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Heart Rate and Heart Rate VariabilityModification in Chronic Insomnia PatientsBenedetto Farina a , Serena Dittoni b , Salvatore Colicchio b , ElisaTestani b , Anna Losurdo b , Valentina Gnoni b , Chiara Di Blasi b ,Riccardo Brunetti a , Anna Contardi a , Salvatore Mazza b & GiacomoDella Marca ba Department of Human Sciences, Cognitive and Clinical PsychologyLab , Università Europea , Rome , Italyb Department of Neurosciences , Catholic University , Rome , ItalyPublished online: 15 Oct 2013.

To cite this article: Benedetto Farina , Serena Dittoni , Salvatore Colicchio , Elisa Testani ,Anna Losurdo , Valentina Gnoni , Chiara Di Blasi , Riccardo Brunetti , Anna Contardi , SalvatoreMazza & Giacomo Della Marca , Behavioral Sleep Medicine (2013): Heart Rate and HeartRate Variability Modification in Chronic Insomnia Patients, Behavioral Sleep Medicine, DOI:10.1080/15402002.2013.801346

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Behavioral Sleep Medicine, 11:1–17, 2013

Copyright © Taylor & Francis Group, LLC

ISSN: 1540-2002 print/1540-2010 online

DOI: 10.1080/15402002.2013.801346

Heart Rate and Heart Rate VariabilityModification in Chronic Insomnia Patients

Benedetto Farina

Department of Human Sciences, Cognitive and Clinical Psychology Lab

Università Europea, Rome, Italy

Serena Dittoni, Salvatore Colicchio, Elisa Testani, Anna Losurdo,

Valentina Gnoni, and Chiara Di Blasi

Department of Neurosciences

Catholic University, Rome, Italy

Riccardo Brunetti and Anna Contardi

Department of Human Sciences, Cognitive and Clinical Psychology Lab

Università Europea, Rome, Italy

Salvatore Mazza and Giacomo Della Marca

Department of Neurosciences

Catholic University, Rome, Italy

Chronic insomnia is highly prevalent in the general population, provoking personal distress and

increased risk for psychiatric and medical disorders. Autonomic hyper-arousal could be a pathogenic

mechanism of chronic primary insomnia. The aim of this study was to investigate autonomic activity

in patients with chronic primary insomnia by means of heart rate variability (HRV) analysis.

Eighty-five consecutive patients affected by chronic primary insomnia were enrolled (38 men and

47 women; mean age: 53.2 ˙ 13.6). Patients were compared with a control group composed of 55

healthy participants matched for age and gender (23 men and 32 women; mean age: 54.2 ˙ 13.9).

Patients underwent an insomnia study protocol that included subjective sleep evaluation, psycho-

metric measures, and home-based polysomnography with evaluation of HRV in wake before sleep,

in all sleep stages, and in wake after final awakening. Patients showed modifications of heart rate

Correspondence should be addressed to Benedetto Farina, Department of Human Sciences, Cognitive and Clinical

Psychology Lab, Università Europea, Via Fleming 110, 00191, Rome, Italy, 0039. E-mail: benfarina@gmail.com

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2 FARINA ET AL.

and HRV parameters, consistent with increased sympathetic activity, while awake before sleep and

during Stage-2 non-REM sleep. No significant differences between insomniacs and controls could

be detected during slow-wave sleep, REM sleep, and post-sleep wake. These results are consistent

with the hypothesis that autonomic hyper-arousal is a major pathogenic mechanism in primary

insomnia, and confirm that this condition is associated with an increased cardiovascular risk.

Chronic insomnia affects up to 10% of the general population, causing personal distress,

impairment in daytime functioning, reduced quality of life, and increased risk for psychiatric

and medical disorders such as anxiety, depression, and cardiovascular diseases (Benca, 2005;

Moul et al., 2002; Ohayon, 2002; Ohayon & Reynolds, 2009; Roth & Ancoli-Israel, 1999).

A limited, but growing, number of studies provide evidence of modifications in heart rate

(HR) and heart rate variability (HRV) in insomnia (Bonnet & Arand, 2010; Fang, Huang,

Yang, & Tsai, 2008; Spiegelhalder et al., 2011; Stepanski & Glinn, 1994). Although the data

from literature is not conclusive, insomnia patients show increased HR during sleep, lower

wake-to-sleep HR reduction, and alterations of HRV measures; overall, these findings seem to

indicate higher sympathetic and lower parasympathetic activation (Bonnet & Arand, 1998; De

Zambotti, Covassin, De Min Tona, Sarlo, & Stegagno, 2011; Fang et al., 2008; Jurysta et al.,

2009; Sforza, Pichot, Cervena, Barthelemy, & Roche, 2007; Spiegelhalder et al., 2011). HR

and HRV modifications are considered evidence in support of the “hyper-arousal” aetiological

hypothesis of primary insomnia, as well as for the increased risk of cardiovascular consequences

(Bonnet & Arand, 2010; De Zambotti et al., 2011; Janackova & Sforza, 2008; Jurysta et al.,

2009; Spiegelhalder et al., 2011; Varkevisser, Van Dongen, & Kerkhof, 2005).

HR is modulated on a beat-to-beat basis by the combined effects of the sympathetic and

parasympathetic nervous system on the sino-atrial node. HRV is a measurement of changes in

HR over time, which provides information about autonomic functioning (Stein & Pu, 2012).

HRV can be analyzed both in the time domain and in the frequency domain. Parameters of

HRV in the time domain are statistical measures derived from the beat file. In the frequency

domain two major components can be calculated from power spectral frequency analysis

performed on a plot of R-wave to R-wave (R–R) intervals, called a tachogram (Task Force of the

European Society of Cardiology, 1996a, 1996b). The high-frequency (HF) spectral component is

associated with parasympathetic activation, whereas the low-frequency (LF) spectral component

reflects both sympathetic and parasympathetic activation. The LF/HF ratio is a dimensionless

measure that is believed to reflect the sympatho-vagal balance (i.e., the ratio of sympathetic to

vagus nerve traffic to the heart; Eckberg, 1997).

