Linear and nonlinear heart rate dynamics in elderly inpatients. Relations with comorbidity and depression.

Abstract

Hospitalization processes are known to increase depressive symptoms arising among elderly population. Meanwhile, dysregulation of cardiac autonomic function has been suggested to link depression and cardiovascular mortality. In this context, analysis of heart rate variability (HRV) is emerging as a powerful mortality risk stratifier clinical tool. The purpose of the study was to examine the relationship among HRV, depression, and comorbidity risk among an elderly inpatient population.
Twenty-six subjects (aged 78±9 years) were recruited from the Short-Term Stay Unit at the Hospital General de Alicante. Before joining a Physical Activity Program aimed to prevent functional impairment and after medical selection and written consent, inpatients were tested for heart rate variability, Yesavage Geriatric Depression Scale, and Charlson comorbidity index score.
Men compared to women showed a significantly larger CCI score. Short-term scaling exponent (α(1)), derived from detrended fluctuation analysis, showed a negative correlation with Charlson comorbidity index. Conversely, a positive correlation was found between sample entropy (SampEn) and Yesavage Scale.
On the one hand, fractal analysis of HRV confirms to be useful as a risk stratifier tool. On the other hand, SampEn is proposed to be reflecting a non-neurally generated complexity when accompanied with low values of α(1). Accordingly, in this regime, it would be indicative of a paradoxical gradual reduction in cardiac autonomic control, accentuated with the severity of depressive symptoms.

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Medicina (Kaunas) 2010; 46(6)
CLINICAL INVESTIGATIONS
Medicina (Kaunas) 2010;46(6):,393-400
Linear and nonlinear heart rate dynamics in elderly inpatients.
Relations with comorbidity and depression
Cristina Blasco-Lafarga1, 2, Ignacio Martínez-Navarro1, María Elisa Sisamón3,
Nuria Caus3, Emilio Yangüez2, Pere Llorens-Soriano4
1Department of Physical Education and Sports, University of Valencia, Spain, 2IES Haygón Institute, Alicante, Spain,
3Department of General Didactic and Special Didactics, University of Alicante, Spain,
4Hospital General Universitario, Alicante, Spain
Correspondence to C. Blasco-Lafarga, Department of Physical
Education and Sports, University of Valencia, C/ Hondón de
las nieves, 5, 4º D, Alicante 03005, Spain
E-mail: m.cristina.blasco@uv.es
Adresas susirašinėti: C. Blasco-Lafarga, Department of Physical
Education and Sports, University of Valencia, C/ Hondón de
las nieves, 5, 4º D, Alicante 03005, Spain
El. paštas: m.cristina.blasco@uv.es
Key words: heart rate variability; comorbidity; depression; elderly; inpatients.
Summary. Background. Hospitalization processes are known to increase depressive symptoms
arising among elderly population. Meanwhile, dysregulation of cardiac autonomic function has been
suggested to link depression and cardiovascular mortality. In this context, analysis of heart rate
variability (HRV) is emerging as a powerful mortality risk stratifier clinical tool. The purpose of
the study was to examine the relationship among HRV, depression, and comorbidity risk among an
elderly inpatient population.
Material and methods. Twenty-six subjects (aged 78±9 years) were recruited from the Short-
Term Stay Unit at the Hospital General de Alicante. Before joining a Physical Activity Program
aimed to prevent functional impairment and after medical selection and written consent, inpatients
were tested for heart rate variability, Yesavage Geriatric Depression Scale, and Charlson comorbid-
ity index score.
Results. Men compared to women showed a signifi cantly larger CCI score. Short-term scal-
ing exponent (α1), derived from detrended fl uctuation analysis, showed a negative correlation with
Charlson comorbidity index. Conversely, a positive correlation was found between sample entropy
(SampEn) and Yesavage Scale.
Conclusions. On the one hand, fractal analysis of HRV confirms to be useful as a risk stratifier
tool. On the other hand, SampEn is proposed to be reflecting a non-neurally generated complexity
when accompanied with low values of α1. Accordingly, in this regime, it would be indicative of a
paradoxical gradual reduction in cardiac autonomic control, accentuated with the severity of depres-
sive symptoms.
