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A Quantitative Systematic Review of Normal Values for Short-Term Heart Rate Variability in Healthy Adults



Heart rate variability (HRV) is a known risk factor for mortality in both healthy and patient populations. There are currently no normative data for short-term measures of HRV. A thorough review of short-term HRV data published since 1996 was therefore performed. Data from studies published after the 1996 Task Force report (i.e., between January 1997 and September 2008) and reporting short-term measures of HRV obtained in normally healthy individuals were collated and factors underlying discrepant values were identified. Forty-four studies met the pre-set inclusion criteria involving 21,438 participants. Values for short-term HRV measures from the literature were lower than Task Force norms. A degree of homogeneity for common measures of HRV in healthy adults was shown across studies. A number of studies demonstrate large interindividual variations (up to 260,000%), particularly for spectral measures. A number of methodological discrepancies underlined disparate values. These include a systematic failure within the literature (a) to recognize the importance of RR data recognition/editing procedures and (b) to question disparate HRV values observed in normally healthy individuals. A need for large-scale population studies and a review of the Task Force recommendations for short-term HRV that covers the full-age spectrum were identified. Data presented should be used to quantify reference ranges for short-term measures of HRV in healthy adult populations but should be undertaken with reference to methodological factors underlying disparate values. Recommendations for the measurement of HRV require updating to include current technologies.
A Quantitative Systematic Review of Normal Values for
Short-Term Heart Rate Variability in Healthy Adults
From the *Division of Public Health and Primary Health Care, University of Oxford, Oxford, UK; †Centre for Sports
and Exercise Science, University of Essex, Colchester, UK; and ‡Research Centre for Society and Health,
Buckinghamshire New University, Chalfont St Giles, UK
Heart rate variability (HRV) is a known risk factor for mortality in both healthy and patient populations.
There are currently no normative data for short-term measures of HRV. A thorough review of short-term
HRV data published since 1996 was therefore performed. Data from studies published after the 1996
Task Force report (i.e., between January 1997 and September 2008) and reporting short-term measures
of HRV obtained in normally healthy individuals were collated and factors underlying discrepant values
were identified. Forty-four studies met the pre-set inclusion criteria involving 21,438 participants. Values
for short-term HRV measures from the literature were lower than Task Force norms. A degree of
homogeneity for common measures of HRV in healthy adults was shown across studies. A number of
studies demonstrate large interindividual variations (up to 260,000%), particularly for spectral measures.
A number of methodological discrepancies underlined disparate values. These include a systematic
failure within the literature (a) to recognize the importance of RR data recognition/editing procedures
and (b) to question disparate HRV values observed in normally healthy individuals. A need for large-
scale population studies and a review of the Task Force recommendations for short-term HRV that
covers the full-age spectrum were identified. Data presented should be used to quantify reference ranges
for short-term measures of HRV in healthy adult populations but should be undertaken with reference
to methodological factors underlying disparate values. Recommendations for the measurement of HRV
require updating to include current technologies. (PACE 2010; 33:1407–1417)
autonomic nervous system,risk factors,norms,homogeneity,populations
In 1996, the European Society for Cardiology
and the North American Society of Pacing and
Electrophysiology supported a Task Force which
issued a seminal paper: “Heart rate variability:
Standards of measurement, physiological inter-
pretation and clinical use” (Circulation, 1996; 93,
1043–1065). Reference normal values for short-
term measures of heart rate variability (HRV) in
healthy adults were published as an appendix
to the paper. Some of these values, however,
were approximated from studies involving small
sample sizes. As a result, these data are considered
as “unsuitable for definite clinical conclusions to
be drawn from.”1The Task Force stressed the
need for large prospective population studies to
Disclosures: The authors have no conflicts of interest to
Address for reprints: David Nunan, Ph.D., MaDOx Group, Uni-
versity of Oxford, Rosemary Rue Building, Old Road Campus,
Roosevelt Drive, Headington, Oxford, United Kingdom. Fax:
441865-289287; e-mail:
Received February 26, 2010; revised May 17, 2010; accepted
June 9, 2010.
doi: 10.1111/j.1540-8159.2010.02841.x
establish normal HRV standards including age and
sex subsets. This need was considered greatest for
HRV values obtained from short-term recordings.
The interest in HRV as a measurement of
autonomic function lies in its clinical importance.
A reduced HRV is a powerful and independent
predictor of an adverse prognosis in patients with
heart disease2–4 and in the general population.5,6
Despite the important prognostic power of HRV, it
is still not a widely used tool in clinical settings.
Key issues relating to this fact include the most
appropriate analysis method(s), the recommended
length of electrocardiogram recordings, and the
conditions in which they should be assessed.7
Arguably an additional key factor is the lack of
agreed normative values for HRV, without which
classifying “abnormal” HRV remains difficult.
In the majority of other clinically health-related
measures (e.g., blood pressure, heart rate, forced
vital capacity), established norms are routinely
compared to provide an indication of current
health status. There is no clear explanation why
this is not the case for HRV.
Since 1996, publications assessing and re-
porting both 24-hour and short-term HRV in
healthy and clinical populations have increased.
Pinna and colleagues8report an increase in the
2010, The Authors. Journal compilation C
2010 Wiley Periodicals, Inc.
PACE, Vol. 33 November 2010 1407
number of yearly publications from 391 to 584
in the period 2000 to 2006, respectively. Taylor
and Studinger9reported an average of 10 articles
related to HRV published weekly during 2005.
These “newer” studies provide a potential source
of normative data for common HRV measures
in healthy populations. Moreover, by comparing
values between publications, it may be possible
to identify factors contributing to discrepancies in
HRV values.
Search Strategy
The PubMed and Ovid databases were
searched using the mesh term “heart rate variabil-
ity” and: “short” “term” “short-term” and “five.” A
second search using 13 terms in conjunction with
the previous search terms was then performed.
Full text articles were then obtained and their
bibliographies searched for further studies not
identified electronically. The full set of search
strategy terms are illustrated in Figure 1.
Selection Criteria and Review Process
Only English language publications involving
healthy adults of at least 18 years were included.
As this study was only interested in short-term
HRV, publications reporting 24-hour measures
of HRV were excluded. A sample size greater
than 50 was originally an inclusion criterion but
was later lowered to 30. The requirement for all
publications to present the mean RR interval was
also an original criterion later revoked. These
two actions were performed as only 22 papers
were eligible for inclusion when the original
criteria were applied. Publications were rejected
if they presented HRV values other than in Task
Force-recommended formats (i.e., absolute, log-
transformed, or back-transformed units).
Data Analysis and Synthesis
The nature of the data (i.e., analysis of group
means) does not support inferential analysis.
Descriptive statistics are presented including:
mean, standard deviation (SD), median, and range.
Measures such as the coefficient of variation
(CV =SD/mean ×100) provide an index of
the dispersion of mean values between studies.
To identify factors underlying between-study
differences, values for measures equating to
greater than 1.5 SD from the mean publication
value were considered discrepant. A value of
1.5 SD was chosen to provide a more conservative
reference range for consideration of discrepant
values. Assessment of possible factors underlying
discrepant values was then made on a study-by-
study and measure-by-measure basis. Percentage
differences were used to assess between-group
differences based on sex, spectral decomposition
technique, and the use of paced versus free
breathing protocols.
Data are presented for time and frequency
measures of HRV most commonly reported within
the literature. Measures of HRV often demonstrate
skewed distributions and are reported as natural
logarithms. Absolute and log-transformed units
are presented for the following measures:
1. Standard deviation of normal-to-normal
(NN) intervals (SDNN);
2. Root mean square of successive differences
between NN intervals (rMSSD);
3. Proportion of successive NN intervals
greater than 50 ms (pNN50%);
4. Very low-frequency spectral power (VLF);
5. Total spectral power (TP), low-frequency
power (LF), and high-frequency power (HF) in
both ms2and normalized units and the ratio of
LF power to HF power (LF:HF).
Measures of TP and VLF from short RR
recordings are physiologically ambiguous and for
this reason their use is not recommended by
the Task Force.1The Task Force also prefers the
use of rMSSD to pNN50 due to its mathematical
robustness. Data from studies reporting TP, VLF,
and pNN50 were included in the initial review
stages but were not entered into the final analysis
of means. These measures were included to
allow the reviewer a complete assessment of the
discrepancy between studies in adherence to the
Task Force recommendations.
Database searches retrieved a total of 3,141
citations (Fig. 1). Shortlisted citations were
retrieved and checked at the title/abstract level
excluding 2,765 papers. Complete articles for the
remaining 376 studies were checked for com-
pliance to inclusion/exclusion criteria. Reasons
for exclusion included measurement of longer-
term HRV (e.g., 24 hours), a sample size <30,
assessment of nonhealthy participants, failure to
present values for, or to measure, traditional time-
and/or frequency-domain HRV, or the paper was
a review article. Therefore, only 44 (12%) eligible
trials were identified. The total sample size from
these 44 papers was 21,438 participants.