A number of studies have investigated HR and HRV measures during normal sleep. Results

have often been discordant. There is a general agreement on the reduction of HR in the

proximity of sleep (Mancia et al., 1983; Trinder et al., 2001; Vanoli et al., 1995). This fall of HR

from wake to sleep becomes more evident during Stage-2 non-REM (NREM) sleep (N2) and

slow-wave sleep (SWS and Stage-3 NREM [N3]; Bonnet & Arand, 1998; Trinder et al., 2001).

In contrast, HR values in REM do not differ substantially from quiet wakefulness (Snyder,

Hobson, Morrison, & Goldfrank, 1964). Some studies of HRV measures during sleep have

shown an increase of HF along with a decrease of LF (Bilan, Witczak, Palusinski, Myslinski,

& Hanzlik, 2005; Burr, 2007; Trinder et al., 2001; Vanoli et al., 1995). LF results have been

controversial and in some studies the LF component was found to be unchanged or even to have

increased (Trinder et al., 2001). However, it has been reported that physiological fluctuations

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HEART RATE VARIABILITY MODIFICATION 3

of the EEG arousal level influence cardiac autonomic activity in normal participants during

sleep (Ferini-Strambi et al., 2000). In general, autonomic control of cardiac measures is thought

to be highly variable between participants: Large inter-individual variations (up to 260,000%)

have been reported, particularly for spectral measures of HRV (Nunan, Sandercock, & Brodie,

2010).

Since the late 1960s, growing evidence has been accumulated about the increase in insomnia

patients of HR and other physiological measures of autonomic arousal, including metabolic

indexes, EEG parameters, body temperatures, adrenocorticotropic hormones, and other stress

hormones (Stepanski & Glinn, 1994).

In 1998, Bonnet and Arand found significantly higher HRs and lower HRVs in wake and

in all sleep stages in 12 insomnia patients. These authors demonstrated a decreased HF and

an increased LF in their sample of insomniacs’ sleep, compared to good sleepers. According

to the authors, this finding constitutes a possible explanation for increased cardiovascular risk

in chronic insomnia (Bonnet & Arand, 1998). In a further study of 50 adult normal sleepers

exposed to stressful conditions, the same authors observed in participants with poor sleep effi-

ciency (situational insomnia group [IG]) the same HR and HRV alterations in primary insomnia

(Bonnet & Arand, 2003). More recently, Fang et al. (2008) examined the differences in LF and

HF measurements in 18 primary insomnia patients—diagnosed according to Diagnostic and

Statistical Manual of Mental Disorders (4th ed. [DSM–IV]; American Psychiatric Association,

1994) criteria—compared with 21 normal sleepers. These authors found a trend toward the

increase of the LF/HF ratio in insomniacs compared to controls, but the differences between

the two groups did not reach statistical significance (Fang et al., 2008). Similar findings were

observed by Jurysta et al. (2009), who studied the relations between EEG and HRV measures

in 14 chronic primary insomnia patients compared to 14 age- and gender-matched controls

in sleep laboratory registrations. Although the LF/HF ratio was slightly increased in wake,

NREM, and REM sleep of insomnia patients, no significant difference was found.

Finally, in a recent study, Spiegelhalder et al. (2011) investigated HR and HRV measures in

58 primary insomnia patients, according to DSM–IV criteria, and 46 healthy matched controls.

Their results confirmed the lower wake-to-sleep HR and HRV reduction in insomnia patients,

but found no other statistically significant modification in HRV measures, like the sympatho-

vagal unbalance or an increase of LF. But, when the researchers restricted the analysis to those

insomniacs with an objective short sleep duration (determined by polysomnography [PSG]),

they also found significant differences between groups with a decrease of HF and other HRV

measures associated with parasympathetic activity (Spiegelhalder et al., 2011).

Moreover, data from sleep and autonomic literature seems to indicate that large differences

do exist between autonomic activity during NREM sleep in the first versus the second part of

the night (Brandenberger, Ehrhart, & Buchheit, 2005), but data on HR and HRV modifications

in insomniacs in different parts of the sleep cycle are scarce.

Autonomic modifications in insomnia may be clinically relevant. It has been suggested that

insomnia may be related to higher mortality due to cardiovascular consequences (Schwartz

et al., 1999). On the other hand, a large population study (i.e., the Sleep Heart Health Study;

Newman et al., 2001) failed to find a relation between insomnia and cardiovascular mortality.

Overall, literature data clearly indicates that sleep duration is associated with mortality in a

u-shaped fashion, with the highest mortality being in women sleeping � 4 hr and � 9 hr

and men sleeping � 5 hr and � 9 hr (Grandner, Hale, Moore, & Patel, 2010). Nevertheless,

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4 FARINA ET AL.

insomnia is not defined on the basis of sleep duration because its diagnosis needs a variety of

criteria, including diurnal symptoms (American Academy of Sleep Medicine [AASM], 2005).

Hyper-arousal, including autonomic hyperactivity, is considered a pathogenic mechanism of

insomnia. For this reason, we decided, in this study, to focus on insomnia, regardless of sleep

duration.

The study was designed to test the hypothesis of autonomic hyper-arousal in insomniacs by

investigating HR and HRV measures in wake and sleep. The first objective of the study was

to confirm the literature data by evaluating a large group of primary insomnia patients because

most previous studies involved small samples. A further objective was to measure HR and HRV

parameters both in insomniacs and in a control group [CG] under different conditions related

to the sleep–wake cycle: particularly, wake before sleep, wake after final awakening, and all

sleep stages (separating early-stage N2 occurring in the first part of the night from late-stage

N2 occurring in the second part of the night; Brandenberger et al., 2005).

MATERIALS AND METHODS

Patients

Eighty-five consecutive patients affected by chronic primary insomnia were enrolled in the study

(38 men and 47 women; mean age: 53.2 ˙ 13.6; range D 27–81 years). All patients were

outpatients recruited from the Catholic University in Rome’s ambulatory for sleep disorders.

