Available online at:
https://cc.arcabc.ca/islandora/object/cc%3Apsycjournal

J Camosun Psyc Res. (2022). Vol. 4(1), pp. 97-129.
Submitted Fall 2021; accepted June 2022.

The Effect of Nutrition on Emotions.
Authors: Chien-Lin Hou*, Molly Mifsud, and Jillian Yendrys
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: jianlin628@gmail.com

ABSTRACT
In this paper, we sought to understand the biological effects of nutrition on mood so that we
could learn how food intake contributes to emotional state. Previous research has predicted that
the effect of chocolate leads to an immediate change in emotional status, that food negatively
affects emotional regulation during states of hunger, and that regular overconsumption of food
can have negative effects on emotional health. In our first (correlational) study, we tested the
strength of these relationships by examining naturalistic daily changes in their variables
longitudinally over a period of one week. We measured chocolate consumption by recording the
amount and concentration of cocoa in chocolate consumed, measured food intake by recording
caloric consumption and perceived levels of hunger through the day, rated level of fullness after
eating, and perceived emotional/mood states using the Shortened Unidirectional but Multifaceted
version of the Profile Of Mood States (SUM-POMS) Scale for the average degree of mood
changes based on these variables experienced throughout each day. Based on the strength of
correlation found between food consumption and mood in our correlational study, we then
conducted a second (experimental) study to test for a causal relationship between the two
variables. Over a 10-day period, participants alternated between either higher calorie
consumption (approximately 2130) calories) or lower calorie consumption (approximately 1460
calories) and measured the effect of this manipulation on mood by recording participant
perceptions of their mood along with observations from a partner. Data pooled across
participants in our correlational study did not find a significant correlation of mood with food
consumption, hunger, overfullness, and or chocolate consumption. However, the results of our
experimental study determined a causal role of food consumption upon mood state. A possible
practical application of these findings could be modestly increasing calorie consumption, within
the amounts one typically eats, as a way to help improve mood states.
1. Introduction
1.1 Research Problem
Food has the potential to induce both
positive and negative emotions in
individuals. Although every individual has
their own preferred food which can in turn

contribute to a positive emotional state, the
underlying biological mechanisms of food
intake can apply to all people. It is also
apparent that states of hunger have the
ability to induce negative emotions and
feelings of exhaustion. In addition,
incidents of overeating can sometimes lead
to feelings of irritability or sadness after the
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episode. The purpose of this study is to
understand the effect of food on emotions so
that we can learn to experience the benefits
of nutrition on emotional well being.
1.2 Literature Review
One factor found in previous research
that contributes to mood swings was
chocolate consumption. An experiment
conducted by Macht and Mueller (2007)
aimed to examine the relationship between
chocolate consumption and changes in mood
states. In their experiment, participants were
randomly assigned into two groups in which
one group received a piece of chocolate (5
g) and the other group received a cup of
spring water (20 ml). Three different
sequences of films (happy, sad, and neutral)
were shown as clips to participants prior to
consumption. Mood states were selfreported for three times before watching the
film, after watching the film, and after
chocolate or water consumption using a 25point scale subdivided into five categories.
The results of mood induction indicated that
depressed mood was reported after seeing
the sad sequence film while the happy film
improved moods and there was no
difference made by the neutral sequence
film. Furthermore, eating a piece of
chocolate improved mood states after a sad
film in comparison with water consumption,
whereas chocolate and water did not show a
difference in mood states after the neutral
film. Results also showed that after the
happy film, participants were more likely to
rate their mood higher with chocolate than
with water. However, it is not clear whether
it was the chocolate itself that made the
difference or its palatability, requiring
further research to determine.
Another factor found in previous research
to affect mood-swings is how states of
hunger negatively impact emotional

