple Sleep Latency Test, and AAT. Secondary outcome variables included the ratio of alpha power during slow-wave sleep to that during remaining sleep stages, UFC, and HRV. Correlations were performed using Spearman’s rho.



Demographic, menopausal, and body mass index data for FMS and control subjects are presented in Table 1. Subjects were matched individually by age and menopausal status. The BMI did not differ between groups. The FMS subjects in comparison to controls reported more symptoms of fatigue, depression, and sleep problems. The FMS patients expressed significantly more pain on the 2-week pain diary (5.0±1.6 vs. 0.0±0.1, p<0.0001), the GBS (12.6±4.0, vs. 0.4±1.1, p<0.0001), and the McGill Pain Questionnaire (10.5±4.8 vs. 0.5±0.8, p<0.0001). Patients with FMS scored higher on the CES-D (11.1±5.6 vs 2.5±3.2, p<0.0001) and 3 met CES-D criteria for depression (>16), though not criteria for major depressive disorder when assessed by structured clinical interview.

Table 1
Study Subjects


No standard polysomnographic measure (Table 2) showed a significant difference between FMS and controls except for the number of stage shifts (p=0.04). The amount of slow-wave sleep obtained ranged from 23 minutes (5.0% of sleep time) to 127 minutes (27.4%). Spectral analysis of these periods and other sleep stages (Table 3) showed that alpha power during slow-wave sleep was not significantly greater among FMS subjects than among controls, and neither was the ratio of alpha power during slow wave sleep divided by alpha power during other stages.

Table 2
Polysomnographic measures, expressed as means (s.d.)*
Table 3
Results of spectral analysis*

On the Multiple Sleep Latency Test, daytime sleepiness showed no significant difference between groups: the mean sleep latency was 11.8±4.8 in the fibromyalgia patients and 13.1±5.2 in controls (p=0.55). However, FMS patients reported more subjective sleepiness on the Stanford Sleepiness Scale prior to their nap attempts (2.9±0.3 vs. 2.0±0.3, p<0.005). The FMS patients, in comparison to controls, also showed significantly higher scores on the Profile of Mood States prior to each nap (all p<0.001).

Auditory Arousal Threshold

There were no significant differences (p=0.39) between awake auditory thresholds for FMS patients (11.1±6.4) and control subjects (11.8±6.7). Similarly, there were no significant differences on net AAT in either stage 2 (34.3±11.5 vs 34.0±15.5, p=0.48) or stage 4 (43.6±13.8 vs 49.7±11.1, p=0.10) sleep, though the AAT for FMS patients during stage 4 sleep was numerically lower.

Urinary Free Cortisol

Overnight UFC was significantly lower in FMS patients than control subjects for the first night (10.4±8.4 vs 18.1±9.1, p=0.02), but not the second night (13.7±13.6 vs 19.4±13.5, p=0.14) or the third night, after the stress of the AAT testing (15.7±13.8 vs 15.8 ± 5.0).

Heart Rate Variability

There were significant group differences in nighttime HRV between FMS and control subjects (Table 4). Longer-term HRV (SDANN, ULF), ratio HRV (normalized LF power), and the short-term fractal scaling exponent were increased in FMS, while normalized HF power and SD12 were decreased. The trend toward higher mean nighttime heart rates in FMS (by 5 bpm) did not reach significance (p=0.160).

Twenty-four-hour recordings (Table 5) suggested similar mean heart rates between groups. However, in contrast to nighttime findings, mean 24-hour SDANN and ULF power showed non-significant decreases in FMS. Other HRV measures were virtually the same for 24-hour and nighttime recordings, except that the higher LF/HF ratio in FMS became statistically significant (p=0.019).

Table 5
Twenty-four-hour Heart Rate Variability (HRV)


This study of women with FMS and age-matched female controls employed several promising physiologic measures to assess which might show promise for consistent discrimination between groups. Standard nocturnal polysomnographic measures showed only non-specific evidence of mild sleep disruption in FMS subjects. Furthermore, FMS and control subjects showed no difference in objectively quantified alpha-delta sleep, even when considered in relation to alpha power during all remaining sleep stages. The common FMS complaint of excessive daytime sleepiness could not be confirmed by a gold-standard sleep laboratory measure. The frequent FMS complaint of easy awakening from sleep could not be corroborated by increased sensitivity, in comparison to controls, to titrated auditory stimuli. Overnight urinary free cortisol also did not show consistent differences between FMS subjects and controls. In contrast, evidence of altered physiologic arousal was obtained from longer-term HRV, and ratio-based and non-linear HRV measures that capture relationships between sympathetic and parasympathetic activity.

