2012. Vol.3, No.8, 590-594
Published Online August 2012 in SciRes (
Copyright © 2012 SciRes.
Mapping the Runner’s Mind: A New Methodology for Real-Time
Tracking of Cognitions
Miguel Qui nt ana 1, O swaldo Rivera 2, Ricardo De La Vega3, Roberto Ruiz3
1Complutense University of Madrid, Madrid, Spain
2Technical University of Madrid, Madrid, Spain
3Autonomus University of Madrid, Madrid, Spain
Received May 15th, 2012; revised June 17th, 2012; a ccepted July 13th, 2012
Recording cognitions in real-time while running has been identified as one of the major limitations in the
field of sport psychology. In this study, a new methodology was developed to overcome this limitation.
For this purpose, 17 competitive long distance runners participated voluntarily. The experimental session
consisted in identifying and storing cognitions (thoughts, emotions, sensations and mental images) while
performing a 30 minutes treadmill run. In addition, participants were asked to register which of these
cognitions were perceived as unpleasant. The mapping task showed a total of 1154 cognitions recorded,
mostly thoughts. In general, during the session, cognitions perceived as unpleasant represented 13.43% of
the total recorded. These cognitions were mainly directed to physical sensations that resulted from stimu-
lations derived from the physical effort of running. Consequently, it is possible to claim an interaction re-
lationship between sensations and exercise workload. The results attempt to demonstrate that the study of
cognitions in real-time is deemed suitable during running.
Keywords: Cognitions; Long Distance Running; Technology; Perceived Exertion; Exercise Workload
Long distance running is one of the most natural ways to ex-
perience psycho-physiological fatigue, (Nowak, 2010). As an
endurance exercise, it represents an important mental and
physical challenge to all athletes who wish to excel (De la Vega,
Rivera, & Ruiz, 2011). It has been suggested that cognitions
play an important role in running performance, especially in
moments of intense fatigue (Buman, Omli, Giacobbi, & Brewer,
2008; LaCaille, Masters, & Heath, 2004). To understand the
influence of cognitions in long distance running, several studies
have been carried out. Of the various approaches (i.e. cognitive
strategies, self talk, attentional focus) in the present study, we
focus on the cognition’s classification. Schomer’s (1986), re-
sults had shown ten categories: feelings and emotions, body
monitoring, command and instruction, pace monitoring, envi-
ronmental feedback, reflective activity thought, personal prob-
lem solving, work and career management, course information
and talk and conversational chatter. In this regard, Nietfeld
(2003) had classified the athlete’s cognitions while running into
the following categories: externally-focused thoughts (not di-
rectly related to the task), planning (related to pre-race prepara-
tions), information management strategy (thoughts that reflect
strategies that runners use during the competition), monitoring
(thoughts runners have about their energy level, pain tolerance
or form), debugging (thoughts that reflect changes in strategies
or adjustments during the race), evaluation (thoughts that re-
flect back on a race). Through a multiple-factor structure Goode
and Roth (1993) concluded that cognitions are conceptualized
in five specific thought-related categories: monitoring of body
responses, daily events, interpersonal or social relationships,
related to external surroundings, and thoughts of a religious or
spiritual nature. Recently, Rose and Parfitt (2010) reported that
during running, cognitions were mainly related to: switching
off from body, association with interoceptive cues, focused on
exercising, not aware of time and thinking about nothing. Nev-
ertheless, most of this research has been performed using a
retrospective approach (Connolly & Tenenbaum, 2010). Hith-
erto the tools used for cognition measurement have been inter-
views (Nietfeld, 2003), post exercise questionnaires; as the
Thoughts During Running Scale, TDRS (Goode & Roth, 1993),
the Schomer’s Classification (Connolly & Tenenbaum, 2010;
Schomer, 1986) and protocol analysis (Blanchard, Rodgers, &
Gauvin, 2004; Ericsson & Simon, 1993; Rose & Parfitt, 2010).
