Health, 2016, 8, 125-132
Published Online January 2016 in SciRes. http://www.scirp.org/journal/health
How to cite this paper: Brandt, R., et al. (2016) The Brunel Mood Scale Rating in Mental Health for Physically Active and
Apparently Healthy Populations. Health , 8, 125-132. http://dx.doi.org/10.4236/health.2016.82015
The Brunel Mood Scale Rating in
Mental Health for Physically
Active and Apparently
Ricardo Brandt1,2, Dafne Herrero3, Thaís Massetti4, Tânia Brusque Crocetta2,5,
Regiani Guarnieri5, Carlos Bandeira de Mello Monteiro4,5, Maick da Silveira Viana2,
Guilherme Guimarães Bevilacqua2, Luiz Carlos de Abreu5, Alexandro Andrade2
1State University of West Parana, Marechal Cândido Rondon, Brazil
2Laboratory of Sport and Exercise Psychology, Santa Catarina State University, Florianópolis, Brazil
3Faculty Public Health, São Paulo, Brazil
4Post-Graduate Program in Rehabilitation Sciences, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
5Department of Morphology and Physiology, Faculty of Medicine of ABC, Santo André, Brazil
Received 27 August 2015; accepted 25 January 2016; published 28 January 2016
Copyright © 2016 by authors and Scientific Research Publishing Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY).
There is a positive relationship between mood states and mental health. The aim of the present
study was to investigate the construct validity and internal consistency of the Brunel Mood Scale
(BRUMS) for use with different populations, which are physically active and apparently healthy.
Measures were obtained from 1295 male (N = 709, 34 ± 20 years, mean ± SD) and female (N = 576,
43 ± 24 years, mean ± SD) volunteers. Factor analysis was used, verifying that six factors (compo-
nents) accounted for 62.65% of the total variance of the scale. The Varimax method with Kaiser
Normalization for the rotation of the factors for the main components, and it was observed that
the 24 scale items loaded on six mood factors (anger, depression, tension, vigor, fatigue, and con-
fusion). Internal consistency was good for all the factors identified. We suggest that the results
provide some support for validity of the BRUMS for use with different populations, which are
physically active and apparently healthy.
Mental Health, Mood States, Psychometrics, Brunel Mood Scale, BRUMS
R. Brandt et al.
Over the past few decades, there has been a growing body of literature on mental health . However, there is
still a need to develop related research into the association between physical activity and mental health , as
problems in this context are a worldwide concern in public health , affecting all age groups and accounting
for significant expenditure by governments .
There is a positive relationship between mood states and mental health  . It is considered that the high
level of vigor associated with lower levels of tension, depression, anger, fatigue and confusion is related to a
better mental health condition  .
Among the instruments that evaluate moods, the POMS (Profile of Mood States) stands out as one of the most
widely used in different populations  . The Brunel Mood Scale (BRUMS), derived from the POMS, the
validation of which in Brazil was performed by Rohlfs et al. , was presented as a tool for detection of the
over-training syndrome. In addition, such scales have been used in different populations and contexts in Brazil
- and other countries -.
The 24-item BRUMS measures six identifiable mood states (Tension, Depression, Anger, Vigor, Fatigue, and
Confusion) through a self-report inventory. The respondents rating a list of adjectives, on a 5-point Likert scale
from 0 (not at all) to 4 (extremely), on the basis of how they had been feeling in the previous week, or in the
moment of evaluation  . The six affective mood states subscales are not diagnostic indicators, but refer
to sub-clinical psychological states (mood states) .
This study aims to investigate the construct validity and internal consistency of the BRUMS for different
populations, which are physically active and apparently healthy.
This is a descriptive cross-sectional study with non-probability sampling (Sample size calculation was not con-
ducted before sampling). The participants in the study were 1,295 individuals from Santa Catarina state, south of
Brazil, of both sexes, physically active and apparently healthy: 709 (54.7%) men with a mean age of 34 years
(±20) and 586 (45.3%) women, with a mean age of 43 years (±24). Data were collected during 2013 year, from
February to November.
