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Fast talkers? Investigating the influence of self-talk on mental toughness and finish times in 800-meter runners

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The purpose of this study was to explore whether a personalized self-talk intervention influenced mental toughness, rating of perceived exertion, sense of the urge to slow down, perceived performance and finish times in a series of 800-meter run time trials. While mental toughness has been associated with improved endurance performance, the effect of changing an individual’s momentary self-talk on mental toughness and finish time has not yet been examined. This single-subject, multiple baseline design case study incorporated three participants who each ran a series of 11 − 13 maximum effort 800-meter time trials on the track, separated by a minimum of two days, across ten weeks. Following an initial series of four to six baseline sessions, they were each then provided a personalized self-talk intervention before running the seven additional sessions. Visual analysis (including review of non-overlapping data points between baseline, intervention, and follow-up sessions) demonstrated the personalized self-talk intervention positively influenced mental toughness and finish times across all three participants but did not consistently affect the rating of perceived exertion, urge to slow down or perceived performance. Additional insights were identified through the integration of social validation interviews informally after each run session and then formally after the intervention. These insights included identifying a new baseline of effort accompanied by different levels of mental toughness and an intrigue on the part of participants about the notable improvement in outcomes in spite of previously perceived “all-out” effort. Lay Summary: Mental toughness variability and 800 meter finish times were both positively influenced by a personalized self-talk intervention in runners. In addition, as mental toughness increased, 800 meter finish times improved.
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Journal of Applied Sport Psychology
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Fast talkers? Investigating the influence of self-
talk on mental toughness and finish times in 800-
meter runners
K. Bradford Cooper, Mark R. Wilson & Martin I. Jones
To cite this article: K. Bradford Cooper, Mark R. Wilson & Martin I. Jones (2020): Fast talkers?
Investigating the influence of self-talk on mental toughness and finish times in 800-meter runners,
Journal of Applied Sport Psychology, DOI: 10.1080/10413200.2020.1735574
To link to this article: https://doi.org/10.1080/10413200.2020.1735574
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Published online: 05 Mar 2020.
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Fast talkers? Investigating the influence of self-talk on
mental toughness and finish times in 800-meter runners
K. Bradford Cooper , Mark R. Wilson, and Martin I. Jones
University of Exeter, Exeter, UK
ABSTRACT
The purpose of this study was to explore whether a personalized
self-talk intervention influenced mental toughness, rating of per-
ceived exertion, sense of the urge to slow down, perceived perform-
ance and finish times in a series of 800-meter run time trials. While
mental toughness has been associated with improved endurance
performance, the effect of changing an individuals momentary
self-talk on mental toughness and finish time has not yet been
examined. This single-subject, multiple baseline design case study
incorporated three participants who each ran a series of 11 13
maximum effort 800-meter time trials on the track, separated by a
minimum of two days, across ten weeks. Following an initial series
of four to six baseline sessions, they were each then provided a per-
sonalized self-talk intervention before running the seven additional
sessions. Visual analysis (including review of non-overlapping data
points between baseline, intervention, and follow-up sessions) dem-
onstrated the personalized self-talk intervention positively influenced
mental toughness and finish times across all three participants but
did not consistently affect the rating of perceived exertion, urge to
slow down or perceived performance. Additional insights were iden-
tified through the integration of social validation interviews infor-
mally after each run session and then formally after the intervention.
These insights included identifying a new baseline of effort accom-
panied by different levels of mental toughness and an intrigue on
the part of participants about the notable improvement in outcomes
in spite of previously perceived all-outeffort.
Lay Summary: Mental toughness variability and 800 meter finish
times were both positively influenced by a personalized self-talk
intervention in runners. In addition, as mental toughness increased,
800 meter finish times improved.
ARTICLE HISTORY
Received 13 August 2019
Revised 21 February 2020
Accepted 23 February 2020
There has been significant interest in developing cognitive and behavioral strategies to
improve athletic performance, given the relatively small physical and skill-based differ-
ences in athletes at the elite level (Tracey & Elcombe, 2016). For example, these same
authors note that mental toughness has been regularly cited within and, importantly,
beyond the literature as the key set of attributes for optimizing performance(p. 1002).
Interest in the concept of mental toughness within the scientific community has
CONTACT K. Bradford Cooper BCooper@USCorporateWellness.com University of Exeter, St. Lukes Campus, 79
Heavitree Rd, Exeter EX1 1TX, UK.
Supplemental data for this article can be accessed on the publishers website
ß2020 Association for Applied Sport Psychology
JOURNAL OF APPLIED SPORT PSYCHOLOGY
https://doi.org/10.1080/10413200.2020.1735574
expanded significantly over the past decade. A Web of Science search on July 27, 2019,
using Mental Toughnessas the topic search criteria revealed just 25 total articles in
publication on the subject before 2006. From 20062015, the number of articles
increased to 189 and since 2016, an additional 246 articles have been published. Recent
narrative (Cowden, 2017) and systematic (Liew et al., 2019) literature reviews and meta-
studies (Anthony et al., 2016) have provided summary insights about the relationship
between mental toughness and improved outcomes, but identify limitations based on
inconsistent definitions and measurement, and a reliance on cross-sectional research
methodologies. The current study sought to address some of these limitations, particu-
larly with respect to the need for longitudinal intervention designs to examine causality
rather than correlation.
