ArticlePDF Available

Factors influencing heart rate variability

Authors:
  • Center for Aerospace Medicine of German Air Force

Abstract and Figures

The measurement and analysis of heart rate variability (HRV), which is based on the variation between consecutive NN intervals, has become an established procedure over the past two decades. A decrease in HRV has been shown to correlate with an increase in mortality. HRV is influenced by a number of physiological factors such as various diseases. Awareness of these mediators or confounders is of great importance in the analysis and assessment of HRV both in scientific studies and in clinical practice. This document, which is based on a selective survey of references and supplemented by information from national and international guidelines, presents the main endogenous, exogenous and constitutional factors. A decrease in HRV has been observed not only in connection with non-influenceable physiological factors such as age, gender and ethnic origin, but also in conjunction with a large number of acute and chronic diseases. Numerous lifestyle factors have both a positive and a negative influence on HRV. There are also physical influences that affect HRV. They must on no account be disregarded. Although the list of the factors is long and not all of them have yet been fully studied, awareness of them is of crucial importance in the measurement of HRV (both under laboratory conditions and during medical practice), its analysis and its assessment. More research also needs to be carried out to close knowledge gaps.
Content may be subject to copyright.
18 | Reviews International Cardiovascular Forum Journal 6 (2016)
DOI: 10.17987/icfj.v6i0.242
Factors Inuencing Heart Rate Variability
Stefan Sammito1,2, Irina Böckelmann2
1. Bundeswehr Medical Service Headquarters, Health Promotion, Sports and Nutrition Medicine
2. Occupational Medicine, Faculty of Medicine, Otto von Guericke University, Magdeburg
Corresponding authors: Bundeswehr Medical Service Headquarters,
Department VI, Health Promotion, Sports and Nutrition Medicine, Cdr (MC) Dr. med. Sammito, Stefan
Von-Kuhl-Str. 56, 56070 Koblenz, Germany
Phone: +0049 2618926130
Fax: +0049 26189626099
Email: drsammito@web.de
Introduction
The measurement and analysis of heart rate variability (HRV),
which is based on the variation between consecutive NN
intervals, has become an established procedure over the past
two decades1-4 since the publication of the rst guidelines5.
Not only have there been advances in recording technology
(smaller, more mobile, more accurate devices)6, but NN
intervals can now also be measured by small chest strap
and pulse watch systems7,8. Technological developments
have decreased the costs of recording and analysis and have
facilitated outpatient applications.
The variability of the successive dierences between the
NN intervals depends on sympathetic and parasympathetic
inuences. Mathematical algorithms can be used to calculate
various HRV parameters from a time series of successive
NN intervals. It is customary to make a distinction between
so-called HRV parameters of the time domain and frequency
domain and so-called non-linear HRV parameters5,7,9.
A decrease in HRV has been shown to correlate with an
increase in mortality, for example after myocardial infarcts10-12,
after bypass operations13, or in connection with cardiac
insuciency14.
HRV is inuenced by a number of physiological factors such as
various diseases. Awareness of these mediators or confounders
is of great importance in the analysis and assessment of HRV
both in scientic studies and in clinical practice.
Methods
This document, which is based on a selective survey of
references and supplemented by information from national
and international guidelines5,7, presents the main endogenous,
exogenous, and constitutional factors. The references primarily
cover metaanalyses and systematic reference surveys on the
subject and is supplemented by extensive cohort studies.
Results
In addition to non-inuenceable physiological parameters, a
number of factors emanate from the lifestyle habits of the test
persons, from the consequences of these habits and from
external circumstances. A host of diseases go hand in hand
with a decrease in HRV, while the inuence on the vegetative
nervous system can be regarded more as a result of diseases
and only rarely as the potential cause of this decrease.
* Corresponding author. drsammito@web.de
Abstract
The measurement and analysis of heart rate variability (HRV), which is based on the variation between consecutive NN intervals,
has become an established procedure over the past two decades. A decrease in HRV has been shown to correlate with an increase
in mortality. HRV is inuenced by a number of physiological factors such as various diseases. Awareness of these mediators or
confounders is of great importance in the analysis and assessment of HRV both in scientic studies and in clinical practice. This
document, which is based on a selective survey of references and supplemented by information from national and international
guidelines, presents the main endogenous, exogenous and constitutional factors. A decrease in HRV has been observed not only in
connection with non-inuenceable physiological factors such as age, gender and ethnic origin, but also in conjunction with a large
number of acute and chronic diseases. Numerous lifestyle factors have both a positive and a negative inuence on HRV. There are
also physical inuences that aect HRV. They must on no account be disregarded. Although the list of the factors is long and not all
of them have yet been fully studied, awareness of them is of crucial importance in the measurement of HRV (both under laboratory
conditions and during medical practice), its analysis and its assessment. More research also needs to be carried out to close
knowledge gaps.
Key words: autonomic nervous system; heart rate; analysis; sympathetic; parasympathicus
Citation: Summit S, Böckelmann I. Factors inuencing heart rate variability. International Cardiovascular Forum Journal.
2016;6:18-22. DOI: 10.17987/icfj.v6i0.242
ISSN: 2410-2636 © Barcaray Publishing
Reviews | 19
International Cardiovascular Forum Journal 6 (2016)
DOI: 10.17987/icfj.v6i0.242
Physiological factors
Non-inuenceable physiological factors include age, gender and
circadian rhythm. A person’s HRV rst increases sharply until they
reach the age of one and continues to increase considerably until
they reach the age of 15, while the resting heart rate decreases15.
Their HRV then decreases as they grow older16-18. It also seems
clear that there is a dierence between men and women in the
way the autonomous nervous system is regulated and thus in
the sympathetic-parasympathetic balance, and this manifests
itself in diering HRVs16,19-26. This dierence between the genders
seems to become less prominent when people reach the age
of 50, a fact that is attributed to the postmenopausal hormonal
changes that take place in women27,28. HRV, like a number of
other physiological parameters, is subject not only to age and
gender, but also to a circadian rhythm29. This must be taken into
account in particular with short-term measurements ranging from
a few minutes to a few hours are made. HRV increases at night
and decreases considerably during the morning hours.
Genetics
While a genetic disposition of the HRV is discussed in twin
studies30, Riese et al.31 did not establish any connection
between eight key genes for the presence of acetylcholine
receptors as part of the autonomous nervous system and the
HRV level in an analysis of several cohort studies involving a
total of 6,470 test persons.
In contrast, ethnic origin seems to have an inuence on HRV.
In a metaanalysis based on a systematic reference survey
involving 17 studies and a total of 11,162 test persons, Hill et
al.32 established a signicantly higher short-term resting HRV
in Afro-American test persons than in American test persons of
European origin.
Diseases
The eects of various diseases on HRV have been examined in
many studies. HRV is lower throughout among patients with the
diseases concerned than among healthy test persons.
Sepsis
There is evidence that HRV decreases among people with
severe acute diseases, including multiple organ failure, and that
this decrease correlates with an increase in mortality33,34.
Heart diseases
A decrease in NRV has been found among people with heart
disease and cardiac insuciency35 or who have suered a
heart attack1. It has been known since the mid-1980s and was
conrmed in a meta-analysis that a decrease in HRV correlates
with an increase in mortality10. Also Hypertension reduced HRV36.
Lung diseases
People with a chronic obstructive pulmonary disease (COPD)
also seem to have lower HRV37 and the degree to which the
HRV is lower correlates with the severity of the COPD.
Renal diseases
HRV is also shown to be lower in patients with chronic kidney
insuciency than in healthy controls38.