regulation. For example, in an experiment
by MacCormack and Lindquist (2019),
researchers examined the relationship
between hunger and negative emotions. In
the experiment, the researchers randomly
selected a group of 218 participants from the
age of 18 to 71 years (46% female, 54%
male). During the experiment, the hunger
level of the participants was self-reported on
a scale of 1-6 (1= not hungry at all, 4=
somewhat hungry, 6= extremely hungry;
range 1-5, median=2.15; standard deviation
=1.32). To test the correlation between
negative emotions and hunger, the
researchers used ambiguous pictographs,
reaction time tests and surveys. The
ambiguous pictographs simultaneously
represented both neutral and negative
images. As the researchers predicted, the
participants who reported a higher rate of
hunger interpreted more of the ambiguous
pictographs as negative. These results
demonstrate that there is potentially a link
between hunger and emotional regulation, as
the interpretation of a negative image likely
indicates that the individual had a negative
mindset due to a lack of food. In addition,
there was a negative correlation between the
self-reported hunger scale and the survey
regarding the difficulty of the QRTEngine
response time test. Thus, the individuals
who reported a higher level of hunger also
reported that they felt that the test was more
difficult. Because a large portion of the
experiment is based on self-reported results,
further testing is needed to determine the
true correlation between hunger and
emotional regulation.
Previous research has also demonstrated
a relationship of eating beyond physical
needs (non-homeostatic eating patterns),
such as binge eating and overeating, with
emotional distress and depression. In a study
by Racine et al. (2019) including 998
undergraduate women and men (54.6%
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female) ranging from 18 to 44 years old,
researchers sought to better understand the
correlation between non-homeostatic eating
patterns and emotional dysregulation and
other relevant clinical conditions, to
discover if some eating patterns were more
strongly related to negative outcomes than
others. Each participant was given the same
online survey package to complete. Each
package contained 12 questionnaires, where
participants were asked to rate their eating
behaviours and experiences with food,
including the Eating Disorder Examination –
Questionnaire (EDE-Q), Eating Pathology
Symptoms Inventory (EPSI), Binge Eating
Scale (BES) and others. Along with specific
food related surveys, external correlate
measures, such as BMI calculator, and the
Inventory of Depression and Anxiety
Symptoms (IDAS-II) were also used. With
other factors controlled for, distress over
non-homeostatic eating was significantly
associated with depressive symptoms and
eating-related clinical conditions in both
women and men. Feeling/thought items
from the Binge eating scale and others
reflected participant feelings of “shame,
guilt, disgust and worry” over nonhomeostatic eating. Interestingly, the study
found that in women, food addiction was
uniquely correlated with depressive
symptoms, emotional dysregulation, and
eating-related clinical impairment.
Overeating was uniquely correlated with the
same symptoms in men. It appears from this
research that there is a strong relationship
between food overconsumption and negative
emotions.
1.3 Hypotheses
Based on the above literature review, we
predicted the following hypotheses:
Hypothesis #1: If chocolate consumption
increases, positive mood states will increase