The construct of mental toughness has been defined as a psychological resource that
is purposeful, flexible, and efficient in nature for the enactment and maintenance of
goal-directed pursuits(Gucciardi, 2017, p. 18). In endurance events, athletes experience
significant adversities and stressors because of the physiologically demanding nature of
the event. When running at intensities approaching ones physical VO
2
max in training
and competition, athletes benefit from having personal resources allowing them to
maintain effort, technique, and motivation in the presence of noxious stimuli (e.g., dis-
comfort, fatigue, perceived effort). Previous research has revealed that mental toughness
is associated with such behavioral perseverance (Gucciardi et al., 2014) and endurance
performance (Blanchfield et al., 2014) and is adopted long-term by ultra-marathon
study participants (McCormick et al., 2017).
However, if interventions are to be developed, it is important that intra-individual varia-
tions in mental toughness can be identified. A series of complementary studies (Cooper
et al., 2018,2019a,2019b) specifically addressed this existing gap in the literature. The
authors of these studies initially identified the presence of mental toughness variability
and then investigated potential optimizers of that variability. Cooper et al. (2018) revealed
that perceived mental toughness varied during a series of high-level endurance events and
importantly, that perceived mental toughness could be increased by self-talk. Cooper et al.,
2019a revealed that Masters Athletes also used self-talk to optimize their performance and
to positively influence their perceived mental toughness (i.e., the belief that they could
achieve their goals, control attention, control emotions). Finally, Cooper et al. (2019b)
revealed that athletes used self-talk as a method to offset the deleterious effects of experi-
mentally manipulated sleep restriction. Specifically, some of the participants in Cooper
et al. (2019b) study reported that when sleep-restricted they would use functional self-talk
to redress the emotional disturbances caused by reduced sleep and to motivate themselves
during periods of sleep-related motivational loss.
The emergence of self-talk as a potential optimizer of mental toughness from these
studies makes it a prime candidate for an explicit intervention strategy. Self-talk has
been defined by Hardy (2006)as(a) verbalizations or statements addressed to the self;
(b) multidimensional in nature; (c) having interpretive elements associated with the
content of statements employed; (d) being somewhat dynamic; and (e) serving at least
two functions; instructional and motivational(p. 84). Self-talk has been shown to be
malleable to perceptions and interpretations of stimuli from the social environment
(Zourbanos et al., 2010, p. 782) and as such, two types of self-talk; organic and strategic
2 K. B. COOPER ET AL.
have been noted (Latinjak, Hatzigeorgiadis, et al., 2019). Extensive research has revealed
an association between self-talk and performance, as well as other related variables
including cognitive, motivational and behavioral mechanisms (Van Raalte et al., 2016).
However, the effect of self-talk on mental toughness is less well understood (Bell et al.,
2013; Cooper et al., 2019a).
Our aim was therefore to examine whether a strategic self-talk intervention (via person-
alized cue words or phrases) could improve mental toughness and running performance.
We were also interested in examining whether the intervention influenced related varia-
bles such as the urge to stop, perceived exertion, and perceived performance. We opted to
use a personalized approach rather than a group-based approach, because the meaning
associated with a specific self-talk strategy is idiosyncratic (Hardy, 2006) and prior system-
atic reviews of strategic self-talk (Hatzigeorgiadis et al., 2011) have called for research
designs to be targeted to the end user. In line with the personalized nature of the interven-
tion, we also chose to measure intervention effects using a single subject multiple baselines
research methodology. Not only do n-of-1 research designs enable the exploration of indi-
vidual changes that can sometimes be hidden in group-based designs (Vieira et al., 2017),
they have been used previously in the self-talk literature (Latinjak, Hernando-Gimeno,
et al., 2019; Latinjak, et al., 2016) with social validation interviews (e.g., Jones et al., 2011)
utilized to check the acceptability of and satisfaction of intervention procedures.
We hypothesized that a personalized strategic self-talk strategy would positively influ-
ence mental toughness as measured by the Mental Toughness Index (Gucciardi et al.,
2015) and performance in an 800 meter run by participants as measured by finish time.
Further, we also hypothesized that the strategic self-talk intervention would extend the
time it takes for the athlete to feel the urge to slow down and reduce the rate of per-
ceived exertion relative to average speed.
Methods
Research approach
Following ethical approval from the authorsinstitutional research ethics committee,
participants were recruited through convenience sampling. This study adopted a mul-
tiple baseline single subject sample design with three experienced female Masters ath-
letes. We chose to sample female Masters athletes because they are an under-researched
population in the literature (Costello, Bieuzen, & Bleakley, 2014). The n-of-1 study
allows hypotheses to be tested within individuals across repeated measurements to
examine the influence of the variable over a specific timeframe (McDonald et al., 2017).
