Content uploaded by Pierluigi Diotaiuti
Author content
All content in this area was uploaded by Pierluigi Diotaiuti on Jan 29, 2020
Content may be subject to copyright.
Available via license: CC BY 4.0
Content may be subject to copyright.
Psychology, 2020, 11, 54-70
https://www.scirp.org/journal/psych
ISSN Online: 2152-7199
ISSN Print: 2152-7180
DOI:
10.4236/psych.2020.111005 Jan. 8, 2020 54 Psychology
Using Sports Tracker: Evidences on
Dependence, Self-Regulatory Modes and
Resilience in a Sample of Competitive Runners
Pierluigi Diotaiuti*, Stefania Mancone, Stefano Corrado
Department of Human Sciences, Society and Health, University of Cassino and Southern Lazio, Cassino, Italy
Abstract
A sample of 111 runners was give
n a survey to illustrate their experience in
using sports monitoring devices. Competitive experience proved to be a de-
termining variable in influencing the strategy of using digital devices, sug-
gesting a specific ergonomic model, so that the functionalities of sport track
ers
were first discovered, then consolidated and finally subjected to a rigorous
selection. A relatively more passive and dependent attitude towards the moni-
toring tools in competitive running was found in subjects with less competi-
tive experience (
p
< .05), less personal resilience (
p
< .01), less target orienta-
tion (
p
< .0001). The more experienced runners, on the other hand, have
shown that over time they have acquired a p
rogressive mastery and internal
control of their performance functions, so that they were sufficiently auto-
nomous to structure the relationship of use with the sport trackers in a strict-
ly instrumental way, for which there was no perception of dependence o
r
submission.
Keywords
Sports Tracker, Device Dependence, Resilience, Self-Regulatory Modes,
Running Agonistic Experience
1. Introduction
Wearable technology is spreading more and more in the global market, achiev-
ing considerable success, especially in the sports and fitness sectors, becoming
essential for those who love sports and need, in real time, to learn fundamental
information such as the time and distance traveled, the position in which he/she
is located, the heart rate and effort made, the calories consumed, and other spe-
How to cite this paper:
Diotaiuti, P.,
Mancone, S
., & Corrado, S. (2020).
Using
Sports Tracker: Evidences on Dependence,
Self
-Regulatory Modes
and Resilience in a
Sample of Competitive Runners
.
Psychol
o-
gy
, 11,
54-70.
https://doi.org/10.4236/psych.2020.111005
Received:
December 1, 2019
Accepted:
January 5, 2020
Published:
January 8, 2020
Copyright © 20
20 by author(s) and
Scientific
Research Publishing Inc.
This work is licensed under the Creative
Commons Attribution International
License (CC BY
4.0).
http://creativecommons.org/licens
es/by/4.0/
Open Access
P. Diotaiuti et al.
DOI:
10.4236/psych.2020.111005 55 Psychology
cific parameters to check the reactions of the body subjected to prolonged effort
and monitor individual athletic performance (Wang et al., 2016; Coughlin &
Stewart, 2016; Henriksen et al. 2018; Godfrey et al., 2018). The wearable tech is
currently offered in various forms, from the classic watch to the smart bracelet,
from fitness bands to sensors of every shape and nature, to monitors indicating
the physical performances useful in different sports, and thanks to their ability to
connect to digital multimedia technology or to a smartphone, they are able to
manage the data relating to the activity so as to be able to keep a real archive,
with which to constantly monitor the progress achieved and obtainable (Rabin &
Bock, 2011; Kinnunen et al., 2012; Kaewkannate & Kim, 2016; Hanton et al.,
2017; Leone, 2018). Among the lovers of the race can be distinguished two cate-
gories of runners, the “jogger” and the “runners” that, unlike the first, train in-
tensely in order to compete in sporting events in a systematic way (Schenkenfelder
& Selinger, 2016). For “joggers” the use of a simple smartphone could be suffi-
cient, as long as it is equipped with the GPS antenna to accomplish the moni-
toring mission very well while running or walking (Aughey, 2011; Seshadri et al.,
2019). When running on the other hand goes beyond being in shape, it becomes
essential to use more particular instruments, that is “sport tracker”, wearable at
an advanced level, which can improve the quality of the training and the ability
to make information more complete and detailed of those provided by smart-
phone applications, not able to guarantee that effectiveness and efficiency ne-
cessary for carrying out increasingly complex physical exercises (Janssen et al.,
2017; Wang, 2015).
Competitive and professional athletes, who carry out intensive training, have
very different needs from those of a “jogger” runner, as well as having the need
for a clock with repetitive timer, chronometer with intermediate times and speed
and distance function, personal diary suitable for memorizing all the training
sessions carried out with the relative times and speeds obtained, allowing an
immediate comparison of performance between one session and the previous
one, and naturally the function that monitors the heart muscle (Case et al., 2015;
Phan et al., 2015). The GPS integrated in the device provides precise calculations
regarding speed, distance, altitude and allows the runner to observe the route on
the map after the running session (Cummins et al., 2013; Pobiruchin et al.,
2017). The altimetry of the route is automatically detected as well as the maxi-
mum height difference during training and the race are directly visible in real
time on the display of the device, like many other fundamental parameters (Bpm,
distance, cardio, calories, etc.) (Crouter, 2004). An added value of most sports
trackers is the ability to view statistics either in real time, while running or after
training is finished on the computer monitor or directly on the watch display
(Evenson et al., 2015). For example, the times of each km can be recorded with
real-time statistics on distance and timing, and it is possible to set an audible
warning after each kilometer or a particular heart rate value (Li et al., 2016;
Aroganam et al., 2019). In addition can be shared workouts with followers, and
P. Diotaiuti et al.
DOI:
10.4236/psych.2020.111005 56 Psychology
then relive the sessions of their own and others’ training on all digital multime-
dia devices (PCs, smart-phones, tablets, etc.), in short, keeping a real open win-
dow to the world and talking, sharing, exchanging experiences and feelings for a
constant personal, social and competitive growth (Stragier et al., 2015; Chang et
al., 2016).
Every athlete who uses a digital device as a support for the running of the race,
is progressively aware of so-called advanced functions, as the level of training
and preparation increases (Bourdon et al., 2017; Ng & Ryba, 2018; Goodyear et
al., 2019). Preliminarily it can be said that the choice of the device is often dic-
tated by factors external to the activity, such as the purchase price and word of
mouth, but once the same is available and one learns to use it, a true and proper
dependency bond that hardly a runner can do without wearing his device during
training or competitions (Maher et al., 2017; Johnston & Heiderscheit, 2019).