Introduction
Aging is an irreversible multifactorial and sto-
chastic impairment process. Since the second or
third decade of life, the functional capacity of our
systems begins to decrease progressively. Irrespec-
tive of our health status, emotional or cognitive
state, age causes signifi cant losses in our physical
condition motor capacities (1, 2); metabolic, car-
diovascular, respiratory, and endocrine functions
(3–5); nervous system and neural-motor control (5,
6), and others.
At the same time, depression is known to cause
not only personal suffering, but to be also related
with higher morbidity and mortality due to its as-
sociation with an increased risk of cardiovascular
disease (7, 8). Moreover, hospitalized elderly people
are at higher risk for the development of depression
(9). Specifi cally, prevalence of depressive symptoms
has been described to rise up to 27% among elderly
inpatients (10).
Similarly, the loss of functional capacities result-
ing from hospitalization is a matter of fact among
elderly inpatients. Hospitalization often results in a
severe restriction of activity that leads to a great im-
pairment of mobility, a potentially loss of independ-
ence, and an increased morbidity risk, in all cases
the greater, the longer the stay in hospital (11, 12).
Eventually, this functional impairment is clear even
48 hours after admission (12).
In order to minimize those negative consequenc-
es of hospitalization processes and to avoid as much
as possible the rising of the aforementioned depres-
sive symptoms, the Short-Term Stay Unit (STSU)
at the Hospital General de Alicante together with
the Department of Physical Education and Sports,
University of Valencia, launched a physical activity

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Medicina (Kaunas) 2010; 46(6)
Cristina Blasco-Lafarga, Ignacio Martínez-Navarro, María Elisa Sisamón, et al.
program specifi cally designed to be done during the
hospitalization period. Before the admission into the
program, patients went through a multimodal initial
assessment jointly conducted by the medical team
and the geriatric personal trainers (GPT), within an
interdisciplinary approach. The above-mentioned
evaluation included information on demographics,
comorbidity, cognitive status, physical health, func-
tional abilities, depressive symptoms, and recording
of resting heart rate (HR). In the present paper, co-
morbidity and depression assessment together with
the analysis of the resting HR recording are pre-
sented.
Heart rate variability (HRV) analysis is commonly
used as an index of cardiac autonomic functioning.
Decreased HRV, measured in any time or frequen-
cy domain, has been associated with a poor health
status in numerous clinical studies. Moreover, re-
cent investigations suggest that abnormal values for
nonlinear HRV measures, refl ecting augmented ran-
domness of the HR, are even more strongly associ-
ated with increased mortality (13–15).
Therefore, because elderly population is at high
risk of developing depressive symptoms, and this
risk is highly increased when becoming embedded
patients at a hospital, an examination of the rela-
tionship among heart rate dynamics, depression,
and comorbidity among this group seems to be of
clinical interest.
Material and methods
Twenty-six subjects (17 males and 9 females, age
78±9 years) were recruited from the STSU at the
Hospital General de Alicante, as a part of a larger
study aimed to prevent functional impairment in
elderly hospitalized patients. Only in the event of
patients’ extremely weakness, cardiovascular shock
risk or communication incapacity, cases were ex-
cluded. All participants and their relatives gave their
written consent after being informed about the re-
search purposes, test and training procedures. This
investigation is currently being jointly conducted by
the aforementioned institution and the Department
of Physical Education and Sports of the University
of Valencia. The protocol was approved by the Re-
search Ethics Committee of the Hospital General de
Alicante.
Charlson Comorbidity Index (CCI) was devel-
oped in 1987 based on 1-year mortality data from
internal medicine patients admitted in a New York
Hospital (16). The index encompasses 19 medical
conditions (i.e., myocardial infarction, congestive
heart failure, dementia, diabetes mellitus, cancer,
AIDS, etc.) weighted 1–6 depending on the risk
of dying associated with this condition, with total
scores ranging from 0–37 (17). Depression was eval-
uated by means of the 15-question Spanish Version
of the Yesavage Geriatric Depression Scale (GDS),
a scale aimed to diagnose depression in population
aged 65 years and more (18). This short version
contains 15 dichotomous questions; each is valued
with 1 point.