Psychiatric diseases
People suering from a series of psychiatric symptoms such
as anxiety disorder39, panic attacks39,40, posttraumatic stress
disorders41, epilepsy42, anorexia43, borderline personality
disorder44 and depressions39,45,46 have been found to have lower
HRV parameters.
It is in discussion if the reduction of HRV in patient with
depression is caused by the depression itself or by the
medication. O´Regan et al. have shown in a study with 4,750
peoples from Ireland that the medications lead to be the factor
that reduced the HRV.47 On the other hand Yeh et al. have
detected, that the depression itself reduced the HRV and not
the medication. They compared 618 patients with a major
depression with 506 healthy peoples48.
Metabolic diseases
HRV is also shown to be lower among people with metabolic
diseases such as diabetes mellitus2,4. With respect to the
metabolic syndrome, however, only women have been found to
have lower HRV, and not men49.
Other diseases
While HRV studies concern a wide range of other diseases,
there are at present only isolated studies on a major share
of these diseases and most of them cover small groups
of patients. A systematic review has revealed that only
headaches50 correlated with a decrease in HRV.
Diseases with no inuence on HRV
Systematic reviews have revealed that some diseases, e.g.
rheumatoid arthritis, cause no clear changes in the HRV of
those suering from them51.
Lifestyle habits
In addition to these non-inuenceable physiological factors, there
are further factors, notably those related to the lifestyle habits of
the test persons. These can have both a positive and a negative
inuence on HRV. They include physical tness or sporting
activity, increased body weight, which is sometimes negatively
associated with the rst two factors, active and passive smoking
and regular alcohol abuse. People who have an active lifestyle
and maintain a good or high level of physical tness or above-
average sporting activity can achieve an increase in their basic
parasympathetic activity and thus an increase in their HRV52-56.
Cumulative or too intensive sporting activity (e.g. competition
series, overtraining syndrome), however, brings about a decrease
in HRV52,54. In contrast, an elevated body weight or elevated
free-fat mass57 correlates with a decrease in HRV. Both active and
passive smoking lead to an increase in HRV58. Regular chronic
alcohol abuse above the alcohol quantity of a standard drink
for women or two standard drinks for men reduces HRV, while
moderate alcohol consumption up to these quantities does not
change the HRV and is not associated with an increase59.
External factors
In addition to climatic conditions and job-related parameters,
several harmful substances and medications also have a direct
or indirect inuence on HRV. Climatic factors lead to changes in
HRV due to the physiological reaction of the vegetative nervous
system. Heat increases sympathetic nervous system activity,
reducing HRV60,61. Long-term exposure to cold (e.g. at work
or during the winter months) has not been found to have an
inuence on HRV60,62,63 due to adaptation eects, e.g. after 60
days. Exposure to noise likewise leads to a decrease in HRV
because it increases sympathetic nervous system activity64-66.
Induced pain also results in a lowering of HRV due to the
activation of the physiological sympathetic nervous system67.
Night shift work over many years results in lower HRV due to
the chronodisruption68-70. There seems to be a connection here
between the length of time a person has done such shift work
and the degree of the decrease in HRV.
20 | Reviews International Cardiovascular Forum Journal 6 (2016)
DOI: 10.17987/icfj.v6i0.242
Some harmful substances (including acute diesel inhalation71,
chronic exposure to lead72-73, cadmium74 and neurotoxic
styrene75,76) and some medications (e.g. beta blockers, ACE
inhibitors, antiarrhythmics and psychotropic drugs5) have been
found to have a direct or indirect inuence on HRV. In contrast, a
systematic review by Gribble et al.77 merely revealed indications
that exposure to mercury brings about a reduction in HRV. With
respect to the eects of very early exposure to mercury, only a
14-year follow-up study involving children from the Faroe Islands
has established an association between reduced HRV among
children of seven and fourteen years of age and the mercury
content in the umbilical cord blood at their births only77,78.
Summary
A decrease in HRV has been observed not only in connection
with non-inuenceable physiological factors such as age,
gender and ethnic origin, but also in conjunction with a large
number of acute and chronic diseases. Numerous lifestyle
factors have both a positive and a negative inuence on HRV.
There are also physical inuences that aect HRV. These must
on no account be disregarded. Although not all the factors on
the list have yet been fully studied, awareness of the many
factors is of crucial importance in the measurement of HRV
(both under laboratory conditions and during medical practice),
its analysis and its assessment.
Declarations of Interest
The authors declare no conicts of interest
Acknowledgements
The authors agree to abide by the requirements of the
“Statement of publishing ethics of the International
Cardiovasular Forum Journal”79.
References
01. Huikuri HV, Mäkikallio TH. Heart rate variability in ischemic heart disease.
Auton Neurosci 2001; 90: 95-101.
02. Karayannis G, Giamouzis G, Cokkinos DV, Skoularigis J, Triposkiadis F.
Diabetic cardiovascular autonomic neuropathy: clinical implications.
Expert Rev Cardiovasc Ther 2012; 10: 747-765. doi: 10.1586/erc.12.53
03. Korpelainen JT, Sotaniemi KA, Huikuri HV, Myllylä VV. Circadian rhythm
of heart rate variability is reversibly abolished in ischemic stroke. Stroke
1997; 28: 2150-2154.
04. Kuehl M, Stevens MJ. Cardiovascular autonomic neuropathies as
complications of diabetes mellitus. Nat Rev Endocrinol 2012; 8: 405-416.
doi: 10.1038/nrendo.2012.21
05. European Society of Cardiology und North American Society of Pacing
and Electrophysiology. 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.
06. Körber T, Ismer B, von Knorre GH. Influences of Holter recording
technology on time domain heart rate variability - laboratory
investigations. Herzschr Elektrophys 2000; 11: 59-65.
07. Sammito S, Thielmann B, Seibt R, Klussmann A, Weippert M, Böckelmann
I.. Guideline for the application of heart rate and heart rate variability
in occupational medicine and occupational science. ASU International
Edition 2015, doi: 10.17147/ASUI.2015-06-09-03.
08. Wallén MB, Hasson D, Theorell T, Canlon B, Osika W. Possibilities and
limitations of the Polar RS800 in measuring heart rate variability at rest.
Eur J Appl Physiol 2012; 112: 1153-1165. doi: 10.1007/s00421-011-2079-
9.
09. Sassi R, Cerutti S, Lombardi F, Malik M, Huikuri HV, Peng CK, Schmidt G,
Yamamoto Y, Gorenek B, Lip GY, Grassi G, Kudaiberdieva G, Fisher JP,
Zabel M, Macfadyen R. Advances in heart rate variability signal analysis:
joint position statement by the e-Cardiology ESC Working Group and
the European Heart Rhythm Association co-endorsed by the Asia Pacific
Heart Rhythm Society. Europace 2015; 17 (9): 1341-1353. doi: 10.1093/
europace/euv015.
10. Buccelletti E, Gilardi E, Scaini E, Galiuto L, Persiani R, Biondi A, Basile
F, Silveri NG. Heart rate variability and myocardial infarction: systematic
literature review and metaanalysis. Eur Rev Med Pharmacol Sci
2009;13(4): 299-307. doi: 10.1002/14651858.CD006577.pub3.
11. Huikuri HV, Stein PK. Heart rate variability in risk stratification of cardiac
patients. Prog Cardiovasc Dis 2013; 56 (2): 153-159. doi: 10.1016/j.
pcad.2013.07.003.