This feeling will be more intense depending on the user’s perception of being
able to take advantage of a usable and ergonomic device that allows him to ex-
ploit his own characteristics and potential to the fullest (Lee & Drake, 2013). It is
not excluded that device also performs for the athlete a function of limiting per-
ceived stress, especially in preparation for the competitions, ensuring through
the various monitoring functions, an exercise of control and greater awareness
of their performance in situations of pressure (see Foster et al., 2017; Roos et al.,
2017; Rieder et al., 2019). Some studies (e.g. Lucidi et al., 2016; Pica et al., 2019)
have recently explored in competitive athletes the relationships between regula-
tory modes and stressful experiences such as the training for a competition or
the retirement. For this reason, we have considered it valid for exploratory pur-
poses to examine the relationship between the regulatory mode orientations
(locomotion and assessment) and the use of sport devices. A second frequently
reported association in the runners’ literature is that between perceived stress
and resilience (Sarkar & Fletcher, 2013; Sarkar & Fletcher, 2014; Codonhato et
al., 2018). Athletes who practice endurance sports are well aware that their activ-
ity can be critical, causing unexpected events or random factors related to their
physical and mental state. Resilience for an athlete is the ability to resist pur-
suing challenging goals, effectively coping with the difficulties and adverse mo-
ments encountered along the way, facing frustrations, and stress after a negative
event such as defeat. Being resilient also means being able to recognize one’s
limits and accept them, and have the strength to look beyond difficulties opti-
mistically (see Gerber et al., 2013; Fletcher & Sarkar, 2013; Galli & Gonzalez,
2015; Fletcher & Sarkar, 2016). Considering this, we wanted to explore if ath-
lete’s dimensions of the resilience (according to Richardson’s model of 2002)
were related to the mode of use of the sport device. The mode of use, as indi-
cated by Schukat et al. (2016), could configure a bond with the device that can
also be structured as a proper dependency bond. Users may become obsessed
with self-monitoring beyond what can be considered a healthy level of attention
to oneself. Individual reports of addictive behaviors regarding wearable fitness
P. Diotaiuti et al.
DOI:
10.4236/psych.2020.111005 57 Psychology
devices used by healthy individuals (see Attig & Franke, 2019) provide insight
into the unintended power that these devices yield and how they can shape the
way users manage their daily life. Consequently we decided to test whether in
agonistic runners regulatory modes orientation and the resilient capability both
showed a particular relationship amongst themselves, and with the device de-
pendency. As far as we are aware, there have been no previous studies dealing
with the relationship between individual differences (regulatory modes orienta-
tion and resilience dimensions) and experience of device use.
2. Procedure
2.1. Hypotheses
Sport experience would significantly impact the use and the experience with
sports monitoring devices. Therefore first hypothesis put to the test was that the
level of experience of the runner would be associated with the way of use and
with a diversified appreciation of the functions of the device. Through the analy-
sis of the percentage frequencies of use of the functions, it was expected that, of
the numerous functions offered by the most common devices, only a limited
number would actually be used, and that among these the hierarchy of use
would change in relation to the level of experience of the runner. Secondly, indi-
vidual differences (in particular regulatory modes orientation and resilience di-
mensions of the subjects) would in turn be significantly associated amongst
themselves, and with the experience of using the device itself (specifically with
dependence experiences). It was expected to find such evidence from correla-
tional and variance analysis.
2.2. Tools
In order to collect the data necessary to carry out the study a questionnaire was
built up and articulated into the following sections: 1) socio-demografic info:
gender; age; 2) specifications as a runner: athletic specialty, i.e. 10 km, half ma-
rathon, marathon, cross country; years of experience in agonistic running; 3)
opinion on the preponderant factor for a good agonistic preparation: choice be-
tween
careful planning
,
intensive training
,
continuous monitoring
; 4) use of the
sport tracker device: perceived utility (1 - 5 points from
useless
to
essential
);
frequency of use of each function (ones most frequently used—multiple response);
satisfaction with basic and advanced functions (1 - 5 points from
very low
to
very good
); evaluation of usability and device ergonomics: precision, ease of use,
quality/price ratio, reliability, completeness, aesthetics, handling skills (1 - 5
points from
very low
to
excellent
, Cronbach’s alpha .75); 5) expectations on the
device: it could help to further improve its own performances (1 - 5 points from
completely disagree to completely agree); 6) psychometric measurements: a)
Regulatory-Modes Scale
(RMS; Higgins et al., 2003; Pierro et al., 2006) com-
posed of 24 items (12 for the measure of
Assessment Mode
and 12 for the meas-
ure of
Locomotion Mode
) 6-points Likert (from 1 = completely disagree to 6 =
P. Diotaiuti et al.
DOI:
10.4236/psych.2020.111005 58 Psychology
completely agree). In this study Cronbach’s alpha resulted .80.
Assessment
is the
comparative component of the system of regulation of the Self, as a tendency to
critically assess the state in which we are in relation to other alternatives for to
achieve the goals in the best possible way. The
Locomotion
, on the contrary, is
the component of our self-adjusting system dedicated to control the movement
by state and its maintenance to achieve an objective in a simple way and without
distractions or delays. In this study Cronbach’s alpha resulted .67; b) The
Resi-
lience Process Questionnaire
(RPQ; Laudadio et al., 2011) consists of 15 items
based on a five-step Likert scale (1 = disagree; 5 = totally agree). It deepens three
dimensions of resilience, according to Richardson perspective (2002): i)
Resilient
Recovery
: typical of a subject who is able to overcome traumatic or stressful
events: scores above 8 are associated with a strong resilience of the subject. E.g.:
“I think that a painful situation can make me better”; Cronbach’s alpha in this
study resulted .83; ii)
Return to Homeostasis
: scores above 8 are characteristic of
subjects who, in the face of the trauma, try to restore the state of equilibrium
before the event. E.g.: “When I am in a difficult situation, I do everything to re-
gain the strength I had”; Cronbach’s alpha in this study resulted .84; iii)
Reinte-
gration with Loss
: scores above 8 indicate the difficulty in facing, accepting and
overcoming traumatic or stressful events. In this dimension, the test authors also
included
Dysfunctional Recovery
(Richardson, 2002). e.g.: “When something
bad happens to me, I cannot get a reason”; Cronbach’s Alpha in this study re-
sulted .75. c) In order to assess addiction bond with the device a
Dependency
Index
on the tracker device
has been constructed taking into account the overall
averages of the scores for the following four questions (Likert 1 - 5): i) “If you
could not have your device with you, would you still train?”; ii) “If you were to
be unable to use your device during training, how much discomfort do you think
this would cause?”; iii) “How much do you feel
tied
to the use of your device?”;
iv) “Do you think that at this moment your sporting performance is influenced
by the use of your device?”. The index was subjected to an PCA exploratory veri-
fication and showed a monofactorial structure, Determinant .192, KMO .709,
Rotated Component Matrix Oblimin, Test Bartlett Sphericity sig. .000. The in-
dex has demonstrated good reliability by presenting a Cronbach’s alpha coeffi-
cient equal to .79.