Resting HR measurements were performed under
a standardized protocol between 9:30 AM and 10:30
AM, in a quiet environment with stable temperature.
Subjects were asked to remain still, with eyes closed
but without falling asleep, and to avoid disruptive
movements of the head or hands throughout the re-
cording period. Participants were equipped with an
electrode transmitter belt (T61, Polar Electro, Kem-
pele, Finland) fi tted just above the chest muscles,
after application of conductive gel as recommended
by the manufacturer. Resting heart rate was contin-
uously monitored and recorded for 10 min using a
Polar RS800 HR monitor set to R-R interval mode
(Polar Electro, Kempele, Finland). This instrument
was previously validated for the accurate measure-
ment of R-R intervals and for the purpose of analyz-
ing HRV (19, 20).
Data were transferred to the Polar Pro Trainer 5
software (Polar Electro, Kempele, Finland) through
an infrared interface, and each downloaded R-R
interval fi le was then exported as a *.txt fi le and
further analyzed by means of Kubios HRV Analysis
Software 2.0 (The Biomedical Signal and Medical
Imaging Analysis Group, Department of Applied
Physics, University of Kuopio, Finland). The whole
analysis process was carried out by the same re-
searcher to ensure consistency. After proper artifact
inspection and correction, time domain and spectral
analysis was performed on 5-min artifact-free ep-
ochs. For the time domain, the standard deviation of
normal R-R intervals (SDNN) and the root-mean-
square difference of successive normal R-R intervals
(rMSSD) were calculated. Before the power frequen-
cy analysis, R-R data were detrended (21) and resa-
mpled at 4 Hz. The fast Fourier transform spectrum
was then calculated using a Welch’s periodogram
method. Low-frequency power (LF, 0.04–0.15 Hz),
high-frequency power (HF, 0.15–0.4 Hz), and total
power (TP, 0–0.4 Hz) were calculated as integrals of
the respective power spectral density curve. LF/HF
ratio was also retained for statistical analysis.
Besides time domain and spectral analysis, HR
dynamics was nonlinearly analyzed using measures
of fractal scaling properties and complexity. De-
trended fl uctuation analysis (DFA) technique was
applied to the R-R interval data in order to quantify
self-similarity correlations. A detailed description of
this technique has been previously provided by Peng
et al. (22). Briefl y, the root-mean-square fl uctua-
tions of the integrated and detrended data are meas-
ured in observation windows of different sizes and
then plotted against the size of the window on a log-

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Medicina (Kaunas) 2010; 46(6)
log scale. The result of this calculation is the scal-
ing exponent α, which represents the slope of this
line and relates (log) fl uctuation to (log) windows
size. Typically, in DFA, the correlations are divided
into short-term and long-term fl uctuations. Based
on previous research (14, 23) and because of our
relatively short recording time, we decided to utilize
the short-term (4 to 11 beats) scaling exponent (α1)
to analyze our R–R interval data. HR complexity
analyses provide a general indication of predictabil-
ity of a time series. In this study, complexity was
calculated using sample entropy (SampEn), which
has been previously described in detail (24). By def-
inition, SampEn is a negative natural logarithm of
an estimate for the predictability in fi nding specifi c
matches in a short-time series. To characterize the
stringency of match recognition, the length (m) of
the subseries and the tolerance (r) of the matches
are previously set. Those adjustable parameters were
fi xed at m= 2 and r =20% of the SD of the datasets,
as previously described in the literature (25–27).
All statistical analyses were carried out using the
Statistical Package for the Social Sciences software
(SPSS version 15.0, SPSS Inc., Chicago, USA). The
distribution of each variable was examined with the
Kolmogorov-Smirnov normality test. When data
were skewed, as it was the case for spectral meas-
ures, data were transformed by taking the natural
logarithm to allow parametric statistical compari-
sons that assume a normal distribution. Therefore,
TP, HF, LF, and LF/HF variables will henceforth
be referred as lnTP, lnHF, lnLF, and lnLF/HF re-
spectively.