Hypothesis #2: If food consumption
decreases, negative emotions will increase.
Hypothesis #3: If food overconsumption
occurs, negative emotions will increase.
2. Methods
2.1 Participants
The three authors of this paper served as
the participants in its studies. The
participants ranged in age from 21 - 34 years
old, with an average age of 25, and included
two females and one male. The participants
were all undergraduate students at Camosun
College who completed the current studies
as an assignment for Psyc 215 (“Biological
Psychology”) and were grouped together
due to their mutual interest in the effects of
food on emotions.
2.2 Correlational Study Methods
We first performed a correlational study
to test concurrently all three of our
hypotheses by examining naturalistic daily
changes in their variables longitudinally.
Each participant kept a study journal with
them at all times over this study’s one week
period in order to record self-observations of
the following five variables: (1) chocolate
consumption, (2) food consumption, (3)
levels of hunger, (4) feelings of fullness
after eating, and (5) mood state.
2.2.1 Chocolate Consumption
To measure chocolate consumption, each
participant recorded in their study journal
the concentrations and amounts of chocolate
that they consumed during this study. The
concentration of chocolate was calculated
and labeled by denoting the cocoa
percentage which is measured by weight of
all ingredients. The amount of chocolate per
consumption was measured in grams.
2.2.2 Food Consumption
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To measure food consumption,
participants recorded what they ate through
the day using the MyFitnessPal application,
which also recorded the calories caloric
content of the food eaten.
2.2.3 Levels of Hunger
To measure states of hunger, participants
recorded their perceived levels of hunger
throughout the day using a hunger scale of 1
- 4 (1 – not hungry, 2 – somewhat hungry, 3
– very hungry, 4 – extremely hungry).
2.2.4. Feelings of Fullness
To measure feelings of fullness after
eating, participants rated their level of
satiety within 20 - 30 minutes of completing
every meal. Participants recorded their
perceived level of fullness on a scale of 0 - 5
(0 - Very hungry, 1 - somewhat hungry, 2 satisfied, 3 - full, 4 - overfull, 5 - extremely
overfull).
2.2.5 Mood States
To measure mood states, each participant
rated how they felt using the Shortened
Unilateral Multifaceted Profile of Mood
States (SUM-POMS) scale. Participants
indicated how they felt from a list of eight
basic emotions: Sadness, Anxiety, Anger,
Tiredness, Confusion, Joy, Contentment,
Love, Energy, or Acuity. Each emotion was
rated on a scale of 0 - 100 to indicate the
intensity of the emotion felt: 0 = not at all,
25= a little, 50 = moderately, 75 = quite a
lot, 100 = extremely. Participants also rated
their overall mood/valence on a scale of 100 to 100: -100 = extremely unpleasant, 50 = moderately unpleasant, 0 = neutral, 50
moderately pleasant, and 100 = extremely
pleasant. (See Appendix for complete list of
items in this questionnaire.)
2.3 Correlational Study Planned 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
(amount and concentration of chocolate
consumed, caloric consumption, perceived
levels of hunger, feelings of fullness after
eating) with their outcome variable
(emotional/mood states). For testing
Hypothesis #1, we correlated the amount of
chocolate consumed each day and the
concentration of cocoa, with the
participants’ emotional state after
consumption. For testing Hypothesis #2, we
correlated the participants’ caloric
consumption with the impact on their
emotional state. We also correlated the
participants' perceived levels of hunger with
their emotional state. For testing Hypothesis
#3, we correlated the daily measure of
feelings of fullness after eating of each
participant with their emotional states. We
performed all of the above correlations
separately for each participant as well as
using data pooled across all of the
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).
2.4 Experimental Study Methods
Based on the strength of the correlation
between amount of food consumed and
mood found in our correlational study, we
chose to conduct an experimental study to
test for a causal relationship between these
two variables from Hypothesis #2.
We manipulated the independent
variable, calories consumed, over a week
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period by assigning participants to
alternating days of either a higher calories
experimental condition or a lower calorie
control condition. High and low-calorie
amounts were determined by assessing each
participant's typical calorie consumption
during our correlation study.
On higher calorie experimental days,
participants consumed approximately the
number of calories that was comparable to
the higher amount of their recorded daily
calorie intake, which averaged out to 2,130
calories. On lower calorie control days,
participants consumed the number of
calories that was similar to the lowest
amount of their recorded intake, which was
an average of 1,460 calories. We based the
caloric amount on the approximate average
participants typically ate across the oneweek period of the correlational study.
In order to account for experimenter
expectancy effects, each participant
recruited a roommate or partner to act as an
observer and record an outside perspective
of the participant’s mood. The partners were
unaware of the conditions of the experiment,
and were informed to observe the
participant, and rate their mood using the
same mood scale described in the
correlational study.
The order of conditions was assigned
using an alternating method, with
participants alternating daily between
experimental and control conditions over the
course of the week.
The dependent variable, mood, was then
measured by the participant at the end of the
day using the same mood scale and
procedure described above in the
correlational study. Both the participant’s
perceived mood and the observer's
perception of the participant’s mood were
recorded.
2.5 Experimental Study Planned Analyses

To assess the statistical significance of
differences seen in mood on higher-calorie
experimental days versus lower-calorie
control days, participant’s t-tests were
performed.
We performed t-tests separately for each
participant as well as using data pooled
across all of the participants. For the t-tests
using pooled data, in addition to using the
raw data, we also performed t-tests 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. An average difference between
conditions was considered statistically
significant if, using a one-tailed distribution
(i.e., to determine if there is a difference
between groups in a specific direction), the
probability of its random occurrence (p) was
< .05 (i.e., less than 5% of the time expected
by chance alone).
3. Results
3.1 Correlational Study Results
As shown in Table 1, overall our results
were not statistically significant. Food
consumption and feelings of fullness were
significantly correlated with mood for one
participant (for Participant #3, r = .81, p =
.02, and r = -.88, p = .005, respectively).
However, food consumption was not
significantly correlated with mood using
pooled raw data (r = -.01, p = .95; see Figure
3) or standardized data (r = .34, p = .13; see
Figure 4). Feelings of fullness was also not
significantly correlated with mood across
pooled raw data (r = -.03, p = .88; see Figure
7) or pooled standardized data (r = -.27, p =
.24; see Figure 8). Chocolate consumption
and mood were not significantly correlated
for any single participant, or using pooled
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raw data (r = -.01, p =.98; see Figure 1) or
pooled standardized data (r = -.06, p = .79;
see Figure 2). Similarly, level of hunger and
mood did not demonstrate a significant
correlation for any participant, or by using
pooled raw data (r =.03, p =.88; see Figure
5) or standardized raw data (r =.09, p =.70;
see Figure 6). Based on a comparison of the
correlation coefficients using either the
pooled raw data or the pooled standardized
data, food consumption showed the
strongest correlation with mood out of all
the variables examined.