This design ideally incorporates a baseline phase long enough to demonstrate a clear
pattern of outcome values in order to clarify the differences between baseline and inter-
vention (Hedges et al., 2012). It is a type of research for which a sample size of between
one (Horner et al., 2005; Hrycaiko & Martin, 1996) and five (Jones et al., 2011) partici-
pants is standard. In the seminal text on the topic, Barlow and Hersen (1984) compared
and contrasted the benefits of single-case replications to alternative research designs; In
terms of validity or generality of findings, a series of single-case designs in similar cli-
ents in which the experiment is directly replicated three or four times can far surpass
the experimental group/no treatment control group design(p. 57).
Participants
Three female athletes volunteered for the study. All three met the screening criteria of
current run training of three or more days per week; absence of any injury that limited
running for over one week in the past three months; and being over the age of 18 years.
Furthermore, each participant was asked to consider whether she had availability and
willingness to meet the lead researcher at a specific running track 1113 times over a
specific ten-week period. The three participants were P1 a 43-year-old experienced
Ironman triathlete, P2 a 35-year-old All-American triathlete and P3 a 40-year-old
experienced high school track coach and trail runner.
Measures
Mental toughness was assessed following each session using the Mental Toughness
Index (MTI: Gucciardi et al., 2015), an eight-item, uni-dimensional measure. This
assessment uses the sum of items from a 7-point Likert scale ranging from 1 (False,
100%of the time)to7(True, 100%of the time). We adjusted the wording of the ori-
ginal eight items to fit the context of this setting without affecting the outcome of the
assessment. As an example, question one of the MTI reads, I believe in my ability to
achieve my goals.This item was adjusted to read, I believe in my ability to achieve
my goals throughout the 800 meters.This adjustment was pre-reviewed with the devel-
oper of the study and was consistent with adjustments made in Study IV of Gucciardi,
et al, 2015. Previous studies examining the internal reliability of the MTI demonstrated
both a high Cronbachsa(0.90) and composite reliability (0.90) levels (Jones &
Parker, 2018).
Perceived effort levels were collected using the 620 point Borg Rating of Perceived
Exertion scale (Borg, 1982) following individual training of each participant in the use
of the tool. Perceived quality of the performance was documented by participants fol-
lowing similar training by marking on a 100 mm visual analog scale (VAS) ranging
from Worst Imaginable Performanceto Best Imaginable Performance.Participants
also received training on the use of the Sportcount 200 Lap Counter and Timer (finger
click stopwatch), used to aid in the identification of when they first felt the urge to slow
down. Finish times and 200-meter lap splits (available in the Supplementary table) were
collected for each session using an iPhone digital stopwatch application, with faster
times (fewer total seconds, as shown in Figure 5) demonstrating improvement.
Procedure
Participants individually met the lead researcher at a local 400-meter track between 6:30
and 7:30 AM on 1113 different times over ten weeks, with each session being sepa-
rated by a minimum of two days. Scheduling was arranged so participants would not
overlap with each another, and they were instructed to maintain a consistent morning
routine leading into each session (including pre-run fuel, caffeine intake, activity levels,
and pre-session warm-up). Participants initially completed four (P1), five (P2) or six
(P3) baseline 800-meter runs, for which they were instructed to complete the distance
as fast as they could run, prior to completing four intervention and three follow-up
4 K. B. COOPER ET AL.
800-meter runs. The multiple baseline design staggered the number of baseline sessions
completed by each participant to make it easier to attribute any change identified to the
intervention itself, rather than the number of sessions (Rhoda et al., 2011). This method
compared positively to previous run intervention studies (Yamamoto et al., 2008) that
often include a single session or no baseline performance for comparison.
During each trial, participants pressed the finger click stopwatch to indicate when
they first felt the urge to slow down. Following the 800-meter run, the participant
would complete the MTI, RPE, and VAS. The first author then walked the track with
the participant while she cooled down and discussed the run. Open-ended questions
from the researcher focused on patterns of thoughts, feelings, and organic self-talk and
other insights on the part of the participant regarding her run performance. These
informal qualitative interviews lasted between 510 minutes and were included for the
purpose of garnering insights from participants as they were experiencing it (Adhabi &
Anozie, 2017). They were not audio-recorded but lasted between 510 minutes, and any
notable highlights were immediately recorded by the first author on the spreadsheet
related to that specific days data, as the participant was leaving the track. These data
were then used to prompt questions in the formal social validity interviews and to
reveal participantsqualitative experiences of completing the run (and intervention).
Intervention
No instruction or coaching took place during the baseline sessions. Following the com-
pletion of the baseline sessions, participants were provided with a personalized strategic
self-talk strategy to be utilized during the intervention run sessions. Initially, each ath-
lete was provided with a series of self-talk cue words to utilize during the first interven-
tion session. The wording for the initial intervention session was developed through a
combination of an 18-month pilot study conducted by the first author on himself (an
elite Masters athlete) and feedback provided by participants during the discussions that
followed their baseline sessions. These were then replaced or modified further over the
ensuing intervention sessions to create personalized verbal cues, based on participant
feedback regarding perceived effectiveness.
