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Heart-rate variability: A biomarker to study the influence of nutrition on physiological and psychological health?


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As the influence of diet on health may take place over a period of decades, there is a need for biomarkers that help to identify those aspects of nutrition that have either a positive or a negative influence. The evidence is considered that heart-rate variability (HRV) (the time differences between one beat and the next) can be used to indicate the potential health benefits of food items. Reduced HRV is associated with the development of numerous conditions for example, diabetes, cardiovascular disease, inflammation, obesity and psychiatric disorders. Although more systematic research is required, various aspects of diet have been shown to benefit HRV acutely and in the longer term. Examples include a Mediterranean diet, omega-3 fatty acids, B-vitamins, probiotics, polyphenols and weight loss. Aspects of diet that are viewed as undesirable, for example high intakes of saturated or trans-fat and high glycaemic carbohydrates, have been found to reduce HRV. It is argued that the consistent relationship between HRV, health and morbidity supports the view that HRV has the potential to become a widely used biomarker when considering the influence of diet on mental and physical health.
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Heart-rate variability: a biomarker to study the influence of
nutrition on physiological and psychological health?
Hayley A. Young and David Benton
As the influence of diet on health may take place over a
period of decades, there is a need for biomarkers that help to
identify those aspects of nutrition that have either a positive
or a negative influence. The evidence is considered that heart-
rate variability (HRV) (the time differences between one beat
and the next) can be used to indicate the potential health
benefits of food items. Reduced HRV is associated with the
development of numerous conditions for example, diabetes,
cardiovascular disease, inflammation, obesity and psychiatric
disorders. Although more systematic research is required,
various aspects of diet have been shown to benefit HRV
acutely and in the longer term. Examples include a
Mediterranean diet, omega-3 fatty acids, B-vitamins,
probiotics, polyphenols and weight loss. Aspects of diet that
are viewed as undesirable, for example high intakes of
saturated or trans-fat and high glycaemic carbohydrates,
have been found to reduce HRV. It is argued that the
consistent relationship between HRV, health and morbidity
supports the view that HRV has the potential to become a
widely used biomarker when considering the influence of diet
on mental and physical health. Behavioural Pharmacology
29:140151 Copyright © 2018 The Author(s). Published by
Wolters Kluwer Health, Inc.
Behavioural Pharmacology 2018, 29:140151
Keywords: diet, disease, health, heart-rate variability, nutrition
Department of Psychology, University of Wales Swansea, Swansea, Wales, UK
Correspondence to David Benton, DSc, Department of Psychology, University of
Wales Swansea, Swansea SA2 8PP, Wales, UK
Received 19 May 2017 Accepted as revised 14 January 2018
Biomarkers are important as proxy measures when
studying health or disease states that develop over long
periods. As a disease can develop over decades, this is an
area where there is a need for biomarkers that identify
aspects of life style that are potentially beneficial or
problematic. The case will be made for using heart-rate
variability (HRV) as an indicator of the physiological
response to food by those interested in the association
between diet and various health outcomes, and by
manufacturers developing functional foods with potential
health benefits. HRV is of interest as a wide range of
diseases are associated with decreased variability,
including diabetes, cardiovascular disease and psychiatric
disorders. In addition, there is an increasing literature
that reports that HRV responds to various aspects of the
diet, raising the possibility that HRV offers a convenient
measure of potential benefit.
Traditionally, heart rate (HR) has been considered a
product of emotional response or stress, but it is
becoming apparent that the interval between beats is a
marker of the capacity to regulate internal and external
demands. The intervals are not constant, but differ from
beat to beat: essentially a higher HRV indicates better
general health (Jarczok et al., 2015). In essence, the
multitude of ways in which different physiological
mechanisms modulate each other ensures that studying
one aspect of the body in isolation limits our
In 1948, the WHO defined health as a state of complete
physical, mental and social well-being and not merely the
absence of disease or infirmity. As complete well-being
is practically impossible, more recent definitions have
emphasized resilience, the capacity to cope and the
ability to maintain a sense of well-being. To assess the
level of existing health and to evaluate dietary inter-
ventions, such definitions need to be operationalized.
With this in mind, HRV may serve as a biomarker
relating to the above definition, and thus serve as an
indicator of the response to diet. In this review, the
control of HR and the measurement of HRV are first
outlined and then associations between HRV, health and
aspects of diet are considered.
Control of heart rate
Figure 1 shows an ECG trace from which the R-to-R
intervals are measured, although it is the variability in the
differences between these intervals that is of interest. In
the brain stem, the medulla oblongata controls HR
through the vagus, the tenth cranial nerve: vagal tone
reduces HR by inhibiting the sinoatrial node, the hearts
pacemaker. Although there is a tendency to view HR as
an involuntary mechanism, the medulla oblongata
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140 Review article
0955-8810 Copyright © 2018 The Author(s). Published by Wolters Kluwer Health, Inc. DOI: 10.1097/FBP.0000000000000383
receives information from the rest of the brain: including
the central autonomic network (CAN) (Benarroch, 1993),
with the prefrontal cortex playing a leading role (Thayer
and Lane, 2009). Indeed, the cortical regulation of the
CAN has been well described; there are both direct and
indirect pathways (involving the cingulate and insula
cortices, amygdala, hypothalamus and medulla oblon-
gata) linking the frontal cortex to autonomic motor cir-
cuits responsible for both the excitatory and inhibitory
effects on the heart (see Thayer and Lane, 2009 for a
review). Therefore, the moment-to-moment control of
HR reflects complex interactions between physiology,
emotion and cognition (Benarroch, 1993; Thayer and
Lane, 2009), encompassing the integration of a wide
variety of information. Thus, it has been suggested that
HRV reflects the overall capacity of the body to deal with
on-going demands (Young and Benton, 2015). HRV may
serve as a proxy for the vertical integrationof the brain
mechanisms that guide flexible control over behaviour
with peripheral physiology, and as such provides an
important window into understanding stress and health
(Thayer et al., 2012). In this way, HRV may act as a
biomarker when considering the influence of diet on
health-related mechanisms.
Indices of heart-rate variability
Three approaches are used to monitor HRV: time-
domain and frequency-domain measures are linear in
nature, whereas complexity measures offer a nonlinear
approach. The simplest approach is to summarize dif-
ferences in the interbeat interval as a mean and SD, so-
called time-domain measures (Fig. 2). Second, a spectral
analysis allows frequency domain indices to be calculated
and describes the interbeat intervals as a complex sum
of waveforms. The variance in HR is distinguished in
terms of the underlying rhythms that occur at different
frequencies. There is growing evidence that frequency-
domain indices measure the functioning of the autonomic
nervous system. The high-frequency (HF) measure
(power in the range 0.150.4 Hz) is more easily under-
stoodasitissaidtoreflectthe parasympathetic nervous
systems (PNSs) (Katona et al., 1970; Appel et al., 1989;
Billman and Hoskins 1989; Camm et al., 1996; Thayer
et al., 2010). In contrast, low-frequency (LF) (power in
the range: 0.040.15 Hz) is less readily interpreted as it
is influenced by both the PNS and also baroreceptor
activity (Houle and Billman, 1999; Billman, 2013). A
frequently considered index is the LF/HF ratio, which
is said to reflect the balance between PNS and the
sympathetic nervous system activity (Malik, 1998),
although this contention has been challenged recently
(Billman, 2013).
More recently, nonlinear indices have been developed:
as one example, approximate entropy provides an indi-
cation of the irregularity or randomness in a data set. The
time series 1, 2, 3, 1, 2, 3 has the same variability as the
time series 1, 1, 2, 2, 3, 3 and 3, 1, 2, 2, 3, 1, but different
underlying patterns. Nonlinear analysis enables the
quantification of this extra information. Essentially, the
nonlinear approach measures the complexity of the sig-
nal; how frequently do similar patterns reoccur? Although
frequency measures in particular have been studied
widely, it has become apparent that nonlinear indices of
complexity reflect different underlying mechanisms
(Young and Benton, 2015). For example, a recent study
reported that nonlinear measures of HRV were better
able to predict the performance of cognitive tests than
linear measures (Young and Benton, 2015). In addition, a
meta-analysis of the impact of depression and anti-
depressant treatment on HRV found that the effects
were most apparent with nonlinear measures of HRV
(Kemp et al., 2010). There is an impression that those
capable of generating more complex signals have a
greater ability to respond to environmental demands in a
more subtle manner (de la Torre-Luque et al., 2016).