Fig. 1. The dierent factors inuencing HRV grouped into four main areas, * = HRV decrease as a result of a physiological reaction to
a physical stimulus. Provides a summary of the results referring to the factors and covers the four main areas, i.e. non-inuenceable
physiological factors, illnesses, inuenceable lifestyle factors, and external factors.
Reviews | 21
International Cardiovascular Forum Journal 6 (2016)
DOI: 10.17987/icfj.v6i0.242
12. Song T, Qu XF, Zhang YT, Cao W, Han BH, Li Y, Piao JY, Yin LL, Da Cheng
H. Usefulness of the heart-rate variability complex for predicting cardiac
mortality after acute myocardial infarction. BMC Cardiovasc Disord 2014;
14: 59. doi: 10.1186/1471-2261-14-59.
13. Lakusic N, Mahovic D, Sonicki Z, Slivnjak V, Baborski F. Outcome of
patients with normal and decreased heart rate variability after coronary
artery bypass grafting surgery. Int J Cardiol 2013; 166 (2): 516-518. doi:
10.1016/j.ijcard.2012.04.040.
14. Sandercock GR, Brodie DA. The role of heart rate variability in prognosis
for different modes of death in chronic heart failure. Pacing Clin
Electrophysiol 2006; 29 (8): 892-904.
15. Eyre ELJ, Duncan MJ, Birch SL, Fisher JP. The influence of age and weight
status on cardiac autonomic control in healthy children: a review. Auton
Neurosci 2014; 186: 8-21. doi: 10.1016/j.autneu.2014.09.019.
16. Abhishekh HA, Nisarga P, Kisan R, Meghana A, Chandran S, Trichur Raju,
Sathyaprabha TN. Influence of age and gender on autonomic regulation of
heart. J Clin Monit Comput 2013; 27 (3): 259-264. doi: 10.1007/s10877-
012-9424-3
17. Shiogai Y, Stefanovska A, McClintock PV. Nonlinear dynamics of
cardiovascular ageing. Phys Rep 2010 488 (2-3): 51-110.
18. Voss A, Schulz S, Schroeder R, Baumert M, Caminal P. Methods derived
from nonlinear dynamics for analysing heart rate variability. Phil Trans R
Soc A 2009; 367 (1887): 277-296. doi: 10.1098/rsta.2008.0232.
19. Agelink MW, Malessa R, Baumann B, Majewski T, Akila F, Zeit T, Ziegler D.
Standardized tests of heart rate variability: normal ranges obtained from
309 healthy humans, and effects of age, gender, and heart rate. Clin Auton
Res 2001;11 (2): 99-108.
20. Barantke M, Krauss T, Ortak J, Lieb W, Reppel M, Burgdorf C,
Pramstaller PP, Schunkert H, Bonnemeier H. Effects of gender and
aging on differential autonomic responses to orthostatic maneuvers. J
Cardiovasc Electrophysiol 2008;19(12): 1296-303. doi: 10.1111/j.1540-
8167.2008.01257.x.
21. Jensen-Urstad K, Storck N, Bouvier F, Ericson M, Lindblad LE, Jensen-
Urstad M. Heart rate variability in healthy subjects is related to age and
gender. Acta Physiol Scand 1997; 160 (3): 235-241.
22. Ramaekers D, Ector H, Demyttenaere K, Rubens A, Van de Werf F.
Association between cardiac autonomic function and coping style in
healthy subjects. Pacing Clin Electrophysiol. 1998; 21 (8): 1546-1552.
23. Snieder H, van Doornen LJ, Boomsma DI, Thayer JF. Sex differences and
heritability of two indices of heart rate dynamics: a twin study. Twin Res
Hum Genet 2007;10 (2): 364-372.
24. Sookan T, McKune AJ. Heart rate variability in physically active individuals:
reliability and gender characteristics. Cardiovasc J Afr 2012; 23 (2): 67-72.
doi: 10.5830/CVJA-2011.108.
25. Tsuji H, Venditti FJ Jr, Manders ES, Evans JC, Larson MG, Feldman CL,
Levy D. Determinants of heart rate variability. J Am Coll Cardiol 1996; 28
(6): 1539-1546.
26. Umetani K, Singer DH, McCraty R, Atkinson M. Twenty-four hour time
domain heart rate variability and heart rate: relations to age and gender
over nine decades. J Am Coll Cardiol 1998; 31 (3): 593-601.
27. Fagard RH. A population-based study on the determinants of heart rate
and heart rate variability in the frequency domain. Verh K Acad Geneeskd
Belg 2001; 63 (1): 57-89.
28. Huikuri HV, Pikkujamsa SM, Airaksinen KE, Ikäheimo MJ, Rantala AO,
Kauma H, Lilja M, Kesäniemi YA. Sex-related differences in autonomic
modulation of heart rate in middle-aged subjects. Circulation 1996; 94 (2):
122-125.
29. Rodríguez-Colón S, He F, Bixler EO, Fernandez-Mendoza J, Vgontzas AN,
Berg A, Kawasawa YI, Liao D. The circadian pattern of cardiac autonomic
modulation and obesity in adolescents. Clin Auton Res 2014; 24: 265-273.
doi: 10.1007/s10286-014-0257-7.
30. Riese H, Rosmalen JGM, Ormel J, van Roon AM, Oldehinkel AJ, Rijsdijk
FV. The genetic relationship between neuroticism and autonomic function
in female twins. Psychol Med 2007; 37 (2): 257-267.
31. Riese H, Muñoz LM, Hartman CA, Ding X, Su S, Oldehinkel AJ, van Roon
AM, van der Most PJ, Lefrandt J, Gansevoort RT, van der Harst P, Verweij
N, Licht CM, Boomsma DI, Hottenga JJ, Willemsen G, Penninx BW,
Nolte IM, de Geus EJ, Wang X, Snieder H. Identifying genetic variants for
heart rate variability in the acetylcholine pathway. PloS ONE 2014; 9 (11):
e112476. doi: 10.1371/journal.pone.0112476.
32. Hill LK, Hu DD, Koenig J, Sollers JJ 3rd, Kapuku G, Wang X, Snieder H,
Thayer JF. Ethnic differences in resting heart rate variability: a systematic
review and meta-analysis. Psychosom Med 2015; 77 (1): 16-25. doi:
10.1097/PSY.0000000000000133.
33. Schmidt H, Hoyer D, Hennen R, Heinroth K, Rauchhaus M, Prondzinsky
R, Hottenrott K, Buerke M, Müller-Werdan U, Werdan K. Autonomic
dysfunction predicts both 1- and 2-month mortality in middle-aged
patients with multiple organ dysfunction syndrome. Crit Care Med 2008;
36 (3): 967-970. doi: 10.1097/CCM.0B013E3181653263.
34. Schmidt H, Lotze U, Ghanem A, Anker SD, Said SM, Braun-Dullaeus R,
Oltmanns G, Rose S, Buerke M, Müller-Werdan U, Werdan K, Rauchhaus
M. Relation of impaired interorgan communication and parasympathetic
activity in chronic heart failure and multiple-organ dysfunction syndrome.
J Crit Care 2014; 29 (3): 367-373. doi: 10.1016/j.jcrc.2013.12.015.
35. Guzzetti S, Magatelli R, Borroni E, Mezzetti S. Heart rate variability in
chronic heart failure. Auton Neurosci 2001 90 (1-2): 102-5.
36. Carthy ER. Autonomic dysfunction in essential hypertension: A
systematic review. Ann Med Surg (Lond) 2013; 3 (1): 2-7. doi: 10.1016/j.
amsu.2013.11.002.