The intervention drew on both instructional and motivational cue words (Hardy
et al., 2001) and was based on positive reappraisal of negative emotions (Lane et al.,
2017), approach for orientation motivation (Elliot & Harackiewicz, 1996) and accept-
ance of noxious stimuli or pain (Jones & Parker, 2018). The self-talk cue words were
then modified differently for each individual participant based on feedback garnered
from participants during the brief discussions that followed the intervention 800 meter
runs to combat the athletescompeting thoughts. For example, P1 used Smooth &
Fast,There you are!,You got thisand (Countdown and) Launch!in the initial
intervention. Her final intervention involved a similar but adjusted series of cue words
and phrases: Smooth & Fast,Embrace, You got this!, Counting down and Youre
there launch!The amended scripts of the specific self-talk cues are provided in
Supplementary data.
As with the baseline and intervention sessions, the final three sessions (follow-up)
were also performed in the presence of the lead researcher to track the run splits, final
finish time, and collect the assessments. While the lead researcher worked with each
athlete to make personalized adjustments to the specific self-talk strategy leading into
each intervention session, the athletes received no additional guidance or instruction
prior to or during the follow-up sessions (to replicate the baseline sessions). Athletes
were simply instructed to utilize anything they had previously learned as a participant
in the study.
Data analysis plan
A visual analysis procedure incorporating a review of level, trend, and variability at
baseline and intervention (Horner et al., 2005) was utilized to determine the occurrence
of an effect regarding perceived performance, strong urge to slow, rating of perceived
exertion, mental toughness and finish times (Figures 15). Also, we identified criteria
for a meaningful minimal benefit (MMB) and harm (MMH; Stoov
e & Andersen, 2003)
by identifying the absolute highest and lowest outcome variable during the individual
Perceived Performance
P1
P2
P3
0
20
40
60
80
100
0
20
40
60
80
100
0
20
40
60
80
100
Baseline Intervention Follow-u
p
Figure 1. Perceived Performance rating by participants in baseline, intervention, and follow-up
sessions. Horizontal lines represent Minimal Meaningful Benefit and Harm, indicating the highest and
lowest perceived performance during baseline for each participant.
6 K. B. COOPER ET AL.
athletes baseline sessions. The determination of this MMB and MMH is beneficial in
interpreting the data and helping ensure the intervention is unlikely to cause harm and
more likely to provide a meaningful benefit to the individual (Stoov
e & Andersen,
2003). We also calculated a Standard Mean Difference (SMD) for each of the measured
items, which has been recommended as a method for detecting the effect of interven-
tions (Olive & Smith, 2005). SMD is calculated by subtracting the mean baseline from
the mean intervention and then dividing by the standard deviation of the baseline. An
SMD of 0.1 would represent a small effect size, while an SMD of 0.51 or higher would
represent a large intervention effect.
Social validity interviews
Based on the recommendations of Wolf (1978), we conducted formal social validity inter-
views within three weeks of the last follow-up 800-meter run session to garner additional
insights from participants. Social validation interviews are recognized as an effective way
Urge to Slow Running (Seconds)
P1
P2
P3
0
50
100
150
200
0
50
100
150
200
0
50
100
150
200
Baseline Intervention Follow-u
p
Figure 2. Recognition of initial onset of the urge to slow down in baseline, intervention, and follow-
up sessions. Horizontal lines represent Minimal Meaningful Benefit and Harm, indicating the earliest
and latest onset of urge during baseline for each participant.
to further substantiate behavioral research outcomes (Wolf, 1978), and have been used to
provide valuable feedback about the practical application and experience of a psycho-
logical intervention (e.g., Jones et al., 2011). These interviews lasted an average of
55 minutes in length with a range of 4765 minutes and were recorded, transcribed, and
analyzed. The interviews were also supplemented with written (by the first author) brief
individual post-session feedback notes. Wolf offered a three-part framework for validating
the social importance of interventions that comprised goals, procedures, and effects. To
address the dimensions of social validity questions included a combination of prepared
items (e.g., How did self-talk influence your mental toughness?or What did you learn
about yourself as a participant in this study?) and free-flowing questions related to vari-
ous participant responses to previous questions (interview transcription provided as
Supplemental file). We analyzed the social validation data using a thematic analysis
(Braun & Clarke, 2006) of each participants experience. Therefore, the results are not
presented as group level themes or patterns, but rather the themes that reflected how
Rate of Perceived Exertion
P1
P2
P3
6
8
10
12
14
16
18
20
6
8
10
12
14
16
18
20
6
8
10
12
14
16
18
20
Baseline Intervention Follow-up
Figure 3. Rating of Perceived Exertion (RPE) by participants during baseline, intervention, and
follow-up sessions. Horizontal lines represent Minimal Meaningful Benefit and Harm, indicating the
highest and lowest RPE during baseline for each participant.
8 K. B. COOPER ET AL.
each participant experienced the intervention in terms of the goals, procedures, and
effects. The thematic analysis involved the searching across a participantsdatatofind
repeated meaning that helped to illuminate the social validity of the intervention.