The period lasted one year, during which 127 consecutive primary insomnia patients were

screened. Of these, 85 agreed to participate. The mean duration of their insomnia was > 2

years. The criterion for inclusion was a diagnosis of primary insomnia, lasting over three

months. Criteria for exclusion were insomnia secondary to medical, neurologic, or psychiatric

diseases, heart diseases, arrhythmias, intake of cardiovascular active drugs, diabetes, uncon-

trolled hypertension, severe obesity (body mass index [BMI] > 35 kg/m2), chronic respiratory

disease, or thyroid diseases. No patient was taking cardiovascular drugs, antidepressants, pain,

sleeping, or other psychoactive medication at the time of the study, or in the previous two weeks.

The diagnosis of primary insomnia was assessed by an interdisciplinary team of neurologists

and psychiatrists on a clinical basis using the International Classification of Sleep Disorders–

Second Edition (ICSD–2) criteria (AASM, 2005).

All patients underwent a full medical and neurological evaluation. To rule out psychiatric

disorders, all patients and controls received a complete psychiatric interview by a trained psy-

chiatrist (Benedetto Farina), and were screened for any possible diagnoses using Diagnostic and

Statistical Manual of Mental Disorders (4th ed., text rev. [DSM–IV-TR]; American Psychiatric

Association, 2000) criteria.

CG

Patients were compared with a CG of 55 healthy participants matched for age and gender

(23 men and 32 women; mean age: 54.2 ˙ 13.9; range D 27–76 years). Control participants

were chosen from a group of healthy volunteers who underwent ambulatory EEG record-

ing for the specific purpose to serve as a CG for the sleep studies. Controls underwent a

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HEART RATE VARIABILITY MODIFICATION 5

full psychiatric, medical, and neurological evaluation, and a hypnological interview to rule

out present or previous history of sleep disorders. The same exclusion criteria were applied

to the CG as had been to the insomniacs. The study was approved by the local ethics

committee; The study was designed in accordance with the Helsinki Declaration of 1975.

All patients and control participants were fully informed, and all gave a written consent to

participate.

Sleep Quality Evaluation

Subjective evaluation of sleep quality was performed using the Pittsburgh Sleep Quality Index

(PSQI) (Buysse, Reynolds, Monk, Berman, & Kupfer, 1989). The Italian version (Curcio et al.,

2013) has been translated from English into Italian, then retranslated for comparison with the

original version. A global score > 5 was considered an indicator of relevant sleep disturbances

(Buysse et al., 1989). For the evaluation of excessive daytime sleepiness, the validated Italian

version of the Epworth Sleepiness Scale (ESS) was applied (Vignatelli et al., 2003). Moreover,

on all participants, an evaluation was performed of the symptoms and clinical signs or predictors

of obstructive sleep apnea syndrome using the Berlin Questionnaire (Netzer, Stoohs, Netzer,

Clark, & Strohl, 1999). The Berlin Questionnaire classifies patients according to the risk

of sleep apnea into two groups: low risk or high risk. This evaluation included measuring

neck circumference, BMI, presence of habitual snoring, nocturia, morning headache, arterial

hypertension, and apneas reported by bed partners. The clinical evaluation included the search

for symptoms of restless legs syndrome using established diagnostic criteria (Allen et al., 2003;

AASM, 2005).

To measure severity and intensity of eventual psychopathological symptoms, the following

scales were used: the Self-administered Anxiety Scale (SAS; Zung, 1971), the Beck Depression

Inventory (BDI; Beck, Ward, Mendelson, Mock, & Erbaugh, 1961), the Maudsley Obsessive–

Compulsive Inventory (MOCI; Dominguez, Jacobson, de la Gandara, Goldstein, & Steinbook,

1989), the Snaith–Hamilton Pleasure Scale (SHAPS; Snaith et al., 1995), and the Eating

Attitude Test–26 Items (EAT–26; Garner, Olmsted, Bohr, & Garfinkel, 1982).

The SAS (Zung, 1971) is a method of measuring levels of anxiety in patients who have

anxiety-related symptoms. It uses a 4-point Likert-type scale ranging from 1 (a little of the

time) to 4 (most of the time). The SAS contains 20 items with 15 increasing anxiety level

questions and five reverse-scored items. High scores correspond to higher levels of anxiety.

Below 45 is the normal range (Zung, 1971). The BDI (Beck et al., 1961) is a 21-item, well-

validated, self-report instrument measuring characteristic attitudes and symptoms of depression

over the previous two weeks. Scores range from 0 to 36. Scores > 9 indicate mild to severe

depression. The MOCI (Dominguez et al., 1989) is a self-report questionnaire with a “true”

or “false” format developed for evaluating obsessive-compulsive symptoms and discriminating

obsessive patients from other neurotic patients and from nonclinical people. The total score

ranges between 0 (absence of symptoms) and 34 (maximum presence of symptoms). The MOCI

has four subscales that measure the following traits: checking, cleaning, slowness, and doubting.

The SHAPS (Snaith et al., 1995) is a 14-item, self-administered instrument that is used to

measure hedonic capacity. Each of the items has a set of four response categories: definitely

agree, agree, disagree, and strongly disagree. Total scores range from 0 to 14. A higher total

SHAPS score indicates higher levels of the present state of anhedonia. Scores > 3 indicate

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6 FARINA ET AL.

pathological anhedonia (Snaith et al., 1995). The EAT–26 (Garner et al., 1982) is an objective,

self-report measure of anorexia nervosa symptoms. The scale has 23 items, where responses are

mutually exclusive and exhaustive. Participants are required to judge whether the item applies

“always,” “very often,” “often,” “sometimes,” “rarely,” or “never.” In this 6-point, forced-choice,

Likert scale, each extreme response in the “anorexic” direction is scored as 3 points, whereas

the adjacent alternatives were weighted as 2 points and 1 point, respectively. Scores > 20 are

considered clinically relevant for eating disorders (Garner et al., 1982).