Data from the present study are presented as
Table SI—Participant demographics and
details of the methodologies employed for all
publications that met the inclusion criteria;
1408 November 2010 PACE,Vol.33
1. ‘Heart rate variability’ –
limited to human studies
involving adults aged 18+
published in English from
Jan ’97 – Sept ’08
2. ‘Short,’ ‘Term,’
3. ‘Five’
4. ‘Normal’
5. ‘Healthy’
6. ‘Control’
7. ‘Sedentary’
8. ‘Population’
9. ‘Heritability’
10. ‘Genetic’
11. ‘Ethnic’
12. ‘Environment’
13. ‘Longitudinal’
14. ‘Athlete’
15. ‘Tr ained’
16. ‘Untrained’
3141 citations with ‘heart
rate variability’ in title or
376 Potentially
relevant citations
2765 Citations excluded
because of irrelevance to
the systematic review
44 Citations finally
included in
systematic review
332 Citations excluded due
90 long-term HRV measured
134 sample size <30
78 unhealthy participants
29 non-traditional measures
1 review article
Phase 1 Phase 2 Phase 3
Figure 1. Schematic of search “strings” added to the PubMed and Ovid databases for retrieval of
citations assessing short-term measures of HRV in healthy adults. Also indicated are the inclusion
and exclusion processes.
Table SII—Values for the HRV measures
corresponding to each study in Table SI;
Table I—Summary data including the
overall mean, SD, CV, and range in values for each
of the HRV measures in Table SII;
Table II—Summates data in Table SII based
on sex;
Table III—Summates data in Table SII
based on breathing protocol and spectral method;
Table IV—Summates data from studies
displaying interindividual range in a number of
HRV measures.
Data from Rajendra Acharya et al.10 listed in
Table SI are not included in Table SII. This study
only presented the range in values for measures of
HRV without providing a mean value. These data
are included in Table IV.
Analysis of Short-Term HRV Data
from the Literature
Descriptive statistics for data from all the in-
cluded studies are presented in Table I. There was
a large range in values between studies. Compared
with frequency-domain measures, time-domain
measures of HRV demonstrated less variation be-
tween studies. For measures reported in absolute
units, the largest variation was observed for HF
(CV =118%) with a range in values across studies
of 3,548 ms2. Mean RR interval demonstrated the
smallest variation (CV =10%; range =375 ms).
In log-transformed units, HF again demonstrated
the largest variation between studies (CV =37%,
range =6.87 ln units). The SDNN demonstrated
the smallest variation (CV =6%, range 0.50 ln
Compared with males, females demonstrated
slightly lower values (8–11%) for all time-domain
measures of HRV expressed in absolute units
(Table II). In the frequency domain, males demon-
strated lower values for LF (14%) and HF (8%)
power. Males showed substantially higher values
for LFnu (17%) but HFnu was similar between
sexes. Values for LF (20%) and HF (18%) were
substantially lower in females when expressed
in log units. Females also demonstrated a lower
LF:HF ratio regardless of the unit of expression.
PACE,Vol.33 November 2010 1409
Tab le I .
Summary of Data from Table SII: Cross Study Overall Mean and Range in Values for Approved Task Force Measures of
Short-Term HRV
Absolute Values Log-Transformed Values
HRV No. of CV No. of CV
Measure Studies Mean SD (%) Median Range Studies Mean SD (%) Median Range
mRR (ms) 30 926 90 10 933 785–1,160 n/a n/a n/a n/a n/a n/a
SDNN (ms) 27 50 16 32 51 32–93 4 3.82 0.23 6 3.71 3.57–4.07
rMSSD (ms) 15 42 15 37 42 19–75 4 3.49 0.26 7 3.26 3.26–3.41
LF (ms2) 35 519 291 56 458 193–1,009 18 5.01 1.76 35 5.02 2.05–7.31
LFnu 29 52 10 19 54 30–65 n/a n/a n/a n/a n/a n/a
HF (ms2) 36 657 777 118 385 82–3,630 18 4.76 1.78 37 4.96 0.08–6.95
HFnu 30 40 10 25 38 16–60 n/a n/a n/a n/a n/a n/a
LF:HF 25 2.8 2.6 93 2.1 1.1–11.6 7 0.69 0.73 106 0.58 0.16–1.98
n/a =nonapplicable; SD =standard deviation; CV =coefficient of variation (SD/mean ×100); mRR =mean RR interval; SDNN =
standard deviation of normal-to-normal intervals; rMSSD =root mean square of successive differences; LF =low-frequency spectral
power; HF =high-frequency spectral power; LF:HF =ratio of low-frequency power to high-frequency power; nu =normalized units; ln =
natural logarithm.
When compared with data derived using
autoregressive methods, spectral measures of
HRV derived using the fast Fourier transform
(FFT) method were markedly different (Table III).
Studies utilizing the FFT method demonstrate
lower LF power, a higher HF power (absolute and
log-transformed units), and, therefore, a higher
LF:HF ratio.
Table II.
Comparison of Absolute and Log-Transformed HRV Values from Included Publications According to Sex
Mean Absolute Values from Mean Log-Transformed Values from
All Studies According to Sex*All Studies According to Sex
No. of Studies Measure Value No. of Studies Measure Value
HRV Difference Difference
Measure M F M F (%) M F M F (%)
mRR (ms) 9 7 922 885 8 No data§No data No data
SDNN (ms) 3 4 40369 No data No data No data
rMSSD (ms) 2 1 211911 No data No data No data
LF (ms2) 9 8 356 414 14 8 4 5.04 4.19 20
LFnu 6 9 53 46 17 No data No data No data
HF (ms2) 10 8 475 516 8 8 4 4.86 4.10 18
HFnu 7 7 39 38 3 No data No data No data
LF:HF 3 6 2.3 1.2 91 2 1 0.360.15140
*Data are means regardless of spectral method; Data from AR studies only; Data from FFT studies only. §Refers to the fact that no
comparable data between males and females were available from any of the included studies; mRR =mean RR interval; SDNN =
standard deviation of normal-to-normal intervals; rMSSD =root mean square of successive differences; LF =low-frequency spectral
power; HF =high-frequency spectral power; LF:HF =ratio of low-frequency power to high-frequency power; nu =normalized units.
There were large discrepancies in values
for HRV measures when obtained under paced
versus free breathing conditions (Table III). When
conducted under paced breathing conditions,
values were higher for all measures of HRV
except LF power which was higher during free
breathing. Finally, a number of studies revealed
large interindividual variation for the majority
1410 November 2010 PACE,Vol.33
Table III.
Comparison of Absolute HRV Values from Included Publications According to Breathing Protocol and Spectral
Decomposition Methods
Mean Absolute Values According to Mean Absolute Values According to
Free or Paced Breathing Pattern*Spectral Decomposition Method
No. of Studies Measure Value No. of Studies Measure Value
HRV Difference Difference
Measure NB PB NB PB (%) AR FFT AR FFT (%)
mRR (ms) 20 6 928 948 2 12 14 881 943 7
SDNN (ms) 20 5 49 59 17 11 14 42 59 29
rMSSD (ms) 8 4 43 55 22 5 10 26 44 40
LF (ms2) 26 15 500 440 12 17 13 484 441 10
LFnu 25 3 51 58 12 13 13 55 47 18
HF (ms2) 27 16 434 963 54 17 14 348 647 45
HFnu 27 2 39 43 9 13 16 36 40 10
LF:HF 21 6 2.72 2.76 0.01 12 12 2.9 1.7 71
*Values are means from all studies using free breathing (NB) and paced breathing (PB) protocols without accounting for sex or spectral
decomposition method; Data are means from all AR or FFT studies without accounting for sex. mRR =mean RR interval; SDNN =
standard deviation of normal-to-normal intervals; rMSSD =root mean square of successive differences; LF =low-frequency spectral
power; HF =high-frequency spectral power; LF:HF =ratio of low-frequency power to high-frequency power; nu =normalized units.
of HRV measures, with values for one measure
(HF) differing by as much as 260,000% between
individuals within the same study (Fagard et al.11;
Table IV).
Results of Literature Retrieval for Normal
Values of Short-Term HRV
From over some 3,100 citations, only 44
reported short-term measures of HRV in healthy
adult participants (n 30) and were in ac-
cordance with Task Force methodological stan-
dards/recommendations. The number of studies
was limited by the following factors:
Many studies of HRV assessed longer term
24-hour monitoring;
Studies were powered for the use of small
sample sizes;
Studies often include clinical populations
without the inclusion of a healthy cohort and/or
reference to healthy values;
Adherence to the Task Force methodologi-
cal recommendations was poor.
Some of the factors pertaining to the above
findings can be more easily explained than others.
A preferred use of 24-hour measurements to that
of short-term measurements could lie in their
greater prognostic power,3,52–54 or the additional
information such as night:day ratios that can
only be determined from 24-hour monitoring.