37. Roque AL, Valenti VE, Massetti T, da Silva TD, Monteiro CB, Oliveira FR,
de Almeida Junior ÁD, Lacerda SN, Pinasco GC, Nascimento VG, Granja
Filho LG, de Abreu LC, Garner DM, Ferreira C. Chronic obstructive
pulmonary disease and heart rate variability: a literature update. Int Arch
Med 2014; 7: 43. doi: 10.1186/1755-7682-7-43.
38. Zhang J, Wang N. Prognostic significance and therapeutic option of heart
rate variability in chronic kidney disease. Int Urol Nephrol 2014; 46: 19-25.
doi: 10.1007/s11255-013-0421-3.
39. Birkhofer A, Schmidt G, Förstl H. Heart and brain -- the influence of
psychiatric disorders and their therapy on the heart rate variability.
Fortschr Neurol Psychiat 2005; 73 (4): 192-205.
40. Friedmann BH, Thayer JF. Autonomic balance revisited: panic anxiety and
heart rate variability. J Psychosom Res 1998; 44 (1): 133-51.
41. Sammito S, Thielmann B, Zimmermann P, Böckelmann I. Influence of
post-traumatic stress disorder on heart rate variability as marker of the
autonomic nervous system – a systematic review. Forts Neurol Psych
2015;83: 30-37. doi: 10.1055/s-0034-1398779.
42. Lotufo PA, Valiengo L, Benseñor IM, Brunoni AR. A systematic review and
meta-analysis of heart rate variability in epilepsy and antiepileptic drugs.
Epilepsia 2012; 53 (2): 272-282. doi: 10.1111/j.1528-1167.2011.03361.x.
43. Chalmers JA, Quintana DS, Abbott MJA, Kemp AH. Anxiety Disorders
are associated with reduced heart rate variability: a meta-analysis. Front
Psychiatry 2014; 5: 80. doi: 10.3389/fpsyt.2014.00080.
44. Koenig J, Kemp AH, Feeling NR, Thayer JF, Kaess M. Resting state
vagal tone in borderline personality disorder: A meta-analysis. Prog
Neuropsychopharmacol Biol Psychiatry 2016; 64: 18-26. doi: 10.1016/j.
pnpbp.2015.07.002.
45. Kapfhammer HP. The relationship between depression, anxiety and heart
disease - a psychosomatic challenge. Psychiatr Danub 2011 23 (4): 412-
424.
46. Stapelberg NJ, Hamilton-Craig I, Neumann DL, Shum DH, McConnell H.
Mind and heart: heart rate variability in major depressive disorder and
coronary heart disease - a review and recommendations. Aust N Z J
Psychiatry 2012; 46 (10): 946-957. doi: 10.1177/0004867412444624.
47. O´Regan C, Kenny RA, Cronin H, Finucane C, Kearney PM.
Antidepressants strongly influence the relationship between depression
and heart rate variability: findings from The Irish Longitudinal Study
on Ageing (TILDA). Psychol Med 2015; 45 (3): 626-36. doi: 10.1017/
S0033291714001767.
48. Yeh TC, Kao LC, Tzeng NS, Kuo TB, Huang SY, Chang CC, Chang HA.
Heart rate variability in major depressive disorder and after antidepressant
treatment with agomelatine and paroxetine: Findings from the Taiwan
Study of Depression and Anxiety (TAISDA). Prog Neuropsychopharmacol
Biol Psychiatry 2016; 64: 60-67. doi: 10.1016/j.pnpbp.2015.07.007.
49. Stuckey MI, Tulppo MP, Kiviniemi AM, Petrella RJ. Heart rate variability and
the metabolic syndrome: a systematic review of the literature. Diabetes
Metab Res Rev 2014; 30 (8): 784-793. doi: 10.1002/dmrr.2555.
50. Koenig J, Williams DWP, Kemp AH, Thayer JF. Vagally mediated heart rate
variability in headache patients - a systematic review and meta-analysis.
Cephalgia 2015; pii: 0333102415583989
51. Adlan AM, Lip GYH, Paton JFR, Kitas GD, Fisher JP. Autonomic function
and rheumatoid arthritis: a systematic review. Semin Arthritis Rheum
2014; 44 (3): 283–304. DOI: 10.1016/j.semarthrit.2014.06.003.
52. Aubert AE, Seps B, Beckers F. Heart rate variability in athletes. Sports Med
2003;33: 889-919.
53. Bernardi L, Piepoli ME. Autonomic nervous system adaption during
physical exercise. Ital Heart J 2001;2: 831-839.
54. Hottenrott K, Hoos O, Esperer HD. Heart rate variability and physical
exercise. Current status. Herz 2006 31 (6): 544-552.
55. Routledge FS, Campbell TS, McFetridge-Durdle JA, Bacon SL.
Improvements in heart rate variability with exercise therapy. Can J Cardiol
2010; 26 (6): 303-312.
56. Sandercock GRH, Bromley PD, Brodie DA. Effects of exercise on heart rate
variability: inferences from meta-analysis. Med Sci Sports Exerc 2005; 37
(3): 433-439.
57. Fraley MA, Birchem JA, Senkottaiyan N, Alpert MA. Obesity and the
electrocardiogram. Obes Rev 2005; 6 (4): 275-81.
58. Dinas PC, Koutedakis Y, Flouris AD. Effects of active and passive tobacco
cigarette smoking on heart rate variability. Int J Cardiol 2013; 163 (2): 109-
115. doi: 10.1016/j.ijcard.2011.10.140.
59. Karpyak VM, Romanowicz M, Schmidt JE, Lewis KA, Bostwick JM.
Characteristics of heart rate variability in alcohol-dependent subjects and
nondependent chronic alcohol users. Alcohol Clin Exp Res 2014; 38 (1):
9-26. doi: 10.1111/acer.12270
22 | Reviews International Cardiovascular Forum Journal 6 (2016)
DOI: 10.17987/icfj.v6i0.242
60. Ren C, O’Neill MS, Park SK, Sparrow D, Vokonas P, Schwartz J. Ambient
temperature, air pollution, and heart rate variability in an aging population.
Am J Epidemiol 2011; 173 (9): 1013-1021. doi: 10.1093/aje/kwq477.
61. Wu S, Deng F, Liu Y, Shima M, Niu J, Huang Q, Guo X. Temperature, traffic-
related air pollution, and heart rate variability in a panel of healthy adults.
Environ Res 2013; 120: 82-89. doi: 10.1016/j.envres.2012.08.008.
62. Bortkiewicz A, Gadzicka E, Szymczak W, Szyjkowska A, Koszada-
Włodarczyk W, Makowiec-Dabrowska T. Physiological reaction to work in
cold microclimate. Int J Occup Med Environ Health 2006;19 (2): 123-131.
63. Harinath K, Malhotra AS, Pal K, Prasad R, Kumar R, Sawhney RC.
Autonomic nervous system and adrenal response to cold in man at
Antarctica. Wilderness Environ Med 2005;16 (2): 81-91.
64. Kraus U, Schneider A, Breitner S, Hampel R, Rückerl R, Pitz M, Geruschkat
U, Belcredi P, Radon K, Peters A. Individual daytime noise exposure
during routine activities and heart rate variability in adults: A repeated
measures study. Environ Health Perspect 2013; 121 (5): 607-612.
65. Lee GS, Chen ML, Wang GY. Evoked response of heart rate variability
using short-duration white noise. Auton Neurosci 2010; 155 (1-2): 94-97.
doi: 10.1016/j.autneu.2009.12.008.