In the morning following the PSG study, the patients were asked to subjectively evalu-

ate their sleep by indicating the following: time needed to fall asleep, number of hours of

sleep, number of awakenings, and overall sleep quality (by means of a 0–10 visual analogic

scale).

PSG

Twenty-four hours of ambulatory (home-based) PSG were recorded. This recording technique

was chosen to allow the patients to sleep in their habitual home setting and to maintain their

spontaneous-usual sleep–wake schedule (McCall, Erwin, Edinger, Krystal, & Marsh, 1992).

Ambulatory recordings were routinely diagnostic PSG evaluations in primary insomnia patients.

Therefore, patients and controls were asked to keep a spontaneous schedule without fixed

light-off or light-on times. A montage of electrodes was performed in the sleep lab, which

included EEG leads filled with electrolyte applied to the following locations: right frontal

(F4), right central (C4), and right occipital (O2); reference electrodes applied to the contra-

lateral mastoid (M1); two EOG channels recorded by surface electrodes applied 1 cm from

each ocular cantus and referred to contra-lateral mastoids surface EMG of sub-mental muscles,

EKG. Sleep recordings were analyzed on a computer monitor, and sleep stages were visually

classified according to the criteria of the AASM (2007; Iber, Ancoli-Israel, Chesson, & Quan,

2007). The beginning and the end of the analyzed interval (i.e., times of “lights off” and

“lights on”) were indicated by the patients in their sleep logs, which they compiled during

the ambulatory recording. The participants were required to lie down for at least 10 min after

their final awakening to record EKG in the post-sleep wake phase. The following parameters

were calculated in the analysis of sleep: sleep onset latency (SOL), time in bed (TIB), total

sleep time (TST), sleep period time (SPT), Sleep Efficiency Index (SEI), Awakenings > 1

min, wake after sleep onset (WASO), percentages of wake, REM, Stage-1 NREM (N1), N2,

and N3.

HRV

An HRV study was carried out on the EKG trace obtained in the PSG study. Following

established criteria, the EKG was recorded using a modified Lead II derivation (with the right

shoulder negative and the left lower torso positive). Sampling rate was 256 Hz, with a digital

resolution of 16 bits per sample. Impedance was kept below 5K�.

All patients and controls were evaluated at the following times (see Figure 1): Time 1 D

pre-sleep wake (defined as the last consecutive 5 min of wake prior to sleep onset), Time 2 D

early light sleep (the first consecutive 5 min of N2 not interrupted by awakenings or stage

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HEART RATE VARIABILITY MODIFICATION 7

FIGURE 1 Sleep hypnogram. Note. Circles and arrows indicate the sleep epochs selected for heart rate

variation analysis. T1 D Time 1; N1 D Stage-1 non-REM sleep. (Color figure available online.)

shifts), Time 3 D SWS (the first consecutive 5 min of N3 not interrupted by awakenings or

stage shifts), Time 4 D stage REM (the first consecutive 5 min of stage REM not interrupted

by awakenings or stage shifts), Time 5 D N2 (the last consecutive 5 min of N2 in the

night not interrupted by awakenings or stage shifts), and Time 6 D post sleep (defined as

5 consecutive min of wake after the final awakening). “Early-stage” N2 was, therefore, in the

first sleep cycle, whereas “late-stage” N2 was collected from the last sleep cycle, preceding

final awakening. We analyzed two intervals of N2 separately because this sleep stage may have

large differences when it occurs in proximity of SWS or REM sleep. In particular, progressive

decrease in HRV sympathetic indexes during the transition toward SWS has been described, in

contrast with high and stable levels during N2 that evolves toward REM (Brandenberger et al.,

2005).

Physiological mechanisms of heart period modulations responsible for LF and HF power

components cannot be considered stationary during the longer-lasting periods (Task Force of the

European Society of Cardiology, 1996a, 1996b). Therefore, the decision to evaluate 5-min sleep

intervals was motivated by the necessity to collect homogenous samples of ECGs from homo-

geneous samples of each sleep stage, of comparable duration, without interruptions due to EEG

arousals, stage shifts, or awakenings. Artefact rejection was performed visually; periods of EKG

recordings characterized by ventricular extrasystoles, movements, muscular artefacts, or other

artefacts were excluded from the analysis. Dedicated software (REMbrandt SleepView, Medcare

Automation®, Amsterdam, Netherlands) recognized the individual electrocardiographic R-wave

peaks and calculated the R–R intervals (using a tachogram). Successively, the tachogram, an

Excel file, was converted into an ASCII file and analyzed by means of dedicated software

freely available from the Web (HRV Analysis Software: University of Kuopio, Department of

Applied Physics, Biomedical Signal Analysis Group, Finland; Niskanen, Tarvainen, Ranta-Aho,

& Karjalainen, 2004).

HRV analysis was performed both in a time domain and in a frequency domain. Because

many of the measures closely correlate with others, the following parameters were considered

in the time domain: mean and standard deviation of HRs; mean and standard deviation of R–

R intervals, root mean square of the differences between consecutive normal-to-normal (NN)

intervals (RMSSD), number of consecutive R–R intervals differing by > 50 msec (NN50) and

the percentage value of NN50 intervals (pNN50), and standard deviation of NN intervals (inter-

vals between consecutive QRS complexes resulting from sinus node depolarization [STDNN]).

STDNN reflects total HRV, whereas pNN50 and RMSSD reflect vagal activity with normal

sinus rhythm (Stein & Pu, 2012).

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8 FARINA ET AL.

In the frequency domain, HRV was analyzed using the parametric Autoregressive Model

analysis, which allowed for an accurate estimate of power spectral density when analyzing short

time intervals, during which the signal is supposed to maintain stationarity (1996a, 1996b). The

frequency bands considered were the LF (0.04–0.15 Hz) and the HF (0.15–0.4 Hz) ones. In

the frequency domain, the power of LF and HF bands were expressed in normalized units

(nu), and the LF/HF ratio was calculated. Normalization was performed using the following

formula:

Z DX � �

�;

where � D EŒX� is the mean and � Dp

Var.X/ is the standard deviation of the probability

distribution of X. HF is purported to reflect parasympathetic nervous system activity, LF is

purported to reflect sympathetic nervous system activity, and the LF/HF ratio is a measure of

the sympathetic/parasympathetic balance.

