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J Camosun Psyc Res. (2023). Vol. 5(1), pp. 78-87.
Submitted Fall 2022; accepted June 2023.

What Physiological Factors Influence State Anger?
Authors: Gauri Arora* and Hayden Frederick
Supervising Instructor: Michael Pollock, Psyc 215 (“Biological Psychology”)
Department of Psychology, Camosun College, 3100 Foul Bay Road, Victoria, BC, Canada V8P 5J2
*Corresponding author email: gauriarora133@gmail.com

ABSTRACT
In this paper, we sought to understand what physiological factors influence state anger in the
hopes of improving awareness of potential triggers. Previous research has predicted that an
increase in heart rate, hunger, and headache pain intensity all lead to an increase in state anger. In
our correlational study, we tested the strength of these relationships by examining naturalistic
daily changes in their variables longitudinally over a period of 11 days. We measured heart rate
by reading our pulse for one minute three times a day, and we measured our hunger, headache
pain and anger levels by using a Likert scale three times a day. For each variable measured, we
added the total of their three daily values together to get an average value for each day. Data
pooled across participants in our correlational study showed significant positive correlations of
anger with hunger and headache pain, and a significant negative correlation with heart rate.
These results provide some insight into what individuals might avoid, such as becoming hungry
or not treating a painful headache, to decrease the likelihood of becoming angry.
1. Introduction
1.1 Research Problem
Anger is useful because it motivates us
into protecting and defending ourselves from
perceived slights. If someone trespasses into
our boundaries or they undermine our
authority, we are liable to become angry and
act to rectify the situation. However, anger is
not always useful – it can be harmful as
well. For example, one might angrily snap at
their partner over dinner plans, or a child
may lash out at a helping hand after stubbing
their toe. In these instances, anger is
unnecessary and can create stress on the
relationships of the involved individuals.
Identifying potential influential factors that
increase the likelihood of becoming angry

and then minimizing the effect of those
factors would be highly beneficial in
reducing this stress.
1.2 Literature Review
One factor that was previously found to
predict entering an angry state is an increase
in heart rate (Lupis et al., 2014). For
example, in a correlational study conducted
by Lupis et al. (2014), they measured
participants heart rate and state anger before
and after a social stress test (Trier Social
Stress Test). The participants were equipped
with a transmitter belt that was placed
around their chests and wrists. They allowed
participants to sit comfortably and relax for
30 minutes while answering benign
questionnaires about their health so as to
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establish a base line heart rate. Five minutes
before the next stage of the testing,
participants were asked to fill out a selfreport questionnaire on their current mood
(the Positive and Negative Affect Schedule).
This questionnaire consisted of 20 items –
10 for positive affect and 10 for negative
affect. Each item was rated on a scale of 1-5
(1 = “very slightly or not at all” and 5 =
“extremely”). The participants were then led
to another room where the social stress test
was conducted. The stress test consisted of a
hypothetical dream job interview where the
participants had to give a five-minute speech
on the suitability of their personality traits
for their dream jobs. The participants
presented in front of a panel of observers
who were instructed to take notes and
remain neutral. If a participant stopped
talking for more than 10 seconds before their
five-minute speech was over, a member of
the panel would ask the participant to
continue. Upon completion of the speech,
the participant was then asked to count
backwards by increments of 17 starting at
2043 as fast and concisely as possible. A
panel member asked the participant to start
back at 2043 if any errors were made. Once
the social stress test was completed, the
affect questionnaire was administered again.
Participants were asked to fill out the
questionnaire while imagining how they felt
during the social stress test. The researchers
analyzed the data for males and females
separately in order to assess gender
differences and found significant
correlations between heart rate and state
anger for men but not for women. The
researchers made note that the social stress
test did not elicit any specific emotion, but
rather encouraged the expression of that
individual’s unique stress response –
whatever emotion that might be.
Additionally, they pointed out that the affect
questionnaires may not have been a reliable