The BRUMS  has 24 items arranged into six subscales: anger, confusion, depression, fatigue, tension and
vigor (Table 1), each with four items. The research participant selects, from a numerical rating scale of zero to
four (0 = not at all, 1 = a bit, 2 = moderate, 3 = enough; 4 = extremely), the option they believe best represents
the situation at that time, using questions such as “How do you feel now?”, “How have been feeling in the past
week, including today?”, or “How have you been feeling?”.
The items on each subscale are:
• Anger: annoyed, bitter, angry, bad-tempered;
• Confusion: confused, muddled, mixed-up, uncertain;
• Depression: depressed, downhearted, unhappy, miserable;
• Fatigue: worn out, exhausted, sleepy, tired;
• Tension: panicky, anxious, worried, nervous;
• Vigor: lively, energetic, active, alert.
Table 1. Dimensions of BRUMS.
State of musculoskeletal tension and worry.
Emotional state of despondency, sadness, unhappiness.
State of hostility, for others.
State of energy, physical force.
State of tiredness, low energy.
State of feeling stunned, instability in emotions.
Reference: Brandt et al. .
R. Brandt et al.
The sum of the responses of each subscale results in a score that ranges from zero to 16. The questionnaire
does not generate an overall score, and each scale should be examined individually, although the constructs are
Survey participants were characterized with respect to other variables, based on the study of Brandt et al. ,
regarding self perception of sleep quality and self-related health. Self perceived health status and quality of sleep,
composed Likert scale with responses from 0 (“very bad”) to 4 (“excellent”) . These questions were used to
compare means of moods, depending on the variables mentioned in the literature, allowing the visualization of
the use of the scale in the research.
All survey participants signed an informed consent and it was approved by the Research Ethics Committee
(44/2011), according to Resolution 196/96 of the National Health Council. A previously trained researcher ad-
ministered the sample individually. The research procedures were explained and the participants asked to point
out if the matter was not clear. For elderly participants, a printed sheet was presented with the response options.
The response time was no longer than six minutes.
Data were tabulated and analyzed using SPSS software version 21.0. The internal consistency of the subscales
was assessed using Cronbach’s alpha. The authors of the original instrument , found the alpha to be greater
than 0.76, so it is considered an instrument with good internal consistency.
Construct validity was assessed through exploratory factor analysis, which identified the common compo-
nents in a large number of variables. The factor analysis was performed according to the steps proposed by
Dancey and Reidy .
We used the principal components method for extracting the factors and considered only those that presented
an eigenvalue of one. For selected factors, a correlation matrix was generated, where relationships between
items and factors were observed through factor loadings. For the purposes of the matrix, the orthogonal rotation
Varimax method was applied, which maximizes high correlations and minimizes casualties, facilitating analysis.
To analyze the results of the mood states, descriptive and inferential statistics (mean and standard deviation)
were used (Kruskal-Wallis and Mann-Whitney).
In order to confirm the theoretical factors, factor analysis was used, verifying that the six factors (components)
accounted for 62.65% of the total variance of the scale (Table 2). The KMO (Kaiser-Mayer-Olkin) test (X2 =
0.909, p < 0.001) indicated the proportion of the data variance and their values can be considered suitable, as
well as the Bartlett sphericity test (X2 = 11259.9, p < 0.05), concerning the correlation between the data.
Table 3 shows the correlations (factor loadings) for each item with each factor, respectively. We used the
method of the main components with the Varimax method rotation of the factors, with Kaiser normalization.
The saturation with values was greater than 0.30 and the items appear ordered by factor.
It is observed that the 24 scale items loaded on six mood factors (anger, depression, tension, vigor, fatigue and
confusion), corresponding to the analyses found by Rohlfs et al.  in the BRUMS validation to search for
Brazilian athletes and non-athletes.
Table 2. Eigenvalues and explained variance components of the BRUMS.
