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Heart Rate Variability as a Marker of Self-Regulation

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Self-regulation is central to many of the most important individual and societal problems today. We sought to determine whether the relationship between self-regulation and heart rate variability (HRV) could be replicated and extended. We hypothesized that baseline HRV would predict persistence on an anagram task, and that under conditions requiring greater self-control, HRV would increase. Two groups were given the same set of difficult and unsolvable anagrams. To induce self-regulatory fatigue, the suppression group was asked to try to not think of a white bear while the expression group was asked to try to think of a white bear. Baseline HRV predicted persistence on the unsolvable anagram. Both groups demonstrated changes in HRV relative to baseline, although we were unable to replicate findings that HRV was elevated during high self-regulatory effort. We were, however, able to replicate findings that the expression group persisted longer on the anagram task compared to the suppression group but only when accounting for physical activity scores. The present study advances our knowledge of the relationship between HRV and self-regulation, so that we can more successfully treat those with seriously impaired self-control.
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Heart Rate Variability as a Marker of Self-Regulation
Alison Reynard Richard Gevirtz Rustin Berlow
Milton Brown Kerri Boutelle
ÓSpringer Science+Business Media, LLC 2011
Abstract Self-regulation is central to many of the most
important individual and societal problems today. We
sought to determine whether the relationship between self-
regulation and heart rate variability (HRV) could be rep-
licated and extended. We hypothesized that baseline HRV
would predict persistence on an anagram task, and that
under conditions requiring greater self-control, HRV would
increase. Two groups were given the same set of difficult
and unsolvable anagrams. To induce self-regulatory fati-
gue, the suppression group was asked to try to not think of
a white bear while the expression group was asked to try to
think of a white bear. Baseline HRV predicted persistence
on the unsolvable anagram. Both groups demonstrated
changes in HRV relative to baseline, although we were
unable to replicate findings that HRV was elevated during
high self-regulatory effort. We were, however, able to
replicate findings that the expression group persisted longer
on the anagram task compared to the suppression group but
only when accounting for physical activity scores. The
present study advances our knowledge of the relationship
between HRV and self-regulation, so that we can more
successfully treat those with seriously impaired self-
control.
Keywords Self-regulation Heart rate variability
Thought suppression Self-control Central autonomic
network
Introduction
Self-regulation, our capacity to change or inhibit thoughts,
emotions, impulses, or overt behaviors, plays a pivotal
role in many of the most important individual and societal
problems (Baumeister et al. 1994). Ineffective self-regu-
lation predicts poor physical and emotional health, and
other life problems (Baumeister et al. 1994; Tangney
et al. 2004). The ability to delay gratification and override
impulses are both self-regulatory behaviors that allow one
to stay focused on long-term goals and sacrifice imme-
diate pleasure for rewarding outcomes. Failure of self-
control correlates with psychopathology such as depres-
sion (Psyszczynski et al. 1987; Wenzlaff et al. 1988),
obsessive thoughts (Martin and Tesser 1996; Wegner
et al. 1987), and aggression (Baumeister et al. 1998; Tice
and Baumeister 1993). Additionally, the inability to
control impulses may play a part in the development of
health related issues, such as obesity (Francis and Susman
2009; Seeywave et al. 2009) and substance abuse and
addiction (Baumeister 2003; Muraven et al. 2002; Hustad
et al. 2009).
The strength model (Muraven et al. 1998) views self-
regulation as a limited resource, analogous to muscle
capacity, insofar that it depletes with use and requires rest
for re-use. Self-regulation can improve over time with
practice, much like a muscle can be strengthened with
repeated challenge. Support for this model comes from
over 50 studies (Gailliot and Baumeister 2007), which have
found that the ability to regulate the self depends on a
This data has been previously published as the first author’s
dissertation with UMI Dissertation Publishing.