measurement because individuals may not
have provided the most accurate account of
their emotional states. A final observation
that the researchers made was that females
in general may be more likely to express
rather than suppress their emotions, the latter
being more common in males, and so were
able to cope better than males during the
social stress test. This may have led to
limited physiological arousal (heart rate
increase) in the females during the social
stress test. With all the above factors in
mind, the researchers concluded that further
testing with more reliable instruments was
required to confirm their gender-varied
findings.
Another factor previously found to
predict an increased likelihood of entering
an angry state is higher hunger levels
(Swami et al., 2022). For example, in a
correlation study conducted by Swami et al.
(2022), they measured participants levels of
hunger, irritability, and anger at various
times throughout the day. The participants
were prompted via an app on their
cellphones to rate each of these states on a
VAS (visual analogue scale), a horizontal
line resembling a ruler that is scaled 0-100,
with 0 being none and 100 being intolerably
extreme. The participants self-reports were
collected via this app five times a day, two
of which were at random times, for a period
of 21 days. The participants were asked to
conduct their lives as they normally would
and to continue with their normal eating
routines. Once the data collection period was
over, researchers used Russell’s affect grid
to visually represent the correlation between
hunger levels and positive or negative affect.
The findings were significant: higher hunger
was correlated with higher negative affect.
Based on these results, the researchers
suggested that both state and trait hunger
were predictors of the intensity of an
individual's negative affects – specifically
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anger and irritability. If individuals allow
themselves to become overly hungry it is
possible that they may find themselves more
prone to becoming angry.
A final factor previously found to
exacerbate state anger was headache pain
(Nicholson et al., 2003). For example, in a
correlational study conducted by Nicholson
et al. (2003), they compared participants
headache and anger tendencies using
questionnaires specifically designed to
screen for either headaches or for anger,
anxiety, and depression, and found a
correlation between individuals that were
prone to headaches and those that
experienced higher levels of anger. The
researchers were able to isolate and thus
eliminate anxiety and depression as possible
confounds, therefore ensuring only anger
was being correlated with headaches
intensity. Interestingly, participants that
tended to hold in their anger, labelled as
anger-in, had the highest rates and highest
intensity of headaches. Consideration was
given to the relationship between increases
in muscle tension due to anger-in expression
and headaches intensity. Based on these
results, the researchers suggested further
consideration be given to those who inhibit
angry feelings and how this maladaptive
coping strategy may be negatively affecting
pain, specifically headaches and migraines.
It is possible that a feedback loop is created,
in that higher levels of pain may lead to even
higher levels of anger. Further testing is
required to investigate this possibility.
1.3 Hypotheses
Based on the above literature review, we
predicted the following hypotheses:
• Hypothesis #1: If heart rate increases,
then anger will increase.
• Hypothesis #2: If hunger increases, then
anger will increase.

• Hypothesis #3: If headache pain
increases, then anger will increase.
2. Methods
2.1 Participants
The two authors of this paper served as
the participants in its studies. The
participants were 20 and 25 years old, with
an average of 22.5 years, and included two
cisgender women. The participants were
both undergraduate students at Camosun
College who completed the current study as
an assignment for Psyc 215 (Biological
Psychology) and were grouped together due
to their mutual interest in Anger. Both the
participants felt they were prone to anger,
and so believed there was ample opportunity
to assess angry states and the factors that
might affected them.
2.2 Materials and Procedures
We performed a correlational study to
test concurrently all our hypotheses by
examining naturalistic daily changes in their
variables longitudinally. Both participants
kept a study journal with them at all times
over the course of this 11-day study period
in order to record self-observations of the
following four variables: (1) heart rate, (2)
hunger, (3) headache pain, and (4) anger.
Heart Rate - To measure heart rate over
the period of 11 days, each participant's
pulse rate was measured for one minute
three times a day. The participants cell
phones had preset timers for 8 a.m., 12 p.m.,
and 6 p.m. Each value was logged in their
study journal, and at the end of each day an
average heartrate was calculated.
Hunger - To measure hunger levels over
the period of 11 days, a Likert scale was
used to rate current hunger three times a day.
The participants cell phones had preset
timers for 8 a.m., 12 p.m., and 6 p.m. At
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each alarm, a rating was made using the
Likert scale and was logged in their study
journal. At the end of each day, an average
value was calculated. The question used for
the Likert scale was: “How hungry are you
at the moment (0 = not hungry at all, 100 =
very hungry)?”
Headache Pain - To measure headache
pain over the period of 11 days, a Likert
scale was used to rate current headache pain
three times a day. The participants cell
phones had preset timers for 8 a.m., 12 p.m.,
and 6 p.m. At each alarm, a rating was made
using the Likert scale and was logged in
their study journal. At the end of each day,
an average value was calculated. The
question used for the Likert scale was: “How
intense is your headache pain at the moment
(0 = no pain at all, 100 = unbearable pain)?”
Anger - To measure anger levels over the
period of 11 days, a Likert scale was used to
rate current their anger levels three times a
day. The participants cell phones had preset
timers for 8 a.m., 12 p.m., and 6 p.m. At
each alarm, a rating was made using the
Likert scale and was logged in their study
journal. At the end of each day, an average
value was calculated. The question used for
the Likert scale was: “How angry are you at
the moment (0 = not angry at all, 100 =
extremely angry)?”
2.3 Statistical Analyses
To assess the strength and statistical
significance of associations between
variables predicted by our three hypotheses,
we performed Pearson product moment
correlations of their predictor variables
(heartrate, hunger, and headache pain) with
their outcome variable (anger). For testing
Hypothesis #1, we correlated the average
heart rate of each participant with the
average anger they felt each day. For testing
Hypothesis #2, we correlated the average