2.3. Sample Selection and Questionnaire Administration
Procedures
The data necessary to verify the working hypotheses were collected through the
administration of a questionnaire specifically structured to a representative sam-
ple of athletes on a regional scale, all strictly accumulated by the performance of
competitive activities supported by the use of digital wrist devices for monitor-
ing of individual performances. The reference population concerned runners
belonging to the 15 running sports associations of the Province of Frosinone,
members of the Fidal (Italian Athletics Federation), which in June 2019 regis-
tered 794 athletes. The sample size determination was made by setting a 1-alpha
P. Diotaiuti et al.
DOI:
10.4236/psych.2020.111005 59 Psychology
confidence level at 95%, therefore with z normal value at the confidence level of
1.96. The following two formulas were applied, where the second operated the
correction for small populations (<20,000): 1)
( )
22
*X z pq b
ο
=
, with
p
as the
proportion to be estimated and
q
the proportion of complementary character and
b the desired precision set at 7%. Hence: 3.8416 (0.84 * 0.16)/0.0045 = 143.41; 2)
( )
1X X X pop
οο
= +
, with pop the numerical value of the reference popula-
tion. Hence: 143.41/[1 + (143.41/794)] = 105.36. Participants were recruited
through a preliminary contact with the presidents of the running sports associa-
tions, who ensured the dissemination of the questionnaire to their members,
through the forwarding of an email in which they were indicated the objectives
and purpose of the study and in which, at the same time, subjects were invited to
access a special link contained in the same communication and then to fill in and
transmit the answers in digital and telematic mode. Participants were assured
anonymity and the use of data in aggregate form for research purposes only. The
average length of time for the compilation was about 20 minutes. One month after
the first email was sent, the sample was asked again, both through a resubmission
of the mail containing the link to access the questionnaire, and through individual
contacts activated with athletes during running competitions held in the region
Lazio in July and August 2019. A total of 111 questionnaires were collected. The
response rate of the subjects to the submission of the compilation link was rather
modest (1:7), but compatible with the fixed sample size (111 > 105).
2.4. Statistical Analysis
The data were processed using the statistical software SPSS version 22. The main
analyzes performed were: descriptive statistics to illustrate socio-demografic in-
fo, specifications as a runner, opinion on a good agonistic preparation, use of the
sport tracker device; Pearson and Spearman bivariate correlations for all main
measures (Device Dependence, Running Experience, Regulatory Modes, Resi-
lience) significant at
p
< .005 and at
p
< .001, 2-tailed); PCA (Principal Compo-
nent Analysis) as exploratory factor verification for Dependence Index; Cron-
bach’s alpha as scale reliability coefficient; Anova univariate test with Post-hoc
Tukey HSD and
p
< .05 to explore significances between Running Experience,
Devise Dependence, Regulatory Modes and Resilience.
3. Results
3.1. Descriptive Analysis
There were a total of 111 participants (Males = 93 (83.8%); Females = 18 (16.2%).
The age of the sample was between 20 and 65 years (M = 43.32; SD = 8.92). The
experience in running ranged from 1 to 34 years (M = 7.96; SD = 7.35). In rela-
tion to the preferred athletic specialty, the 51.4% declared to compete desirably
for races of 10 km, the 27% for the half marathon, the 16.2% for the marathon,
the 5.4% for the cross-country race. Overall the 47.7% of the sample declared to
have competed in at least one marathon in the last three years. Where it was
P. Diotaiuti et al.
DOI:
10.4236/psych.2020.111005 60 Psychology
asked to indicate a preponderant factor for a good preparation for race, the
57.7% indicated careful planning, the 27.9% intensive training, the 13.5% conti-
nuous monitoring of performance.
3.2. Running and Digital Devices
When asked about the weight of the digital device for the preparation of the
race, only a small portion of subjects said that the use of the tracker was
not very
useful
(7.2%) or even
useless
(1.8%), while for all the others digital devices were
deemed
quite useful
(45.0%),
necessary
(31.5%) or
indispensable
(13%). With
regard to the type of device used, 41.4% of the subjects declared to own a Gar-
min brand device, 20.7% Polar, 9.8% Timex, 7.2% Tom Tom, the remaining
subjects referred to devices that covered percentages of less than 3%.
3.3. Use of the Device Functionalities
With regard to the distribution of the functions mainly used, sorted by frequen-
cy (“Of the following functions, please indicate which ones you use most fre-
quently”—multiple response), it was possible to observe (see Figure 1) that out
of 15 functions considered, 5 covered the percentage ranging from 85.6% to 36%
(calculation of distance, stopwatch, heart rate measurement, activity diary and
calculation of calories burned). The remaining functions covered a percentage
ranging from 25% to 0.9%.
Crossing the levels of competitive experience (quartiles of the total years of
participation in competitive races) with the frequency of use of device functions,
the following variations in percentage resulted, as shown in Figure 2.
First level
(1 - 3 years): calculation of distance 96.3; chronometer 81.1; activity
diary 44.4; heart rate measurement 40.7; calculation of calories burned 40.7; re-
maining functions ranged from a percentage of 22.2% to 0.
Figure 1. Prevalent use rates of device functions.
P. Diotaiuti et al.
DOI:
10.4236/psych.2020.111005 61 Psychology
Figure 2. Competitive experience and use of devise functions.
Second level
(4 - 5 years): chronometer 82.8; distance 79.3; heart rate 55.2; al-
timeter 41.4; calculation of calories burned 37.9; diary of activities 34.5; training
programs 31.0; remaining functions ranged from a percentage of 13.8% to 0.