We also hypothesized that if amount of food
consumed decreased (Hypothesis #2), or
food overconsumption increased
(Hypothesis #3), then negative feelings
would increase. Data pooled across
participants in our correlational study did
not support the predicted relationship of
mood states with chocolate consumption,
amount of food consumed, or food
overconsumption (Hypothesis #1, #2 and
#3). However, the results of our
experimental study were able to establish a
causal relationship between food
consumption and mood.

3.2 Experimental Study Results
4.2 Relation of Results to Past Research
As shown in Table 2, significant
differences were found in participants' selfreported mood between the high-calorie
condition and low-calorie conditions.
Statistically significant differences between
these conditions were seen with two of the
three single participants’ data (p ≥ 0.05),
pooled raw data (p = 0.035; see Figure 9),
and pooled standardized data (p = 0.015; see
Figure 10).
As shown in Table 3, significant
differences were found in the observers’
accounts of participants’ mood between the
high-calorie condition and low-calorie
conditions. Statistically significant
differences between these conditions were
seen with one participant’s data (p = 0.04)
and pooled standardized data (p = 0.042; see
Figure 12), but not with pooled raw data (p
= 0.069; see Figure 11).
4. Discussion
4.1 Summary of Results
Based on previous research, we
hypothesized that increases in chocolate
consumption would be followed by
increased positive mood (Hypothesis #1).

Based on the results we obtained from
our correlation study, we did not report a
relationship between chocolate consumption
and induced mood states that was found by
previous research. Participants in the
experiment conducted by Macht and Mueller
(2007) reported higher mood states after
watching a happy film and indicated that
chocolate improved their mood after
watching a clip of a sad film. Our
correlational study differed from previous
research by not introducing mood induction
to the participants. In their experiment,
participants were shown excerpts from
different emotion-related movies to induce
mood; in our correlation study, however,
participants reported their mood states after
chocolate consumption without having any
access to mood induction. The lack of
induced mood states in the correlation study
could be responsible for the different results
obtained in our study.
Our correlational study did not confirm
the relationship between hunger and
negative emotions reported by previous
research. MacCormack and Lindquist (2019)
found that individuals who reported a higher
hunger level displayed poorer emotional
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regulation and had a higher tendency to
exert negative emotions than those who
were not hungry. The methodology of our
correlational study differed from that of the
MacCormack and Lindquist study in that we
used a mood scale and a hunger scale to
determine the effect of hunger, whereas the
MacCormack and Lindquist study used
ambiguous pictographs and a QRTEngine
response time test in addition to a mood and
hunger scale to determine the effect of
hunger. The discrepancy in the results could
be due to our correlational study not using as
many methods to record emotions and
therefore not yielding as accurate of results.
Another difference seen between our
correlational study and the MacCormack and
Lindquist study is seen in the timing of the
data collection. In the MacCormack and
Lindquist (2019) study, the participants
completed the test and immediately reported
hunger at the end of the test, whereas in our
correlational study mood was reported as a
daily average. Future studies could increase
accuracy of the relationship of two variables
by implementing multiple mood scales
throughout the day rather than reporting a
single number daily. This improvement
could more accurately depict how hunger
influences mood by observing how mood
changes after and between meals.
The results of our study on the correlation
between food overconsumption and negative
emotions did not demonstrate consistency
with the research done by Racine et al
(2019). The Racine et al.’s (2019) study
gave participants multiple questionnaires
about their eating behaviours and emotions
and found relationships between negative
emotions and non-homeostatic eating
patterns, while our study involved
evaluating participant’s subjective mood
experience in relation to how full that they
felt. Our participants did not consistently
report negative mood on days where they

may have experienced increased feelings of
fullness after eating. There are a couple of
ways that our methodology may have
affected our results. First, we were
measuring multiple variables at once, which
may have had a confounding effect on the
daily average of each participant's perceived
mood state. Second, our study relied on a
daily average of perceived mood rather than
mood states immediately following a bout of
excessive food consumption. Future studies
should consider the time of reporting when
considering the relationship between food
overconsumption and mood.
4.3 Implications of Results
We conducted this research to better
understand the effect of food on emotions so
that we could experience the benefits of
nutrition on emotional well being. Based on
our correlational study, we hypothesized that
there is a threshold in which food
consumption produces improvements in
mood but surpassing this threshold can
result in decreased mood. Based on our
experimental results, we can suggest
consuming the recommended amount of
calories each day to maintain an overall
positive mood.