Heart-rate variability, inflammation and
Inflammation is part of the immune response, with too
much and too little inflammation indicating, or leading to,
a wide range of diseases. Hotamisligil (2006) noted that
metabolic regulation and the immune system are highly
integrated: he suggested that this interaction reflects a
central homeostatic mechanism that, if dysfunctional,
results in chronic metabolic disorders, particularly obe-
sity, type 2 diabetes and cardiovascular disease. An
inadequate response creates immunodeficiency that can
result in infection or cancer (Mellemkjaer et al., 2002). As
the inflammatory response is in part regulated by the
autonomic nervous system, HRV is of interest.
The vagus plays a role in the inflammatory reflex that
controls innate immune responses when tissue is injured
Fig. 1
A heart-rate trace. A typical electrocardiogram trace is illustrated. R is
the peak of the QRS complex (electrocardiogram trace) and heart-rate
variability is measured by considering consecutive RR intervals. The
RR interval is not constant, but varies within a normal range of
0.61.2 s. It is the degree of this RR variability that is of interest as
greater variability is associated with better health.
HRV: a biomarker for health Young and Benton 141
or there is a pathogen invasion (Pavlov and Tracey, 2012).
The inflammatory reflex has an afferent component that
is activated by cytokines that relay information to the
hypothalamus. This input can then initiate an anti-
inflammatory response that prevents the release of
inflammatory products into the blood stream (Tracey,
2002). It is becoming increasingly clear that the nervous
system, through the vagal nerve, both monitors
Fig. 2
Linear domain - Time indices
Index Interpretation
Mean RR interval (ms) The average of NN intervals. A longer NN interval indicates a
lower heart rate.
SDNN (ms) Standard deviation of the NN intervals. Indicates total variability
with a high value is thought to associate with better health.
RMSSD (ms) The square root of the mean of the sum of the squares of
differences between adjacent NN intervals (RMSSD) is thought
to indicate parasympathetic nervous system activity.
Linear domain - Frequency indices
Index Interpretation
Total power (ms2) Variance of all inter-beat intervals ( 0.4 Hz). Indicates total
variability with a high value is thought to associate with better
ULF (ms2) Power in the ultra-low frequency range ( 0.003 Hz). Might
reflect circadian, neuroendocrine, activity, other unknown
VLF (ms2) Power in the very low frequency range (0.003–0.04 Hz). Might
reflect vasomotor changes, thermoregulatory and possibly
parasympathetic influences on heart rate.
LF (ms2) Power in the low frequency range (0.04–0.15 Hz). Thought to
reflect a combination of parasympathetic and baroreceptor
HF (ms2) Power in the HF range (0.15–0.4 Hz). Reflects parasympathetic
LF/HF Ratio LF (ms2)/HF (ms2). Frequently used index of autonomic
balance although this has been debated.
Non-linear analysis
Index Interpretation
ApEn Approximate Entropy measures irregularity or randomness in a
set of data, such that smaller values reflect a greater regularity.
Sample entropy (SampEn) is a similar measure that is more
suitable for short recordings.
DFA Detrended Fluctuation Analysis quantifies the fractal like
correlation properties of a time series and reveals short-range
(DFA1) and long-range (DFA2) correlations within a series.
Various measures of heart-rate variability. The variability in consecutive differences in the RR interval can be expressed using a number of different
approaches that are one of three types: time or frequency based or nonlinear. HF, high-frequency; LF, low-frequency.
142 Behavioural Pharmacology 2018, Vol 29 No 2&3
peripheral inflammation and dampens the immune
response (Boeckxstaens, 2013). The sympathetic arm of
the autonomic nervous system also plays a role: it releases
noradrenaline, which appears to act at sites other than the
synapse, as many immune cells, including lymphocytes
and macrophages, have adrenergic receptors (Kavelaars
et al., 1999).
HRV is associated with the levels in the blood of
C-reactive protein (CRP), a protein that is produced by
the liver as a response to inflammation, with the levels
increasing following the secretion of interleukin (IL)-6
from macrophages and T cells. The role of the protein is
to bind to lysophosphatidyl choline on the surface of
dead cells and to enhance the ability of antibodies and
phagocytic cells to remove pathogens. Higher levels of
CRP are associated with a greater risk of hypertension,
diabetes and cardiovascular disease (Dehghan et al.,
2007). Jarczok et al. (2014) reported that the HF com-
ponent of HRV predicted the level of CRP when it was
measured 4 years later; this is important as both inflam-
mation and HRV have been implicated in conditions
such as diabetes and cardiovascular disease (Kudat et al.,
2006; Thayer et al., 2010).
Madsen et al. (2007) related low-grade inflammation to
autonomic dysfunction in those with suspected coronary
heart disease. The mean SD of normal to normal RR
(SDNN) was higher in those with lower levels of CRP,
with the association being greater in those with a pre-
vious history of myocardial infarction. However, CRP
remained associated with HRV independent of disease, a
finding interpreted as a relationship occurring between
low-grade inflammation and autonomic dysfunction.
Experimental evidence is beginning to emerge and
support these associations. For example, a recent study
found that vagus nerve-stimulation in epilepsy patients
inhibited peripheral blood production of tumour necrosis
factor, IL-1βand IL-6 (Koopman et al., 2016).
Whether by influencing inflammation or by other
mechanisms, HRV has been associated with a range of
diseases, in some cases predicting subsequent problems
and in others being an index of disease progression
(RenuMadhavi and Ananth, 2012). As a generalization, a
healthy biological system tends to be both variable and
complex, characteristics that decline with disease.
Irrespective of disease, HRV declines with age, reaching,
in some older than 65 years of age, levels that are a risk
factor for mortality (Umetani et al., 1998).
The American Heart Association states that diabetes
increases the risk of heart disease or a stroke and thus
early diagnosis of complications is the key to decreasing
mortality. A systematic review of those with diabetes
concluded that HRV can help to predict cardiac mor-
bidity and mortality, and that it can be used at an early
stage to indicate the future risk of complications
(Fakhrzadeh et al., 2012). As one example, Yoshioka and
Terasaki (1994) reported that the HF component, which
indicates parasympathetic nervous activity, was lower in
diabetics rather than controls, and there were inverse
correlations between the LF and HF. It was concluded
that these measures of HRV are useful when evaluating
diabetic autonomic and peripheral neuropathies. The
Atherosclerosis Risk in Communities study (Schroeder
et al., 2005) also found that diabetics differenced in HF
power and that HF in nondiabetics was greater in those
with lower levels of fasting insulin. Thus, there was a
relationship between insulin resistance, as indicated by
higher levels of fasting insulin, and lower HRV. In
addition, after a 9-year followup, there was a general
decline in HRV. Such findings suggest that an impair-
ment of the functioning of the autonomic nervous sys-
tem, reflected in HRV, occurs during the early stages of
diabetes and becomes progressively worse over time.
A more general question is the association between HRV
and blood glucose levels. The Framingham Heart Study
(Singh et al., 2000) related HRV to fasting levels of blood
glucose: LF and HF power was reduced in those with
diabetes or impaired levels of fasting glucose. In addition,
a recent study found that resting HRV (average RR
interval, HF power, sample entropy) predicted the
increase in blood glucose following a high glycaemic load
drink (Young and Watkins, 2016). Overall, HRV appears
to be associated inversely with plasma glucose levels. It is
plausible that the links between insulin resistance, blood
glucose and HRV are attributable to deficits in the
inflammatory reflex (Pavlov and Tracey, 2012). Vinik
(2012) noted that activation of inflammatory cytokines in
newly diagnosed type 2 diabetes correlated with changes
in sympathovagal balance. As, in type 2 diabetes,
changes in the autonomic nervous system predict sudden
death, such measures offer the chance of an early
As mentioned above, a reduction in HRV predicts mac-
rovascular disease, for example carotid artery athero-
sclerosis (Gottsäter et al., 2006). Indeed, a recent study
found that insulin resistance mediated the association
between HF-HRV and carotid intimamedia thickness
(Kemp et al., 2016). In fact, it has been known for many
years that following myocardial infarction, HRV is related
to the risk of consequent morbidity and mortality (La
Rovere et al., 1998). In addition, the rate at which con-
gestive heart failure and arrhythmias occur has been
related to a reduced HRV (Sandercock and Brodie, 2006)
and lower HRV complexity is associated with a worse
outcome in cardiac patients (Souza et al., 2015). Although
fewer studies have considered those without a history of
coronary heart disease, a meta-analysis examined those
without any problem at baseline (Hillenbrand et al.,
2013). A lower HRV, measured as SDNN, was associated
with a subsequent 40% increase in the risk of suffering a
first cardiovascular event.