Gender differences in CCI and GDS scores were
evaluated using a Student’s t test model for two
samples of unequal variance. Homogeneity of vari-
ance was verifi ed by the Levene’s test. A one-way
ANCOVA model was employed to elucidate differ-
ences in linear HRV indices (i.e., SDNN, rMSSD,
lnTP, lnHF, lnLF, lnLF/HF) and nonlinear meas-
ures (i.e., α1 and SampEn), between males and fe-
males, using CCI and age as covariables.
Partial correlations were used to assess the rela-
tionship between linear and nonlinear HRV indices,
CCI and GDS scores, controlling for age. Moreover,
gender-specifi c partial correlations (controlling for
age and CCI) between linear and nonlinear HRV in-
dices and GDS score were conducted. The purpose
of this further analysis was to verify whether the
association between depressive symptoms and car-
diac autonomic regulation differed between elderly
males and females, as recently proposed by Chen
et al. (28). The magnitudes of correlations were
defi ned according to Cohen (29), whereby correla-
tions >0.5 are considered large, 0.3–0.5 are con-
sidered moderate and 0.1–0.3 are considered small.
A P value of <0.05 was considered statistically sig-
nifi cant. Data are presented as means and standard
deviations (±SD).
Results
Three subjects (3 males) were excluded from the
analysis due to an excessive number of artifacts in
their HR recordings. Men as compared to women
showed a signifi cantly larger CCI score (8.07±2.49
vs. 6.33±1.12, P=0.034). On the contrary, women
displayed a higher, although innsignifi cant, GDS
score (4.78±2.95 vs. 3.36±3.01, P=0.277). Table
1 shows differences in linear and nonlinear HRV
indices between men and women. No signifi cant
gender differences were found either in linear HRV
indices (SDNN, rMSSD, lnTP, lnLFP, lnHFP,
lnLF/HF) or amongst nonlinear measures (α1 and
SampEn).
Men WomenP
SDNN, ms
rMSSD, ms
lnTP, ms2
lnHF, ms2
lnLF, ms2
lnLF/HF
α1
SampEn
17.98±10.50
21.16±12.54
5.09±1.33
4.20±1.45
4.07±1.51
–0.11±0.87
0.87±0.30
1.44±0.36
26.57±29.69
31.39±33.78
4.99±2.35
4.14±2.55
3.93±2.54
–0.24±0.99
0.86±0.20
1.41±0.26
0.635
0.535
0.566
0.723
0.514
0.471
0.717
0.456
Values are provided as means±SD. SDNN, standard devia-
tion of R-R intervals; rMSSD, root-mean-square difference of
successive R-R intervals; lnTP, total-frequency power of R-R
intervals; lnLF, low-frequency power of R-R intervals; lnHF,
high-frequency power of R-R intervals; lnLF/HF, ratio of low-
frequency to high-frequency power; α1, short-term fractal scal-
ing exponent; SampEn, sample entropy.
Table 1. Gender differences in measures of linear
and nonlinear heart rate dynamics
No signifi cant correlations between any time or
frequency domain indices and CCI or GDS scores
were found. Nevertheless, α1 displayed a negative
moderate signifi cant correlation with the CCI score
(r=–0.42, P<0.05). Conversely, a positive moder-
ate signifi cant correlation was found between Sam-
pEn and GDS score (r=0.57, P<0.01). The results
of all partial correlations (i.e., controlling for age)
considering the sample as a whole are presented in
Table 2.
When analyzing separately (i.e., men and wom-
en) the abovementioned relationships, amongst
women, both linear and nonlinear measures failed to
correlate signifi cantly with the GDS score. On the
contrary, among men, SampEn showed a positive
strong signifi cant correlation with the GDS score
(r=0.80, P<0.01). The results of all gender-specifi c
partial correlations (i.e., controlling for age and CCI
score) are presented in Table 3.
Heart rate dynamics in elderly inpatients

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Medicina (Kaunas) 2010; 46(6)
396
SDNN, standard deviation of R-R intervals; rMSSD, root-mean-square difference of successive R-R intervals; lnTP, total-frequency power of R-R intervals; lnLF, low-frequency power
of R-R intervals; lnHF, high-frequency power of R-R intervals; lnLF/HF, ratio of low-frequency to high-frequency power; α1, short-term fractal scaling exponent; SampEn, sample entropy.