References
MacCormack, J. K., & Lindquist, K. A.
(2019). Feeling hangry? When hunger is
conceptualized as emotion. Emotion,
19(2), 301–319.
https://doi.org/10.1037/emo0000422.supp
Macht, M., & Mueller J. (2007). Immediate
effects of chocolate on experimentally
induced mood status. Appetite, 49(3),
667-674.
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https://doi.org/10.1016/j.appet.2007.05.0
04
Racine, S.E., Hagan, K. E., & Schell, S. E.
(2019). Is all nonhomeostatic eating the
same? Examining the latent structure of

nonhomeostatic eating processes in
women and men. Psychological
Assessment, 31(10), 1220-1233
https://doi.org/10.1037/pas0000749.supp

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Table 1
Correlations for Study Variables

Participant
#1

Participant
#2

r

n

r

n

r

n

r

n

Pooled
standardized
data
r
n

0.10

7

0.12

7

0.81*

7

-0.01

21

0.34

21

Hunger & mood

-0.34

7

0.70

7

-0.09

7

0.03

21

0.09

21

Overfullness & mood

0.20

7

-0.13

7

-0.88*

7

-0.03

21

-0.27

21

0.36

7

-0.24

7

-0.32

7

-0.01

21

-0.06

21

Variables

Food consumption &
mood

Participant
#3

Pooled raw
data

Chocolate
consumption &
mood
* p < .05.

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Table 2
Descriptive Statistics for Self Observed Mood States Across Different Conditions

Higher calories

M

18

58*

44*

Pooled
raw
data
40*

consumed

SD

45.5

19.24

15.17

32.51

0.82

n

5

5

5

15

15

Lower calories

M

28

20

4

17.33

-0.38

consumed

SD

38.34

37.42

24.08

33.05

0.97

n

5

5

5

15

15

Condition

Statistic

Participant Participant Participant
#1
#2
#3

Pooled
standardized
data
0.38*

Note. M, SD, and n, represent mean, standard deviation, and sample size, respectively. Mood was
measured on a scale from -100 – to +100, where -100 = extremely unpleasant and +100=
extremely pleasant.
* p < .05 for comparison of higher calories condition with its respective lower calories condition.

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Table 3
Descriptive Statistics for Partner Observed Mood States Across Different Conditions

Higher calories

M

34

68*

48

Pooled
raw
data
50

consumed

SD

31.3

22.8

12.55

26.12

0.94

n

5

5

5

15

15

Lower calories

M

40

40

31

37

-0.3

consumed

SD

22.36

21.21

19.17

19.89

0.92

n

5

5

5

15

15

Condition

Statistic

Participant Participant Participant
#1
#2
#3

Pooled
standardized
data
0.3*

Note. M, SD, and n, represent mean, standard deviation, and sample size, respectively. Mood was
measured on a scale from -100 – to +100, where -100 = extremely unpleasant and +100=
extremely pleasant.
* p < .05 for comparison of higher calories condition with its respective lower calories condition.

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Figure 1
Association Between Chocolate Consumption and Overall Mood Using Pooled Raw Data

Notes. Maker colour differences participants: red = participant #1, orange = participant #2, and
yellow = participant #3. Some data might not be visible in the figure due to overlapping
markers.

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Figure 2
Association Between Chocolate Consumption and Overall Mood Using Pooled Standardized
Data

Notes. Maker colour differences participants: red = participant #1, orange = participant #2, and
yellow = participant #3. Some data might not be visible in the figure due to overlapping markers.

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Figure 3
Association Between Calories and Overall Mood Using Pooled Raw Data

Notes. Maker colour differences participants: red = participant #1, orange = participant #2, and
yellow = participant #3. Some data might not be visible in the figure due to overlapping markers.

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Figure 4
Association Between Calories and Overall Mood Using Pooled Standardized Data

Notes. Maker colour differences participants: red = participant #1, orange = participant #2, and
yellow = participant #3. Some data might not be visible in the figure due to overlapping markers.

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Figure 5
Association Between Hunger and Overall Mood Using Pooled Raw Data

Notes. Maker colour differences participants: red = participant #1, orange = participant #2, and
yellow = participant #3. Some data might not be visible in the figure due to overlapping markers.