A more plausible explanation lies in the fact
that many studies of HRV are retrospective
in nature, reporting data from 24-hour Holter
monitoring carried out as part of standard cardiac
The fact that studies utilize only a small
sample size may be explained by the nature of
the study, limitations in resources, and/or the
calculations of statistical power.55 Other factors,
such as the failure to report the actual values
for measures of HRV, were found to occur when
studies were interested in change scores56 or
preferred to present results graphically.57
The failure to report mean RR interval
by 54% of the studies is a concern. Because
of the reciprocal nature of HR and mean RR
interval, studies reporting measures of HRV often
choose to report only mean HR36,43 or in some
cases, neither.25,41 This error can be likened
to assessing the suspension behavior of a car
without acknowledging the car’s speed. Such
errors also reflect, on the part of both author and
publishing editor, failures in understanding of the
fundaments of HRV data and their analysis.
Thirty-six percent of included studies re-
ported TP and VLF which are not recommended
from short RR recordings due to their ambiguous
physiological meaning under such conditions.1
The use of units that differ from standard units
(e.g., beats per minute/Hz58) further limited
the number of eligible studies. When such
studies are published, they reflect a weakness in
PACE,Vol.33 November 2010 1411
Tab le I V.
Publications Presenting Interindividual Variation in Approved Task Force Short-Term Measures of HRV
Author Number of mRR SDNN rMSSD LF HF
and Date Participants (ms) (ms) (ms) (ms2) LFnu (ms2) HFnu LF:HF
Agelink et al.13 (1998) 69 NR NR 6.9–99.4 NR NR NR NR 0.29–11.00
Fagard et al.11 (1998) 587 NR NR NR 4–6,397*NR 4–10,751*NR NR
Sinnreich et al.16 (1998) 293 NR 3.39–4.05 ln2.88–3.57 ln4.63–6.24 lnNR 4.07–5.49 lnNR NR
23 (2001) 392 573–1,402 13–168 NR 35–5,941 11–98 10–7,231 3–72 0.24–17.10
3.56–8.69 ln 2.30–8.89 ln
Sucharita et al.25 (2002) 93 NR NR NR NR NR 13- 6,830 19–93 NR
Rajendra Acharya10 et al. 125 NR 41–67 53.6–70.4 NR NR NR NR 1.6–1.9
(2004) (mean lower and upper
values from three age groups)
Kurosawa et al.45 (2007) 66 NR NR NR 86–1,874NR 98–3,938NR NR
*Value is geometric; Values are 25th –75th percentile; Values are 5 and 95 percentiles; NR =not reported; mRR =mean RR interval; SDNN =standard deviation of
normal-to-normal intervals; rMSSD =root mean square of successive differences; LF =low-frequency spectral power; HF =high-frequency spectral power; LF:HF =ratio of
low-frequency power to high-frequency power; nu =normalized units; ln =natural logarithm.
1412 November 2010 PACE,Vol.33
adherence to Task Force recommendations. This
also demonstrates a lack of coherence between
authors and editors as to how and what to present
when reporting short-term measures of HRV.
Comparisons between Literature and Task
Force Values
The Task Force does not provide norm values
for short-term time-domain measures of HRV and
therefore comparisons can only be made between
spectral measures. The Task Force figures are as
follows: 1,170 ms2for LF power, 975 ms2for
HF power, 54 and 29 for normalized LF and HF,
and 1.5–2.0 for the LF:HF ratio. The Task Force
LF value is more than 1.5 SD above the mean
literature value (519 ms2). The Task Force HF
value is also higher compared with that from the
literature (657 ms2). Task Force and literature-
normalized measures of LF and HF power are
more homogenous but the Task Force value for
LF:HF (1.5–2.0) is considerably lower than the
value gained from the literature (2.8).
Reasons for these discrepancies could be
due a number of factors including differing
characteristics of participants and differences in
spectral decomposition methods. The studies from
which the norms were obtained were not cited by
the Task Force authors so comparisons in terms
of participants are not possible. The Task Force
report does provide details as to the frequency
bandwidths used for determining LF and HF
power distributions. Oscillations in RR intervals
occurring at LF were assessed between 0.04 and
0.15 Hz and at HF between 0.15 and 0.4 Hz. Forty-
seven percent of the studies presented here report
values for LF and HF power obtained at frequency
bandwidths differing from those recommended by
the Task Force. Some considered oscillations in
heart periods at frequencies as low as zero to
0.003 as part of the LF component.19,40 Others
utilized much lower cutoff values (0.3 Hz) for
the HF component.21 Discrepancies in LF and HF
frequency bands could lead to the inclusion and/or
exclusion of oscillations of differing physiological
origins and would certainly result in varying
values for LF, HF, and/or both. It is both interesting
and somewhat telling then that these studies
report some of the largest discrepancies for
spectral measures of HRV.
From Table SI, it can be seen that the
following population-based studies report values
for short-term HRV measurements from large
samples (1,000): Rennie et al.,6Kuo et al.18
Dekker et al.,20 Liao et al.,32 Hemingway et al.,36
Britton et al.43 On closer examination, a number
of these studies were based upon ongoing
longitudinal and/or cross-sectional assessments
of the same participant populations. While these
studies present different sized samples and were
testing different hypotheses, there is a potential
for significant overlap between their respective
samples. This may explain the similarity in values
between Dekker et al.20 and Liao et al.32 and
among Rennie et al.,6Hemingway et al.,36 and
Britton et al.43 (Table SII). For these reasons, it
could be argued that only three large populations
have been assessed since the 1996 Task Force
report.6,18,43 Moreover, the lowest participant age
across these three populations was 40 years. This
means that there are currently no published data
for short-term HRV measures obtained in a large
population including adults aged less than 40. The
negative relationship between HRV and age may
also explain the relatively low values for HRV
measures observed by these studies. The impact
these large samples have on the mean publication
values presented here is also noteworthy.
Studies Reporting Discrepant Absolute
HRV Values
Approximately 85% of studies demonstrated
values within 1.5 SD of the mean publications
value for one or more short-term HRV measure.
Closer scrutiny of the 15% of studies demon-
strating values greater than 1.5 SD can help
identify conditions leading to disparate values
for short-term measures of HRV. Discussion of
the following studies demonstrating discrepant
values will adopt a measure-by-measure approach:
Melanson21 (mRR, SDNN, rMSSD, LF, HF),
Sandercock et al.34 (LF), Evrengul et al.40 (SDNN),
Mehlsen et al.48 (SDNN), Sandercock et al.50
(SDNN, rMSSD), Nunan et al.51 (LF).
A closer look at the characteristics of the
above studies revealed a number of similarities
and differences related to study participants,
RR interval data recording, artifact identification,
and interpolation and spectral decomposition
protocols. As these factors can have differing
effects depending on the measure, they will be
discussed separately for time- and frequency-
domain measures, respectively.
Time-Domain Measures
The high RR values reported by Melanson21
and the high SDNN values reported by both
Melanson21 and Sandercock et al.50 might be
explained by their use of young and moderate-
to-well trained participants. There is a well-
established link between age and HRV, with a
decrease in HR for increasing age with younger
individuals demonstrating higher values.1,16,18,59
SDNN is also a function of the recording
length, with longer analyzed recordings producing
larger values.60 For this reason, the Task Force
recommends a standardized duration of 5 minutes
PACE,Vol.33 November 2010 1413
for short-term SDNN (and other measures of
HRV). These factors most likely explains the larger
values observed by Evrengul and colleagues40 who
determined the SDNN of RR interval data recorded
over a 1-hour period. No justification for such a
recording length was given by the authors.
Parasympathetic nerve traffic enacts its effects
at a much faster (<1 second) rate than sympa-
thetic outflow (>5 seconds); therefore, beat-to-beat
changes in RR intervals (rMSSD) are considered
a reflection of vagal outlfow.52,54 Measures of
rMSSD are highly variable under conditions of
enhanced vagal outflow.61 One such condition
is paced breathing, particularly in the supine
position. In addition, the bradycardia observed
for more highly trained individuals is commonly
accompanied by augmented markers of cardiac
vagal modulation,62,63 although this relationship is
not always observed.64 The discrepant values for
rMSSD reported by Melanson21 and Sandercock
et al.50 are likely to result from the combined
effect of young, trained individuals with higher
baseline vagal tone and the use of supine and
paced breathing protocols.
Frequency-Domain Measures
A number of human and animal studies have
demonstrated findings of both sympathetic65–67
and parasympathetic68 origins for LF oscilla-
tions and spectral power. An augmented and
diminished LF power under parasympathetic
blockade has implications for studies where
vagal conditions are enhanced, such as during
paced breathing conditions.68 The higher values
observed by Melanson21 may be the consequence
of a vagally mediated augmentation of LF power
resulting from the paced breathing condition.
In healthy normotensive controls, a value of
82 ms2was reported by Piccirillo et al.33 Moreover,
this value was used to determine “abnormal” HF
power in chronic heart failure (CHF) patients.