HRV: a biomarker for health Young and Benton 143
Heart-rate variability, obesity and weight loss
Decreased HRV is associated with a significantly
increased risk of death from cardiovascular disease
(Bigger et al., 1993; Makikallio et al., 1999). Therefore,
Kim et al. (2005) considered whether the high rate of
cardiovascular disease in obese individuals might be
associated with changes in HRV. They found that the
RMSSD (a frequently used index of parasympathetic
activity) was correlated negatively with fat mass and the
hip-to-waist ratio. Similar effects were noted for LF
power, although as noted above, this index is not easily
interpretable. Nonetheless, it seems that obesity can
alter HRV.
For example, Mouridsen et al. (2013) examined the
impact of weight loss on HR and HRV in overweight
postmenopausal women. An average weight loss of 3.9 kg
was associated with a decrease in HR and increased
HRV, as indicated by SDNN and the interbeat interval.
Previously, Karason et al. (1999) had considered obese
patients, who, after surgery, had lost 28% of their body
weight, on average 32 kg. SDNN and HF power were
attenuated in those with obesity compared with lean
patients; HRV improved with weight loss. Adachi et al.
(2011) examined the influence of obesity on autonomic
activity during sleep. Volunteers were allocated randomly
to a standard diet or one that over 8 weeks increased
weight by 4 kg. After weight gain, HF power decreased
both when awake and when asleep, changes that resolved
with weight loss. A related finding was that caloric
restriction may reverse the autonomic changes that occur
as we age. A decline in HRV with age is well described:
for example, Zulfiqar et al. (2010) compared HRV in
patients of ages ranging from 10 to 99 years. Several
measures of HRV all decreased rapidly from the second
to fifth decades, although the decline then slowed. They
concluded that a healthy long life depends on the pre-
servation of autonomic functioning, more specifically, the
influence of the PNS on HRV. Stein et al. (2012) found
that in patients who had practiced caloric restriction, for
on average 7 years, HR was lower and several measures of
HRV values were significantly higher. In fact, HRV was
comparable to the norms for those 20 years younger. The
overall impression is that weight gain adversely influ-
ences HRV, although this effect may be reversible with
weight loss and/or dietary restriction.
Heart-rate variability and eating behaviour
HR variables have also been related to dieting and eating
behaviour. A recent review concluded that most,
although not all, studies investigating HRV in those with
anorexia nervosa find parasympathetic dominance
(Mazurak et al., 2011). Similarly, those with bulimia
nervosa are characterized by higher vagal activity, parti-
cularly HF-HRV (Peschel et al., 2016). In a healthy
sample, Meule et al. (2012b) found that restrained eating,
which is the intentional restriction of food intake to
prevent weight gain or to promote weight loss, was
associated with low cardiac vagal control (Table 1). The
restraint score and the extent to which weight fluctuated
predicted HF-HRV negatively. In a further study, Meule
et al. (2012a) qualified this finding by considering suc-
cessful versus unsuccessful dieters: success was asso-
ciated positively with HF-HRV. They concluded that
vagalcardiac control reflected the strength of self-
regulation such that successful restrained eaters were
characterized by higher cardiac vagal control. These
findings are in line with two studies that found reduced
HR variability and complexity (RR interval, HF power,
sample entropy) in those with a propensity towards dis-
inhibited eating, which is a tendency to overeat in the
presence of palatable foods or other disinhibiting stimuli,
such as emotional stress (Young and Watkins, 2016;
Young et al., 2017a). Importantly, these effects remained
after controlling for the healthiness of the diet. Thus, it
appears that tonic HRV might index individual differ-
ences in the capacity to self-regulate in the face of
temptation. Indeed, Geisler et al. (2016) reported that
restrained eaters who suppressed their feelings about a
distressing film had higher vagally mediated HRV
(RMSSD) during subsequent exposure to a palatable
food (jelly beans). In an earlier study, Segerstrom and
Nes (2007) found an increase in RMSSD when partici-
pants were told to resist warm cookies and instead eat
carrots. A caveat is that caloric restriction and intermittent
fasting, behaviours commonly observed in restrained
eaters, have been shown to increase HF oscillatory
components in HR (Mager, 2006). Thus, the higher HF-
HRV observed in this population may be because of
lifestyle factors rather than differences in self-regulatory
capacity. Future research should explore this possibility.
That said, these associations between eating behaviour
and HRV offer the possibility of examining the associa-
tion between the ability of different types of food to
influence HRV and their ability to reduce or prevent
pathological eating behaviours.
Heart-rate variability and psychiatric disorders
A dysfunctional autonomic nervous system, with an
associated reduction in HRV, has been found in a wide
range of psychiatric disorders, a contention supported by
a number of recent meta-analyses. Kemp et al. (2010)
examined the association between depression and HRV
in those without cardiovascular disease. When depressed
patients and healthy controls were compared, the former
had a lower HRV; it was particularly lower in those with
more severe symptoms. Similar meta-analytic reviews
have shown HRV reductions in a range of psychiatric
disorders: bipolar disorder (Faurholt-Jepsen et al., 2017),
anxiety disorders (Chalmers et al., 2014), post-traumatic
stress disorder (Nagpal et al., 2013) and schizophrenia
(Clamor et al., 2016). Notably, such effects are most
consistently examined using HF-HRV or other vagally
mediated HRV indices (e.g. RMSSD), although on
144 Behavioural Pharmacology 2018, Vol 29 No 2&3
occasion, larger effect sizes have been noted with non-
linear indices (Kemp et al., 2010). Importantly, HRV
predicted the onset of psychological illness 10 years later
(Jandackova et al., 2016). Given the links between HRV,
emotion regulation and executive functioning (Williams
et al., 2015; Zahn et al., 2016; Holzman and Bridgett,
2017), it has been proposed that HRV is a transdiagnostic
biomarker of mental illness (Beauchaine and Thayer,
This brief overview illustrates that HRV is a risk for, or a
marker of, a wide range of disorders. As such, any inter-
vention that impacted positively on HRV has the
potential to benefit health. Evidence suggests that life-
style may be one such factor. Young et al. (2017a)
reported that smoking cigarettes and drinking alcohol
negatively influenced HRV, measured using linear (HF
power) and nonlinear (sample entropy) measures. In
addition, these aspects of the HR time series were
increased in those who took regular exercise and con-
sumed a healthy diet. Notably, diet quality at least par-
tially explained the association between mood,
disinhibition and HRV (Young et al., 2017a); thus, dietary
modification may improve HRV in those at risk of psy-
chological disorders.
The nature of the diet and heart-rate variability
In an early study, Lu et al. (1999) examined the post-
prandial changes in HRV following a 500 kcal test meal
(turkey sandwich: 32.4% fat, 17.5% protein and 50.1%
carbohydrate). HF power decreased during the first and
second 30 min following consumption, suggesting a
reduction in vagal tone. Similarly, after a mixed meal of
501.8 kcal that comprised 31% protein, 18% lipids and
51% carbohydrates, a decrease in HF power was
observed from 40 to 120 min after the meal, an effect that
correlated negatively with ghrelin concentrations (Chang
et al., 2010). In relation to specific macronutrients,
Kanaley et al. (2007) studied the influence of an acute
glucose load on HRV in obese women with and without
diabetes. Total power decreased in response to the glu-
cose challenge compared with what was observed in the
fasted state. In addition, a high-carbohydrate meal was
reported to augment HRV reactivity (in response to
mental stress) compared with a high-protein meal: HR
decreased more quickly poststress after the carbohydrate
meal (Uijtdehaage et al., 1994). Over a 2-day period,
Lima-Silva et al. (2010) examined the impact of a low-
carbohydrate diet in those who had been exercising.