Through a retrospective recall, it is likely that participants do
not verbalize everything that they were thinking during that
time (Rose & Parfitt, 2010). In addition, retrospective data (i.e.
thought content) is not as reliable as obtaining moment-by-moment
data, especially, when the activity is longer than 10 seconds
(Ericsson & Simon, 1993; Connolly & Tenenbaum, 2010). In
order to assess cognitions while running, the standard method
has been to ask participants to verbalize what they were think-
ing. A more dynamic process is necessary for monitoring cog-
nitions during running (Blanchard, Rodgers, & Gauvin, 2004;
Rose & Parfitt, 2010). The primary aim of this study was to
map participant’s cognitions while running. To attain this pur-
pose, we developed a new methodology to measure the real
time frequency of cognitions, without a retrospective approach
or asking the athletes to verbalize. The secondary aim was to
describe which of these cognitions are perceived as unpleasant.
A total of 17 competitive long distance runners (all male),
age range from 19 to 53 years (M = 35.2, SD = 10.5), weight
(M = 70.4, SD = 6.8 kg) and height (M = 177.2, SD = 6.8 cm)
with a running experience of (M = 11.2, SD = 9.8 years) par-
ticipated voluntarily. For this study, long distance runners were
defined as those who regularly ran more than 10 km (Benyo &
Herderson, 2002). All athletes signed an informed consent prior
to participation.
Background and demographic Questionnaire: in this brief
questionnaire, all participants were asked questions related to
identifying their running background (i.e. years of running,
preferred distance).
Cognitions: The mapping task consisted in identifying and
recording cognitions while running. Cognitions were defined as
the mental activities involved in acquiring and processing in-
formation (Colman, 2009), and consisted of four specific direc-
tions: Images: cognitions related to the mental representation in
which the runner observes himself doing something. Emotions:
content related to an affective state during the running session.
Sensations: cognitions related to the subjective experience of
feeling that resulted from stimulations derived from the physi-
cal effort of running. Thoughts: those that appeared during the
running session but do not have to do with images, emotions or
Perceived Exertion: The 15-point scale of the Rating of Per-
ceived Exertion RPE (Borg, 1998) was used as a measurement
of fatigue during exercise. The RPE is a category-ratio scale
ranging from 6 (no exertion at all) to 20 (maximal exertion).
Flow State: In order to assess participant’s experience; we
used the flow construct, which refers to the optimal psycho-
logical state associated with a very positive experience (Jackson
& Eklund, 2002). The flow state was measured using the Span-
ish version of the Flow State Scale (FSS, Jackson & Marsh,
1996). The FFS consists of 36 items that assess nine dimen-
sions of flow (balance between ability and challenge, merging
of action and awareness, clear goals, direct feedback, concen-
tration on task, sense of control, loss of self-consciousness,
distorted sense of time and autotelic experience); each dimen-
sion has 4 items that are rated on a 10-point Likert-type scale.
The possible range of scores for each dimension is 4 to 40. The
scale has a value of Cronbach’s alpha of over .70 (García et al.,
Technical equipment: The real-time recording was carried
out by two dynamic measurement tools: MindFocus® software
(O3WellBeing Solutions, Spain), installed in a mobile phone
(Nokia N85, Finland) and wireless controller Zeemote JS1®
(Zeemote Technology Inc., United Kingdom) as previously
used by De la Vega et al. (2011) for a cognitive strategies re-
search. As a portable (100 × 25 mm), ergonomic and very
lightweight (25.5 g without batteries) device, the controller fits
perfectly in the runner’s hand, allowing him to run in a natural
way. The mobile application recorded every controller action
with the precise time reference (hour, minute and second) in
which it was performed.
Setting. Data were collected in a controlled environment on a
motorized treadmill with zero gradient (Spazio Forma® Tech-
nogym®, Italy), at a temperature of (M = 24.4, SD = .6˚C) an d
relative humidity of (M = 28.6, SD = 6.4%). The workload
speed was determined by the maximum heart rate (%HRmax)
calculated by the formula [220-age (years)], which is consid-
ered a valid method in order to control and prescribe exercise
intensity (Yamaji, Iguchi, & Hashisume, 2008). Each runner
performed a running session of 30 minutes with two speed in-
tensities (A = 20 minutes run, workload speed at 80%HRmax
and B = 10 minutes, 90% HRmax) plus warm up (2 minutes at
7 km·h–1) and cool down. Heart rate was continuously meas-
ured with a Polar T31® Transmitter, (Polar Electro Oy, Finland).