Inclusion of these values in the present study
may explain the lower overall mean value for
HF power. An important observation is that these
values are considerably lower than the Task Force
norm value for HF and the mean studies value
presented here. As is common throughout the
literature, consideration as to the “normality” of
the so-called “healthy” values is ignored.
Spectral measures are highly sensitive to tech-
nical errors within RR data such as artifacts, mis-
placement of missing data, poor pre-processing,
and nonstationarity. Information regarding error
detection methods for 1-hour Holter RR interval
data was not provided by Evrengul et al.40 and
no indication as to the number of errors observed
and/or removed was given. The fact that Mehlsen
et al.48 do not report the performance of any
error identification, removal, and/or correction
procedures suggest a failure to understand the
importance of correct RR interval data in the
analysis of its variation. RR intervals were also
considered to be “within normal range,” yet the
authors provide no reference for this so-called
“normal” range.
The Task Force1recommendations stress
the need for manual editing of RR interval
data. Evidence of a strong prognostic value for
fully automated measures of HRV12 and their
accurate and reliable determination compared to
traditional methods34,51 suggests that the Task
Force1recommendations may be outdated. At the
very least, they require updating to account for
the computational power of current automated RR
recording and HRV analysis devices.
Studies Reporting Discrepant Log-Transformed
HRV Values
Of the studies reporting log-transformed mea-
sures of HRV, only one demonstrated discrepant
values for HRV measures.12 In the study by Ho
et al.12 data for spectral measures of HRV were
obtained in a healthy control group matched for
age and sex to a group of patients suffering from
CHF. The participants in the control group were
44% female, with a mean age of 72 years and a
resting HR of 76 beats/min. There is a well-known
age-related decline in HRV that particularly affects
measures related to vagal modulations of HR in
females.18 Data presented elsewhere demonstrate
a negative correlation between HR and spectral
measures of HRV.69 These two factors alone may
explain the low values for LF (2.05 ln ms2)and
particularly for HF power (0.08 ln ms2) observed
by Ho et al.12 As with the majority of studies
utilizing a control “reference” group, the values
presented in the control group are not questioned
by the authors as to their normality/abnormality.
Summary of Main Factors Underlining
Discrepant Values in Short-Term HRV
from Healthy Individuals
The measure-by-measure analysis performed
for those studies reporting discrepant values
revealed a number of underlying factors including:
1. Moderate to high level of participant
habitual physical activity;
2. The use of paced breathing protocols,
particularly when performed in participants with
moderate to high physical activity levels;
3. Where younger participants are measured,
values for HRV are typically higher;
1414 November 2010 PACE,Vol.33
4. Poor reporting and/or performance of
RR interval error recognition, removal, and/or
correction procedures;
5. The use of differing frequency bandwidths
and normalization methods for LF and HF spectral
6. Wide variation in HRV measures between
healthy participants of the same study;
7. The misclassification of participants as
8. A failure of studies to recognize the nor-
mality/abnormality of values obtained in healthy
Some of the points above (1, 2, 3, and 6)
were not unexpected. Of some surprise was the
failure to perform error correction procedures by a
number of studies and the poor reporting of these
procedures by others. The last three summary
points are particularly important and highlight the
inherent problem of defining a so-called “normal”
These points are also inter-related in that
the failure to question the normality of data
when obtained in healthy participants possibly
stems from the fact that even in homogonous
healthy groups, measures of HRV can display wide
interindividual variations (as high as 260,000%,
Fagard et al.11; Table IV).
It is important, however, to recognize other
factors could influence discrepancies between
studies. Measures of HRV are influenced by diet
(caffeine and alcohol intake) and physical and
mental stress. Very few of the studies included
here include information on these factors and their
impact on values presented cannot be determined.
When assessing studies reporting so-called normal
HRV, readers should employ close scrutiny of the
factors outlined above as well as potential other
factors (e.g., diet, stress) related to the individual
aspects of each study. With consideration of these
factors, the data presented in this study may
provide users of HRV with reference ranges by
which to determine disparate values for common
measures of short-term HRV.
Study Limitations
It is possible that some papers meeting the
inclusion criteria for the present study would have
been missed by the search strategy employed.
Arbitrary selection of the selected search terms
may have meant that some studies reporting short-
term HRV in healthy adults may have been missed.
Alternatively, it could be argued that studies
missed despite the comprehensive list of search
terms may be too ambiguous in terms of the
context in which short-term measures of HRV were
Study Recommendations
To facilitate between-study comparisons and
aid standardization of measurements, studies need
to report the outcomes of RR interval data editing
procedures. In addition, measures of stationarity
or measures taken to address nonstationary
signals should be provided. Moreover, editors
and reviewers need to adopt greater diligence in
ensuring that papers using HRV provide details
of data treatment before accepting the paper(s) for
Despite the call for large population-based
studies to determine normal HRV standards by the
1996 Task Force paper, there are no studies with
participants from the full age spectrum. There is
still a need for a population-based study assessing
short-term HRV measurements and involving the
full age spectrum. A multicenter approach may be
the most feasible approach. Such a study would
require stringent methodological standards and
participant inclusion criteria and awareness of
methodological and participant factors known to
affect HRV.
There is a need for a revision of current recom-
mendations and standards for the measurement of
short-term HRV. These should be made in light
of significant developments in the computational
power and accuracy of automated RR interval
and HRV analysis systems. There is a particular
need to stress clarity and transparency by the
manufacturers as to the QRS, RR interval, and HRV
analysis procedures of new technologies.
Data presented here should be used to
quantify reference ranges for short-term measures
of HRV in healthy adult populations but should
be undertaken with reference to methodological
factors underlying disparate values. These include
but are not limited to: participant demographic
characteristics, including age, sex, and habitual
physical activity levels; poor RR interval data
editing procedures; poor classification of healthy
participants; and a failure to recognize values as
disparate. Studies reporting HRV need to recog-
nize the normality of data even when obtained
in individuals considered as healthy. The need
for large-scale population studies assessing short-
term HRV in normally healthy adults still remains.
Current recommendations require updating to
account for the era of completely automated HRV
analysis. Clarification of measurement standards
in light of the discrepancies observed between
studies is also needed.
PACE,Vol.33 November 2010 1415
1. Task Force. Heart rate variability: Standards of measurement,
physiological interpretation and clinical use. Task Force of the
European Society of Cardiology and the North American Society
of Pacing and Electrophysiology. Circulation 1996; 93:1043–1065.
2. Kleiger RE, Miller JP, Bigger JT Jr, Moss AJ. Decreased heart rate
variability and its association with increased mortality after acute
myocardial infarction. Am J Cardiol 1987; 59:256–262.
3. Nolan J, Batin PD, Andrews R, Lindsay SJ, Brooksby P, Mullen
M, Baig W, et al. Prospective study of heart rate variability and
mortality in chronic heart failure: Results of the United Kingdom
Heart Failure Evaluation and Assessment of Risk Trial (UK-heart).
Circulation 1998; 98:1510–1516.
4. La Rovere MT, Pinna GD, Maestri R, Mortara A, Capomolla S, Febo
O, Ferrari R, et al. Short-term heart rate variability strongly predicts
sudden cardiac death in chronic heart failure patients. Circulation
2003; 107:565–570.
5. Tsuji H, Larson MG, Venditti FJ, Manders ES Jr, Evans JC, Feldman
CL, Levy D. Impact of reduced heart rate variability on risk for
cardiac events. The Framingham Heart Study. Circulation 1996;
6. Rennie KL, Hemingway H, Kumari M, Brunner E, Malik M, Marmot
M. Effects of moderate and vigorous physical activity on heart rate
variability in a British study of civil servants. Am J Epidemiol 2003;
7. Perki¨
aki JS. Heart rate variability: Recent developments. Ann
Noninvasive Electrocardiol 2002; 7:83–85.
8. Pinna G, Maestri R, Torunski A, Danilowicz-Szymanowicz L,
Szwoch M, La Rovere MT, Raczak G. Heart rate variability measures:
A fresh look at reliability. Clin Sci 2007; 113;131–140.
9. Taylor JA, Studinger P. Counterpoint: Cardiovascular variability is
not an index of autonomic control of the circulation. J Appl Physiol
2006; 101:678–681.
10. Rajendra Acharya U, Kannathal N, Ong Wai Sing, Luk Yi Ping, Tji
Leng Chua. Heart rate analysis in normal participants various age
groups. Biomed Eng Online 2004; 3:24.
11. FagardRH, Pardaens K, Staessen JA, Thijs L. Power spectral analysis
of heart rate variability by autoregressive modelling and fast Fourier
transform: Repeatability and age-sex characteristics. Acta Cardiol
1998; 53:211–218.
12. Ho KL, Moody GB, Peng C-K, Mietus JE, Larson MG, Levy
D, Goldberer AL. Predicting survival in heart failure case and
control participants by use of fully automated methods for
deriving nonlinear and conventional indices of heart rate dynamics.