A. Reynard (&)R. Gevirtz M. Brown
CSPP, Alliant International University, Daley Hall, 10455
Pomerado Rd, San Diego, CA 92131, USA
e-mail: Areynard@alliant.edu
R. Berlow K. Boutelle
University of California, San Diego, La Jolla, CA, USA
123
Appl Psychophysiol Biofeedback
DOI 10.1007/s10484-011-9162-1
limited, exhaustible resource. However, unlike physical
fatigue or emotional stress, we are unaware of our vul-
nerability to self-regulatory failure; we do not know when
this ability will fail or how much of this resource is left
prior to failure (Baumeister 2003).
Self-control occurs by means of certain brain structures,
including areas in the frontal cortex, which communicate
with the central autonomic network (CAN; Thayer and
Lane 2000). This self-regulation process involves cortical
awareness (input), cortical control of brainstem autonomic
centers like the nucleus ambiguus, and vagal outflow to the
heart (Porges 2001). One measure of vagal parasympa-
thetic activity is heart rate variability (HRV), the beat-to-
beat difference in heart rate measured in milliseconds
(Task Force 1996). Since brain structures involved in self-
regulation also control the autonomic nervous system,
HRV has recently been identified as a measure of self-
regulatory capacity.
Segerstrom and Solberg Nes (2007) tested five outcome
measures following stress and self-regulatory challenge
(eating carrots versus eating cookies) to determine if HRV
could be used to predict self-regulatory capacity and fati-
gue. In this study, baseline HRV predicted persistence on
an anagram task. Stress lowered HRV while self-regulatory
effort elevated HRV. The authors concluded that stress
incites a sympathetic response, which increases energy use,
while self-regulatory effort involves restraint, which lowers
energy use to conserve glucose and other resources for the
brain.
Although the work of Segerstrom and Solberg Nes
(2007) represents seminal work in the area of self-regula-
tion, there are a number of limitations in this study. While
food provided a desirable substance that participants had to
resist and thereby exert self-control, glucose may have
been introduced by using food to manipulate self-regula-
tion. Since those who ate the cookies ingested sucrose,
which was readily converted to glucose, it could explain
why the low self-regulation group persisted longer on the
anagram task and could account for group differences in
HRV. Additionally, Segerstrom and Solberg Nes (2007)
did not include measurement of physical activity levels.
Previous research has shown a consistent relationship
between HRV and exercise (Sandercock et al. 2005);
baseline fitness levels or frequency of exercise was not
controlled for in this study.
Thus, the objective of the current study was to replicate
the findings of Segerstrom and Solberg Nes (2007)
regarding the relationship between HRV and self-regula-
tion, and to extend these findings by removing confounds
such as physical activity levels and food (i.e., glucose)
ingestion. We hypothesized that there would be a signifi-
cant, positive correlation between persistence time and
baseline HRV. We also predicted a positive association
between self-regulatory effort and HRV. Finally, we pre-
dicted that participants in the low self-regulation group
would persist longer on the unsolvable anagram than those
in the high self-regulation group.
Methods
Participants
Participants were recruited using two methods. First, an
e-mail advertisement was sent to all students at the
California School of Professional Psychology—San Diego
at Alliant International University. Participants were also
recruited through online advertising sites such as Craig’s
List. Those who responded were sent an introductory email
response containing the participants’ bill of rights, consent
form and a screening demographic questionnaire. Upon
initial contact, participants were also informed of a mon-
etary incentive of ten dollars to be given upon completion
of the study. Inclusion criteria required participants to be
psychologically and physically healthy as well as fluent
readers and writers of English. Psychological health was
defined as a raw score of less than 1.35 (below the 80th
percentile, Tscore =80) on the Global Severity Index
(GSI) of the Brief Symptom Inventory. Those scoring
above this cut off were excluded from participation.
Physical health was assessed via self-report of medical
conditions and medications using the demographic ques-
tionnaire. Those with cardio respiratory illness or those
who were taking medication interfering with autonomic
function (i.e. opiates, tricyclic antidepressants, beta
blockers, and stimulants) were excluded from participation.
Participants who met full inclusion and exclusion criteria
were randomly assigned to a high self-regulation, experi-
mental group versus a low self-regulation, control group.