66. Schnell I, Potchter O, Epstein Y, Yaakov Y, Hermesh H, Brenner S, Tirosh E.
The effects of exposure to environmental factors on heart rate variability:
An ecological perspective. Environ Pollut 2013;183: 7-13. doi: 10.1016/j.
envpol.2013.02.005.
67. Koenig J, Jarczok MN, Ellis RJ, Hillecke TK, Thayer JF. Heart rate variability
and experimentally induced pain in healthy adults: a systematic review.
Eur J Pain 2014; 18 (3): 301-314. doi: 10.1002/j.1532-2149.2013.00379.x.
68. Ha M, Kim J, Park J, Chung HK. Blood pressure and heart rate variability in
workers of 8-hour shifts. J Hum Ergol (Tokyo) 2001; 30(1-2): 229-33.
69. Lindholm H, Sinisalo J, Ahlberg J, Hirvonen A, Hublin C, Partinen M,
Savolainen A. Attenuation of vagal recovery during sleep and reduction of
cortisol/melatonin ratio in late afternoon associate with prolonged daytime
sleepiness among media workers with irregular shift work. Am J Ind Med.
2012; 55 (7): 643-649. doi: 10.1002/ajim.22042.
70. Wehrens SM, Hampton SM, Skene DJ. Heart rate variability and
endothelial function after sleep deprivation and recovery sleep among
male shift and non-shift workers. Scand J Work Environ Health 2012 38
(2): 171-181. doi: 10.5271/sjweh.3197.
71. Brito JM, Belotti L, Toledo AC, Antonangelo L, Silva FS, Alvim DS, Andre
PA, Saldiva PH, Rivero DH. Acute cardiovascular and inflammatory toxicity
induced by inhalation of diesel and biodiesel exhaust particles. Toxicol
Sci. 2010 Jul;116(1):67-78. doi: 10.1093/toxsci/kfq107.
72. Böckelmann I, Pfister EA, McGauran N, Robra B-P. Assessing the
suitability of cross-sectional and longitudinal cardiac rhythm tests with
regard to identifying effects of occupational chronic lead exposure. J
Occup Environ Med 2002; 44 (1): 59-65.
73. Murata K, Araki S, Yokoyama K, Nomiyama K, Nomiyama H, Tao YX, Liu
SJ. Autonomic and central nervous system effects of lead in female glass
workers in China. Am J Ind Med 1995; 28 (2): 233-244.
74. Feng W, He X, Chen M, Deng S, Qiu G, Li X, Liu C, Li J, Deng Q, Huang
S, Wang T, Dai X, Yang B, Yuan J, He M, Zhang X, Chen W, Kan H, Wu T.
Urinary metals and heart rate variability: a cross-sectional study of urban
adults in Wuhan, China. Environ Health Perspect 2015; 123 (3): 217-22.
doi: 10.1289/ehp.1307563.
75. Murata K, Araki S, Yokoyama K, Maeda K. Autonomic and peripheral
nervous system dysfunction in workers exposed to mixed solvents. Int
Arch Occup Environ Health 1991; 63: 335-340.
76. Murata K, Araki S, Yokoyama K. Assessment of the peripheral, central, and
autonomic nervous system function in styrene workers. Am J Ind Med
1991; 20: 775-784.
77. Gribble MO, Cheng A, Berger RD, Rosmann L, Guallar E. Mercury
exposure and heart rate variability: a systematic review. Curr Envir Health
Rep 2015; 2 (3): 304-14. doi: 10.1007/s40572-015-0053-0.
78. Grandjean P, Murata K, Budtz-Jørgensen E, Weihe P. Cardiac autonomic
activity in methylmercury neurotoxicity: 14-year follow-up of a Faroese
birth cohort. J Pediatr 2004; 144 (2): 169–76.
79. Shewan LG, Coats AJS, Henein M. Requirements for ethical publishing in
biomedical journals. International Cardiovascular Forum Journal 2015;2:2
DOI: 10.17987/icfj.v2i1.4
... 2 On the one hand, chronic stress, inflammation, reduced regulatory capacity, obesity, smoking, and cardiovascular diseases are associated with reduced indices of HRV. 4 On the other hand, an optimal HRV indicates a healthy organism, adaptability, and well-being. 3,5 Hence, HRV is a promising marker to detect pathological states. 1 A link between autonomic and cognitive processes is supported by several studies demonstrating a positive relationship between cognitive functioning and HRV. ...
Article
Full-text available
Background Given that, up to date, there is no effective strategy to treat dementia, a timely start of interventions in a prodromal stage such as mild cognitive impairment (MCI) is considered an important option to lower the overall societal burden. Although autonomic functions have been related to cognitive performance, both aspects have rarely been studied simultaneously in MCI. Objective The aim of the present study was to investigate cardiac autonomic control in older adults with and without MCI. Methods Cardiac autonomic control was assessed by means of heart rate variability (HRV) at resting state and during cognitive tasks in 22 older adults with MCI and 29 healthy controls (HCs). Resting HRV measurement was performed for 5 minutes during a sitting position. Afterwards, participants performed three PC-based tasks to probe performance in executive functions and language abilities (i.e., Stroop, N-back, and a verbal fluency task). Results Participants with MCI showed a significant reduction of HRV in the frequency-domain (high frequency power) and nonlinear indices (SD2, D2, and DFA1) during resting state compared to HCs. Older individuals with MCI exhibited decreases in RMSSD and increases in DFA1 from resting state to Stroop and N-back tasks, reflecting strong vagal withdrawal, while this parameter remained stable in HCs. Conclusion The results support the presence of autonomic dysfunction at the early stage of cognitive impairment. Heart rate variability could help in the prediction of cognitive decline as a noninvasive biomarker or as a tool to monitor the effectiveness of therapy and prevention of neurodegenerative diseases.
... In addition, an increase in global autonomic activity was observed in the mid-point assessment, indicated by a significant increase in LF (nu)on week 6 (Vanderlei et al., 2009), which remained higher than the baseline values in week 12, but without significance. It is important to highlight that, although we strictly follow all precautions for the collection and analysis of HRV indices (Catai et al., 2020), several external factors could indirectly influence HRV, such as, weather conditions, job-related parameters and noises (Sammito and Böckelmann, 2016). In relation to weather, heat reduces HRV due to increased sympathetic nervous system activity. ...
Article
Background: In Cardiovascular Rehabilitation (CR), patient adherence to the maintenance phase is a major challenge. Virtual reality-based therapy (VRBT) promotes acute hemodynamic and autonomic repercussions similar to traditional rehabilitation and can increase patient adherence to the program. However, it is unknown whether the combination of VRBT to a traditional CR manages to maintain or even improve clinical and autonomic variables in long term. Objective: To analyze whether VRBT combination in a traditional CR can maintain or improve clinical and autonomic variables in cardiac patients in the maintenance phase of these programs. Methods: Twenty-six volunteers (62.04 ± 12.22 years) were evaluated, who underwent an initial assessment and two other assessments (in the sixth and 12th week) of the following outcomes: systolic and diastolic blood pressure, respiratory rate, pulse saturation of oxygen, heart rate, perceived exertion, and cardiac autonomic modulation, using linear and non-linear heart rate variability methods. Results: Except for the apparent lack of clinical significance observed in Shannon Entropy, LF (nu), and HF (nu), the combination of VRBT as routine in a traditional program did not cause significant changes in the analyzed variables. Conclusion: combination of VRBT was able to maintain the chronic hemodynamic and autonomic repercussions caused by traditional CR.
... The total duration of each experiment was approximately 1 h. All experiments were carried out from 8 am to 4 pm to avoid major HRV fluctuations (Sammito & Böckelmann, 2016). ...