Compared with a high-carbohydrate diet, the low-
carbohydrate diet increased LF and decreased HF
power. There were, however, no differences in HR or
RR interval. As this study considered those who had
been exercising, there is a need to look at those simply
going about their everyday life.
Nagai et al. (2005) reported that a high-fat meal increased
the VLF component of HRV. Although the physiological
processes underlying VLF-HRV are unclear, Nagai et al.
(2005) interpreted this as an indication of thermo-
regulatory sympathetic nervous system activity. Other
components of HRV may also be influenced by the
nature of dietary fat, at least in the longer term:
Soares-Miranda et al. (2012) reported that in two cohorts,
either 19 or 72 years of age, a greater consumption of
trans-fats was associated with a less favourable HRV;
SDNN, RMSSD and total power were lower.
Importantly, trans-fat consumption predicted lower HRV
5 years later.
When considering the influence of diet, by far the
majority of work has involved supplementation with
Table 1 The relation between heart-rate variability and eating behaviour
References Sample Design Outcome
Segerstrom and
Nes (2007)
168 students aged
19 years
Asked to resist eating palatable food and consume
less palatable. Then, they performed an anagram
task that needed persistence
Increase in RMSSD when eating cookies was resisted,
and carrots consumed. HRV seen as an index of self-
regulatory strength
Meule et al.
47 females aged
24 years
BMI 22
Compared those who were and were not retrained
A high HF associated with less weight fluctuation. Weight
fluctuation predicted LH/HF
Meule et al.
50 females aged
1840 years
BMI between
17.5 and 25
Compared those who were or were not currently
Successful dieting positively associated with HF and
negatively with LH/H F
Geisler et al.
111 students aged
23 years
Participants took on a task that did or did not require
self-control. Then,, they were exposed to palatable
food. Effortable self-control measures as H RV
Restrained eaters, who suppressed feelings, had higher
RMSSD when exposed to a palatable food, but not
when self-control had not been exercised. Suggested
dieting required self-control as indicated by HRV
Young and Watkins
66 females between
18 and 29 years
BMI 1731
Studied the relation between HRV and glucose
Disinhibited eaters had reduced HRV (HF and sample
entropy). Those with low HRV had poorer glucose
tolerance the levels remained higher for longer
Young et al.
156 healthy adults
from 18 to 34 years,
122 healthy adults
From 18 to 30 years
Cross sectional study asked whether diet mediated
the association between H RV and mood
Diet shown to mediate the relation between HRV (H F and
sample entropy) and mood
HF, high-frequency; HRV, heart-rate variability; LF, low-frequency.
HRV: a biomarker for health Young and Benton 145
omega-3 fatty acids. For example, in a secure forensic
inpatient facility, Hansen et al. (2014) randomly allocated
patients to a diet containing salmon three times a week
compared with meat in the control group. The changes in
HRV associated with fish consumption correlated nega-
tively with sleep latency and positively with a measure of
daily functioning. Table 2 lists a representative sample of
studies that show that research has been driven by an
interest in disease states, particularly heart disease, and
has typically involved the examination of older adults.
There is a general impression that omega-3 supple-
mentation resulted in greater parasympathetic activity,
although greater confidence in this conclusion comes
from a meta-analysis. Xin et al. (2013) integrated fifteen
studies. The time-domain measures, SDNN and
RMSSD, did not differ after supplementation. However,
although there was no effect on the frequency measure
LF, fish oil significantly increased HF. The authors
concluded that the enhancement of vagal tone may be
an important mechanism underlying the antiarrhythmic
and other clinical effects of fish oil. Similarly, the review
of Christensen and Schmidt (2007) concluded that: In
most (Billman et al., 1994; Christensen et al., 1999;
Christensen and Schmidt 2007; Billman and Harris, 2011;
Christensen, 2011), although not all, studies, dietary n-3
PUFA levels and n-3 PUFA supplementation are related
to improved HR variability. The findings suggested that
an increased parasympathetic regulation of cardiac func-
tioning occurred.
More generally, the Mediterranean diet, which has fish as
a component, is also associated with HRV. Dai et al.
(2010), using a food-frequency questionnaire, established
the extent to which middle-aged male twins ate a
Mediterranean diet. After adjusting for genetic con-
tributions, those who conformed to the Mediterranean
diet had higher HRV across a range of indices: SDNN,
RMSSD, ULF, VLF, HF and LF. Although it was
unclear which aspect of the diet was beneficial, it is
tempting to suggest that one factor is eating fish and in
this way increasing the intake of omega-3 fatty acids.
Billman (2013) reviewed the evidence that the intake of
omega-3 fatty acids influenced cardiac rhythms. He
concluded that supplementation with n3-PUFAs affects
ion channels and calcium-regulatory proteins, although
these depended on the route of administration.
Immediately, there is a direct effect of the fatty acids on
ion channels, although over the longer term, after the
incorporation of the fatty acids into the cell membrane,
cardiac electrical activity changes. HR is reduced and
HRV increases, reflecting alterations in the intrinsic
pacemaker rather than regulation by the activity of the
autonomic nervous system.
Other contributions to a Mediterranean diet have also
been found to influence HRV. In a randomized trial, the
influence of a multivitamin-mineral preparation on HRV
was assessed (Pomportes et al., 2015). Faster reactions in a
test of the ability to inhibit responses were associated
with a stable HF, indicative of PNS activity. In those
taking micronutrients, HF remained stable, whereas in
the placebo condition, HF decreased over time. Jaatinen
et al. (2014) studied the influence of yoghurt enriched
with α-lactalbumin, bioactive peptides and B vitamins in
individuals with high-trait anxiety. In the active group,
vagally mediated HRV (RMSSD) was higher. They
concluded that this yoghurt combination may aid coping
with stress. As nut consumption is associated with a lower
risk of cardiovascular disease, Sauder et al. (2014) studied
its influence on HRV. The pistachio diet was associated
with a higher HF and greater total variation in beat-to-
beat intervals. Similar effects have been observed in
relation to the consumption of polyphenol-rich red wine.
Intake of wine, but not of spirits or beer, is positively and
independently (after controlling for other health beha-
viours) associated with HRV (SDNN, total power, VLF
power, LF power and HF power) in women with CHD
(Janszky et al., 2005).
There is also some evidence that particular nutrients
influence HRV. Sucharita et al. (2014) considered HRV in a
healthy elderly Indian sample with either a high or a low
vitamin B
status. The LF was lower in those with a
poorer vitamin B
status, although no effects on HF power
or HR were observed. Supplementation for 3 months
increased LF to levels comparable to those with an initially
good vitamin B
status. Sodium intake is also linked to
HRV. In a randomized-controlled trial, Allen et al. (2014)
asked participants, with normal blood pressure, to consume
for 5 days a diet with low (10 mmol/day), normal
(150 mmol) or high (400 mmol) levels of sodium. The
response to low sodium was consistent with sympathetic
activation and reduced vagal activity; LFnu (normalized)
increased and SDNN, RMSSD and HFnu (normalized)
decreased compared with both normal and high sodium
In summary, although there has been limited systematic
study, there is a series of reports that relate various
indices of HRV to the intake of a range of food items. As
such, there is good reason to support the further con-
sideration of HRV as a biomarker with the potential to
indicate the potential influence of food on health.
There is growing evidence that a range of diseases are
accompanied by a decrease in HRV, including, amongst
others, diabetes (Yoshioka and Terasaki, 1994), cardio-
vascular disease (Gottsäter et al., 2006) and psychiatric
disorders (Kemp et al., 2014). Although indices of HRV
do not distinguish between types of disorder, there is a
consistent pattern of a reduced variability in HR being
associated with disease: a higher HRV is associated with
psychological flexibility and allostatic resilience. Indeed,
longitudinal studies support the view that reduced HRV
predicts psychological and physiological morbidity years
146 Behavioural Pharmacology 2018, Vol 29 No 2&3
Table 2 The influence of fish oil supplementation and eating fish on heart-rate variability
References Sample Design Intervention Outcome
Christensen et al. (1996) 49 patients with CAD aged 63 years Parallel 4.3 g EPA/DHA a day or placebo for
12 weeks
SDNN and mean RR increased from baseline in the active, but not the control
condition. No significant difference between active and control
Christensen et al. (1998) 17 patients with RF aged 52 years Parallel 4.7 g EPA/DHA a day or placebo for
12 weeks
High cell fatty acids associated with higher SDNN
Those with CRF had lower HRV
Christensen et al. (1999) 40 healthy individuals aged 38 years Parallel 1.68, 5.9 g EPA/DHA a day or
placebo for 12 weeks
Baseline positive correlation between SDN N, RMSSD and levels of DHA in cell.