Fifty-nine healthy adults, including 16 males (mean
age =26.8; range =18–34) and 43 females (mean
age =27.3; range =18–39) qualified and completed this
study. In terms of education, 15.3% had some college
experience, 52.5% completed a bachelor’s degree, and
32.2% completed a master’s degree. Ethnic composition of
the sample was as follows: 55.9% Caucasian, 3.4% African
American, 11.9% Asian/Pacific Islander, 18.6% Hispanic,
6.8% Biracial, and 3.4% identified themselves as Other.
The modal participant was female, single, Caucasian, col-
lege educated, and spoke English as a first language.
Procedure
When participants arrived the consent form was verbally
reviewed. This was followed by administration of the Brief
Symptom Inventory (BSI) to screen for psychological
Appl Psychophysiol Biofeedback
123
problems. Upon completion, participants were seated in
a room with heart rate leads attached to the chest in a
Lead II configuration. A baseline physiological profile was
obtained for all participants, during which participants sat
quietly for 5 min.
After the baseline phase of the study, participants were
instructed to write their thoughts in sentences on paper
(Muraven et al. 1998). To precisely follow the protocol of
Muraven et al. (1998), who obtained their method from
Wegner et al. (1987), we used the verbatim wording of
those sources. Participants in the low self-regulation con-
dition were told to try to think of a white bear and those in
the high self-regulation group were told to try to not think
about a white bear. The experimenter returned after 6 min
and administered a task appraisal measurement.
After the experimental manipulation, participants
worked on several moderately difficult anagrams and one
unsolvable anagram (Solberg Nes et al. 2005). Anagrams
were presented in a uniform and standard sequence, in an
identical fashion as Segerstrom and Solberg Nes (2007).
They were allowed a maximum time of 5 min to work on
the unsolvable anagram, and 120 s to work on ten other
difficult, but solvable anagrams. Total time for the anagram
task was 20 min for all participants. Following the anagram
task, a second task appraisal measurement was adminis-
tered. Participants then rested quietly for 5 min and were
debriefed.
Physiological Monitoring
HRV was assessed using physiological monitoring equip-
ment to measure interbeat intervals (IBI). Physiological
data was measured using a noninvasive biofeedback system
(I-330 C-2?interface and USE2 Software from J & J
Engineering, Poulsbo, WA) with a laptop computer.
Persistence Measurement
Persistence was measured by using a stopwatch to record
time in seconds before giving up on each individual ana-
gram, as well as to record total time in seconds before
giving up on the anagram task.
Demographic Questionnaire
Basic data used for descriptive information including age,
gender, ethnic background, annual income and highest
level of education completed was collected. Additionally,
the demographics questionnaire requested information
regarding the health history of each participant. This sec-
tion included questions about medical conditions, psychi-
atric illness and current medications.
Brief Symptom Inventory
The Brief Symptom Inventory (BSI; Derogatis 1993) was
used to screen for psychological symptoms. This 53-item
measure of self-report uses a 4-point Likert type scale, with
0 being ‘‘Not at All’’ and 4 being ‘‘Extremely.’’ The BSI
has high internal reliability (Cronbach’s Alpha of .71–.85)
and good test–retest reliability in a sample of 60 non-
patients (Derogatis and Melisaratos 1983). Convergent
validity for this measure was demonstrated with compari-
sons to the Wiggins and Tryons scales of the MMPI with
good correlations ranging from .3 to .7 (Conoley and
Kramer 1989; Derogatis 1993).
Godin Leisure-Time Exercise Questionnaire
The Godin Leisure Time Exercise Questionnaire (GLTEQ;
Godin and Shephard 1985) was used to rank participants on
physical activity. This 4-item self-report measure accounts
for levels of strenuous, moderate, and mild exercise in a
7-day period (Godin and Shephard 1985). From this
questionnaire, a global exercise score in metabolic equiv-
alents [i.e., METS(1)] can be calculated to determine
energy expenditure during physical activity. Strenuous,
moderate, and mild forms of exercise are given MET
values of 9, 5, and 3. The self-reported total for strenuous,
moderate, and mild forms of exercise are multiplied by
their respective MET values. This instrument has been
found to be useful and valid with respect to related mea-
surement questionnaires of physical activity (Jacobs et al.