A detailed description of HRV analysis, standards of measurement, physiological interpre-

tation, and clinical use is available in the report by the Task Force of the European Society of

Cardiology and the North American Society of Pacing and Electrophysiology (1996a, 1996b).

Statistical Analysis

The gender composition of the groups (insomniacs and controls) was compared by means

of a chi-square test. Before comparing the two groups, the distribution of the samples was

tested using the Shapiro–Wilk test, with a significance level of p < :05. When the distribution

was normal in both the samples (insomniacs and controls) a comparison was made using

the student t test, followed by the Bonferroni correction for multiple comparisons. When the

distribution was not normal, a nonparametric test was applied (i.e., the Mann–Whitney U test).

The significance level was set at p < :05. HRV parameters, in each of the recording conditions,

were compared between insomnia patients and controls. Because the distribution of HRV values

cannot be presumed as a normal distribution, we decided to compare the data by means of a

nonparametric test—namely, the Mann–Whitney U test. The level of significance was set to

p < :05. Within the IG, correlations were tested between PSQI, ESS, SAS, and BDI scores and

HRV parameters by means of Spearman’s correlation index. The critical value of Spearman’s

correlation coefficient was set to r.83/ D :216, corresponding to a significance level p < :05.

Statistics were performed using Statistical Package for Social Sciences software Version 19

(SPSS, Inc., Chicago, IL).

RESULTS

Useful recordings were obtained from all patients and controls. Data are reported as Mean

Age ˙ Standard Deviation. No differences in age and gender compositions of the two groups

could be detected—(a) age: IG, 53:2 ˙ 13:6 years; CG, 54:2 ˙ 13:9 years; and t test, 0.677

.p D :932/; (b) gender: IG, 38 men and 47 women; and CG, 23 men and 32 women; and

(c) Fisher’s exact test: �2 D 0:110 .p D :736/.

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HEART RATE VARIABILITY MODIFICATION 9

Sleep Analysis Results

Insomniacs showed significant differences in their main sleep parameters compared to controls:

longer SOL (IG: 27.8 ˙ 79.4 min; CG: 26.0 ˙ 22.9 min; U test: 1,246, p < :001), reduced

SEI (IG: 82.4 ˙ 22.0; CG: 86.9 ˙ 14.2; U test: 1,340, p < :001), increased number of

awakenings (IG: 8.0 ˙ 4.9; CG: 6.1 ˙ 3.6; U test: 3,058, p D :002), and, consequently,

increased WASO (IG: 178.5 ˙ 125.0 min; CG: 91.3 ˙ 78.8 min; U test: 4,008, p < :001).

However, no significant differences were observed in the length of the SPT (IG: 473.5 ˙ 135.0

min; CG: 442.6 ˙ 69.6 min; U test: 2,616, p D :236) and TST (IG: 388.3 ˙ 91.8 min; CG:

387.2 ˙ 66.0 min; student t test: 0.774, p D 1:000). In the IG, the TIB was longer than that of

controls (IG: 599.2 ˙ 224.0 min; CG: 523.4 ˙ 185.0 min; U test: 3,640, p < :001). Detailed

results of the sleep analysis in the two groups, and results of the statistical comparison, are

listed in Table 1.

HRV Analysis Results

Compared to controls, the insomniacs showed increased HR in wake before sleep (IG: 67.8 ˙

8.7 bpm; CG: 59.0 ˙ 7.3 bpm; student t test: p < :001) and both in early-stage N2 (IG:

64.8 ˙ 7.6 bpm; CG: 49.0 ˙ 5.4 bpm; student t test: p < :001) and late-stage N2 (IG: 60.9 ˙

8.0 bpm; CG: 53.5 ˙ 10.4 bpm; U test: 3,345, p < :001), and no significant modifications

were observed in N3 (IG: 63.5 ˙ 8.5 bpm; CG: 57.1 ˙ 21.6 bpm; U test: 2,256, p D :614),

REM (IG: 64.7 ˙ 9.0 bpm; CG: 67.5 ˙ 9.3 bpm; student t test: p D :086), and wake final

awakening (IG: 75.3 ˙ 13.4 bpm; CG: 72.2 ˙ 13.1 bpm; student t test: p D :175).

In the frequency domain, an increased LF (using nu) component was observed in insomniacs

in pre-sleep wake (IG: 49.8 ˙ 24.5; CG: 41.5 ˙ 20.5; U test: 2,853, p D :028) and in

early-stage N2 (IG: 47.1 ˙ 24.1; CG: 33.4 ˙ 17.6; t test: p < :001), whereas no significant

differences were seen in the other sleep and wake stages. No modifications of the HF component

were observed in wake or sleep. The sympatho-vagal balance, expressed by the LF/HF ratio, was

increased in insomnia patients, but only during early-stage N2 (IG: 2.5 ˙ 3.4; CG: 1.5 ˙ 2.3; U

test: 1,813, p D :025), although a trend to increase this balance (but not significant) could also

be observed in pre-sleep wake (IG: 3.7 ˙ 3.3; CG: 2.9 ˙ 2.9; U test: 1,950, p D :098).

Notably, no significant differences between insomniacs and controls could be detected in

HRV parameters during deep SWS (N3), REM sleep, and post-sleep wake. Detailed results of

the HRV analysis in the two groups, and results of the statistical comparison, are shown in

Table 2.