Dose-dependent increase in HRV
Effects found in men, but not women
Dyerberg et al. (2004) 49 healthy individuals aged 38 years Parallel 3.2 g EPA/DHA a day or placebo for
8 weeks
SDNN, RMSSD, NN50, NN6% were not significantly
Holguin et al. (2005) 52 elderly individuals aged 77 years Parallel 1.55 EPA/DHA a day or placebo for
16 weeks
Supplementation increased LF, HF and RMSSD
OKeefe et al. (2006) 36 patients with CAD aged 68 years Cross-over 0.8 EPA/DHA a day or placebo for
16 weeks
Supplement increased HF and reduced HR at rest
SDNN and LF not effected
Hamaad et al. (2006) 38 patients with CAD aged 60 years Parallel 0.8 EPA/DHA a day or placebo for
16 weeks
No significant influence on SDNN, RMSSD, LF, HF and LF/HF
Radaelli et al. (2006) 25 patients with CAD aged 60 years Parallel 1.68 EPA/DHA a day or placebo for
16 weeks
Supplementation increased LF, HF, SDNN, RMSSD
Ninio et al. (2008) 46 overweight patients aged 50 years Parallel 0.8 EPA/DHA a day or placebo for
12 weeks
Supplement increased HF but did not affect LF or LF/HF. Resting HR decreased after
Nodari et al. (2009) 41 patients with Idiopathic dilated
cardiomyopathy aged 63 years
Parallel 1.44 g E PA/DHA a day or placebo for
24 weeks
The LF/HF ratio increased after supplement when under mental stress. VLF
decreased in the placebo group, but not in the active group
Carney et al. (2010) 72 depressed patients with CAD aged
57 years
Parallel 1.68 g E PA/DHA a day or placebo for
10 weeks
No effect on LF or HF
Sjoberg et al. (2010) 67 overweight patients aged 52 years Parallel 0.64, 1.28, and 1.92 g EPA/DHA a
day or placebo for 12 weeks
There was a dose-dependent reduction in the LF/ HF, although neither LH or HF was
significantly influenced
Valera et al. (2014) 180 French Polynesian adults Cross-
The DHA in red cells was related to
Increasing DHA concentrations were associated with lower HR and greater HF,
Fish intake
Mozaffarian et al. (2008) 4263 ECGs 1152 24 h holter Cross-
Eating tuna and other fish assessed
using FFQ
Eating fish associated with greater RMSSD and HF and lower LF. A lower Poincaré
ratio and higher short-term fractal scaling exponent suggested less erratic sinoatrial
node firing
Hansen et al. (2014) 95 male forensic inpatients Parallel Ate either fish or meat meals three
times a week for 6 months
Fish group showed a significant increase in HF.
The increased variability associated with fish consumption correlated negatively with
sleep latency and positively with a measure of daily functioning
Grung et al. (2015) 49 male forensic inpatients Parallel Ate either fish or meat meals three
times a week for 6 months
Principal component analysis of HRV parameters produced a parasympathetic factor
that increased when fish was eaten
CAD, coronary artery disease; CRF, chronic renal failure; DHA, docosahexaenoic acid; EPA, eicosapentaenoic acid; FFQ, food frequency questionnaire; HF, high-frequency; HR, heart rate; HRV, heart-rate variability; LF, low-
frequency; SDDN, SD of normal to normal RR.
HRV: a biomarker for health Young and Benton 147
later (Carnethon et al., 2003; Jandackova et al., 2016).
Thus, HRV may serve as a biomarker for future health.
Various aspects of diet have been found to be associated
with HRV. In general, the types of diet and particular
foods that have been found to be associated with a
healthy life-style are associated with higher HRV. For
example, a Mediterranean diet (Mozaffarian et al., 2008;
Soares-Miranda et al., 2012), fish consumption
(Mozaffarian et al., 2008), multivitamins (Pomportes et al.,
2015) and losing weight (Zulfiqar et al., 2010) all
increased HRV. However, aspects of diet that are com-
monly viewed as undesirable, for example a high fat or
trans-fat diet, reduced HRV (Soares-Miranda et al., 2012).
Although it is clear that diet influences HRV, the
mechanisms and pathways underlying such effects are
multifactorial. In relation to physical health, it is clear that
vagal tone is central to the regulation of a number of
allostatic systems, including the cardiovascular system,
glucose regulation, the hypothalamicpituitaryadrenal
axis function, and inflammatory processes (Thayer and
Sternberg, 2006; Young and Benton, 2015; Viljoen and
Claassen, 2017). Thus, reduced vagal tone may directly
contribute towards increased allostatic load (Koopman
et al., 2016). However, chronic diseases such as diabetes
contribute towards autonomic neuropathy (Vinik et al.,
2003), which is associated with reduced HRV
(Fakhrzadeh et al., 2012). Such bidirectional effects make
causality difficult to determine.
Similarly, it has been argued that vagal afferent signals
might mediate the influence that diet has on psycholo-
gical health (Kemp et al., 2017). Indeed, emerging evi-
dence suggests that cardiac interoceptive sensations
contribute towards ones mental health (Craig, 2003;
Critchley et al., 2004; Seth, 2013; Young et al., 2017b);
however, it is difficult to isolate diet-related effects on
afferent vagal signals. In addition, influential models such
as the neurovisceral integration model (Thayer and Lane,
2009) consider HRV to be the product of the CAN, which
guides goal-directed behaviour; the prefrontal cortex
plays a central inhibitory role (Thayer and Lane, 2000;
Smith et al., 2017). Thus, HRV may capture primarily
efferent vagal activity. With this in mind, it is plausible
that the effects of diet on HRV operate indirectly through
changes in mental health. That is, diet influences brain
functioning, cognition and mood, which is then reflected
in changes in HRV. Indeed, a study supports the view
that diet influences psychological health not only acutely
(Young and Benton, 2014, 2015) but also chronically
(Jacka et al., 2010). However, whether these effects
mediate or are meditated by HRV remains to be
Furthermore, the link between HRV and pathological
eating behaviours (Young and Watkins, 2016) points
towards the possibility of mutual causation. That is,
reduced HRV, by virtue of its connections with the
prefrontal cortex (Dietrich et al.,, 2006; Chang et al., 2013;
Jennings et al., 2016), may indicate a predisposition to
make poor dietary choices. In turn, a poor-quality diet
could exacerbate the reduction in HRV. In support of this
proposition, Young et al. (2017a) found that disinhibited
eating and poor diet quality were associated indepen-
dently with deficits in HRV. Notably, HRV is related
positively to interoceptive sensitivity (Ainley et al., 2012),
raising the possibility that diet-related reductions in vagal
tone may diminish the ability to detect interoceptive
signals, including information about ones current
homeostatic state (Attuquayefio et al., 2017, Smith et al.,
2017), and contribute towards mental illnesses (Paulus
and Stein, 2010). Irrespective of the mechanisms
involved, the association between HRV and so many
types of disease state suggests that it is a measure of
factors with a wide-spread significance. Thus, HRV may
serve as a useful biomarker for identifying potentially
beneficial or detrimental aspects of diet.
A distinction should be drawn between phasic and tonic
HRV as this will impinge upon interpretation of research
findings. In relation to emotion, demanding situations
may give rise to an increase or a decrease in phasic HRV.
The former might arise when an individual successfully
self-regulates to deal with the demands of the situation
(Park et al., 2014; Geisler et al., 2016) and the latter may
arise when the situation is perceived as threatening and
an individual shows an autonomic stress response
(Segerstrom and Nes, 2007).
The vast majority of research linking health and HRV has
focused on tonic resting-state HRV (Chalmers et al., 2014;
Kemp et al., 2014, 2016). Although tonic HRV predicts
phasic changes in vagal tone, lower tonic HRV was
associated with phasic HRV suppression and higher tonic
HRV was associated with phasic HRV enhancement
(Park et al., 2014). This is an important methodological
consideration when considering the effects of diet.