1993). Test–retest reliability (r=.74) and construct and
predictive validity were sufficiently established (Godin and
Shephard 1985).
Task Appraisal
Eleven items were used to measure task appraisal for the
experimental manipulation. The items had a Likert-type
response scale with 1 being ‘‘Strongly Disagree’’ and 5
being ‘‘Strongly Agree.’’ Items included, ‘‘I had to exert a
lot of effort on the task,’’ ‘‘I had to try hard on the task,’
‘The task required a lot of energy,’’ ‘‘I had to fight against
an urge while working on the task,’’ ‘‘I was dealing with
conflicting desires while working on the task,’’ ‘‘I had a lot
of distracting thoughts while working on the task,’’ ‘‘The
task was unpleasant,’’ ‘‘The task was difficult,’’ ‘‘I had to
control myself while working on the task,’’ ‘‘The task was
frustrating,’’ ‘‘I had to concentrate a lot on the task.’’ Alpha
reliability for the 11 items was .83. Six items were used to
measure task appraisal for the anagram task. The items
included, ‘‘It was difficult,’’ ‘‘It was stressful,’’ ‘‘It required
a lot of effort,’’ ‘‘I had to concentrate on the task,’’ ‘‘I had
to force myself to keep going,’’ and ‘‘I wanted to stop
Appl Psychophysiol Biofeedback
123
before it was over.’’ Alpha reliability for the six items was
.74, which differed slightly from Segerstrom and Solberg
Nes (2007) who found an alpha reliability of .81 for the
same six items.
Statistical Analysis and Design Analysis
HRV Measures
Heart rate data was analyzed using root mean squared suc-
cessive differences in the IBI (RMSSD). The raw IBI data
was exported from the J&J EngineeringÓC-2?Physio-
logical Monitoring System and edited in accordance with
Task Force guidelines (Task Force 1996). Data was cleaned
using Kubios HRV, version 2.0 software (Tarvainen and
Niskanen 2008). Ectopic beats or other artifacts were iden-
tified through visual inspection and averaged using the IBI
preceding and following the identified artifact.
Statistical analyses were conducted using the Statistical
Package for Social Sciences (PASW (SPSS) for Macintosh,
version 17.0, 2009, Chicago, IL, USA).
Results
Task Appraisal
Subjective assessment of task difficulty was measured
using targeted questionnaires. The first manipulation check
was given following the experimental manipulation.
Overall, the high self-regulation group did not find the task
significantly more difficult than the low self-regulation
condition (M=2.33, SD =.62 vs. M=2.50, SD =.52),
F(1, 57) =1.26, p=.26, g
2
=.02. The effect size for the
experimental manipulation of cookies and carrots con-
ducted by Segerstrom and Solberg Nes (2007) was signif-
icantly larger (Cohen’s d=.63) compared to the effect
size of the present study (Cohen’s d=.30).
Following the anagram task, another appraisal was given
for participants to rate the difficulty and effort of the ana-
gram task. Overall, the high self-regulation group did not
find the task significantly more difficult than the low self-
regulation condition (M=3.99, SD =.70 vs. M=4.03,
SD =.61), F(1, 57) =.05, p=.82, Cohen’s d=.06.
Baseline HRV and Persistence
We predicted a positive, significant correlation between
persistence time and baseline HRV, such that participants
with a higher HRV at baseline would persist longer on the
anagrams. Pearson correlations were conducted to identify
relationships between baseline HRV and persistence time
on the anagrams as shown in Table 1. The correlation
between HRV at baseline and the total number of seconds it
took to complete all eleven anagrams was not significant at
the 0.05 level (p=.33). However, baseline HRV was
positively correlated with seconds on the unsolvable ana-
gram (p=.02) as shown in Fig. 1. Since there was a
potential education confound, we included it in our pre-
diction model to control for its possible effect on persis-
tence time. Hierarchical multiple regression was used to
assess if baseline HRV, after controlling for education level,
predicted persistence time. Education level was entered at
Step 1, explaining 7% of the variance in persistence time on
the unsolvable anagram (p=.02). After entry of the log of
baseline RMSSD, at Step 2 the total variance explained by
the model was 14%, F(2, 56) =4.73, p=.01. The log of
baseline RMSSD explained an additional 7.5% of the var-
iance in persistence time after controlling for education
level, Fchange (1, 56) =4.94, p=.03. In the final model,
only the log of RMSSD at baseline was statistically
Table 1 Correlations between baseline RMSSD and persistence time
Log
RMSSD
baseline
Total seconds for
all anagrams
Seconds for
unsolvable
anagram
Log
RMSSD
baseline
.13 .30*
Total
seconds
round 1
.53**
* Correlation significant at the 0.05 level (2-tailed)
** Correlation significant at the 0.01 level (2-tailed)
Fig. 1 Persistence on the unsolvable anagram as a function of
baseline RMSSD
Appl Psychophysiol Biofeedback
123
significant, t=2.22, p=.03, squared semi partial =.08.