Transcripts were analyzed for these repeated patterns, and insights related to the data col-
lected for each athlete were highlighted and integrated into the summary of results.
Results
Individual participant results related to finishing time, mental toughness, perceived per-
formance, rating of perceived exertion, and urge to slow are provided visually in
Figures 15. We have also included the data and SMD analysis in Tables 1 and 2 within
the Supplementary Materials. SMD of MTI data were 16.7, 6.6 and 1.6, and finish times
were 4.5, 5.8, and 3.7, for P1, P2 and P3 respectively all well above the 0.51 cutoff for
intervention effectiveness.
Mental Toughness (MTI)
P1
P2
P3
8
16
24
32
40
48
56
8
16
24
32
40
48
56
8
16
24
32
40
48
56
Baseline Intervention Follow-u
p
Figure 4. Mental Toughness Index (MTI) scores provided by participants during baseline, intervention,
and follow-up sessions. Horizontal lines represent Minimal Meaningful Benefit and Harm, indicating
the highest and lowest RPE during baseline for each participant.
Participant results and insights of note
Participant one (P1)
This participant demonstrated an immediate learning curve improvement from her
first baseline run of 9 seconds (3.3%) and then improved an additional 7 seconds (2.6%)
over the last three baseline sessions. MTI, RPE, urge to slow, and perceived
performance were generally constant throughout the baseline sessions. Upon initiation
of the self-talk intervention, her MTI more than doubled from an average of 19 at base-
line to 40 during intervention and 51 during follow-up. Her finish times also demon-
strated a significant improvement, as she trimmed an additional 25 seconds (9.8%) from
her best baseline session and 33 seconds (12.5%) off her baseline average. Perceived per-
formance improved, and the timing of her urge to slow were extended with the
intervention.
800m Finish Time (seconds)
P1
P2
P3
160.00
180.00
200.00
220.00
240.00
260.00
280.00
160.00
180.00
200.00
220.00
240.00
260.00
280.00
160.00
180.00
200.00
220.00
240.00
260.00
280.00
Baseline Intervention Follow-up
Figure 5. Finish time in seconds (less time indicates improved time) by participants in baseline, inter-
vention, and follow-up sessions. Horizontal lines represent Minimal Meaningful Benefit and Harm, indi-
cating the slowest and fastest finish times during baseline for each participant.
10 K. B. COOPER ET AL.
Interestingly, while she was running significantly faster than her baseline, her RPE
remained similar to her baseline during the intervention and follow-up. She shared
some of her thoughts on this occurrence as:
I learned that there are many different levels of mental toughness. There are so many tools
that we dont even know exist until theyre given to us and the human body is capable of
so much more than we think it is.
When comparing her eventual improvement to her baseline times, she expanded
her thoughts:
Theres just a lot to take from knowing that youre basically relying on (an assumption
that) oh well thats my baseline I cant go up and above what Ive always done. What
was it you said (when we discussed self-talk strategies after the conclusion of the study)
about breaking the algorithm? That we remember what weve done before and think I
cant do better than that.But you can do better than that. Break the algorithm. Just
because thats how youve performed in the past doesnt dictate your future. Use these
tools that youve been given and look at what youre capable of. Wow thats amazing!
Perhaps most interesting with this participant was her RPE rating (Figure 3). During
her baseline sessions, she rated every session as an 18 or 19 on the 20-point RPE scale.
However, when she made significant improvements in her time and mental toughness
levels during the intervention and follow-up sessions, her average RPE remained the
same (18 average). She shared her thoughts on what was occurring as:
The first four as hard as I thought I was pushing myself, looking back obviously they
were more on the easierscale. But on each of those specific days, I felt like I was pushing
myself as hard as I could. So it really blew my mind that I was then able to go 30 seconds
faster throughout those middle three or four (intervention sessions).
In terms of her thoughts about the influence of the self-talk, she noted:
The mental can overcome the physical the physical can cause the mental to struggle
and vice-versa. Having those different things (self-talk items) to focus on throughout the
800 and to perform based on those cues was very helpful. Otherwise Im just running. I
think (I had more mental toughness) because of the focus that was now enabled. You are
giving me something to focus on and that, in turn, pushed that scale higher and higher
because when you have tools in your toolbox to reach those goals, they feel
more attainable.
Participant two (P2)
The second participant completed five baseline all-out 800-meter time trial runs before
moving into the intervention sessions. P2 was the fastest of our three participants, even-
tually running 2:58.5 during the intervention portion. However, despite her experience
and emphasis on short course racing, she showed a significant improvement in both
mental toughness and finish time with the self-talk intervention. Her average MTI
increased by 14 (37%) from average baseline to average intervention while her run times
dropped a notable 18 seconds (9.2%) from her best baseline to her best intervention.
RPE did increase with the intervention, and her urge to slow was extended with the
intervention. In reference to why she thought her mental toughness improved (and the
urge to stop was pushed back) with the self-talk, she noted:
Having more focus on that self-talk helped quiet down the little voices that the body
might have had with the little aches and pains. Your head can wander in the wrong
direction, but with mental toughness, you regain control, you steer your head in the
right direction.