*P<0.05, **P<0.01.
Table 2. Results of partial correlations (r), controlling for age and considering the sample as a whole,
between linear and nonlinear HR dynamics measures, CCI and GDS.
SDNN
rMSSD
lnTP
lnLF
lnHF
lnLF/HF
α1
SamplEn
r
P
r
P
r
P
r
P
r
P
r
P
r
P
r
P
CCI
GDS
–0.26
0.21
0.236 0.342
–0.16
0.20
0.4670.375
–0.25
0.22
0.257 0.317
–0.26
0.18
0.2310.434
–0.10
0.29
0.6530.191
–0.34–0.26
0.1190.245
–0.42 –0.14
0.049*
0.524
0.200.57
0.379
0.006**
CCI, Charlson comorbidity index; GDS, Yesavage Geriatric Depression Scale; SDNN, standard deviation of R-R intervals; rMSSD, root-mean-square difference of successive R-R
intervals; lnTP, total-frequency power of R-R intervals; lnLF, low-frequency power of R-R intervals; lnHF, high-frequency power of R-R intervals; lnLF/HF, ratio of low-frequency to high-
frequency power; α1, short-term fractal scaling exponent; SampEn, sample entropy. *P<0.05, **P<0.01.
SDNN
rMSSD
lnTP
lnLF
lnHF
lnLF/HF
α1
SamplEn
r
P
r
P
r
P
r
P
r
P
r
P
r
P
r
P
Men
Women
0.02 0.39
0.9420.384
0.080.32
0.801
0.0485
0.260.36
0.406 0.422
0.200.41
0.5410.365
0.41 0.33
0.184 0.471
–0.47
0.27
0.128 0.552
–0.23
0.43
0.4730.332
0.800.04
0.002**
0.933
Table 3. Results of gender-specifi c partial correlations (r), controlling for age and CCI score,
between linear and nonlinear HR dynamics measures, and GDS
Cristina Blasco-Lafarga, Ignacio Martínez-Navarro, María Elisa Sisamón, et al.

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Discussion
Fractal scaling properties of HR dynamics have
been shown to yield powerful prognostic informa-
tion compared with conventional measures of HRV.
Specifi cally, a growing body of evidence is emerg-
ing regarding prognostic power of short-term fractal
scaling properties analyzed by means of the DFA
technique. Eventually, a breakdown of short-term
fractal organization in human HR dynamics, ex-
pressed as a reduced scaling exponent α1, has been
observed in various disease states, and it has been
indicative of an increased risk of mortality and life-
threatening arrhythmias in patients with and without
structural heart disease. Moreover, in non–heart-
diseased elderly population, α1 has been suggested
to be an specifi c risk marker of cardiac death (23).
Interestingly, in the above-mentioned study, α1 dis-
played an association with overall mortality, whereas
ApEn shoedw no prognostic power. Similarly, in our
study, α1 displayed a signifi cant correlation with the
CCI score (P=0.049), but SampEn was far from sig-
nifi cantly correlating with the CCI score (P=0.524).
It may imply that among nonlinear measures, those
addressed to assess fractal correlation properties, are
more accurate as risk stratifi ers than those analyzing
HR complexity.
Some authors have already advocated for gener-
alizing the application of α1 as a risk stratifi er of sud-
den cardiac death beyond the patient populations
considered at increased risk of fatal arrhythmias to
the general elderly population (14, 23). Notwith-
standing, as pointed out by Huikuri et al. (13) in a
recent review article, DFA of HR dynamics is not
yet in widespread clinical use. Unlike the above-
mentioned approach, CCI is worldwide and com-
monly utilized for risk adjustment. Therefore, our
statically signifi cant correlation between α1 and CCI
further reinforces the application of α1 as a mortality
risk stratifi er and should encourage its widespread
clinical use, especially among elderly populations
and/or pluripathologic patients.