Circulation 1997; 96:842–848.
13. Agelink MW, Lemmer W, Malessa R, Zeit T, Majewski T,
Klieser E. Improved autonomic neurocardial balance in short-term
abstinent alcoholics treated with acamprosate. Alcohol Alcohol
1998; 33:602–605.
14. Kageyama T, Nishikido N, Kobayashi T, Kurokawa Y, Kaneko
T, Kabuto M. Long commuting time, extensive overtime, and
sympathodominant state assessed in terms of short-term heart
rate variability among male white-collar workers in the Tokyo
megalopolis. Ind Health 1998; 36:209–217.
15. Piccirillo G, Bucca D, Bauco C, Cinti AM, Michele D, Fimognari
FL, Cacciafesta M, Marigliano V. Power spectral analysis of heart
rate in participants over a hundred years old. Int J Cardiol 1998; 63:
16. Sinnreich R, Kark JD, Friedlander Y, Sapoznikov D, Luria MH. Five
minute recordings of heart rate variability for population studies:
repeatability and age-sex characteristics. Heart 1988; 80:156–162.
17. Steinberg AA, Mars RL, Goldman DS, Percy, RF. Effect of end-stage
renal disease on decreased heart rate variability. Am J Cardiol 1998;
18. Kuo TBJ, Lin T, Yang CCH, Li C-L, Chen C-F, Chou P. Effect of
aging on gender differences in neural control of heart rate. Am J
Physiol-Heart C 1999; 46:H2233–H2239.
19. Notarius CP, Butler GC, Ando S, Polland MJ, Senn BL, Floras JS.
Dissociation between microneurographic and heart rate variability
estimates of sympathetic tone in normal participants and patients
with heart failure. Clin Sci (Lon) 1999; 96:557–565.
20. Dekker JM, Crow RS, Folsom AR, Hannan PJ, Liao D, Swenne A,
Schouten EG, et al. Low heart rate variability in a 2-minute rhythm
strip predicts risk of coronary heart disease and mortality from
several causes: The Aric study. Circulation 2000; 102:1239–1244.
21. Melanson EL. Resting heart rate variability in men varying in
habitual physical activity. Med Sci Sports Exerc 2000; 32:1894–
22. Fagard RH. A population-based study on the determinants of heart
rate and heart rate variability in the frequency domain. Verh K Acad
GeneeskdBelg 2001; 63:57–89.
23. Pikkuj¨
a SM, Makikallio TH, Airaksinen KEJ, Huikuri HV. De-
terminants and interindividual variation of R-R interval dynamics
in healthy middle-aged participants. Am J Physiol-Heart C 2001;
24. Tulppo MP, Hughson RL, M ¨
akikallio TH, Juhani Airaksinen KE,
anen T, Huikuri H. Effects of exercise and passive head-up tilt
on fractal and complexity properties of heart rate dynamics. Am J
Physiol-Heart C 2001; 280:H1081–H1087.
25. Sucharita S, Bharathi AV, Kurpad AV, Vaz M. A comparative study
of tests of cardiac parasympathetic nervous activity in healthy
human participants. Physiol Meas 2002; 23:347–354.
26. Yildirir A, Kabakci G, Akgul E, Tokgozoglu L. Effects of menstrual
cycle on cardiac autonomic innervations as assessed by heart rate
variability. Ann Noninvasive Electrocardiol 2002; 7:60–63.
27. Geelen A, Zock PL, Swenne CA, Brouwer IA, Schouten EG, Katan
MB. Effect of n-3 fatty acids on heart rate variability and baroreflex
sensitivity in middle-aged participants. Am Heart J 2003; 146:344–
28. Gerritsen J, TenVoorde BJ, Dekker JM, Kingma R, Kostone PJ, Bouter
LM, Heethaar RM, et al. Measures of cardiovascular autonomic
nervous function: Agreement, reproducibility, and reference values
in middle age and elderly participants. Diabetologia 2003; 46:330–
29. Virtanen R, Jula A, Salminen JK, Voipio-Pulkki L-M, Helenius H,
Kuusela T, Airaksinen J, et al. Anxiety and hostility are associated
with reduced baroreflex sensitivity and increased beat-to-beat blood
pressure variability. Psychosom Med 2003; 65:751–756.
30. Jurca R, Church TS, Morss GM, Jordan AN, Earnest CP. Eight
weeks of moderate-intensity exercise training increases heart rate
variability in sedentary postmenopausal women. Am Heart J 1984;
31. Laitinen T, Niskanen L, Geelen G, L¨
ansimies E, Hartikainen J. Age
dependency to head-up tilt in healthy participants. J Appl Physiol
2004; 96:2333–2340.
32. Liao D, Duan Y, Whitsel EA, Zheng Z-J, Heiss G, Chinchilli VM,
Lin H-M, et al. Association of higher levels of ambient criteria
pollutants with impaired cardiac autonomic control: A population-
based studies. Am J Epidemiol 2004; 160:768–777.
33. Piccirillo G, Magri D, Naso C, di Carlo S, Mois`
e A, De Laurentis T,
Torrini A, et al. Factors influencing heart rate variability power
spectral analysis during controlled breathing in patients with
chronic heart failure or hypertension and in healthy normotensive
participants. Clin Sci 2004; 107:183–190.
34. Sandercock GRH, Shelton C, Bromley P, Brodie DA. Agreement
between three commercially available instruments for measuring
short-term heart rate variability. Physiol Meas 2004; 25:1115–
35. Schroeder EB, Whitsel EA, Evans GW, Prineas RJ, Chambless
LE, Heiss G. Repeatability of heart rate variability measures. J
Electrocardiol 2004; 37:163–172.
36. Hemingway H, Shipley M, Brunner E, Britton A, Malik M, Marmot
M. Does autonomic function link social position to coronary risk?
The Whitehall II Study. Circulation 2005; 111:3017–3077.
37. Lucini D, Di Fede G, Parati G, Pagani M. Impact of chronic
psychosocial stress on autonomic cardiovascular regulation in
otherwise healthy participants. Hypertension 2005; 46:1201–1206.
38. M¨
orner S, Wiklund U, Rask P, Olofsson B, Kazzam E, Waldenstr¨
A. Parasympathetic dysfunction in hypertrophic cardiomyopathy
assessed by heart rate variability: Comparison between short-term
and 24-h measurements. Clin Physiol Funct Imaging 2005; 25:90–
39. Buchheit M, Gindre C. Cardiac parasympathetic regulation:
Respective associations with cardiorespiratory fitness and training
load. Am J Physiol-Heart C 2006; 291:H451–H458.
40. Evrengul H, Tanriverdi H, Kose S, Amasyali B, Kilic A, Celik T,
Turhan H, et al. The relationship between heart rate recovery and
heart rate variability in coronary artery disease. Ann Noninvasive
Electrocardiol 2006; 11:154–162.
41. Park SK, Schwartz J, Weisskopf M, Sparrow D, Vokonas PS, Wright
RO, Coull B, et al. Low-level lead exposure, metabolic syndrome,
and heart rate variability: The VA normative aging study. Environ
Health Perspect 2006; 114:1718–1724.
42. Pichon A, Roulard M, Antoine-Jonville S, de Bisschop C, Denjean,
A. Spectral analysis of heart rate variability: Interchangeability
1416 November 2010 PACE,Vol.33
between autoregressive analysis and fast Fourier transform. J
Electrocardiol 2006; 39:31–37.
43. Britton A, Shipley M, Malik M, Hnatkova K, Hemingway H,
Marmot M. Changes in heart rate and heart rate variability over
time in middle-aged men and women in the general population
(from the Whitehall II cohort study). Am J Cardiol 2007; 100:524–
44. Kobayashi H. Inter- and intra-individual of heart rate variability in
Japanese males. J Physiol Anthropol 2007; 26:173–177.
45. Kurosawa T, Iwata T, Dakeishi M, Ohno T, Tsukada M, Murata
K. Interaction between resting pulmonary ventilation function and
cardiac autonomic function assessed by heart rate variability in
young adults. Biomed Res 2007; 28:205–211.
46. Uusitalo ALT, Vanninen E, Lev ¨
alahti E, Batti´
e MC, Videman T,
Kaprio J. Role of genetic and environmental influences on heart
rate variability in middle-aged men. Am J Physiol-Heart C 2007;
47. Huang S-T, Chen G-Y, Wu C-H, Kuo C-D. Effect of disease activity
and position on autonomic nervous system modulation in patients
with systemic lupus erythematosus. Clin Rheumatol 2008; 27:295–
48. Mehlsen J, Kaijer MN, Mehlsen A-B. Autonomic and electrocar-
diographic changes in cardioinhibatory syncope. Europace 2008;
49. Nunan D, Jakovljevic DG, Donovan G, Hodges L, Sandercock GRH,
Brodie DA. Levels of agreement for RR intervals and short-term
heart rate variability obtained from the Polar S810 and an alternative
system. Eur J App Physiol 2008; 103:529–537.