Third level
(6 - 10 years): distance 91.7; chronometer 87.5; heart rate 58.3;
calories burned 45.8; activity diary 41.7; training programs 25.0; altimeter
20.8,%; remaining functions ranged from a percentage of 12.5 to 0.
Fourth level
(over 11 years): chronometer 77.8; distance 72.2; remaining func-
tions ranged from a percentage of 27.8 to 0.
Among the functions declared as less used by the athletes resulted: sleep
tracking (76.7%), culinary diary monitoring (67.7%), blood pressure measure-
ment (57.7%), accelerometer (51.4%), aerobic capacity measurement (45%), per-
formance network sharing (43.2%), specific training analysis programs (37.8%),
mobile device association (36.9%), altimeter (36.9%).
The satisfaction degree in using basic functions of the device was rated for the
24.5% “very good”, 62.3% “good”, 11.30% “sufficient”, and only for the 0.9% re-
spectively “low” and “very low”. If considering the different competitive expe-
riences, the trend has instead shown a significant decrease in satisfaction in the
fourth level, falling to a minimum percentage of 11.1.
Satisfaction in using advanced functions was for the 15% “very good”, 57.4%
“good”, 19.80% “sufficient”, and 3% and 4% respectively “low” and “very low”.
Also here, the comparative analysis of the data referred to the experience showed
a significant decrease in satisfaction in the level 4, in particular the figure of
maximum satisfaction falls to a minimum with a percentage of 11.0, while for
the judgment “good” at 38.9%, also appeared the level “very low” which was
11.1%.
As regards the evaluation of the quality of usability and ergonomics of the de-
P. Diotaiuti et al.
DOI:
10.4236/psych.2020.111005 62 Psychology
vice (precision, ease of use, quality/price ratio, reliability, completeness, aesthet-
ics, handling skills) it was noted that almost all of them were appreciated by al-
most all of the sample. The positive judgment showed, however, a lowering with
the growth of the experience, since the percentages moved towards the indica-
tion of sufficiency, compared to that of “good” and “excellent”.
For 63.1% of the sample, however, there was a defect in their own device that
should be corrected and in particular it has been indicated more problems of
night brightness and inconvenience to the strap. The percentage has risen con-
sidering the experience gained by the athletes, the most experienced group dec-
lares in fact for 83.3% the presence of defects to be corrected.
3.4. Device Dependence and Individual Differences
Following Table 1 reports Pearson’s correlation coefficients for the main va-
riables considered in the study.
Considering the hypotheses of the study, worthy of attention was first of all
the negative coefficient −.259** that associated
Resilient Recovery
and
Device
Dependence
. A strong positive link (.460**) resulted between
Resilient Recovery
and
Locomotion Regulatory Mode
, while a remarkable negative link (−.289**)
associated
Resilient Recovery
with
Assessment Regulatory Mode
. Assessment
also showed a strong positive link (.397**) with the
Dysfunctional Recovery
.
Table 2 shows the average values of the variables being measured in our sam-
ple of runners:
Device Dependence
,
Regulatory Modes
and
Resilience.
3.5. Running Experience and Devise Dependence
The Anova test compared experience levels showing a significant difference be-
tween the dependency averages of the fourth and second groups. F (3, 107) =
4.766
p
= .004 Eta2 = .12 OP = .891. The group with the most experience (more
than 10 years of competitive practice) was less dependent, especially compared
to the group of those who fell within the range of 4 to 6 years of competitive
Table 1. Correlation matrix for all main measures of the study.
Device
Dependence
Age
Running
Experience
Locomotion
Mode
Assessment
Mode
Hom.
Rec.
Dys.
Rec.
Resilient
Recovery
Device Dependence
1
Age
−.154
1
Running Experience
−.086
.335**
1
Locomotion Mode
−.133
.016
−.004
1
Assessment Mode
.084
−.066
.051
−.296**
1
Homeostatic Recovery
−.082
−.053
.086
.474**
−.211*
1
Dysfunctional Recovery
.021
.127
.016
−.114
.397**
−.470**
1
Resilient Recovery
−.259**
.133
.096
.460**
−.289**
.539**
−.081
1
Note: Pearson’s correlation matrix for the means of the scales with the exception of Age and Running Experience, for which the Spearman coefficient was
used (
n
= 111; **, correlation is significant at
P
< .005 2-tailed; *, correlation is significant at
p
< .001 2-tailed).
P. Diotaiuti et al.
DOI:
10.4236/psych.2020.111005 63 Psychology
Table 2. Mean values of device dependence, regulatory modes and resilience (Sample: N
= 111).
Mean
SD
Sk
Ku
Device Dependence
2.49
.89
.087
−.546
Locomotion Mode
5.16
.58
−.276
−.491
Assessment Mode
2.87
.88
.012
−.320
Homeostatic Recovery
3.92
.62
.094
−.858
Dysfunctional Recovery
2.15
.51
.042
−.078
Resilient Recovery
3.19
.83
.300
−.290
Legend: SD = Standard Deviation; Sk = Skewness; Ku = Kurtosis.
practice. Post-hoc Tukey HSD
p
< .05 M2 = 2.83 SD = .15 M4 = 1.99 SD = .17
95% CI [.252; 1.43]. An equally significant result emerged between the two
groups (fourth and second) in relation to the expectation that the use of the de-
vice could contribute to further improve their own performance: F (3, 107) =
4.094
p
= .009 Eta2 = .11. OP = 831. The group with competitive experience 4 - 6
years attributed more weight to the contribution that the device could continue
to exert on their performance level. Post-hoc Tukey HSD
p
< .05 M2 = 2.48 SD
= .19 M4 = 2.50 SD = .22 95% CI [.413; 1.56].
3.6. Regulatory Modes and Resilience
Worthy of attention was the difference resulted between average values of
Lo-
comotion Regulatory Mode
(M = 5.16) and
Assessment Regulatory Mode
(M =
2.87) stressing that in the sample of runners the orientation towards the objec-
tive with respect to the evaluation/control of the process was particularly pro-
nounced. Comparing average values of both scales with other samples of
non-competitive subjects, it was possible to note that values of
Locomotion Reg-
ulatory Mode
in runners were significantly higher than those of non-competitive
subjects, while the values of
Assessment Regulatory Mode
tended to be lower:
runners sample: MLoc = 5.16 and MAss = 2.87; mixed adults and young people
sample: MLoc = 3.82 and MAss = 3.57; university students sample: MLoc = 3.93 and
MAss = 3.15).