hunger felt by each participant with the
average anger they felt each day. For testing
Hypothesis #3, we correlated the average
headache pain felt by each participant with
the average anger they felt each day. We
performed all the above correlations
separately for each participant as well as
using data pooled across both participants.
For the correlations using pooled data, in
addition to using the raw data, we also
performed correlations after we had first
transformed the data from each participant
into z-scores in order to standardize
differences in averages and variability seen
between the participants in their data and
thus make them more comparable. A
correlation coefficient was considered
statistically significant if the probability of
its random occurrence (p) was < .05 (i.e.,
less than 5% of the time expected by chance
alone).
3. Results
As shown in Table 1, hunger, headache
pain, and heart rate were significantly
correlated with anger. Although being
statistically significant for participant one (r
= .86, p = .0003), hunger was not
statistically significant for participant two (r
=.20, p = .56). However, a significant
correlation between hunger and anger was
found by using pooled raw data (r = .70, p
=.0001; see Figure 1) and pooled
standardized data (r = .53, p = .01; see
Figure 2). Likewise, statistically significant
correlations were found between headache
pain and anger for participant one (r =.85, p
= .0004), but not participant two (r = .26, p
= .45), and both the pooled raw data (r = .71,
p = 0.00009; see Figure 3) and the pooled
standardized data (r = .55, p = .007; see
Figure 4) were statistically significant.
Finally, heart rate and anger were
significantly correlated for participant one (r
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= -.67, p = .02), but not for participant two (r
= -.21, p = .54) and while pooled raw data
was not significant (r =-.26, p = .24; see
Figure 5), pooled standardized data was
statistically significant (r = -.44, p = .04; see
Figure 6). Based on a comparison of the
correlation coefficients using either the
pooled raw data or the pooled standardized
data, headache pain showed the strongest
correlation with anger.
4. Discussion
4.1 Summary of Results
Based on past research, we hypothesized
that increases in three variables would be
followed by an increase in anger. These
variables are as follows: the heart rate
(Hypothesis #1), the level of hunger
(Hypothesis #2), and the intensity of
headache pain (Hypothesis #3). Data pooled
across participants in our correlational study
supported the predicted relationships of
hunger (Hypothesis #2) and headache pain
intensity (Hypothesis #3), but not heart rate
(Hypothesis #1) as a heart rate showed a
negative correlation with anger.
4.2 Relation of Results to Past Research
The ability of our correlational study to
predict anger based on heart rate is not in
line with previous research. Lupis et al.
(2014) found that an increase in heart rate is
positively correlated with an increase in
anger – at least in males. However, our
study found a negative correlation between
heart rate and anger: as heart rate increased,
anger level decreased. It is possible we
found a negative correlation because our
study consisted of only females, whereas
Lupis et al. (2014) included both males and
females in their study. Additionally, the
method used to measure heart rate was not