50. Sandercock GR, Hardy-Shepherd D, Nunan D, Brodie DA. The
relationships between self-assessed habitual physical activity and
non-invasive measures of cardiac autonomic modulation in young
healthy volunteers. J Sport Sci 2008; 26:1171–1177.
51. Nunan D, Donovan G, Jakovljevic DG, Hodges L, Sandercock
GRH, Brodie DA. Validity and reliability of short-term heart rate
variability from the Polar S810. Med Sci Sports Exerc 2009; 41:243–
52. Bigger JT, Albrecht P, Steinman RC, Rolnitzky LM, Fleiss JL, Cohen
RJ. Comparison of time and frequency domain-based measures
of cardiac parasympathetic activity in Holter recordings after
myocardial infarction. Am J Cardiol 1989; 61:208–215.
53. Fei L, Copie X, Malik M, Camm J. Short- and long-term assessment
of heart rate variability for risk stratification after acute myocardial
infarction. Am J Cardiol 1996; 77:681–684.
54. Kleiger RE, Stein PK, Bigger JT Jr, Heart rate variability:
Measurement and clinical utility. Ann Noninv Electrocardiol 2005;
55. Guijt AM, Sluiter JK, Frings-Dresen MH. Test-retest reliability of
heart rate variability and respiration rate at rest and during light
physical activity in normal participants. Arch Med Res 2007;
56. Reland S, Ville NS, Wong S, Carrault G, Carre F. Reliability of heart
rate variability in healthy older women at rest and during orthostatic
testing. Aging Clin Exp Res 2005; 17:316–321.
57. Buchheit M, Simon C, Charloux A, Doutreleau S, Piquard F,
Brandenberger G. Heart rate variability and intensity of habitual
physical activity in middle-aged persons. Med Sci Sports Exer 2005;
58. Fluckiger L, Boivin J-M, Quillot D, Jeandel C, Zannad F. Differential
effects of aging on heart rate variability and blood pressure
variability. J Gerontol 1999; 54A:B219–B224.
59. Migliaro ER, Contreras P, Bech S, Etxagibal A, Castro M, Ricca R,
Vicente K, et al. Relative influence of age, resting heart rate and
sedentary life style in short-term analysis of heart rate variability.
Braz J Med Biol Res 2001; 34:493–500.
60. Saul JP, Albrecht P, Berger RD, Cohen J. Analysis of long term heart
rate variability: Methods, 1/f scaling and implications. Comput
Cardiol 1988; 14:419–422.
61. ˝
Ori Z, Monir G, Weiss J, Sayhouni X, Singer DH. Heart rate
variability: Frequency domain analysis. Cardiol Clin 1992; 10:499–
62. Puig J, Freitas J, Carvalho MJ, Puga N, Ramos J, Fernandes P, Costa
O, et al. Spectral analysis of heart rate variability in athletes. J Sports
Med Phys Fitness 1993; 33:44–48.
63. Shin K, Minamitani H, Onishi, S, Yamazaki H, Myoungho L.
Autonomic differences between athletes and nonathletes: Spectral
analysis approach. Med Sci Sports Exerc 1997; 29:1482–1490.
64. Sandercock GRH, Bromley PD, Brodie DA. The reliability of short-
term measurements of heart rate variability. Int J Cardiol 2005;
65. Guzzetti S, Piccaluga E, Casati R, Cerutti S, Lombardi F, Pagani M,
Malliani A. Sympathetic predominance in essential hypertension:
A study employing spectral analysis of heart rate variability. J
Hypertens 1984; 6:711–717.
66. Pomeranz B, Macaulay RJ, Caudill MA, Kutz I, Adam D, Gordon D,
Kilborn KM, et al. Assessment of autonomic function in humans by
heart rate spectral analysis. Am J Physiol 1985; 248:H151–H153.
67. Rimoldi O, Pierini S, Ferrari A, Cerutti S, Pagani M, Malliani A.
Analysis of short-term oscillations of RR and arterial pressure in
conscious dogs. Am J Physiol-Heart C 1999; 27:H967–H976.
68. Akselrod S, Gordon D, Ubel FA, Shannon DC, Berger AC, Cohen RJ.
Power spectrum analysis of heart rate fluctuation: A quantitative
probe of beat-to-beat cardiovascular control. Science 1981; 213:220–
69. Coumel P, Maison-Blance P, Catuli D. Heart rate and heart rate
variability in normal young adults. J Cardiovasc Electr 1994; 5:899–
Supporting Information
The following supporting information is available for this article:
Table SI. Publications Reporting Short-Term Measures of HRV in Normally Healthy Adults from 1996
to September 2008: Comparison of Methodologies.
Table SII. Publications Reporting Short-Term Measures of HRV in Normally Healthy Adults from 1996
to September 2008: HRV Measurement Values.
Supporting Information may be found in the online version of this article.
(This link will take you to the article abstract).
Please note: Wiley-Blackwell are not responsible for the content or functionality of any supporting
information supplied by the authors. Any queries (other than missing material) should be directed to
the corresponding author for the article.
PACE,Vol.33 November 2010 1417
... Spectral analysis allows the intensity of the HRV spectral components [i.e., the high-frequency band (HF), lowfrequency band (LF), and very low-frequency band (VLF)] to be determined. The HF component is believed to be mediated primarily by cardiac parasympathetic outflows and thus may provide a direct index of vagal activity; whereas the LF is commonly viewed as a product of both sympathetic and parasympathetic activity (Gianaros et al., 2004;Jennings & McKnight, 1994;Malliani et al., 1991;Nunan et al., 2010;Pumprla et al., 2002;Task Force, 1996). ...
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Understanding transient dynamics of the autonomic nervous system during fear learning remains a critical step to translate basic research into treatment of fear-related disorders. In humans, it has been demonstrated that fear learning typically elicits transient heart rate deceleration. However, classical analyses of heart rate variability (HRV) fail to disentangle the contribution of parasympathetic and sympathetic systems, and crucially, they are not able to capture phasic changes during fear learning. Here, to gain deeper insight into the physiological underpinnings of fear learning, a novel frequency-domain analysis of heart rate was performed using a short-time Fourier transform, and instantaneous spectral estimates extracted from a point-process modeling algorithm. We tested whether spectral transient components of HRV, used as a noninvasive probe of sympathetic and parasympathetic mechanisms, can dissociate between fear conditioned and neutral stimuli. We found that learned fear elicited a transient heart rate deceleration in anticipation of noxious stimuli. Crucially, results revealed a significant increase in spectral power in the high frequency band when facing the conditioned stimulus, indicating increased parasympathetic (vagal) activity, which distinguished conditioned and neutral stimuli during fear learning. Our findings provide a proximal measure of the involvement of cardiac vagal dynamics into the psychophysiology of fear learning and extinction, thus offering new insights for the characterization of fear in mental health and illness.
... Age was associated with a decreased RMSSD. Indeed, the levels of the HRV time domain parameters decrease with age, especially after 50 years [82,83] and the prevalence of hypothyroidism increases with age up to 10-15% in elderly patients [4]. We demonstrated that increased diastolic and systolic blood pressure were associated with decreased LF and HFnu power, respectively. ...
Full-text available
Introduction Hypothyroidism may be associated with changes in the autonomic regulation of the cardiovascular system, which may have clinical implications. Objective To conduct a systematic review and meta-analysis on the impact of hypothyroidism on HRV. Materials and methods PubMed, Cochrane, Embase and Google Scholar were searched until 20 August 2021 for articles reporting HRV parameters in untreated hypothyroidism and healthy controls. Random-effects meta-analysis were stratified by degree of hypothyroidism for each HRV parameters: RR intervals (or normal to normal-NN intervals), SDNN (standard deviation of RR intervals), RMSSD (square root of the mean difference of successive RR intervals), pNN50 (percentage of RR intervals with >50ms variation), total power (TP), LFnu (low-frequency normalized unit), HFnu (high-frequency), VLF (very low frequency), and LF/HF ratio. Results We included 17 studies with 11438 patients: 1163 hypothyroid patients and 10275 healthy controls. There was a decrease in SDNN (effect size = -1.27, 95% CI -1.72 to -0.83), RMSSD (-1.66, -2.32 to -1.00), pNN50 (-1.41, -1.98 to -0.84), TP (-1.55, -2.1 to -1.00), HFnu (-1.21, -1.78 to -0.63) with an increase in LFnu (1.14, 0.63 to 1.66) and LF/HF ratio (1.26, 0.71 to 1.81) (p <0.001). HRV alteration increased with severity of hypothyroidism. Conclusions Hypothyroidism is associated with a decreased HRV, that may be explained by molecular mechanisms involving catecholamines and by the effect of TSH on HRV. The increased sympathetic and decreased parasympathetic activity may have clinical implications.