In our sample the runners with the highest target orientation (
Locomotion
Regulatory Mode
) were also those who showed significantly higher values of
Re-
silient Recovery
: F (1, 109) = 16.325
p
= .000 Eta2 = .14 OP = .98 M1 = 2.87 SD
= .66; M2 = 3.50 SD = .88. 95% CI [−.938; −.320]. Furthermore runners with the
highest target orientation (
Locomotion Regulatory Mode
) were also those who
showed significantly higher values of
Omeostatic Recovery
: F (1, 109) = 10.4
p
= .002 Eta2 = .09 OP = .88 M1 = 3.77 SD = .55; M2 = 4.14 SD = .63. 95% CI
[−.604; −.140]. In addition the runners with the highest process control orienta-
tion (
Assessment Regulatory Mode
) were also those who showed significantly
higher values of
Dysfunctional Recovery
: F (1, 109) = 9.825
p
= .002 Eta2 = .08
OP = .87 M1 = 2.03 SD = .48; M2 = 2.33 SD = .48. 95% CI [−.485; −.109].
P. Diotaiuti et al.
DOI:
10.4236/psych.2020.111005 64 Psychology
Lastly the runners with the highest process control orientation (
Assessment
Regulatory Mode
) were also those who showed significantly lower values of
Re-
silient Recovery
: F (1, 109) = 6.052
p
= .015 Eta2 = .05 OP = .68 M1 = 3.34 SD
= .80; M2 = 2.95 SD = .82. 95% CI [.075; .697].
3.7. Device Dependence and Resilience
For the purposes of this study, the significant association that emerged between
Device
Dependence
and
Resilient Recovery
of the athlete was particularly rele-
vant: F (1, 109) = 12.612
p
= .001 Eta2 = .10. OP = .94. The increased dependence
was significantly associated with lower levels of resilient recovery: M1 = 2.77 SD
= .80; M2 = 2.20 SD = .89. 95% CI [.254; .894] (See Table 3).
4. Discussion
Worth of interest is underline the relationship between the judgment on the
utility of the device for the preparation of the competition and the specific use of
the functions: on the one hand the growth of the competitive experience in-
creases the belief that the devices are essential (from 7.7% to 33%), at the same
time the number of functions constantly used by users decreases over time (5
functions for the first level of experience, 7 respectively for the second and third
level, 2 for the fourth level). The trend suggested that functions are first “discov-
ered”, then consolidated and finally subjected to strict selection. Evidently most
experienced subjects acquire over time a progressive mastery and (internal con-
trol) of their own performative functions. As far as the passage from the first to
the second experience level was concerned, it could be observed that hierarchical
weight between the calculation of the distance and the chronometer were re-
versed. Reasonably the subject after having measured, in a first phase, primarily
with the distance, then values the running time and it is in this second moment
that the need to monitor the heart rate is added, in order to modulate the run-
ning speed looking for his own physical limit. At the same time, it is possible to
detect the appearance of two complementary functions, the altimeter and the
Table 3. Mean values of dependence, regulatory modes and resilience according to the level of running experience.
Running Experience
Level 1 (1 - 3 Years)
Level 2 (4 - 6 Years)
Level 3 (7 - 9 Years)
Level 4 (>10 Years)
N Total:
111
n: 27
n: 34
n: 24
n: 25
Mean
SD
Sk
Ku
Mean
SD
Sk
Ku
Mean
SD
Sk
Ku
Mean
SD
Sk
Ku
Device Dependence
2.54
.83
.026
−.263
2.83
.66
−.486
.414
2.60
.89
.124
−.666
1.99
.98
1.10
1.24
Locomotion Mode
5.11
.59
.307
−1.17
5.15
.52
−.190
−.755
5.18
.66
−.866
.904
5.19
.60
−.291
−.667
Assessment Mode
2.99
1.00
−.286
−.541
2.90
.81
.042
−.507
2.78
.63
.154
−.985
2.87
1.00
.204
−.132
Homeostatic Recovery
3.95
.69
.038
−.872
3.88
.61
−.023
−.881
3.89
.54
.197
−.618
3.91
.63
.209
−.927
Dysfunctional Recovery
2.23
.61
−.014
.075
2.17
.44
−.270
−.377
1.96
.47
.486
.684
2.23
.50
−.310
−.149
Resilient Recovery
3.24
.92
.363
−.797
3.07
.90
.334
−.140
3.11
.64
.314
−.055
3.30
.72
.069
.088
Legend: SD = Standard Deviation; Sk = Skewness; Ku = Kurtosis.
P. Diotaiuti et al.
DOI:
10.4236/psych.2020.111005 65 Psychology
specific training programs. Obviously, the runner need additional information
and support to obtain the best athletic performance. At the third level of expe-
rience it could be seen that again the hierarchical weight between distance and
chronometer is reversed. The new priority acquired by the extension of the dis-
tance is associated with a new increase in the function of control of burned calo-
ries, as exceeded the distance of 20 kilometers, necessarily requires a continuous
caloric replenishment to continue in conditions of efficiency. At the fourth level,
priority is given to the chronometer and distance functions for the reasons men-
tioned above. With reference to the satisfaction in the use of both basic and ad-
vanced functions of the device, a progressive critical judgment could be detected
in the most expert group, probably due to the fact that outside the functions
considered indispensable, the others no longer corresponded to their explicit
need. As far as the evaluation of the quality of usability and ergonomics of the
device was concerned, it was noted that they were appreciated by almost the en-
tire reference sample. In relation to the detection of defects detected in the de-
vices as experience grows, there was a greater propensity to indicate the presence
of defects to be corrected. Specifically, problems relating to night-time illumina-
tion of the device and to the wrist strap were more widely reported. With regard
to the section that intended to assess the bond with the device, it is worthy of at-
tention the fact that describes the discomfort caused by not being able to use
own device. The highest dependence was found in runners with competitive ex-
perience between 4 and 6 years, while the lowest average dependence was found
in runners with the highest experience (>10 years). In relation to the judgment
on the influence of the device on sports performance, resulted a progressive de-
crease in the weight of the perceived influence related to the growth of the expe-
rience. This result can be interpreted as a consolidation of one’s awareness and
active control role in the management of one’s performance. In other words,
they do not deny the use of the device, but claim to “use” it as an aid, consider-
ing themselves the only protagonists for the achievement and improvement of
their athletic performance. A plausible interpretation is that the “independents”
are the subjects who have acquired greater autonomy in monitoring their com-
petitive performance, while the “dependents” are subjects strongly linked to the
indispensability of support in monitoring ensured by the device.