The education level was not statistically significant in the
whole equation, t=1.93, p=.06, squared semi par-
tial =.06. Therefore, education level did not explain the
relationship between baseline HRV and persistence on the
anagram task.
Self-Regulatory Effort and HRV
We also predicted a positive association between self-
regulatory effort and HRV, such that participants in the
high self-regulation condition would have increased HRV
relative to baseline compared to participants in the low
self-regulation condition. A paired samples t-test was used
to compare RMSSD at baseline and RMSSD during the
experimental manipulation for each group. There was a
significant change in RMSSD at baseline compared to the
experimental manipulation for the high self-regulation
group (t=2.61, p=.01, r
2
=.16) as well as for the low
self-regulation group (t=3.92, p=.00, r
2
=.31) as
shown in Fig. 2and Table 2. The time by group interaction
effect failed to show that the two groups differed with
regard to change in RMSSD (F=.35, p=.56, g
2
=.01,
power =.09).
Self-Regulatory Effort, Persistence, and Exercise
Since there was a significant difference in GLTEQ scores
between groups, we split the high self-regulation and low
self-regulation groups into high and low GLTEQ levels to
explore whether this difference affected persistence time
on the unsolvable anagram and on the first round of ana-
grams. A significant difference was found only with per-
sistence levels on the unsolvable anagram (t(29) =2.88,
p\.01, x
2
=.19) for the group low on the GLTEQ as
shown in Fig. 3. For the participants who scored high on
the GLTEQ, no difference was found between the two
conditions. After adjusting for pre-intervention GLTEQ
scores, there was a significant difference between the two
groups on persistence time for the unsolvable anagram
F(1, 56) =4.06, p=.05, partial eta squared =.07. Since
there was no difference in persistence time for participants
who scored high on the GLTEQ, these findings suggest that
physical exercise may serve as a buffer against self-regu-
latory fatigue.
Discussion
The main purposes of the present study were twofold; first,
to replicate findings by Segerstrom and Solberg Nes (2007)
regarding self-regulation and its relationship with HRV,
and secondly, to remove the confound of blood glucose and
exercise levels present in their study. As shown previously,
baseline HRV significantly predicted persistence on the
unsolvable anagram. Therefore, it does appear that there is
a replicable relationship between baseline HRV and per-
sistence. The association between persistence and high
HRV supports empirical evidence demonstrating the posi-
tive benefits of high HRV. Patients who have been trained
using biofeedback to raise their HRV show improvement in
several different areas, including: Asthma (Lehrer et al.
2004), Chronic Obstructive Pulmonary Disorder (Giardino
et al. 2004), Fibromyalgia (Hassett et al. 2007), Depression
(Karavidas et al. 2007), Hypertension (Reinke et al. 2007),
Congestive Heart Failure (Swanson et al. 2006), Irritable
Bowel Syndrome (Humphreys and Gevirtz 2000; Sowder
et al. 2005) and PTSD (Zucker et al. 2009). Experimental
research has also shown a predictive value for low HRV
for several negative health outcomes, including cardiac
Fig. 2 Heart rate variability (measured by the log of RMSSD) from
baseline to the experimental manipulation as a function of group
Table 2 Log RMSSD baseline compared to log RMSSD bear: group
comparison
Suppression Expression
RMSSD M SD M SD
Baseline 1.61 .17 1.66 .22
Bear 1.53 .22 1.56 .22
Fig. 3 Persistence on the unsolvable anagram in participants above
and below median GLTEQ scores for those in the suppression and
expression groups
Appl Psychophysiol Biofeedback
123
mortality (Bigger et al. 1993), and cardiac morbidity
(Kristal-Boneh et al. 1995). The salient clinical question is
whether increasing HRV for these populations will result in
a concomitant improvement in self-regulation. Since the
results of the present research support the correlation
between HRV and self-regulation, it may be appropriate to
include measures of self-regulation along with HRV
monitoring in future studies of psychosomatic conditions.