... Under normoxia we found values similar to the general population (926 ± 90 ms, Frontiers in Physiology | May 2022 | Volume 13 | Article 899636 9 resting, normoxic) (Nunan et al., 2010). Sleep was accompanied by an RR elongation that has been reported both in nighttime (Herzig et al., 2018) and daytime sleep . ...
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Context: The use of daytime napping as a countermeasure in sleep disturbances has been recommended but its physiological evaluation at high altitude is limited. Objective: To evaluate the neuroendocrine response to hypoxic stress during a daytime nap and its cognitive impact. Design, Subject, and Setting: Randomized, single-blind, three period cross-over pilot study conducted with 15 healthy lowlander subjects (8 women) with a mean (SD) age of 29(6) years (Clinicaltrials identifier: NCT04146857, Interventions: Volunteers underwent a polysomnography, hematological and cognitive evaluation around a 90 min midday nap, being allocated to a randomized sequence of three conditions: normobaric normoxia (NN), normobaric hypoxia at FiO2 14.7% (NH15) and 12.5% (NH13), with a washout period of 1 week between conditions. Results: Primary outcome was the interbeat period measured by the RR interval with electrocardiogram. Compared to normobaric normoxia, RR during napping was shortened by 57 and 206 ms under NH15 and NH13 conditions, respectively (p < 0.001). Sympathetic predominance was evident by heart rate variability analysis and increased epinephrine levels. Concomitantly, there were significant changes in endocrine parameters such as erythropoietin (∼6 UI/L) and cortisol (∼100 nmol/L) (NH13 vs. NN, p < 0.001). Cognitive evaluation revealed changes in the color-word Stroop test. Additionally, although sleep efficiency was preserved, polysomnography showed lesser deep sleep and REM sleep, and periodic breathing, predominantly in men. Conclusion: Although napping in simulated altitude does not appear to significantly affect cognitive performance, sex-dependent changes in cardiac autonomic modulation and respiratory pattern should be considered before napping is prescribed as a countermeasure.
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We have previously proposed that mothers and infants co-regulate one another’s autonomic state through an autonomic conditioning mechanism, which starts during gestation and results in the formation of autonomic socioemotional reflexes (ASRs) following birth. Theoretically, autonomic physiology associated with the ASR should correlate concomitantly with behaviors of mother and infant, although the neuronal pathway by which this phenomenon occurs has not been elucidated. In this paper, we consider the neuronal pathway by which sensory stimuli between a mother and her baby/child affect the physiology and emotional behavior of each. We divide our paper into two parts. In the first part, to gain perspective on current theories on the subject, we conduct a 500-year narrative history of scientific investigations into the human nervous system and theories that describe the neuronal pathway between sensory stimulus and emotional behavior. We then review inconsistencies between several currently accepted theories and recent data. In the second part, we lay out a new theory of emotions that describes how sensory stimuli between mother and baby unconsciously control the behavior and physiology of both. We present a theory of mother/infant emotion based on a set of assumptions fundamentally different from current theories. Briefly, we propose that mother/infant sensory stimuli trigger conditional autonomic socioemotional reflexes (ASRs), which drive cardiac function and behavior without the benefit of the thalamus, amygdala or cortex. We hold that the ASR is shaped by an evolutionarily conserved autonomic learning mechanism (i.e., functional Pavlovian conditioning) that forms between mother and fetus during gestation and continues following birth. We highlight our own and others research findings over the past 15 years that support our contention that mother/infant socioemotional behavior is driven by mutual autonomic state plasticity, as opposed to cortical trait plasticity. We review a novel assessment tool designed to measure the behaviors associated with the ASR phenomenon. Finally, we discuss the significance of our theory for the treatment of mothers and infants with socioemotional disorders.
Patients with chronic kidney disease are at risk of developing cardiovascular disease. To facilitate out-of-clinic evaluation, we piloted wearable device-based analysis of heart rate variability and behavioral readouts in patients with chronic kidney disease from the Chronic Renal Insufficiency Cohort and controls (n = 49). Time-specific partitioning of heart rate variability readouts confirm higher parasympathetic nervous activity during the night (mean RR at night 14.4 ± 1.9 ms vs. 12.8 ± 2.1 ms during active hours; n = 47, analysis of variance (ANOVA) q = 0.001). The α2 long-term fluctuations in the detrended fluctuation analysis, a parameter predictive of cardiovascular mortality, significantly differentiated between diabetic and nondiabetic patients (prominent at night with 0.58 ± 0.2 vs. 0.45 ± 0.12, respectively, adj. p = 0.004). Both diabetic and nondiabetic chronic kidney disease patients showed loss of rhythmic organization compared to controls, with diabetic chronic kidney disease patients exhibiting deconsolidation of peak phases between their activity and standard deviation of interbeat intervals rhythms (mean phase difference chronic kidney disease 8.3 h, chronic kidney disease/type 2 diabetes mellitus 4 h, controls 6.8 h). This work provides a roadmap toward deriving actionable clinical insights from the data collected by wearable devices outside of highly controlled clinical environments.
Purpose This study investigated the effects of lung volume and trigeminal nerve stimulation (TS) on diving responses in breath-hold divers (BHDs) and non-divers (NDs). Methods Eight BHDs and nine NDs performed four breath-hold trials at different lung volumes, with or without TS, and one trial of TS. Haemodynamic parameters and electrocardiograms were measured for each trial. Results During the TS trial, the total peripheral resistance increased more in BHDs. Breath-hold performed at total lung capacity showed a more pronounced decrease in stroke volume and cardiac output in BHDs. The decrease in heart rate and increase in total peripheral resistance were more pronounced in BHDs when breath-holding was performed with TS. Conclusion The more pronounced diving response in BHDs was attributed to the greater increase in total peripheral resistance caused by TS. Furthermore, the lower stroke volume and cardiac output in BH performed at total lung capacity could also cause a more pronounced diving response in BHDs. Availability of data and materials The datasets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request. Keywords
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Dynamics of the autonomic nervous system (ANS) are hypothesized to play a role in the emergence of interpersonal violence. In the present study, we examined continuous activities of the inhibitory parasympathetic pathway of the ANS through the root mean square of successive differences between normal heartbeats (RMSSD) in 22 male offenders who committed interpersonal violence and 24 matched controls from the general population across three successive phases: resting baseline, while performing an emotional Go/No-Go task, and post-task recovery. Results showed that across the three phases, the offender group presented lower RMSSD at baseline (pFDR = .003; Cohen’s d = − 1.11), but similar levels during the task, attributed to a significant increase in their RMSSD level (pFDR = .027, Cohen’s d = − 1.26). During recovery, while no distinction between the two groups was found, both groups showed signs of recovering toward baseline values. These findings suggest that violent incarcerated offenders can flexibly engage parasympathetic resources to meet environmental challenges. This underscores the necessity of considering parasympathetic dynamics and its respective mobilization/flexibility to better understand ANS profiles underlying interpersonal violence as well as its potential utility in designing more tailored interventions.
Background: Adverse cardiovascular effects are associated with both diesel exhaust and road traffic noise, but these exposures are hard to disentangle epidemiologically. We used an experimental setup to evaluate the impact of diesel exhaust particles and traffic noise, alone and combined, on intermediary outcomes related to the autonomic nervous system and increased cardiovascular risk. Methods: In a controlled chamber 18 healthy adults were exposed to four scenarios in a randomized cross-over fashion. Each exposure scenario consisted of either filtered (clean) air or diesel engine exhaust (particle mass concentrations around 300 µg/m3), and either low (46 dB(A)) or high (75 dB(A)) levels of traffic noise for 3 h at rest. ECG was recorded for 10-min periods before and during each exposure type, and frequency-domain heart rate variability (HRV) computed. Endothelial dysfunction and arterial stiffness were assessed after each exposure using EndoPAT 2000. Results: Compared to control exposure, HRV in the high frequency band decreased during exposure to diesel exhaust, both alone and combined with noise, but not during noise exposure only. These differences were more pronounced in women. We observed no synergistic effects of combined exposure, and no significant differences between exposure scenarios for other HRV indices, endothelial function or arterial stiffness. Conclusion: Three-hour exposure to diesel exhaust, but not noise, was associated with decreased HRV in the high frequency band. This indicates activation of irritant receptor-mediated autonomic reflexes, a possible mechanism for the cardiovascular risks of diesel exposure. There was no effect on endothelial dysfunction or arterial stiffness after exposure.