Psychometric Measures

In the evaluation of subjective sleep of the IG, the mean PSQI score was 12.5 ˙ 3.6 and the

ESS score was 4.6 ˙ 4.2. The mean scores at the psychometric evaluation of the IG were

as follows: SAS D 49.5 ˙ 9.7 (11 patients had severe anxiety scores), BDI D 7.8 ˙ 6.7 (3

patients had moderate to severe depression scores), MOCI D 9.5 ˙ 6.3, EAT–26 D 7.2 ˙ 8.1,

and SHAPS D 0.7 ˙ 1.5. No significant correlations were found between HRV parameters

(HR, HRV, LF, HF, and LF/HF) and psychometric measures.

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TABLE 1

Age and Polysomnographic Parameters of Patients and Controls

Insomniacs (n D 85) Controls (n D 55) Mann–Whitney Student t Test

Variable M SD Mdn Shapiro–Wilk p M SD Mdn Shapiro–Wilk p U Test p t p

Age 53.2 13.6 53 0.980 .206 54.18 13.9 55 0.964 .099 0.677 0.932

Sleep onset latency 27.8 79.4 9 0.321 .000 26.00 22.9 24 0.910 .001 3,355 .000

Time in bed 599.2 224.0 574 0.666 .000 523.40 185.0 490 0.970 .186 1,035 .000

Total sleep time 388.3 91.8 384 0.995 .991 387.20 66.0 388 0.976 .328 0.774 1.000

Sleep period time 473.5 135.0 458 0.825 .000 442.60 69.6 444 0.954 .035 2,059 .236

Sleep Efficiency Index 82.4 22.0 81 0.792 .000 86.90 14.2 94 0.930 .003 3,335 .000

Awakenings > 1 min 8.0 4.9 7 0.908 .000 6.10 3.6 5 0.929 .003 1,617 .002

Wake after sleep onset 178.5 125.0 167 0.859 .000 91.30 78.8 44 0.880 .000 667 .000

REM 15.5 5.9 16 0.986 .468 17.00 7.5 17 0.972 .227 0.162 1.000

N1 (Stage-1 non-REM) 8.1 4.5 8 0.915 .000 9.70 7.1 9 0.816 .000 2,778 .060

N2 (Stage-2 non-REM) 37.2 11.8 37 0.971 .056 38.60 11.9 41 0.987 .800 0.250 1.000

N3 (Stage-3 non-REM) 23.0 11.7 22 0.983 .321 21.80 11.0 19 0.946 .015 2,028 .187

Wake 16.2 13.2 12 0.887 .000 26.40 39.1 10 0.704 .000 2,368 .895

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TABLE 2

Results of Heart Rate and Heart Rate Variability Measures in Wake (Before and After Sleep) and all Sleep Stages

Insomniacs (n D 85) Controls (n D 55) Mann–Whitney Student t Test

Variable M SD Mdn Shapiro–Wilk p M SD Mdn Shapiro–Wilk p U Test p p

Pre-sleep wake

Mean HR 67.8 8.7 67 0.978 .145 59.0 7.3 58 0.962 .076 <.001

STDNN 47.0 23.0 30 0.861 <.001 41.0 19.0 30 0.869 .000 2,694 .128

RMSSD 31.8 15.9 28 0.926 <.001 28.9 13.6 27 0.948 .019 2,109 .330

pNN50% 11.1 13.9 6 0.772 <.001 11.0 12.2 7 0.836 <.001 2,305 .890

LF 238.0 359.6 140 0.578 <.001 230.1 362.2 141 0.561 <.001 2,252 .715

HF 150.4 211.2 84 0.599 <.001 140.8 146.5 74 0.786 <.001 2,378 .863

LF (nu) 49.8 24.5 53 0.966 .023 41.5 20.5 45 0.957 .050 2,853 .028

HF (nu) 38.0 22.9 35 0.935 <.001 35.2 20.2 32 0.934 .005 2,478 .549

LF/HF 3.7 3.3 2.4 0.854 <.001 2.9 2.9 1.6 0.796 <.001 1,950 .098

Early-stage N2

Mean HR 64.8 7.6 64 0.971 .054 49.0 5.4 48 0.959 .061 <.001

STDNN 42.0 19.0 30 0.956 .006 31.0 14.0 20 0.945 .013 3,190 <.001

RMSSD 35.9 17.5 32 0.931 <.001 27.2 13.5 25 0.935 .005 1,650 .003

pNN50% 13.0 15.2 7 0.812 <.001 10.1 11.2 6 0.836 <.001 2,534 .402

LF 343.3 440.4 186 0.732 <.001 238.5 328.2 117 0.698 <.001 1,949 .097

HF 225.7 274.0 129 0.711 <.001 170.1 186.7 94 0.770 <.001 2,068 .250

LF (nu) 47.1 24.1 46 0.972 .064 33.4 17.6 32 0.975 .314 <.001

HF (nu) 40.7 22.5 37 0.961 .011 32.4 15.8 33 0.971 .208 2,792 .052

LF/HF 2.5 3.4 1.3 0.664 <.001 1.5 2.3 0.6 0.592 <.001 1,813 .025

Stage N3

Mean HR 63.5 8.5 63 0.974 .108 57.1 21.6 62 0.709 <.001 2,256 .614

STDNN 36.0 16.0 20 0.835 <.001 33.0 19.0 20 0.888 <.001 2,220 .732

RMSSD 34.4 17.4 33 0.917 <.001 32.6 20.5 33 0.953 0.031 2,299 .870

pNN50% 12.0 14.7 7 0.793 <.001 11.8 14.9 9 0.769 <.001 2,229 .702

LF 135.9 200.5 71 0.608 <.001 115.6 142.2 71 0.770 <.001 2,192 .533

HF 198.2 245.7 124 0.657 <.001 201.5 274.8 116 0.633 <.001 2,279 .803

LF (nu) 34.0 22.1 30 0.943 .001 29.6 22.3 27 0.940 .009 2,430 .304

HF (nu) 54.4 25.5 53 0.916 <.001 52.0 30.8 53 0.922 .002 2,271 .751

LF/HF 1.0 1.4 0.5 0.685 <.001 0.8 1.0 0.4 0.704 <.001 2,025 .182

(continued )

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TABLE 2

(Continued)