With regard to the weight of individual differences, significance has emerged
in the measurement of the resilient capacity of the subjects. Resilient Recovery is
the resilience dimension that has shown a remarkable inverse relationship with
the device dependency. Among the regulatory modes the Locomotion orienta-
tion revealed an interesting link with the Resilient Recovery, while the Assess-
ment mode was associated with the Dysfunctional Recovery. These associations
that have emerged in the study suggest a potential explanatory articulation that
could be the subject of a further model investigation. Considering the articula-
tion into levels of experience, it appeared that the second and third levels were
composed of subjects with a higher component of resilient recovery. The orien-
P. Diotaiuti et al.
DOI:
10.4236/psych.2020.111005 66 Psychology
tation to the objective (locomotion mode) of the athletes, resulted significantly
associated with the function of sharing the own performances on internet. Evi-
dently, the subjects most focused on their own objectives were aware that the in-
teraction and sharing, was a useful tool for further growth and pursuit of their
final objectives. The need for control in the process can turn out, in some cases,
to be excessively rigid, preventing the subject from focusing attention on the
dynamism of the purpose-oriented process, crystallizing the projection of atten-
tion on the internal control components. Having control of the situation does
not mean being obsessively focused on the details in order not to let anything
escape, on the contrary, a resilient subject manifests his sense of control by
showing some flexibility in the face of unexpected events, whether they be nega-
tive or positive. Numerous studies have already consolidated the hypothesis of a
greater effectiveness in performative terms of the outsourcing of the focus of at-
tention (see Schücker et al., 2009; Neumann & Piercy, 2013; Zep Iin et al., 2014).
Therefore, a goal setting with a pronounced internal focus component can pro-
duce less significant final results compared to a focus orientation of the attention
decidedly oriented towards the objective.
5. Conclusion
The results of the study supported the initial hypothesis that the level of compet-
itive experience was a determining variable in modulating the strategy of use of
digital devices. Participants expressed very positive usability evaluations of both
the functions and the device as a whole, revealing the development of a progres-
sive interaction between the opportunities offered by the functions and the spe-
cific needs of the athlete. The association of some specific individual trait with
the experience of using the device was confirmed. It was also confirmed the in-
fluence that subjects attributed to the device on individual athletic performance
and the overall impact on the personal and relational sphere of the subject. In
conclusion, the profile of the agonist runner revealed a pronounced orientation
to the objective which constituted a primary focus of attention for all the sub-
jects. This provision also influences the normal relations of use of the digital de-
vice in the face of a recognized functionality and usefulness of the same, the
subject is sufficiently autonomous to structure the relationship of use in strictly
instrumental function, so that in the face of a wide response in terms of diffusion
of the devices, there is no perception of dependence or “subjugation”, rather the
subject is well aware that the appropriate and intelligent use can promote the
achievement of clear objectives and predetermined with full awareness by the
most experienced runner. On the other hand, it was possible to observe a rela-
tively more “passive” attitude towards the monitoring tool in subjects who be-
longed to the levels of minor experience or in those subjects whose individual
profile showed higher values in the assessment regulatory mode, which corres-
ponds to an attitude of goal-setting oriented more towards the evaluation and
interpretation of the individual performative moments.
P. Diotaiuti et al.
DOI:
10.4236/psych.2020.111005 67 Psychology
6. Limitations
The study certainly has some limitations. The first is related to the modest
number of participants. A replication of the study with a wider sample base
would be desirable so that further inferential statistics and structural testing can
be used to investigate the predictive weight of resilience variables and regulatory
modes on device dependence in the runner. It is also likely that significant va-
riables not considered in this study, such as the components of anxiety (somatic,
cognitive and social) or the level of stress perceived by the athlete, will also come
into play in the relationship with addiction. It would be appropriate to think of a
replication of the study by assessing the incidence of these additional compo-
nents or other specifications related to the context (including cultural attitudes)
in which the athlete operates. Given the widespread use of sports trackers in
many sports disciplines, it would also be reasonable to undertake exploratory and
comparative studies on sports device addiction in other disciplines than running.
Authors’ Contributions
PD, SM, and SC designed the study. PD, SC, and SM analyzed the data and dis-
cussed the results. PD and SC drafted the manuscript, and SM and SC revised
the manuscript. All authors approved the final manuscript. Finally, the authors
have agreed to be accountable for all aspects of the manuscript in ensuring that
questions related to the accuracy or integrity of any part of it are appropriately
investigated and resolved.
Conflicts of Interest
The authors declare no conflicts of interest regarding the publication of this paper.
References
Aroganam, G., Manivannan, N., & Harrison, D. (2019). Review on Wearable Technology
Sensors Used in Consumer Sport Applications.
Sensors, 19,
1983.
https://doi.org/10.3390/s19091983
Attig, C., & Franke, T. (2019). I Track, Therefore I Walk—Exploring the Motivational
Costs of Wearing Activity Trackers in Actual Users.
International Journal of Human-
Computer Studies, 127,
211-224. https://doi.org/10.1016/j.ijhcs.2018.04.007
Aughey, R. J. (2011). Applications of GPS Technologies to Field Sports
. International
Journal of Sports Physiology and Performance, 6,
295-310.
https://doi.org/10.1123/ijspp.6.3.295
Bourdon, P. C., Cardinale, M., Murray, A., Gastin, P., Kellmann, M., Varley, M. C., & Ca-
ble, N. T. (2017). Monitoring Athlete Training Loads: Consensus Statement
. Interna-
tional Journal of Sports Physiology and Performance, 12,
161-170.
https://doi.org/10.1123/IJSPP.2017-0208
Case, M. A., Burwick, H. A., Volpp, K. G., & Patel, M. S. (2015). Accuracy of Smartphone
Applications and Wearable Devices for Tracking Physical Activity Data.
The Journal of
the American Medical Association, 313,
625-626.
https://doi.org/10.1001/jama.2014.17841
Chang, R. C. S., Lu, H. P., Yang, P., & Luarn, P. (2016). Reciprocal Reinforcement be-
P. Diotaiuti et al.
DOI:
10.4236/psych.2020.111005 68 Psychology
tween Wearable Activity Trackers and Social Network Services in Influencing Physical
Activity Behaviors.