Before the self-talk intervention, P2 stated she was running all-out but rating her
RPE between 15 and 18 (Figure 3). Once the intervention started, her RPE was consist-
ently rated 1920, and she was running significantly faster. When discussing this,
P2 explained:
I think on the baselines, its just not realizing how much harder I truly could go. So
looking back, yes I would probably scale those (baseline RPEs) back. At the time that I
was doing the baselines I didnt really know that I had more. Clearly, it proved I had more
in me. I proved that digging deep theres room to dig deeper. Reflecting on it, Imin
awe! Imwow! Maybe (its) like a positive feedback loop knowing that I could, I had
more in me or was pushing that boundary. So then, ok can we do it again? Can we do it
a little faster? Can we do a little more?
Participant three (P3)
Our third participant ran her best baseline time during session one (Figure 5). Then, as
with the other participants, she demonstrated a dramatic improvement upon initiation
of the self-talk intervention, lowering her finish time by over 17 seconds (8%) between
her last baseline and first intervention session. Also, while her six baseline sessions had
a range of 9 seconds from fastest to slowest, her seven intervention and follow-up ses-
sions were all within 5 seconds of one another. Her average level of mental toughness
(Figure 4) correlated closely with this pattern, increasing by 38% from baseline to inter-
vention. Her perceived performance improved, and her urge to slow and RPE increased
nominally with the intervention. When reflecting on these various elements, her
thoughts about what affected her change in mental toughness included:
That was huge to show that much improvement because I had really thought I was
running at an all-out effort in those six (baseline) 800s. The (self-talk) gave me mental
purpose, mental focus. I was no longer just running kind of mindless I now had focus
and purpose. As your mind starts to wander, you start to feel that pain. You just naturally
start to slow down and you start that negativity. (This) gets you some focus some
positive focus. It (self-talk) would push them (negative thoughts) aside or kind of dampen
them because they were still there I could still feel them but it just felt like whatever it
was in my head (mental toughness) was stronger and taking the focus more than what I
was feeling physically. My mental toughness is there [pause] the motivation to use it has to
be there in order for it to really work effectively. Otherwise, I was just pulling bits and
pieces of mental strength to kind of get throughthe 800.
She went on to note:
My intention is always there, but maybe my efforts not. My effort is not always where my
intentions are. Realizing that my perception is off on my physical effort that Im putting into
what Imdoingthat I could probably be stronger and be faster than I think I am maybe
Im holding myself back somehow. The easy choice is to just cruise because you donthave
all the extra thoughts or decisions you have to make. Its the comfortable choice the
comfortable path. It (mental toughness) allowed me to push those boundaries just a little bit
to get out of my comfort zone to get out of my safe zone and allowed me to have some
confidence to go outside that comfort zone a little bit and to trust that I would be ok.
12 K. B. COOPER ET AL.
She also thought she had several insights about her approach as a high school
track coach:
When I coach, itsno crutches’–right? We always talk about no crutches. It is what it is.
This is the moment you either take it or you dont. Then in actuality (as she personally
experienced it), its eye opening. Its easier said than done its helping me to unpackage
those pieces and kind of repackage it in a way that works for them (kids being coached).
That mental piece after having done this study, you realize its huge its huge!
Specific to social validity of self-talk, participants noted dimensions of both goals and
procedures from Wolf (1978) three-part framework. P3 noted:
I liked using the self-talk strategy, mostly on runs when I am not feeling my best. Ive used
it (as a coach) with the varsity girlscross-country team this past season. We started it
about mid-season using it on specific, higher intensity training days and then
transitioned those moments into how it could be used in the races.
P2 also stated she liked using the self-talk strategies and had continued to utilize it in
her athletic pursuits. In addition, she shared also integrating self-talk outside of her ath-
letic pursuits as follows:
(We) have been remodeling our house and there have been days of labor intense work that
self-talk has popped into my head. It changed the outlook of the work from having the
feeling of ugh, we gotta do thisand dragging our feet to more of a positive alright! We
can do this, lets get this done.Granted it was a long project and werent the same words
as when running an 800 but it was a great tool to have for our mental approach to
overcome and achieve our monstrous goal.
Discussion
The purpose of this study was to examine whether a personalized strategic self-talk
intervention in the form of predetermined cue words could increase the perception of
mental toughness and running performance in female Masters athletes. Specifically,
we hypothesized that a personalized self-talk strategy using predetermined cue
words would positively influence mental toughness as measured by the Mental
Toughness Index (Gucciardi et al., 2015) and performance in an 800-meter run by par-
ticipants as measured by finish time. We also hypothesized that this strategic self-talk
intervention would extend the time it takes for the athlete to feel the urge to stop,
increase the rating of perceived performance, and reduce the rate of perceived exertion
relative to speed.