Article
Full-text available
Loneliness has emerged as a public health concern. Previous research has reported its deleterious effects on physical and mental health; however, some specific psychophysiological mechanisms in healthy adults remain to be elucidated. The aim of the current study is to investigate whether self-reported social support and social touch (giving and receiving social touch), as well as resting heart rate variability (HRV), are significant negative predictors of loneliness in healthy adults. The study sample consists of 120 healthy students (50% female) with a mean age of 21.85 years old (DP= 2.21). The students were asked to complete a psychiatric screening questionnaire utilizing loneliness, social support, and social touch scales. HRV was derived from an electrocardiographic signal recorded for 15 min, with the participant relaxed in a supine position. Linear regression analyses were conducted to evaluate loneliness as a function of social support, social touch (giving or receiving touch), and resting HRV. The results show that social support (p< 0.001) and social touch, specifically receiving touch (p< 0.002), accounted for a significant proportion of the variance in loneliness. However, neither giving touch nor resting HRV was a significant predictor of loneliness. The current study highlights specific psychosocial factors in healthy adults that should be considered as promising pathways in order to reduce or work toward preventing loneliness, thus promoting better health and well-being. Keywords: Loneliness; Social touch; Social support; Heart rate variability
... HRV is useful to assess training needs; however, HRV measurements may be influenced by other factors such as physiological and genetic conditions, diseases, lifestyle habits, and even external factors (Shaffer et al., 2014;Sammito & Böckelmann, 2016). The impact of stress (Kim, Cheon, Bai, Lee, & Koo, 2018), sleep (Sajjadieh et al., 2020), and fatigue (Tran, Wijesuriya, Tarvainen, Karjalainen, & Craig, 2009) are the modulating factors of HRV getting more attention from the research community (Ltd., 2014;Plews et al., 2012). ...
Thesis
Full-text available
Alleviating the burden of breast cancer has become in one of the biggest challenges of our times. The advances in surgery, radiotherapy, and systemic therapy have improved the survival rates of patients with breast cancer, but have also produced a higher number of patients suffering short- and long-term side effects, with high the risk of recurrence, developing comorbidities, and death. Therapeutic exercise poses a means to address this issues; however, exercise interventions in patients with cancer are often adhered to the same therapeutic exercise guidelines. This results in one-size-fits-all exercise prescriptions for all adults, regardless their individual exercise capabilities and needs, which may lead to inadequate training adaptation. The mobile health (mHealth) paradigm has enabled the remote and individual monitoring of health through wearable sensors and smartphones. Personalizing training adaptation with an mHealth approach has already been successfully conducted in sports settings, and the literature suggests that similar strategies may translated to patients with chronic conditions such as breast cancer. However, recent works do not target the adjustment of training doses to the individual needs of the patients. This thesis presents three contributions to support the personalization of therapeutic exercise intervention in patients with breast cancer. First, ATOPE+, an mHealth system to support the remote monitoring of patients’ training load through heart rate variability (HRV), self-reported wellness, and Fitbit physical activity and sleep data. ATOPE+ also integrates a decision-support system with expert rules that automatically trigger daily exercise recommendations for patients. Second, the ATOPE+Breast dataset, an open dataset describing the continuous evolution of training load during therapeutic exercise intervention for 23 patients with breast cancer. Third, a clustering approach to assess training needs in patients with breast cancer. Data science and artificial intelligence (AI) are leveraged in this approach to better understand the different states of the patient throughout an exercise intervention, and eventually serve as a tool to make more informed decisions when prescribing an exercise dose. The potential of these contributions may lead to new research directions in the personalization of therapeutic exercise interventions in real-life scenarios, specially regarding the application of mHealth and AI to improve chronic conditions.
Article
Full-text available
Cesareans antibiotic prophylaxis aims to prevent the clinical manifestations of SSI expected to occur. Theoretically, Cefazoline is considered more advantageous as a cesarean prophylactic antibiotic than Ceftriaxone. This study aims to compare the effectiveness of Cefazoline and Ceftriaxone as cesarean prophylactic antibiotics at RSIA Trisna Medika Tulungagung from October–December 2021. The study used a cross-sectional research design using 214 medical records data of patients with preoperative prophylactic antibiotics Cefazoline and Ceftriaxone (1:1). Data analysis includes antibiotic use profiles and therapeutic effectiveness data. Statistical analysis used the Mann-Whitney U Test. Research results showed that Cefazoline and Ceftriaxone have the same effectiveness as cesarean prophylactic antibiotics at RSIA Trisna Medika Tulungagung October – December 2021 based on pulse frequency, breath frequency, 10d post-op wound pain, purulent, dry wound, SSI, and LOS.
Thesis
Hintergrund und Ziele Bei der spontanen Subarachnoidalblutung (SAB) handelt es sich mit einer mittleren Inzidenz von 6 bis 9 pro 100.000 Einwohnern in Europa und den USA um eine seltene Form des Schlaganfalls. Trotz des im Vergleich zu den anderen Schlaganfallsubtypen durchschnittlich jungen Patientenalters um das 50. Lebensjahr sind Morbidität und Mortalität jedoch relativ hoch. Ein wesentlicher Grund hierfür liegt in der Vielzahl möglicher Sekundärkomplikationen, deren Ursache unter anderem in einer Dysregulation des autonomen Nervensystems vermutet wird. In der vorliegenden Studie sollte untersucht werden, inwieweit eine autonome kardiovaskuläre Modulationsstörung in der Akutphase der spontanen SAB nachzuweisen ist und ob diese vom klinischen Schweregrad der betroffenen Patienten abhängt. Des Weiteren sollte ein möglicher Zusammenhang zwischen einer autonomen Funktionsstörung innerhalb von 24 Stunden nach Symptombeginn und dem funktionellen Zustand bei Entlassung aus dem Akutkrankenhaus analysiert werden. Methoden und Patienten Bei insgesamt 51 Patienten (52,9% Frauen, Alter 54 (50 – 63) Jahre) erfolgte in dieser prospektiven Beobachtungsstudie innerhalb von 24 Stunden nach Symptombeginn einer spontanen SAB die Messung der Herzratenvariabilität. Basierend auf den RR-Intervallen (RRI) eines jeweils fünfminütigen EKG-Rohsignals und dem systolischen Blutdruck (SBP) wurden mittels linearer Analyseverfahren die Parameter der Zeit- und Frequenzdomäne der autonomen kardiovaskulären Modulation berechnet. Dabei wurden die Parameter RRI- und SBP-low frequency power bzw. RRI- und SBP-LF-Leistung als Surrogatmarker für die sympathische Modulation, die RRI-high frequency power bzw. RRI-HF-Leistung und RRI-root mean square of successive differences bzw. RRI-RMSSD für die parasympathische Modulation, RRI-Variationskoeffizient bzw. RRI-CV, RRI-Standardabweichung bzw. RRI-SD und RRI-totale autonome Leistung für die autonome Gesamtmodulation und RRI-LF/HFVerhältnis als Marker für das sympatho-vagale Gleichgewicht errechnet. Gleichzeitig erfolgte ein Gruppenvergleich an Hand des klinischen Schweregrade mittels der Hunt-und-Hess-Skala (H&H; von 0, d.h. keine Symptome, bis 5, d.h. tiefes Koma und Strecksynergismen, reichend) zwischen der klinisch leichter betroffenen Subgruppe H&H<3 (Symptome wie Kopfschmerzen und