Phasic changes represent real-time adaptations to the
external or the internal environment; as such, a tempor-
ary decline in HRV need not necessarily be pathological.
An example can be considered in relation to the literature
on fluid consumption. Hypohydration necessitates a
counter-regulatory increase in HR to maintain blood
pressure: for every 1% decrease in body mass during
exercise, there is an increase in HR of 3.29 bpm (Adams
et al., 2014). Even in the absence of hypohydration, water
ingestion is followed by an increase in cardiac vagal
control (Helen et al., 2002). Under such circumstances, an
inability to flexibly alter the variability of HR may be
viewed as detrimental. Thus, whether tonic HRV influ-
ences ones ability to effectively counter regulate during
the postprandial period is an important question. Young
and Watkins (2016) provided the first evidence that this
might be the case: individual differences in tonic vagal
tone moderated the postprandial response to drinks that
148 Behavioural Pharmacology 2018, Vol 29 No 2&3
differed in glycaemic load. Whether a higher resting-state
HRV confers greater metabolic resilience during the
post-prandial period is an important question for future
Although we have argued that decreased HRV may serve
as a biomarker of future health, there are caveats that
need to be considered. For example, it has been
observed that black individuals have higher HRV, and
yet are at greater risk of future cardiovascular disease
(Hill et al., 2017). In addition, although most psychiatric
disorders are characterized by reduced HRV, those with
anorexia nervosa (Mazurak et al., 2011) or bulimia nervosa
(Peschel et al., 2016) tend to have parasympathetic
dominance (higher HF-HRV). Although both these
conundrums may be explained by dietary differences in
these populations for instance, there are clear racial
differences in nutritional behaviour the literature to
date has typically not explored this possibility. This will
be an important avenue for future research.
There are three types of approach by which HRV can be
measured: the linear time-based and frequency-based
approaches and also nonlinear methods. To date, limited
research has considered nonlinear methods when study-
ing diet, although there are reasons to believe that they
contribute additional information to that supplied by the
linear methods (Young and Benton, 2015). In addition,
there is some evidence that nonlinear HRV indices cap-
ture the influence of endogenous hormonal fluctuations
and thermoregulation (Bai et al., 2009; Young and
Benton, 2015), which may be pertinent when considering
the influence of diet. Thus, future studies should incor-
porate both linear and nonlinear approaches into their
In summary, the consistent relationship between HRV,
health and morbidity supports the view that HRV can be
considered an index of psychological and physiological
resilience. An increasing number of studies report that
particular foods, nutrients and dietary styles influence
HRV, supporting its future use when examining the
impact of what we eat. The insidious influence of diet
will be cumulative and take place over a period of many
decades (Benton, 2010). As such HRV may prove to be a
means for identifying beneficial or detrimental aspects of
Conflicts of interest
There are no conflicts of interest.
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HRV: a biomarker for health Young and Benton 151
... HRV is a biomarker of future physical and psychological health (Young & Benton, 2018), an indicator of stress (Kim et al., 2018), an index of resilience , and emotional regulation (Appelhans & Luecken, 2006), a correlate of cognitive load (Solhjoo et al., 2019), and an associate of memory consolidation (van Schalkwijk et al., 2020). The list could go on and on; the point is that HRV has ubiquitous relations to wide-ranging components of physical and mental health. ...
... Since HRV predicts the treatment effectiveness, it could be interesting to start early with interventions that are known to increase vagal activity. Possible interventions include auricular acupuncture and transcutaneous vagal nerve stimulation (Bretherton et al., 2019;La Marca et al., 2010), HRV biofeedback (Caldwell & Steffen, 2018;Wheat & Larkin, 2010), yoga (Chu et al., 2017;Tyagi & Cohen, 2016), supplementation with omega-3 fatty acids or Mediterranean diet (Xin et al., 2013;Young & Benton, 2018), and physical exercise (Sandercock et al., 2005). ...
Depression is one of the most prevalent mental disorders, with treatment outcomes generally being unsatisfactory. The identification of outcome predictors could contribute to improving diagnosis, treatment, and outcome. Heart rate variability (HRV), an index of cardiovagal activity, has been proposed as a potential correlate of depression as well as a predictor of treatment effectiveness. The aim of the present study was to examine if HRV at baseline could predict the outcome of inpatient treatment for stress-related depressive disorder (SRDD). Depressive symptoms of n = 57 inpatients with an SRDD, who were treated in a specialized burnout ward, were assessed using the Beck Depression Inventory (BDI) at the beginning, the end of treatment, and at 3-month follow-up. HRV (i.e., RMSSD, the root mean square of successive RR interval differences) was determined from a five-minute measurement in the supine position. RMSSD was not significantly associated with the BDI score at the beginning, end, and follow-up. Higher RMSSD was revealed to be a significant predictor of a stronger decrease in depressive severity from the beginning to the end of the treatment. Thereby, the regression model explained 7.6% of the total variance in the BDI decrease. The results revealed initial HRV to predict a larger decrease in depressive severity. Therefore, resting HRV represents a physiological resource and index of successful neurovisceral interaction, which supports inpatients in benefitting from specialized treatment.
... Heart rate estimation is particularly important for monitoring users in a range of frequent situations, such as driving a vehicle [1], engaging in physical activities [2], working in hazardous conditions [3], and during investigative police interviews [4]. Heart rate or its variability may be used to track and detect stress-related aspects [1], tiredness [5], emotions [6], health [7], and social behavior [8]. ...
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Estimating heart rate is important for monitoring users in various situations. Estimates based on facial videos are increasingly being researched because they allow the monitoring of cardiac information in a non-invasive way and because the devices are simpler, as they require only cameras that capture the user’s face. From these videos of the user’s face, machine learning can estimate heart rate. This study investigates the benefits and challenges of using machine learning models to estimate heart rate from facial videos through patents, datasets, and article review. We have searched the Derwent Innovation, IEEE Xplore, Scopus, and Web of Science knowledge bases and identified seven patent filings, eleven datasets, and twenty articles on heart rate, photoplethysmography, or electrocardiogram data. In terms of patents, we note the advantages of inventions related to heart rate estimation, as described by the authors. In terms of datasets, we have discovered that most of them are for academic purposes and with different signs and annotations that allow coverage for subjects other than heartbeat estimation. In terms of articles, we have discovered techniques, such as extracting regions of interest for heart rate reading and using video magnification for small motion extraction, and models, such as EVM-CNN and VGG-16, that extract the observed individual’s heart rate, the best regions of interest for signal extraction, and ways to process them.
... [6][7][8] In contrast, higher HRV is found to be associated with reduced morbidity, mortality, improved quality of life and psychological well-being. [9][10][11] Earlier studies have reported that obese individuals are relatively more susceptible to ventricular arrhythmias, which has been found to be a powerful indicator of sudden death. [12][13][14][15] Several researchers have shown decreased HRV in obese people (BMI ≥30) and this suggests that autonomic disturbances could be involved in the processes stimulating arrhythmia in such people. ...
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Introduction: Obesity is a chronic relapsing disease process and serious public health concern that can lead to chronic diseases, medical complications and a higher risk of disability. Another significant feature of obesity is dysfunction in cardiac autonomic function, which leads to changes in parasympathetic and sympathetic regulation, which can be measured using heart rate variability (HRV). The objective of this review is to estimate the extent to which exercise doses impacts on HRV among individuals living with overweight and obesity class I and II. Methods and analysis: A systematic literature search will be performed using PubMed/Medline, Scopus, EMBASE, ProQuest, CINAHL, Web of Science and the Cochrane Library for articles dating from 1965 to December 2021. Inclusion criteria include studies designed as parallel-arm randomised trials, enrolling adolescent and adult individuals with overweight (body mass index, BMI≥25 to ≤29.9) and obesity (class I BMI: 30-34.9 and class II BMI: 35-39.9) undergoing aerobic or resistance training or concurrent exercise training. For data synthesis, sensitivity analysis, subgroup analysis and risk of bias assessment, Stata V.13.0 software will be used. Ethics and dissemination: Formal ethical approval is not required. This systematic review will be submitted to a peer-reviewed journal. Prospero registration number: CRD42019104154.