Self-talk was one of the strategies previously noted (Cooper et al., 2019a) as poten-
tially influencing mental toughness, but further experimental investigation was war-
ranted. As hypothesized, strategic self-talk was associated with changes in both mental
toughness levels and run performance. Specifically, the strategic self-talk intervention
utilizing predetermined cue words improved self-assessed levels of mental toughness by
an average of 62% and improved 800-meter run times by an average of 9% over the
baseline average. The 62% figure was skewed by P1, who more than doubled her self-
assessed level of mental toughness. However, the range of change in self-assessed MTI
noted in P2 and P3 were consistent with the ranges seen previously in that of runners
(Cooper, et al., 2019a). The present study is thus unique in showing an association
between a specific optimizer of mental toughness and a concurrent relationship between
mental toughness levels and run performance improvement. These results support pre-
vious research (Bandura et al., 1987) that indicated training in cognitive control
strengthened perceived self-efficacy and ability to withstand pain. The results also pro-
vide an additional example of the influence of psychological skills training, including
that of self-talk strategies on mental toughness and resultant performance, shown
previously among elite military recruits (Fitzwater et al., 2018). Further, while the three
participants in our study were not 800-meter specialists, they were all experienced
athletes and runners who completed not just a single but also rather a series of baseline
time trials before utilization of the self-talk intervention. They all demonstrated a
notable increase in self-assessed mental toughness and improvement in 800-meter finish
times from baseline.
In reference to self-talk specifically, the intervention is not free of critique. Strategic
self-talk cue words may be less effective for more experienced athletes (Hardy et al.,
2015). It was for this reason we noted our participants were experienced runners but
not 800-meter specialists. Further, it has been noted that while mental control may
promote the intended consequence, it has also been found to result in ironic opposite
consequences to those intended due to enhanced sensitivity to mental contents
(Wegner, 1994).
The results of this study identified a consistent association between a personalized
strategic self-talk intervention and mental toughness scores, 800-meter run times, and
perceived performance. In reference specifically to RPE, the literature (Marcora &
Staiano, 2010; Noakes, 2008) indicates that the perception of effort is one of the primary
limiters of exercise performance leading to disengagement from the task. All three of
our participants reported they were running all-outduring baseline. Following the
intervention, participants were able to increase their RPE and speed or hold their RPE
relatively constant while running significantly faster. This pattern of indicating an all-
outeffort but then exceeding that effort by a significant margin in future sessions, may
provide further insights regarding the use of the RPE scale in future studies in addition
to similar findings noted previously (Hardy et al., 2019). While we did not find self-talk
reduced RPE as has been demonstrated previously (Blanchfield et al., 2014), it was
reduced in comparison to the relative running speed (e.g., faster running at similar
RPE). Perhaps the strategic self-talk is also helping the individual discover what the
higher levels of exertion feel like for the future or influencing the ability to cope with
the higher levels of exertion.
This study also continues to build upon previous research indicating that mental
toughness functions more similar to a state (Gucciardi et al., 2014; Harmison, 2011)
than a trait. It also provides expanded insights on ways in which that functional state
can be optimized (Cooper et al., 2019a) through specific steps taken by the individual
either independently or via a coach or other trusted advisor. If self-talk were to be
shown to influence mental toughness, it might then feed into the resource caravan
(Neal et al., 2017), which is an aggregation of multiple personal resources that inter-
weave to positively influence goal-directed outcomes. For example, the comments
shared by P1 about the different levels of mental toughness and the influence on what
she then noted she could achieve likely ties into this aggregation.
14 K. B. COOPER ET AL.
Limitations
One limitation might be related to the novelty of the specific event for these partici-
pants. Experienced 800-meter runners may still benefit, but not likely to the same extent
as these non-specialists. Selecting participants who were not 800-meter specialists may
reduce the direct application and comparison for track coaches. However, as can be
seen in Figure 5, two of the three participants showed evidence of a flat performance at
baseline, followed by a notable jump immediately after the initial intervention. The
remaining participant (P1) did show some improvement during the baseline sessions
and may have benefited from one or two additional baseline sessions, but her improve-
ment post-intervention was non-linear from the baseline trend.
The urge to stop did not appear to be as sensitive to the intervention as our self-
report measure of mental toughness or other performance measures (e.g., speed). This
may be due to involving an unnatural activity (i.e., considering the point at which they
had urge to stop) that may require additional training. We chose to measure perceived
mental toughness through self-report. However, we recognize researchers can also meas-
ure mentally tough behavior through observation or validated tools (e.g., Bell et al.,
2013). It is likely that perceived mental toughness and behavioral mental toughness are
distinct variables that are intricately related. Future researchers may wish to build on
the current study by integrating the combination of behavioral mental toughness and
perceived mental toughness when studying the effect of self-talk interventions.
Completing this study outside the context of a laboratory limited our ability to inte-
grate precise consistency regarding temperature and surface conditions. However, our
goal with this study design was to create a real-world setting outside the traditional lab,
and we were able to modify the additional variables by scheduling all sessions in the
early morning at the same track during generally temperate months of April June in
Colorado. The potential for participant bias is also a limitation of note. Participants
knew from the information sheets that the study was examining the influence of self-
talk on mental toughness and finish times, and there is potential they wanted to show it
worked.However, it is unlikely that such a Hawthorne effect can fully explain the
level of change detected across the three participants.