Our findings demonstrate that FMS subjects were not clearly sleepier than controls: the discrepancy did not approach statistical significance, and the clinical significance of a one-minute difference in mean sleep latency on a Multiple Sleep Latency Test is questionable (the score ranges from 0 to 20 minutes). At the same time that participants made their nap attempts, FMS subjects’ self-ratings in comparison to those of controls suggested considerably more subjective sleepiness. The FMS subjects in comparison to controls also simultaneously rated their mood state as lower. These uniquely time-paired results highlight differences between subjective perception and objective measures in FMS subjects.

Similarly, although FMS patients do not have physical abnormalities associated with ear disease (47), awake subjects do show increased sensitivity to sounds of all magnitudes including those encountered in everyday activities (48). Our results of auditory arousal threshold testing during sleep remain somewhat ambiguous. Arousal from stage 4 sleep trended towards a lower auditory threshold for FMS subjects compared to controls, and the lack of significance could well have arisen from the sample size. In this case, a difference of 6 dB could potentially suggest clinical significance. We are not aware of any similar data, obtained during sleep, with which to compare to our results. However, experimental disruption of deep non-REM sleep can reduce pain threshold and induce fatigue in FMS (19). Medications that augment slow wave sleep may improve FMS symptoms (49). Such data combine with our findings to suggest that a larger and more definitive assessment of arousal thresholds during deep non-REM sleep would be worthwhile.

Although severe nocturnal sleep disruption might have been expected to increase overnight urinary free cortisol, the only significant difference we found between FMS and control subjects occurred during the first night, and in the opposite direction. We hesitate to draw strong conclusions from this observation because it was not replicated on the next two nights. However, we can speculate that the initial difference could have reflected differences in established circadian rhythms between the groups, one that perhaps began to ameliorate after the first night with a continued stay in a parallel environment.

Of the objective measures we chose to explore, heart rate variability measures that reflect psychophysiologic arousal seemed to be the most sensitive discriminators between FMS and control subjects. Heart rate variability was assessed over 24 hours and also separately during usual nocturnal sleep hours. Significant and consistent between-group differences were seen for 24-hour and nighttime ratio HRV measures, and for non-linear HRV measures. Ratio measures, while not quantifying sympathetic activity per se can be interpreted as reflecting sympathetic and parasympathetic balance. Measures that reflect intermediate-term HRV, like SDNNIDX, the average variability over 5 min, were not different between groups. This suggests that total autonomic activity was similar in FMS and control subjects. However, the marked increase in normalized LF power combined with the marked decrease in normalized HF power (each of which quantifies the relative contribution of oscillations in these bands to intermediate-term HRV) are consistent with a shift towards greater sympathetic control of heart rate, both during the nighttime and over the 24-hour cycle in FMS patients. Relative increases in sympathetic activity during sleep would be consistent with our observation of a trend toward more frequent arousals in FMS subjects as compared to controls. Sympathetic surges are known to occur during and after EEG-defined arousals triggered by sleep apnea or artificial tones (50,51), and before arousals associated with periodic leg movements (52). Sleep apneas and periodic limb movements most typically occur at frequencies between every 20 seconds to every 2 minutes, resulting in increased power in the very low frequency HRV band, which measures underlying HRV changes at these frequencies. However, no such difference between FMS and control subjects was observed in this study.

Of interest was the finding, for the first time, of markedly increased DFA1 (short-term fractal scaling exponent) and concomitantly decreased SD12 in FMS patients. The DFA1 reflects the degree to which heart rate patterns are correlated (higher DFA1) vs. random (lower DFA1) on a scale of 4–11 beats. Normal DFA1, as found in the controls in this study, is about 1.1. Decreased SD12 in FMS subjects was consistent with increased DFA1 and also suggests a difference in the underlying structure of heart rate patterns in FMS. Both results suggest a lack of normal complexity in the regulation of heart rate patterns in FMS. Decreased heart rate variability has been reported previously in FMS (53,54). These observations combine with ours to support the recent hypothesis that “decomplexification” of the autonomic nervous system, with persistent and inflexible sympathetic predominance across the circadian cycle, may play a key role in FMS, as well as related conditions such as irritable bowel, chronic fatigue, and Gulf War syndrome (55). Sympathetic hyperactivity could potentially explain not only HRV changes in FMS, but also important features of the syndrome, and chronic pain or allodynia in particular (56), to the extent that the sympathetic nervous system can activate primary afferent nociceptors (57).