COMPONENT Eigenvalues initials
Total % Variance % cumulative
1 (Anger) 7.27 30.33 30.33
2 (Depression) 2.62 10.92 41.25
3 (Tension) 1.68 7.03 48.28
4 (Vigor) 1.41 5.91 54.20
5 (Fatigue) 1.13 4.72 58.92
6 (Confusion) 1.01 3.73 62.65
Extraction method: Principal component analysis.
R. Brandt et al.
Table 3. Exploratory factor load for each item in the six factors extracted from the BRUMS.
Component 1 2 3 4 5 6 Cronback Alfa
ITENS Anger Depression Tension Vigor Fatigue Confusion
Annoyed 0.722 0.830
Bitter 0.785 0.826
Angry 0.813 0.831
Bad tempered 0.671 0.830
Depressed 0.648 0.831
Downhearted 0.621 0.831
Unhappy 0.647 0.832
Miserable 0.440 0.832
Panicky 0.388 0.829
Anxious 0.806 0.831
Worried 0.724 0.834
Nervous 0.741 0.831
Lively 0.725 0.851
Energetic 0.773 0.851
Active 0.789 0.851
Alert 0.639 0.852
Worn out 0.776 0.830
Exhausted 0.803 0.829
Sleepy 0.491 0.835
Tired 0.785 0.831
Confused 0.693 0.828
Muddled 0.486 0.828
Mixed-up 0.614 0.832
Uncertain 0.601 0.828
Extraction method: Principal component analysis. Rotation Method: Varimax, with Kaiser normalization.
Anger and Vigor had factor loadings above 0.63 in all items, without existing cross-loading. In Depression,
the items, “depressed”, “downhearted”, and “unhappy” showed high factor loadings, greater than 0.62. The item
“miserable” showed a lower factor loading (0.440). There was cross-loading with the item “confused”. For Ten-
sion, the items “anxious”, “worried”, and “nervous” obtained factor loadings higher than 0.72, with no cross-
loading. Fatigue items obtained factor loadings above 0.70 except for “sleepy”, which showed a lower factor
loading (0.491). Confusion presented three items with factor loadings above 0.60. The item “muddled” had a
lower factor loading and introduced cross-loading with the Depression factor.
The internal consistency of the 24 items was high (α = 0.85). Internal consistency was good for all the factors
identified: Anger α = 0.65; Confusion α = 0.63; Depression α = 0.66; Fatigue α = 0.60; Tension α = 0.65, and
Vigor α = 0.81.
The participants, both men and women, showed high levels of Vigor and low levels of Tension, Depression,
Anger, Fatigue and Confusion (Figure 1), and there are significant differences in the variables Anger, Vigor and
Fatigue between men and women.
R. Brandt et al.
Figure 1. Mood states of men and women engaged in physical activity, apparently healthy.
*Significant difference at p < 0.05. **Significant difference at p < 0.001.
Separating the participants into age groups (Table 4), there is a significant difference between the moods of
the youngest participants (under 18), adults (between 18 and 60 years) and the elderly (over 60 years).
By analyzing the mood depending on self-perceived health status, participants who showed better perception
had lower levels of Depression, Fatigue and Confusion and Vigor, when compared to those with poorer self-
rated health. With the relationship between sleep and moods, all factors are significantly different between those
with a better perception of quality of sleep.
The aim of this study was to investigate the construct validity and internal consistency of the BRUMS, so as to
recognize it as an instrument for measuring mental health in different populations, which are physically active
and apparently healthy.
The BRUMS has been used in different populations of athletes and non-athletes, young people and adults 
, with heart disease , and with fibromyalgia  , among others. Its validation for physically active
and apparently healthy populations showed consistent results, with good reliability and construct validity, as
evidenced by the alpha coefficient and factor loadings, found to be higher than other instrument validation stu-
Generally, the factors were properly loaded in their respective domains. The low cross-existence between the
loading factors is a positive element in the present study, given that other validations showed a higher amount of
cross-loading which does not compromise their results . It has been found that there are six factors with ei-
genvalues above one, similar to those found in Rohlfs et al. . A high internal consistency was observed, with
values of 0.85, whereas all areas had values appropriate for its validation.