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There are few reports about the coexistence of Sjögren's syndrome (SS) and ankylosing spondylitis (AS). To evaluate the frequency of SS in patients with AS. We studied 70 patients with AS presenting to the university outpatient clinic between January 2002 and November 2003. All the patients were asked about sicca symptoms by using sicca questionnaire. Rheumatoid factor, anti-nuclear antibody, anti-Ro, and anti-La antibodies were examined for each of the patients. Salivary flowmetry for the existence of xerostomia, Schirmer's test, and break-up time for the existence of xerophtalmia were performed in all patients with AS. Minor salivary gland biopsy was performed on the patients with at least three positive responses to the sicca questionnaire and positive xerosto-mia/ xerophtalmia tests. Biopsies were regarded as pathological when they showed focal grade III and grade IV sialoadenitis according to Chisholm grading criteria. Among 70 AS cases, 56 (80%) were men, 14 (20%) were women, and the mean age was 42 years old. Minor salivary gland biopsy was performed on the 16 patients. Of 16 minor salivary gland biopsies, 7 were assessed as pathological—5 of them showed grade III, and 2 of them showed grade IV sialoadenitis. Of these seven patients, one was anti-Ro-positive, and two were anti-La-positive. There was no patient with normal salivary gland biopsy and anti-Ro and/or anti-La positivity. In our study group, 7 (10%) of 70 AS patients had concomitant SS. Therefore, it seems likely that AS may have pathogenetic association with SS.
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Our aim was to estimate causal relationships of genetic factors and different specific environmental factors in determination of the level of cardiac autonomic modulation, i.e., heart rate variability (HRV), in healthy male twins and male twins with chronic diseases. The subjects were 208 monozygotic (MZ, 104 healthy) and 296 dizygotic (DZ, 173 healthy) male twins. A structured interview was used to obtain data on lifetime exposures of occupational loading, regularly performed leisure-time sport activities, coffee consumption, smoking history, and chronic diseases from 12 yr of age through the present. A 5-min ECG at supine rest was recorded for the HRV analyses. In univariate statistical analyses based on genetic models with additive genetic, dominance genetic, and unique environmental effects, genetic effects accounted for 31-57% of HRV variance. In multivariate statistical analysis, body mass index, percent body fat, coffee consumption, smoking, medication, and chronic diseases were associated with different HRV variables, accounting for 1-11% of their variance. Occupational physical loading and leisure-time sport activities did not account for variation in any HRV variable. However, in the subgroup analysis of healthy and diseased twins, occupational loading explained 4% of the variability in heart periods. Otherwise, the interaction between health status and genetic effects was significant for only two HRV variables. In conclusion, genetic factors accounted for a major portion of the interindividual differences in HRV, with no remarkable effect of health status. No single behavioral determinant appeared to have a major influence on HRV. The effects of medication and diseases may mask the minimal effect of occupational loading on HRV.
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: To assess the validity and the reliability of short-term resting heart-rate variability (HRV) measures obtained using the Polar S810 heart-rate monitor and accompanying software. : Measures of HRV were obtained from 5-min R to R wave (RR) interval data for 19 males and 14 females during 10 min of quiet rest on three separate occasions at 1-wk intervals using the Polar S810. Criterion measures of HRV were obtained simultaneously using the CardioPerfect (CP; Medical Graphics Corporation, St Paul, MN) 12-lead ECG module. Measures of validity of the Polar S810 were estimated by regression analysis, and measures of reliability of both devices were estimated by analysis of change scores. Measures of the SD of normal-to-normal intervals (SDNN), the root mean square of successive differences (RMSSD), and the low-frequency (LF) and the high-frequency (HF) spectral power and their ratio (LF/HF) were analyzed after log transformation, whereas mean RR and LF and HF in normalized units were analyzed without transformation. : There were marginal differences between the Polar and the CP mean measures of HRV, and the uncertainty in the differences was small. The Polar S810 demonstrated high correlations (0.85-0.99) with CP for all measures of HRV indicating good to near-perfect validity. Except for the low- and the high-frequency normalized units, Polar S810 did not add any substantial technical error to the within-subject variability in the repeated measurements of HRV. : HRV measures obtained with the Polar S810 and accompanying software have no appreciable bias or additional random error in comparison with criterion measures, but the measures are inherently unreliable over a 1-wk interval. Reliability of HRV from longer (e.g., 10 min) and/or consecutive 5-min RR recordings needs to be investigated with the Polar and criterion instruments.
Determinants and intersubject variations of fractal and complexity measures of R-R interval variability were studied in a random population of 200 healthy middle-aged women (age 51 +/- 6 yr) and 189 men (age 50 +/- 6 yr) during controlled conditions in the supine and sitting positions. The short-term fractal exponent (alpha (1)) was lower in women than men in both the supine (1.18 +/- 0.20 vs. 1.12 +/- 0.17, P< 0.01) and sitting position (P< 0.001). Approximate entropy (ApEn), a measure of complexity, was higher in women in the sitting position (1.16 +/-17 vs. 1.07 +/-19, P<0.001), but no gender-related differences were observed in ApEn in the supine position. Fractal and complexity measures were not related to any other demographic, laboratory, or lifestyle factors. Intersubject variations in a fractal measure, (1) (e.g., 1.15 +/- 0.20 in the supine position, z value 1.24, not significant), and in a complexity measure, ApEn (e.g., 1.14 +/- 0.18 in the supine position, z value 1.44, not significant), were generally smaller and more normally distributed than the variations in the traditional measures of heart rate variability (e.g., standard deviation of R-R intervals 49 +/- 21 ms in the supine position, z value 2.53, P< 0.001). These results in a large random population sample show that healthy subjects express relatively little interindividual variation in the fractal and complexity measures of heart rate behavior and, unlike the traditional measures of heart rate variability, they are not related to lifestyle, metabolic, or demographic variables. However, subtle gender-related differences are also present in fractal and complexity measures of heart rate behavior.
Spectral analysis of spontaneous heart rate fluctuations were assessed by use of autonomic blocking agents and changes in posture. Low-frequency fluctuations (below 0.12 Hz) in the supine position are mediated entirely by the parasympathetic nervous system. On standing, the low-frequency fluctuations increase and are jointly mediated by the sympathetic and parasympathetic nervous systems. High-frequency fluctuations, at the respiratory frequency, are decreased by standing and are mediated solely by the parasympathetic system. Heart rate spectral analysis is a powerful noninvasive tool for quantifying autonomic nervous system activity.
An association between air pollution and increased cardiovascular disease (CVD) mortality has been reported, but underlying mechanisms are unknown. The authors examined short-term associations between ambient pollutants (particulate matter less than 10 µm in aerodynamic diameter (PM 10 ), ozone, carbon monoxide, nitrogen dioxide, and sulfur dioxide) and cardiac autonomic control using data from the fourth cohort examination (1996– 1998) of the population-based Atherosclerosis Risk in Communities Study. For each participant, the authors calculated PM 10 and gaseous pollutant exposures as 24-hour averages and ozone exposure as an 8-hour average 1 day prior to the randomly allocated examination date. They calculated 5-minute heart rate variability indices and used logarithmically transformed data on high-frequency (0.15–0.40 Hz) and low-frequency (0.04– 0.15 Hz) power, standard deviation of normal R-R intervals, and mean heart rate. Linear regression was used to adjust for CVD risk factors and demographic, socioeconomic, and meteorologic variables. Regression coefficients for a one-standard-deviation increase in PM 10 (11.5 µg/m3) were –0.06 ms2 (standard error (SE), 0.018), –1.03 ms (SE, 0.31), and 0.32 beats/minute (SE, 0.158) for log-transformed high-frequency power, standard deviation of normal R-R intervals, and heart rate, respectively. Similar results were found for gaseous pollutants. These cross-sectional findings suggest that higher ambient pollutant concentrations are associated with lower cardiac autonomic control, especially among persons with existing CVD, and highlight a putative mechanism through which air pollution is associated with CVD. air pollution; cardiovascular diseases; heart rate
The Task Force was established by the Board of the European Society of Cardiology and co-sponsored by the North American Society of Facing and Electrophysiology. It was organised jointly by the Working Groups on Arrhythmias arzd on Computers of Cardiology of the European Society of Cardiology. After exchanges of written views on the subject, the main meeting of a writing core of the Task Force took place on May 8-10. 1994, on Necker Island. Following external reviews, the tent of this report was approved by the Board of the European Society of Cardiology on August 19,1995, and by the Board of the North American Society of Facing and Electrophysiology on October 3, 1995.
Background —Low heart rate variability (HRV) is associated with a higher risk of death in patients with heart disease and in elderly subjects and with a higher incidence of coronary heart disease (CHD) in the general population. Methods and Results —We studied the predictive value of HRV for CHD and death from several causes in a population study of 14 672 men and women without CHD, aged 45 to 65, by using the case-cohort design. At baseline, in 1987 to 1989, 2-minute rhythm strips were recorded. Time-domain measures of HRV were determined in a random sample of 900 subjects, for all subjects with incident CHD (395 subjects), and for all deaths (443 subjects) that occurred through 1993. Relative rates of incident CHD and cause-specific death in tertiles of HRV were computed with Poisson regression for the case-cohort design. Subjects with low HRV had an adverse cardiovascular risk profile and an elevated risk of incident CHD and death. The increased risk of death could not be attributed to a specific cause and could not be explained by other risk factors. Conclusions —Low HRV was associated with increased risk of CHD and death from several causes. It is hypothesized that low HRV is a marker of less favorable health.