Our data supporting the relationship between self-regu-
latory effort and persistence time was also consistent with
previous research. However, findings were only consistent
with those of Segerstrom and Solberg Nes (2007) when
accounting for exercise scores. Previous research has also
shown a strong connection between HRV and exercise
(Sandercock et al. 2005). Findings of the present study
regarding exercise and its role in enhancing self-regulatory
stamina and preventing self-regulatory fatigue highlight the
importance of controlling for physical activity levels in
future research.
Data gathered regarding the relationship between self-
regulatory effort and HRV was inconsistent with previous
findings (Segerstrom and Solberg Nes 2007). We predicted
that the low self-regulation group would demonstrate a
decrease in HRV relative to baseline, whereas the high
self-regulation group would demonstrate an increase in
HRV relative to baseline. In our study, both groups showed
a decrease in HRV relative to baseline. In terms of sensory
stimulation, the cookies and carrots paradigm used by
Segerstrom and Solberg Nes (2007) involved olfactory and
gustatory sensations, which are particularly compelling,
and have different brain pathways, which do not neces-
sarily involve thalamic circuits and therefore may have a
different relationship with the CAN. Our paradigm was
much more cognitive in nature, requiring visual imagina-
tion and its suppression. Results differed from those of
Segerstrom and Solberg Nes (2007) as HRV did not
decrease during the anagram task for the suppression
group. The discrepancies above can be, in part, attributed
to the fact that our thought suppression manipulation was
not perceived as requiring as much effort as their experi-
mental manipulation of resisting cookies and instead hav-
ing to eat carrots. Or, this difference may be attributed to
the fact that their paradigm involved the ingestion of
cookies, containing sugar (sucrose), which is readily con-
verted into glucose, raising blood glucose levels and
therefore affecting self-regulation (Galliott and Baumeister
2007). Finally, other contributing factors such as screening
protocols or education levels may have contributed to the
differences. Future studies should evaluate these variables
as moderators.
Limitations of the present study include a relatively
small sample size (N=59) that was largely female (74%)
and half Caucasian (56.7%). Other limitations include the
experimental nature of the study, which may not reflect
what happens outside of the laboratory. Furthermore,
because this is a homogenous group of educated individ-
uals, the range of self-regulation may be narrow and
therefore we experienced a ceiling effect, making it more
difficult to perceive HRV differences. Despite these limi-
tations, there are a number of strengths of this study, which
include controlling for medication affecting autonomic
function, controlling for baseline physical activity levels,
replication of the finding that HRV can be used as a marker
or indicator of self-regulatory strength, and elucidation of
the relationship between self-regulation, exercise, and
persistence.
Further exploration of the relationship between HRV
and self-regulation could be taken in several directions.
Can HRV biofeedback training improve self-regulation? If
this causal relationship is established, it could aid treatment
of conditions, which require self-regulation such as obesity.
By increasing HRV, obese patients could be helped to
increase persistence with regard to an exercise regimen or
decrease intake of energy rich foods to aid weight loss.
Other disordered behavior requiring self-regulatory effort
such as medication compliance, sleep hygiene, and therapy
attendance could also potentially be improved by an
increase in HRV.
Acknowledgments The authors would like to thank Suzanne C.
Segerstrom, Ph.D., and Lise Solberg Nes, Ph.D., for sharing gener-
ously their protocols, procedures, and anagrams. Thanks to Starr
MacKinnon for her assistance with this study.