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Figure 6
Association Between Hunger and Overall Mood Using Pooled Standardized Data

Notes. Maker colour differences participants: red = participant #1, orange = participant #2, and
yellow = participant #3. Some data might not be visible in the figure due to overlapping markers.

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Figure 7
Association Between Fullness and Overall Mood Using Pooled Raw Data

Notes. Maker colour differences participants: red = participant #1, orange = participant #2, and
yellow = participant #3. Some data might not be visible in the figure due to overlapping markers.

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Figure 8
Association Between Fullness and Overall Mood Using Pooled Standardized Data

Notes. Maker colour differences participants: red = participant #1, orange = participant #2, and
yellow = participant #3. Some data might not be visible in the figure due to overlapping markers.

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Figure 9
Average Mood Across Different Calorie Conditions Using Pooled Raw Data

Notes. Overall mood scores are shown for high-calorie and low-calorie conditions using
pooled raw data from all participants. Errors bars show ± 95% confidence levels. Overlapping
scatterplot shows data from each participant. Marker colour differentiates participants: red =
participant #1, orange = participant #2, and yellow = participant #3

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Figure 10
Average Mood Across Different Calorie Conditions Using Pooled Standardized Data

Notes. Overall mood scores are shown for high-calorie and low-calorie conditions using
pooled standardized data from all participants. Errors bars show ± 95% confidence levels.
Overlapping scatterplot shows data from each participant. Marker colour differentiates
participants: red = participant #1, orange = participant #2, and yellow = participant #3

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Figure 11
Partner Observed Average Mood Across Different Calorie Conditions Using Pooled Raw Data

Notes. Overall mood scores are shown for high-calorie and low-calorie conditions using
pooled raw data from partner observations. Errors bars show ± 95% confidence levels.
Overlapping scatterplot shows data from each participant. Marker colour differentiates
participants: red = participant #1, orange = participant #2, and yellow = participant #3

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Hou, Mifsud,, & Yendrys - J Camosun Psyc Res. (2022). Vol. 4(1), pp. 97-129.

Figure 12
Partner Observed Average Mood Across Different Calorie Conditions Using Pooled Raw Data

Notes. Overall mood scores are shown for high-calorie and low-calorie conditions using
pooled standardized data from partner observations. Errors bars show ± 95% confidence levels.
Overlapping scatterplot shows data from each participant. Marker colour differentiates
participants: red = participant #1, orange = participant #2, and yellow = participant #3

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Appendix

The Shortened Unidirectional but Multifaceted version of the Profile Of Mood States (SUMPOMS) Scale
by Dr. Michael Pollock, Camosun Psychology

Part A. – Individual Negative & Positive Moods
Using the 0 to 100 range scale shown below, for each of the following moods indicate HOW
MUCH YOU ARE FEELING THEM RIGHT NOW.
0

25

50

75

100

Not at all

A little

Moderately

Quite a lot

Extremely

Negative Moods

Positive Moods

MOOD

SCORE

MOOD

SCORE

Sadness

_____________

Joy

_____________

Adjectives: Depressed, Unhappy,
Blue, Hopeless, Miserable,
Helpless, Worthless, and Sad

Anxiety

_____________

Adjectives: Elevated, Optimistic,
Cheerful, Euphoric, and Joyful

Contentment

_____________

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Adjectives: Tense, On Edge,
Uneasy, Restless, Nervous, and
Anxious

Anger

_____________

Adjectives: Peeved, Grouchy,
Annoyed, Resentful, Bitter, Hostile,
Furious, and Angry

Tiredness

_____________

Adjectives: Worn Out, Fatigued,
Exhausted, Weary, Bushed, and
Tired

Confusion

_____________

Adjectives: Unable to Concentrate,
Bewildered, Forgetful, Uncertain
About Things, and Confused

Adjectives: At Peace, Satisfied,
Fulfilled, Relaxed, and Contented

Love

_____________

Adjectives: Fond, Adoring,
Affectionate, Tender, Warm-hearted,
and Loving

Energy

_____________

Adjectives: Lively, Active, Full of Pep,
Vigorous, and Energetic

Acuity

_____________

Adjectives: Mentally Sharp, Focused,
Clear Headed, Efficient, Perceptive,
Insightful, and Acute

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Part B. – Overall Mood/Valence
Using the -100 to 100 range scale shown below, indicate OVERALL HOW YOU ARE
FEELING RIGHT NOW.

-100

-50

0

50

100

Extremely

Moderately

Neutral

Moderately

Extremely

Unpleasant

Unpleasant

Pleasant

Pleasant

Overall Mood Score:

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