Session speed (M = 13.1, SD = 1.5 km·h–1) was automatically
set by the treadmill computer using the target heart rate for each
stage. RPE was measured every 5 minutes; the scale was auto-
matically projected on a screen and participants were asked to
verbalize only the number that represented their perceived ex-
ertion. By setting the session duration to 30 minutes, the re-
searcher was able to monitor the cognitions-mapping task in
real time.
Experimental Session
Athletes were informed about session duration and workload
speed. They were instructed about the RPE scale and the map-
ping task, specifically, the differences between: thoughts-images
and emotions-sensations. Moving the joystick forward indicates
a thought, to the right for images, to the left when it was a sen-
sation and backward when it was an emotion. Pressing the con-
troller button immediately after the joystick indicates that the
cognition was perceived as unpleasant (See Figure 1). Partici-
pants were informed that the task was based on their own opin-
ion; and on what each runner wanted to identify according to
the instructions received. Warm up. A trial of the mapping task
and RPE measures was conducted before the session started.
Mapping task. In order to help participants to remember how
they should indicate their cognitions, a diagram showing the
joystick direction was projected on a screen. The only interac-
tion between the athlete and the researcher was when partici-
pants verbalized the number corresponding to their perceived
exertion. Running output (HR, speed and time) was not visible
on the treadmill control screen; athletes did not receive any
feedback. Other psychological variables were recorded during
the experimental condition for the purpose of a different study
not covered in this work. Participants completed the FSS im-
mediately after the cooling down.
Data Analysis
Descriptive means and standard deviations are presented for
all variables. The percentage of cognitions perceived as un-
pleasant was calculated from the average frequency recorded.
sensation image
Figure 1.
Mapping task and tools.
Copyright © 2012 SciRes. 591
Copyright © 2012 SciRes.
Rating of perceived exertion. Perceived exertion range was
from 9.67 to 15.33 (M = 12.89, SD = 1.52), which represents a
somewhat hard effort (Borg, 1998).
In addition, to explore the relation between perceived exertion
and flow state, Spearman’s rank correlation coefficient was
used. Significance level was (p < .05). Participants experience. FSS showed an average global
flow state of 257.76 (SD = 37.47). This result represents 71.6%
of a maximum scale score of 360. This percentage suggests that
articipant’s session appraisal was mainly positive. Spearman’s
rank correlation coefficient showed that as the rating of per-
ceived exertion increases the global flow state decreases (r =
–.56, p = .01). This inverse relation was present also in the flow
factors of balance between ability and challenge (r = –.61, p
= .009), clear goals (r = –.52, p = .03), clear, direct feedback (r
= –.63, p = .006) and sense of control (r = –.51, p = .03).
Mapping Task. A new methodology was developed in order
to map participant’s cognitions while running. The mapping
task showed a total of 1154 cognitions recorded (M = 67.88, SD
= 40.06). Average cognition frequency recorded during the
session is presented in Figure 2.
Participants identified and registered an average of 58.76 (SD
= 37.63) cognitions, distributed as follows: thoughts (M = 31.06,
SD = 23.23), images (M = 13.05, SD = 10.25), emotions (M =
7.29, SD = 5.25) and sensations (M = 18, SD = 13.36).
Table 1.
Figure 3 illustrates the fluctuation of unpleasant cognitions
during the session. Unpleasant cognitions (M = 9.12, SD =
8.03), 15.52% of the total recorded, were distributed into:
thoughts (M = 3. 88, SD = 3.77) representing 12.49 %, images
(M = .65, SD = 1.05) 4.86%, emotions (M = .71, SD = .92)
9.73% and sensations (M = 4.82, SD = 5.91) 26.77%, which
represents the highest percentage. In general, the mapping task
shows that during the session unpleasant cognitions (N = 155)
represents 13.43% of the total recorded.
Cognitions frequency.
A (80%HRmax) B (90%HRmax)
Cognitions Unpleasanta Cognitions Unpleasanta
M (SD)
Thoughts19.35 (14.16)2.06 (2. 35) 11.76 (10.01) 1.88 (2.14)
Emotions 4.29 (3.96) .52 (.94) 3 (2.52) .17 (.39)
Images 8.17 (5.96 ) .41 (.6 1) 5.17 (5.55) .35 (.60)
Sensations 10.64 (9.20)2 (3.14) 7.35 (6.14) 2.64 (3.74)
The frequency analysis showed in Table 1, reveals that dur-
ing the first 20 minutes, 18.79% of the sensations registered
were perceived as unpleasant, this percentage increases to
35.91% in the last 10 minutes. Note: aIndividual’s perception.
nitions Ma
24681012 14 16 18 2022 24 26 28 30
80% HR max90% HR max
Time (min)
Thoughts Emotions Images Sensations
Figure 2.