Insomniacs (n D 85) Controls (n D 55) Mann–Whitney Student t Test

Variable M SD Mdn Shapiro–Wilk p M SD Mdn Shapiro–Wilk p U Test p p

Stage REM

Mean HR 64.7 9.0 64 0.980 .199 67.5 9.3 66 0.970 .188 .086

STDNN 52.0 22.0 30 0.978 .153 53.0 23.0 40 0.972 .238 .663

RMSSD 32.3 18.5 26 0.848 <.001 34.4 19.6 27 0.854 <.001 2,536 0.397

pNN50% 9.5 12.3 4 0.745 <.001 10.8 13.7 4 0.749 <.001 2,162 0.453

LF 278.6 341.3 152 0.721 <.001 267.6 325.3 154 0.714 <.001 2,362 0.917

HF 148.7 205.3 68 0.657 <.001 165.5 215.2 65 0.685 <.001 2,531 0.409

LF (nu) 56.4 25.2 60 0.952 .003 54.7 25.4 60 0.943 .012 2,366 0.903

HF (nu) 35.4 51.0 27 0.357 .000 42.8 64.2 31 0.375 <.001 2,017 0.171

LF/HF 3.4 4.2 2.2 0.645 <.001 3.0 3.6 1.7 0.690 <.001 2,211 0.588

Late-stage N2

Mean HR 60.9 8.0 60 0.982 .311 53.5 10.4 53 0.810 .000 3,345 <.001

STDNN 48.0 25.0 30 0.906 <.001 41.0 24.0 20 0.913 .001 2,686 0.080

RMSSD 41.1 25.5 36 0.850 <.001 36.8 24.6 33 0.841 <.001 2,098 0.307

pNN50% 17.2 18.2 10 0.852 <.001 16.4 16.7 11 0.866 <.001 2,361 0.734

LF 342.7 827.1 170 0.329 <.001 332.6 883.5 154 0.302 <.001 2,232 0.653

HF 297.6 355.8 155 0.757 <.001 279.1 331.6 121 0.763 <.001 2,221 0.619

LF (nu) 42.1 22.5 41 0.976 .126 36.6 20.0 35 0.973 .242 .132

HF (nu) 46.8 25.1 41 0.889 <.001 42.6 24.6 36 0.880 <.001 2,545 0.254

LF/HF 1.5 1.8 1 0.698 <.001 1.3 1.7 1 0.636 <.001 2,105 0.320

Post-sleep wake

Mean HR 75.3 13.4 74 0.974 .087 72.2 13.1 71 0.977 .378 .175

STDNN 71.0 35.0 60 0.900 <.001 69.0 34.0 60 0.901 <.001 2,395 0.806

RMSSD 33.8 18.7 30 0.854 <.001 33.0 19.9 28 0.771 <.001 2,184 0.513

pNN50% 8.4 10.2 4 0.755 <.001 8.1 9.7 4 0.771 <.001 2,383 0.846

LF 350.0 414.9 180 0.772 <.001 350.4 423.3 171 0.777 <.001 2,282 0.813

HF 142.3 217.7 75 0.541 <.001 151.1 235.4 71 0.543 <.001 2,325 0.957

LF (nu) 64.2 75.5 63 0.353 <.001 52.1 26.6 60 0.890 <.001 2,649 0.184

HF (nu) 40.7 123.4 23 0.183 <.001 28.2 19.4 24 0.916 .001 2,331 0.978

LF/HF 4.4 5.2 3 0.749 <.001 4.1 5.4 2 0.703 <.001 2,139 0.396

Note. HR D heart rate; STDNN D standard deviation of NN (normal-to-normal) intervals; pNN50(%) D percentage of NN intervals > 50 msec different from

previous NN; RMSSD D root mean square of successive differences of NN intervals; LF (ms2) D low frequency (HR rhythms from 0.04–0.15 Hz [absolute value]);

HF (ms2) D high frequency (HR rhythms from 0.15–0.40 Hz [normalized unit]); nu D normalized units; LF (nu) D HR rhythms from 0.04–0.15 Hz (absolute

value); HF (nu) D HR rhythms from 0.15–0.40 Hz; LF/HF D LF/HF ratio.

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HEART RATE VARIABILITY MODIFICATION 13

DISCUSSION

The objective of this study was to measure HR and HRV parameters to test the hypothesis

of autonomic hyper-arousal in chronic primary insomnia patients. In particular, we wanted

to evaluate HR and HRV parameters in the different sleep and wake stages separating the

first versus the second part of the night. As expected, in our study, the chronic insomnia

patients showed an increased HR. The data confirm the results of previous studies (Bonnet

& Arand, 1998, 2010; Covassin et al., 2011; De Zambotti et al., 2011; Spiegelhalder et al.,

2011; Stepanski & Glinn, 1994), which showed higher HR in insomnia patients during wake

and NREM sleep stages, as compared to healthy controls. However, unlike other research, this

increase was found to be significant only in wake before sleep and in N2 (pre- and post SWS),

but not in N3, REM sleep, and WASO. In our opinion, this is the major finding of this study

because it could be evidence that arousal in insomniacs is maximal in the wake before sleep

and that it progressively decreases with the deepening of sleep.

The difference to other studies could be due to methodological differences, as we measured

HRV in each sleep stage separately, whereas most of the previous studies considered all NREM

stages as a whole (Bonnet & Arand, 1998, 2010; Covassin et al., 2011; De Zambotti et al.,

2011; Spiegelhalder et al., 2011; Stepanski & Glinn, 1994). A further relevant finding is the

lower wake-to-sleep HR decrease in insomnia patients compared to controls (see Figure 1),

which was previously reported by several authors (Bonnet & Arand, 1998, 2010; Covassin

et al., 2011; De Zambotti et al., 2011; Spiegelhalder et al., 2011).

Our study also revealed consistent differences in HRV between insomniacs and controls.