Hirnnervenausfälle, n=19, 42,1% Frauen, Alter 50,0 (43,0 – 57,0) Jahre) und der schwerer betroffenen Subgruppe H&H≥3 (klinisch mit beispielhaft Halbseitenlähmungen oder Vigilanzstörungen, n=32, 59,4% Frauen, Alter 58,5 (51,5 – 66,5) Jahre). Sämtliche Daten wurden mit einer gesunden Kontrollgruppe verglichen (n=20, 55,0% Frauen, Alter 50,0 (37,5 4 – 55,8) Jahre). Abschließend erfolgte eine weitere Subgruppenanalyse unter Verwendung des RRI-CV zwischen den beiden Gruppen „normale“ (n=34) und „pathologische“ autonome kardiovaskuläre Modulation (n=17). Dabei wurde in beiden Gruppen der funktionelle Zustand bei Entlassung mittels der modifizierten Rankin-Skala verglichen. Ergebnisse und Beobachtungen Während die Biosignale Herzfrequenz bzw. RRI und SBP keine signifikanten Unterschiede zwischen allen Gruppen aufwiesen, zeigte sich zwischen der Gesamtkohorte der Patienten im Vergleich zu den gesunden Probanden eine signifikante Reduktion der Parameter der sympathischen (RRI-LF-Leistung 141,0 (18,9 – 402,4) vs. 442,3 (246,8 – 921,2) ms²/Hz, p=0,001) und totalen autonomen kardiovaskulären Modulation (RRI-CV 2,4 (1,2 – 3,7) vs. 3,7 (3,1 – 5,3) %, p=0,001) sowie der sympatho-vagalen Balance (RRI-LF/HF-Verhältnis 1,1 (0,7 – 1,9) vs. 4,0 (1,7 – 10,0), p<0,001). Zusätzlich konnte eine signifikante inverse Korrelation in den Rangkorrelationskoeffizienten nach Spearman zwischen einer autonomen Dysfunktion und den beiden Subgruppen H&H<3 und H&H≥3 für die Parameter der sympathischen (RRI-LF-Leistung 338,6 (179,7 – 710,4) vs. 72,1 (10,1 – 175,9) ms²/Hz, p=0,001, rho = -0.524) und totalen autonomen Modulation (RRI-CV 3,5 (2,3 – 5,4) vs. 1,6 (1,0 – 2,8) %, p<0,001, rho = -0,519) nachgewiesen werden. In den binär logistischen Regressionen konnte im Vergleich zu möglichen Störfaktoren (Alter, prä-mRS, intraventrikuläre Blutung und maschinelle Beatmung) der signifikante Einfluss des H&H auf die sympathische (RRI-LF-Leistung, aOR 9,20, p=0,033) und totale autonome kardiovaskuläre Modulation (RRI-CV, aOR 12,16, p=0,040) unterstrichen werden. Zuletzt ließ sich ein signifikanter Unterschied zwischen einem „normalen“ und „pathologischen“ RRI-CV innerhalb der ersten 24 Stunden nach Symptombeginn und dem funktionellen Outcome bei Entlassung nachweisen (mRS bei Entlassung 1 (0 – 4) vs. 4 (2 – 5), p=0,046). Teile der Ergebnisse werden in einem wissenschaftlichen Fachjournal submittiert werden. Schlussfolgerungen und Diskussion Innerhalb der Akutphase der spontanen SAB lässt sich abhängig vom klinischen Schweregrad, gemessen anhand des H&H-Wertes, eine signifikante Reduktion der sympathischen und totalen autonomen Modulation der Herzkreislauffunktion beobachten. Eine bereits innerhalb der ersten 24 Stunden pathologisch veränderte autonome kardiovaskuläre Modulationsstörung ist darüber hinaus mit einem schlechten funktionellen Outcome bei Entlassung aus dem Akutkrankenhaus assoziiert. Ein höherer H&H-Wert hängt folglich mit einer zunehmenden autonomen Dysfunktion zusammen und stellt so vermutlich einen wichtigen Aspekt bezüglich des erhöhten Risikos für die multiplen Sekundärkomplikationen (zerebrale Vasospasmen, neurogenes Lungenödem, Kontraktionsbandnekrosen des Herzens etc.) und einem schlechten Outcome nach SAB dar.
Article
Full-text available
Measuring mental workload at the workplace using (psycho-) physiological measurement techniques seems desirable but is difficult to implement. Conventional analysis techniques are designed to cover longer measurement durations, neglecting the demands of modern work places: high worker flexibility and constantly fluctuating mental workload. As an alternative analysis approach, measurement (resp. analysis) duration can be shortened and event-based pattern analysis of various physiological parameters can be performed. The effects of such approaches are demonstrated by experimental examples. Furthermore, an event-timestamp independent framework is presented. Focusing on occasionally occurring peaks and longer lasting plateaus in mental workload trajectories, an automatized analysis of workload during work processes becomes possible. Practical relevance: With steadily increasing cognitive demands at work the risk of mental fatigue increases too. Mental workload is not directly observable at the workplace and the objective measurement and interpretation is complicated. Improving the overall assessment and analysis strategies for (physiological) mental workload indicators can benefit the quality of risk assessments of workplaces and processes as well as enable the possibility of demand-orientated control of (informational) assistance systems to prevent mental overload and resulting health constraints.
Article
Full-text available
This review has summarized the methods currently available for cardiac sympathetic assessment in clinical or under research, with emphasis on the principles behind these methodologies. Heart rate variability (HRV) and other methods based on heart rate pattern analysis can reflect the dominance of sympathetic nerve to sinoatrial node function and indirectly show the average activity level of cardiac sympathetic nerve in a period of time. Sympathetic neurotransmitters play a key role of signal transduction after sympathetic nerve discharges. Plasma or local sympathetic neurotransmitter detection can mediately display sympathetic nerve activity. Given cardiac sympathetic nerve innervation, i.e., the distribution of stellate ganglion and its nerve fibers, stellate ganglion activity can be recorded either directly or subcutaneously, or through the surface of the skin using a neurophysiological approach. Stellate ganglion nerve activity (SGNA), subcutaneous nerve activity (SCNA), and skin sympathetic nerve activity (SKNA) can reflect immediate stellate ganglion discharge activity, i.e., cardiac sympathetic nerve activity. These cardiac sympathetic activity assessment methods are all based on the anatomy and physiology of the heart, especially the sympathetic innervation and the sympathetic regulation of the heart. Technological advances, discipline overlapping, and more understanding of the sympathetic innervation and sympathetic regulation of the heart will promote the development of cardiac sympathetic activity assessment methods.
Preprint
Full-text available
The Neurovisceral Integration Model posits that shared neural networks support the effective regulation of emotions and heart rate, with heart rate variability (HRV) serving as an objective, peripheral index of prefrontal inhibitory control. Prior neuroimaging studies have predominantly examined both HRV and associated neural functional connectivity at rest, as opposed to contexts that require active emotion regulation. The present study sought to extend upon previous resting-state functional connectivity findings, examining HRV and corresponding amygdala functional connectivity during a cognitive reappraisal task. Seventy adults (52 old and 18 young adults, 18-84 years, 51% male) received instructions to cognitively reappraise negative and neutral affective images during functional MRI scanning. HRV measures were derived from a finger pulse signal throughout the scan. During the task, young adults exhibited a significant inverse association between HRV and amygdala-medial prefrontal cortex (mPFC) functional connectivity, in which higher HRV was correlated with weaker amygdala-mPFC coupling, whereas old adults displayed a slight positive, albeit non-significant correlation. Furthermore, voxelwise whole-brain functional connectivity analyses showed that higher HRV was linked to weaker right amygdala-posterior cingulate cortex connectivity across old and young adults, and in old adults, higher HRV positively correlated with stronger right amygdala-right ventrolateral prefrontal cortex connectivity. Collectively, these findings highlight the importance of assessing HRV and neural functional connectivity during active regulatory contexts to further identify neural concomitants of HRV and adaptive emotion regulation.