The most promising sleep-specific measures in our study for physiological discrimination between FMS and control subjects were the longer-term nighttime heart rate variability (SDANN and ULF) measures. A significant increase in SDANN and ULF was found in FMS subjects vs. controls during the nighttime, in contrast to a trend towards relatively decreased values in FMS subjects for the same parameters over the 24-hour cycle. This surprising result suggests a difference in the strength of underlying autonomically-mediated ultradian rhythms of heart rate, perhaps reflecting more disturbed sleep not measured by standard parameters or an abnormality of neurocardiac integration in FMS.

Both these changes suggest higher levels of sympathetic activity in FMS vs. control subjects and confirm findings of a previous investigation (12). However, in that study the difference in 24-hour HRV arose mainly at midnight and 3:00 AM measurement points. In contrast, our data showed no significant difference between daytime and nighttime, suggesting that the increased physiological arousal represented by this measure may permeate both sleep and wakefulness.

Among the limitations of our study was small sample size, and failure to find more sleep-related differences between FMS and control subjects cannot be taken as proof that those relationships do not exist. Multiple tests in a limited sample, without adjustment for simultaneous comparisons, were performed because the priority was to explore a range of potential physiologic measures for a potentially robust approach. This means however that identified associations, especially those with marginal p-values, deserve prospective replication. Finally, stringent inclusion and exclusion criteria also limit, to some extent, applicability of the findings to clinical practice, where co-morbid psychopathology, medication use, and primary sleep disorders are common and potentially influential. In particular, HRV changes, reflecting increased sympathetic and decreased parasympathetic activity, have been reported in a range of psychiatric conditions, including major depression (58). Although none of our subjects met criteria for major depression, we cannot exclude the possibility that differences between FMS and control subjects could have been influenced by more subtle levels of psychiatric comorbidity. However, the increase in nighttime values for longer-term HRV and the marked increase in the short-term fractal scaling exponent have not been reported in depression, and therefore may be more specific to FMS.

In short, our comparison of carefully selected FMS and control subjects failed to show many differences in standard polysomnographic measures, an objective measure of daytime sleepiness, sensitivity to an auditory stimulus, or overnight cortisol levels. However, FMS subjects did show HRV changes consistent with decreased parasympathetic control of heart rate and relatively high sympathetic activity during both daytime and nighttime hours. Whereas the HRV changes themselves seem unlikely to mediate the symptoms of fibromyalgia, the underlying autonomic dysfunction potentially could affect pain. To assess whether specifically nocturnal HRV differences between FMS subjects and controls reflect abnormalities of sleep, as opposed to other pathophysiology in FMS, will require longitudinal or interventional study designs. Our present results more broadly suggest that efforts to distinguish sleep of FMS and non-FMS subjects may benefit from newer, non-standard approaches to analysis of polysomnographic data. In particular, one largely unexplored analytic approach (59), though not part of our original prospective protocol, demonstrated retrospectively the potential value of focus on durations of uninterrupted, specified sleep stages (22). Beyond manipulations of traditionally scored sleep patterns, however, analyses of HRV and other autonomic measures may offer the most immediate promise for consistent physiologic measures that will distinguish sleep of FMS patients with reasonable sensitivity and specificity.


The authors thank Beth Malow, M.D., Thomas Roth, Ph.D., Gary Richardson, M.D., and Susan A. Martin, MPH, for their expert advice on research design, and Cory Martin, RPSGT for his careful assistance in the execution of this protocol.


This work was performed at the University of Michigan, Ann Arbor, Michigan and was supported by the University of Michigan GCRC (NIH M01 RR000042), NIH K24 AR02139 to Dr. Crofford, and grants from Pfizer, Inc. and the Arthritis Foundation, Michigan Chapter.

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