A positive correlation between severity of de-
pressive symptoms and HR complexity (see Table
2) found in the present study is in complete disa-
greement with some previous investigations con-
cerning this relationship (30, 31). However, this
contradiction may be simply due to a methodologi-
cal issue. Unlike the above-mentioned authors, who
used an approximate entropy (ApEn) algorithm,
we employed a SampEn algorithm to measure the
complexity of our RR interval data. SampEn was
proposed by Richman and Moorman (24) to over-
come limitations associated with ApEn. Specifi cally,
SampEn excludes counting self-matches and does
not employ a template-wise strategy for calculating
probabilities as ApEn does. Therefore, SampEn is
widely accepted as a more consistent and less biased
complexity measure. And accordingly, ApEn results
should be interpreted with caution (32). However,
irrespective of methodological considerations, larg-
er values of HR complexity are usually associated
with a healthier cardiac autonomic functioning (26,
33, 34).
Notwithstanding, this unidirectional view of
changes in HR complexity has been thoroughly dis-
cussed (35), and it remains an open and somewhat
controversial question (32, 36). As proposed for lin-
ear HRV indices, it could be that larger values do not
necessarily mean “better” values (15). Platisa and Gal
(37) interestingly assessed resting HR dynamics, by
means of both SampEn and DFA, in four groups of
people: young healthy subjects, elderly individuals,
congestive heart failure subjects, and a patient with
transplanted heart. They found that illness was char-
acterized by concomitant loss of regularity (i.e., high
SampEn) and short-term fractal correlation proper-
ties of RR interval dynamics (i.e., low α1). Similar
HR dynamics has been described during high inten-
sity exercise (38, 39). Hence, it may be suggested
that high values of SampEn should be interpreted
bidirectionally. On the one hand, together with
“good” values of α1 (i. e., nearing 1), larger values of
SampEn should be interpreted as healthier. On the
other hand, when accompanied with low values of
α1, high values of SampEn might be indicative of a
gradual reduction in cardiac autonomic control via
the sinus node. In this regime, SampEn would be
refl ecting a non-neurally generated complexity (i.e.,
intrinsic heart control mechanisms) (32, 37, 40, 41).
In alignment with this notion, Greiser et al. (42)
suggested that increasing HRV in men aged 75 years
and more might be explained by a higher prevalence
of sinus node disease (compared to women).
Our α1 results (0.87±0.26) are far from those
considered as “healthy”; on the contrary, they are
indicative of an increased risk of cardiac mortal-
ity in our sample (14, 23). Accordingly, a positive
correlation showed between severity of depressive
symptoms and HR complexity leads us to conclude
that depression, even in an already frail popula-
tion (78±9 years, CCI 7.39±2.21), further impairs
cardiac autonomic regulation. Interestingly, in the
unique depression-related study, to the best of our
knowledge, in which HR dynamics was analyzed by
means of SampEn (43), patients with major depres-
sive disorder (compared to healthy subjects) showed
higher, although insignifi cant statistically, values in
that variable (1.77 vs. 1.92). Moreover, in Chen et
al. study (28), although SampEn was not measured,
a concomitant decrease in HF and LF/HF among
severe depressed elderly participants was interpreted
as a “pervasive decline of their cardiac autonomic
function.”
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Medicina (Kaunas) 2010; 46(6)
Therefore, the present investigation contributes
further to previous investigations examining depres-
sion-related cardiac autonomic dysregulation, espe-
cially those using HR complexity measures in their
analysis (30, 31, 43). Moreover, consistent with pre-
vious research (39, 44–46), nonlinear approaches
(compared to linear indices) showed superior for de-
tecting subtle changes in HR behavior in an already
poor HRV background. Nevertheless, further stud-
ies with larger samples are needed to confi rm our
hypothesis and clarify the underlying mechanisms
of the “non-healthy” higher SampEn-RR proposed
in the present paper. At the same time, a reanalysis
of our RR interval data using the recently developed
Multiscale Sample Entropy technique would enable
us to reach more robust conclusions (47, 48).