Conflicts of interest No conflicts of interest are present for authors
Reynard, Gevirtz, Berlow, Brown or Boutelle.
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... Moreover, Porges advocated that the evolution of the ANS, played also a crucial role to the development of emotional experience and the social engagement [10]. That is, by measuring time and frequency domains of HRV, we can estimate central nervous processes, such as attentional control and emotional regulation [11][12][13], or even personality characteristics, such us adaptability or resilience [12,[14][15][16]. ...
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Background The interaction between high physical performance under extreme conditions and simultaneous control of the cognitive executive functioning has been a subject of research in literature for the past few decades. Stroop test and Heart-rate variability (HRV), have been verified clinical tools for the assessment of cerebral and autonomic/ cardiovascular stress responses respectively. Purpose The investigation of HRV adaptive response to stress and cognitive stress resilience under extremely strenuous conditions. Methods 34 consecutive subjects were enrolled. Of them, 18 were candidates under intense preparation for their enlistment in the Hellenic Navy SEALs (HNS) and 16 were healthy controls (HC). All subjects underwent stroop tasks, along with mental state and personality examination. HRV variables in time and frequency domains recordings were acquired, during each aforementioned cognitive testing procedure. Results HNS's performance on both cognitive and emotion stroop tasks were equivalent to controls. During the size comparison of the number stroop and emotion stroop, HC had statistically significantly higher power content at different HRV frequency bands compared to HNS participants (p=0.036 and p=0.06 respectively). Finally, in a between group comparison of the psychometric tools, HNS had significantly higher somatization (p<0.01), anxiety (p=0.037) and neuroticism (p=0.047) than HC. Correlation was conducted for each group separately, between the psychometric tools and the measurements of HRV for both number and emotion stroop. Moderate negative correlations were found between SDNN (Standard deviation of normal-to-normal RR-intervals) during the size comparison of number stroop and three out of nine categories of psychometric questionnaire; somatization (r(23)=−0.452, p<0.05), anxiety (r(23)=−0.457, p<0.05) and hostility (r(23)=−0.445, p<0.05) Conclusion The above findings suggest that HNS display flexibility in their autonomic regulation during cognitive and emotional tasks. This characteristic is closely related to problem solving or adaptability skills. Additionally, HRV can be a promising clinical index regarding the assessment of psychophysiological resilience especially in the neurovisceral integration (NVI) model. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Region of Attica
... We used the Empatica E4 wearable sensor to collect physiological data in real-life settings and heart rate variability (HRV) analysis software (Kubios HRV) to calculate the self-regulation of the entrepreneur participants (Geisler and Schröder-Abé 2015;Holzman and Bridgett 2017;Koval et al., 2013;McCraty and Shaffer 2015;Reynard et al., 2011;Segerstrom and Nes 2007;Zahn et al., 2016). Kubios bases its calculations on the standards defined by the HRV Task Force (Task Force, 1996) and we applied automatic artefact correction method (Tarvainen et al., 2019). ...
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Entrepreneurs apply self-regulation to achieve their entrepreneurial goals and to achieve the best combination of what one has available. Many patterns of self-regulation break down when people are challenged, under stress or fatigued; typical conditions for entrepreneurial activity. Through the adoption of unobtrusive wearable sensors this study makes a methodological contribution by visualizing the depletion and recovery cycles and associating these with the ups and downs of the entrepreneurial journey. We show how the cycles of self-regulation depletion and recovery are impacted by the entrepreneur journey, demonstrating the importance of maintaining self-regulation and potential consequences on performance and wellbeing when self-regulation is not maintained. We put forward a research agenda for the study of entrepreneurial action, calling on researchers to expand the theory of entrepreneurial action by adding an entrepreneur centred explanation for entrepreneurial activities.
... A large body of evidence indicates that reduced HRV in part mediates the relationship between depression and cardiac mortality [5,[47][48][49]. However, research on HRV and depression has generally been conducted in patients who already have CVD so that the association between depression and HRV may have been overestimated. ...