Future research directions
The insights provided in this study provide a complement to and build upon previous
research regarding the optimization of mental toughness (Beattie et al., 2019; Cook,
Crust, Littlewood, Nesti, & Allen-Collinson, 2014; Powell & Myers, 2017) but there
remain numerous opportunities to build further. The Thrive Prepare Activate model
(Cooper et al., 2019a) identifies a range of possible mental toughness influencers rang-
ing from fuel and caffeine to relational support and stress facilitation. Investigating one
of the influencers outside the specific context of athletics may also provide an intriguing
direction of study. For example, does self-talk influence mental toughness related to an
individuals chosen activity levels, high caloric food selection, or amount of screen time?
Such studies could help bring the mental toughness discussion into that of the general
population well-being conversation (Hannan et al., 2015).
Further, the investigation into mediation models to understand why self-talk influen-
ces performance and mental toughness is of value. Is it a result of changes in mental
toughness via elements such as self-efficacy and perceived control? Alternatively, is
mental toughness effectively a collection of cognitive tools and behavioral strategies that
improve outcomes across a variety of individual pursuits?
Application for practitioners
There has been increased attention on self-talk in the literature in recent years
(Hatzigeorgiadis et al., 2017) and a particular interest in the influence on performance
and competitive outcomes (Funatsu, 2018). Based on the apparent impact of strategic
self-talk on mental toughness and 800-meter finish times in this study, opportunities for
application are likely to be of great interest to coaches, consultants, and athletes. Some
of these were directly identified by one or more of our participants in terms of improv-
ing their coaching, athletic, professional, and even personal pursuits. In terms of coach-
ing, this study highlighted the value of a consistent approach to mental toughness and
its related constructs. Even our highly educated, respected, and up-to-date coach (P3)
indicated she would be translating many of her discoveries to how she is coaching her
athletes. For example, she noted her perspective of telling her athletes no crutches
(excuses) would be something she could instead adjust to provide them with specific
self-talk strategies to utilize during the difficult periods of a race. All three participants
made the connection between what they learned about their mental toughness patterns
and processes and both their future athletic and non-athletic pursuits. A typical com-
ment revolved around the discovery through their participation in the study that they
were capable of more than they realized, and the additional pursuits for which that dis-
covery might now be a catalyst. Also, the need for a personalized approach to self-talk
was clarified as the participants did note overlap between some of the most effective
self-talk strategies but also identified others that were specific to their individual history
and pursuits. Encouragingly, our findings also indicated that participants were able to
maintain the majority of their improved performance during the follow-up sessions
without the continued instruction from the outside advisor, providing a long-term value
for potential participants.
Conclusion
Performance enhancement in athletics and other aspects of life can take many forms.
The data related to the impact of alternatives noted here, such as caffeine or special
shoes are of interest as individuals look for ways in which to create that enhanced out-
come. Self-talk provides similar outcomes (at least in the current sample), and being
volitional and non-intrusive, is not accompanied by any side effects. Our findings
related to the influence of strategic self-talk providing results consistently beyond the
range of minimal benefit and harm for each of these categories appears to demonstrate
a relatedness that can now be expanded upon elsewhere and with other categories seen
as mental toughness optimizers.
16 K. B. COOPER ET AL.
ORCID
K. Bradford Cooper http://orcid.org/0000-0002-8207-8033
Martin I. Jones http://orcid.org/0000-0002-8878-6532
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... On the other hand, other work has linked both MT and PWB to sporting success. 21 There was no check on whether the intervention group listened to the mindful exercise tracks, or conversely if the controls practised similar techniques. However, both would have reduced differences between the groups. ...
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Objectives Research has shown that mindfulness in athletes is associated with positive outcomes. We therefore assessed the effect of mindfulness training on increasing mental toughness (MT) and psychological well-being (PWB). Methods This was a parallel-group, pre-and post-test, randomised controlled pilot trial. Forty-five female athletes from Iranian universities in Tehran were randomly assigned into experimental ( n = 23) and control groups ( n = 22). Forty-two completed the trial. The mindfulness training group received the Mindfulness–Acceptance–Commitment programme for 7 weeks. All subjects completed the Mindfulness Inventory for Sport (MIS), Sports Mental Toughness Questionnaire and PWB Scale. Data were analysed using mixed analysis of variance. Results Mindfulness training increased mindfulness in the experimental group ( p < 0.01). In turn, increased mindfulness led to increased MT and PWB at 2-month follow-up ( p < 0.05). Conclusions Given that mindfulness training may increase the MT and PWB of athletes, these techniques should be considered for inclusion in sports coaching.
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... Thus, researchers suggest that "MT is likely to guide the formation of behavioral intention, and also influenced the likelihood of an individual enacting those behaviors" (Crust et al., 2014;Gucciardi, 2015). Previous studies have found a positive correlation between MT and PA (Crust et al., 2014;Gucciardi, 2015;Brand et al., 2016;Serge et al., 2016;Eskandarnejad, 2018;Vaughan et al., 2018;Hegerstad et al., 2019;Cooper et al., 2020;Roncone et al., 2020). ...
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