In the analysis of the results for the BRUMS application, it is evident that there is a difference in the moods of
men and women, already presented in other studies, as well as for the different age groups    .
Moreover, in the latter, there is a significant difference in all mood factors. When analyzing the results of the
mood states, it is suggested that researchers investigate these characteristic differences in their populations, the-
reby reducing the possibility of error in the data analysis.
In analyzing the results of the self-assessment of health and sleep, it is clear who has a tendency to better
health and sleep, has a mood with greater vigor and less tension, depression, anger, fatigue and confusion. This
would be consistent with the proposed profile by Morgan  entitled the ‘iceberg’ profile (Figure 1), this being
an ideal mental health model. Corroborating this study demonstrates the importance of sleep to mental health, in
the sense of insufficient or poor sleep can cause mental disorders, impairing cognitive function and performance
From these analyses it is evident that the use of BRUMS beyond the detection of the over-training syndrome
, where it has been used in research to delineate the mood profile of different populations, is that it may also
R. Brandt et al.
Table 4. Factors of mood about age, self perceived health status and self perception of sleep quality in physically active sub-
jects, apparently healthy.
Tension Depression Anger Vigor Fatigue Confusion
Age group ** ** ** ** ** **
Less than 18 years (n = 271) 4.8 2.9 1.1 1.2 1.4 2.2 10.7 2.8 3.1 2.7 2.4 2.6
Between 18 and 60 (n = 624) 4.4 3.2 1.6 2.6 1.9 2.9 10.9 3.1 3.6 3.4 2.1 2.6
More than 60 years (n = 385) 2.1 2.4 1.2 2.1 0.7 1.7 9.7 2.8 2.5 2.9 1.1 1.9
Health assessment ** ** * *
Excellent (n = 292) 3.7 3.2 0.8 1.7 1.2 2.3 11.6 2.9 2.7 3.1 1.4 2.1
Good (n = 589) 4.1 2.9 1.3 2.2 1.6 2.5 10.6 2.7 3.2 3.1 2.2 2.4
Regular (n = 134) 3.7 3.1 1.9 2.9 1.7 2.8 9.8 3.1 3.5 3.1 2.1 2.6
Poor (n = 11) 5.7 4.5 3.9 3.5 2.3 3.1 8.7 3.8 4.8 3.8 2.6 3.9
Very bad (n = 5) 2.2 1.6 1.2 2.1 0.6 1.3 10.4 2.9 1.4 1.9 1.0 1.7
Sleep quality perception * ** ** ** ** **
Excellent (n = 105) 4.1 3.0 0.8 1.6 1.4 2.0 12.0 3.1 2.6 2.5 1.5 2.0
Good (n = 419) 4.3 2.9 1.2 2.1 1.5 2.4 11.1 2.7 3.1 2.9 1.9 2.2
Regular (n = 231) 4.8 3.0 1.4 2.4 1.9 2.7 10.5 3.1 3.9 3.6 2.4 2.7
Poor (n = 44) 5.5 3.9 2.7 3.8 3.7 4.2 10.4 2.9 5.3 3.6 3.4 3.4
Very bad (n = 5) 6.2 5.7 7.2 3.9 5.8 4.8 8.0 2.2 6.4 3.7 6.8 4.9
*Significant difference at p < 0.05. **Significant difference at p < 0.001.
be used as a mental health indicator.
From the above, considering that researchers are in different contexts and with different populations, their use of
the BRUMS can investigate mental health in different populations, which are physically active and apparently
All authors participated in the acquisition of data and revision of the manuscript. All authors determined the de-
sign, interpreted the data and drafted the manuscript. All authors read and gave final approval for the version
submitted for publication.
Declaration of Interest
The authors report no conflict of interest. All authors were responsible for the content and writing of this paper.
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