Article
Full-text available
Following the publication of the Task Force document on heart rate variability (HRV) in 1996, a number of articles have been published to describe new HRV methodologies and their application in different physiological and clinical studies. This document presents a critical review of the new methods. A particular attention has been paid to methodologies that have not been reported in the 1996 standardization document but have been more recently tested in sufficiently sized populations. The following methods were considered: Long-range correlation and fractal analysis; Short-term complexity; Entropy and regularity; and Nonlinear dynamical systems and chaotic behaviour. For each of these methods, technical aspects, clinical achievements, and suggestions for clinical application were reviewed. While the novel approaches have contributed in the technical understanding of the signal character of HRV, their success in developing new clinical tools, such as those for the identification of high-risk patients, has been rather limited. Available results obtained in selected populations of patients by specialized laboratories are nevertheless of interest but new prospective studies are needed. The investigation of new parameters, descriptive of the complex regulation mechanisms of heart rate, has to be encouraged because not all information in the HRV signal is captured by traditional methods. The new technologies thus could provide after proper validation, additional physiological, and clinical meaning. Multidisciplinary dialogue and specialized courses in the combination of clinical cardiology and complex signal processing methods seem warranted for further advances in studies of cardiac oscillations and in the understanding normal and abnormal cardiac control processes. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2015. For permissions please email: journals.permissions@oup.com.
Article
In the past, most scientific or routine studies on heart rate variability (HRV) were done using commercial Holter ECG devices with traditional magnetic tapes. All methods to calculate parameters of HRV are based on the measurement of time intervals between successive sinus cardiac beats, in practice RR intervals. Hence, results of HRV calculation depend on the quality of primary data acquisition. However, technical standards on accuracy of RR interval acquisition in magnetic tape and digital solid state Holter ECG systems do not exist as yet. Therefore in a laboratory investigation accuracy of HRV parameter calculation after traditional standard tape with and without real-time analysis and digital solid state data acquisition was compared in three commercial Holter ECG systems [Ela medical (E), Oxford Medilog Excel (O) and Medset Cardiolight (M)]. A computer-based ECG simulator was used to synthesize a test signal consisting of artificial P-QRS complexes of fixed rate, quartz stabilized 'sinus rhythms'. Two channel analog tape and digital 24 h recordings of the fixed rate test signal were performed simultaneously. The influence of recording technology on accuracy of RR interval measurement and consequently on time domaine HRV was shown by comparing five standard 24-h time domain parameters. As expected, in the three Holter systems the analog tape recordings showed a higher, technically induced, artificial RR variability. The SDNN values (E: 9.38 ms vs 2.68 ms; p<0.001; O: 3.89 ms vs 0.97 ms; p<0.001; M: 10.47 ms vs 2.89 ms; p=0.001), SDNN-Index values (E: 8.22 ms vs 0.90 ms; p<0.001; O: 3.75 ms vs 0.90 ms; p<0.001; M: 7.81 ms vs 1.49 ms, p<0.001) and RMSSD-values (E: 10.39 ms vs 2.16 ms; p<0.001; O: 10.05 ms vs 1.88 ms, p<0.001; M: 8.62 ms vs 1.69 ms; p<0.001) were significantly lower if acquisition was done by digital solid devices in all systems, SDANN (M: 9.83 ms vs 5.37 ms; p<0.001) in the Medset system too. The used method is a possible way to validate commercial Holter ECG systems for HRV analysis. Clinical significance of artifical HRV seems to be low.
Article
Methods from nonlinear dynamics (NLD) have shown new insights into heart rate (HR) variability changes under various physiological and pathological conditions, providing additional prognostic information and complementing traditional time-and frequency-domain analyses. In this review, some of the most prominent indices of nonlinear and fractal dynamics are summarized and their algorithmic implementations and applications in clinical trials are discussed. Several of those indices have been proven to be of diagnostic relevance or have contributed to risk stratification. In particular, techniques based on mono-and multifractal analyses and symbolic dynamics have been successfully applied to clinical studies. Further advances in HR variability analysis are expected through multidimensional and multivariate assessments. Today, the question is no longer about whether or not methods from NLD should be applied; however, it is relevant to ask which of the methods should be selected and under which basic and standardized conditions should they be applied.
Article
Mercury affects the nervous system and has been implicated in altering heart rhythm and function. We sought to better define its role in modulating heart rate variability, a well-known marker of cardiac autonomic function. This is a systematic review study. We searched PubMed, Embase, TOXLINE, and DART databases without language restriction. We report findings as a qualitative systematic review because heterogeneity in study design and assessment of exposures and outcomes across studies, as well as other methodological limitations of the literature, precluded a quantitative meta-analysis. We identified 12 studies of mercury exposure and heart rate variability in human populations (ten studies involving primarily environmental methylmercury exposure and two studies involving occupational exposure to inorganic mercury) conducted in Japan, the Faroe Islands, Canada, Korea, French Polynesia, Finland, and Egypt. The association of prenatal mercury exposure with lower high-frequency band scores (thought to reflect parasympathetic activity) in several studies, in particular the inverse association of cord blood mercury levels with the coefficient of variation of the R-R intervals and with low-frequency and high-frequency bands at 14 years of age in the Faroe Islands birth cohort study, suggests that early mercury exposure could have a long-lasting effect on cardiac parasympathetic activity. Studies with later environmental exposures to mercury in children or in adults were heterogeneous and did not show consistent associations. The evidence was too limited to draw firm causal inferences. Additional research is needed to elucidate the effects of mercury on cardiac autonomic function, particularly as early-life exposures might have lasting impacts on cardiac parasympathetic function.
Article
Objective: Vagal nerve activity-indexed by heart rate variability (HRV)-has been linked to altered pain processing and inflammation, both of which may underpin headache disorders and lead to cardiovascular disease (CVD). Here we examined the evidence for differences in parasympathetic (vagal) activity indexed by time- and frequency-domain measures of HRV in patients with headache disorders compared to healthy controls (HCs). Methods: A systematic review and meta-analysis was conducted on studies investigating group differences in vagally mediated HRV (vmHRV) including time- (root-mean-square of successive R-R-interval differences (RMSSD)) and frequency- (high-frequency HRV) domain measures. Studies eligible for inclusion were identified by a systematic search of the literature, based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Results: Seven studies reporting a total of 10 comparisons of patients with headache disorders (HF-HRV n = 67, RMSSD n = 122) and HCs (HF-HRV n = 64, RMSSD n = 125) were eligible for inclusion. Random-effects meta-analysis revealed a significant main effect on RMSSD (Z = 2.03, p = 0.04; Hedges' g = -0.63; 95% CI (-1.24, -0.02); k = 6) and similar pooled effect size estimates for HF-HRV when breathing was controlled (g = -0.30; 95% CI (-0.69; 0.10)) but not when breathing was not controlled (g = 0.02; 95% CI (-0.69; 0.74)). Controlling for breathing had no effect on RMSSD. Conclusion: vmHRV is reduced in patients with headache disorders, findings associated with a medium effect size. Suggestions for future research in this area are provided, emphasizing a need to investigate the impact of headache disorders and commonly comorbid conditions-including mental disorders-as well as the investigation of the risk for CVD in migraine in particular. We further emphasize the need for large-scale studies to investigate HRV as a mechanism mediating the association of migraine and CVD.