Average cognition frequency calculated every two minutes.
leasant Co
nitions Ma
80% HR max90% HR max
Time (min)
Unpleasant ThoughtsUnpleasant EmotionsUnp leas ant ImagesUnp leasant S ensatio ns
Figure 3.
Average cognitions perce ived as unpleasant calculated every two minutes.
The main objective of this study was to mapping runner’s
cognitions. For this purpose we used a new methodology that
allows a real-time recording of cognitions. We considered four
cognitions directions that are presented in all categories of pre-
vious studies (i.e. emotions and sensations). These cognitions
are described as frequent while running in previous findings
(e.g. Goode & Roth, 1993; Nietfeld, 2003; Schomer, 1986;
Rose & Parfitt, 2010). As Figure 2 illustrates, the mapping task
showed that during long distance running, cognitions are in
constant fluctuation. Our results replicate the findings by Niet-
feld (2003) showing the dynamic cognitions stream while run-
ning. From a methodological point of view, data obtained in
real time represent a breakthrough. Moreover, details about the
cognitions (frequency and the time that they were identified)
are presented in a novel way. Interestingly, thoughts presented
the highest frequency. Furthermore, emotions showed the low-
est frequency. Our results showed that unpleasant cognitions
represent 15.52% of the total recorded. In fact, the mapping
task showed that sensations (M = 18) were frequent during the
session. These findings are in line with those obtained by
Goode and Roth (1993), Nietfeld (2003), Schomer (1986), and
Rose and Parfitt (2010). Apparently, during the last 10 minutes
of the session (workload of 90%HRmax) the percentage of
unpleasant sensations increased (see Table 1) from 18.79% to
35.91%. This descriptive data attempt to confirm the interaction
between workload and cognitions. As proposed by Tenenbaum
(2001), as one run faster attentional focus becomes internal.
Our results showed that when the workload increases, the ath-
lete’s cognitions are directed to the stimuli derived from physi-
cal effort. These findings are consistent with those obtained
through categories, where body monitoring is a frequent thought
content (e.g. Nietfeld, 2003). Nevertheless, previous studies
have only described cognitions content, without exploring how
the athlete has perceived them. In this study the workload in-
creased from 80 to 90% HRmax and the RPE values indicate
that participants perceived the session as somewhat hard. When
participants are experienced runners, as in our study, a 10%
increase may not represent a significant one. Participants re-
ported an average long distance running experience of 11.2
years, 58.82% normally ran 10 kilometres, and the remaining
41.48% usually compete in half marathon and marathon.
According to Goudas and Theodorakis (2007), receiving
heart rate feedback could influence perceived exertion and cog-
nitions. To avoid bias, our participants did not receive any
feedback related to heart rate, speed or time. Further studies
could analyze the influence of exercise feedback over cogni-
tions. Based on the FSS results (M = 257.76, of a maximum
score of 360), it is possible to claim that the session experience
was considered mainly as positive. According to Neil et al.
(2011), the experience appraisals, which involve all cognitions
directions and how they are perceived (pleasant or unpleasant)
could influence the upcoming performance.
Interestingly, as the rating of perceived exertion increased,
the global flow state decreased (r = –.56, p = .01). Supporting
the findings of Connolly and Tenenbaum (2010), our results
show that this inverse relation was also present in the flow fac-
tor: sense of control. It appears that as the perceived exertion
increases, the athlete’s sense of control decreases, altering the
experience appraisal. Future studies could analyze the influence
of experience appraisal over running performance. In addition,
it may be interesting for future research to identify how cogni-
tions can be influenced by outside inputs (i.e. environment
stimulus, verbal feedback, performance information).
In conclusion, our results attempt to demonstrate that a new
dynamic and non-intrusive methodology is suitable for the
study of cognitions during running.
We thank volunteers for their participation and anonymous
experts in the field for their review of an earlier manuscript. We
as author are thankful to the companies that have facilitated the
technology for this study. Mindfocus® is available for research.
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