... (Guzik et. al. 2007, Massaro et al. 2019, Zangróniz et al. 2018, Shaffer et al. 2017, Mohan et al. 2016, Young et al. 2018). In the next subsection each of the time domain features and frequency domain feature will be discussed in details. ...
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In this study a heart-beat-interval counterbased a low-power PPG microsystem is proposed for accurate assessment of the mental stress. The designed microsystem incorporates a new low power PPG sensing readout with a Time-to-digital converter for the long-time continuous heart-beat-interval estimation. Further the analog front-end circuit is implemented in the integrated chip having an area of 1.4 mm² and fabricated using the TSMC 0.18 µm process. Measured linear sensing range of the designed readout is 20 nA to 110 µA. With the 1.8 V standard supply, measurement results show that the power consumption of the PPG readout circuit is 52.2 µW, while the total measured power consumption of the designed chip is 100.2 µW. To evaluate the performance of the proposed microsystem in mental stress assessment, the designed circuit is integrated with OLED-OPD sensor and then applied to the wrist of two healthy subjects under different stressors, (e.g., laughing, solving a mathematical problem, hearing loud audio/sound, and moving neck). The statistical analysis of the detected PPG signal and measured on-chip-heart rate interval in time domain shows that the mean value of peak-to-peak interval, entropy, and stress-induced vascular index increases during the stress. In addition, in the frequency domain analysis of the heart rate variability (HRV) shows that the ratio of the low frequency component to the high frequency component is increased during the stress. Thus, the indices of HRV measured directly from the designed readout system can serve effectively as indication of heart rate variability and mental stress.
... It is probable that an SMI can only change HRV when it triggers longstanding changes in attitude, i.e., learning how to recognize and cope with stress at an early stage. Behaviors that increase HRV include sport [59], nutrition [60], relaxation and breathing techniques [61], and biofeedback [62]. BMI, nutrition behavior, and physical activity did not change over time in the intervention group nor in the control group, while the attitude toward stressful situations, as measured by the stress reactivity score, changed significantly [43]. ...
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Stress management interventions aim to reduce the disease risk that is heightened by work stress. Possible pathways of risk reduction include improvements in the autonomous nervous system, which is indexed by the measurement of heart rate variability (HRV). A randomized controlled trial on improving stress management skills at work was conducted to investigate the effects of intervention on HRV. A total of 174 lower management employees were randomized into either the waiting list control group (CG) or the intervention group (IG) receiving a 2-day stress management training program and another half-day booster after four and six months. In the trial, 24 h HRV was measured at baseline and after 12 months. Heart rate (HR), root mean square of successive differences (RMSSD), standard deviation of normal-to-normal intervals (SDNN), and standard deviation of the average of normal-to-normal intervals (SDANN) were calculated for 24 h and nighttime periods. Age-adjusted multilevel mixed effects linear regressions with unstructured covariance, time as a random coefficient, and time × group interaction with the according likelihood-ratio tests were calculated. The linear mixed-effect regression models showed neither group effects between IG and CG at baseline nor time effects between baseline and follow-up for SDANN (24 h), SDNN (24 h and nighttime), RMSSD (24 h and nighttime), and HR (24 h and nighttime). Nighttime SDANN significantly improved in the intervention group (z = 2.04, p = 0.041) compared to the control group. The objective stress axis measures (SDANN) showed successful stress reduction due to the training. Nighttime SDANN was strongly associated with minimum HR. Though the effects were small and only visible at night, it is highly remarkable that 3 days of intervention achieved a measurable effect considering that stress is only one of many factors that can influence HR and HRV.
... During chronic stress, the sympathetic nervous system is hyperactivated causing physical, psychological and behavioral abnormalities. However, previous research detected the HRV as a potential biomarker for psychological stress indicator (Kim et al., 2018;Tonello et al., 2014), as well as for psychological and physiological resilience and health (Young & Benton, 2018). ...
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Physical activity can improve health as well as reduce stress and the risk of developing several widespread diseases. However, there exists no accepted standard biomedical examination-method for stress evaluation. The purpose of this study was to investigate the effect of regular physical activity on stress and wellness as well as the evaluation of potential biomarkers in this field. This study included 105 people (mean age = 36.57 ± 1.4 years) who were randomly assigned into the exercise group 1 (EG-1) (n = 41), the exercise group 2 (EG-2) (n = 30), and the control group (CG) (n = 34). Measurements of stress and wellness were obtained by Multiscan BC-OXI before and after experimental period. This device presents a multifrequency segmental body composition 3D analyser with digital pulse oximeter. The key indicators of stress as well as for wellness were significantly improved in the EG-1. Parasympathetic activity showed significant changes as potential stress biomarker. Statistically significant gender differences were not observed in the comparable groups. The results suggest that the stress resistance and well-being significantly improved in the EG-1 due to regular physical activity. However, further research is necessary to determine effects of physical activity on integral health indicators.
... The influence of diet on health has been studied for decades, being necessary to determine some biomarkers that could identify those nutritional factors that could have a positive influence on long-term health [14]. Some examples include a Mediterranean diet, omega-3 fatty acids, polyphenols, and probiotics. ...
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This investigation aimed to identify the effect of a synbiotic in athletes and sedentary people, and their potential varying responses regarding the immune system, autonomic regulation and body composition. Twenty-seven participants were involved in the protocol: 14 sedentary and 13 semi-professional soccer players. Both groups were randomly divided into an experimental and control group. A synbiotic (Gasteel Plus®®, Heel España S.A.U.) comprising a blend of probiotic strains, including Bifidobacterium lactis CBP-001010, Lactobacillus rhamnosus CNCM I-4036, and Bifidobacterium longum ES1, was administered to the experimental group, and a placebo was given to the control group for 30 days. Heart rate variability, body composition, and immune/inflammatory cytokines were determined. Statistically significant differences were observed between sedentary individuals and athletes in heart rate variability but not between the experimental and control groups. A difference between the athletic and sedentary group is observed with the influence of training on the effects of the synbiotic on the levels of fat mass and body-fold sum. No significant differences were shown in cytokines after the protocol study. No changes occur with the synbiotic treatment between the athlete and sedentary groups, while no negative effect was produced. Further research will be necessary to see chronic effects in the analyzed biomarkers.
The purpose of this study was to investigate the effect of transcranial pulsed current stimulation (tPCS) on fatigue delay after medium-intensity training. Materials and methods: Ninety healthy college athletes were randomly divided into an experimental group (n = 45) and control group (n = 45). The experimental group received medium-intensity training for a week. After each training, the experimental group received true stimulation of tPCS (continuous 15 min 1.5 mA current intensity stimulation). The control group received sham stimulation. The physiological and biochemical indicators of participants were tested before and after the experiment, and finally 30 participants in each group were included for data analysis. Results: In the experimental group, creatine kinase (CK), cortisol (C), time-domain heart rate variability indices root mean square of the successive differences (RMSSD), standard deviation of normal R-R intervals (SDNN), and frequency domain indicator low frequency (LF) all increased slowly after the intervention. Among these, CK, C, and SDNN values were significantly lower than those in the control group (p < 0.05). Testosterone (T), T/C, and heart rate variability frequency domain indicator high frequency (HF) in the experimental group decreased slowly after the intervention, and the HF value was significantly lower than that in the control group (p < 0.05). The changes in all of the indicators in the experimental group were smaller than those in the control group. Conclusion: The application of tPCS after medium-intensity training enhanced the adaptability to training and had a significant effect on the maintenance of physiological state. The application of tPCS can significantly promote the recovery of autonomic nervous system function, enhance the regulation of parasympathetic nerves, and delay the occurrence of fatigue.