Similarly to us, Rozzini et al. (49) fi ndings
pointed to a higher prevalence of depressive symp-
toms among female inpatients, while comorbidity
risk was greater amongst males (i.e., higher val-
ues of CCI). Notwithstanding, these gender dif-
ferences were attenuated from 70 s to 90 s, almost
disappearing in the last decade. Meanwhile, dif-
ferences in resting HRV between men and women
among elderly population have been thoroughly
examined; however, results are partly contradic-
tory. Within a large community study (1742 par-
ticipants), Felber Dietrich et al. (50) showed that
women aged 65–73 had a signifi cantly higher HF
but lower LF and LF/HF than men of the same age
group. In a similar size sample (1779 participants),
Greiser et al. (42) corroborated these gender dif-
ferences in resting HRV (higher HF but lower LF
and LF/HF in women), furthermore including
subjects up to 83 years of age. Notwithstanding,
even recently, Chen et al. (28) showed no signifi -
cantly gender differences in LF, HF, and LF/HF
ratio in a homogeneous sample of 606 participants
aged 65 or more.
Besides, only a handful of studies concerning
gender differences among elderly population have
included nonlinear HRV measures in their analysis.
Kojima et al. (30) measured α1 and ApEn in a sample
of 119 hemodialysis patients aged 55.2±10.5 years.
By using an ANCOVA model, where age and serum
albumin were entered as covariables, they found
that both variables displayed signifi cantly lower val-
ues in women compared to men. We utilized a simi-
lar statistical approach in our study, covariating for
age and CCI score in our analysis. Nevertheless, we
failed to fi nd any signifi cant gender differences (see
Table 1). This difference may be explained not only
by our smaller sample (23 vs. 116 subjects), but also
because of our participants were much older (78±9
vs. 55.2±10.5 years), and gender differences are
known to disappear as a function of time (51, 52).
Despite fi nding no gender differences in all HR
dynamics measures (included SampEn), we decided
to conduct stratifi ed (i.e., separating men and wom-
en) partial correlations between linear and nonlinear
HRV indices and GDS. The purpose of this further
analysis was to examine whether gender plays an in-
teractive role on the relationship between depression
and cardiac autonomic regulation. Interestingly, we
found a stronger correlation between SampEn and
GDS score when considering only men than when
considering the entire sample (r=0.80 vs. r=0.57).
Meanwhile, among women, SampEn failed to cor-
relate with GDS score (see Table 3). This more
robust association between depression and cardiac
autonomic dysregulation in elderly males compared
to females is in accordance with Chen et al. (28).
Assuming that increased complexity of RR inter-
val data at rest may be indicative of reduced cardiac
autonomic control in some cases (i.e., when it is ac-
companied with low values of α1), the absence of re-
lationship between severity of depressive symptoms
and HR complexity among females could be due to
women (compared to men) lagging behind several
years in developing cardiovascular diseases (i.e., si-
nus node impairment) (42).
Conclusions
In the present study, two major fi ndings should
be highlighted. Firstly, measurement of fractal
properties of heart rate dynamics kept a signifi cant
relationship with CCI score, thus emphasizing their
use as a risk stratifi er tool. Secondly, exceedingly
higher values of SampEn among severely depressed
elderly may be refl ecting a progressive loss in car-
diac autonomic control. This latter observation
further reinforces depression deleterious effect on
inpatients’ health and utterly justify interventions
aimed to avoid or reduce the appearance of depres-
sive symptoms associated with hospitalization proc-
esses. However, as above-mentioned, further studies
with larger samples and a reanalysis of the RR in-
terval data using the recently developed multiscale
sample entropy technique are needed to delve into
this phenomenon.
Nevertheless, as a main conclusion, according to
the results here presented, interventions aimed to
avoid or reduce the appearance of depressive symp-
toms associated with hospitalization processes are
fully justifi ed.
Acknowledgments
Special thanks to Arturo Ruíz, chief-respon-
sible of the Delegation of the Consell Valencià de
l’Esport in Alicante, and to Dr. Raúl P. Garrido, Dr.
at the General Hospital de Alicante, for their help
and support in the building up of this project.
Cristina Blasco-Lafarga, Ignacio Martínez-Navarro, María Elisa Sisamón, et al.

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399
Medicina (Kaunas) 2010; 46(6)
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Received 17 May 2010, accepted 7 June 2010
Straipsnis gautas 2010 05 17, priimtas 2010 06 07
Cristina Blasco-Lafarga, Ignacio Martínez-Navarro, María Elisa Sisamón, et al.