JMIR mHealth and uHealth, 4,
e84.
https://doi.org/10.2196/mhealth.5637
Codonhato, R., Vissoci, J. R. N., Nascimento Junior, J. R. A. D., Mizoguchi, M. V., &
Fiorese, L. (2018). Impact of Resilience on Stress and Recovery in Athletes.
Revista
Brasileira de Medicina do Esporte, 24,
352-356.
https://doi.org/10.1590/1517-869220182405170328
Coughlin, S. S., & Stewart, J. (2016). Use of Consumer Wearable Devices to Promote
Physical Activity: A Review of Health Intervention Studies.
Journal of Environment
and Health Sciences, 2
.
Crouter, S. E. (2004). Validity of 10 Electronic Pedometers for Measuring Steps, Distance,
and Energy Cost.
Medicine & Science in Sports & Exercise, 36,
331-335.
Cummins, C., Orr, R., O’Connor, H., & West, C. (2013). Global Positioning Systems
(GPS) and Microtechnology Sensors in Team Sports: A Systematic Review.
Sports Medi-
cine, 43,
1025-1042. https://doi.org/10.1007/s40279-013-0069-2
Evenson, K. R., Goto, M. M., & Furberg, R. D. (2015). Systematic Review of the Validity
and Reliability of Consumer-Wearable Activity Trackers.
International Journal of Be-
havioral Nutrition and Physical Activity, 12,
159.
https://doi.org/10.1186/s12966-015-0314-1
Fletcher, D., & Sarkar, M. (2013). Psychological Resilience: A Review and Critique of
Definitions, Concepts and Theory.
European Psychologist, 18,
12-23.
https://doi.org/10.1027/1016-9040/a000124
Fletcher, D., & Sarkar, M. (2016). Mental Fortitude Training: An Evidence-Based Ap-
proach to Developing Psychological Resilience for Sustained Success
. Journal of Sport
Psychology in Action, 7,
135-157. https://doi.org/10.1080/21520704.2016.1255496
Foster, C., Rodriguez-Marroyo, J. A., & De Koning, J. J. (2017). Monitoring Training
Loads: The Past, the Present, and the Future.
International Journal of Sports Physiolo-
gy and Performance, 12,
2-8. https://doi.org/10.1123/IJSPP.2016-0388
Galli, N., & Gonzalez, S. P. (2015). Psychological Resilience in Sport: A Review of the Li-
terature and Implications for Research and Practice.
International Journal of Sport and
Exercise Psychology, 13,
243-257. https://doi.org/10.1080/1612197X.2014.946947
Gerber, M., Kalak, N., Lemola, S., Clough, P. J., Perry, J. L., Pühse, U., Elliot, C., Hols-
boer-Trachsler, E., & Brand, S. (2013). Are Adolescents with High Mental Toughness
Levels More Resilient against Stress?
Stress and Health, 29,
164-171.
https://doi.org/10.1002/smi.2447
Godfrey, A., Hetherington, V., Shum, H., Bonato, P., Lovell, N. H., & Stuart, S. (2018).
From A to Z
:
Wearable Technology Explained
. Maturitas, 113,
40-47.
https://doi.org/10.1016/j.maturitas.2018.04.012
Goodyear, V. A., Kerner, C., & Quennerstedt, M. (2019). Young People’s Uses of Weara-
ble Healthy Lifestyle Technologies; Surveillance, Self-Surveillance and Resistance.
Sport, Education and Society, 24,
212-225.
https://doi.org/10.1080/13573322.2017.1375907
Hanton, C. R., Kwon, Y. J., Aung, T., Whittington, J., High, R. R., Goulding, E. H., Bona-
sera, S. J. et al. (2017). Mobile Phone-Based Measures of Activity, Step Count, and Gait
Speed: Results from a Study of Older Ambulatory Adults in a Naturalistic Setting.
JMIR
mHealth and uHealth, 5,
e104. https://doi.org/10.2196/mhealth.5090
Henriksen, A., Mikalsen, M. H., Woldaregay, A. Z., Muzny, M., Hartvigsen, G., Hops-
tock, L. A., & Grimsgaard, S. (2018). Using Fitness Trackers and Smartwatches to
Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables.
P. Diotaiuti et al.
DOI:
10.4236/psych.2020.111005 69 Psychology
Journal of Medical Internet Research, 20,
e110. https://doi.org/10.2196/jmir.9157
Higgins, E. T., Kruglanski, A. W., & Pierro, A. (2003). Regulatory Mode: Locomotion and
Assessment as Distinct Orientations.
Advances in Experimental Social Psychology, 35,
294-344. https://doi.org/10.1016/S0065-2601(03)01005-0
Janssen, M., Scheerder, J., Thibaut, E., Brombacher, A., & Vos, S. (2017). Who Uses Run-
ning Apps and Sports Watches? Determinants and Consumer Profiles of Event Run-
ners’ Usage of Running-Related Smartphone Applications and Sports Watches.
PLoS
ONE, 12,
e0181167. https://doi.org/10.1371/journal.pone.0181167
Johnston, W., & Heiderscheit, B. (2019). Mobile Technology in Running Science and
Medicine: Are We Ready?
Journal of Orthopaedic & Sports Physical Therapy, 49,
122-
125. https://doi.org/10.2519/jospt.2019.0604
Kaewkannate, K., & Kim, S. (2016). A Comparison of Wearable Fitness Devices.
BMC
Public Health, 16,
433. https://doi.org/10.1186/s12889-016-3059-0
Kinnunen, H., Tanskanen, M., Kyröläinen, H., & Westerterp, K. R. (2012). Wrist-Worn
Accelerometers in Assessment of Energy Expenditure during Intensive Training.
Phy-
siological Measurement, 33,
1841. https://doi.org/10.1088/0967-3334/33/11/1841
Laudadio A., Mazzocchetti L., & Fiz Pérez J. (2011)
Valutare la resilienza. Teorie
,
modelli
e strumenti
. Roma: Editore Carocci.
Lee, V. R., & Drake, J. (2013). Digital Physical Activity Data Collection and Use by En-
durance Runners and Distance Cyclists.
Technology, Knowledge and Learning, 18,
39-63. https://doi.org/10.1007/s10758-013-9203-3
Leone, F. (2018). Using Wearables in Sports and Fitness.
Eracle. Journal of Sport and So-
cial Sciences, 1,
5-12.