... Physiological indices are insightful indicators of emotion dysregulation (Beauchaine et al., 2007;Davies et al., 2015) as visceral reactions are promising biomarkers of having experienced a traumatic event (Schuettler & Boals, 2010). Heart rate variability (HRV) is a recognized physiological index of self-regulatory capacity (Applehans & Luecken, 2006;Reynard et al., 2011) with diminished HRV indicating emotional dysregulation (Chalmers et al., 2014;Godfrey et al., 2019). Decreased HRV is evident in PTSD (Gillie & Thayer, 2014) and associated with poor performance on cognitive control tasks (Gillie & Thayer, 2014;Nagpal et al., 2013;Norte et al., 2013). ...
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Background and objectives Few studies have evaluated the link between working memory (WM) and post-traumatic stress disorder (PTSD). Further, it is unknown whether this relationship is accounted for by other relevant variables including negative affect, emotional dysregulation, or general non-WM-related cognitive control deficits, which are associated with PTSD. The purpose of this study was to determine the extent to which a computerized WM task could predict PTSD symptomology incrementally beyond the contribution of other relevant variables associated with PTSD. Methods Thirty veterans were eligible to complete emotional symptom questionnaires, a heart-rate variability measure, and computerized tasks (i.e., emotional Stroop and automated complex span tasks). A three-stage hierarchical regression was conducted with the PCL-5 total score and symptom clusters (i.e., re-experiencing, avoidance, hyperarousal, and negative cognition/mood) as the dependent variable. Results Results revealed that only the re-experiencing symptom cluster was significantly predicted by executive, verbal, and visuospatial WM tasks, which explained an additional 29.7% of the variance over and above other relevant variables. Most notably, the visuospatial task was the only WM task that significantly explained PCL-5 re-experiencing symptoms. Limitations This study was based on a small sample of veterans with PTSD and causality cannot be determined with this cross-sectional study. Conclusions Overall, the results suggest that deficits in visuospatial WM are significantly associated with PTSD re-experiencing symptoms after controlling for other relevant variables. Further research should evaluate whether an intervention to improve visuospatial WM capacity can be implemented to reduce re-experiencing symptoms.
... People use her/his emotion regulation process (including fuzzy neural intonation) to manage positive and negative emotions, as shown by HRV continuity. Leonard et al. [26] it is believed that low HRV may indicate poor physical and mental health and other life problems. ...
... A considerable number of studies have associated vagally mediated HRV to self-regulatory capacity [29], emotion regulation [30], and the feeling of coherence of an individual [31]. The term cardiac coherence or physiological coherence is used to describe the measurement of the orderliness and stability in the synchronous and harmonious functioning of the regulatory systems of the body at any given time [28]. ...
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This study aimed at assessing the value of heart rate variability (HRV) as a stress indicator before and after a final re-sit exam among healthy sixth grade medical students. Fifty participants were recruited for the study (test group, n = 30; control group, n = 20). Each participant was examined for 5 minutes pre and post exam periods using the Heartmath proprietary protocol. EmWave equipment was used to detect, record and analyze the HR and to plot out the variability in discrete percentages for low, medium and high coherences. Results indicated that mean percentage coherence score was significantly higher in the test group (p < 0.05) at low cardiac coherence domain, but lower (p < 0.05) at the high coherence domain, compared with the control. Coherence score was significantly higher (p < 0.05) after the exam indicating release from stress, as compared to before the examination when stress was observable among the exam candidates. There were no significant gender differences observed in cardiac coherence scores before and after examination. Our findings indicate that HRV is a reliable indicator of real-time exam stress and supports future clinical use of HRV as a non-invasive and simple stress test.
... It is also important to consider our results in light of the fact that RSA levels are highest during non-stressed or relaxed states. In fact, greater effort has long been associated with parasympathetic withdrawal (Luft, Takase, & Darby, 2009;Lundberg & Frankenhaeuser, 1980), and researchers have documented RSA withdrawal when using effortful emotion regulation inductions and tasks (LeMoult et al., 2016;Reynard, Gevirtz, Berlow, Brown, & Boutelle, 2011). Thus, the effort of applying self-compassion may have influenced RSA levels during the SR induction. ...
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