as precise in our study as was in the study
conducted by Lupis et al. (2014). More
research is necessary to confirm our results
and should include both genders as well as
more precise methods of measurement.
Our correlational study on the
relationship between hunger and anger
supports the findings of Swami et al. (2022).
Levels of hunger, which can also be
described as blood glucose levels, appear to
affect anger in people. However, Swami at
al. (2022) measured not only anger, but also
irritability and negative feelings in general,
while we only investigated the correlation
between hunger and anger in our study.
With this in mind, future research might
focus on other negative emotions, such as
anxiety, irritability, and depression so as to
give a fuller understanding of the
relationship that hunger might have with
emotions.
Finally, our correlational study on the
relationship between headache pain and
anger supported the study conducted by
Nicholson et al. (2003), despite using both a
different demographic, and more complex
anger measurements and parameters. In their
study, they used chronic headache
participants vs headache-free participants
and not only measured trait anger but also
anger-in and anger-out expression as well as
aggression and hostility (Nicholson et al.,
2003). The participants in our study would
have met the criteria for the headache-free
group, yet we still found a correlation
between the variables. This speaks to the
potential generalizability between the
relationship of headache pain and anger,
despite the chronicity of the pain. Future
studies would benefit from investigating
different modes of anger expression (angerin vs. anger-out), specifically which mode is
most likely to be expressed when suffering
from headache pain.
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4.3 Implications of Results

References

Possible practical applications of our
current findings are avoiding physiological
factors, such as hunger and headache pain,
that increase the likelihood of becoming
angry. For example, encouraging individuals
to keep close at hand both a snack to satiate
hunger and pain medication to lessen the
intensity of a headache may be beneficial in
reducing the amount of time spent in an
unnecessary and unjustified angry state.
The desire to lessen the amount of time
we ourselves spend in an angry state was the
inspiration for this study. Understanding the
negative impact that being angry can have
on those around us encouraged us to find
ways of reducing our own anger. Based on
our results, it may be that keeping well fed
and managing our headache pain effectively
could be a useful starting point in this
pursuit.

Lupis, S. B., Lerman, M., & Wolf, J. M.
(2014). Anger responses to psychosocial
stress predict heart rate and cortisol stress
responses in men but not women.
Psychoneuroendocrinology, 49, 84-95.
https://www.ncbi.nlm.nih.gov/pmc/article
s/PMC4165699/
Nicholson, R. A., Gramling, S. E., Ong, J.
C., & Buenevar, L. (2003). Differences in
anger expression between individuals
with and without headache after
controlling for depression and anxiety.
Headache, 43(6), 651-663.
https://headachejournal-onlinelibrarywileycom.libsecure.camosun.bc.ca:2443/doi/ep
df/10.1046/j.15264610.2003.03108.x
Swami, V., Hochstoger, S., Kargl, E., &
Stieger, S. (2022). Hangry in the field:
An experience sampling study on the
impact of hunger on anger, irritability,
and affect. PLoS ONE, 17(7).
https://journals.plos.org/plosone/article?i
d=10.1371/journal.pone.0269629#sec005

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Table 1
Correlations for Study Variables
Variables

Hunger level &
Anger level
Headache pain
& Anger level
Heart rate &
Anger level

Participant #1

Participant #2

Pooled raw data

r
.86*

n
11

r
.20

n
11

r
.70*

n
22

Pooled
standardized
data
r
n
.53*
22

.85*

11

.26

11

.71*

22

.55*

22

-.67*

11

-.21

11

-0.26

22

-.44*

22

Notes. r = correlation coefficient, n = number of days sampled.
* p < .05

Figure 1
Association Between Hunger Level and Anger Level Using Pooled Raw Data

Notes. Marker colour differentiates participants: red = participant #1, orange = participant #2.
Some data might not be visible due to overlapping markers.

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Figure 2
Association Between Hunger Level and Anger Level Using Pooled Standardized Data

Notes. Marker colour differentiates participants: red = participant #1, orange = participant #2.

Figure 3
Association Between Headache Pain and Anger Level Using Pooled Raw Data

Notes. Marker colour differentiates participants: red = participant #1, orange = participant #2.
Some data might not be visible due to overlapping markers.

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Figure 4
Association Between Headache Pain and Anger Level Using Pooled Standardized Data

Notes. Marker colour differentiates participants: red = participant #1, orange = participant #2.
Some data might not be visible due to overlapping markers.

Figure 5
Association Between Heart Rate and Anger Level Using Pooled Raw Data

Notes. Marker colour differentiates participants: red = participant #1, orange = participant #2.

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Figure 6
Association Between Heart Rate and Anger Level Using Pooled Standardized Data

Notes. Marker colour differentiates participants: red = participant #1, orange = participant #2.

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