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According to estimates from Public Health England, by 2034 70% of adults are expected to be overweight or obese, therefore understanding the underpinning aetiology is a priority. Eating in response to negative affect contributes towards obesity, however, little is known about the underlying mechanisms. Evidence that visceral afferent signals contribute towards the experience of emotion is accumulating rapidly, with the emergence of new influential models of 'active inference'. No longer viewed as a 'bottom up' process, new intero-ceptive facets based on 'top down' predictions have been proposed, although at present it is unclear which aspects of interoception contribute to aberrant eating behaviour and obesity. Study one examined the link between eating behaviour, body mass index and the novel interoceptive indices; interoceptive metacognitive awareness (IAw) and interoceptive prediction error (IPE), as well as the traditional measures; interoceptive accuracy (IAc) and interoceptive sensibility (IS). The dissociation between these interoceptive indices was confirmed. Emotional eaters were characterised by a heightened interoceptive signal but reduced meta-cognitive awareness of their interoceptive abilities. In addition, emotional eating correlated with IPE; effects that could not be accounted for by differences in anxiety and depression. Study two confirmed the positive association between interoceptive accuracy and emotional eating using a novel unbiased heartbeat discrimination task based on the method of constant stimuli. Results reveal new and important mechanistic insights into the processes that may underlie problematic affect regulation in overweight populations.
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In animals, a Western style diet–high in saturated fat and added sugar–causes impairments in hippocampal-dependent learning and memory (HDLM) and perception of internal bodily state (interoception). In humans, while there is correlational support for a link between Western-style diet, HDLM, and interoception, there is as yet no causal data. Here, healthy individuals were randomly assigned to consume either a breakfast high in saturated fat and added sugar (Experimental condition) or a healthier breakfast (Control condition), over four consecutive days. Tests of HDLM, interoception and biological measures were administered before and after breakfast on the days one and four, and participants completed food diaries before and during the study. At the end of the study, the Experimental condition showed significant reductions in HDLM and reduced interoceptive sensitivity to hunger and fullness, relative to the Control condition. The Experimental condition also showed a markedly different blood glucose and triglyceride responses to their breakfast, relative to Controls, with larger changes in blood glucose across breakfast being associated with greater reductions in HDLM. The Experimental condition compensated for their energy-dense breakfast by reducing carbohydrate intake, while saturated fat intake remained consistently higher than Controls. This is the first experimental study in humans to demonstrate that a Western-style diet impacts HDLM following a relatively short exposure–just as in animals. The link between diet-induced HDLM changes and blood glucose suggests one pathway by which diet impacts HDLM in humans.
Heart rate variability (HRV) indexes functioning of the vagus nerve, arguably the most important nerve in the human body. The Neurovisceral Integration Model has provided a structural framework for understanding brain-body integration, highlighting the role of the vagus in adaptation to the environment. In the present paper, we emphasise a temporal framework in which HRV may be considered a missing, structural link between psychological moments and mortality, a proposal we label as Neurovisceral Integration Across a Continuum of Time (or NIACT). This new framework places neurovisceral integration on a dimension of time, highlighting implications for lifespan development and healthy aging, and helping to bridge the gap between clearly demarcated disciplines such as psychology and epidemiology. The NIACT provides a novel framework, which conceptualizes how everyday psychological moments both affect and are affected by the vagus in ways that have long-term effects on mortality risk. We further emphasize that a longitudinal approach to understanding change in vagal function over time may yield novel scientific insights and important public health outcomes.
Background: Uncertainty often exists about the comparability of results obtained by different health risk indicator systems. Objectives: To compare two health risk indicator systems, i.e, allostatic load and heart rate variability (HRV). Additionally, to investigate the feasibility of inclusion of HRV indicators into allostatic load assessments and which HRV indicators are best to introduce. Methods: Allostatic loads were calculated based on blood pressure, waist-to-hip ratio, BMI, cholesterol, HDL-C, LDL-C, CRP, albumin, glycosylated haemoglobin, blood glucose and cortisol excretion. Allostatic load scores were compared to HRV results obtained by frequency domain, time domain and Poincaré analyses. Results: Negative correlations were found between allostatic loads and total HRV, for all periods and all HRV analytical techniques (r=-0.67, p=0.0001 to r=-0.435, p=0.035), and between allostatic loads and vagal measures of HRV for supine (r=-0.592, p=0.001 to r=-0.584, p=0.001) and the first 5 minutes standing (r=-0.443, p=0.021 to r=-0.407, p=0.035), with all HRV techniques. Heart rate responses declined with increases in allostatic loads. Conclusion: HRV and allostatic load scores give comparable results as health risk indicators. Baseline total HRV and vagal, rather than sympathetic, measures of HRV should be introduced into allostatic load assessments. Results are in line with the concept of vagal tone as a regulator of allostatic systems. Inclusion of heart rate responses to orthostatic stress, into allostatic load assessments, warrants further investigation.
Purpose: Increased left ventricular mass (LVM) is an early precursor of target organ damage due to hypertension. Diminished parasympathetic cardiac control has been linked to both hypertension onset and left ventricular impairment; however, emerging evidence suggests that this pattern may be different in African Americans. The present study sought to determine if race impacts the relationship between parasympathetic cardiac control and LVM. Methods: LVM was assessed via echocardiography in a sample (N = 148) of African American and White adults (Mean age = 33.20 ± 5.71) with normal or mildly elevated blood pressure. Parasympathetic cardiac control was assessed by a measure of high frequency heart rate variability (HF-HRV) determined from electrocardiographic (ECG) recordings during 5 min of rest. Results: In regression analysis, greater HF-HRV was associated with greater LVM among African Americans (P = 0.002), but was not related to LVM in Whites (P = 0.919). Conclusions: These are the first data to demonstrate that race moderates the relationship between HRV and LVM and further suggest that race may be an important factor in the association between parasympathetic cardiac control and other CVD risk factors. This article is protected by copyright. All rights reserved.
Theoretical perspectives posit that heart-rate variability (HRV) reflects self-regulatory capacity and therefore can be employed as a bio-marker of top-down self-regulation (the ability to regulate behavioral, cognitive, and emotional processes). However, existing findings of relations between self-regulation and HRV-indices are mixed. To clarify the nature of such relations, we conducted a meta-analysis of 123 studies (N = 14,347) reporting relations between HRV-indices and aspects of top-down self-regulation (e.g., executive functioning, emotion regulation, effortful control). A significant, albeit small, effect was observed (r = 0.09) such that greater HRV was related to better top-down self-regulation. Differences in relations were negligible across aspects of self-regulation, self-regulation measurement methods, HRV computational techniques, at-risk compared with healthy samples, and the context of HRV measurement. Stronger relations were observed in older relative to younger samples and in published compared to unpublished studies. These findings generally support the notion that HRV-indices can tentatively be employed as bio-markers of top-down self-regulation. Conceptual and theoretical implications, and critical gaps in current knowledge to be addressed by future work, are discussed.
Background: Heart rate variability (HRV) has been suggested reduced in bipolar disorder (BD) compared with healthy individuals (HC). This meta-analysis investigated: HRV differences in BD compared with HC, major depressive disorder or schizophrenia; HRV differences between affective states; HRV changes from mania/depression to euthymia; and HRV changes following interventions. Methods: A systematic review and meta-analysis reported according to the PRISMA guidelines was conducted. MEDLINE, Embase, PsycINFO, The Cochrane Library and Scopus were searched. A total of 15 articles comprising 2534 individuals were included. Results: HRV was reduced in BD compared to HC (g=-1.77, 95% CI: -2.46; -1.09, P<0.001, 10 comparisons, n=1581). More recent publication year, larger study and higher study quality were associated with a smaller difference in HRV. Large between-study heterogeneity, low study quality, and lack of consideration of confounding factors in individual studies were observed. Conclusions: This first meta-analysis of HRV in BD suggests that HRV is reduced in BD compared to HC. Heterogeneity and methodological issues limit the evidence. Future studies employing strict methodology are warranted.
Consistently it has been reported that a depressed mood and low heart rate variability (HRV) are linked. However, studies have not considered that the association might be explained by dietary behaviour. The resting inter-beat interval data of 266 adults (Study 1: 156 (51 M), Study 2: 112 (38 M)) were recorded for six minutes and quantified using linear (HF power: 0.15–0.4 Hz) and nonlinear indices (Sample entropy). Participants also completed the Profile of Mood States and the Three Factor Eating questionnaires. The Alternative Healthy Eating Index was used to quantify diet quality. In study 1 mood was associated with HRV; an effect partially mediated by diet. Study 2 replicated the finding: disinhibited eating (the tendency to lose control over one’s eating) and diet sequentially mediated the association between mood and HRV. Diet plays a role in the link between mood and HRV and studies should consider the influence of this factor.