Li, R. T., Kling, S. R., Salata, M. J., Cupp, S. A., Sheehan, J., & Voos, J. E. (2016). Wearable
Performance Devices in Sports Medicine.
Sports Health, 8,
74-78.
https://doi.org/10.1177/1941738115616917
Lucidi, F., Pica, G., Mallia, L., Castrucci, E., Manganelli, S., Bélanger, J. J., & Pierro, A.
(2016). Running away from Stress: How Regulatory Modes Affect Stress through Pas-
sion.
Scandinavian Journal of Medicine and Science in Sports, 26,
703-711.
https://doi.org/10.1111/sms.12496
Maher, C., Ryan, J., Ambrosi, C., & Edney, S. (2017). Users’ Experiences of Wearable Ac-
tivity Trackers: A Cross-Sectional Study
. BMC Public Health, 17,
880.
https://doi.org/10.1186/s12889-017-4888-1
Neumann, D. L., & Piercy, A. (2013). The Effect of Different Attentional Strategies on
Physiological and Psychological States during Running.
Australian Psychologist, 48,
329-337. https://doi.org/10.1111/ap.12015
Ng, K., & Ryba, T. (2018). The Quantified Athlete: Associations of Wearables for High
School Athletes.
Advances in Human-Computer Interaction, 2018,
Article ID: 6317524.
https://doi.org/10.1155/2018/6317524
Phan, D., Siong, L. Y., Pathirana, P. N., & Seneviratne, A. (2015). Smartwatch: Perfor-
mance Evaluation for Long-Term Heart Rate Monitoring. In
2015 International Sym-
posium on Bioelectronics and Bioinformatics (ISBB)
(pp. 144-147). Beijing.
https://doi.org/10.1109/ISBB.2015.7344944
Pica. G., Mallia, L., Pierro, A., Alivernini, F., Borellini, V., & Lucidi, F. (2019). How
Stressful Is Retirement! Antecedents of Stress Linked to Athletes’ Career Termination.
Journal of Applied Social Psychology, 49,
488-497.
https://doi.org/10.1111/jasp.12599
Pierro, A., Kruglanski, A. W., & Higgins, E. T. (2006). Regulatory Mode and the Joys of
P. Diotaiuti et al.
DOI:
10.4236/psych.2020.111005 70 Psychology
Doing: Effects of Locomotion and Assessment on Intrinsic and Extrinsic Task-Motivation.
European Journal of Personality, 20,
355-375. https://doi.org/10.1002/per.600
Pobiruchin, M., Suleder, J., Zowalla, R., & Wiesner, M. (2017). Accuracy and Adoption of
Wearable Technology Used by Active Citizens: A Marathon Event Field Study.
JMIR
mHealth and uHealth, 5,
e24. https://doi.org/10.2196/mhealth.6395
Rabin, C., & Bock, B. (2011). Desired Features of Smartphone Applications Promoting
Physical Activity.
Telemedicine and e-Health, 17,
801-803.
ttps://doi.org/10.1089/tmj.2011.0055
Richardson, G. E. (2002). The Metatheory of Resilience and Resiliency.
Journal of Clinical
Psychology, 58,
307-321. https://doi.org/10.1002/jclp.10020
Rieder, A., Lehrer, C., & Jung, R. (2019). Understanding the Habitual Use of Wearable
Activity Trackers
.
In
14th International Conference on Wirtschaftsinformatik
(pp.
1002-1016)
.
Siegen, Germany.
Roos, L., Taube, W., Beeler, N., & Wyss, T. (2017). Validity of Sports Watches When Es-
timating Energy Expenditure during Running.
BMC Sports Science, Medicine and Re-
habilitation, 9,
22. https://doi.org/10.1186/s13102-017-0089-6
Sarkar, M., & Fletcher, D. (2013). How Should We Measure Psychological Resilience in
Sport Performers?
Measurement in Physical Education and Exercise Science, 17,
264-
280. https://doi.org/10.1080/1091367X.2013.805141
Sarkar, M., & Fletcher, D. (2014). Psychological Resilience in Sport Performers: A Review
of Stressors and Protective Factors.
Journal of Sports Sciences, 32,
1419-1434.
https://doi.org/10.1080/02640414.2014.901551
Schenkenfelder, R., & Selinger, S. (2016).
A Comparison of Multiple Wearable Devices
Regarding Their User Experience during Running.
http://ffhoarep.fh-ooe.at/bitstream/123456789/686/1/125_218_Schenkenfelder_FullPap
er_en_Final.pdf
Schücker, L., Hagemann, N., Strauss, B., & Völker, K. (2009). The Effect of Attentional
Focus on Running Economy.
Journal of Sports Sciences, 27,
1241-1248.
https://doi.org/10.1080/02640410903150467
Schukat, M., McCaldin, D., Wang, K., Schreier, G., Lovell, N. H., Marschollek, M., &
Redmond, S. J. (2016). Unintended Consequences of Wearable Sensor Use in Health-
care. Contribution of the IMIA Wearable Sensors in Healthcare WG.
Yearbook of
Medical Informatics, No. 1,
73-86.
Seshadri, D. R., Li, R. T., Voos, J. E., Rowbottom, J. R., Alfes, C. M., Zorman, C. A., &
Drummond, C. K. (2019). Wearable Sensors for Monitoring the Internal and External
Workload of the Athlete.
NPJ Digital Medicine, 2,
71.
https://doi.org/10.1038/s41746-019-0149-2
Stragier, J., Evens, T., & Mechant, P. (2015). Broadcast Yourself: An Exploratory Study of
Sharing Physical Activity on Social Networking Sites.
Media International Australia,
155,
120-129. https://doi.org/10.1177/1329878X1515500114
Wang, J. B., Cataldo, J. K., Ayala, G. X., Natarajan, L., Cadmus-Bertram, L. A., White, M.,
& Pierce, J. P. (2016). Mobile and Wearable Device Features that Matter in Promoting
Physical Activity.
Journal of Mobile Technology in Medicine, 5,
2.
https://doi.org/10.7309/jmtm.5.2.2
Wang, K. L. (2015). Application of Wearable Devices to Running during Training.
Inter-
national Journal of Machine Learning and Computing, 5,
445.
https://doi.org/10.18178/ijmlc.2015.5.6.549
Zep Iin, S., Galli, N., Visek, A. J., Durham, W., & Staples, J. (2014). Concentration and
Attention in Sport.
SportPsych Works, 2,
1-2.