Some HRV parameters were significantly modified in the IG in the early-stage N2. In particular,

in the IG, we observed increased RMSSD, LF, and an LF/HF ratio. These findings contrast

with the original observations by Bonnet and Arand (1998), but are in agreement with most

of the subsequent studies (Fang et al., 2008; Jurysta et al., 2009; Spiegelhalder et al., 2011).

The interpretation of these results is controversial. The increase of HR and the LF/HF ratio

could account for increased sympathetic activity; on the other hand, the increase of RMSSD

suggests an increase of vagal activity (Stein & Pu, 2012).

The clinical explanation of HRV modifications is questionable. Two major hypotheses can

be made: autonomic modifications could be an epiphenomenon of sleep fragmentation or,

alternatively, HRV alterations could be an expression of a hyper-arousal state—that is, they

could be a direct expression of the pathogenic mechanism of primary insomnia.

Regarding the first hypothesis, it has been speculated that frequency-domain measures of

HRV (LF, HF, and the LF/HF ratio) represent a nonspecific marker of sleep fragmentation,

unrelated to a specific sleep disorder (Janackova & Sforza, 2008). This issue was addressed in

a study by Sforza and colleagues, who measured HRV frequency-domain variables in a very

large sample of 366 patients with different sleep disorders including insomnia, sleep-related

breathing disorders, and restless leg syndrome with periodic limb movement (Sforza et al.,

2007). They concluded that HRV frequency-domain indexes represent a powerful measure,

capable of differentiating patients with insomnia from those having severe sleep fragmentation

related to respiratory and movement disorders (Sforza et al., 2007). In our observations, an

argument against the “nonspecific” hypotheses arises from the peculiar time course of HR and

HRV modifications, which are relevant during the wake before sleep and persist in N2 of the

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14 FARINA ET AL.

FIGURE 2 Time course of heart rate (HR) in patients and controls during wake and sleep stages.

first part of the night, whereas few or no modifications could be detected in the second part of

the night and in wake after final awakening (see Figure 2 and Table 1).

Alternatively, the HR and HRV modifications in chronic insomnia could be considered ex-

perimental evidence of a trait of autonomic hyper-arousal in those patients. In fact, the increase

of HR and the failure of normal autonomic drop in falling asleep, along with modifications

of other physiologic parameters (body temperatures, stress hormones, and EEGs), could be

considered measures of a state of arousal that predisposes the individual to poor sleep (Bonnet

& Arand, 1998, 2010; De Zambotti et al., 2011; Spiegelhalder et al., 2011). As recently stated

by Bonnet and Arand (2010), insomnia is considered to be the combination of physiological

predisposition provoked by a central nervous system tonic arousal with stressful environmental

factors. However, if there are no longer doubts as to the existence of an arousal state during

the sleep of insomniacs, the debate is still ongoing about the origin of this arousal (Bonnet &

Arand, 2010): It could be considered a somatic trait marker or a consequence of cognitive and

emotional personality features. According to the review by the Standards of Practice Committee

of the AASM, emotional arousal is a key mediating factor in acute or sub-acute insomnia, and

“negative expectations, and progressively desperate efforts to sleep with increasing time in

bed awake, are often associated with [the] development of chronic insomnia” (Sateia et al.,

2000, p. 245). In other words, the worry about poor sleep in chronic insomniacs could become

a stress factor that maintains insomnia. In our opinion, it is possible that part of the hyper-

arousal state demonstrated in chronic insomniacs could be induced by worrying about sleep

problems, which, in turn, increases the original hyper-arousal state in a self-perpetuating cycle.

The time course of HR and HRV modifications in our sample could represent evidence for

this hypothesis: Autonomic arousal is at its maximum during the wake before sleep, and it

progressively decreases with the deepening of sleep. In our opinion, this is the major new

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HEART RATE VARIABILITY MODIFICATION 15

finding of the study and, if confirmed, could be of significant relevance to insomnia research

and therapy.

Whatever the origin of the hyper-arousal state, the increase of cardiac activity during

sleep is a risk factor for the cardiovascular consequences of chronic insomniac. A large

body of experimental evidence records the link between hyper-arousal of sleep expressed by

cardiovascular alterations and cardiovascular disease in chronic insomnia (Bonnet & Arand,

2010). This suggests that HR and HRV measures could be considered useful clinical tools to

evaluate the degree of cardiovascular risk in chronic insomnia (Sforza et al., 2007). However,

this study has two major strengths. The first is the large number of participants. The second

is that it was designed to evaluate HRV in different stages of wake and sleep, allowing a

better description of the fluctuations of autonomic arousal of insomniacs in the course of the

wake–sleep cycle. Finally, some patients exceeded cutoff scores of self-rating scales, although

they were not to a level of clinical diagnosis for a psychiatric disorder.

Limitations of the Study

Methodological issues could account for the differences between some of our results and those

reported in previous studies. First of all, it must be kept in mind that HRV measures show a

large variability between participants—even between healthy participants—and that they may

be modified in a variety of physiological conditions (Nunan et al., 2010). Another possible

bias can derive from the criteria adopted for the diagnosis of insomnia: Most of the previous

studies used DSM–IV rules, whereas we chose the ICSD–2 criteria. The very broad age range

of participants that greatly increases measurement variability in both groups and decreases

the possibility to show group differences represents another limitation of this study. Finally,

the decision to do recordings at home, although more respectful of the patients’ sleep habits,

produces results that are rather different from those obtained with traditional, laboratory-based

PSG. In particular, insomnia patients enrolled in our study showed a very long TIB—longer

than that observed in controls. Increased TIB is a consequence of the recording technique,

and several reasons can account for this effect: Ambulatory or home recordings do not give

the patients a specific schedule, which does happen in laboratory-based studies; moreover, we

asked our patients to keep their usual sleep–wake cycle, and patients were not working on the

day they did the study. The length of TIB may affect some other sleep variables—in particular,

the number of awakenings, WASO, and TST. To minimize the effect of long TIB, we calculated

most of the sleep variables as a function of the SPT: SEI, sleep state percentages, and arousal

indexes.

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